Data processing method and apparatus

Abstract

The application provides a data processing method and device, wherein the data processing method comprises: obtaining user behavior detail data of a target object in a preset historical time interval; determining user attribute information and event attribute information in the user behavior detail data; aggregating the user attribute information and the event attribute information according to a user identifier in the user behavior detail data to obtain aggregation data, wherein the aggregation data comprises user attribute information and event statistical results; and performing format conversion on the aggregation data based on a target data compression structure to obtain user event data of the target object. By aggregating the user attribute information and the event attribute information in the user behavior detail data and converting the aggregation data into user event data, the storage space occupied by the user behavior detail data is reduced, thereby improving the storage efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a data processing method. The present application also relates to a data processing device, a computing device and a computer readable storage medium. BACKGROUND

[0002] With the continuous development of computer technology, the user's demand for data processing is more and more; in order to facilitate the relevant technical personnel can understand the use of the application, the detailed data collected in the application can be analyzed, that is, the execution of different events is determined, and the detailed data can also be retained and analyzed, that is, the attraction of the application to the user is determined, so as to realize the analysis of the application data by the user.

[0003] However, due to the large amount of detailed data and the continuous increase, it will occupy more storage space; and in the case of large amount of detailed data, it will affect the analysis efficiency of the data and the user experience.

[0004] Therefore, how to reduce the data storage space and improve the analysis efficiency of the data has become a technical problem to be solved by the technical personnel in the field. SUMMARY

[0005] Therefore, the embodiments of the present application provide a data processing method. The present application also relates to a data processing device, a computing device and a computer readable storage medium, so as to solve the technical problems of large data occupying storage space and low data analysis efficiency in the prior art.

[0006] According to a first aspect of the embodiments of the present application, a data processing method is provided, comprising:

[0007] obtaining user behavior detailed data of a target object in a preset historical time interval;

[0008] determining user attribute information and event attribute information in the user behavior detailed data;

[0009] aggregating the user attribute information and the event attribute information according to the user identifier in the user behavior detailed data, obtaining aggregated data, wherein the aggregated data includes user attribute information and event statistical results;

[0010] format converting the aggregated data based on a target data compression structure, obtaining user event data of the target object.

[0011] According to a second aspect of the embodiments of the present application, a data processing device is provided, comprising:

[0012] The acquisition module is configured to acquire user behavior detail data of a target object in a preset historical time interval;

[0013] The determination module is configured to determine user attribute information and event attribute information in the user behavior detail data;

[0014] The aggregation module is configured to aggregate the user attribute information and the event attribute information according to a user identifier in the user behavior detail data, to obtain aggregated data, wherein the aggregated data includes user attribute information and event statistical results;

[0015] The conversion module is configured to perform format conversion on the aggregated data based on a target data compression structure, to obtain user event data of the target object.

[0016] According to a third aspect of an embodiment of the present application, a computing device is provided, including a memory, a processor, and computer instructions stored in the memory and executable on the processor, and the processor implements the steps of the data processing method when executing the computer instructions.

[0017] According to a fourth aspect of an embodiment of the present application, a computer readable storage medium is provided, which stores computer instructions, and the computer instructions implement the steps of the data processing method when executed by a processor.

[0018] The data processing method provided by the present application acquires user behavior detail data of a target object in a preset historical time interval, determines user attribute information and event attribute information in the user behavior detail data, aggregates the user attribute information and the event attribute information according to a user identifier in the user behavior detail data, to obtain aggregated data, wherein the aggregated data includes user attribute information and event statistical results, and performs format conversion on the aggregated data based on a target data compression structure, to obtain user event data of the target object.

[0019] An embodiment of the present application realizes preliminary compression of user behavior detail data by aggregating user attribute information and event attribute information in the user behavior detail data, to obtain aggregated data, realizes further compression of the user behavior detail data by performing format conversion on the aggregated data based on a target data compression structure, to obtain user event data, and reduces the storage space occupied by the user behavior detail data. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a system structure schematic diagram of a data processing system to which the data processing method provided by an embodiment of the present application is applied;

[0021] Figure 2is a flowchart of a data processing method provided by an embodiment of the present application;

[0022] Figure 3 is a display interface schematic diagram of a label user event statistical chart provided by an embodiment of the present application;

[0023] Figure 4 is a processing flowchart of a data processing method applied to a video playing application provided by an embodiment of the present application;

[0024] Figure 5 is a schematic diagram of a data processing method provided by an embodiment of the present application;

[0025] Figure 6 is a structural schematic diagram of a data processing device provided by an embodiment of the present application;

[0026] Figure 7 is a structural block diagram of a computing device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0027] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced without the specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to obscure the present application. Some portions of the detailed description which follow are presented in terms of algorithms, symbolic representations of operations on data bits or binary digital signals stored within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art.

[0028] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of one or more embodiments of the present application. As used in one or more embodiments of the present application and the accompanying claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in one or more embodiments of the present application, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0029] It will be understood that, although the terms first, second, etc. can be used herein to describe various information, these terms are not intended to denote a temporal or chronological order. Rather, these terms are used only as a shorthand notation to first, second, etc. For example, in one or more embodiments of the present application, the first can be termed the second, and similarly, the second can be termed the first, without departing from the scope of one or more embodiments of the present application. As used herein, the term "if' can be construed to mean "when" or "upon" or "in response to determining" terms denoting the occurrence of stated events or actions, depending on the context.

[0030] First, the noun terms related to one or more embodiments of the present application are explained.

[0031] Traffic event, retention analysis: Event refers to the tracking, recording and description of user behavior or business process. Retention rate is an important indicator for measuring product, business and activity strategy. The higher the retention rate represents the greater the continuous attraction of the application to users. Event and retention analysis can be used for daily function analysis or active user number analysis.

[0032] DWD: (Data Warehouse Detail), also known as ODS layer, is the isolation layer between business layer and data warehouse.

[0033] DWB: (Data Warehouse Base), which stores objective data, is generally used as an intermediate layer and can be considered as a data layer for a large number of indicators.

