Methods, devices, equipment, media and products for processing data

Abstract

The present disclosure provides a method for processing data, which can be used in the fields of big data and finance. The specific implementation scheme is: obtaining m pieces of resource transfer data for a user; preprocessing the m pieces of resource transfer data based on a predetermined classification model to obtain the preprocessed m pieces of data; using a predetermined dimensionality reduction algorithm to perform dimensionality reduction processing on the preprocessed m pieces of data to obtain m pieces of three-dimensional data; and using a three-dimensional engine tool to generate a three-dimensional display model of the m pieces of three-dimensional data, so as to realize the three-dimensional display of the m pieces of resource transfer data based on virtual reality. The present disclosure also provides a device, equipment, medium and product for processing data.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, specifically to the field of finance and the field of big data, and more specifically to a method, device, equipment, medium and product for processing data. Background Art

[0002] With the development of economic level and computer technology, online statistics of data can improve statistical efficiency. In order to facilitate users to understand the statistical results of the data, the statistical results can also be displayed.

[0003] In the related art, the statistical results of data are usually displayed in the form of one-dimensional items or in the form of two-dimensional tables. These display methods all remain at the loose and direct low-level display, and users cannot intuitively recognize the changes in data through numbers. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a method, apparatus, device, medium and product for processing data to improve user experience and intuitiveness of data presentation.

[0005] According to a first aspect of the present disclosure, a method for processing data is provided, the method comprising: obtaining m pieces of data to be displayed; preprocessing the m pieces of data to be displayed based on a predetermined classification model to obtain the preprocessed m pieces of data; performing dimensionality reduction processing on the preprocessed m pieces of data using a predetermined dimensionality reduction algorithm to obtain m pieces of three-dimensional data; and generating a three-dimensional display model of the m pieces of three-dimensional data using a three-dimensional engine tool, so as to realize three-dimensional display of the m pieces of data to be displayed based on virtual reality technology, wherein m is a natural number greater than 1.

[0006] According to an embodiment of the present disclosure, preprocessing m pieces of data to be displayed based on a predetermined classification model includes: converting the m pieces of data to be displayed into m pieces of standard data in a standard format; and classifying the m standard data using a predetermined classification model to obtain m pieces of standard data with classification results.

[0007] According to an embodiment of the present disclosure, the predetermined classification model includes a naive Bayes polynomial classification model.

[0008] According to an embodiment of the present disclosure, among the m preprocessed data, n standard data having the same classification result are arranged adjacent to each other, where n is a natural number greater than 1, and n is less than or equal to m.

[0009] According to an embodiment of the present disclosure, using a predetermined dimensionality reduction algorithm to perform dimensionality reduction processing on the preprocessed m pieces of data includes: determining a first probability matrix of the preprocessed m pieces of data; obtaining a second probability matrix of the preprocessed m pieces of data projected into a three-dimensional space; determining the relative entropy between the first probability matrix and the second probability matrix; using a gradient descent algorithm to minimize the relative entropy to adjust the second probability matrix; and determining the three-dimensional data corresponding to the adjusted second probability matrix as the m pieces of three-dimensional data.

[0010] According to an embodiment of the present disclosure, the predetermined dimensionality reduction algorithm includes a T-distributed random neighbor embedding algorithm.

[0011] According to an embodiment of the present disclosure, using a three-dimensional engine tool to generate a three-dimensional display model of m three-dimensional data includes: using a three-dimensional engine tool to generate a three-dimensional data model of m three-dimensional data; and using skybox technology to render a three-dimensional display scene for the three-dimensional data model to obtain the three-dimensional display model.

[0012] A second aspect of the present disclosure provides a device for processing data, including: a data acquisition module, used to acquire m pieces of data to be displayed; a data preprocessing module, used to preprocess the m pieces of data to be displayed based on a predetermined classification model to obtain the preprocessed m pieces of data; a data dimension reduction module, used to perform dimension reduction processing on the preprocessed m pieces of data using a predetermined dimension reduction algorithm to obtain m pieces of three-dimensional data; and a three-dimensional model generation module, used to generate a three-dimensional display model of the m pieces of three-dimensional data using a three-dimensional engine tool, so as to realize three-dimensional display of the m pieces of data to be displayed based on virtual reality technology, wherein m is a natural number greater than 1.

[0013] According to an embodiment of the present disclosure, the above-mentioned data preprocessing module includes: a data conversion submodule, used to convert m pieces of data to be displayed into m pieces of standard data in a standard format; and a data classification submodule, used to classify the m pieces of standard data using a predetermined classification model to obtain m pieces of standard data with classification results.

[0014] According to an embodiment of the present disclosure, the above-mentioned predetermined classification model includes a naive Bayes polynomial classification model.

[0015] According to an embodiment of the present disclosure, among the m pieces of preprocessed data, n pieces of standard data having the same classification result are arranged adjacent to each other, where n is a natural number greater than 1, and n is less than or equal to m.

[0016] According to an embodiment of the present disclosure, the above-mentioned data dimensionality reduction module includes: a first matrix determination submodule, used to determine the first probability matrix of the m preprocessed data; a second matrix determination submodule, used to obtain the second probability matrix of the m preprocessed data projected into the three-dimensional space; a relative entropy determination submodule, used to determine the relative entropy between the first probability matrix and the second probability matrix; a matrix adjustment submodule, used to minimize the relative entropy using a gradient descent algorithm to adjust the second probability matrix; and a data determination submodule, used to determine the three-dimensional data corresponding to the adjusted second probability matrix as m three-dimensional data.

