A data processing method, device and electronic device based on multi-platform data

Abstract

The present specification discloses a data processing method, device and electronic device based on multi-platform data, which specifically includes: obtaining the target application data of the target application on each application platform, and storing it in a data table according to each preset field in the data table. Obtain the data analysis indicators corresponding to each application platform, summarize each data analysis indicator, and obtain each summarized data analysis indicator. For each summarized data analysis indicator, determine the analysis result data corresponding to each summarized data analysis indicator according to the data stored in the data table. Perform data processing based on the analysis result data corresponding to each summarized data analysis indicator. The method in this specification effectively improves the work efficiency of the data processing process by unifying, integrating, collating and analyzing the multi-platform application data from multiple angles, and facilitates the subsequent analysis process for the target application, thereby indirectly improving the optimization and update speed for the target application to a certain extent.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a data processing method, device and electronic device based on multi-platform data. Background Art

[0002] With the development of network technology, application software developers often release their applications in multiple countries or regions and on application platforms of different systems (such as IOS, Android, etc.) in order to promote the update and optimization of application software and improve user experience, and collect application data in a unified manner for comprehensive analysis.

[0003] Taking multiple countries or regions as an example, due to the different policies and regulations corresponding to different regions, the same application software will involve different application data and data analysis dimensions on the application platforms in different regions. If the application data and data analysis dimensions of the application platforms in each region are simply integrated, the overall application data volume will be too large when the integrated data is subsequently analyzed comprehensively, and the application data corresponding to the application platforms in each region and the analysis result data corresponding to each data analysis dimension will be mixed together, which will lead to heavy and extremely complicated tasks in the process of statistical data sorting, seriously affecting the efficiency of statistics and analysis.

[0004] Therefore, how to efficiently process application data of application software on multiple application platforms is a crucial issue. Summary of the invention

[0005] This specification provides a data processing method, device and electronic device based on multi-platform data to partially solve the above-mentioned problems existing in the prior art.

[0006] This manual adopts the following technical solutions:

[0007] This specification provides a data processing method based on multi-platform data, including:

[0008] Obtain target application data of the target application on each application platform, wherein for each application platform, the target application data corresponding to the target application on the application platform at least includes user information data, application configuration data, and operation data of the target application on the application platform;

[0009] According to each preset field set in the determined preset data table, storing the target application data on each application platform in the data table;

[0010] Obtaining data analysis indicators corresponding to the application platforms, and summarizing the data analysis indicators corresponding to the application platforms to obtain summarized data analysis indicators;

[0011] For each summarized data analysis indicator, determine the analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table;

[0012] Data processing is performed based on the analysis result data corresponding to each summarized data analysis indicator.

[0013] Optionally, before the step of determining, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table, the method further includes:

[0014] Classify the data stored in the data table according to a preset classification dimension to obtain a classification result;

[0015] The step of determining, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table specifically includes:

[0016] For each summarized data analysis indicator, the analysis result data corresponding to the summarized data analysis indicator is determined according to the classification result.

[0017] Optionally, the step of determining, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table, specifically includes:

[0018] For each summarized data analysis indicator, obtaining historical analysis result data corresponding to the summarized data analysis indicator pre-saved by an application platform using the summarized data analysis indicator;

[0019] The analysis result data corresponding to the summarized data analysis indicator is determined based on the acquired historical analysis result data and the data stored in the data table.

[0020] Optionally, before the step of determining, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table, the method further includes:

[0021] Obtaining time zone information corresponding to each application platform;

[0022] For each application platform, when it is determined that the time zone information corresponding to the application platform does not match the preset reference time zone information, time zone adjustment is performed on the time data of the application platform in the data stored in the data table according to the reference time zone information to obtain a data table after the time zone adjustment;

[0023] The step of determining, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table specifically includes:

[0024] For each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator is determined according to the data stored in the data table after the time zone adjustment.

[0025] Optionally, the step of performing data processing according to the analysis result data corresponding to each summarized data analysis indicator specifically includes:

[0026] According to the preset data cleaning rules, a data cleaning operation is performed on the analysis result data corresponding to each summarized data analysis indicator to determine the cleaned analysis result data corresponding to each summarized data analysis indicator, wherein the data cleaning rules at least include a data replacement processing rule, a data deletion processing rule, and a data desensitization processing rule;

[0027] Data processing is performed based on the cleaned analysis result data corresponding to each summarized data analysis indicator.

[0028] Optionally, the step of aggregating the data analysis indicators corresponding to the application platforms to obtain the aggregated data analysis indicators specifically includes:

[0029] For each data analysis indicator, obtain the historical analysis result data corresponding to the data analysis indicator stored in the application platform using the data analysis indicator, and determine the field information corresponding to the data analysis indicator;

[0030] For any two data analysis indicators from different application platforms, the historical analysis results corresponding to the two data analysis indicators and the field information corresponding to the two data analysis indicators are input into a preset indicator classification model, so as to determine the indicator matching degree between the two data analysis indicators through the indicator classification model;

[0031] According to the indicator matching degree between any two data analysis indicators from different application platforms, the data analysis indicators corresponding to the application platforms are summarized to obtain the summarized data analysis indicators.