[0034] Hive: a data warehouse tool that can map structured data files into a database table and provide SQL query function, which can convert SQL statements into MapReduce (a high-performance parallel computing platform based on clusters) tasks for execution.

[0035] ClickHouse (columnar storage database): full name is ClickStreamDataWareHouse; a columnar storage database (DBMS: Database Management System) for online analytical processing query (OLAP: Online Analytical Processing) MPP architecture, which can generate analysis data reports in real time using SQL queries.

[0036] High-order function: a query method provided by ClickHouse database.

[0037] BitMap technology: can be understood as a data structure for storing specific data through a bit array; since bit is the smallest unit of data, this data structure is often very space-saving.

[0038] RBM (Roaring Bitmap) data structure: RoaringBitMaps (RBM for short) is a compression algorithm. Bitmap is a commonly used data structure, and bit map index is widely used in databases and search engines to quickly locate whether a numerical value exists, which is a high-efficiency data compression algorithm that can significantly speed up query. However, BitMap still occupies a large amount of memory (linear growth), so it is generally necessary to compress BitMap to reduce memory occupation and improve efficiency.

[0039] ClickHouse materialized view: Materialized view is a persistent storage of query result set, which is completely different from ordinary view, but more like a table. The implementation of ClickHouse materialized view is more like a trigger. If the aggregation function is predefined in the view, the aggregation function is only applied to the newly inserted data (without specifying the populate keyword). Changes to the source table data will not change the materialized view, which is one of the unique features of ClickHouse.

[0040] Dictionary mapping: Dictionary is a unique built-in mapping type, which can use any immutable object as the key of the dictionary (such as string, number, tuple, etc.).

[0041] Label: The full name is user portrait label. User label is the core factor of user portrait, which is the adjective with different characteristics generated by analyzing and refining the behavior data of users in the platform.

[0042] User group: User group is a user cluster with different characteristics generated by analyzing and refining the behavior data of users in the platform.

[0043] Spark-udf: A custom function of spark engine, which operates on each element in each column of data and returns only one result.

[0044] Currently, event and retention analysis are used for daily function analysis or active user number analysis. Event and retention analysis are based on detailed data of application program. Through index function of ClickHouse query engine, such as uniq(), single event analysis, multiple event comparison analysis and multiple event composite index operation can be supported. The ratio of users participating in subsequent behavior to users participating in initial behavior within a specified time is calculated. Through filtering, grouping and other components, diversified analysis requirements are met.

[0045] Although ClickHouse query performance is very superior, ClickHouse high-order function can provide analysis support to most event and retention analysis scenarios. However, current event and retention analysis is based on detailed data. The amount of data increases greatly every day, and the storage pressure is large. The analysis query efficiency based on detailed data is not high, and due to the large amount of detailed data, only 30-day time window can be supported, the function is single, and the user experience is poor.

[0046] To solve the above problems, the event and retention analysis of the application compresses the data of 100 billion per day to the data of several billion per day through offline modeling layering, pre-aggregation compression of account event time granularity, spark-udf query acceleration and other means, greatly reduces the storage and improves the query performance, and the user slow query can be reduced to less than 10s per day, and the time window can be expanded to 45 days or even longer. And the analysis scene of high complexity query such as user retention, user grouping and the like can be better supported.

[0047] The event and retention analysis of the application compresses the data of 100 billion per day to the data of several billion per day through offline modeling layering, pre-aggregation compression of account event time granularity, spark-udf query acceleration and other means, greatly reduces the storage and improves the query performance, and the user slow query can be reduced to less than 10s per day, and the time window can be expanded to 45 days or even longer. And the analysis scene of high complexity query such as user retention, user grouping and the like can be better supported.

[0048] In the application, a data processing method is provided, and the application also relates to a data processing device, a computing device and a computer readable storage medium, which are described in detail in the following embodiments.

[0049] Referring to Figure 1 , Figure 1 A system structure schematic diagram of a data processing system to which the data processing method provided by the embodiment of the application is applied is shown.

[0050] Figure 1 The data processing system 100 to which the data processing method provided by the embodiment of the application is applied, wherein the data processing system 100 comprises a data warehouse 102 and a database 104, and the data warehouse 102 comprises a data detail layer and a data base layer.

[0051] It should be noted that the data warehouse can realize collection of user behavior detail data in an application program or a webpage, and hierarchical processing of the user behavior detail data through a hierarchical structure in each data warehouse, and storage of the processed data to the database; the database can store the data output by the data warehouse and support a data query function, and the type of the database is not limited in the embodiment, including but not limited to a ClickHouse database.

[0052] In actual application, the data processing system 100 can be understood as a server corresponding to a data analysis platform, and the data warehouse 102 is used to pre-process the user behavior detail data in the application, and store the processed user behavior data into the database 104, so that when the data processing system 100 receives a data query request, the data query operation can be directly realized in the database 104. In specific implementation, the data detail layer in the data warehouse 102 can obtain all the user behavior detail data of the application in the preset historical time interval, and input into the data base layer of the data warehouse 102, and the user behavior detail data is aggregated by using the data base layer, the user event data is aggregated, and the hive table is obtained. At the same time, the user event data can also be compressed, and then the compressed user event data can be stored in the database 104.

[0053] In summary, the data processing method provided in the embodiment realizes the preliminary compression of the user behavior detail data by aggregating the user attribute information and the event attribute information in the user behavior detail data. The further compression of the user behavior detail data is realized by converting the format of the aggregated data based on the target data compression structure, and the user event data is obtained, which reduces the storage space occupied by the user behavior detail data. Not only the storage space of the user behavior data can be reduced, but also the data query efficiency can be improved by querying the compressed stored data.

[0054] Figure 2 A flowchart of a data processing method according to an embodiment of the present application is shown, which specifically includes the following steps:

[0055] Step 202: Obtain the user behavior detail data of the target object in the preset historical time interval.