[0017] According to an embodiment of the present disclosure, the predetermined dimensionality reduction algorithm includes a T-distributed random neighbor embedding algorithm.

[0018] According to an embodiment of the present disclosure, the above-mentioned three-dimensional model generation module includes: a first model generation sub-module, which is used to generate a three-dimensional data model of m three-dimensional data using a three-dimensional engine tool; and a second model generation sub-module, which is used to render a three-dimensional display scene for the three-dimensional data model using skybox technology to obtain a three-dimensional display model.

[0019] The third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned method of processing data.

[0020] The fourth aspect of the present disclosure further provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above-mentioned method for processing data.

[0021] The fifth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above-mentioned method for processing data when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0023] Figure 1 The application scenario diagram of the method, apparatus, device, medium and product for processing data according to the embodiments of the present disclosure is schematically shown;

[0024] Figure 2 A flowchart schematically shows a method for processing data according to an embodiment of the present disclosure;

[0025] Figure 3 A schematic diagram showing a principle diagram of preprocessing resource transfer data according to an embodiment of the present disclosure is shown;

[0026] Figure 4 The schematic diagram shows the principle diagram of performing dimensionality reduction processing on data according to an embodiment of the present disclosure;

[0027] Figure 5 A flowchart of generating a three-dimensional display model according to an embodiment of the present disclosure is schematically shown;

[0028] Figure 6 The overall flow chart of the method for processing data according to the embodiment of the present disclosure is schematically shown;

[0029] Figure 7 A structural block diagram schematically shows a device for processing data according to an embodiment of the present disclosure; and

[0030] Figure 8 The block diagram schematically shows an electronic device suitable for implementing the method for processing data according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0032] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0033] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0034] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0035] An embodiment of the present disclosure provides a method for processing data, which includes a data acquisition process, a data preprocessing process, a data dimensionality reduction process, and a three-dimensional model generation process. In the data acquisition process, m pieces of data to be displayed are acquired. In the data preprocessing process, the m pieces of data to be displayed are preprocessed based on a predetermined classification model to obtain the preprocessed m pieces of data. In the data dimensionality reduction process, a predetermined dimensionality reduction algorithm is used to perform dimensionality reduction processing on the preprocessed m pieces of data to obtain m three-dimensional data. In the three-dimensional model generation process, a three-dimensional display model of the m pieces of three-dimensional data is generated using a three-dimensional engine tool to realize the three-dimensional display of the m pieces of data to be displayed based on virtual reality technology. Wherein, m is a natural number greater than 1.

[0036] Figure 1 The application scenario diagram of the method, apparatus, device, medium and product for processing data according to the embodiments of the present disclosure is schematically shown.

[0037] like Figure 1 As shown, the application scenario 100 may include a user 110, a terminal device 120 and a server 130. The terminal device 120 and the server 130 may be connected via a network communication, for example. The network may include a wired or wireless communication link.

[0038] The terminal device 120 may be, for example, an electronic device having a display screen and installed with various client applications, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, etc., wherein the client applications may include, for example, payment applications, online banking applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0039] According to an embodiment of the present disclosure, the terminal device 120 may be provided with hardware devices such as a gyroscope and an acceleration sensor. The terminal device 120 may support the display of holographic images, so that the user 110 can see a three-dimensional displayed image when viewing the holographic image displayed by the terminal device 120 via the virtual reality device 140, thereby giving the user a sense of immersion and reality. The virtual reality device 140 may be, for example, a virtual reality head-mounted display device. The aforementioned various client applications may, for example, have an online payment function to achieve resource transfer.

[0040] The user 110 can use the terminal device 120 to interact with the server 130 through the network to receive or send messages, etc. For example, the user can use the client application with data statistics function provided by the terminal device 120 to send a data acquisition request to the server 130. The data can be, for example, financial data, item in and out data, item sales data, and other data with at least four maintenance items.

[0041] The server 130 may be, for example, a server that provides various services. When the statistical data is financial data, the server may be a background management server (for example only) that supports a client application with an online payment function used by the user 110 using the terminal device 120. The server 130 may, for example, respond to a resource transfer record acquisition request, retrieve resource transfer data related to the personal account of the user 110, and process the resource transfer data and return it to the terminal device 120, so that the terminal device 120 can render the received processed resource transfer data into a display screen to display the resource transfer data.

[0042] In one embodiment, the processed resource transfer data returned by the server 130 to the terminal device 120 may be, for example, a three-dimensional display model of the data, so that when the user 110 views the data displayed by the terminal device 120 through the virtual reality device 140 worn by the user 110, the user can view the three-dimensional display of the data.

[0043] In one embodiment, if Figure 1 As shown, the application scenario 100 may further include a database 150 for storing resource transfer data of each user of the client application supported by the server 130. Accordingly, in response to the resource transfer record acquisition request, the server 130 may first acquire the asset transfer data from the database 150, and then process the asset transfer data.

[0044] It should be noted that the method for processing data provided by the present disclosure may be executed by, for example, the terminal device 120 or the server 130, or may be partially executed by the terminal device 120 and partially executed by the server 130. Accordingly, the apparatus for processing data provided by the present disclosure may be set in, for example, the terminal device 120 or the server 130, or some modules may be set in the terminal device 120 and some modules may be set in the server 130.

