Data storage method and device, equipment and medium

Abstract

The invention discloses a data storage method and device, equipment and a medium, and the method comprises the steps: obtaining table configuration data from a demand document of a to-be-built data table, the table configuration data being used for representing attribute features of the to-be-built data table; performing feature extraction on the table configuration data to obtain storage feature information of the table configuration data, classifying the to-be-established data table based on the storage feature information of the table configuration data, and determining a storage format corresponding to the to-be-established data table; based on the table configuration data, the storage format and a preset SQL script template, generating an SQL script corresponding to the to-be-established data table; and executing the SQL script corresponding to the to-be-established data table to establish the to-be-established data table, and storing the to-be-established data table according to the storage format. According to the method, the accuracy of the determined storage format of the to-be-established data table can be improved, the storage efficiency and the storage resource utilization rate are improved, and storage space waste is reduced.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data processing, and in particular to a data storage method, apparatus, device, and medium. Background Art

[0002] With the advent of the big data era, data storage, processing, and analysis have become crucial topics in modern information technology. Traditional database management systems and data storage solutions often face challenges with processing complex and massive amounts of data, including inefficiency, wasted storage space, and inappropriate data format selection. In particular, automated and intelligent selection of the most appropriate storage format to improve storage efficiency and query performance in large-scale data processing and storage has become a pressing technical challenge.

[0003] Currently, existing data storage methods typically rely on manual experience to determine the storage format of the data table to be created, and then store the data table according to the determined storage format. However, this approach has problems such as low accuracy of the determined storage format, low storage efficiency, poor query performance, and potential waste of storage resources. Summary of the Invention

[0004] Embodiments of the present application provide a data storage method, apparatus, device, and medium for improving the accuracy of a storage format of a determined data table to be created, improving storage efficiency and storage resource utilization, and reducing storage space waste.

[0005] In a first aspect, an embodiment of the present application provides a data storage method, including:

[0006] Acquire table configuration data from a requirement document of a data table to be created, wherein the table configuration data is used to characterize attribute characteristics of the data table to be created;

[0007] Performing feature extraction on the table configuration data to obtain storage feature information of the table configuration data, and classifying the data table to be created based on the storage feature information of the table configuration data to determine a storage format corresponding to the data table to be created;

[0008] Generate an SQL script corresponding to the data table to be created based on the table configuration data, the storage format and a preset structured query language SQL script template;

[0009] Execute the SQL script corresponding to the data table to be created to create the data table to be created and store it according to the storage format.

[0010] In an embodiment of the present application, table configuration data representing the attribute characteristics of the data table to be built is obtained from the requirements document of the data table to be built, and feature extraction is performed on the table configuration data to obtain storage feature information of the table configuration data to more comprehensively reflect the inherent characteristics and distribution patterns of the table configuration data. The data table to be built is classified based on the storage feature information of the table configuration data, and the storage format corresponding to the data table to be built is determined, thereby improving the accuracy of the storage format corresponding to the determined data table to be built. In addition, the present application generates an SQL script corresponding to the data table to be built based on the table configuration data, the storage format, and a preset SQL script template, and executes the SQL script corresponding to the data table to be built to establish the data table to be built and store it according to the storage format without manual intervention, thereby improving storage efficiency and storage resource utilization, reducing storage space waste, and improving data query and processing performance. Therefore, this application can flexibly select the appropriate storage format based on the requirement documents of different data tables to be built, rather than a single, fixed format, thereby improving the accuracy of the storage format corresponding to the determined data table to be built, improving the utilization rate of storage resources, reducing storage space waste, and improving data query and processing performance. It is especially suitable for big data processing and complex business scenarios, and meets the high requirements of enterprises for storage efficiency and resource optimization.

[0011] In one possible design, extracting features from the table configuration data to obtain storage feature information of the table configuration data, classifying the data table to be created based on the storage feature information of the table configuration data, and determining a storage format corresponding to the data table to be created includes:

[0012] Inputting the table configuration data into a feature extraction layer of a trained classification model, and extracting storage feature information of the table configuration data through the feature extraction layer;

[0013] Inputting the storage feature information of the table configuration data into the output layer of the classification model, classifying the data table to be created based on the storage feature information of the table configuration data through the output layer, and obtaining a probability value of each storage format corresponding to the data table to be created;

[0014] The storage format corresponding to the maximum probability value is used as the storage format.

[0015] In an embodiment of the present application, the table configuration data is input into the trained classification model to obtain the probability value of each storage format corresponding to the data table to be built, and the storage format corresponding to the maximum probability value is used as the storage format, so that the appropriate storage format can be flexibly selected according to the requirement documents of different data tables to be built, thereby improving the accuracy of the storage format corresponding to the determined data table to be built, improving the utilization rate of storage resources, and reducing storage space waste.

[0016] In a possible design, the storage characteristic information of the table configuration data includes part or all of the data table characteristic information, field characteristic information, and operation characteristic information;

[0017] The data table characteristic information is used to characterize the basic attribute characteristics of the data table to be created;

[0018] The field characteristic information is used to characterize the field distribution characteristics of the data table to be created;

[0019] The operation characteristic information is used to characterize the operation characteristics of the data table to be created.

[0020] In a possible design, after determining the storage format corresponding to the data table to be created, the method further includes:

[0021] Displaying the storage format corresponding to the data table to be created on the display page;

[0022] In response to a storage format modification instruction triggered by a user, determining a modified storage format, and determining a target label based on the modified storage format and each preset storage format, wherein the target label includes an actual probability value of each storage format corresponding to the data table to be created;

[0023] Inputting the table configuration data and the target label into the classification model to obtain a predicted probability value corresponding to each storage format of the data table to be built;

[0024] Based on the predicted probability value of each storage format corresponding to the data table to be built and the target label, a target loss value is determined, and the parameters of the classification model are updated using the target loss value.