[0056] In order to meet the data analysis requirements, the detail data generated by the user in the process of using the application can be collected and recorded in real time before receiving the data analysis request, so as to facilitate subsequent data analysis. Since different users will generate different detail data in the process of using the application, the number of detail data is large in the case of large number of users. Therefore, the detail data in the set time interval can be obtained for subsequent analysis.

[0057] The target object refers to an object outputting user behavior detail data, for example, application software, a webpage, and the like; the preset historical time interval refers to a time interval preset in a historical period, for example, the preset historical time interval is from June 5, 2021 to June 6, 2021; and the user behavior detail data refers to data generated by a user using a target object, for example, data generated by the user using a chat function in instant messaging software, data generated by the user browsing a webpage in a shopping webpage, and the like.

[0058] In an embodiment of the present application, user behavior detail data of a shopping application in a preset historical time interval from June 1, 2021 to June 7, 2021 is obtained, and the user behavior detail data includes data of user 1 browsing a shopping page, order data of user 2 purchasing goods, and the like.

[0059] By obtaining user behavior detail data of a target object in a preset historical time interval, subsequent further processing of the user behavior detail data in the preset historical time interval is facilitated.

[0060] Step 204: determining user attribute information and event attribute information in the user behavior detail data.

[0061] In actual application, in order to facilitate subsequent further processing of user behavior detail data, different types of information in the user behavior detail data can be determined.

[0062] The user attribute information refers to attribute information corresponding to a user, for example, the user attribute information includes at least one of user device information, user identification information, and user level information; and the event attribute information refers to attribute information corresponding to an event type, for example, the event attribute information is at least one of event type information, event time information, and event state information.

[0063] Specifically, the method of determining user attribute information and event attribute information in the user behavior detail data can include:

[0064] Determining information corresponding to a user identification in the user behavior detail data as user attribute information.

[0065] Determining information corresponding to an event type in the user behavior detail data as event attribute information.

[0066] The user identification refers to a field that can uniquely represent a user, for example, a user IP address, a user number, and the like; and the event type refers to a type of event, for example, a click type, a browsing type, a point type, an exposure type, and the like.

[0067] Specifically, at least one user identifier is determined in the user behavior detail data, user attribute information corresponding to each user identifier is determined in the user behavior detail data based on each user identifier, at least one event type is determined in the user behavior detail data, and event attribute information corresponding to each event type is determined.

[0068] In an embodiment of the present application, the user identifiers determined in the user behavior detail data include user ID 1, user ID 2 and user ID 3, the user attribute information corresponding to each user identifier is determined in the user behavior detail data, that is, the user IP address, user equipment information and user level information corresponding to user ID 1, user ID 2 and user ID 3 are obtained respectively, the event types in the user behavior detail data include the browsing event type, the clicking event type and the exposure event type, and the event attribute information corresponding to each event type is determined in the user behavior detail data, that is, the event execution times, the event execution times and the event execution states corresponding to the browsing event type, the clicking event type and the exposure event type are obtained respectively.

[0069] By determining the user attribute information and the event attribute information in the user behavior detail data, the user behavior detail data can be further processed based on different types of attribute information in the user behavior detail data.

[0070] Step 206: aggregating the user attribute information and the event attribute information according to the user identifiers in the user behavior detail data to obtain aggregated data, wherein the aggregated data includes user attribute information and event statistical results.

[0071] After determining different types of attribute information in the user behavior detail data, the different types of attribute information can be processed respectively, and the processed data can be aggregated to obtain aggregated data corresponding to the user detail data.

[0072] The aggregated data refers to data obtained by aggregating the user attribute information and the event attribute information based on the user identifiers, and the event statistical results refer to statistical results obtained by statistically processing the event attribute information.

[0073] In actual application, the method of aggregating the user attribute information and the event attribute information according to the user identifiers in the user behavior detail data to obtain aggregated data includes:

[0074] Determining a target user identifier in the user behavior detail data;

[0075] Determining user behavior sub-data in the user behavior detail data according to the target user identifier;

[0076] Perform deduplication processing on the user attribute information in the user behavior sub-data corresponding to the target user identifier to obtain user attribute information corresponding to the target user identifier;

[0077] Perform aggregation processing on the event attribute information in the user behavior sub-data according to the user attribute information corresponding to the target user identifier to obtain aggregation data corresponding to the user attribute information.

[0078] Among them, the target user identifier refers to one of the user identifiers determined in the user behavior detail data, for example, user identifier 1, user identifier 2 and user identifier 3 are determined in the user behavior detail data, then user identifier 1 can be used as the target user identifier; the user behavior sub-data refers to the data corresponding to the target user identifier, for example, in the user behavior detail data, the corresponding user behavior detail data is determined as the user behavior sub-data according to the target user identifier 1; the deduplication processing refers to the data processing mode of merging multiple identical data into one data, for example, data 1 "user a, device type b" and data 2 "user a, device type b" are two identical data, then only data 1 or only data 2 can be retained to complete the deduplication processing of the data; the user attribute information corresponding to the target user identifier refers to the user attribute information obtained after the deduplication processing.

[0079] Specifically, the target user identifier is determined in the user behavior detail data; the user behavior detail data corresponding to the target user identifier is screened in the user behavior detail data according to the target user identifier as the user behavior sub-data; the user attribute information in each user behavior sub-data is determined, and the user attribute information is deduplicated to obtain the user attribute information corresponding to the user identifier; the event attribute information in each user behavior sub-data is determined, and the event attribute information is aggregated according to the user attribute information to obtain the aggregation data corresponding to the user attribute information; the processed user attribute information and event attribute information can be aggregated in the same data structure, such as both in the map data structure to obtain the aggregation data.

[0080] In an embodiment of the present application, the target user identifier 2 is determined in the user behavior detail data; the corresponding user behavior sub-data 1 (user ID: 2, device type: e1, user level v1, event type: browse, event execution times: 3) and user behavior sub-data 2 (user ID: 2, device type: e1, user level v1, event type: click, event execution times: 5) are determined in the user behavior detail data according to the target user identifier 2; the user attribute information in each user behavior sub-data is determined, specifically, the user attribute information "user ID: 2, device type: e1, user level v1" in the user behavior sub-data 1 and the user attribute information "user ID: 2, device type: e1, user level v1" in the user behavior sub-data 2 are determined; the user attribute information is de-duplicated to obtain the user attribute information "user ID: 2, device type: e1, user level v1" corresponding to the target user identifier 2; the event attribute information in the user behavior sub-data 1 and the user behavior sub-data 2 is combined according to the user attribute information to obtain the aggregation data corresponding to the user attribute information.

