Data processing method, device, electronic device and storage medium

Abstract

The present disclosure is about a data processing method, device, electronic device and storage medium, and the method includes: obtaining benchmark account data and test account data respectively, wherein the benchmark account data is data generated when an account accesses a benchmark program, and the test account data is data generated when an account accesses a test program; obtaining a benchmark value corresponding to the benchmark account data and a test value corresponding to the test account data, wherein the benchmark value is obtained based on processing sparse data in the benchmark account data, and the test value is obtained based on processing sparse data in the test account data; comparing the test value with the benchmark value to obtain a test result of the test account data. Since the test value and the benchmark value are obtained respectively by processing the sparse data in the benchmark account data and the test account data, the variance of the data can be reduced, thereby narrowing the confidence interval, avoiding the influence of extreme values ​​of the sparse data, and greatly improving the test effect.

Description

Technical Field

[0001] The present disclosure relates to the field of computers, and in particular to a data processing method, device, electronic device and storage medium. Background Art

[0002] AB experiment can be when developing a new version of the program. Under the same dimension, groups of visitors with the same or similar structure are allowed to randomly or regularly access the original baseline version and the new test version of the program. The test data corresponding to the test version is obtained through AB experiment, and the problems with the test version are determined based on the test data.

[0003] However, in AB experiments, sparse and thick-tailed indicators usually affect the accuracy of AB experiments, making it impossible to accurately evaluate the task objectives of the test version, and thus unable to determine whether the test version can be adopted. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the present disclosure provides a data processing method, device, electronic device and storage medium.

[0005] According to a first aspect of an embodiment of the present disclosure, a data method is provided, including:

[0006] Respectively acquiring benchmark account data and test account data, wherein the benchmark account data is data generated when an account accesses a benchmark program, and the test account data is data generated when an account accesses a test program;

[0007] Obtaining a reference value corresponding to the reference account data and a check value corresponding to the test account data, wherein the reference value is obtained based on processing sparse data in the reference account data, and the check value is obtained based on processing sparse data in the test account data;

[0008] The test value is compared with the reference value to obtain a test result of the test account data.

[0009] Optionally, obtaining a reference value corresponding to the reference account data includes:

[0010] Obtaining a first expected value of the benchmark account data and a first proportion of sparse data in the benchmark account data, wherein the first expected value is determined by a mean and a variance of the sparse data in the benchmark account data;

[0011] Based on a first expected value of the benchmark account data and a first proportion of the sparse data in the benchmark account data, a benchmark value corresponding to the benchmark account data is obtained.

[0012] Optionally, obtaining a verification value corresponding to the test account data includes:

[0013] obtaining a second expected value of the test account data and a second proportion of sparse data in the test account data, wherein the second expected value is determined by a mean and a variance of the sparse data in the test account data;

[0014] Based on the second expected value of the test account data and the second proportion of the sparse data in the test account data, a check value corresponding to the test account data is obtained.

[0015] Optionally, the method further comprises:

[0016] It is predetermined that a product of the first expected value and the first ratio has a corresponding relationship with a product of the second expected value and the second ratio.

[0017] Optionally, the sparse data is non-zero data in the benchmark account data or the test account data.

[0018] According to a second aspect of an embodiment of the present disclosure, there is provided a data processing device, including:

[0019] A data acquisition module, configured to respectively acquire benchmark account data and test account data, wherein the benchmark account data is data generated when an account accesses a benchmark program, and the test account data is data generated when an account accesses a test program;

[0020] a value acquisition module configured to acquire a reference value corresponding to the reference account data and a check value corresponding to the test account data, wherein the reference value is obtained by processing sparse data in the reference account data, and the check value is obtained by processing sparse data in the test account data;

[0021] The comparison module is configured to compare the test value with the reference value to obtain a test result of the test account data.

[0022] Optionally, the value acquisition module includes:

[0023] A first proportion determination submodule is configured to obtain a first expected value of the benchmark account data and a first proportion of sparse data in the benchmark account data, wherein the first expected value is determined by a mean and a variance of the sparse data in the benchmark account data;

[0024] The reference value acquisition submodule is configured to obtain a reference value corresponding to the reference account data based on a first expected value of the reference account data and a first proportion of the sparse data in the reference account data.

