Library activity design method and system based on big data analysis

Abstract

The invention provides a library activity design method and system based on big data analysis. The method comprises the steps of obtaining user data in a library management system within a preset time period; performing clustering analysis on the user data to obtain a clustering label of each cluster; obtaining demand information of different clusters based on the cluster label of each cluster; on the basis of the demand information of the different clusters, obtaining active time periods of user groups in the different clusters; matching active user groups in a preset activity time period, and designing an activity scheme data set meeting the demand information of the active user groups based on the demand information of the active user groups; according to the library activity scheme, the activity scheme set meeting the preset threshold value is screened out, the activity scheme set meeting the preset threshold value is predicted and evaluated, the optimal activity scheme is obtained, the participation degree of students in library activities is greatly improved through the library activity scheme provided by the invention, and rich and diversified learning and culture experiences are provided for the students.

Description

Technical Field

[0001] The present application relates to the technical field of library activity design, and specifically to a library activity design method and system based on big data analysis. Background Art

[0002] At present, as an important place on campus for knowledge inheritance and academic exchanges, there is a significant gap between the current status and potential of the library. Although the library is favored by some students who love learning and are eager for knowledge, and has become a place for them to explore the unknown and deepen their understanding, some students have missed the library as a place of learning due to lack of comprehensive understanding of the library's functions or failure to deeply realize the great help of library resources for personal growth. The lack of students' understanding of the library has led to the polarization of library participation, which is not conducive to the formation of a strong learning atmosphere throughout the school.

[0003] As an important part of campus cultural life, the library should be a vibrant and creative communication space that can regularly hold various reading promotion activities, academic lectures, cultural salons, etc. to attract and meet the interests and needs of different students. However, the current situation is that there are few or even almost no activities, which reduces the ways for students to understand and approach the library, and limits the role that the library can play in promoting students' all-round development and broadening their knowledge horizons. Therefore, how to overcome the above-mentioned technical problems and defects has become a key issue that needs to be solved. Summary of the invention

[0004] In order to overcome the problem of few or even almost zero activities in the prior art, which reduces the problem of students understanding and approaching the library, this application provides a library activity design method and system based on big data analysis, which adopts the following technical solutions:

[0005] In the first aspect, the present application provides a library activity design method based on big data analysis, including:

[0006] Obtain user data in the library management system within a preset time period;

[0007] Performing cluster analysis on the user data to obtain a cluster label for each cluster;

[0008] Based on the cluster label of each cluster, obtain the demand information of different clusters;

[0009] Based on the demand information of the different clusters, obtaining active time periods of user groups in the different clusters;

[0010] Matching active user groups in a preset activity time period, and based on the demand information of the active user groups, designing an activity plan data set that meets the demand information of the active user groups;

[0011] Filter out the activity plan set that meets the preset threshold, and perform predictive evaluation on the activity plan set that meets the preset threshold to obtain the optimal activity plan.

[0012] Furthermore, before performing cluster analysis on the user data, it also includes performing a cleaning operation on the user data to obtain cleaned user data, converting non-numeric data in the cleaned user data into numeric data to obtain type conversion data; and normalizing the type conversion data to the same scale to obtain normalized data.

[0013] Furthermore, the cluster analysis is performed on the user data to obtain a cluster label of each cluster, and the specific steps are as follows:

[0014] Extracting feature information of the normalized data, and determining an optimal number of clusters of the feature information based on a silhouette coefficient;

[0015] Randomly initialize the cluster centers of the optimal number of clusters;

[0016] Assign each data point in the feature information to the nearest cluster center; obtain the mean of each cluster and update the cluster center; repeat this step until the cluster center no longer changes or the maximum number of iterations is reached, and obtain the clustering result;

[0017] The clustering results are visualized based on principal component analysis to obtain the cluster labels of each cluster.

[0018] Furthermore, determining the optimal number of clusters of the feature information based on the silhouette coefficient specifically includes:

[0019] Set the range of cluster numbers based on the number of features;

[0020] For each cluster number in the cluster number range, clustering the data points of the feature information based on a clustering algorithm;

[0021] Obtain the average distance a(i) between each data point and other data points in the same cluster, and the average distance b(i) between each data point and the data point in the nearest cluster;

[0022] Obtain the silhouette coefficient s(i) of each data point based on a preset formula;

[0023] The average silhouette coefficients under different numbers of clusters are obtained, and based on the changing trend of the silhouette coefficients, the number of clusters with the highest average silhouette coefficient is selected as the optimal number of clusters.

