Data processing method, device, apparatus, and computer storage medium

Abstract

The application discloses a data processing method and device, equipment and a computer storage medium. The method comprises the following steps: obtaining target behavior data corresponding to a data index, the target behavior data comprising a plurality of target variables and variable values of the plurality of target variables; selecting a plurality of significant variables with a similarity to the data index greater than a preset threshold from the plurality of target variables to obtain first significant variables of the data index; selecting a significant variable with the greatest similarity to the data index from the first significant variables to obtain a first target significant variable of the data index; and counting the variable values of the first target significant variable to obtain statistical information of the first target significant variable; and displaying the statistical information. Through real-time counting and displaying of the statistical information, a user can discover abnormal data in a timely manner. In the case of discovering abnormal data, the user can directly view statistical information corresponding to other associated data indexes, thereby improving the efficiency of finding problems.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a data processing method, apparatus, device, and computer storage medium. Background Technology

[0002] Operational data monitoring is very meaningful. Data monitoring is a timely and effective means of reporting data anomalies. Users can observe whether there are any anomalies by monitoring the data, and then analyze why the data is abnormal.

[0003] In existing technologies, users collect event tracking data from the system according to their needs and perform statistical analysis on this data to check for anomalies. However, this data monitoring method cannot detect anomalies in a timely manner, and when anomalies do occur, users still need to combine their experience to obtain a large amount of event tracking data and check and analyze the reasons for the anomalies step by step, which is inefficient. Summary of the Invention

[0004] This application provides a data processing method, apparatus, device, and computer storage medium. By performing real-time statistics on embedded behavior data corresponding to data indicators and visually displaying the statistical information, users can periodically check for data anomalies based on the visualized statistical information, enabling timely detection of abnormal data. When users discover abnormal data, they can directly view the statistical information corresponding to other related data indicators without needing to retrieve and statistically analyze the user's embedded behavior data again, thus improving the efficiency of problem finding.

[0005] In a first aspect, embodiments of this application provide a data processing method, including:

[0006] Obtain target event behavior data corresponding to data metrics. Target event behavior data includes multiple target variables and the variable values ​​of multiple target variables.

[0007] Using the first selection method, multiple significant variables with a similarity greater than a preset threshold to the data indicator are selected from multiple target variables to obtain the first significant variable of the data indicator;

[0008] Using the second selection method, the significant variable with the highest similarity to the data indicator is selected from the first significant variables to obtain the first target significant variable of the data indicator;

[0009] The values ​​of the first objective significant variable are statistically analyzed to obtain the statistical information of the first objective significant variable;

[0010] Display statistical information.

[0011] In one possible implementation, before acquiring the target tracking behavior data corresponding to the data metrics, the method includes:

[0012] The first tracking point behavior data of the received data metrics includes multiple variables and the variable values ​​of the multiple variables;

[0013] Based on the relationship between data indicators and variables, the first variable and its value corresponding to the data indicator are selected from the first tracking point behavior data to obtain the target tracking point behavior data.

[0014] In one possible implementation, based on the relationship information between data indicators and variables, the target tracking behavior data is obtained by selecting the first variable corresponding to the data indicator and the variable value of the first variable from the first tracking behavior data, including:

[0015] Based on the relationship between data indicators and variables, the first variable and its value corresponding to the data indicator are selected from the first tracking point behavior data to obtain the second tracking point behavior data.

[0016] Based on the preset rule information, the variable values ​​of the second variable and the second variable are selected from the second tracking point behavior data to obtain the target tracking point behavior data.

[0017] In one possible implementation embodiment, before selecting multiple significant variables from multiple target variables whose similarity to the data indicator is greater than a preset threshold using the first selection method to obtain the first significant variable of the data indicator, the method further includes:

[0018] Using the third selection method, several significant variables with a similarity greater than a preset threshold to the data indicators are selected from multiple target variables, and the similarity between the multiple significant variables and the data indicators is determined.