[0045] Understandably, Figure 1 The number and type of terminal devices, servers, virtual reality devices, and databases described are only examples to facilitate understanding of the present disclosure. According to actual needs, there may be any number and type of terminal devices, servers, virtual reality devices, and databases.

[0046] The following will be based on Figure 1 The scene described by Figure 2 to Figure 6 The method for processing data according to the disclosed embodiment is described in detail.

[0047] Figure 2 The flowchart of the method for processing data according to the embodiment of the present disclosure is schematically shown.

[0048] like Figure 2As shown, the method 200 for processing data in this embodiment may include operations S210 to S240.

[0049] In operation S210, m pieces of data to be displayed are obtained.

[0050] According to an embodiment of the present disclosure, resource transfer data can be obtained in response to a user's target operation on a client application with an online payment function. The resource transfer data can be data matching the account logged in by the client application with an online payment function used by the user. Usually, the logged in account corresponds to the user one-to-one, so the resource transfer data obtained is data for the user, or can be data requested by the user. It can be understood that when the data to be displayed is other data such as item sales data or item storage data, a method similar to the method of obtaining resource transfer data can be used to obtain the data to be displayed.

[0051] According to an embodiment of the present disclosure, a user may input an acquisition condition via a client application, for example, and the acquisition condition may include, for example, a data generation period and / or a data type, etc. Accordingly, the data to be displayed may be acquired according to the acquisition condition. For example, the m pieces of resource transfer data acquired may be resource transfer data generated in the past year. For example, the m pieces of data to be displayed acquired may be item sales data or item purchase data, or resource transfer data of resources transferred out in the past year, etc. It is to be understood that the above acquisition conditions are only used as examples to facilitate understanding of the present disclosure, and the present disclosure does not limit this. Wherein, m is a natural number greater than 1. It is to be understood that the value of m may be set according to the acquisition condition, and the present disclosure does not limit this.

[0052] In operation S220, the m pieces of data to be displayed are preprocessed based on a predetermined classification model to obtain the m pieces of preprocessed data.

[0053] According to an embodiment of the present disclosure, when the data to be displayed is resource transfer data in financial data, the predetermined classification model can be used, for example, to classify the transfer-out account of the resource transfer data. For example, accounts corresponding to different categories of users can be divided into different categories. The classified categories can include, for example, catering categories, department store categories, medical categories, leisure categories, clothing categories, etc. When the data to be displayed is data on the entry and exit of items in the warehouse, the predetermined classification model can be used, for example, to classify the outbound items, and the classified categories can include, for example, food, daily necessities, furniture, clothing, etc. It can be understood that the classification result of the predetermined classification model can be set according to the type of data to be displayed.

[0054] In this embodiment, each of the m pieces of data can be used as an input of a predetermined classification model, and the category of each piece of data is output after being processed by the predetermined classification model. The category of each piece of data is added as a label to each piece of data to obtain preprocessed data. The predetermined classification model may include, for example, a Bayesian classifier, a K nearest neighbor model (KNearest Neighbor, KNN) or a decision tree model.

[0055] Each of the m pieces of preprocessed data has a classification result. Through the classification, when performing dimensionality reduction processing, the three-dimensional data converted from the data of the same category can have a greater similarity.

[0056] In operation S230, a predetermined dimension reduction algorithm is used to perform dimension reduction processing on the preprocessed m pieces of data to obtain m pieces of three-dimensional data.

[0057] According to an embodiment of the present disclosure, the principal component analysis algorithm (PCA), the linear discriminant analysis algorithm (LDA), the stochastic neighbor embedding algorithm (SNE), etc. can be used to perform dimensionality reduction processing on the preprocessed m data.

[0058] According to an embodiment of the present disclosure, each of the m pieces of data may be, for example, five-dimensional, six-dimensional, or any other data with a dimension greater than three. In one embodiment, when the data to be displayed is resource transfer data, each of the m pieces of data may include, for example, a resource transfer amount, a resource transfer time, a resource transfer description, a resource transfer identification code, and accounts of both parties to the resource transfer.

[0059] In operation S240, a three-dimensional display model of the m pieces of three-dimensional data is generated by using a three-dimensional engine tool, so as to realize three-dimensional display of the m pieces of data to be displayed based on virtual reality technology.

[0060] According to an embodiment of the present disclosure, a three-dimensional engine tool may be, for example, a WebGL third-party library three.js written in JavaScript, which provides a great number of 3D display functions and is a 3D engine running in a browser that can create various three-dimensional scenes, including cameras, lights and shadows, materials and other objects.

[0061] According to an embodiment of the present disclosure, the three-dimensional engine tool may also be, for example, a three-dimensional modeling software, which may include, for example, 3ds Max, Maya, Autodesk 123D, Blender, etc. Among them, 3ds Max is a three-dimensional animation production and rendering software based on a PC system, which has functions such as three-dimensional modeling, material production, lighting setting, camera use, animation setting and rendering. Maya is a three-dimensional animation software produced by Autodesk. Autodesk 123D is a modeling software released by Tektronix. Blender is an open source, cross-platform, all-round three-dimensional animation production software that provides solutions from modeling, animation, materials, rendering, to audio processing, video editing, etc. It can be understood that the above-mentioned three-dimensional engine tools are only used as examples to facilitate the understanding of the present disclosure, and the present disclosure does not limit this.

