WO2020211299A1

WO2020211299A1 - Data cleansing method

Info

Publication number: WO2020211299A1
Application number: PCT/CN2019/109121
Authority: WO
Inventors: 张礼成
Original assignee: 苏宁云计算有限公司; 苏宁易购集团股份有限公司
Priority date: 2019-04-17
Filing date: 2019-09-29
Publication date: 2020-10-22
Also published as: CA3177209A1; CN110162519A

Abstract

A data cleansing method. The method comprises: acquiring data from a first data source, and establishing an independent data stream by using the acquired data (101); filtering the data in the data stream to obtain data to be cleansed (102); deleting or filling a field comprising a missing value in the data to be cleansed, to obtain preliminary cleansed data (103); detecting whether the preliminary cleansed data conforms to a preset determination rule, and deleting the data not conforming to the determination rule to obtain final cleansed data (104); and outputting the final cleansed data to a second data source (105). By using the above-mentioned method, data security can be improved.

Description

Data cleaning method

Technical field

This application relates to the field of big data processing technology, and in particular to a data cleaning method.

Background technique

With the advent of the Internet age, a large amount of information data continues to flood into the Internet, and the amount of data is increasing at a rate of 50% every year. With the support of huge data sources, corporate decisions are increasingly based on data analysis, rather than relying solely on experience and intuition. Data cleaning is an indispensable link in the entire data analysis process, and the quality of the results is directly related to the model effect and the final data analysis conclusion. Data cleaning refers to the process of re-auditing and verifying data. The purpose is to delete duplicate data, correct existing errors, and ensure data consistency. In actual operation, data cleaning usually takes up 50%-80% of the time of the data analysis process.

Data cleaning includes offline data cleaning and real-time data cleaning. Offline data cleaning can use complex processing to perform more fine-grained data cleaning by sacrificing performance, including missing value processing, outlier value processing, duplicate value processing, and empty value processing. Value filling, unified units, whether to standardize processing, whether to delete unnecessary variables, and whether to sort, etc.; compared with offline data cleaning, real-time data cleaning is more inclined to fill, filter, and check data legality due to real-time requirements. However, the existing data cleaning process is usually integrated with the data analysis process, and the two are highly coupled. The data cleaning process is greatly affected by other codes of data analysis, data loss is prone to occur, and data security is poor.

Summary of the invention

Based on this, it is necessary to provide a data cleaning method for the above technical problems, which can improve data security.

A data cleaning method, the method includes:

Obtain data from the first data source, and use the acquired data to establish an independent data stream;

Filter the data in the data stream to obtain the data to be cleaned;

Delete or fill the fields containing missing values in the data to be cleaned to obtain preliminary cleaned data;

Detect whether the preliminary cleaning data meets the preset judgment rules, delete the data that does not meet the judgment rules, and obtain the final cleaning data;

Output the final cleaning data to the second data source.

In one of the embodiments, the deleting or filling of the fields containing missing values in the data to be cleaned includes:

According to the ratio of the number of missing values in the field to the total number, the missing rate of the field is calculated;

Determine the importance of the attribute of the field according to the indicators to be analyzed;

According to the field's missing rate and attribute importance, the fields containing missing values are deleted or filled.

In one of the embodiments, the deleting or filling the field containing the missing value according to the missing rate and attribute importance of the field includes:

When the missing rate of the field is lower than the preset missing rate threshold and the attribute importance is lower than the preset important rating threshold, fill in the field;

When the missing rate of the field is not lower than the preset missing rate threshold and the attribute importance is lower than the preset important rating threshold, delete the field;

When the missing rate of the field is not lower than the preset missing rate threshold and the attribute importance is higher than the preset important rating threshold, the missing value of the field is completed.

In one of the embodiments, the method further includes:

Exploring the metadata describing data attributes of the data in the first data source, analyzing the quality problems existing in the data according to the metadata analysis, and setting filtering rules according to the quality problems;

The filtering the data in the data stream to obtain the data to be cleaned includes: filtering the data in the data stream according to the filtering rule to obtain the data to be cleaned.

In one of the embodiments, the filtering processing on the data in the data stream includes:

Row-level filtering, remove unnecessary rows in the data;

Column-level filtering. When a row has multiple columns, only the fields corresponding to the required columns are selected and retained.

In one of the embodiments, the preset judgment rule includes a legality rule and a logic rule, and the detecting whether the preliminary cleaning data conforms to the preset judgment rule includes:

If the preliminary cleaning data does not meet the legality rules, set the preliminary cleaning data to the maximum value that meets the legality rules, or delete;

If the preliminary cleaning data does not conform to the logic rule, the preliminary cleaning data is deleted, and a warning instruction is generated.