[0019] Calculate the average similarity for each;

[0020] The third selection method with the largest average value is selected, and this third selection method with the largest average value is used as the first selection method.

[0021] In one possible implementation embodiment, it further includes:

[0022] The second significant variable is obtained by replacing the fourth variable in the first significant variable with the third variable. The third variable is any variable other than the first significant variable among multiple target variables, and the fourth variable is any variable other than the first target significant variable among the first significant variables.

[0023] Using the second selection method, the significant variable with the highest similarity to the data indicator is selected from the second significant variables to obtain the second target significant variable of the data indicator;

[0024] If the first and second objective significant variables are the same, the variable values ​​of the first objective significant variable are statistically analyzed to obtain the statistical information of the first objective significant variable.

[0025] In one possible implementation embodiment, it further includes:

[0026] If the significant variables of the first objective and the significant variables of the second objective are inconsistent, the third selection method with the largest average value among the third selection methods (excluding the third selection method with the largest average value) shall be selected as the first selection method.

[0027] In one possible implementation, statistical information is displayed, including:

[0028] The statistical information of the first target significant variable within the first preset time period is fitted to obtain the relationship information between the target time and the statistical information;

[0029] Based on the relationship between the target time and statistical information, determine the statistical information for the second preset time period after the first preset time period;

[0030] Display statistical information for the first and second preset time periods.

[0031] In one possible implementation, the statistical information includes at least one of the mean of the first target significant variable, the standard deviation of the first target significant variable, and the Cronbach's alpha value of the first target significant variable.

[0032] Secondly, embodiments of this application provide a data processing apparatus, including:

[0033] The acquisition module is used to acquire target tracking behavior data corresponding to data metrics. The target tracking behavior data includes multiple target variables and the variable values ​​of multiple target variables.

[0034] The selection module is used to select multiple significant variables from multiple target variables whose similarity to the data indicator is greater than a preset threshold using a first selection method, thereby obtaining the first significant variable of the data indicator.

[0035] The selection module is also used to select the most similar significant variable to the data indicator from the first significant variables using a second selection method, so as to obtain the first target significant variable of the data indicator;

[0036] The statistics module is used to calculate the variable values ​​of the first objective significant variable and obtain statistical information about the first objective significant variable.

[0037] The display module is used to show statistical information.

[0038] Thirdly, embodiments of this application provide an electronic device, the device comprising:

[0039] Processor and memory storing computer program instructions;

[0040] The data processing method that implements any of the above when the processor executes computer program instructions.

[0041] Fourthly, embodiments of this application provide a computer storage medium on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the data processing method described above is implemented.

[0042] Fifthly, embodiments of this application provide a computer program product, characterized in that, when the instructions in the computer program product are executed by the processor of an electronic device, the electronic device is able to execute any of the above-mentioned data processing methods.

[0043] The data processing method, apparatus, device, and computer storage medium of this application embodiment include: acquiring target tracking behavior data corresponding to a data indicator, the target tracking behavior data including multiple target variables and variable values ​​of the multiple target variables; using a first selection method, selecting multiple significant variables from the multiple target variables whose similarity to the data indicator is greater than a preset threshold to obtain a first significant variable of the data indicator; using a second selection method, selecting the significant variable with the highest similarity to the data indicator from the first significant variables to obtain a first target significant variable of the data indicator; statistically analyzing the variable values ​​of the first target significant variable to obtain statistical information of the first target significant variable; and displaying the statistical information. Thus, by statistically analyzing the tracking behavior data corresponding to the data indicator in real time and visually displaying the statistical information in real time, users can periodically check whether there are any anomalies in the data based on the visualization of the statistical information, and can promptly discover abnormal data. When users discover abnormal data, they can directly view the statistical information corresponding to other related data indicators without needing to acquire user tracking behavior data for statistical analysis, thus improving the efficiency of finding problems. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic flowchart of a data processing method provided in one embodiment of this application;

[0046] Figure 2 This is a flowchart illustrating a data processing method provided in another embodiment of this application;

[0047] Figure 3 This is a schematic diagram of the structure of a data processing apparatus provided in another embodiment of this application;

[0048] Figure 4 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0049] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended only to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0050] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0051] It should be noted that the acquisition, storage, use, and processing of data in this application embodiment all comply with the relevant provisions of national laws and regulations.