[0081] Further, the method for performing aggregation processing on the event attribute information in the user behavior sub-data according to the user attribute information corresponding to the target user identifier to obtain the aggregation data corresponding to the user attribute information comprises:

[0082] determining the target user behavior sub-data corresponding to the user attribute information in the user behavior sub-data;

[0083] counting the execution times of each event in the target user behavior sub-data according to the event attribute information to obtain an event statistical result, wherein the event statistical result comprises an event type and an execution time corresponding to the event type;

[0084] splicing the user attribute information and the event statistical result to obtain the aggregation data corresponding to the user attribute information.

[0085] Wherein, the target user behavior sub-data refers to the data in the user behavior sub-data, which is determined according to the user attribute information, for example, the target user behavior sub-data m and the target user behavior sub-data n containing the user attribute information are determined in the user behavior sub-data according to the user attribute information "user ID: 5, device type: a1"; the execution times of each event refer to the total execution times of each event in the target user behavior sub-data.

[0086] Specifically, after obtaining the user attribute information after deduplication processing, the target user behavior sub-data corresponding to the user attribute information is filtered in the user behavior sub-data; the event attribute information in the target user behavior sub-data is determined, the execution times of each event in the target user behavior sub-data are counted according to the event attribute information, and the event statistical result is obtained; the user attribute information and the event statistical result are spliced to obtain the aggregation data corresponding to the user attribute information; for example, the real-time generation of the aggregation data can be realized by using a spark algorithm.

[0087] In a specific embodiment of the present application, the target user behavior sub-data m1 (user ID: 23, user equipment type: d1, event type: click, event times: 5, event type: browse, event times: 3) and the target user behavior sub-data m2 (user ID: 23, user equipment type: d1, event type: click, event times: 7, event type: browse, event times: 6) corresponding to the user attribute information "user ID: 23, user equipment type: d1" are determined; the event attribute information "event type: click, event times: 5, event type: browse, event times: 3" and "event type: click, event times: 7, event type: browse, event times: 6" in the target user behavior sub-data are determined, and the total times of the browse event corresponding to the target user behavior sub-data are counted according to the event attribute information, that is, the total execution times of the click event are 12, and the event statistical result is "event type: click, event times: 12; event type: browse, event times: 9"; the user attribute information and the event statistical result are spliced to obtain the aggregation data "user ID: 23, user equipment type: d1, event type: click, event times: 12; event type: browse, event times: 9".

[0088] By aggregating the user attribute information and the event attribute information according to the user identifier, the aggregation data is obtained, which realizes the effect of compressing the storage space of the user behavior detail data and avoids the repeated storage of the same data.

[0089] Step 208: Format conversion is performed on the aggregation data based on the target data compression structure, and the user event data of the target object is obtained.

[0090] After obtaining the aggregation data corresponding to the user behavior detail data, the aggregation data can be further processed, thereby realizing further compression of the user behavior detail data.

[0091] The target data compression structure refers to a data result that can realize compression of data, for example, a BitMap data structure, an RBM data structure, etc.; the format conversion refers to converting the aggregated data into a format corresponding to the target data compression structure; and the user event data refers to data obtained after the aggregated data is format-converted based on the target data compression structure.

[0092] In actual application, the method for format-converting the aggregated data based on the target data compression structure to obtain the user path data of the target object can include the following steps.

[0093] The user identifier in the user attribute information of the aggregated data is format-converted based on the target data compression structure to obtain the user event data of the target object.

[0094] Specifically, the user identifier in the user attribute information of the aggregated data is determined; the user identifier is format-converted based on the target data compression structure to obtain the user identifier of the target data compression structure; and the user event data corresponding to the target object is composed of the user identifier of the target data compression structure and the user attribute information and event attribute information of the aggregated data except the user identifier.

[0095] In a specific embodiment of the present application, the aggregated data K and the target data compression structure BitMap are determined; the data dictionary corresponding to the target data compression structure BitMap is determined, and the user identifier in the user attribute information of the aggregated data K is converted into the BitMap data structure based on the data dictionary; and the user event data corresponding to the application program G in the preset time interval is spliced from the user identifier of the BitMap data structure and the user attribute information and event attribute information of the aggregated data K except the user identifier.

[0096] By converting the user identifier in the aggregated data into the target data compression structure and then generating the user event data based on the user identifier of the target data compression structure, further compression of the aggregated data is realized, so that the user behavior detail data is further reduced.

[0097] In actual application, in order to facilitate data analysis based on the label, after the aggregated data is format-converted based on the target data compression structure to obtain the user path data of the target object, the label data of the target object can also be obtained, so as to facilitate subsequent data analysis. The specific method includes the following steps.

[0098] The user identifier of the target object and the attribute label corresponding to the user identifier are obtained.

[0099] The user identifier is format-converted based on the target data compression structure, and the user label data of the target object is determined based on the converted user identifier and the attribute label.

[0100] The attribute label refers to a label field corresponding to the user identifier, for example, the labels selected by user A when registering the application H are "animation" and "entertainment", that is, the attribute labels of user A are "animation" and "entertainment"; the user label data refers to data composed of the attribute label and the converted user identifier.

[0101] In an embodiment of the present application, the user identifier in the application J and the attribute label corresponding to each user identifier are obtained, specifically, the user identifier "s1" and the attribute label "animation, film, and food" corresponding to the user identifier "s1" are obtained; a data dictionary corresponding to the BitMap data structure, and the user identifier "s1" is mapped to the BitMap data structure based on the data dictionary; the user label data is composed of the attribute label "animation, film, and food" corresponding to the user identifier "s1" and the user identifier "s1" of the BitMap data structure.