[0024] Furthermore, the preset formula is The value of the silhouette coefficient is between -1 and 1. The closer the value of the silhouette coefficient is to 1, the better the clustering effect is. The closer the value of the silhouette coefficient is to 0, the data point is at the edge of the cluster. A negative value indicates a poor clustering effect.

[0025] Furthermore, based on the cluster label of each cluster, the demand information of different clusters is obtained, and the specific contents include:

[0026] Extracting key features based on the cluster labels;

[0027] Convert the data points in each cluster into pre-formatted data;

[0028] Based on the key features of each cluster and the data in the preset format, the user data of each cluster is mined for association rules to generate frequent item sets that meet the support threshold;

[0029] Generate associated requirements from the frequent item set, filter out requirements that meet the minimum confidence level, and obtain requirement information of different clusters.

[0030] Furthermore, based on the demand information of the different clusters, the active time periods of the user groups in the different clusters are obtained, and the specific contents include:

[0031] Get the time data of each user in each cluster;

[0032] Based on the cluster label and time data of each cluster, calculate the number of borrowings in each hour;

[0033] The mean and standard deviation of the borrowing times are calculated, and the time period exceeding the preset threshold is regarded as the active time period.

[0034] Furthermore, the activity plan set that meets the preset threshold is screened out, and the activity plan set that meets the preset threshold is predicted and evaluated to obtain the optimal activity plan, and the specific contents include:

[0035] Determine the evaluation indicators for the activity plan set;

[0036] Based on the preset threshold of each evaluation indicator, select the activity plans that meet the threshold and obtain the candidate plan set;

[0037] Assign weights to different evaluation indicators for each candidate solution based on the analytic hierarchy process;

[0038] Obtain the evaluation values ​​of different indicators of each candidate solution based on the preset evaluation model, and obtain the comprehensive scores of different indicators based on the weights and evaluation values ​​of different indicators;

[0039] Sort all candidate activity plans from high to low according to their comprehensive scores, and select the plan with the highest score as the optimal plan.

[0040] In the second aspect, the present application also provides a library activity design system based on big data analysis, including:

[0041] A user data acquisition module is used to acquire user data in a preset time period in the library management system;

[0042] A user feature acquisition module, used to perform cluster analysis on the user data and obtain a cluster label for each cluster;

[0043] A demand information acquisition module is used to acquire demand information of different clusters based on the cluster label of each cluster;

[0044] An active time acquisition module, used to acquire active time periods of user groups in different clusters based on the demand information of the different clusters;

[0045] An activity plan data set acquisition module is used to match active user groups in a preset activity time period, and based on the demand information of the active user groups, design an activity plan data set that meets the demand information of the active user groups;

[0046] The optimal activity plan acquisition module is used to screen out the activity plan set that meets the preset threshold, and to predict and evaluate the activity plan set that meets the preset threshold to obtain the optimal activity plan.

[0047] In a third aspect, the present application provides an electronic device, including:

[0048] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions, which, when executed by the device, cause the device to perform the method as described in the first aspect.

[0049] In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer executes the method described in the first aspect.

[0050] In a fifth aspect, the present application provides a computer program, which, when executed by a computer, is used to execute the method described in the first aspect.

[0051] In one possible design, the program in the fifth aspect may be stored in whole or in part on a storage medium packaged together with the processor, or may be stored in whole or in part on a memory not packaged together with the processor.

[0052] This application has the following beneficial effects:

[0053] 1. This application obtains user data in a preset time period from a library management system; performs cluster analysis on the user data to obtain cluster labels for each cluster; builds a user profile for each cluster based on the cluster labels for each cluster, and obtains demand information for different clusters. This application divides users into different groups through cluster analysis, and then identifies the characteristics of users in different groups, so that the library can more accurately understand the reading habits and interests of users in the process of formulating activities, and then provide personalized data support for library activity design.