In one of the embodiments, the first data source and the second data source are different data types of the same distributed messaging system. Further, the distributed messaging system is Kafka, and the first data source and the second data source are Kafka The two different topics of the data stream are based on Spark Streaming.

A data cleaning device, the device includes:

The data acquisition module is used to acquire data from the first data source, and use the acquired data to establish an independent data stream;

The data filtering module is used to filter the data in the data stream to obtain the data to be cleaned;

The preliminary cleaning module is used to delete or fill the fields containing missing values in the data to be cleaned to obtain preliminary cleaning data;

The final cleaning module is used to detect whether the preliminary cleaning data meets the preset judgment rules, delete the data that does not meet the judgment rules, and obtain the final cleaning data;

The data output module is used to output the final cleaning data to the second data source.

A computer device includes a memory, a processor, and a computer program stored on the memory and running on the processor, and the processor implements the following steps when the computer program is executed:

Filter the data in the data stream to obtain the data to be cleaned;

Output the final cleaning data to the second data source.

A computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

Filter the data in the data stream to obtain the data to be cleaned;

Output the final cleaning data to the second data source.

Compared with the prior art, the beneficial effects of the present invention are:

A data cleaning method, device, computer equipment, and storage medium. Data cleaning is performed by establishing an independent data stream. The data obtained from a first data source is cleaned and then put into another data source for subsequent business To process, make the data cleaning process independent from the data analysis code, reduce the coupling between codes, and effectively improve the security of data;

Further, the present invention puts data filtering in the first step of data cleaning, thereby reducing the amount of data that needs to be cleaned in the future, and greatly improving the efficiency of data cleaning.

Description of the drawings

FIG. 1 is a schematic flowchart of a data cleaning method in an embodiment;

Fig. 2 is a structural block diagram of a data cleaning device in an embodiment.

detailed description

In order to make the purpose, technical solutions, and advantages of this application clearer, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the application, and not used to limit the application.

In an embodiment, as shown in FIG. 1, the present application provides a data cleaning method, including the following steps:

Step 101: Obtain data from a first data source, and use the acquired data to establish an independent data stream.

Among them, the first data source is the source from which the data is obtained; the data stream is an ordered data sequence of bytes with a starting point and an ending point.

Specifically, the present invention performs data cleaning by establishing an independent data stream, separates the data cleaning process from the data analysis code, and reduces the coupling between codes.

Step 102: Perform filtering processing on the data in the data stream to obtain data to be cleaned.

Specifically, data filtering is placed in the first step of data cleaning, which can effectively reduce the amount of data that needs to be cleaned later and greatly improve the efficiency of data cleaning.

Step 103: Delete or fill fields in the data to be cleaned that contain missing values to obtain preliminary cleaned data;

Missing value refers to the lack of information in the data, that is, the value of one or some attributes of the data is incomplete.

Step 104: Detect whether the preliminary cleaning data meets the preset judgment rule, delete the data that does not meet the judgment rule, and obtain the final cleaning data;

Step 105: Output the final cleaning data to the second data source.

The second data source is another data source different from the first data source, and is used to store data for subsequent business use or processing.

Specifically, the data cleaning process of the present invention is independent of other processing processes of data analysis, is not affected by other codes, and has higher data security.

In the above data cleaning method, data cleaning is performed by establishing an independent data stream, and the data obtained from the first data source is cleaned and then put into another data source for subsequent business processing, so that the data cleaning process is changed from The data analysis code is independent, which reduces the coupling between the codes and effectively improves the security of the data.

As a specific implementation manner, the first data source and the second data source are different data types of the same distributed messaging system. For example, the distributed messaging system is Kafka, and the first data source and the second data source are Kafka. The two different topics of the data stream are based on Spark Streaming.

The missing rate of the field is the ratio of the number of missing values of the field to the total number;

For example: There are 100 records in the salary field, 20 records are missing values, and the missing rate is 20%.

The criteria for judging the importance of the attributes of the fields are determined based on the indicators that need to be analyzed. If you need to give a user portrait or label to provide data for subsequent precision marketing, then you need to collect user attribute information, such as the user’s age, gender and other attribute information It is an important field.

Specifically, if the field attribute is numeric data, the field can be filled according to the data distribution. More specifically, if the data is evenly distributed, the average value is used to fill the field; if the data distribution is skewed, the median is used to fill the field Fill it.