[0102] By determining the user identifier of the target object, the attribute label corresponding to the user identifier is obtained; the user identifier is format-converted, and the user label data is generated based on the converted user identifier and the attribute label, thereby enriching the data used for data analysis.

[0103] In actual application, after the user event data of the target object is obtained by format-converting the aggregation data based on the target data compression structure, the method further includes:

[0104] The user event data of the target object is stored in the database.

[0105] Correspondingly, after the user label data of the target object is determined based on the converted user identifier and the attribute label, the method further includes:

[0106] The user label data of the target object is stored in the database.

[0107] The database refers to a database that can store the target data compression structure, for example, a ClickHouse database; specifically, in the case of the database being a ClickHouse database, the data query results executed in the ClickHouse database can be stored based on the ClickHouse materialized view, thereby improving the data query efficiency.

[0108] By storing the compressed user label data and user event data instead of the user detail data in the database, the database storage space is saved, and due to the reduction of data volume, the subsequent data analysis efficiency is improved.

[0109] Specifically, after the user event data of the target object is stored in the database, the method further includes:

[0110] receiving a user event data query request for a target object, wherein the user event data query request carries a basic configuration query condition;

[0111] querying corresponding user event data in the user event data in the database based on the basic configuration query condition, wherein the basic configuration query condition comprises at least one of an event time condition, a buried point event condition, and a user device condition;

[0112] generating a user event statistical chart based on the user event data and sending the user event statistical chart to a user event statistical chart display interface of the target object.

[0113] In the actual application, the user event data query request refers to a request for querying user event data that meets the query condition in the database; the basic configuration query condition refers to a condition for querying user event data in the database, and the basic configuration query condition comprises at least one of an event time condition, a buried point event condition, and a user device condition; the user event statistical chart refers to a statistical chart obtained by processing user event data, and in the actual application, the user event statistical chart can be a statistical chart form such as a line chart and a table that is convenient for users to view.

[0114] In the actual application, after receiving the user event data query request for the target object, the basic configuration query condition in the user event data query request is determined; user event data that meets the basic configuration query condition is filtered from the user event data in the database, for example, high-order functions are used for data query in the ClickHouse database; a user event statistical chart is generated based on the statistical chart generation mode corresponding to the user event statistical chart and the user event data that meets the basic configuration query condition and the statistical chart generation mode; the generated user event statistical chart can be sent to the user event statistical chart display interface of the target object, such as the screen of a computer device, and the user event statistical chart is displayed by the user event statistical chart display interface.

[0115] Further, before generating the user event statistical chart based on the user event data, the user event data needs to be decoded based on a custom function to obtain data for generating the user event statistical chart, wherein the custom function can be spark-udf, ClickHouse-udf, etc.

[0116] In a specific embodiment of the present application, the server receives a user event data query request with a burying event condition of "play page browsing"; based on the burying event condition, the server queries the target user event data corresponding to the burying event condition in the user event data in the database; generates a line graph based on the target user event data and the line graph generation method, and sends the line graph to the user event graph display interface.

[0117] By querying data in the user event data in the database based on basic configuration query conditions, the processing efficiency of query requests can be improved because the user event data stored in the database is compressed and the amount of data is smaller than the user behavior detail data.

[0118] Specifically, after storing the user tag data of the target object in the database, the method further includes:

[0119] Receive a user event data query request for a target object, wherein the user event data query request carries a basic configuration query condition and a tag data query condition;

[0120] Determining the user event data to be processed from the user event data in the database based on the basic configuration query condition, and determining the user tag data to be processed from the user tag data in the database based on the tag data query condition;

[0121] Processing the to-be-processed user event data and the to-be-processed user tag data according to a preset data processing method to obtain tag user event data;

[0122] A tagged user event statistical graph is generated based on the tagged user event data, and the tagged user event statistical graph is sent to a user event statistical graph display interface of the target object.

[0123] Among them, the user event data query request refers to a request to query the database for user event data and user tag data that meet the query conditions; the tag data query condition refers to the condition for querying user tag data in the database. For example, you can enter the corresponding tag data in the tag query condition box in the front-end interface of the application, such as post-90s, girls, animation, and entertainment.

[0124] In actual application, after the database receives a user event data query request for an application program, the query request carries a basic configuration query condition and a tag data query condition; then, according to the basic configuration query condition, the database determines the to-be-processed user event data from the user event data in the database, wherein the to-be-processed user event data is the user event data filtered in the database according to the basic configuration query condition, facilitating subsequent intersection and union calculation and processing of the to-be-processed user event data; then, according to the tag data query condition, the database determines the to-be-processed user tag data from the user tag data in the database, facilitating subsequent intersection and union calculation and processing of the to-be-processed user tag data; it should be noted that the user event data and the user tag data stored in the database have their user identifiers converted into RBM storage structures, so that the to-be-processed user event data and the to-be-processed user tag data can be processed according to a preset data processing mode to obtain tag user event data, and then a tag user event statistical chart is generated according to the tag user event data, and the tag user event statistical chart is sent to a user event statistical chart display interface of the application program.

[0125] Referring to Figure 3 , Figure 3 A display interface schematic diagram of a tag user event statistical chart provided by an embodiment of the present application is shown.

[0126] Figure 3 The part of the embedded point event selection in the interface can be understood as an input and selection box of the basic configuration query condition and the tag data query condition. After the user determines the query conditions, the user can click the “query” button in the interface, and then the user event statistical chart in the lower half of the interface can be displayed for the user, wherein the user event statistical chart can be understood as a user event statistical chart queried in the database according to the above query conditions, facilitating subsequent direct provision of data analysis basis for relevant personnel according to the user event statistical chart. In addition, the above two query conditions can also be stored by clicking the “save” button, facilitating subsequent quick query of the corresponding query conditions. Figure 3 Figure 3 The part of the embedded point event selection in the interface can be understood as an input and selection box of the basic configuration query condition and the tag data query condition. After the user determines the query conditions, the user can click the “query” button in the interface, and then the user event statistical chart in the lower half of the interface can be displayed for the user, wherein the user event statistical chart can be understood as a user event statistical chart queried in the database according to the above query conditions, facilitating subsequent direct provision of data analysis basis for relevant personnel according to the user event statistical chart. In addition, the above two query conditions can also be stored by clicking the “save” button, facilitating subsequent quick query of the corresponding query conditions.