[0052] Operational data monitoring is very meaningful. Data monitoring is a timely and effective means of reporting data anomalies. Users can observe whether there are any anomalies by monitoring the data, and then analyze why the data is abnormal.

[0053] In existing technologies, users collect event tracking data from the system according to their needs and perform statistical analysis on this data to check for anomalies. However, this data monitoring method cannot detect anomalies in a timely manner, and when anomalies do occur, users still need to combine their experience to obtain a large amount of event tracking data and check and analyze the reasons for the anomalies step by step, which is inefficient.

[0054] To address the problems of the prior art, embodiments of this application provide a data processing method, apparatus, device, and computer storage medium. The data processing method provided in this application embodiment will be described first below.

[0055] Figure 1 A flowchart illustrating a data processing method provided in one embodiment of this application is shown.

[0056] like Figure 1 As shown, the data processing method provided in this application includes the following steps.

[0057] S110. Obtain the target tracking behavior data corresponding to the data indicators. The target tracking behavior data includes multiple target variables and the variable values ​​of the multiple target variables.

[0058] Here, data metrics are pre-defined indicators, each including corresponding target event tracking data. For example, a data metric could be the forwarding rate, the frequency of visits to a target page, the product usage rate, or the depth of product usage. Target event tracking data is data generated when users input information, such as clicks on a forward button. The target variable includes the number of clicks on the forward button, with the corresponding numerical value being the variable value. In some embodiments, the target event tracking data corresponding to the data metric is acquired in real time. One data metric can correspond to multiple target variables.

[0059] S120. Using the first selection method, select multiple significant variables from multiple target variables whose similarity to the data indicator is greater than a preset threshold to obtain the first significant variable of the data indicator.

[0060] Here, the preset threshold is set in advance.

[0061] In some embodiments, using a first selection method, the similarity between the target variable and the data indicator is calculated, and multiple significant variables with a similarity greater than a preset threshold are selected from multiple target variables to obtain the first significant variable of the data indicator.

[0062] In some embodiments, the first selection method may be a first preset function, which may include, but is not limited to, regression equations and correlation equations. The first selection method may also be implemented using a tool for identifying significant variables, such as SPSS software. Here, the first selection method is not specifically limited; it is sufficient to identify significant variables.

[0063] In some embodiments, the plurality of target variables includes a first significant variable.

[0064] In some embodiments, the first significant variable can be determined based on the number of significant variables, the similarity between the target variable and the data indicator can be calculated, the similarity can be sorted from largest to smallest, and the top n variables can be selected as significant variables to obtain the first significant variable of the data indicator.

[0065] S130. Using the second selection method, select the significant variable with the highest similarity to the data indicator from the first significant variables to obtain the first target significant variable of the data indicator.

[0066] In some embodiments, using a second selection method, the similarity between the first significant variable and the data indicator is calculated, and the significant variable with the highest similarity to the data indicator is selected from the first significant variables to obtain the first target significant variable of the data indicator.

[0067] In some implementation examples, the second selection method may include, but is not limited to, more advanced regression analysis, mediation effects, and moderating effects. There are no specific limitations on the second selection method here; it is sufficient to identify significant variables.

[0068] In some embodiments, before selecting the significant variable with the highest similarity to the data indicator from the first significant variables using the second selection method to obtain the first target significant variable of the data indicator, the significant variable with the highest similarity to the data indicator from the first significant variables is selected using the fourth selection method, and the similarity between the data indicator and the significant variable is determined. The fourth selection method with the highest similarity is selected as the second selection method.