[0032] Optionally, the step of determining each preset field set in the preset data table specifically includes:

[0033] For each application platform, the target application data corresponding to the application platform is identified to determine the field types contained in the target application data corresponding to the application platform as the field types used by the application platform;

[0034] For any field type used by each application platform, masking is performed on at least part of the field value corresponding to the field type to obtain masked data corresponding to the field type;

[0035] For any two field types, determine the field matching degree between the two field types according to the masked data corresponding to the two field types;

[0036] According to the field matching degree between any two field types, each preset field to be set in the preset data table is determined.

[0037] This specification provides a data processing device based on multi-platform data, including:

[0038] An acquisition module is used to acquire target application data of a target application on each application platform. For each application platform, the target application data corresponding to the target application on the application platform at least includes user information data, application configuration data and operation data of the target application on the application platform;

[0039] A storage module, configured to store the target application data on each application platform in the data table according to each preset field set in the determined preset data table;

[0040] An indicator aggregation module is used to obtain the data analysis indicators corresponding to the application platforms, and aggregate the data analysis indicators corresponding to the application platforms to obtain the aggregated data analysis indicators;

[0041] A determination module, for determining, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table;

[0042] The processing module is used to process the analysis result data corresponding to each summarized data analysis indicator.

[0043] This specification provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned data processing method based on multi-platform data is implemented.

[0044] This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned data processing method based on multi-platform data when executing the program.

[0045] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0046] It can be seen from the above method that in a data processing method based on multi-platform data provided in this specification, the target application data of the target application on each application platform can be obtained, and the target application data can be stored in the data table according to the preset fields in the determined data table. Then, while obtaining the data analysis indicators corresponding to each application platform, the indicators of each data analysis indicator are summarized to obtain each summarized data analysis indicator. Next, for each summarized data analysis indicator, the analysis result data corresponding to the data analysis indicator is determined according to the data stored in the data table. Finally, data processing is performed according to the analysis result data corresponding to each summarized data analysis indicator.

[0047] From the above content, it can be seen that the data processing method based on multi-platform data provided in this specification can determine the data table storing the target application data corresponding to the target application in each application platform, and conduct a comprehensive analysis of the data table according to the data analysis indicators corresponding to each application platform, determine the analysis result data corresponding to each data analysis indicator, and then perform data processing according to each analysis result data. The method in this specification effectively broadens the overall perspective range for the target application, and the unified integration and multi-angle analysis of multi-platform application data effectively improves the efficiency of the subsequent analysis process. In addition, the method in this specification can efficiently process the data of the target application of each application platform, while also indirectly improving the optimization and update efficiency of the target application. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings:

[0049] Figure 1 A flowchart of a data processing method based on multi-platform data provided in this specification;

[0050] Figure 2 This is a schematic diagram of an example of a data processing method based on multi-platform data provided in this specification;

[0051] Figure 3 A schematic diagram of a data processing device based on multi-platform data provided in this specification;

[0052] Figure 4 A method corresponding to the Figure 1 Schematic diagram of the structure of an electronic device. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0054] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0055] Figure 1 The following is a flow chart of a data processing method based on multi-platform data provided in this specification, comprising the following steps:

[0056] S101: Acquire target application data of a target application on each application platform.

[0057] Nowadays, with the continuous development and progress of network technology, in order to speed up the update and optimization of application software, application software developers often promote and release application software in multiple countries or regions and on different systems. This multi-platform promotion and operation method can indeed expand the scope of data collection and analysis for application software, but it also has obvious defects. Taking the perspective of countries or regions as an example, there are differences in data formats and contents between application platforms in different regions, and even the focus of data analysis on each platform may be different. When it is necessary to conduct a comprehensive analysis of the application for optimization and update, these diverse data formats and contents may slow down the analysis efficiency of the analysis process and affect the execution efficiency of subsequent tasks for application adjustment and update. Therefore, how to efficiently process the application data of application software on multiple application platforms is an urgent problem to be solved.

[0058] To this end, this specification provides a data processing method based on multi-platform data, wherein the execution subject adopted by the method provided in this specification can be a server, or a terminal device such as a desktop computer, a laptop computer, etc. In addition, the execution subject of this specification can also be a subject in the form of software, such as a client installed in a terminal device, etc. For the sake of convenience of description, the following specification only takes the server as the execution subject to describe the data processing method based on multi-platform data provided.

[0059] Based on this, the server that applies the data processing method based on multi-platform data provided in this specification can uniformly organize and comprehensively analyze the target application data obtained on each application platform, so as to facilitate subsequent comprehensive analysis of the target application based on the processed data.