[0025] Optionally, the value acquisition module includes:

[0026] A second proportion determination submodule is configured to obtain a second expected value of the test account data and a second proportion of sparse data in the test account data, wherein the second expected value is determined by a mean and a variance of the sparse data in the test account data;

[0027] The test value acquisition submodule is configured to obtain a test value corresponding to the test account data based on a second expected value of the test account data and a second proportion of the sparse data in the test account data.

[0028] Optionally, the device further comprises:

[0029] The determination module is configured to predetermine that a product of the first expected value and the first ratio has a corresponding relationship with a product of the second expected value and the second ratio.

[0030] Optionally, the sparse data is non-zero data in the benchmark account data or the test account data.

[0031] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, including:

[0032] processor;

[0033] a memory for storing processor-executable instructions;

[0034] Wherein, the processor is configured to execute any of the above-mentioned data processing methods.

[0035] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the above-mentioned data processing method.

[0036] According to a fifth aspect of the embodiments of the present disclosure, a computer program product is provided, which, when executed on a computer, enables the computer to execute the steps of the data processing method described in any one of the above embodiments.

[0037] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0038] The data processing method, device, electronic device, and storage medium provided by the embodiments of the present disclosure obtain the benchmark account data and the test account data respectively, obtain the benchmark value corresponding to the benchmark account data and the test value corresponding to the test account data, and obtain the test result of the test account data by comparing the test value with the benchmark value. Since the embodiments of the present disclosure process the sparse data in the benchmark account data and the test account data to obtain the test value and the benchmark value respectively, the variance of the data can be reduced, thereby narrowing the confidence interval, avoiding the influence of extreme values ​​of the sparse data, and greatly improving the test effect.

[0039] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0041] Figure 1 is a flow chart of a data processing method according to an exemplary embodiment;

[0042] Figure 2 yes Figure 1 Flowchart of step S120;

[0043] Figure 3 Yes Yes Figure 1 Another flow chart of step S120;

[0044] Figure 4 is a block diagram of a data processing device according to an exemplary embodiment;

[0045] Figure 5 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0046] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0047] Figure 1 is a flow chart of a data processing method according to an exemplary embodiment. Figure 1 As shown, the method is used in a terminal and may include the following steps:

[0048] In step S110 , the base account data and the test account data are acquired respectively.

[0049] Among them, the benchmark account data is the data generated when the account accesses the benchmark program, and the test account data is the data generated when the account accesses the test program.

[0050] It is understandable that during the development process, a program will be continuously updated. The program before the update can be called the benchmark program, and the program after the update can be called the test program. The data generated by the account in the process of accessing the benchmark program can be called the benchmark data, and the data generated by the account in the process of accessing the test program can be called the test data.

[0051] It should be noted that the account information involved in this disclosure (including but not limited to account device information, account personal information, etc.) is all information authorized by the account or fully authorized by all parties.

[0052] In step S120, a reference value corresponding to the reference account data and a check value corresponding to the test account data are obtained.

[0053] The benchmark value is obtained based on the processing of sparse data in the benchmark account data, and the test value is obtained based on the processing of sparse data in the test account data.

[0054] Since data with sparse and thick-tailed distribution characteristics are easily affected by extreme values ​​in AB experiments, making it impossible to efficiently and accurately evaluate the experimental results, the embodiments of the present disclosure pre-process the sparse data to avoid the impact of sparse data on the results of AB experiments. Sparse data refers to data with a large number of zero-value points, and the proportion of valid data points is relatively small. This causes the indicator to converge to a normal distribution more slowly (requiring a larger sample size), and at the same time, due to excessive fluctuations, the effectiveness of the experimental effect test is reduced.

[0055] For example, in a short video platform with a live broadcast function, sometimes only a small number of accounts will watch the live broadcast, but some accounts that watch the live broadcast will watch it for a long time. At this time, when a hypothesis test is performed on the account's live broadcast viewing time on all active accounts, there will be problems with extreme values ​​and a large number of zero values, resulting in low statistical effectiveness. The statistical effectiveness can be improved by the method provided in the embodiments of the present disclosure.