[0025] In an embodiment of the present application, the storage format corresponding to the data table to be created is displayed on a display page, and in response to a storage format modification instruction triggered by the user, the modified storage format is determined. Based on the modified storage format and the preset storage formats, the target label is determined, and the classification model is incrementally optimized using the table configuration data and the target label, thereby improving the performance of the classification model, improving the classification accuracy of the classification model, and improving the applicability of the classification model to adapt to the needs of dynamic business scenarios.

[0026] In one possible design, the classification model is trained in the following manner:

[0027] Acquire a training data set, wherein the training data set includes a plurality of training samples, each training sample includes historical table configuration data and a label of a historical data table to be built, wherein the label is used to characterize a storage format corresponding to the historical data table to be built;

[0028] Based on the training samples in the training data set, performing cyclic iterative training on the preset classification model, and obtaining the classification model when a preset iteration end condition is met;

[0029] The following operations are performed during one cycle of iterative training:

[0030] Input any training sample in the training data set into a preset classification model for processing to obtain a predicted probability value of the training sample corresponding to each storage format;

[0031] Determine a loss value corresponding to the any one training sample based on a predicted probability value of each storage format corresponding to the any one training sample and a label of the any one training sample;

[0032] The parameters of the preset classification model are updated using the loss value corresponding to any one of the training samples.

[0033] In an embodiment of the present application, a training data set including a plurality of training samples is used to train a preset classification model to obtain a classification model, thereby improving the classification accuracy of the classification model.

[0034] In a second aspect, an embodiment of the present application provides a data storage device, comprising:

[0035] An acquisition module, configured to acquire table configuration data from a requirement document of a data table to be created, wherein the table configuration data is used to characterize attribute characteristics of the data table to be created;

[0036] a determination module, configured to perform feature extraction on the table configuration data to obtain storage feature information of the table configuration data, and classify the data table to be created based on the storage feature information of the table configuration data to determine a storage format corresponding to the data table to be created;

[0037] A generation module, configured to generate an SQL script corresponding to the data table to be created based on the table configuration data, the storage format and a preset structured query language SQL script template;

[0038] The execution module is used to execute the SQL script corresponding to the data table to be created, so as to create the data table to be created and store it according to the storage format.

[0039] In one possible design, the determining module is configured to:

[0040] Inputting the table configuration data into a feature extraction layer of a trained classification model, and extracting storage feature information of the table configuration data through the feature extraction layer;

[0041] Inputting the storage feature information of the table configuration data into the output layer of the classification model, classifying the data table to be created based on the storage feature information of the table configuration data through the output layer, and obtaining a probability value of each storage format corresponding to the data table to be created;

[0042] The storage format corresponding to the maximum probability value is used as the storage format.

[0043] In a possible design, the storage characteristic information of the table configuration data includes part or all of the data table characteristic information, field characteristic information, and operation characteristic information;

[0044] The data table characteristic information is used to characterize the basic attribute characteristics of the data table to be created;

[0045] The field characteristic information is used to characterize the field distribution characteristics of the data table to be created;

[0046] The operation characteristic information is used to characterize the operation characteristics of the data table to be created.

[0047] In a possible design, after determining the storage format corresponding to the data table to be created, the determination module is further configured to:

[0048] Displaying the storage format corresponding to the data table to be created on the display page;

[0049] In response to a storage format modification instruction triggered by a user, determining a modified storage format, and determining a target label based on the modified storage format and each preset storage format, wherein the target label includes an actual probability value of each storage format corresponding to the data table to be created;

[0050] Inputting the table configuration data and the target label into the classification model to obtain a predicted probability value corresponding to each storage format of the data table to be built;

[0051] Based on the predicted probability value of each storage format corresponding to the data table to be built and the target label, a target loss value is determined, and the parameters of the classification model are updated using the target loss value.

[0052] In one possible design, the determination module is used to train a classification model in the following manner:

[0053] Acquire a training data set, wherein the training data set includes a plurality of training samples, each training sample includes historical table configuration data and a label of a historical data table to be built, wherein the label is used to characterize a storage format corresponding to the historical data table to be built;

[0054] Based on the training samples in the training data set, performing cyclic iterative training on the preset classification model, and obtaining the classification model when a preset iteration end condition is met;

[0055] The following operations are performed during one cycle of iterative training:

[0056] Input any training sample in the training data set into a preset classification model for processing to obtain a predicted probability value of the training sample corresponding to each storage format;

[0057] Determine a loss value corresponding to the any one training sample based on a predicted probability value of each storage format corresponding to the any one training sample and a label of the any one training sample;

[0058] The parameters of the preset classification model are updated using the loss value corresponding to any one of the training samples.

[0059] In a third aspect, the present application provides an electronic device, comprising:

[0060] a memory for storing program instructions;

[0061] The processor is configured to call the program instructions stored in the memory and execute the steps included in any one of the methods of the first aspect according to the obtained program instructions.

[0062] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a computer, the computer executes any one of the methods according to the first aspect.

[0063] In a fifth aspect, the present application provides a computer program product, comprising: a computer program code, which, when executed on a computer, enables the computer to execute any one of the methods described in the first aspect.

[0064] The technical effects brought about by the second to fifth aspects and any one of their designs can be referred to the technical effects brought about by the corresponding designs in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 Schematic diagram of application scenarios provided by embodiments of the present application;

[0066] Figure 2 A flowchart of a data storage method provided in an embodiment of the present application;

[0067] Figure 3A flowchart of a method for determining a storage format corresponding to a data table to be created provided in an embodiment of the present application;

[0068] Figure 4 A flowchart of a method for incrementally optimizing a classification model provided in an embodiment of the present application;

[0069] Figure 5 A flowchart of a classification training method provided in an embodiment of the present application;

[0070] Figure 6 A flowchart of a method for performing a one-cycle iterative training on a classification model provided in an embodiment of the present application;

[0071] Figure 7 A detailed flow chart of a data storage method provided in an embodiment of the present application;

[0072] Figure 8 A schematic structural diagram of a data storage device provided in an embodiment of the present application;

[0073] Figure 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Unless there is a conflict, the embodiments in the present application and the features in the embodiments can be combined with each other in any way. In addition, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in an order different from that here.