[0062] Based on the above analysis, the embodiments of the present disclosure can realize three-dimensional visualization of the data to be displayed by converting multi-dimensional data to be displayed into three-dimensional data and generating a three-dimensional display model based on the three-dimensional data, thereby improving the intuitiveness of information display and user experience to a certain extent. Furthermore, the present disclosure can make the distribution of three-dimensional data obtained by dimensionality reduction fit the distribution of the initial data to be displayed by preprocessing the data using a predetermined classification model before performing data dimensionality reduction processing, thereby alleviating the clustering congestion problem existing in data dimensionality reduction to a certain extent. Through this three-dimensional display, while improving the user experience, it can also provide a reference for users to perform business planning, thereby improving the rationality of planning.

[0063] Figure 3 The flowchart of preprocessing the data to be displayed according to the embodiment of the present disclosure is schematically shown.

[0064] According to the embodiments of the present disclosure, Figure 3 As shown, the aforementioned operation of preprocessing the data to be displayed may include, for example, operations S321 to S322.

[0065] In operation S321 , m pieces of to-be-displayed data are converted into m pieces of standard data in a standard format.

[0066] According to an embodiment of the present disclosure, the m pieces of data to be displayed for a user may include data in different scenarios, and the data in different scenarios may have different formats and / or data dimensions. When preprocessing the data to be displayed, this embodiment may first convert each piece of data to be displayed into data in a standard format, so as to improve the efficiency and accuracy of subsequent data processing.

[0067] According to an embodiment of the present disclosure, the standard data in a standard format may be, for example, data in a JS object notation (JSON) format or a key-value pair format. Exemplarily, when the data to be displayed is resource transfer data, the elements included in the standard data in the standard format may include, for example, a resource transfer identifier, a resource transfer time, a resource transfer purpose, and remarks. In one embodiment, when the transferred resource is a circulating currency, the elements included in the standard data may include a remittance identifier, a transaction time, a fund user, and remarks. Among them, JSON is a lightweight data exchange format.

[0068] In operation S322, the m standard data are classified using a predetermined classification model to obtain m pieces of standard data with classification results.

[0069] According to an embodiment of the present disclosure, the predetermined classification model may include, for example, a naive Bayesian polynomial classification model. In the naive Bayesian polynomial classification model, it is assumed that each standard data is independent of each other and each element included in each data is equally important. In this embodiment, training data may be first obtained to train the naive Bayesian polynomial classification model, and the trained model may be used to classify the m standard data.

[0070] According to an embodiment of the present disclosure, when training a naive Bayesian polynomial classification model, all categories of sample data can be determined first, and then the number of sample data in each category is calculated. Subsequently, for each sample data, the category to which each sample data belongs is determined according to the prior classification result, and then the conditional probability P (element category) of each element in the sample data included in each category for each category is obtained, and the conditional probability P (element category) = the number of times the element appears in all sample data included in each category / the total number of elements included in all sample data included in each category. Subsequently, the conditional probability P (all elements of category sample data) that each sample data belongs to each category in all categories is calculated based on the conditional probability. The conditional probability P (all elements of category sample data) is the product of the conditional probabilities of each element in the sample data for each category. By looping through each piece of data in the sample data, the conditional probability that each piece of data belongs to each category in all categories can be obtained, and finally the category corresponding to the maximum conditional probability is determined to be the category to which each piece of data actually belongs. Among them, the prior classification result is the classification result set by the client application according to a predetermined rule. By correcting the parameters of the naive Bayesian polynomial classification model according to the difference between the actual category and the prior classification result, a trained naive Bayesian polynomial classification model is obtained.

[0071] According to the embodiment of the present disclosure, by classifying m pieces of standard data, the pre-processed m pieces of data can be sorted according to categories when performing dimensionality reduction processing, so that the arrangement positions of n pieces of standard data with the same classification results are adjacent to each other. Therefore, when the SNE algorithm is used for dimensionality reduction, the three-dimensional data with high similarity in the initial m pieces of three-dimensional data projected can be arranged as close as possible. Wherein, n is a natural number greater than 1, and n is less than or equal to m.

[0072] Figure 4 The schematic diagram shows the principle of performing dimensionality reduction processing on data according to an embodiment of the present disclosure.

[0073] According to the embodiments of the present disclosure, Figure 4 As shown, when the embodiment 400 uses a predetermined dimensionality reduction algorithm to perform dimensionality reduction processing on the data, the SNE algorithm can be used. In this way, the preprocessed m data 410 can be projected into the three-dimensional space to obtain the initial three-dimensional data 420. Subsequently, the first probability matrix 430 of the preprocessed m data 410 is determined, and the second probability matrix 440 of the initial three-dimensional data 420 projected into the three-dimensional space is determined. After obtaining the first probability matrix 430 and the second probability matrix 440, the relative entropy 450 between the first probability matrix 430 and the second probability matrix 440 is determined, and the relative entropy 450 is minimized by using the gradient descent algorithm 460. The second probability matrix 440 is continuously adjusted during the minimization process, thereby adjusting the initial three-dimensional data 420 corresponding to the second probability matrix 440. Finally, the three-dimensional data corresponding to the second probability matrix (i.e., the adjusted second probability matrix) when the relative entropy 450 is minimum is determined as the optimized three-dimensional data 470, and the m three-dimensional data obtained by the dimensionality reduction processing are obtained.