[0060] Specifically, the specific application scenario in which the server performs data fusion through this method can be determined according to actual application requirements. For example, for a game application software released by a game manufacturer on multiple application platforms and regions, the server can obtain the application data corresponding to the game application software on each application platform, and perform format unification processing for each application data, as well as analysis index fusion. As a result, the game manufacturer can efficiently operate and analyze the game application software based on the processed data, such as obtaining user preferences, game defects, etc., to optimize and update the game application software. For another example, for a shopping application software released by a trade manufacturer on multiple application platforms and regions, the server can obtain the application data corresponding to the shopping application software on each application platform, and perform data format alignment, comprehensive analysis index and other data processing operations on the application data. As a result, the trade manufacturer can analyze various types of data information in the acquired shopping application software based on the processed data, such as determining user shopping habits, optimizing product push information, etc.

[0061] In this specification, the server can obtain the target application data of the target application on each application platform. For each application platform, the target application data corresponding to the target application in the application platform can at least include user information data, application configuration data and operation data of the target application on the application platform.

[0062] Specifically, taking game application software as an example, the target application data of the target application in the application platform is explained. Assuming that the server needs to process and integrate data for a game application software released on multiple regional platforms, the server can obtain the target application data corresponding to the target application (game application software) from the corresponding servers of each application platform. For each application platform, the target application data of the target application can at least include the basic information corresponding to the user who registers and uses the game application software (such as user account basic information, game account information, user recharge record information, etc.), that is, the user information data mentioned above, and the application information corresponding to the game application software on the application platform (such as application version and content information, application security authentication information, application platform-based privacy policy and user agreement information, etc.), that is, the application configuration information mentioned above. In addition, the target application data can also include the application task execution information and event record information of the game application software on the application platform (such as application update records and content information, application regular recharge income statistics, application operation vulnerabilities or crashes and other error record information, etc.), that is, the operation data mentioned above.

[0063] It should be noted that this specification does not strictly limit the methods or means of how to obtain the target application data of the target application on each application platform. For example, the target application in each application platform can be pre-set to send the target application data to the open source message system project platform (Apache Kafka). By utilizing the platform's efficient and reliable information transfer capabilities, the server can directly obtain the target application data of the target application on each application platform. The data collection method of the above open source message system project platform is only used for illustration, and the specific implementation application and implementation can be determined according to the actual application scenario and requirements.

[0064] In addition, in this specification, the server can be real-time when acquiring data. The server can obtain the target application data of the target application on each application platform in real time, thereby effectively ensuring the authenticity and effectiveness of the target fusion information in the subsequent analysis process. Of course, the server can also choose to perform timed data acquisition in order to ease network pressure and equipment technical configuration, and collect the target application data of the target application on each application platform according to a preset time period. The specific acquisition form and time limit can be determined according to the actual situation, and are not strictly limited in this specification.

[0065] It should also be explained that the relationship between the various application platforms mentioned in the above and subsequent steps is similar to the relationship between different application platforms corresponding to different regions. For example, taking game applications as the target application, the same game application can be published in game application platforms corresponding to different countries or regions, such as being published and operated on the game application platform of country A, and also being published and operated on the game application platform of country B. The server that applies the method in this specification can obtain the target application data of the target application on the application platform of country A, and the target application data of the target application on the application platform of country B. When it comes to language issues or data format issues in different countries, the server can convert the language or data format of the target application data into a unified language or the same data format through preset conversion rules. The specific conversion rules, basic language and basic data format are not strictly limited in this specification and can be flexibly selected according to the actual application scenario.

[0066] S102: According to each preset field set in the determined preset data table, the target application data on each application platform is stored in the data table.

[0067] In this specification, the server stores the target application data on each application platform in the determined preset data table according to each preset field set in the preset data table.

[0068] Specifically, the server can divide and fill in the target application data on each application platform according to the different data contents represented by the preset fields. Taking online shopping type application software as an example, it is assumed that the server obtains the target application data of a certain online shopping application on each application platform, and the preset field of the data table for the target application data may be the user ID as the primary key, and the remaining fields corresponding to the primary key may be the user nickname used by the user in the target application, user registration information (email or mobile phone number, etc.), user account status (logged in or offline), etc. The server can store the various data in the target application data of the online shopping application on each application platform according to the various fields in the data table, and then obtain the corresponding data table of the online shopping application. The above examples are only used to understand the expression form of the data table and the preset fields. The specific data table format and the content and number of the preset fields are not strictly limited in this manual and can be flexibly set according to the actual application scenario.

[0069] Regarding the specific determination process of each preset field set in the preset data table, in this specification, the server can identify the target application data corresponding to each application platform obtained, thereby determining the field types contained in the target application data corresponding to each application platform. Then, for each field type of each application platform, the server can perform mask processing on at least part of the content of the field value corresponding to the field type, thereby determining the masked data corresponding to the field type.