[0127] Further, the to-be-processed user event data and the to-be-processed user tag data are processed according to a preset data processing mode to obtain tag user event data, including:

[0128] determining an association relationship between the basic configuration query condition and the tag data query condition, and determining the preset data processing mode based on the association relationship;

[0129] processing the to-be-processed user event data and the to-be-processed user tag data based on the preset data processing mode to obtain tag user event data.​

[0130] The preset data processing manner is a manner of mutual calculation of data determined according to the two query conditions, such as intersection calculation, union calculation, etc.

[0131] In actual application, the association between the basic configuration query condition and the tag data query condition can be determined, and the processing manner of the data filtered out by the two conditions, that is, the intersection calculation manner or the union calculation manner of the data set, is determined. Further, the intersection calculation or the union calculation is performed on the to-be-processed user event data and the to-be-processed user tag data according to the determined preset data processing manner, to obtain the tag user event data.

[0132] In addition, after obtaining the data query result, the data query result can be stored in the database, for example, the query result is stored in the database by using ClickHouse materialized view technology.

[0133] To sum up, the data processing method provided by the embodiment of the application can process a large amount of user behavior detail data, obtain RBM data structure data, and pre-store the data in the database, which can not only compress the storage and reduce the memory space, but also improve the data query efficiency. At the same time, the user behavior data and the user tag data are fused, which is helpful for subsequent determination of the user path data of the people corresponding to the tag according to the query tag data, and then precise people circle selection is realized.

[0134] The following will be described in detail with reference to the accompanying drawings. Figure 4 The data processing method provided by the application will be further described by taking the application of the data processing method in a video playing application program as an example. Wherein, Figure 4 A processing flowchart of a data processing method applied to a video playing application program is shown, which specifically includes the following steps:

[0135] Step 402: Obtain user behavior detail data of the video playing application program in a preset historical time interval.

[0136] Specifically, as shown in FIG. 1, Figure 5 FIG. 1 is a schematic diagram of the data processing method of the application, Figure 5 FIG. 1 is a schematic diagram of the data processing method of the application, Figure 5 The DWD layer (data detail layer) of the data warehouse collects user behavior detail data of the video playing application program in a preset historical time interval.

[0137] Step 404: Determine the information corresponding to the user identifier in the user behavior detail data as user attribute information, and determine the information corresponding to the event type in the user behavior detail data as event attribute information.

[0138] Step 406: Deduplication processing is performed on the user attribute information to obtain user attribute information corresponding to the user identifier.

[0139] Step 408: Count the number of executions of each event according to the event attribute information to obtain event statistical results.

[0140] Step 410: Combine the user attribute information and the event statistics results to obtain aggregated data corresponding to the user attribute information.

[0141] Step 412: Convert the format of the user identifier in the user attribute information of the aggregated data based on the target data compression structure to obtain user event data of the video playback application.

[0142] Specifically, such as Figure 5 As shown, the DWD layer (data detail layer) of the data warehouse transmits the user behavior detail data to the DWB layer (basic data layer), and the DWB layer (basic data layer) executes the contents of steps 404 to 412.

[0143] Step 414: Obtain the user ID and the attribute tag corresponding to the user ID in the video playback application.

[0144] Step 416: Format conversion is performed on the user identifier based on the target data compression structure, and user tag data of the video playback application is determined based on the converted user identifier and attribute tag.

[0145] Step 418: Store the user event data and user tag data of the video playback application into the database.

[0146] Specifically, such as Figure 5 As shown, the user event data of the video playback application and the user tag data of the video playback application generated above are stored in the database.

[0147] The data processing method of the present application obtains detailed user behavior data for a target object within a preset historical time interval; determines user attribute information and event attribute information in the user behavior detailed data; aggregates the user attribute information and event attribute information according to the user identifier in the user behavior detailed data to obtain aggregated data, wherein the aggregated data includes user attribute information and event statistics; and converts the format of the aggregated data based on the target data compression structure to obtain user event data of the target object. By aggregating the user attribute information and event attribute information in the user behavior detailed data and converting the aggregated data into user event data, the storage space occupied by the user behavior detailed data is reduced, thereby improving storage efficiency and saving storage space.

[0148] Corresponding to the method embodiments, the application further provides data processing device embodiments, Figure 6 A structure diagram of a data processing device is shown. As shown in the figure, Figure 6 The device comprises:

[0149] The acquisition module 602 is configured to acquire user behavior detail data of a target object in a preset historical time interval;

[0150] The determination module 604 is configured to determine user attribute information and event attribute information in the user behavior detail data;

[0151] The aggregation module 606 is configured to aggregate the user attribute information and the event attribute information according to a user identifier in the user behavior detail data, to obtain aggregation data, wherein the aggregation data comprises user attribute information and event statistical results;

[0152] The conversion module 608 is configured to perform format conversion on the aggregation data based on a target data compression structure, to obtain user event data of the target object.

[0153] Optionally, the aggregation module 606 is further configured to:

[0154] determine a target user identifier in the user behavior detail data;

[0155] determine user behavior sub-data in the user behavior detail data according to the target user identifier;

[0156] perform deduplication processing on user attribute information in the user behavior sub-data, to obtain user attribute information corresponding to the target user identifier;

[0157] perform aggregation processing on event attribute information in the user behavior sub-data according to the user attribute information corresponding to the target user identifier, to obtain aggregation data corresponding to the user attribute information.

[0158] Optionally, the aggregation module 606 is further configured to:

[0159] determine target user behavior sub-data corresponding to the user attribute information in the user behavior sub-data;

[0160] statistically determine the number of times each event is executed in the target user behavior sub-data according to the event attribute information, to obtain event statistical results, wherein the event statistical results comprise an event type and the number of times the event type is executed;

[0161] splice the user attribute information and the event statistical results, to obtain aggregation data corresponding to the user attribute information.