[0075] The terms "first" and "second" in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the term "comprising" and any of its variations are intended to cover non-exclusive protection. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices. "Multiple" in this application can mean at least two, for example, two, three or more, and the embodiments of this application are not limited thereto.

[0076] In the technical solution of this application, the collection, dissemination, and use of data comply with the requirements of relevant national laws and regulations.

[0077] Before introducing a data storage method provided by an embodiment of the present application, in order to facilitate understanding, the technical background of the embodiment of the present application is first introduced in detail below.

[0078] With the advent of the big data era, data storage, processing, and analysis have become crucial topics in modern information technology. Traditional database management systems and data storage solutions often face challenges with processing complex and massive amounts of data, including inefficiency, wasted storage space, and inappropriate data format selection. In particular, automated and intelligent selection of the most appropriate storage format to improve storage efficiency and query performance in large-scale data processing and storage has become a pressing technical challenge.

[0079] Currently, existing data storage methods typically rely on manual experience to determine the storage format of the data table to be created, and then store the data table according to the determined storage format. However, this approach has problems such as low accuracy of the determined storage format, low storage efficiency, poor query performance, and potential waste of storage resources.

[0080] In order to solve the above problems, the present application proposes a data storage method, device, equipment and medium to improve the accuracy of the storage format of the determined data table to be built, improve storage efficiency and storage resource utilization, and reduce storage space waste.

[0081] First reference Figure 1, which is a schematic diagram of an application scenario of an embodiment of the present application, including a collector 11 and a server 12, wherein the collector 11 and the server 12 are connected to each other through a network, which can be a local area network, a wide area network, etc. The collector 11 can be a convenient device (for example: a mobile phone, a tablet, a laptop, etc.), or a personal computer (PC). The server 12 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, and big data and artificial intelligence platforms. The collector 11 is used to collect table configuration data from the requirement document of the data table to be built and send it to the server 12. The server 12 is used to determine the storage format corresponding to the data table to be built based on the table configuration data, generate the SQL script corresponding to the data table to be built based on the table configuration data, the storage format and the preset Structured Query Language (SQL) script template, and execute the SQL script corresponding to the data table to be built to build the data table to be built and store it according to the storage format.

[0082] In an embodiment of the present application, as an optional implementation method, the server 12 obtains the table configuration data collected by the collector 11 from the requirement document of the data table to be built, wherein the table configuration data is used to characterize the attribute characteristics of the data table to be built; performs feature extraction on the table configuration data to obtain storage feature information of the table configuration data, and classifies the data table to be built based on the storage feature information of the table configuration data to determine the storage format corresponding to the data table to be built; generates an SQL script corresponding to the data table to be built based on the table configuration data, the storage format and the preset SQL script template; executes the SQL script corresponding to the data table to be built to establish the data table to be built and store it according to the storage format.

[0083] Reference below Figure 2 The flowchart of a data storage method shown in FIG. 1 illustrates the technical solution provided in the embodiment of the present application:

[0084] Step 201: Acquire table configuration data from a requirement document of a data table to be created.

[0085] The requirements document for the data table to be created includes information such as the table size, each table field, the corresponding field type for each table field, query mode, query frequency, and update frequency. Query modes include query engines such as MR (Map Reduce), Tez (a data processing framework), and Spark (a computing engine). Update frequency includes some or all of the deletion frequency, modification frequency, and addition frequency. Table configuration data is used to characterize the attributes of the data table to be created. This includes information such as the table size, the number of table fields, and the field types.

[0086] For example, the requirements document for a data table to be created includes a table name of user_data, a table size of a megabyte (MB), table fields including user_id, user_name, email, and signup_date, the field type corresponding to the user_id field is INT (integer type), the field type corresponding to the user_name field is STRING (string type), the field type corresponding to the email field is STRING, and the field type corresponding to the signup_date field is STRING. The query mode is Spark, the query frequency is b, the deletion frequency is c, the modification frequency is d, and the addition frequency is e.

[0087] The data table to be created is a table that needs to be connected to a data warehouse. The data warehouse can be Hive (data warehouse system), which is a data warehouse system built on Hadoop (an intelligent development platform based on a code generator). It provides a SQL-like query language (called HiveQL) for managing, querying, and analyzing big data stored in Hadoop HDFS (Hadoop Distributed File System). Hive enables data analysts to use the SQL query language to operate on distributed stored data without having to master complex MapReduce (programming model) programming. HDFS is a distributed file system designed for processing big data. It is mainly used to store massive amounts of data and provides high fault tolerance, high throughput, and the ability to adapt to large-scale data processing needs.

[0088] Step 202 : extracting features from the table configuration data to obtain storage feature information of the table configuration data, classifying the data table to be built based on the storage feature information of the table configuration data, and determining the storage format corresponding to the data table to be built.

[0089] In an embodiment of the present application, the table configuration data is input into a trained classification model to perform feature extraction on the table configuration data to obtain storage feature information of the table configuration data, and the data table to be built is classified based on the storage feature information of the table configuration data to determine the storage format corresponding to the data table to be built.

[0090] The classification model can be LightGBM (Light Gradient Boosting Machine) or other multi-classification models. LightGBM is an efficient framework based on the Gradient Boosting algorithm. It is primarily used to process large datasets and perform machine learning tasks, especially in classification, regression, and ranking tasks.

[0091] Step 203: Generate an SQL script corresponding to the data table to be created based on the table configuration data, storage format and preset SQL script template.

[0092] The preset SQL script template can be set according to actual conditions. SQL script refers to an executable file written in a database query and programming language.