[0070] Then, the server can determine the field matching degree between any two field types according to the masked data corresponding to the two field types determined by the above process. Then, according to the field matching degree and the preset field matching degree threshold, determine whether the two field types are fields of the same type, and according to the magnitude relationship between the field matching degree between any two field types and the field matching degree threshold, determine the preset fields to be set in the data table.

[0071] For ease of understanding, the determination process of the above preset fields is explained by taking the user ID information of the application software as an example. Assume that the server obtains the target application data of an application software on platform A and platform B, where the user's ID information is called "user account" in platform A, and the specific data content format of the "user account" of a user on platform A is ID: 10001 (the specific number is only used as an example and is not strictly limited). In platform B, the user's ID information is called "user name", and the specific data format of the "user account" of a user on platform B is ID: 10002. The server can perform data identification on the target application data of platform A, determine that one of the field types is "user account", and the data of "ID: 10001" is the field value corresponding to the field type "user account". Similarly, the server will also perform data identification on the target application data of platform B, determine that one of the field types is "user name", and "ID: 10002" is the field value corresponding to the field type "user name".

[0072] Then, the server may at least partially mask the field values ​​corresponding to the field type "user account" and the field type "user name", that is, partially mask "ID: 10001" and "ID: 10002" respectively, and obtain the masked data "IDxxxx01" and "IDxxxx02" respectively. Then, for each field type, the field matching degree between the masked data of the field type and the masked data of other field types is calculated. In this example, the field matching degree between the masked data "IDxxxx01" of the field type "user account" and the masked data "IDxxxx02" of the field type "user name" is calculated. The specific format of the field matching degree may be in percentage form.

[0073] Furthermore, the server can determine whether the field type "user account" matches the field type "user name" according to the field matching degree and the preset field matching degree threshold, that is, determine whether they are the same type of field. It is not difficult to see that the field matching degree of these two types is close to 100%. Assuming that the preset field matching degree threshold is 90%, the server will regard the field type "user account" of platform A and the field type "user name" of platform B as the same type of field, and determine the total field type of the two field types as the preset field according to their corresponding field values ​​("ID: 10001" and "ID: 10002"). In this example, the server can use the user ID as the total field type of the two field types according to "ID: 10001" and "ID: 10002", and set it as a preset field in the data table. The user account data in platform A and the user name data in platform B can be filled in the part of the preset field in the data table that is the user ID.

[0074] Through the above-mentioned process of determining preset fields, the server can unify the different field names of the same type of data content in the target application on different application platforms, thereby improving the uniformity of the overall data and avoiding confusion or errors in the subsequent analysis process due to inconsistent fields to a certain extent.

[0075] S103: Acquire data analysis indicators corresponding to the application platforms, and aggregate the data analysis indicators corresponding to the application platforms to obtain aggregated data analysis indicators.

[0076] S104: For each summarized data analysis indicator, determine the analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table.

[0077] In this specification, the server can obtain the data analysis indicators for the target application on each application platform, and summarize the data analysis indicators corresponding to each application platform, so as to determine each summarized data analysis indicator. Next, the server can determine the analysis result data corresponding to each summarized data analysis indicator based on the data table determined in the above steps for each summarized data analysis indicator. Among them, the data analysis indicator can specifically refer to the data analysis and collection angle for the target application set on a certain application platform. For example, the data analysis indicator of a certain application platform for the user download method of a certain application software can be specifically divided into: users click on promotional advertising links to download, users download in the application store, users download on the official website, etc.

[0078] Specifically, regarding the process of determining each summarized data analysis indicator, in this specification, the server can obtain the historical analysis result data corresponding to each data analysis indicator obtained, as well as the field information corresponding to the data analysis indicator, which is pre-saved from the corresponding application platform.

[0079] Then, the server can input the historical analysis results corresponding to the two data analysis indicators and the field information corresponding to the two data analysis indicators into the preset indicator classification model for any two data analysis indicators from different application platforms, so as to determine the indicator matching degree between the two data analysis indicators through the indicator classification model. The server can judge whether the two data analysis indicators are of the same or similar type based on the preset indicator matching degree threshold and indicator matching degree, and treat the two data analysis indicators of the same or similar type as one type of data analysis indicators, and treat the two data analysis indicators of different types as two types of data analysis indicators. Furthermore, the server can summarize the data analysis indicators corresponding to each application platform obtained according to the indicator matching degree between any two data analysis indicators from different application platforms determined by the indicator classification model, and obtain the summarized data analysis indicators.