[0056] Therefore, in the embodiments of the present disclosure, when the benchmark account data and the test account data are obtained respectively, the benchmark account data and the test account data usually contain a large amount of zero-value data, which is data that does not generate user behavior in the account, such as zero viewing time. Therefore, it is necessary to consider the sparse data in the data to improve the inspection effectiveness.

[0057] In order to describe the solution clearly, the specific implementation method of obtaining the benchmark value corresponding to the benchmark account data and obtaining the test value corresponding to the test account data will be described in detail in the following embodiments.

[0058] In step S130, the test value is compared with the reference value to obtain a test result of the test account data.

[0059] In the embodiments provided by the present disclosure, by obtaining the benchmark value corresponding to the benchmark account data and the inspection value corresponding to the test account data, and comparing and analyzing the inspection value with the benchmark value, the test result of the test account data can be obtained. In the embodiments, the distribution of sparse data in the benchmark account data and the test account data is considered to obtain the benchmark value corresponding to the benchmark account data and the inspection value corresponding to the test account data, respectively, avoiding directly using the mean of the sample data as the inspection value and the benchmark value for comparative analysis, which can effectively improve the accuracy.

[0060] It should be noted that the sample in the embodiment may be a specific presentation form of benchmark account data or test account data, and the benchmark account data or test account data may respectively include one or more sample data, but is not limited thereto.

[0061] In the embodiment, the samples in the experimental group and the benchmark group can be repeatedly sampled several times without replacement, and the test value can be calculated using the samples obtained in each sampling to obtain the empirical distribution of the test value, and statistical inference can be performed based on the empirical distribution. The variance of the benchmark account data and the test account data can also be calculated based on the theoretical variance to obtain the variance of the test value, and then the statistical hypothesis test can be used to compare whether there is a significant difference between the benchmark account data and the test account data, thereby obtaining the test result of the test account data.

[0062] The data processing method provided by the embodiment of the present disclosure obtains the benchmark account data and the test account data respectively, obtains the benchmark value corresponding to the benchmark account data and the test value corresponding to the test account data, and obtains the test result of the test account data by comparing the test value with the benchmark value. Since the embodiment of the present disclosure processes the sparse data in the benchmark account data and the test account data to obtain the test value and the benchmark value respectively, the variance of the data can be reduced, thereby narrowing the confidence interval, avoiding the influence of extreme values ​​of the sparse data, and greatly improving the test effect.

[0063] In another embodiment provided by the present disclosure, based on the above embodiment, in order to determine how to obtain the reference value corresponding to the reference account data, such as Figure 2 As shown, step S120 may further include the following steps:

[0064] In step S121 , a first expected value of the reference account data and a first proportion of sparse data in the reference account data are obtained.

[0065] The first expected value is determined by the mean and variance of the sparse data in the benchmark account data.

[0066] In step S122, based on the first expected value of the benchmark account data and the first proportion of sparse data in the benchmark account data, a benchmark value corresponding to the benchmark account data is obtained.

[0067] In the embodiment provided by the present disclosure, the behavior of the base account can be divided into two steps:

[0068] The first step is to determine whether the behavior of the benchmark account occurs. If the behavior of the benchmark account does not occur, that is, no corresponding behavior data is generated, for example, the live broadcast viewing time corresponding to the benchmark account is 0, then its value is also 0; if the behavior of the benchmark account occurs, that is, corresponding behavior data is generated, then its value is greater than 0, for example, the viewing time corresponding to the benchmark account is greater than 0. Specifically, the proportion of sparse data in the test account data, p1, can be determined by the proportion of non-zero values ​​in the benchmark account data.

[0069] The second step is to estimate that the benchmark account data follows a log-normal distribution, and estimate the mean and variance of the log-normal distribution. Specifically, the mean of the sparse data in the benchmark account data, i.e., the data whose values ​​are not 0, is denoted as μ1; the standard deviation of the sparse data in the benchmark account data, i.e., the data whose values ​​are not 0, is denoted as σ1.

[0070] The expected value corresponding to the benchmark account data is p1*(exp(μ1+σ1 2 / 2)), where p1 is the proportion of sparse data, i.e., data with values ​​other than 0, in the benchmark account data, μ1 is the mean of the sparse data, i.e., data with values ​​other than 0, in the benchmark account data, and σ1 is the variance of the sparse data, i.e., data with values ​​other than 0, in the benchmark account data.