[0093] For example, when the storage format of the data table to be created is Parquet (columnar storage format), based on the table configuration data, storage format, and preset SQL script template, the generated SQL script corresponding to the data table to be created is as follows:

[0094] CREATE TABLE IF NOT EXISTS user_data( / / Create a data table named user_data;

[0095] user_id INT, / / The field type of the user_id field in this data table is INT;

[0096] user_name STRING, / / The field type of the user_name field in this data table is STRING;

[0097] email STRING, / / The email field in this data table has a field type of STRING;

[0098] signup_date STRING / / The field type of the signup_date field in this data table is STRING; )

[0100] STORED AS PARQUET / / The data table is stored in the Parquet storage format;

[0101] LOCATION'hdfs: / / user / hive / warehouse / user_data / ' / / The storage location of the data table is 'hdfs: / / user / hive / warehouse / user_data / ';

[0102] TBLPROPERTIES('parquet.compression' = 'SNAPPY'); / / Use SNAPPY (a development kit for compression and decompression) to compress and store the data table in the Parquet storage format;

[0103] In an embodiment of the present application, the data table name, data table fields, and field types corresponding to the data table fields are directly obtained from the table configuration data. The prefix "hdfs: / / user / hive / warehouse / " of the storage location of the data table is pre-configured by the user, and the storage location " / user_data / " of the data table is determined based on the data table name. The storage format of the data table is predicted by the classification model.

[0104] In an embodiment of the present application, a trained classification model is used to process the input table configuration data of the data table to be built, and the storage format corresponding to the data table to be built is output. Based on the table configuration data, storage format and preset SQL script template, an SQL script corresponding to the data table to be built is generated, and automatic configuration of compression format and storage path is supported.

[0105] Step 204 : Execute the SQL script corresponding to the data table to be created to create the data table to be created and store it according to the storage format.

[0106] In the embodiment of the present application, after executing the SQL script corresponding to the data table to be created, a collector is used to collect at least one piece of data, and each piece of collected data is loaded into the established data table to be created.

[0107] In the embodiment of the present application, after executing the SQL script corresponding to the data table to be created, in response to a new data instruction of the established data table to be created triggered by the user, the data is loaded into the established data table to be created.

[0108] In an embodiment of the present application, table configuration data representing the attribute characteristics of the data table to be built is obtained from the requirements document of the data table to be built, and feature extraction is performed on the table configuration data to obtain storage feature information of the table configuration data to more comprehensively reflect the inherent characteristics and distribution patterns of the table configuration data. The data table to be built is classified based on the storage feature information of the table configuration data, and the storage format corresponding to the data table to be built is determined, thereby improving the accuracy of the storage format corresponding to the determined data table to be built. In addition, the present application generates an SQL script corresponding to the data table to be built based on the table configuration data, the storage format, and a preset SQL script template, and executes the SQL script corresponding to the data table to be built to establish the data table to be built and store it according to the storage format without manual intervention, thereby improving storage efficiency and storage resource utilization, reducing storage space waste, and improving data query and processing performance. Therefore, this application can flexibly select the appropriate storage format based on the requirement documents of different data tables to be built, rather than a single, fixed format, thereby improving the accuracy of the storage format corresponding to the determined data table to be built, improving the utilization rate of storage resources, reducing storage space waste, and improving data query and processing performance. It is especially suitable for big data processing and complex business scenarios, and meets the high requirements of enterprises for storage efficiency and resource optimization.

[0109] In the embodiment of the present application, table configuration data is obtained from the requirement document of the data table to be built, and the table configuration data needs to be normalized or labeled, and missing field information is processed, default values ​​are filled in, etc. The table configuration data includes field values ​​corresponding to different fields.

[0110] For example, table configuration data M={f1,f2,…,f n}, where f i is the field value of the i-th field, and n is the number of fields in the table configuration data. After M is standardized, the processed table configuration data M is obtained. ′ ={f1 ′ ,f2 ′ ,…,f n ′}, where f i ′ The processed field value of the i-th field.

[0111] In the embodiment of the present application, the field value f of the i-th field i There are two situations in which standardization is performed:

[0112] In the first case, the field value f of the i-th field i If it is a numeric value, then based on the maximum value of the i-th field, the minimum value of the i-th field and the field value f of the i-th field i , determine the processed field value f of the i-th fieldi ′ . Among them, the maximum value of the i-th field and the minimum value of the i-th field can be set according to actual conditions, or can be determined based on the field value of the i-th field in multiple historical table configuration data, that is, the maximum value of the i-th field and the minimum value of the i-th field in multiple historical table configuration data.

[0113] In this embodiment of the present application, the processed field value f of the i-th field can be determined using the following formula: i ′ :

[0114]

[0115] Among them, f i is the field value of the i-th field, min(f i ) is the minimum value of the i-th field, max(f i ) is the maximum value of the i-th field.

[0116] In the second case, the field value f of the i-th field i If it is a non-numeric value, the field value f of the i-th field is encoded using the set encoding method. i Perform encoding processing to obtain the processed field value f of the i-th field i ′ The encoding method may be set according to actual conditions.

[0117] In this embodiment of the present application, the processed field value f of the i-th field can be determined using the following formula: i ′ :

[0118] f i ′ =Encode(f i );

[0119] Among them, Encode() is the encoding method, f i The field value of the i-th field.

[0120] In the embodiment of the present application, the storage format corresponding to the data table to be created is determined based on the table configuration data. Figure 3 A flowchart of a method for determining a storage format corresponding to a data table to be created is provided in an embodiment of the present application. Figure 3 As shown, the above step 202 at least includes the following steps 301-303:

[0121] Step 301: input the table configuration data into the feature extraction layer of the trained classification model, and extract the storage feature information of the table configuration data through the feature extraction layer.

[0122] In the embodiment of the present application, the storage characteristic information of the table configuration data includes part or all of the data table characteristic information, field characteristic information and operation characteristic information;

[0123] The data table characteristic information is used to characterize the basic attribute characteristics of the data table to be created;

[0124] Field feature information is used to characterize the field distribution characteristics of the data table to be created;

[0125] The operation characteristic information is used to characterize the operation characteristics of the data table to be created.