[0054] 2. This application obtains the active time periods of user groups in different clusters based on the demand information of the different clusters; matches the active user groups in the preset activity time periods, and designs an activity plan data set that meets the demand information of the active user groups based on the demand information of the active user groups; screens out the activity plan set that meets the preset threshold, and predicts and evaluates the activity plan set that meets the preset threshold to obtain the optimal activity plan. This application obtains the active time periods of different user groups, so that specific user groups can be more accurately targeted in the process of library activity design; the library activity plan designed based on the demand information of the active user groups can better attract the participation interest of the target users and enhance the user experience in the activities; unnecessary waste of resources can be avoided by screening the activity plans that meet the preset threshold. Data analysis makes the design decisions of library activity plans more scientific and objective. This application greatly improves students' participation in library activities and provides students with rich and diverse learning and cultural experiences. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is an exemplary system architecture diagram to which the embodiments of the present application can be applied;

[0056] Figure 2 A flow chart of a library activity design method based on big data analysis according to an embodiment of the present application;

[0057] Figure 3 A flowchart of obtaining a cluster label for each cluster according to an embodiment of the present application;

[0058] Figure 4 A flowchart for determining the optimal number of clusters according to an embodiment of the present application;

[0059] Figure 5 A flowchart of obtaining demand information of different clusters according to an embodiment of the present application;

[0060] Figure 6 A flowchart of determining active time periods of user groups in different clusters according to an embodiment of the present application;

[0061] Figure 7A flowchart of obtaining the optimal activity plan according to an embodiment of the present application;

[0062] Figure 8 This is a system flow chart of an embodiment of the present application;

[0063] Fig. 9 It is a schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0065] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0066] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0067] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0068] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0069] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4) players, laptop computers and desktop computers, etc.

[0070] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0071] It should be noted that the library activity design method based on big data analysis provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the library activity design system based on big data analysis is generally set in the server / terminal device.

[0072] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0073] Continue to refer Figure 2 , the figure shows a flow chart of a library activity design method based on big data analysis of the present application, the method comprising the following steps:

[0074] Step 201, obtaining user data within a preset time period in the library management system.

[0075] In the embodiment of the present application, the fields for obtaining the user data include user ID, borrowing time, book ID, and book type, wherein user ID represents a number that uniquely identifies a user; borrowing time represents the specific date when a user borrows a book; book ID represents a number that uniquely identifies a book; book type represents the classification of books, such as novels, science, history, etc.; book title represents the name of the book. For example: through the school's library management system, obtain user data within three months; the preset time period in the embodiment of the present application is set according to actual needs. By obtaining user data in the library management system within a preset time period, it can help understand the user's reading preferences, and then provide personalized data support for library activity design.

[0076] Step 202: Perform cluster analysis on the user data to obtain a cluster label for each cluster.

[0077] In an embodiment of the present application, before clustering analysis is performed on the user data, a cleaning operation is also performed on the user data to obtain cleaned user data, non-numeric data in the cleaned user data is converted into numeric data to obtain type conversion data; and the type conversion data is normalized to the same scale to obtain normalized data.

[0078] In an embodiment of the present application, the type conversion data is normalized to the same scale to obtain normalized data, and the type conversion data can be normalized using Z-score standardization or Min-Max scaling.

[0079] In the embodiment of the present application, cluster analysis is performed on the user data to obtain the cluster label of each cluster. Please refer to Figure 3 , the specific steps are:

[0080] Step 301: extract feature information of normalized data, and determine the optimal number of clusters of the feature information based on the silhouette coefficient.

[0081] Step 302: randomly initialize cluster centers of the optimal number of clusters.

[0082] Step 303, assign each data point in the feature information to the nearest cluster center; obtain the mean of each cluster and update the cluster center; repeat this step until the cluster center no longer changes or the maximum number of iterations is reached, and obtain the clustering result.

[0083] Step 304: Visualize the clustering results based on principal component analysis to obtain cluster labels for each cluster. For example, some clusters may prefer books of a specific type or borrow books in a specific time period.

[0084] In an embodiment of the present application, extracting the characteristic information of the normalized data includes: using the borrowing time as a periodic feature of the characteristic information, using the book ID as a distribution feature of the book type, and using the book type as a feature of the user preference type.