[0069] S140. Calculate the variable values ​​of the first objective significant variable to obtain the statistical information of the first objective significant variable.

[0070] In some implementation examples, the values ​​of the first target significant variable can be inserted into a data table, and statistical information about the variable values ​​can be calculated and inserted into the data table as well. Here, the values ​​of the first target significant variable change over time.

[0071] S150, Display statistical information.

[0072] In some embodiments, visualization tools are used to display the statistical information of the first target significant variable for each data indicator within a preset time period.

[0073] In some embodiments, the first significant variable is the variable that has the greatest impact on the data metric. For example, the data metric could be the forwarding rate, and the first significant variable could be the number of clicks on the forward button (it can be understood that the forwarding rate is not only related to the number of clicks on the forward button, but also to the total number of viewers, etc.). When a user finds that the forwarding rate is low, they can promptly check whether the number of clicks on the forward button is abnormal. If an abnormality is found, they can directly check the statistical information of the first significant variable corresponding to the data metric related to the forwarding rate. For example, the data metric related to the forwarding rate could be the playback rate. By checking whether the statistical information of the first significant variable corresponding to the playback rate (e.g., the number of times the page is opened) is problematic, the source of the problem can be determined.

[0074] In this way, by statistically analyzing the event tracking data corresponding to the data metrics in real time and visualizing the statistical information, users can periodically check for data anomalies based on the visualization of the statistical information, enabling timely detection of abnormal data. If users discover abnormal data, they can directly view the statistical information corresponding to other related data metrics without needing to retrieve and analyze the user's event tracking data again, thus improving the efficiency of problem finding.

[0075] Based on this, in some embodiments, such as Figure 2 As shown, before S110 above, the method may also include S101 and S102.

[0076] S101, Receive the first tracking point behavior data of the data indicator. The first tracking point behavior data includes multiple variables and the variable values ​​of the multiple variables.

[0077] In some embodiments, event tracking is used to record user behavior processes and results to meet the needs of fast, efficient, and rich data applications. Event tracking is a commonly used data collection method. By collecting data through event tracking, one can analyze the usage of a website or app, or user behavior habits, etc., which is the foundation for building data products such as user profiles and user behavior paths. For a website or app, event tracking can be used for monitoring.

[0078] In some embodiments, the first event tracking data is generated through data tracking.

[0079] S102. Based on the relationship information between data indicators and variables, select the first variable and the variable value of the first variable corresponding to the data indicator from the first embedding behavior data to obtain the target embedding behavior data.

[0080] In some embodiments, the relationship between data metrics and variables is pre-defined.

[0081] In some embodiments, based on the relationship information between data metrics and variables, a first variable and its value corresponding to the data metric are selected from the first event tracking behavior data, and variables and their values ​​that are not related to the data metric are filtered out to obtain the target event tracking behavior data. Here, the first variable is the target variable.

[0082] In some embodiments, the first tracking point behavior data is cleaned to remove duplicate variables and variable values. It should be noted that when the same variable has the same value at the same time, it is considered a duplicate variable value.

[0083] In this way, by selecting the first data point behavior data that is relevant to the data indicators and removing impurity data, the accuracy of determining significant variables is improved, thereby improving the accuracy of statistical information.

[0084] Based on this, in some embodiments, the above-mentioned S102 may specifically include:

[0085] Based on the relationship between data indicators and variables, the first variable and its value corresponding to the data indicator are selected from the first tracking point behavior data to obtain the second tracking point behavior data.

[0086] Based on the preset rule information, the variable values ​​of the second variable and the second variable are selected from the second tracking point behavior data to obtain the target tracking point behavior data.

[0087] Here, the preset rule information is set in advance, including the selection information of variables.

[0088] In some embodiments, after filtering out variables and variable values ​​that are irrelevant to the data metrics, the variable values ​​of the second variable and the second variable are selected from the second tracking behavior data according to preset rule information, and variables of target type, target code (Stock Keeping Unit, SKU), or target brand are filtered out to obtain target tracking behavior data. Here, the second variable is the target variable.