[0080] Taking the above data analysis indicators of user downloading methods as an example, the determination process of the above summarized data analysis indicators is explained. Assume that the field information of a data analysis indicator of the target application of application platform A is "regularly counting the number of times users download in the application store", and the field information of a data analysis indicator of the target application of application platform B is "daily counting the number of times users obtain the target application through the application store", and the historical analysis result data corresponding to the two data analysis indicators are both in the form of specific number records (for example: 100 times). The server can input the historical analysis result data and field information corresponding to the two data analysis indicators into the preset indicator classification model to calculate the indicator matching degree between the two data analysis indicators through the indicator classification model.

[0081] Then, the server can determine whether the two data analysis indicators are of the same or similar type based on the relationship between the preset indicator matching threshold and the indicator matching degree. The data form of the indicator matching degree and the indicator matching threshold can be in percentage form. It is not difficult to see that in this example, the data analysis indicator of application platform A "regularly counting the number of times users download in the application store" is extremely similar to the data analysis indicator of application platform B "daily counting the number of times users obtain the target application through the application store", and the indicator matching degree obtained through the indicator classification model is close to 100%. When the indicator matching degree threshold is set to 95%, the server can use these two data analysis indicators as the same type of data classification indicators, that is, "regularly counting the number of times users download in the application store" of application platform A and "daily counting the number of times users obtain the target application through the application store" of application platform B can be identified by the server as the same type of data analysis indicators. For example, the data of the two data analysis indicators can be classified under the data analysis indicator "regular number of times users download the target application in the application store", and "regular number of times users download the target application in the application store" will be used as one of the data analysis indicators in the summarized data analysis indicators, including the indicators of application platform A and application platform B mentioned above. If the indicator matching degree obtained through the indicator classification model is less than the indicator matching degree threshold, the server will use the two data analysis indicators as two data analysis indicators of different types in each summarized data analysis indicator.

[0082] It should be noted that the indicator classification model involved in the determination process of the above-mentioned summarized data analysis indicators is a pre-trained mathematical model. The specific training process can be to input the historical analysis result data and field information corresponding to the two historical data analysis indicators with known indicator matching degrees into the indicator classification model to be trained, so that the indicator classification model to be trained can determine the indicator matching degree between the two historical data analysis indicators, thereby training the indicator classification model with the optimization goal of minimizing the deviation between the true indicator matching degree and the indicator matching degree determined by the indicator classification model. As for the specific model category of the indicator classification model, mathematical models such as Sentence-BERT (SBERT) and twin neural networks can be used. The specific model type and structure of the indicator classification model are not strictly limited in this manual, and can be flexibly selected according to actual needs and application scenarios.

[0083] Furthermore, the server can obtain, for each data analysis indicator in the aggregated data analysis indicators, the historical analysis result data corresponding to the data analysis indicator pre-saved by the application platform using the data analysis indicator, and determine the analysis result data corresponding to the data analysis indicator based on the historical analysis result data and the data stored in the data table. If it is determined that the historical analysis result data corresponding to the data analysis indicator is not stored in a certain application platform, or the application platform does not use the data analysis indicator to perform data analysis on the target application data, the server can perform data analysis on the corresponding data stored in the data table of the application platform based on the data analysis indicator, thereby determining the historical analysis result data corresponding to the application platform and the data analysis indicator, and then the server can use the data set of each historical analysis result data corresponding to the data analysis indicator as the analysis result data corresponding to the data analysis indicator.

[0084] In order to facilitate understanding of the specific determination process of the above analysis result data, the following still uses the user download method of the application software mentioned above as an example for explanation. Assume that the server determines that the various summarized data analysis indicators for the user download method of the application software specifically include: "Users click on promotional advertising links to download", "Users search and download in the application store", and "Users download on the official website", a total of three data analysis indicators. However, the data analysis indicators for the target application in a certain application platform A only include the two data analysis indicators of "Users click on promotional advertising links to download" and "Users search and download in the application store", and do not include the data analysis indicator of "Users download on the official website". Due to some specific reasons or regulations, application platform A classifies the data of "Users download on the official website" into the corresponding data of "Users click on promotional advertising links to download".

[0085] When the server determines the analysis result data corresponding to the data analysis indicator "users click on promotional advertising links to download" in each summarized data analysis indicator, it cannot directly obtain the corresponding historical analysis result data from application platform A, but all target application data corresponding to the target application in application platform A have been filled in the data table. At this time, the server can directly determine the historical analysis result data corresponding to application platform A and the data analysis indicator "users download on the official website" based on the data corresponding to application platform A in the data table, and integrate it with the historical analysis result data corresponding to the data analysis indicator "users download on the official website" from other platforms, and use the data set of each historical analysis result data as the analysis result data corresponding to the data analysis indicator.

[0086] In addition, in this specification, before determining the analysis result data for each data analysis indicator in each summarized data analysis indicator, the server can also classify the data stored in the data table according to the pre-set classification dimensions to obtain the classification results. The pre-set classification dimensions can at least include classification based on user registration method, classification based on application operation error record type, etc. The specific content and number of classification dimensions are not strictly limited in this specification and can be flexibly set according to actual application scenarios and application requirements.