[0085] In the embodiment of the present application, the optimal number of clusters for determining the feature information based on the silhouette coefficient can be found in Figure 4 , specifically including:

[0086] Step 401, setting a cluster number range based on the number of features;

[0087] Step 402, clustering the data points of the feature information based on a clustering algorithm for each cluster number in the cluster number range;

[0088] Step 403: Obtain the average distance a(i) between each data point and other data points in the same cluster, and the average distance b(i) between each data point and the data point in the nearest cluster.

[0089] Step 404, obtaining the silhouette coefficient s(i) of each data point based on a preset formula;

[0090] Step 405, obtaining average silhouette coefficients under different numbers of clusters, and based on the variation trend of the silhouette coefficients, selecting the number of clusters with the highest average silhouette coefficient as the optimal number of clusters.

[0091] The preset formula is The value of the silhouette coefficient is between -1 and 1. The closer the value of the silhouette coefficient is to 1, the better the clustering effect is. The closer the value of the silhouette coefficient is to 0, the data point is at the edge of the cluster. A negative value indicates a poor clustering effect.

[0092] Step 203: based on the cluster label of each cluster, obtain the demand information of different clusters.

[0093] In the embodiment of the present application, the cluster label based on each cluster is used to obtain the demand information of different clusters. Please refer to Figure 5 , the specific steps include:

[0094] Step 501, extracting key features based on the clustering labels; key features include but are not limited to basic features, behavioral features, interest features, etc.; basic features may be average age, gender ratio, education level, etc.; behavioral features may be borrowing frequency, types of borrowed books, borrowing duration, etc.; interest features may be types of frequently borrowed books, user feedback (such as comments, ratings), etc.

[0095] Step 502, converting the data points in each cluster into preset format data, the preset format data can be set as Data1: {age: 25, gender: male, borrowed books: novels} Data1; 2: {age: 30, gender: female, borrowed books: science fiction}.

[0096] Step 503, based on the key features of each cluster and the data in the preset format, the user data of each cluster is mined for association rules to generate frequent item sets that meet the support threshold;

[0097] Step 504 , generating associated requirements from the frequent item set, screening out requirements that meet the minimum confidence level, and obtaining requirement information of different clusters.

[0098] Step 204: based on the demand information of the different clusters, obtain the active time periods of the user groups in the different clusters. Figure 6 , the specific contents include:

[0099] Step 601: Obtain time data of each user in each cluster.

[0100] Step 602, based on the cluster label and time data of each cluster, calculate the number of borrowings in each hour; for example, the activity data.groupby(['cluster label', 'hour']).count() can be used to count the number of borrowings in each hour.

[0101] Step 603, calculating the mean and standard deviation of the borrowing times, and taking the time period exceeding the preset threshold as the active time period.

[0102] Step 205 , matching active user groups in a preset activity time period, and based on the demand information of the active user groups, designing an activity plan data set that meets the demand information of the active user groups.

[0103] In an embodiment of the present application, a preset activity time period is used as a query condition to screen out active user groups that meet the preset activity time period, and obtain demand information that meets the active user groups; based on the demand information of the active user groups, an activity plan is determined, wherein the activity plan includes the activity type (such as lectures, reading sharing, training, etc.), activity theme and content, activity time, activity location and activity form (online or offline).

[0104] Step 206 , screen out a set of activity plans that meet a preset threshold, and perform a prediction evaluation on the set of activity plans that meet the preset threshold to obtain an optimal activity plan.

[0105] In the embodiment of the present application, a set of activity plans that meet the preset threshold is screened out, and a prediction evaluation is performed on the set of activity plans that meet the preset threshold to obtain the optimal activity plan. Please refer to Figure 7 , the specific contents include:

[0106] Step 701, determining evaluation indicators of the activity plan set, wherein the evaluation indicators may be expected number of participants, user satisfaction, cost-effectiveness ratio, resource utilization, etc.;

[0107] Step 702, based on the preset threshold of each evaluation indicator, select the activity plans that meet the threshold and obtain a set of candidate plans;

[0108] Step 703, assigning weights to different evaluation indicators of each candidate solution based on the analytic hierarchy process;

[0109] Step 704, obtaining evaluation values ​​of different indicators of each candidate solution based on a preset evaluation model, and obtaining comprehensive scores of different indicators based on weights and evaluation values ​​of different indicators.