[0126] Table characteristic information includes part or all of the table size and number of table fields. Field characteristic information includes part or all of the field type distribution and nesting of the table fields. Operation characteristic information includes part or all of the query mode, query frequency, and update frequency. Update frequency includes part or all of the deletion frequency, modification frequency, and addition frequency.

[0127] Specifically, based on the processed table configuration data M ′ , generate part or all of the data table feature information, field feature information and operation feature information, and perform fusion processing to obtain storage feature information.

[0128] For example, based on the processed table configuration data M ′ , generating data table feature information F, field feature information D, and operation feature information Q. Based on the data table feature information F, field feature information D, and operation feature information Q, we obtain storage feature information X = F∪D∪Q, where ∪ represents a union operation. The data table feature information F includes the data table size file_size and the number of data table fields column_count. Therefore, the data table feature information F = {file_size, column_count}. The field feature information D includes the data table field type distribution type_distribution and the nested structure nested_structure. Therefore, the field feature information D = {type_distribution, nested_structure}. The operation feature information Q includes the query mode query_mode, the query frequency query_frequency, and the update frequency update_frequency. Therefore, the operation feature information Q = {query_mode, query_frequency, update_frequency}.

[0129] Step 302: input the storage feature information of the table configuration data into the output layer of the classification model, and classify the data table to be built based on the storage feature information of the table configuration data through the output layer to obtain the probability value of each storage format corresponding to the data table to be built.

[0130] In the embodiment of the present application, the preset storage format set can be set according to actual conditions, and the storage format set includes multiple storage formats. The storage format set may include storage formats such as Text (text file), Parquet (columnar storage format), ORC (Optimized Row Columnar), Avro (row-based storage format), RCfile (Record Columnar File, columnar record file), and SequenceFile (binary serialized file). For example, the storage format set Y includes 6 storage formats, that is, the storage format set Y = {Text, Parquet, ORC, Avro, RCfile, SequenceFile}.

[0131] Step 303: Use the storage format corresponding to the maximum probability value as the storage format.

[0132] In an embodiment of the present application, after obtaining the probability value of each storage format corresponding to the data table to be built, the probability values ​​of each storage format can be sorted in descending order, and the storage format corresponding to the maximum probability value is used as the storage format.

[0133] In an embodiment of the present application, taking the classification model as LightGBM as an example, the trained LightGBM multi-classification model is loaded, and based on the input table configuration data M of the data table to be built, the probability value of each storage format corresponding to the data table to be built is output, that is, the probability distribution of the storage format of the data table to be built, and the storage format corresponding to the maximum probability value is used as the storage format corresponding to the data table to be built.

[0134] This application can use the following formula to obtain the probability distribution P(Y|M) of the storage format of the data table to be created:

[0135] P(Y|M)=LightGBM(M);

[0136] Among them, Y is the storage format set, LightGBM() is LightGBM, and M is the table configuration data of the data table to be built.

[0137] In the embodiment of the present application, after obtaining the probability distribution of the storage format of the data table to be built, the storage format Y corresponding to the data table to be built can be determined based on the probability distribution of the storage format of the data table to be built using the following formula: recommend :

[0138]

[0139] Where P(Y|M) is the probability distribution of the storage format of the data table to be created, argmax is used to determine the input parameter or index position at which the objective function achieves its maximum value, Y is the storage format set, and M is the table configuration data of the data table to be created.

[0140] In the embodiment of the present application, after determining the storage format corresponding to the data table to be created, the storage format corresponding to the data table to be created is displayed on the display page, so that the user can manually review the storage format corresponding to the data table to be created and determine whether the storage format corresponding to the data table to be created is reasonable. If the storage format corresponding to the data table to be created is reasonable, then continue to execute the above steps 203-204. If the storage format corresponding to the data table to be created is unreasonable, then based on the modified storage format determined by the user, the classification model is incrementally optimized. Figure 4 A flowchart of a method for incrementally optimizing a classification model provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, it at least includes the following steps 401-404:

[0141] Step 401: Display the storage format corresponding to the data table to be created on the display page.

[0142] In an embodiment of the present application, in addition to displaying the storage format corresponding to the data table to be created on the display page, the requirement document or table configuration data of the data table to be created can also be displayed, so that the user can judge whether the storage format corresponding to the data table to be created is reasonable based on the requirement document of the data table to be created.

[0143] Step 402 : In response to a storage format modification instruction triggered by a user, determining a modified storage format, and determining a target tag based on the modified storage format and various preset storage formats.

[0144] The target label includes the actual probability value for each storage format corresponding to the data table to be created. For example, the storage format set is {storage format 1, storage format 2, storage format 3, storage format 4}. Based on the table configuration data of the data table to be created, the classification model determines that the storage format corresponding to the data table to be created is storage format 2. The display page shows that the storage format corresponding to the data table to be created is storage format 2. In response to a storage format modification instruction triggered by the user, the modified storage format is determined to be storage format 3. Based on the modified storage format and the preset storage formats, the target label is determined to be {0, 0, 1, 0}.

[0145] Step 403: Input the table configuration data and the target label into the classification model to obtain the predicted probability value corresponding to each storage format of the data table to be built.

[0146] Step 404 : Based on the predicted probability values ​​and target labels corresponding to each storage format of the data table to be built, a target loss value is determined, and the parameters of the classification model are updated using the target loss value.

[0147] Optionally, the target loss value is determined based on the predicted probability value and target label corresponding to each storage format of the data table to be built, including: based on the predicted probability value and target label corresponding to each storage format of the data table to be built, the target loss value is determined using a set loss function.

[0148] The loss function may be set according to actual conditions. For example, the loss function may be a cross entropy loss function, a KL divergence loss function, or the like.