[0089] In some embodiments, the system may also receive user input to select a target variable and its value.

[0090] In this way, the values ​​of the second variable and the second variable can be selected from the second tracking point behavior data according to user needs, thereby improving the user experience.

[0091] Based on this, in some embodiments, prior to the above-described S120, the method may further include:

[0092] Using the third selection method, several significant variables with a similarity greater than a preset threshold to the data indicators are selected from multiple target variables, and the similarity between the multiple significant variables and the data indicators is determined.

[0093] Calculate the average similarity for each;

[0094] The third selection method with the largest average value is selected, and this third selection method with the largest average value is used as the first selection method.

[0095] In some embodiments, the third selection method is pre-defined. Multiple third selection methods are included.

[0096] In this way, the optimal selection method can be chosen from multiple selection methods, which improves the accuracy of identifying significant information and thus improves the accuracy of statistical information.

[0097] Based on this, in some embodiments, the method may further include:

[0098] The second significant variable is obtained by replacing the fourth variable in the first significant variable with the third variable. The third variable is any variable other than the first significant variable among multiple target variables, and the fourth variable is any variable other than the first target significant variable among the first significant variables.

[0099] Using the second selection method, the significant variable with the highest similarity to the data indicator is selected from the second significant variables to obtain the second target significant variable of the data indicator;

[0100] If the first and second objective significant variables are the same, the variable values ​​of the first objective significant variable are statistically analyzed to obtain the statistical information of the first objective significant variable.

[0101] In some embodiments, any variable other than the first significant variable from among multiple target variables is used to replace any variable other than the first significant variable from among the first significant variables. Using a second selection method, the significant variable with the highest similarity to the data indicator is selected from the second significant variables to obtain the second target significant variable of the data indicator. It is then determined whether the first target significant variable and the second target significant variable are the same. If they are the same, the value of the first target significant variable is statistically analyzed to obtain the statistical information of the first target significant variable.

[0102] In some embodiments, robustness and heterogeneity tests can be performed to determine whether the determination of the first significant variable is accidental. Robustness tests examine the robustness of the evaluation methods and indicators' explanatory power; that is, whether the evaluation methods and indicators still maintain a relatively consistent and stable interpretation of the evaluation results when certain parameters are changed. In simpler terms, it involves changing a specific parameter, conducting repeated experiments, and observing whether the empirical results change with the parameter settings. If the sign and significance of the results change after changing the parameter settings, it indicates a lack of robustness. Heterogeneity tests describe the differences and diversity of measurement results among participants, interventions, and a range of studies, or the variations in the intrinsic validity of those studies. In other words, robustness and heterogeneity tests can determine whether the first significant target variable changes after variable substitution.

[0103] In this way, after replacing the variable, comparing whether the first and second significant variables are consistent avoids the randomness in determining the first significant variable.

[0104] Based on this, in some embodiments, the method may further include:

[0105] If the significant variables of the first objective and the significant variables of the second objective are inconsistent, the third selection method with the largest average value among the third selection methods (excluding the third selection method with the largest average value) shall be selected as the first selection method.

[0106] In some embodiments, if the first target significant variable and the second target significant variable are inconsistent, it indicates that the determination of the first significant variable is accidental and it is necessary to reselect the selection method to select the first target significant variable.

[0107] In this way, by changing the selection method and selecting the first significant variable again, the randomness in determining the first significant variable is avoided.

[0108] Based on this, in some embodiments, the above-mentioned S150 may specifically include:

[0109] The statistical information of the first target significant variable within the first preset time period is fitted to obtain the relationship information between the target time and the statistical information;

[0110] Based on the relationship between the target time and statistical information, determine the statistical information for the second preset time period after the first preset time period;

[0111] Display statistical information for the first and second preset time periods.

[0112] In some embodiments, users can analyze statistical information from multiple dimensions by displaying statistical information, such as trends and comparisons.