[0087] For example, after the server determines the data table corresponding to a certain application software, it can classify the data table according to the preset classification dimension. Assuming that the preset classification dimension is "classification based on user registration information", the server can classify users with the same registration method in the data table (such as registration by mobile phone number, registration by binding a specific application account, registration by email, etc.) into the same part of the data table, so that if relevant personnel actively analyze the user registration behavior data in the data table later, it can be more organized and neat. For another example, if the preset classification dimension is "classification based on application operation error record type", the server can classify the error causes based on the error record information of the application operation crash in the data table, and organize the error record information with the same or similar error causes.

[0088] In addition, in this specification, before determining the analysis result data for each data analysis indicator in the summarized data analysis indicators, the server can also obtain the time zone information corresponding to each application platform, and for each application platform, if it is determined that the time zone information corresponding to the application platform does not match the preset reference time zone information, the server can perform a time zone adjustment on the time in the data stored in the data table of the application platform according to the reference time zone information, thereby obtaining a data table after the time zone adjustment.

[0089] For example, suppose that the time zone information corresponding to a certain application platform among the application platforms corresponding to the target application is West 4th District (UTC-4), and the preset reference time zone information in the server is (UTC+8). If the data stored in the data table of the application platform contains user login information and login time information, and it is assumed that the login time information of a certain user is 9:00 am in West 4th District, the server can adjust the time zone according to the basic time zone information, that is, convert it to 21:00 pm. It should be noted that the server will not replace the original time data with the adjusted time data, but will save and record two or more time data together in the data table, so that when the subsequent analysis process may involve separate analysis of different time zones or regions, it is easier to perform data statistics, thereby improving the analysis efficiency.

[0090] It should be noted that the order of the above-mentioned classification processing operations and time zone adjustment operations for the data table and whether they are necessary are not strictly limited in this manual, and can be flexibly set and adjusted in order according to the actual application scenario.

[0091] S105: Perform data processing according to the analysis result data corresponding to each summarized data analysis indicator.

[0092] In this specification, the server may perform data processing according to the analysis result data corresponding to each summarized data analysis indicator.

[0093] Specifically, the server can perform data cleaning operations on the analysis result data corresponding to each summarized data analysis indicator according to the preset data cleaning rules, and determine the cleaned analysis result data corresponding to each summarized data analysis indicator. The above-mentioned data cleaning rules can at least include data replacement processing rules, data deletion processing rules and data desensitization processing rules.

[0094] It should be noted that the process of data cleaning the analysis result data of each summarized data analysis indicator can be used to reduce erroneous data and duplicate data in each analysis result data, and can avoid some special text data or other sensitive information that may exist in the analysis result data from violating or implying violations of the laws and regulations on which the server is based.

[0095] For example, suppose that a server built based on the laws and regulations of the region where application platform A is located uses the method of this specification to uniformly process the data of target applications on application platform A and application platform B in other regions, but certain specific text data in the target application data corresponding to application platform B does not comply with the laws and regulations of the region where application platform A is located. After determining the analysis result data of each data analysis indicator, the server can perform data cleaning operations on the analysis result data according to preset data cleaning rules based on the laws and regulations of the region where application platform A is located. Specifically, the data that does not comply with the regulations can be deleted, replaced, or desensitized (for example, partial content can be covered, etc.).

[0096] Furthermore, the server can perform data processing according to the cleaned analysis result data corresponding to each summarized data analysis indicator. Specifically, the server can perform data fusion of the cleaned analysis result data corresponding to each summarized data analysis indicator with the data table storing the target application data corresponding to each application platform determined in the above steps. The specific fusion method is not strictly limited in this specification. It can be simply adding the cleaned analysis result data corresponding to each summarized data analysis indicator to the data table in parallel, or it can be adjusting the specific data content and preset fields in the data table according to the cleaned analysis result data corresponding to each summarized data analysis indicator. Then, the server can use the data processing results after data fusion as the target fusion data corresponding to the target application on each application platform. The subsequent analysis process involved can conduct a comprehensive analysis of the target application based on the target fusion data, or it can conduct targeted analysis based on the classification of each data analysis indicator in the target fusion data, the division of time zones in different regions, etc.

[0097] In summary, in order to facilitate understanding of the overall process of the data processing method based on multi-platform data provided in this specification, an example schematic diagram of the data processing method based on multi-platform data is used for illustration below. Figure 2 as shown in .

[0098] Figure 2 This is a schematic diagram of an example of a data processing method based on multi-platform data provided in this specification.