[0074] According to an embodiment of the present disclosure, the SNE algorithm is a machine learning algorithm for dimensionality reduction, proposed by Hinton and Roweis in 2002. The main principle of the SNE algorithm is to select sample data of neighbors and represent the similarity between high-dimensional data with probability distribution. The more similar two high-dimensional data are, the greater the possibility that the two data appear around each other. The algorithm is a nonlinear dimensionality reduction algorithm, which is very suitable for reducing high-dimensional data to 2 or 3 dimensions, and has excellent visualization results.

[0075] The SNE algorithm maps data points to probability distribution through affine transformation, that is, converting Euclidean distance into conditional probability to express the similarity between points. Each data point represents a piece of preprocessed data. The SNE algorithm calculates the probability distribution between high-dimensional data, so that the more similar a piece of data is to the compared data, the greater the possibility of it appearing in the surrounding area. Then, the SNE algorithm constructs a new probability distribution in the low-dimensional space, so that the probability distribution of the low-dimensional space is as consistent as possible with the probability distribution of the high-dimensional space.

[0076] For example, the following describes the dimension reduction process in detail by taking the preprocessed m data as five-dimensional data. The preprocessed m data can form a data set X = {x1, x2..., x m}. m is the number of data, data item x i is a high-dimensional data. i With x j The similarity probability between them is expressed as p ij , the similarity probability can be calculated by the following formula.

[0077]

[0078] Among them, p ii The value of is 0, that is, the value of the diagonal elements of the first probability matrix is ​​0. At this time, because we only need to pay attention to the high-dimensional distance between the data, we calculate the data x i The distance from itself is meaningless. ij It can be calculated using the following formula.

[0079]

[0080] Among them, σ i is x i The variance of the corresponding Gaussian probability distribution, according to x i The variance is generally given by x i The K nearest neighbor algorithm calculates and evaluates the K data closest to the data to be estimated, and then obtains the predicted value of the data to be estimated.

[0081] After calculating the first probability matrix corresponding to the high-dimensional data set, a similar probability calculation method is used for the low-dimensional data after dimensionality reduction. First, a probability distribution Q is initialized in the low-dimensional space, and the similarity with the probability distribution in the high-dimensional space is compared. The probability distribution Q in the low-dimensional space is gradually adjusted, and finally a probability distribution that is extremely similar to that in the high-dimensional space is obtained. i and x j , and then use y to reduce the dimension to a low-dimensional space. i andj For low-dimensional y i , we can specify the Gaussian distribution as the inverse of the square root of 2, so the similarity between data in the low-dimensional space is expressed by the following formula.

[0082]

[0083] Similarly, setting q ii = 0. If the dimensionality reduction effect is good and the local features are preserved intact, then q ii =p ii .

[0084] Next, it is necessary to determine the distance between the probability distribution in the high-dimensional space and the probability distribution in the low-dimensional space, so as to adjust the probability distribution in the low-dimensional space to be close to the probability distribution in the high-dimensional space. Here, the concept of relative entropy (also known as KL divergence) is introduced to determine the similarity between the probability distribution in the high-dimensional space and the low-dimensional space. The relative entropy can be expressed, for example, by the following formula.

[0085]

[0086] Among them, P i Indicates x i For example, the conditional probability distribution of all other data points. When adjusting the probability distribution of low-dimensional space, different relative distances will produce different penalty weights. When calculating the result of relative entropy, if two points farther apart in low-dimensional space are used to represent two points closer in high-dimensional space, then there will be a larger penalty value when adjusting next time. If two points closer in low-dimensional space are used to represent two points farther apart in high-dimensional space, then there will be a smaller penalty value when adjusting.

[0087] For example, in high-dimensional space, p ji = 0.7, let the corresponding q in the current low-dimensional space be ji =0.3. At this time, the distance between the two points in the high-dimensional space is closer, while the distance between the two points in the low-dimensional space is farther. It is necessary to reduce the distance between the two points in the low-dimensional space. At this time, the penalty value is c1 = p*log(p / q). If in the high-dimensional space, p ji = 0.3, let the corresponding q in the current low-dimensional space be ji =0.7, at this time, the distance between the two points in the high-dimensional space is far, while the distance between the two points in the low-dimensional space is close, and the distance between the two points needs to be increased in the low-dimensional space. At this time, the penalty value is c2 = p*log(p / q). c2 is obviously smaller than c1. It can be seen that the asymmetry at this time makes the adjustment strength different at different distances.

[0088] According to an embodiment of the present disclosure, a gradient descent method may be used to minimize relative entropy, so that after each correction, the distribution of the low-dimensional space is closer to the distribution of the high-dimensional space. In the objective function of the SNE algorithm, the calculation formula for the gradient is as follows.

[0089]

[0090] Finally, the solution at the tth iteration can be expressed by the following formula.

[0091]

[0092] Among them, Y (t) is the solution, η is the learning rate, and α(t) is the momentum value.

[0093] According to the embodiments of the present disclosure, it is considered that when reducing the dimension of data, it may be difficult to express the relationship between data in the high-dimensional space in the low-dimensional space. For example, in the high-dimensional space, a certain area may gather more data. When represented in the low-dimensional space, the space between these gathered data will be further compressed, resulting in more data being gathered in the certain area. This will undoubtedly affect the observation of the data and cause the crowding problem of the clustering results.