[0113] As an example, users can see the trend and fluctuations of statistical information by comparing the relationship between a target time and statistical information (which could be a curve). They can also compare statistical information from July of the same year with that from June to see the changes and analyze the reasons for these changes based on the corresponding time periods.

[0114] In this way, users can intuitively see the changes in statistical information. It not only displays past and present statistical changes, but also predicts future statistical information, allowing users to make timely adjustments based on the predicted data and reduce anomalies.

[0115] Based on this, in some embodiments, the statistical information includes at least one of the mean of the first target significant variable, the standard deviation of the first target significant variable, and the Cronbach's alpha value of the first target significant variable.

[0116] In some embodiments, the statistical information may include, but is not limited to, at least one of the mean of the first target significant variable, the standard deviation of the first target significant variable, and the Cronbach's alpha value of the first target significant variable.

[0117] In this way, users can intuitively see how the primary target significant variable changes based on statistical information, without having to calculate it themselves according to their needs.

[0118] Based on the data processing method provided in the above embodiments, this application also provides specific implementations of a data processing apparatus. Please refer to the following embodiments.

[0119] See Figure 3 The data processing apparatus 300 provided in this application embodiment includes:

[0120] The acquisition module 310 is used to acquire target tracking behavior data corresponding to data metrics. The target tracking behavior data includes multiple target variables and the variable values ​​of multiple target variables.

[0121] The selection module 320 is used to select multiple significant variables from multiple target variables whose similarity to the data indicator is greater than a preset threshold using a first selection method, thereby obtaining the first significant variable of the data indicator.

[0122] The selection module 320 is also used to select the most similar significant variable to the data indicator from the first significant variables using the second selection method, so as to obtain the first target significant variable of the data indicator;

[0123] The statistics module 330 is used to count the variable values ​​of the first target significant variable and obtain the statistical information of the first target significant variable;

[0124] Display module 340 is used to display statistical information.

[0125] Based on this, in some embodiments, the device 300 may further include:

[0126] The receiving module is used to receive the first tracking behavior data of the data indicator before acquiring the target tracking behavior data corresponding to the data indicator. The first tracking behavior data includes multiple variables and the variable values ​​of the multiple variables.

[0127] The selection module 320 is also used to select the first variable and the variable value of the first variable corresponding to the data indicator from the first tracking point behavior data based on the relationship information between the data indicator and the variable, so as to obtain the target tracking point behavior data.

[0128] Based on this, in some embodiments, the selection module 320 can specifically be used for:

[0129] Based on the relationship between data indicators and variables, the first variable and its value corresponding to the data indicator are selected from the first tracking point behavior data to obtain the second tracking point behavior data.

[0130] Based on the preset rule information, the variable values ​​of the second variable and the second variable are selected from the second tracking point behavior data to obtain the target tracking point behavior data.

[0131] Based on this, in some embodiments, the device 300 may further include:

[0132] The selection module 320 is also used to select multiple significant variables with a similarity greater than a preset threshold to the data indicator from multiple target variables using the first selection method to obtain the first significant variable of the data indicator, and to select multiple significant variables with a similarity greater than a preset threshold to the data indicator from multiple target variables using the third selection method, and to determine the similarity between the multiple significant variables and the data indicator.

[0133] The calculation module is used to calculate the average similarity score for each item.

[0134] The selection module 320 is also used to select the third selection method with the largest average value, and to use the third selection information with the largest average value as the first selection method.

[0135] Based on this, in some embodiments, the device 300 may further include:

[0136] The replacement module is used to replace the fourth variable in the first significant variable with the third variable to obtain the second significant variable. The third variable is any variable other than the first significant variable among multiple target variables, and the fourth variable is any variable other than the first target significant variable among the first significant variables.