[0071] In the AB experiment scenario, the sample is usually a bucket or a group. If the sample is a bucket, the average of the expected values ​​obtained in the previous step of each bucket is used as the expected value corresponding to the benchmark account data; if the sample is a group, the expected value obtained in the previous step is directly used as the expected value corresponding to the benchmark account data.

[0072] In another embodiment provided by the present disclosure, based on the above embodiment, in order to determine how to test the verification value corresponding to the account data, such as Figure 3 As shown, step S120 may further include the following steps:

[0073] In step S123, a second expected value of the test account data and a second proportion of sparse data in the test account data are obtained.

[0074] The second expected value is determined by the mean and variance of the account data of the sparse data in the test account data.

[0075] In step S124, based on the second expected value of the test account data and the second proportion of sparse data in the test account data, a test value corresponding to the test account data is obtained.

[0076] Similarly, in the embodiment provided by the present disclosure, the behavior of the test account can also be divided into two steps:

[0077] The first step is to determine whether the test account's behavior occurs. If the test account's behavior does not occur, that is, no corresponding behavior data is generated, for example, the live broadcast viewing time corresponding to the test account is 0, then its value is also 0; if the test account's behavior occurs, that is, corresponding behavior data is generated, then its value is greater than 0. Specifically, the proportion of sparse data in the test account data, p2, can be determined through the proportion of values ​​in the test account data that are not 0.

[0078] The second step is to estimate that the base test account data follows a log-normal distribution, and estimate the mean and variance of the log-normal distribution. Specifically, the mean of the sparse data in the test account data, i.e., the data whose values ​​are not 0, is denoted as μ2; the standard deviation of the sparse data in the test account data, i.e., the data whose values ​​are not 0, is denoted as σ2.

[0079] The expected value of the test account data is p2*(exp(μ2+σ2 2 / 2)), where p2 is the proportion of sparse data in the test account data, i.e., data with values ​​other than 0, μ2 is the mean of the sparse data in the test account data, and σ2 is the variance of the sparse data in the test account data.

[0080] In the AB experiment scenario, the sample is usually a bucket or a group. If the sample is a bucket, the average of the expected values ​​obtained in the previous step of each bucket is used as the expected test value corresponding to the test account data; if the sample is a group, the expected value obtained in the previous step is directly used as the test value corresponding to the test account data.

[0081] In practical applications, if the sparse data in the benchmark account data and the test account data are significantly different, it will also affect the test results of the AB experiment. Therefore, in one embodiment, the data processing method may also include: predetermining that the product of the first expected value and the first ratio has a corresponding relationship with the product of the second expected value and the second ratio.

[0082] The corresponding relationship may be that the product of the first expected value and the first ratio is the same as the product of the second expected value and the second ratio. Of course, the corresponding relationship between the product of the first expected value and the first ratio and the product of the second expected value and the second ratio may also be set according to actual conditions, and the embodiments of the present disclosure do not specifically limit this.

[0083] Taking the example that the product of the first expected value and the first proportion is the same as the product of the second expected value and the second proportion, specifically, it can be predetermined that: p1*E(X1)=p2*E(X2), subscripts 1 and 2 represent the benchmark account data and the test experimental data, where p1 is the first proportion of sparse data in the benchmark account data, i.e., data with values ​​other than 0, E(X1) is the first expected value of the benchmark account data, p2 is the second proportion of sparse data in the benchmark account data, i.e., data with values ​​other than 0, and E(X2) is the second expected value of the test account data. The open source toolkit bootstrap or theoretical variance can be used to perform hypothesis testing. The theoretical variance can be solved using the law of total variance to obtain the conditional variance formula VAR(X)=p*(exp(2μ+σ2))*(exp(σ2)-p), where μ is the mean of the distribution and σ is the standard deviation of the distribution.

[0084] It can be seen that by setting conditions in advance, the test results can be prevented from being affected by inconsistencies in sparse data between the benchmark account data and the test account data.