[0110] For example, assuming that the evaluation indicators are the expected number of participants, cost-effectiveness ratio, and user satisfaction, the comprehensive score method for calculating these indicators is: comprehensive score = w1×expected number of participants + w2×cost-effectiveness ratio + w3×user satisfaction, among which w1, w2, and w3 are the weights of the expected number of participants, cost-effectiveness ratio, and user satisfaction.

[0111] Step 705: sort all candidate activity plans from high to low according to their comprehensive scores, and select the plan with the highest score as the optimal plan.

[0112] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0113] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence 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 a part of the steps in the flowchart 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 part of the sub-steps or stages of other steps.

[0114] Continue to refer Figure 8 The library activity design system based on big data analysis described in this embodiment includes: a user data acquisition module 801, a user feature acquisition module 802, a demand information acquisition module 803, an active time acquisition module 804, an activity plan data set acquisition module 805, and an optimal activity plan acquisition module 806. Among them:

[0115] The user data acquisition module 801 is used to acquire user data in a preset time period in the library management system;

[0116] A user feature acquisition module 802, configured to perform cluster analysis on the user data and obtain a cluster label for each cluster;

[0117] The demand information acquisition module 803 is used to acquire demand information of different clusters based on the cluster label of each cluster;

[0118] An active time acquisition module 804 is used to acquire active time periods of user groups in different clusters based on the demand information of the different clusters;

[0119] The activity plan data set acquisition module 805 is used to match the active user group in the preset activity time period, and based on the demand information of the active user group, design an activity plan data set that meets the demand information of the active user group;

[0120] The optimal activity plan acquisition module 806 is used to screen out an activity plan set that meets a preset threshold, and perform prediction and evaluation on the activity plan set that meets the preset threshold to obtain the optimal activity plan.

[0121] To solve the above technical problems, the present application also provides a computer device. Fig. 9 , Fig. 9 This is a basic structural block diagram of the computer device in this embodiment.

[0122] The computer device 9 includes a memory 9a, a processor 9b, and a network interface 9c that are interconnected through a system bus. It should be noted that the figure only shows a computer device 9 with components 9a-9c, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0123] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0124] The memory 9a includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 9a can be an internal storage unit of the computer device 9, such as a hard disk or memory of the computer device 9. In other embodiments, the memory 9a can also be an external storage device of the computer device 9, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 9. Of course, the memory 9a can also include both the internal storage unit of the computer device 9 and its external storage device. In this embodiment, the memory 9a is generally used to store the operating system and various application software installed on the computer device 9, such as the program code of the library activity design method based on big data analysis, etc. In addition, the memory 9a can also be used to temporarily store various types of data that have been output or are to be output.

[0125] The processor 9b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 9b is generally used to control the overall operation of the computer device 9. In this embodiment, the processor 9b is used to run the program code stored in the memory 9a or process data, such as running the program code of the library activity design method based on big data analysis.

[0126] The network interface 9c may include a wireless network interface or a wired network interface. The network interface 9c is generally used to establish a communication connection between the computer device 9 and other electronic devices.

[0127] The present application also provides another embodiment, namely, providing a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a program of a library activity design method based on big data analysis, and the library activity design based on big data analysis can be executed by at least one processor so that the at least one processor executes the steps of the library activity design method based on big data analysis as described above.

[0128] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0129] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A library activity design method based on big data analysis, characterized by: include: Obtain user data in the library management system within a preset time period; Performing cluster analysis on the user data to obtain a cluster label for each cluster; Based on the cluster label of each cluster, obtain the demand information of different clusters; Based on the demand information of the different clusters, obtaining active time periods of user groups in the different clusters; Matching active user groups in a preset activity time period, and based on the demand information of the active user groups, designing an activity plan data set that meets the demand information of the active user groups; Filter out the activity plan set that meets the preset threshold, and perform predictive evaluation on the activity plan set that meets the preset threshold to obtain the optimal activity plan.

2. The library activity design method based on big data analysis according to claim 1 is characterized in that: Before performing cluster analysis on the user data, the method further includes performing a cleaning operation on the user data to obtain cleaned user data, converting non-numeric data in the cleaned user data into numeric data to obtain type conversion data; and normalizing the type conversion data to the same scale to obtain normalized data.