Specifically, the completion of the missing value of the field includes:

Complete with other information, such as using the ID number to calculate gender, hometown, date of birth, age, etc.;

Through data completion before and after, for example, when the time series lacks data, the average value before and after can be used as the completion value, and when there are many missing values, the value obtained by smoothing can be used as the completion value;

If you cannot complete it, you must delete it, but don't delete it, you can use it later.

As a specific implementation manner, the missing rate threshold may be any value from 90% to 95%.

In one of the embodiments, before the data in the data stream is filtered, the metadata describing the data attributes of the data in the first data source is first explored, and then the quality problems existing in the data are obtained according to the metadata analysis. A filtering rule is set for the quality problem, and the step 102 performs filtering processing on the data in the data stream according to the filtering rule to obtain the data to be cleaned.

Metadata, also known as intermediary data and relay data, is data describing data, mainly information describing data attributes, used to support functions such as indicating storage locations, historical data, resource search, and file recording.

Specifically, encapsulating the data attributes that need to be processed as metadata can make the program more scalable. At the same time, formulating corresponding filtering rules for data quality issues is conducive to improving the efficiency of data filtering.

Row-level filtering, remove unnecessary rows in the data;

Specifically, the combination of row-level filtering and column-level filtering can effectively speed up data filtering.

For example, the process of calculating pv/uv by channel:

The log data includes nearly 200 fields such as the visitor’s IP address, browser information, client terminal device information, specific access time, specific pages accessed, superior interview pages, and access duration. The requirement of this embodiment is to count each The traffic volume of each channel and the traffic volume of independent IP.

Row-level filtering, only choose to keep the log data related to the channel, so as to filter out the log data that does not contain the channel;

Column-level filtering, select cid (channel name), uid (device identification), ip address from nearly 200 fields included in the log data related to the channel, filter out unnecessary fields, and then you can get statistics for each channel Pv/nv;

pv is the abbreviation of Page View, that is, the number of page views. Every time a user visits each page in the website, it is recorded once, and the number of multiple visits to the same page by the user is accumulated into the total number of pv;

uv is the abbreviation of unique visitor, which refers to a natural person who visits and browses this webpage through the Internet.

In this embodiment, considering scalability, for example, subsequent data processing may require statistics on user retention rates, and data such as the access time of each IP address may be further recorded.

The user retention rate is the ratio of old users to total users.

In one of the embodiments, the preset judgment rule includes a legality rule and a logic rule, and the detecting whether the preliminary cleaning data meets the preset judgment rule includes:

The legality rules are the format requirements rules for values, dates, and field contents.

Specifically, the field type legality rule: the date field format is "YYYY-MM-DD"

The legality rules of the field content: gender is male, female or unknown; the date of birth is earlier than or equal to today;

Logic rules are common sense rules used to determine whether data conforms to logic; for example, people’s age is generally between 0 and 120, and if there is an age of 200 years old, it is judged that this piece of data is abnormal.

After the data is cleaned by legality rules and logic rules, data that does not meet the format requirements and logic rules is removed, and effective final cleaned data is obtained.

It should be understood that although the various steps in the flowchart of FIG. 1 are displayed in sequence as indicated by the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless specifically stated in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least some of the steps in FIG. 1 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution of these sub-steps or stages The sequence is not necessarily performed sequentially, but may be performed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

In one embodiment, as shown in FIG. 2, a data cleaning device is provided, which includes: a data acquisition module, a data filtering module, a preliminary cleaning module, a final cleaning module, and a data output module, wherein:

The final cleaning module is used to detect whether the preliminary cleaning data meets the preset judgment rules, delete the data that does not meet the judgment rules, and obtain the final cleaning data.

In specific implementation, the first data source and the second data source are different data types of the same distributed messaging system.

In an embodiment, the preliminary cleaning module includes a missing rate submodule, an importance degree submodule, and a missing value processing submodule, wherein:

The missing rate sub-module is used to calculate the missing rate of the field according to the ratio of the number of missing values in the field to the total number;

The importance degree sub-module is used to determine the attribute importance degree of the field according to the index to be analyzed;

The missing value processing sub-module is used to delete or fill fields containing missing values according to the field’s missing rate and attribute importance.

Further, the missing value processing sub-module includes a comparison unit and a primary processing unit, wherein:

The comparison unit is used to compare the missing rate and attribute importance of the field with preset missing rate thresholds and important rating thresholds, respectively; the primary processing unit is used to fill, delete, or complete the fields.

In one embodiment, the data cleaning device further includes a data exploration module. The data exploration module is used to first explore the metadata describing data attributes of the data in the first data source before filtering the data in the data stream, and then according to The metadata analysis obtains the quality problems existing in the data, and the filtering rules are set according to the quality problems.