[0099] from Figure 2 It can be seen from the shown content that the server can obtain the target application data of the target application on each application platform from application platform A, application platform B, and application platform C. Then, the server can determine the preset fields in the data table based on the target application data, and fill the target application data in the data table to determine the data table corresponding to the target application. Then, according to the data analysis indicators corresponding to each application platform, the indicators are summarized to determine the analysis indicators of each summarized data. Furthermore, the server can also perform classification processing and time zone adjustment according to the classification dimension and time zone information, and determine the analysis result data corresponding to each summarized data analysis indicator based on the processed data. Finally, the server can perform data cleaning according to the preset data cleaning rules, perform data processing on the data after the cleaning operation, and determine the comprehensive data corresponding to the target application.

[0100] From the above content, it can be seen that the data processing method based on multi-platform data provided in this specification can determine the data table storing the target application data corresponding to the target application in each application platform, and conduct a comprehensive analysis of the data table according to the data analysis indicators corresponding to each application platform, determine the analysis result data, and then perform data processing based on the analysis result data. The method in this specification effectively broadens the global perspective on the target application itself and the operation trend, and the integration of multi-platform application data improves the analysis efficiency of the subsequent data analysis process to a certain extent. In addition, while improving the data statistical efficiency through the method in this specification, it also indirectly improves the optimization and update efficiency of the target application.

[0101] The above are one or more implementation methods of this specification. Based on the same idea, this specification also provides corresponding devices, such as Figure 3 shown.

[0102] Figure 3 A schematic diagram of a data processing device based on multi-platform data provided in this specification, comprising:

[0103] An acquisition module 301 is used to acquire target application data of a target application on each application platform. For each application platform, the target application data corresponding to the target application on the application platform at least includes user information data, application configuration data and operation data of the target application on the application platform;

[0104] A storage module 302 is used to store the target application data on each application platform in the data table according to each preset field set in the determined preset data table;

[0105] The indicator summary module 303 is used to obtain the data analysis indicators corresponding to the application platforms, and summarize the data analysis indicators corresponding to the application platforms to obtain the summarized analysis indicators;

[0106] A determination module 304 is used to determine, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table;

[0107] The processing module 305 is used to perform data processing according to the analysis result data corresponding to each summarized data analysis indicator.

[0108] Optionally, the indicator summary module 303 is specifically used to classify the data stored in the data table according to a preset classification dimension to obtain a classification result;

[0109] The indicator summary module 303 is specifically used to determine, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the classification result.

[0110] Optionally, the determination module 304 is specifically used to obtain, for each summarized data analysis indicator, historical analysis result data corresponding to the summarized data analysis indicator pre-saved by an application platform that uses the summarized data analysis indicator; and determine the analysis result data corresponding to the summarized data analysis indicator based on the acquired historical analysis result data and the data stored in the data table.

[0111] Optionally, the determination module 304 is specifically used to obtain the time zone information corresponding to each application platform; for each application platform, when it is determined that the time zone information corresponding to the application platform does not match the preset reference time zone information, the time data of the application platform in the data stored in the data table is adjusted according to the reference time zone information to obtain a data table after the time zone adjustment;

[0112] The determining module 304 is specifically configured to determine, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table after the time zone adjustment.

[0113] Optionally, the processing module 305 is specifically used to perform data cleaning operations on the analysis result data corresponding to each summarized data analysis indicator according to preset data cleaning rules, determine the cleaned analysis result data corresponding to each summarized data analysis indicator, and the data cleaning rules at least include data replacement processing rules, data deletion processing rules and data desensitization processing rules; perform data processing according to the cleaned analysis result data corresponding to each summarized data analysis indicator.

[0114] Optionally, the indicator summary module 303 is specifically used to, for each data analysis indicator, obtain the historical analysis result data corresponding to the data analysis indicator saved by the application platform using the data analysis indicator, and determine the field information corresponding to the data analysis indicator; for any two data analysis indicators from different application platforms, input the historical analysis results corresponding to the two data analysis indicators and the field information corresponding to the two data analysis indicators into a preset indicator classification model to determine the indicator matching degree between the two data analysis indicators through the indicator classification model; based on the indicator matching degree between any two data analysis indicators from different application platforms, summarize the data analysis indicators corresponding to the application platforms to obtain the summarized data analysis indicators.

[0115] Optionally, the storage module 302 is specifically used to, for each application platform, identify the target application data corresponding to the application platform to determine the field types contained in the target application data corresponding to the application platform as the field types used by the application platform; for any field type used by each application platform, mask at least part of the field value corresponding to the field type to obtain the masked data corresponding to the field type; for any two field types, determine the field matching degree between the two field types based on the masked data corresponding to the two field types; and determine the preset fields to be set in the preset data table based on the field matching degree between any two field types. This specification also provides a computer-readable storage medium storing a computer program, which can be used to execute the above-mentioned Figure 1 A data processing method based on multi-platform data is provided.

[0116] This manual also provides Figure 4 The one shown corresponds to Figure 1 A schematic diagram of the electronic device. Figure 4 As shown, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The data processing method based on multi-platform data.