[0149] In the embodiment of the present application, taking the classification model as LightGBM as an example, based on the table configuration data M of the data table to be built, LightGBM is used to determine the storage format Y corresponding to the data table to be built. recommend In response to the storage format modification instruction triggered by the user, the modified storage format Y is determined manual Based on the table configuration data M and the modified storage format Y manual Incremental training of LightGBM. In this embodiment of the present application, the following formula can be used to perform incremental optimization on LightGBM:

[0150] LightGBM new =Train(LightGBM,D feedback );

[0151] Among them, LightGBM new is the LightGBM after incremental optimization, that is, the LightGBM after the parameters of LightGBM are updated, Train() is the model training function, LightGBM is the LightGBM before incremental optimization, D feedback is the input data of LightGBM, D feedback Includes the table configuration data M of the data table to be created and the modified storage format Y manual , that is, D feedback ={(M,Y manual )}.

[0152] In an embodiment of the present application, after determining the storage format corresponding to the data table to be built, the storage format corresponding to the data table to be built is displayed on a display page, so that the user can manually review the storage format corresponding to the data table to be built and determine whether the storage format corresponding to the data table to be built is reasonable. If the storage format corresponding to the data table to be built is reasonable, an SQL script corresponding to the data table to be built is generated and executed to establish the data table to be built and store it according to the storage format; if the storage format corresponding to the data table to be built is unreasonable, the classification model is incrementally optimized based on the modified storage format determined by the user, thereby improving the performance of the classification model, improving the classification accuracy of the classification model, and improving the applicability of the classification model to adapt to the needs of dynamic business scenarios.

[0153] In the embodiment of the present application, a classification model is trained based on the acquired training data set. Figure 5 A flow chart of a classification training method provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, it at least includes the following steps 501-502:

[0154] Step 501: Obtain a training data set.

[0155] The training data set includes multiple training samples, each training sample includes historical table configuration data and a label of the historical data table to be built, and the label is used to represent the storage format corresponding to the historical data table to be built.

[0156] In an embodiment of the present application, a label of the historical data table to be created is determined based on the storage format corresponding to the historical data table to be created and each storage format in the set storage format set. Specifically, the label of the historical data table to be created is determined by the following method: the storage format corresponding to the historical data table to be created is marked as a first label value, and each other storage format in the set storage format set is marked as a second label value, and the label of the historical data table to be created is obtained based on the label value corresponding to each storage format. The first label value and the second label value can be set according to actual conditions. For example, the first label value can be 1 and the second label value can be 0.

[0157] For example, the storage format set is {storage format A, storage format B, storage format C, storage format D}. If the storage format corresponding to the historical data table to be built is storage format B, the label of the historical data table to be built is {0, 1, 0, 0}.

[0158] Step 502: Based on the training samples in the training data set, a preset classification model is trained in a loop, and a classification model is obtained when a preset iteration end condition is met.

[0159] Among them, the above-mentioned preset iteration end condition can be set according to actual conditions. For example, the preset iteration end condition can be that the current number of iterations reaches the set number of iterations, and the set number of iterations can be set according to actual conditions; the preset iteration end condition can also be that the preset classification model converges.

[0160] The following is a detailed description of the method for performing a cyclic iterative training of the classification model. Figure 6 As shown:

[0161] Step 601: Input any training sample in the training data set into a preset classification model for processing to obtain a predicted probability value of any training sample corresponding to each storage format.

[0162] In the embodiment of the present application, the basic model used to train the classification model, that is, the preset classification model, can be various models with prediction functions, such as a convolutional neural network model, a neural network model, etc.

[0163] Step 602: Determine the loss value corresponding to any training sample based on the predicted probability value of each storage format corresponding to any training sample and the label of any training sample.

[0164] Step 603: Use the loss value corresponding to any training sample to update the parameters of the preset classification model.

[0165] Optionally, determining a loss value corresponding to any training sample based on the predicted probability value of any training sample corresponding to each storage format and the label of any training sample includes: determining the loss value corresponding to any training sample using a set loss function based on the predicted probability value of any training sample corresponding to each storage format and the label of any training sample. The set loss function can be set according to actual conditions, for example, the set loss function can be a cross entropy loss function, a KL divergence loss function, or other such loss functions.

[0166] The specific steps of the data storage method provided above are described in detail below. Figure 7 As shown:

[0167] Step 701: Acquire table configuration data from the requirement document of the data table to be created.

[0168] Step 702: Input the table configuration data into the feature extraction layer of the trained classification model, and extract the storage feature information of the table configuration data through the feature extraction layer.

[0169] Step 703: Input the storage feature information of the table configuration data into the output layer of the classification model. The output layer classifies the data table to be built based on the storage feature information of the table configuration data to obtain the probability value of each storage format of the data table to be built.

[0170] Step 704: Use the storage format corresponding to the maximum probability value as the storage format corresponding to the data table to be created.

[0171] Step 705: Display the storage format corresponding to the data table to be created on the display page.

[0172] Step 706 : In response to the storage format modification instruction triggered by the user, determine the modified storage format, and determine the target tag based on the modified storage format and the preset storage formats.

[0173] In an embodiment of the present application, after determining the modified storage format, it also includes: generating an SQL script corresponding to the data table to be created based on the table configuration data, the modified storage format and the preset SQL script template; executing the SQL script corresponding to the data table to be created to create the data table to be created and store it according to the modified storage format.

[0174] Step 707: Input the table configuration data and the target label into the classification model to obtain the predicted probability value corresponding to each storage format of the data table to be built.

[0175] Step 708: Determine a target loss value based on the predicted probability value and target label corresponding to each storage format of the data table to be built, and use the target loss value to update the parameters of the classification model.

[0176] Step 709 : In response to the storage format determination instruction triggered by the user, an SQL script corresponding to the data table to be created is generated based on the table configuration data, the storage format and the preset SQL script template.

[0177] Step 710: Execute the SQL script corresponding to the data table to be created to create the data table to be created and store it according to the storage format.