[0137] The selection module 320 is also used to select the significant variable with the greatest similarity to the data indicator from the second significant variables using the second selection method, so as to obtain the second target significant variable of the data indicator;

[0138] The statistics module 330 is also used to calculate the variable value of the first target significant variable and obtain statistical information of the first target significant variable when the first target significant variable and the second target significant variable are the same.

[0139] Based on this, in some embodiments, the device 300 may further include:

[0140] The selection module 320 is also used to select, when the first objective significant variable and the second objective significant variable are inconsistent, the third selection method with the largest average value other than the third selection method with the largest average value from the third selection methods as the first selection method.

[0141] Based on this, in some embodiments, the display module 340 may specifically include:

[0142] The fitting unit is used to fit the statistical information of the first target significant variable within the first preset time period to obtain the relationship information between the target time and the statistical information.

[0143] The determining unit is used to determine the statistical information within a second preset time period after the first preset time period based on the relationship information between the target time and the statistical information;

[0144] The display unit is used to display statistical information within the first preset time period and the second preset time period.

[0145] Based on this, in some embodiments, the statistical information includes at least one of the mean of the first target significant variable, the standard deviation of the first target significant variable, and the Cronbach's alpha value of the first target significant variable.

[0146] Each module of the data processing apparatus provided in this application embodiment can realize the functions of each step of the data processing method provided above and achieve its corresponding technical effects. For the sake of brevity, it will not be described in detail here.

[0147] Based on the same inventive concept, embodiments of this application also provide an electronic device.

[0148] Figure 4 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0149] An electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0150] Specifically, the processor 401 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0151] Memory 402 may include mass storage for data or instructions. For example, and not limitingly, memory 402 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 402 is non-volatile solid-state memory.

[0152] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0153] The processor 401 implements any of the data processing methods described in the above embodiments by reading and executing computer program instructions stored in the memory 402.

[0154] In one example, the electronic device may also include a communication interface 403 and a bus 410. For example, Figure 4 As shown, the processor 401, memory 402, and communication interface 403 are connected through bus 410 and complete communication with each other.

[0155] The communication interface 403 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0156] Bus 410 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Linear Predictive Coding (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (Peripheral Component Interconnect-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VESA Local Bus, VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 410 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application contemplates any suitable bus or interconnection. The electronic device can perform the data processing methods described in the embodiments of the present invention, thereby implementing the data processing methods described above.

[0157] Furthermore, in conjunction with the data processing methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the data processing methods in the above embodiments.

[0158] This application also provides a computer program product, wherein the instructions in the computer program product, when executed by the processor of an electronic device, cause the electronic device to perform various processes implementing any of the above-described data processing method embodiments.

[0159] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0160] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memory (ROM), flash memory, erasable read-only memory (EROM), floppy disks, compact disc read-only memory (CD-ROM), optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0161] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0162] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0163] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A data processing method, characterized by, The method comprises the following steps: obtaining target behavior data corresponding to a set data index, the target behavior data comprising a plurality of target variables and variable values of the plurality of target variables, the target behavior data being data generated when a user inputs information; selecting, by using a first selection method, a plurality of significant variables from the plurality of target variables, the plurality of significant variables having a similarity to the data index greater than a preset threshold, to obtain first significant variables of the data index; selecting, by using a second selection method, a significant variable having a maximum similarity to the data index from the first significant variables, to obtain first target significant variables of the data index, the first target significant variables being variables having the greatest impact on the data index; counting variable values of the first target significant variables to obtain statistical information of the first target significant variables; displaying the statistical information. The method further comprises the following steps: replacing, by using a third variable, a fourth variable in the first significant variables to obtain second significant variables, the third variable being any one of the plurality of target variables other than the first significant variables, and the fourth variable being any one of the first significant variables other than the first target significant variables; selecting, by using the second selection method, a significant variable having a maximum similarity to the data index from the second significant variables, to obtain second target significant variables of the data index; in a case where the first target significant variables and the second target significant variables are consistent, counting variable values of the first target significant variables to obtain statistical information of the first target significant variables.