[0085] It should be noted that there are many existing ways to determine the difference between two sets of data through expectation and variance in AB experiments, which will not be repeated here. However, for sparse and thick-tailed indicators in the disclosed embodiments, compared with directly using the sample mean as the test value, the disclosed embodiments can effectively narrow the confidence interval and reduce the second type error rate. In this way, by respectively obtaining the benchmark account data and the test account data, obtaining the benchmark value corresponding to the benchmark account data and the test value corresponding to the test account data, and comparing the test value with the benchmark value, the test result of the test account data is obtained. Since the disclosed embodiments will process the sparse data in the benchmark account data and the test account data to obtain the test value and the benchmark value respectively, the variance of the data can be reduced, thereby narrowing the confidence interval, avoiding the influence of extreme values ​​of sparse data, and greatly improving the test effect.

[0086] Figure 4 is a block diagram of a data processing device according to an exemplary embodiment. Figure 4 The device includes a data acquisition module 10, a value acquisition module 20 and a comparison module 30.

[0087] The data acquisition module 10 is configured to respectively acquire the benchmark account data and the test account data, wherein the benchmark account data is the data generated when the account accesses the benchmark program, and the test account data is the data generated when the account accesses the test program;

[0088] A value acquisition module 20, configured to acquire a reference value corresponding to the reference account data and a check value corresponding to the test account data, wherein the reference value is obtained by processing sparse data in the reference account data, and the check value is obtained by processing sparse data in the test account data;

[0089] The comparison module 30 is configured to compare the test value with the reference value to obtain a test result of the test account data.

[0090] In another embodiment provided by the present disclosure, the value acquisition module includes:

[0091] A first proportion determination submodule is configured to obtain a first expected value of the benchmark account data and a first proportion of sparse data in the benchmark account data, wherein the first expected value is determined by a mean and a variance of the sparse data in the benchmark account data;

[0092] The reference value acquisition submodule is configured to obtain a reference value corresponding to the reference account data based on a first expected value of the reference account data and a first proportion of sparse data in the reference account data.

[0093] In another embodiment provided by the present disclosure, the value acquisition module includes:

[0094] A second proportion determination submodule is configured to obtain a second expected value of the test account data and a second proportion of sparse data in the test account data, wherein the second expected value is determined by a mean and a variance of account data of the sparse data in the test account data;

[0095] The test value acquisition submodule is configured to obtain a test value corresponding to the test account data based on a second expected value of the test account data and a second proportion of sparse data in the test account data.

[0096] In another embodiment provided by the present disclosure, the device further includes:

[0097] The determination module is configured to predetermine that a product of the first expected value and the first ratio has a corresponding relationship with a product of the second expected value and the second ratio.

[0098] In another embodiment provided by the present disclosure, the sparse data is non-zero data in the benchmark account data or the test account data.

[0099] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0100] The data processing device provided by the embodiment of the present disclosure obtains the benchmark account data and the test account data respectively, obtains the benchmark value corresponding to the benchmark account data and the test value corresponding to the test account data, and obtains the test result of the test account data by comparing the test value with the benchmark value. Since the embodiment of the present disclosure processes the sparse data in the benchmark account data and the test account data to obtain the test value and the benchmark value respectively, the variance of the data can be reduced, thereby narrowing the confidence interval, avoiding the influence of extreme values ​​of the sparse data, and greatly improving the test effect.

[0101] Figure 5 8 is a block diagram of a data processing apparatus 800 according to an exemplary embodiment. For example, the apparatus 800 is an electronic device, which may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0102] Reference Figure 5 , the device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0103] The processing component 802 generally controls the overall operation of the device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0104] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0105] The power supply component 806 provides power to the various components of the device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 800.

[0106] The multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0107] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the device 800 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0108] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0109] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the device 800, and the sensor assembly 814 can also detect the position change of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration / deceleration of the device 800, and the temperature change of the device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0110] The communication component 816 is configured to facilitate wired or wireless communication between the device 800 and other devices. The device 800 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0111] In an exemplary embodiment, the apparatus 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above-mentioned data processing method.

[0112] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by the processor 820 of the device 800 to perform the above method. For example, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0113] The embodiment of the present disclosure also provides a computer-readable storage medium. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the above-mentioned data processing method.