In one embodiment, the data filtering module includes a row-level filtering unit and a column-level filtering unit, wherein: the row-level filtering unit is used to remove unnecessary rows in the data; the column-level filtering unit is used for When a row has multiple columns, only the fields corresponding to the required columns are selected and retained.

In an embodiment, the final cleaning module includes a legality detection unit, a logic detection unit, and a final processing unit, wherein:

The legality detection unit is used to detect whether the preliminary cleaning data conforms to a preset legality rule;

The logic detection unit is used to detect whether the preliminary cleaning data meets a preset logic rule;

The final processing unit is configured to set the preliminary cleaning data that does not meet the legality rule to the maximum value that meets the legality rule or delete, delete the preliminary cleaning data that does not meet the logic rule, and generate a warning instruction.

For the specific definition of the data cleaning device, please refer to the above definition of the data cleaning method, which will not be repeated here. Each module in the above-mentioned data cleaning device can be implemented in whole or in part by software, hardware and a combination thereof. The foregoing modules may be embedded in the form of hardware or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the foregoing modules.

In one embodiment, a computer device is provided, and the computer device may be a terminal. The computer equipment includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide calculation and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to realize a data cleaning method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, or it can be a button, a trackball or a touchpad set on the computer equipment shell , It can also be an external keyboard, touchpad, or mouse.

In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the following steps are implemented: From a first data source Obtain data, use the acquired data to establish an independent data stream; filter the data in the data stream to obtain the data to be cleaned; delete or fill in the fields containing missing values in the data to be cleaned to obtain preliminary cleaned data; Whether the cleaning data meets the preset judgment rule, delete the data that does not meet the judgment rule, and obtain the final cleaning data; output the final cleaning data to the second data source.

In one embodiment, the processor further implements the following steps when executing the computer program: calculate the missing rate of the field according to the ratio of the number of missing values of the field to the total number; determine the attribute importance of the field according to the index to be analyzed ; According to the missing rate of the field and the importance of the attribute, delete or fill the field containing the missing value.

In one embodiment, the processor further implements the following steps when executing the computer program: when the missing rate of the field is lower than the preset missing rate threshold and the attribute importance is lower than the preset important rating threshold, fill in the field; When the missing rate of the field is not lower than the preset missing rate threshold and the attribute importance is lower than the preset important rating threshold, delete the field; when the missing rate of the field is not lower than the preset missing rate threshold and the attribute importance is higher than When the important rating threshold is preset, the missing value of the field is completed.

In one embodiment, the processor further implements the following steps when executing the computer program: exploring the metadata describing the data attributes of the data in the first data source, analyzing the quality problems existing in the data according to the metadata analysis, and setting according to the quality problems The filtering rule is to perform filtering processing on the data in the data stream according to the filtering rule to obtain the data to be cleaned.

In one embodiment, the processor also implements the following steps when executing the computer program: row-level filtering, which removes unnecessary rows from the data; column-level filtering, when a row has multiple columns, only select and retain the required The field corresponding to the column.

The preset judgment rules include legality rules and logic rules. In one embodiment, the processor further implements the following steps when executing the computer program: if the preliminary cleaning data does not meet the legality rules, set the preliminary cleaning data to Meet the maximum value of the legality rule, or delete; if the preliminary cleaning data does not meet the logic rule, delete the preliminary cleaning data and generate a warning instruction.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: obtain data from a first data source, and use the obtained data to establish an independent The data stream of the data stream; the data in the data stream is filtered to obtain the data to be cleaned; the fields containing missing values in the data to be cleaned are deleted or filled to obtain the preliminary cleaned data; whether the preliminary cleaned data meets the preset judgment rules, Delete the data that does not meet the judgment rule to obtain the final cleaned data; output the final cleaned data to the second data source.

In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: according to the ratio of the number of missing values of the field to the total number, the missing rate of the field is calculated; and the attribute of the field is determined to be important according to the indicators to be analyzed. Degree; according to the missing rate of the field and the importance of the attribute, the field containing the missing value is deleted or filled.

In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: when the missing rate of the field is lower than the preset missing rate threshold and the attribute importance is lower than the preset important rating threshold, the field is filled; When the missing rate of the field is not lower than the preset missing rate threshold and the attribute importance is lower than the preset important rating threshold, delete the field; when the missing rate of the field is not lower than the preset missing rate threshold and the attribute importance is high In the preset important rating threshold, the missing value of the field is completed.

In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: the metadata describing the data attributes of the data in the first data source is explored, the quality problem of the data is obtained according to the metadata analysis, and the quality problem is set according to the quality problem. The filtering rules are determined, and the data in the data stream is filtered according to the filtering rules to obtain the data to be cleaned.