[0162] Optionally, the determining module 604 is further configured to:

[0163] determine information corresponding to a user identifier in the user behavior detail data as user attribute information;

[0164] determine information corresponding to an event type in the user behavior detail data as event attribute information.

[0165] Optionally, the conversion module 608 is further configured to:

[0166] format-convert a user identifier in the user attribute information of the aggregated data based on a target data compression structure, to obtain user event data of the target object.

[0167] Optionally, the apparatus further comprises a conversion submodule configured to:

[0168] obtain a user identifier for a target object and an attribute label corresponding to the user identifier;

[0169] format-convert the user identifier based on a target data compression structure, and determine user label data of the target object based on the converted user identifier and the attribute label.

[0170] Optionally, the apparatus further comprises a storage module configured to:

[0171] store the user event data of the target object to a database;

[0172] Correspondingly, after the determining of the user label data of the target object based on the converted user identifier and the attribute label, the apparatus further comprises:

[0173] storing the user label data of the target object to a database.

[0174] Optionally, the apparatus further comprises a first query module configured to:

[0175] receive a user event data query request for a target object, wherein the user event data query request carries a basic configuration query condition;

[0176] query corresponding user event data in the user event data in the database based on the basic configuration query condition, wherein the basic configuration query condition comprises at least one of an event time condition, a buried point event condition, and a user device condition;

[0177] generate a user event statistical chart based on the user event data, and send the user event statistical chart to a user event statistical chart display interface of the target object.

[0178] Optionally, the apparatus further comprises a second query module configured to:

[0179] receive a user event data query request for a target object, wherein the user event data query request carries a basic configuration query condition and a tag data query condition;

[0180] determine to-be-processed user event data from user event data of a database based on the basic configuration query condition, and determine to-be-processed user tag data from user tag data of the database based on the tag data query condition;

[0181] process the to-be-processed user event data and the to-be-processed user tag data according to a preset data processing mode, to obtain tag user event data;

[0182] generate a tag user event statistical chart based on the tag user event data, and send the tag user event statistical chart to a user event statistical chart display interface of the target object.

[0183] Optionally, the second query module is further configured to:

[0184] determine an association relationship between the basic configuration query condition and the tag data query condition, and determine a preset data processing mode based on the association relationship;

[0185] process the to-be-processed user event data and the to-be-processed user tag data based on the preset data processing mode, to obtain tag user event data.

[0186] Optionally, the user attribute information includes at least one of user device information, user identification information and user level information; and the event attribute information includes at least one of event type information, event time information and event state information.

[0187] The data processing apparatus provided in the application includes an acquisition module configured to acquire user behavior detail data of a target object in a preset historical time interval; a determination module configured to determine user attribute information and event attribute information in the user behavior detail data; an aggregation module configured to aggregate the user attribute information and the event attribute information according to a user identifier in the user behavior detail data, to obtain aggregation data, wherein the aggregation data includes user attribute information and event statistical results; and a conversion module configured to perform format conversion on the aggregation data based on a target data compression structure, to obtain user event data of the target object. By aggregating the user attribute information and the event attribute information in the user behavior detail data and converting the aggregation data into user event data, the storage space occupied by the user behavior detail data is reduced, and thus the storage efficiency is improved and the storage space is saved.

[0188] The above is a schematic scheme of the data processing apparatus of the embodiment. It should be noted that the technical scheme of the data processing apparatus and the technical scheme of the data processing method described above belong to the same concept, and the details of the technical scheme of the data processing apparatus that are not described in detail can be referred to the description of the technical scheme of the data processing method.

[0189] Figure 7 A structural block diagram of a computing device 700 according to an embodiment of the application is shown. The components of the computing device 700 include, but are not limited to, a memory 710 and a processor 720. The processor 720 is connected to the memory 710 through a bus 730, and a database 750 is used to save data.

[0190] The computing device 700 also includes an access device 740, which enables the computing device 700 to communicate via one or more networks 760. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 740 can include one or more of any type of network interface (e.g., network interface card (NIC)) such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a near field communication (NFC) interface, and the like, wired or wireless.

[0191] In an embodiment of the application, the above-described components of the computing device 700 and other components not shown in the Figure 7 should be understood that the components can be connected to each other through a bus. Figure 7The illustrated computing device architecture diagram is for the purpose of example only and is not intended to limit the scope of the present application. Other components can be added or substituted as desired by those skilled in the art.

[0192] The computing device 700 can be any type of stationary or mobile computing device including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or PC. The computing device 700 can also be a mobile or stationary server.

[0193] The processor 720 implements the steps of the data processing method when executing the computer instructions.

[0194] The above is a schematic scheme of the computing device of the embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the data processing method described above belong to the same concept, and the details of the technical scheme of the computing device which are not described in detail can be referred to the description of the technical scheme of the data processing method.

[0195] The embodiment of the present application further provides a computer readable storage medium which stores computer instructions, and the computer instructions are executed by a processor to implement the steps of the data processing method.

[0196] The above is a schematic scheme of the computer readable storage medium of the embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the data processing method described above belong to the same concept, and the details of the technical scheme of the storage medium which are not described in detail can be referred to the description of the technical scheme of the data processing method.

[0197] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0198] The computer readable medium can include any entity or apparatus capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, software distribution medium, etc. It should be noted that the computer readable medium can include appropriate additions or subtractions according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0199] It should be noted that for the foregoing method embodiments, the descriptions are expressed as a combination of a series of actions for the sake of simplicity, but those skilled in the art should know that the present application is not limited by the order of the described actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0200] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0201] The preferred embodiments of the present application disclosed above are only used to help explain the present application. The alternative embodiments do not describe all the details and do not limit the present application to the specific embodiments described. Obviously, according to the content of the present application, many modifications and changes can be made. The present application selects and describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and utilize the present application. The present application is limited by the claims and their entire scope and equivalents.