[0114] Since the embodiments of the present disclosure process sparse data in the benchmark account data and the test account data to obtain the test value and the benchmark value respectively, the variance of the data can be reduced, thereby narrowing the confidence interval, avoiding the influence of extreme values ​​of the sparse data, and greatly improving the test effect.

[0115] According to an embodiment of the present disclosure, a computer program product is also provided. In another embodiment provided by the present disclosure, a computer program product containing instructions is also provided. When the computer program product is run on a computer, the computer executes the steps of the data processing method described in any one of the above embodiments.

[0116] Since the embodiments of the present disclosure process sparse data in the benchmark account data and the test account data to obtain the test value and the benchmark value respectively, the variance of the data can be reduced, thereby narrowing the confidence interval, avoiding the influence of extreme values ​​of the sparse data, and greatly improving the test effect.

[0117] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present disclosure is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, DSL (Digital Subscriber Line, digital subscriber line)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD (Digital Versatile Disc)), or a semiconductor medium (eg, an SSD (Solid State Disk)), etc.

[0118] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0119] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0120] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A data processing method, characterized in that: include: Respectively acquiring benchmark account data and test account data, wherein the benchmark account data is data generated when an account accesses a benchmark program, and the test account data is data generated when an account accesses a test program; Obtaining a reference value corresponding to the reference account data and a check value corresponding to the test account data, wherein the reference value is obtained based on processing sparse data in the reference account data, and the check value is obtained based on processing sparse data in the test account data; Comparing the test value with the reference value to obtain a test result of the test account data; Wherein, the obtaining of the reference value corresponding to the reference account data includes: Obtaining a first expected value of the benchmark account data and a first proportion of sparse data in the benchmark account data, wherein the first expected value is determined by a mean and a variance of the sparse data in the benchmark account data; Obtaining a reference value corresponding to the reference account data based on a first expected value of the reference account data and a first proportion of the sparse data in the reference account data; The obtaining the test value corresponding to the test account data includes: obtaining a second expected value of the test account data and a second proportion of sparse data in the test account data, wherein the second expected value is determined by a mean and a variance of the sparse data in the test account data; Obtaining a test value corresponding to the test account data based on a second expected value of the test account data and a second proportion of the sparse data in the test account data; The sparse data is data that is not zero in the benchmark account data or the test account data.

2. The method according to claim 1, characterized in that The method further comprises: It is predetermined that a product of the first expected value and the first ratio has a corresponding relationship with a product of the second expected value and the second ratio.

3. A data processing device, characterized in that: include: A data acquisition module, configured to respectively acquire benchmark account data and test account data, wherein the benchmark account data is data generated when an account accesses a benchmark program, and the test account data is data generated when an account accesses a test program; a value acquisition module configured to acquire a reference value corresponding to the reference account data and a check value corresponding to the test account data, wherein the reference value is obtained by processing sparse data in the reference account data, and the check value is obtained by processing sparse data in the test account data; a comparison module, configured to compare the test value with the reference value to obtain a test result of the test account data; Wherein, the value acquisition module includes: A first proportion determination submodule is configured to obtain a first expected value of the benchmark account data and a first proportion of sparse data in the benchmark account data, wherein the first expected value is determined by a mean and a variance of the sparse data in the benchmark account data; A reference value acquisition submodule, configured to obtain a reference value corresponding to the reference account data based on a first expected value of the reference account data and a first proportion of the sparse data in the reference account data; The value acquisition module further includes: A second proportion determination submodule is configured to obtain a second expected value of the test account data and a second proportion of sparse data in the test account data, wherein the second expected value is determined by a mean and a variance of the sparse data in the test account data; a test value acquisition submodule, configured to obtain a test value corresponding to the test account data based on a second expected value of the test account data and a second proportion of the sparse data in the test account data; The sparse data is data that is not zero in the benchmark account data or the test account data.

4. The device according to claim 3, characterized in that The device also includes: The determination module is configured to predetermine that a product of the first expected value and the first ratio has a corresponding relationship with a product of the second expected value and the second ratio.

5. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to execute the data processing method described in any one of claims 1-2.

6. A computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute a data processing method, which includes the steps of the data processing method described in any one of claims 1-2.

7. A computer program product, characterized in that When it is run on a computer, it enables the computer to execute the steps of the data processing method described in any one of claims 1-2.

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