[0094] For example, in order to reduce the problem of data crowding, in high-dimensional space, for example, Gaussian distribution can be used to express the probability distribution between data. When simulating in low-dimensional space, t-distribution is used to express the relationship between the similarity and distance between data points. At this time, the similarity between data in low-dimensional space is expressed by the following formula.

[0095]

[0096] Accordingly, the calculation formula for the gradient can be optimized as follows.

[0097]

[0098] According to an embodiment of the present disclosure, a T-distributed random neighborhood embedding algorithm can also be used to perform dimensionality reduction processing on the preprocessed m data. The T-distributed random neighborhood embedding algorithm optimizes the relative entropy between the high-dimensional space and the low-dimensional space, and replaces the conditional probability distribution with the joint probability distribution. The optimized relative entropy is expressed as shown in the following formula.

[0099]

[0100] Where p is the joint probability distribution of each point in the high-dimensional space, and Q is the joint probability distribution of each point in the low-dimensional space. For any i and j, there is p ij =p ji ,qij =q ji At this time, the T-distributed random neighborhood embedding algorithm has symmetry, and the elements in the second probability matrix can be expressed as:

[0101]

[0102] In high-dimensional space, the elements in the first probability matrix can be expressed as:

[0103]

[0104] In this case, when the data point x in the high-dimensional space i In case of exception, all ||x i -x j || 2 For x i For j, p ij This change makes y i The impact on the penalty value becomes very small. At this time, the error of simulating the probability distribution of high-dimensional space in low-dimensional space will be too large. ij Make corrections and let p ij =(p ji +p ij ) / 2m, which ensures that every point contributes to the penalty function. Symmetric t-SNE is simpler and faster to calculate. The gradient formula can be expressed as follows.

[0105]

[0106] With the above optimized calculation formula, we can first set the parameters of the cost function, the perplexity value Perp, and then the optimization parameters for each iteration, including the number of times t, the learning rate η, and the momentum α(t). Calculate the first probability matrix of the high-dimensional space under a given perplexity value, and then let p ij =(p ji +p ij ) / 2m. The iteration starts within the learning number, and in the second probability matrix representing the three-dimensional space, q is calculated. ij , then calculate the information entropy of the first probability matrix and the second probability matrix, and calculate the next solution Y in the three-dimensional space by solving the formula (t) , after each update, the distance between the probability distribution in the three-dimensional space and the probability distribution in the high-dimensional space will be further reduced. Iterate in sequence until the goal is achieved. Finally, the probability matrix in the three-dimensional space can basically represent the probability distribution matrix between the data in the high-dimensional space, and the three-dimensional data corresponding to the probability matrix in the three-dimensional space is the final determined m three-dimensional matrix.

[0107] Figure 5The flowchart of generating a three-dimensional display model according to an embodiment of the present disclosure is schematically shown.

[0108] According to the embodiments of the present disclosure, Figure 5 As shown, when generating a three-dimensional display model, it can be achieved through operations S541 to S542.

[0109] In operation S541 , a 3D data model of m pieces of 3D data is generated using a 3D engine tool.

[0110] In operation S542, a three-dimensional display scene for the three-dimensional data model is rendered using a skybox technology to obtain a three-dimensional display model.

[0111] According to an embodiment of the present disclosure, when generating a three-dimensional display model, the first step is to reference the file. Three.js can be used to create a skybox first, and the first reference is the three.js file. The second file referenced is OrbitControls.js, and the functions in it are used to control the camera. By calling the functions in the OrbitControls.js file, a 360° conversion viewing angle can be achieved in the skybox. Among them, the skybox is a common technology for enhancing the expressiveness of the scene. It is generally achieved by wrapping a texture around the camera. The idea is to draw a large cube. If the observer is at the center of the cube, when the camera moves, the cube also moves with the camera, so that the camera will never move to the edge of the scene.

[0112] This embodiment uses the skybox technology to render the three-dimensional display scene, so that the display of the three-dimensional display model can be more in line with the actual stereoscopic scene, and thus it can facilitate improving the user experience.

[0113] Figure 6 The overall flow chart of the method for processing data according to an embodiment of the present disclosure is schematically shown.

[0114] like Figure 6 As shown, the method for processing data in this embodiment includes data preprocessing operation S610, data processing and three-dimensional visualization conversion operation S620, and data display operation S630. The data to be displayed in this embodiment is financial data, specifically, user's fund transaction data.

[0115] According to an embodiment of the present disclosure, operation S610 may include operations S611 to S614.

[0116] In operation S611, in response to a data query request from a user, a query service is initiated to query the user's fund transaction data.

[0117] In operation S612, the data is sorted into a standard format. The data converted into the standard format is similar to the standard data described above and will not be described in detail here.

[0118] After acquiring the data in the standard format, a classification algorithm may be used to classify the data in the standard format to obtain a classification result data set 612. The number of the classification result data set 612 is equal to the number of categories.

[0119] According to an embodiment of the present disclosure, before using the classification algorithm for classification, the training data 611 may be obtained first, and then the category of the training data 611 may be determined using the initial classification algorithm. Operation S613 is then performed to determine whether there is a difference between the determined category and the actual category of the training data (i.e., the judgment result). If there is a difference, the classification algorithm is corrected to correct the determined category (i.e., the correction result). If the determined category is the same as the actual category of the training data, a trained classification algorithm may be obtained, and the trained algorithm may be used to classify the m pieces of data (i.e., perform operation S614). The classification algorithm is similar to the predetermined classification model described above, and will not be described in detail here.