2. The data processing method according to claim 1, characterized in that, Before obtaining the target behavior data corresponding to the data index, the method comprises the following steps: receiving first behavior data of the data index, the first behavior data comprising a plurality of variables and variable values of the plurality of variables; selecting, according to relationship information between the data index and the variables, a first variable corresponding to the data index and a variable value of the first variable from the first behavior data, to obtain the target behavior data.

3. The data processing method according to claim 2, characterized in that, The step of selecting, according to relationship information between the data index and the variables, a first variable corresponding to the data index and a variable value of the first variable from the first behavior data, to obtain the target behavior data, comprises the following steps: selecting, according to relationship information between the data index and the variables, a first variable corresponding to the data index and a variable value of the first variable from the first behavior data, to obtain second behavior data; selecting, according to preset rule information, a second variable and a variable value of the second variable from the second behavior data, to obtain the target behavior data.

4. The data processing method of claim 1, wherein, Before selecting, by using a first selection method, a plurality of significant variables from the plurality of target variables, the plurality of significant variables having a similarity to the data index greater than a preset threshold, to obtain first significant variables of the data index, the method further comprises the following steps: selecting, by using a third selection method, a plurality of significant variables from the plurality of target variables, the plurality of significant variables having a similarity to the data index greater than a preset threshold, and determining similarities of the plurality of significant variables to the data index; calculating average values of the similarities, respectively. The third selection method with the largest average value is selected, and the third selection information with the largest average value is used as the first selection method.

5. The data processing method according to claim 4, characterized in that, Also includes: If the first target significant variable and the second target significant variable are inconsistent, the third selection method with the largest average value other than the third selection method with the largest average value shall be selected as the first selection method.

6. The data processing method of claim 1, wherein, The presentation of the statistical information includes: The statistical information of the first target significant variable within the first preset time period is fitted to obtain the relationship information between the target time and the statistical information; Based on the relationship between the target time and the statistical information, determine the statistical information within the second preset time period after the first preset time period; Display statistical information for the first preset time period and the second preset time period.

7. The data processing method according to any one of claims 1 to 6, characterized in that, The statistical information includes at least one of the mean of the first target significant variable, the standard deviation of the first target significant variable, and the Cronbach's alpha value of the first target significant variable.

8. A data processing apparatus, characterized by, include: The acquisition module is used to acquire target tracking behavior data corresponding to the set data indicators. The target tracking behavior data includes multiple target variables and the variable values ​​of the multiple target variables. The target tracking behavior data is the data generated when the user inputs information. The selection module is used to select multiple significant variables from the multiple target variables whose similarity to the data indicator is greater than a preset threshold using a first selection method, thereby obtaining the first significant variable of the data indicator; The selection module is further configured to use a second selection method to select the most similar significant variable to the data indicator from the first significant variables, thereby obtaining the first target significant variable of the data indicator, wherein the first target significant variable is the variable that has the greatest impact on the data indicator; The statistics module is used to count the variable values ​​of the first target significant variable and obtain the statistical information of the first target significant variable; The display module is used to display the statistical information; The replacement module is used to replace the fourth variable in the first significant variable with a third variable to obtain a second significant variable. The third variable is any one of the plurality of target variables other than the first significant variable, and the fourth variable is any one of the first significant variables other than the first target significant variable. The selection module is further configured to use the second selection method to select the significant variable with the highest similarity to the data indicator from the second significant variables, thereby obtaining the second target significant variable of the data indicator; The statistical module is further configured to, when the first target significant variable and the second target significant variable are consistent, statistically analyze the variable value of the first target significant variable to obtain statistical information of the first target significant variable.

9. An electronic device, comprising: The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the data processing method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer program instructions, and the computer program instructions are executed by a processor to implement the data processing method in any one of claims 1-7.

11. A computer program product, characterised in that, The instructions in the computer program product are executed by a processor of an electronic device, so that the electronic device can execute the data processing method in any one of claims 1-7.

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