In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: row-level filtering, which removes unnecessary rows from the data; column-level filtering, when a row has multiple columns, only select and retain all The corresponding fields need to be listed.

The preset judgment rules include legality rules and logic rules. In one embodiment, the computer program also implements the following steps when being executed by the processor: if the preliminary cleaning data does not meet the legality rules, set the preliminary cleaning data to In order to comply with the maximum value of the legality rule, or delete; if the preliminary cleaning data does not meet the logic rule, the preliminary cleaning data is deleted, and a warning instruction is generated.

A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage. In the medium, when the computer program is executed, it may include the procedures of the above-mentioned method embodiments. Wherein, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and/or volatile memory. Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not a limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (SynchLink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction between the combinations of these technical features, they should It is considered as the range described in this specification.

The above-mentioned embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the invention patent. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can be made, and these all fall within the protection scope of this application. Therefore, the scope of protection of the patent of this application shall be subject to the appended claims.

Claims

A data cleaning method, the method includes:

Obtain data from the first data source, and use the acquired data to establish an independent data stream;

Filtering data in the data stream to obtain data to be cleaned;

Deleting or filling fields containing missing values in the data to be cleaned to obtain preliminary cleaned data;

Detecting whether the preliminary cleaning data meets the preset judgment rule, deleting the data that does not meet the judgment rule, and obtaining the final cleaning data;

The final cleaning data is output to the second data source.
The method according to claim 1, wherein the deleting or filling a field containing a missing value in the data to be cleaned comprises:

According to the ratio of the number of missing values in the field to the total number, the missing rate of the field is calculated;

Determine the importance of the attribute of the field according to the indicators to be analyzed;

According to the missing rate of the field and the importance of the attribute, the field containing the missing value is deleted or filled.
The method according to claim 2, wherein the deleting or filling the field containing missing values according to the missing rate and attribute importance of the field comprises:

When the missing rate of the field is lower than the preset missing rate threshold and the attribute importance is lower than the preset important rating threshold, fill in the field;

When the missing rate of the field is not lower than the preset missing rate threshold and the attribute importance is lower than the preset important rating threshold, delete the field;

When the missing rate of the field is not lower than the preset missing rate threshold and the attribute importance is higher than the preset important rating threshold, the missing value of the field is completed.
The method of claim 1, wherein the method further comprises:

Exploring the metadata describing the data attributes of the data in the first data source, analyzing the quality problems existing in the data according to the metadata analysis, and setting filtering rules according to the quality problems;

The filtering processing on the data in the data stream to obtain the data to be cleaned includes: filtering the data in the data stream according to the filtering rules to obtain the data to be cleaned.
The method according to any one of claims 1 to 4, wherein the filtering processing on the data in the data stream comprises:

Row-level filtering, remove unnecessary rows in the data;

Column-level filtering. When a row has multiple columns, only the fields corresponding to the required columns are selected and retained.
The method according to any one of claims 1 to 4, wherein the preset determination rule includes a legality rule and a logic rule, and the detecting whether the preliminary cleaning data meets the preset determination rule includes:

If the preliminary cleaning data does not meet the legality rules, set the preliminary cleaning data to the maximum value that meets the legality rules, or delete;

If the preliminary cleaning data does not conform to the logic rule, delete the preliminary cleaning data, and generate a warning instruction.
The method according to claim 1, wherein the first data source and the second data source are different data types of the same distributed messaging system, and further, the distributed messaging system is Kafka, and the first The first data source and the second data source are two different topics of Kafka; the data stream adopts a data stream based on Spark Streaming.
A data cleaning device, characterized in that the device includes:

The data acquisition module is used to acquire data from the first data source, and use the acquired data to establish an independent data stream;

The data filtering module is used to filter the data in the data stream to obtain the data to be cleaned;

The preliminary cleaning module is used to delete or fill the fields containing missing values in the data to be cleaned to obtain preliminary cleaning data;

The final cleaning module is used to detect whether the preliminary cleaning data meets the preset judgment rule, delete the data that does not meet the judgment rule, and obtain the final cleaning data;

The data output module is used to output the final cleaning data to the second data source.
A computer device comprising a memory, a processor, and a computer program stored on the memory and capable of running on the processor, wherein the processor implements any one of claims 1 to 7 when the computer program is executed The steps of the method.
A computer-readable storage medium with a computer program stored thereon, wherein the computer program implements the steps of the method according to any one of claims 1 to 7 when the computer program is executed by a processor.