3. The library activity design method based on big data analysis according to claim 2 is characterized in that: The cluster analysis is performed on the user data to obtain a cluster label of each cluster, and the specific steps are as follows: Extracting feature information of the normalized data, and determining an optimal number of clusters of the feature information based on a silhouette coefficient; Randomly initialize the cluster centers of the optimal number of clusters; Assign each data point in the feature information to the nearest cluster center; obtain the mean of each cluster and update the cluster center; repeat this step until the cluster center no longer changes or the maximum number of iterations is reached, and obtain the clustering result; The clustering results are visualized based on principal component analysis to obtain the cluster labels of each cluster.

4. The library activity design method based on big data analysis according to claim 3 is characterized in that: The determining the optimal number of clusters of the feature information based on the silhouette coefficient specifically includes: Set the range of cluster numbers based on the number of features; For each cluster number in the cluster number range, clustering the data points of the feature information based on a clustering algorithm; Obtain the average distance a(i) between each data point and other data points in the same cluster, and the average distance b(i) between each data point and the data point in the nearest cluster; Obtain the silhouette coefficient s(i) of each data point based on a preset formula; The average silhouette coefficients under different numbers of clusters are obtained, and based on the changing trend of the silhouette coefficients, the number of clusters with the highest average silhouette coefficient is selected as the optimal number of clusters.

5. The library activity design method based on big data analysis according to claim 1 is characterized in that: The cluster label of each cluster is used to obtain the demand information of different clusters, and the specific contents include: Extracting key features based on the cluster labels; Convert the data points in each cluster into pre-formatted data; Based on the key features of each cluster and the data in the preset format, the user data of each cluster is mined for association rules to generate frequent item sets that meet the support threshold; Generate associated requirements from the frequent item set, filter out requirements that meet the minimum confidence level, and obtain requirement information of different clusters.

6. The library activity design method based on big data analysis according to claim 1 is characterized in that: The acquiring, based on the demand information of the different clusters, active time periods of user groups in the different clusters specifically includes: Get the time data of each user in each cluster; Based on the cluster label and time data of each cluster, calculate the number of borrowings in each hour; The mean and standard deviation of the borrowing times are calculated, and the time period exceeding the preset threshold is regarded as the active time period.

7. The library activity design method based on big data analysis according to claim 1 is characterized in that: The screening out of the activity plan set that meets the preset threshold, and performing prediction and evaluation on the activity plan set that meets the preset threshold to obtain the optimal activity plan, specifically includes: Determine the evaluation indicators for the activity plan set; Based on the preset threshold of each evaluation indicator, select the activity plans that meet the threshold and obtain the candidate plan set; Assign weights to different evaluation indicators for each candidate solution based on the analytic hierarchy process; Obtain the evaluation values ​​of different indicators of each candidate solution based on the preset evaluation model, and obtain the comprehensive scores of different indicators based on the weights and evaluation values ​​of different indicators; Sort all candidate activity plans from high to low according to their comprehensive scores, and select the plan with the highest score as the optimal plan.

8. A library activity design system based on big data analysis, used to implement the library activity design method based on big data analysis of claims 1-7, characterized in that: include: A user data acquisition module is used to acquire user data in a preset time period in the library management system; A user feature acquisition module, used to perform cluster analysis on the user data and obtain a cluster label for each cluster; A demand information acquisition module is used to acquire demand information of different clusters based on the cluster label of each cluster; An active time acquisition module, used to acquire active time periods of user groups in different clusters based on the demand information of the different clusters; An activity plan data set acquisition module is used to match active user groups in a preset activity time period, and based on the demand information of the active user groups, design an activity plan data set that meets the demand information of the active user groups; The optimal activity plan acquisition module is used to screen out the activity plan set that meets the preset threshold, and to predict and evaluate the activity plan set that meets the preset threshold to obtain the optimal activity plan.

9. An electronic device, characterized in that: include: one or more processors; Memory; And one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions, which, when executed by the device, enable the device to perform the steps of the library activity design method based on big data analysis as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed on a computer, enables the computer to execute the steps of the method for designing library activities based on big data analysis as described in any one of claims 1 to 7.

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