[0178] Based on the same technical concept, the present application exemplarily provides a data storage device, such as Figure 8 As shown, the device includes:

[0179] An acquisition module 801 is configured to acquire table configuration data from a requirement document of a data table to be created, wherein the table configuration data is used to characterize attribute characteristics of the data table to be created;

[0180] Determination module 802, configured to extract features from the table configuration data to obtain storage feature information of the table configuration data, classify the data table to be created based on the storage feature information of the table configuration data, and determine a storage format corresponding to the data table to be created;

[0181] A generating module 803 is configured to generate an SQL script corresponding to the data table to be created based on the table configuration data, the storage format and a preset structured query language SQL script template;

[0182] The execution module 804 is configured to execute the SQL script corresponding to the data table to be created, so as to create the data table to be created and store it in accordance with the storage format.

[0183] Optionally, the determining module 802 is configured to:

[0184] Inputting the table configuration data into a feature extraction layer of a trained classification model, and extracting storage feature information of the table configuration data through the feature extraction layer;

[0185] Inputting the storage feature information of the table configuration data into the output layer of the classification model, classifying the data table to be created based on the storage feature information of the table configuration data through the output layer, and obtaining a probability value of each storage format corresponding to the data table to be created;

[0186] The storage format corresponding to the maximum probability value is used as the storage format.

[0187] Optionally, the storage characteristic information of the table configuration data includes part or all of the data table characteristic information, field characteristic information and operation characteristic information;

[0188] The data table characteristic information is used to characterize the basic attribute characteristics of the data table to be created;

[0189] The field characteristic information is used to characterize the field distribution characteristics of the data table to be created;

[0190] The operation characteristic information is used to characterize the operation characteristics of the data table to be created.

[0191] Optionally, after determining the storage format corresponding to the data table to be created, the determining module 802 is further configured to:

[0192] Displaying the storage format corresponding to the data table to be created on the display page;

[0193] In response to a storage format modification instruction triggered by a user, determining a modified storage format, and determining a target label based on the modified storage format and each preset storage format, wherein the target label includes an actual probability value of each storage format corresponding to the data table to be created;

[0194] Inputting the table configuration data and the target label into the classification model to obtain a predicted probability value corresponding to each storage format of the data table to be built;

[0195] Based on the predicted probability value of each storage format corresponding to the data table to be built and the target label, a target loss value is determined, and the parameters of the classification model are updated using the target loss value.

[0196] Optionally, the determining module 802 is configured to obtain a classification model through training in the following manner:

[0197] Acquire a training data set, wherein the training data set includes a plurality of training samples, each training sample includes historical table configuration data and a label of a historical data table to be built, wherein the label is used to characterize a storage format corresponding to the historical data table to be built;

[0198] Based on the training samples in the training data set, performing cyclic iterative training on the preset classification model, and obtaining the classification model when a preset iteration end condition is met;

[0199] The following operations are performed during one cycle of iterative training:

[0200] Input any training sample in the training data set into a preset classification model for processing to obtain a predicted probability value of the training sample corresponding to each storage format;

[0201] Determine a loss value corresponding to the any one training sample based on a predicted probability value of each storage format corresponding to the any one training sample and a label of the any one training sample;

[0202] The parameters of the preset classification model are updated using the loss value corresponding to any one of the training samples.

[0203] Based on the same inventive concept, the embodiment of the present application provides an electronic device that can realize the functions of the data storage device discussed above. Figure 9 , the device includes a processor 901 and a memory 902, wherein the memory 902 is used to store program instructions;

[0204] The processor 901 calls the program instructions stored in the memory and executes the program instructions to implement the following steps:

[0205] Acquire table configuration data from a requirement document of a data table to be created, wherein the table configuration data is used to characterize attribute characteristics of the data table to be created;

[0206] Performing feature extraction on the table configuration data to obtain storage feature information of the table configuration data, and classifying the data table to be created based on the storage feature information of the table configuration data to determine a storage format corresponding to the data table to be created;

[0207] Generate an SQL script corresponding to the data table to be created based on the table configuration data, the storage format and a preset structured query language SQL script template;

[0208] Execute the SQL script corresponding to the data table to be created to create the data table to be created and store it according to the storage format.

[0209] Optionally, the extracting features of the table configuration data to obtain storage feature information of the table configuration data, classifying the data table to be created based on the storage feature information of the table configuration data, and determining a storage format corresponding to the data table to be created includes:

[0210] Inputting the table configuration data into a feature extraction layer of a trained classification model, and extracting storage feature information of the table configuration data through the feature extraction layer;

[0211] Inputting the storage feature information of the table configuration data into the output layer of the classification model, classifying the data table to be created based on the storage feature information of the table configuration data through the output layer, and obtaining a probability value of each storage format corresponding to the data table to be created;

[0212] The storage format corresponding to the maximum probability value is used as the storage format.

[0213] Optionally, the storage characteristic information of the table configuration data includes part or all of the data table characteristic information, field characteristic information and operation characteristic information;

[0214] The data table characteristic information is used to characterize the basic attribute characteristics of the data table to be created;

[0215] The field characteristic information is used to characterize the field distribution characteristics of the data table to be created;

[0216] The operation characteristic information is used to characterize the operation characteristics of the data table to be created.

[0217] Optionally, after determining the storage format corresponding to the data table to be created, the processor 901 is further configured to execute:

[0218] Displaying the storage format corresponding to the data table to be created on the display page;

[0219] In response to a storage format modification instruction triggered by a user, determining a modified storage format, and determining a target label based on the modified storage format and each preset storage format, wherein the target label includes an actual probability value of each storage format corresponding to the data table to be created;

[0220] Inputting the table configuration data and the target label into the classification model to obtain a predicted probability value corresponding to each storage format of the data table to be built;

[0221] Based on the predicted probability value of each storage format corresponding to the data table to be built and the target label, the target loss value is determined, and the parameters of the classification model are updated using the target loss value

[0222] Optionally, the classification model is trained in the following manner:

[0223] Acquire a training data set, wherein the training data set includes a plurality of training samples, each training sample includes historical table configuration data and a label of a historical data table to be built, wherein the label is used to characterize a storage format corresponding to the historical data table to be built;

[0224] Based on the training samples in the training data set, performing cyclic iterative training on the preset classification model, and obtaining the classification model when a preset iteration end condition is met;

[0225] The following operations are performed during one cycle of iterative training:

[0226] Input any training sample in the training data set into a preset classification model for processing to obtain a predicted probability value of the training sample corresponding to each storage format;

[0227] Determine a loss value corresponding to the any one training sample based on a predicted probability value of each storage format corresponding to the any one training sample and a label of the any one training sample;

[0228] The parameters of the preset classification model are updated using the loss value corresponding to any one of the training samples.