[0117] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0118] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0119] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0120] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0121] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0122] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, 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, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0123] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0125] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0126] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0127] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0128] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0129] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0130] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0131] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0132] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. A data processing method based on multi-platform data, characterized in that: include: Obtain target application data of the target application on each application platform, wherein for each application platform, the target application data corresponding to the target application on the application platform at least includes user information data, application configuration data, and operation data of the target application on the application platform; According to each preset field in the preset data table identified according to the target application data of the target application on each application platform, the target application data on each application platform is stored in the data table; Obtaining data analysis indicators corresponding to each of the application platforms, and performing indicator aggregation processing on the data analysis indicators corresponding to each of the application platforms to obtain aggregated data analysis indicators, wherein the indicator aggregation processing at least includes merging and aggregating two data analysis indicators on different application platforms whose indicator matching degree is not less than a preset indicator matching degree threshold; For each summarized data analysis indicator, determine the analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table; Data processing is performed based on the analysis result data corresponding to each summarized data analysis indicator.

2. The method according to claim 1, characterized in that Before the step of determining, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table, the method further includes: Classify the data stored in the data table according to a preset classification dimension to obtain a classification result; The step of determining, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table specifically includes: For each summarized data analysis indicator, the analysis result data corresponding to the summarized data analysis indicator is determined according to the classification result.

3. The method according to claim 1, characterized in that The step of determining, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table specifically includes: For each summarized data analysis indicator, obtaining historical analysis result data corresponding to the summarized data analysis indicator pre-saved by an application platform using the summarized data analysis indicator; The analysis result data corresponding to the summarized data analysis indicator is determined based on the acquired historical analysis result data and the data stored in the data table.

4. The method according to claim 1, characterized in that Before the step of determining, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table, the method further includes: Obtaining time zone information corresponding to each application platform; For each application platform, when it is determined that the time zone information corresponding to the application platform does not match the preset reference time zone information, time zone adjustment is performed on the time data of the application platform in the data stored in the data table according to the reference time zone information to obtain a data table after the time zone adjustment; The step of determining, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table specifically includes: For each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator is determined according to the data stored in the data table after the time zone adjustment.

5. The method according to claim 1, characterized in that The step of performing data processing according to the analysis result data corresponding to each summarized data analysis indicator specifically includes: According to the preset data cleaning rules, a data cleaning operation is performed on the analysis result data corresponding to each summarized data analysis indicator to determine the cleaned analysis result data corresponding to each summarized data analysis indicator, wherein the data cleaning rules at least include a data replacement processing rule, a data deletion processing rule, and a data desensitization processing rule; Data processing is performed based on the cleaned analysis result data corresponding to each summarized data analysis indicator.

6. The method according to claim 1, characterized in that The step of aggregating the data analysis indicators corresponding to the application platforms to obtain the aggregated data analysis indicators specifically includes: For each data analysis indicator, obtain the historical analysis result data corresponding to the data analysis indicator stored in the application platform using the data analysis indicator, and determine the field information corresponding to the data analysis indicator; For any two data analysis indicators from different application platforms, the historical analysis results corresponding to the two data analysis indicators and the field information corresponding to the two data analysis indicators are input into a preset indicator classification model, so as to determine the indicator matching degree between the two data analysis indicators through the indicator classification model; According to the indicator matching degree between any two data analysis indicators from different application platforms, the data analysis indicators corresponding to the application platforms are summarized to obtain the summarized data analysis indicators.

7. The method according to claim 1, characterized in that The step of identifying each preset field according to the target application data of the target application on each application platform specifically includes: For each application platform, the target application data corresponding to the application platform is identified to determine the field types contained in the target application data corresponding to the application platform as the field types used by the application platform; For any field type used by each application platform, masking is performed on at least part of the field value corresponding to the field type to obtain masked data corresponding to the field type; For any two field types, determine the field matching degree between the two field types according to the masked data corresponding to the two field types; According to the field matching degree between any two field types, each preset field to be set in the preset data table is determined.

8. A data processing device based on multi-platform data, characterized in that: include: An acquisition module is used to acquire target application data of a target application on each application platform. For each application platform, the target application data corresponding to the target application on the application platform at least includes user information data, application configuration data and operation data of the target application on the application platform; A storage module, configured to store the target application data on each application platform in the data table according to each preset field identified according to the target application data of the target application on each application platform in the data table; An indicator aggregation module is used to obtain the data analysis indicators corresponding to the application platforms, and perform indicator aggregation processing on the data analysis indicators corresponding to the application platforms to obtain the aggregated data analysis indicators, wherein the indicator aggregation processing at least includes merging and aggregating two data analysis indicators on different application platforms whose indicator matching degree is not less than a preset indicator matching degree threshold; A determination module, for determining, for each summarized data analysis indicator, analysis result data corresponding to the summarized data analysis indicator according to the data stored in the data table; The processing module is used to process the analysis result data corresponding to each summarized data analysis indicator.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

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