Claims

1. A data processing method, characterized in that: include: Obtain detailed user behavior data for the target object within a preset historical time period; Determining user attribute information and event attribute information in the user behavior detailed data; aggregating the user attribute information and the event attribute information according to the user identifier in the user behavior detailed data to obtain aggregated data, wherein the aggregated data includes the user attribute information and event statistical results; Performing format conversion on the aggregated data based on the target data compression structure to obtain user event data of the target object; Wherein, after converting the format of the aggregated data based on the target data compression structure to obtain the user event data of the target object, the method further includes: Obtaining a user ID for a target object and an attribute tag corresponding to the user ID; Performing format conversion on the user identifier based on a target data compression structure, and determining user tag data of the target object based on the converted user identifier and the attribute tag, wherein the target data compression structure is a data structure for compressing data; Wherein, after converting the format of the aggregated data based on the target data compression structure to obtain the user event data of the target object, the method further includes: storing the user event data of the target object in a database; After storing the user event data of the target object in the database, the method further includes: Receive a user event data query request for a target object, wherein the user event data query request carries a basic configuration query condition; Querying the corresponding user event data in the user event data in the database based on the basic configuration query condition, wherein the basic configuration query condition includes at least one of an event time condition, a buried event condition, and a user device condition; A user event statistical graph is generated based on the user event data, and the user event statistical graph is sent to a user event statistical graph display interface of the target object.

2. The method according to claim 1, wherein Aggregating the user attribute information and the event attribute information according to the user identifier in the user behavior detailed data to obtain aggregated data includes: Determining a target user identifier in the user behavior detailed data; Determining user behavior sub-data in the user behavior detailed data according to the target user identifier; Performing deduplication processing on the user attribute information in the user behavior sub-data to obtain user attribute information corresponding to the target user identifier; Aggregation processing is performed on the event attribute information in the user behavior sub-data according to the user attribute information corresponding to the target user identifier to obtain aggregated data corresponding to the user attribute information.

3. The method according to claim 2, wherein Performing aggregation processing on the event attribute information in the user behavior sub-data according to the user attribute information corresponding to the target user identifier to obtain aggregated data corresponding to the user attribute information includes: Determining target user behavior sub-data corresponding to the user attribute information in the user behavior sub-data; Counting the number of executions of each event in the target user behavior sub-data according to the event attribute information to obtain an event statistical result, wherein the event statistical result includes an event type and the number of executions corresponding to the event type; The user attribute information and the event statistics result are spliced ​​together to obtain aggregated data corresponding to the user attribute information.

4. The method according to claim 1, wherein Determining user attribute information and event attribute information in the user behavior detailed data includes: Determining that information corresponding to the user identifier in the user behavior detailed data is user attribute information; The information corresponding to the event type in the user behavior detailed data is determined to be event attribute information.

5. The method according to claim 1, wherein Converting the format of the aggregated data based on the target data compression structure to obtain user event data of the target object includes: The user identifier in the user attribute information of the aggregated data is formatted based on the target data compression structure to obtain user event data of the target object.

6. The method according to claim 1, wherein After converting the format of the aggregated data based on the target data compression structure to obtain the user event data of the target object, the method further includes: Accordingly, after determining the user tag data of the target object based on the converted user identifier and the attribute tag, the method further includes: The user tag data of the target object is stored in a database.

7. The method according to claim 6, characterized in that After storing the user tag data of the target object in the database, the method further includes: Receive a user event data query request for a target object, wherein the user event data query request carries a basic configuration query condition and a tag data query condition; Determining the user event data to be processed from the user event data in the database based on the basic configuration query condition, and determining the user tag data to be processed from the user tag data in the database based on the tag data query condition; Processing the to-be-processed user event data and the to-be-processed user tag data according to a preset data processing method to obtain tag user event data; A tagged user event statistical graph is generated based on the tagged user event data, and the tagged user event statistical graph is sent to a user event statistical graph display interface of the target object.

8. The method according to claim 7, characterized in that Processing the user event data to be processed and the user tag data to be processed according to a preset data processing method to obtain the tag user event data includes: Determining an association between the basic configuration query condition and the tag data query condition, and determining a preset data processing method based on the association; The user event data to be processed and the user tag data to be processed are processed based on the preset data processing method. The data is processed to obtain the tag user event data.

9. The method according to claim 1, wherein The user attribute information includes at least one of user device information, user identification information, and user level information; the event attribute information includes at least one of event type information, event time information, and event status information.

10. A data processing device, characterized in that: include: An acquisition module is configured to acquire detailed user behavior data of a target object within a preset historical time interval; a determination module configured to determine user attribute information and event attribute information in the user behavior detailed data; an aggregation module configured to aggregate the user attribute information and the event attribute information according to the user identifier in the user behavior detailed data to obtain aggregated data, wherein the aggregated data includes the user attribute information and event statistical results; a conversion module configured to perform format conversion on the aggregated data based on a target data compression structure to obtain user event data of the target object; The device further includes a conversion submodule configured to: Obtaining a user ID for a target object and an attribute tag corresponding to the user ID; Performing format conversion on the user identifier based on a target data compression structure, and determining user tag data of the target object based on the converted user identifier and the attribute tag, wherein the target data compression structure is a data structure for compressing data; Optionally, the device further includes a storage module configured to: storing the user event data of the target object in a database; The device further includes a first query module configured to: Receive a user event data query request for a target object, wherein the user event data query request carries a basic configuration query condition; Querying the corresponding user event data in the user event data in the database based on the basic configuration query condition, wherein the basic configuration query condition includes at least one of an event time condition, a buried event condition, and a user device condition; A user event statistical graph is generated based on the user event data, and the user event statistical graph is sent to a user event statistical graph display interface of the target object.

11. A computing device comprising a memory, a processor, and computer instructions stored in the memory and executable on the processor, wherein: When the processor executes the computer instructions, the steps of the method according to any one of claims 1 to 9 are implemented.

12. A computer-readable storage medium storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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