[0229] Based on the same inventive concept, embodiments of the present application provide a computer-readable storage medium, a computer program product comprising: computer program code, which, when executed on a computer, causes the computer to execute any of the data storage methods discussed above. Because the principles underlying the problems solved by the computer-readable storage medium are similar to those of the data storage method, the implementation of the computer-readable storage medium can be referred to as the implementation of the method, and any repetitions will not be repeated here.

[0230] Based on the same inventive concept, embodiments of the present application further provide a computer program product, comprising: computer program code, which, when executed on a computer, causes the computer to perform any of the data storage methods discussed above. Because the principles underlying the problems solved by the aforementioned computer program products are similar to those of the data storage methods, the implementation of the aforementioned computer program products can be referred to as the implementation of the methods, and any repetitions will not be repeated.

[0231] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0232] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0233] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0234] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of user-operated steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0235] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A data storage method, characterized in that: include: Acquire table configuration data from a requirement document of a data table to be created, wherein the table configuration data is used to characterize attribute characteristics of the data table to be created; Performing feature extraction on the table configuration data to obtain storage feature information of the table configuration data, and classifying the data table to be created based on the storage feature information of the table configuration data to determine a storage format corresponding to the data table to be created; Generate an SQL script corresponding to the data table to be created based on the table configuration data, the storage format and a preset structured query language SQL script template; Execute the SQL script corresponding to the data table to be created to create the data table to be created and store it according to the storage format.

2. The method according to claim 1, wherein The extracting features of the table configuration data to obtain storage feature information of the table configuration data, classifying the data table to be created based on the storage feature information of the table configuration data, and determining a storage format corresponding to the data table to be created includes: Inputting the table configuration data into a feature extraction layer of a trained classification model, and extracting storage feature information of the table configuration data through the feature extraction layer; Inputting the storage feature information of the table configuration data into the output layer of the classification model, classifying the data table to be created based on the storage feature information of the table configuration data through the output layer, and obtaining a probability value of each storage format corresponding to the data table to be created; The storage format corresponding to the maximum probability value is used as the storage format.

3. The method according to claim 2, wherein The storage characteristic information of the table configuration data includes part or all of the data table characteristic information, field characteristic information and operation characteristic information; The data table characteristic information is used to characterize the basic attribute characteristics of the data table to be created; The field characteristic information is used to characterize the field distribution characteristics of the data table to be created; The operation characteristic information is used to characterize the operation characteristics of the data table to be created.

4. The method according to claim 2, wherein After determining the storage format corresponding to the data table to be created, the method further includes: Displaying the storage format corresponding to the data table to be created on the display page; In response to a storage format modification instruction triggered by a user, determining a modified storage format, and determining a target label based on the modified storage format and each preset storage format, wherein the target label includes an actual probability value of each storage format corresponding to the data table to be created; Inputting the table configuration data and the target label into the classification model to obtain a predicted probability value corresponding to each storage format of the data table to be built; Based on the predicted probability value of each storage format corresponding to the data table to be built and the target label, a target loss value is determined, and the parameters of the classification model are updated using the target loss value.

5. The method according to any one of claims 1 to 4, characterized in that: The classification model is trained in the following way: Acquire a training data set, wherein the training data set includes a plurality of training samples, each training sample includes historical table configuration data and a label of a historical data table to be built, wherein the label is used to characterize a storage format corresponding to the historical data table to be built; Based on the training samples in the training data set, performing cyclic iterative training on the preset classification model, and obtaining the classification model when a preset iteration end condition is met; The following operations are performed during one cycle of iterative training: Input any training sample in the training data set into a preset classification model for processing to obtain a predicted probability value of the training sample corresponding to each storage format; Determine a loss value corresponding to the any one training sample based on a predicted probability value of each storage format corresponding to the any one training sample and a label of the any one training sample; The parameters of the preset classification model are updated using the loss value corresponding to any one of the training samples.

6. A data storage device, characterized in that include: An acquisition module, configured to acquire table configuration data from a requirement document of a data table to be created, wherein the table configuration data is used to characterize attribute characteristics of the data table to be created; a determination module, configured to perform feature extraction on the table configuration data to obtain storage feature information of the table configuration data, and classify the data table to be created based on the storage feature information of the table configuration data to determine a storage format corresponding to the data table to be created; A generation module, configured to generate an SQL script corresponding to the data table to be created based on the table configuration data, the storage format and a preset structured query language SQL script template; The execution module is used to execute the SQL script corresponding to the data table to be created, so as to create the data table to be created and store it according to the storage format.

7. The device according to claim 6, characterized in that The determining module is used for: Inputting the table configuration data into a feature extraction layer of a trained classification model, and extracting storage feature information of the table configuration data through the feature extraction layer; Inputting the storage feature information of the table configuration data into the output layer of the classification model, classifying the data table to be created based on the storage feature information of the table configuration data through the output layer, and obtaining a probability value of each storage format corresponding to the data table to be created; The storage format corresponding to the maximum probability value is used as the storage format.

8. An electronic device, characterized in that: include: a memory for storing program instructions; A processor is configured to call program instructions stored in the memory and execute the steps of the method according to any one of claims 1 to 5 according to the obtained program instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to perform the method according to any one of claims 1 to 5.

10. A computer program product, characterized in that The computer program product comprises: a computer program code, and when the computer program code is run on a computer, the computer is caused to execute the method according to any one of claims 1 to 5.

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