[0120] After the classification result data set is obtained, operation S620 may be performed, which may include operations S621 to S623.

[0121] In operation S621, the data is reduced in dimension, a high-dimensional data array 621 is obtained based on the classification result data set, and a low-dimensional data array 622 corresponding to the high-dimensional data array 621 is projected. Subsequently, a probability matrix 623 for the high-dimensional data is determined according to the high-dimensional data array 621 as the first probability matrix described above, and a probability matrix 624 for the low-dimensional data is determined according to the low-dimensional data array 622 as the second probability matrix described above.

[0122] In operation S622, the KL divergence of probability matrix 623 and probability matrix 624 is calculated, and it is determined whether the KL divergence reaches the target, that is, whether the KL divergence is less than a predetermined value. If less than, operation S623 is performed to determine that the data dimension reduction is completed and m three-dimensional data are obtained. If not less than, return to adjust the low-dimensional data array 622 to reduce the KL divergence.

[0123] After the data dimension reduction is completed, operation S630 may be performed, and operation S630 may include operations S631 to S633.

[0124] In operation S631, a three-dimensional data model is generated according to the m pieces of three-dimensional data after dimension reduction.

[0125] In operation S632, a skybox technology is used to build a three-dimensional scene for the three-dimensional data model.

[0126] After the three-dimensional scene is constructed, operation S633 may be performed to allow the user to view the three-dimensional visualization data by connecting the VR glasses.

[0127] It is understandable that the above operation S620 can be performed by Figure 4 The above operations S631 to S632 are similar to the operations S541 to S542 described above, and are not described in detail here.

[0128] This embodiment first uses the naive Bayesian polynomial model to preprocess the customer data to obtain a processed data set, uses the t-SNE algorithm to map the data from high dimension to three dimension, generates a three-dimensional data model through Three.js, and uses the skybox technology to build a concise three-dimensional display scene. Therefore, users can view three-dimensional visualization data in a virtual reality scene by connecting the terminal device through VR glasses.

[0129] Based on the above method for processing data, the present disclosure also provides a device for processing data. Figure 7 The device for processing data is described in detail.

[0130] Figure 7 The structure block diagram of the device for processing data according to an embodiment of the present disclosure is schematically shown.

[0131] like Figure 7 As shown, the data processing device 700 of this embodiment includes a data acquisition module 710, a data preprocessing module 720, a data dimension reduction module 730 and a three-dimensional model generation module 740.

[0132] The data acquisition module 710 is used to acquire m pieces of data to be displayed, where m is a natural number greater than 1. In one embodiment, the data acquisition module 710 can be used to perform the operation S210 described above, which will not be described in detail here.

[0133] The data preprocessing module 720 is used to preprocess the m pieces of data to be displayed based on a predetermined classification model to obtain the m pieces of preprocessed data. In one embodiment, the data preprocessing module 720 can be used to perform the operation S220 described above, which will not be described in detail here.

[0134] The data dimension reduction module 730 is used to perform dimension reduction processing on the preprocessed m pieces of data using a predetermined dimension reduction algorithm to obtain m pieces of three-dimensional data. In one embodiment, the data dimension reduction module 730 can be used to perform the operation S230 described above, which will not be described in detail here.

[0135] The 3D model generation module 740 is used to generate a 3D display model of m pieces of 3D data using a 3D engine tool, so as to realize the 3D display of the m pieces of data to be displayed based on virtual reality technology. In one embodiment, the 3D model generation module 740 can be used to perform the operation S240 described above, which will not be described in detail here.

[0136] According to an embodiment of the present disclosure, the above-mentioned data preprocessing module includes: a data conversion submodule, used to convert m pieces of data to be displayed into m pieces of standard data in a standard format; and a data classification submodule, used to classify the m pieces of standard data using a predetermined classification model to obtain m pieces of standard data with classification results.

[0137] According to an embodiment of the present disclosure, the above-mentioned predetermined classification model includes a naive Bayes polynomial classification model.

[0138] According to an embodiment of the present disclosure, among the m pieces of preprocessed data, n pieces of standard data having the same classification result are arranged adjacent to each other, where n is a natural number greater than 1, and n is less than or equal to m.

[0139] According to an embodiment of the present disclosure, the above-mentioned data dimensionality reduction module includes: a first matrix determination submodule, used to determine the first probability matrix of the m preprocessed data; a second matrix determination submodule, used to obtain the second probability matrix of the m preprocessed data projected into the three-dimensional space; a relative entropy determination submodule, used to determine the relative entropy between the first probability matrix and the second probability matrix; a matrix adjustment submodule, used to minimize the relative entropy using a gradient descent algorithm to adjust the second probability matrix; and a data determination submodule, used to determine the three-dimensional data corresponding to the adjusted second probability matrix as m three-dimensional data.

[0140] According to an embodiment of the present disclosure, the predetermined dimensionality reduction algorithm includes a T-distributed random neighbor embedding algorithm.

[0141] According to an embodiment of the present disclosure, the above-mentioned three-dimensional model generation module includes: a first model generation sub-module, which is used to generate a three-dimensional data model of m three-dimensional data using a three-dimensional engine tool; and a second model generation sub-module, which is used to render a three-dimensional display scene for the three-dimensional data model using skybox technology to obtain a three-dimensional display model.

[0142] According to an embodiment of the present disclosure, any multiple of the data acquisition module 710, the data preprocessing module 720, the data dimension reduction module 730, and the three-dimensional model generation module 740 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the data acquisition module 710, the data preprocessing module 720, the data dimension reduction module 730, and the three-dimensional model generation module 740 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the data acquisition module 710, the data preprocessing module 720, the data dimension reduction module 730 and the three-dimensional model generation module 740 can be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function can be performed.

[0143] Figure 8 The block diagram schematically shows an electronic device suitable for implementing the method for processing data according to an embodiment of the present disclosure.

[0144] like Figure 8 As shown, the electronic device 800 according to an embodiment of the present disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 708 into a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include an onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0145] In RAM 803, various programs and data required for the operation of electronic device 800 are stored. Processor 801, ROM 802 and RAM 803 are connected to each other via bus 804. Processor 801 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 802 and / or RAM 803. It should be noted that the program can also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0146] According to an embodiment of the present disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to the bus 804. The electronic device 800 may further include one or more of the following components connected to the I / O interface 805: an input portion 806 including a keyboard, a mouse, etc.; an output portion 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 808 including a hard disk, etc.; and a communication portion 809 including a network interface card such as a LAN card, a modem, etc. The communication portion 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed, so that a computer program read therefrom is installed into the storage portion 808 as needed.

[0147] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0148] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 802 and / or RAM 803 described above and / or one or more memories other than ROM 802 and RAM 803.

[0149] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the item recommendation method provided by the embodiment of the present disclosure.

[0150] The above functions defined in the system / device of the embodiment of the present disclosure are executed when the computer program is executed by the processor 801. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0151] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 809, and / or installed from a removable medium 811. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0152] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0153] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0154] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0155] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0156] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for processing data, comprising: Get m pieces of data to be displayed; Preprocessing the m pieces of data to be displayed based on a predetermined classification model to obtain the preprocessed m pieces of data, including: converting the m pieces of data to be displayed into m pieces of standard data in a standard format; and classifying the m pieces of standard data using the predetermined classification model to obtain m pieces of standard data with classification results; each piece of the preprocessed m pieces of data has the classification result of the corresponding data to be displayed; among the preprocessed m pieces of data, n pieces of standard data with the same classification result are arranged adjacent to each other, wherein n is a natural number greater than 1, and n is less than or equal to m; Using a predetermined dimensionality reduction algorithm to perform dimensionality reduction processing on the m pieces of preprocessed data to obtain m pieces of three-dimensional data; the predetermined dimensionality reduction algorithm includes a random neighborhood embedding algorithm; and A three-dimensional display model of the m pieces of three-dimensional data is generated by using a three-dimensional engine tool, so as to realize the three-dimensional display of the m pieces of data to be displayed based on virtual reality technology. Here, m is a natural number greater than 1.

2. The method according to claim 1, wherein: The predetermined classification model includes a naive Bayes polynomial classification model.

3. The method according to claim 1, wherein: The adopting a predetermined dimensionality reduction algorithm to perform dimensionality reduction processing on the preprocessed m pieces of data includes: Determine a first probability matrix of the m preprocessed data; Obtain a second probability matrix of projecting the preprocessed m pieces of data into a three-dimensional space; determining a relative entropy between the first probability matrix and the second probability matrix; minimizing the relative entropy using a gradient descent algorithm to adjust the second probability matrix; and The three-dimensional data corresponding to the adjusted second probability matrix is ​​determined as the m pieces of three-dimensional data.

4. The method according to claim 3, wherein: The predetermined dimensionality reduction algorithm includes a T-distributed random neighbor embedding algorithm.

5. The method according to claim 1, wherein: The three-dimensional display model of the m pieces of three-dimensional data generated by using a three-dimensional engine tool includes: Using a 3D engine tool to generate a 3D data model of the m pieces of 3D data; and A skybox technology is used to render a three-dimensional display scene for the three-dimensional data model to obtain the three-dimensional display model.

6. A device for processing data, comprising: A data acquisition module is used to acquire m pieces of data to be displayed; A data preprocessing module, used for preprocessing the m pieces of data to be displayed based on a predetermined classification model to obtain the preprocessed m pieces of data, including: converting the m pieces of data to be displayed into m pieces of standard data in a standard format; and classifying the m pieces of standard data using the predetermined classification model to obtain m pieces of standard data with classification results; each piece of the preprocessed m pieces of data has the classification result of the corresponding data to be displayed; among the preprocessed m pieces of data, n pieces of standard data with the same classification result are arranged adjacent to each other, wherein n is a natural number greater than 1, and n is less than or equal to m; a data dimensionality reduction module, configured to perform dimensionality reduction processing on the m pieces of preprocessed data using a predetermined dimensionality reduction algorithm to obtain m pieces of three-dimensional data; the predetermined dimensionality reduction algorithm includes a random neighborhood embedding algorithm; and a three-dimensional model generation module, used to generate a three-dimensional display model of the m pieces of three-dimensional data using a three-dimensional engine tool, so as to realize the three-dimensional display of the m pieces of data to be displayed based on virtual reality technology, Here, m is a natural number greater than 1.

7. An electronic device comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the method according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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