Geographic information data quality detection method and device and electronic device

Abstract

The invention discloses a geographic information data quality detection method. The method comprises the steps that geographic information data are acquired and preprocessed; defining quality indexes based on geographic information data characteristics; constructing a geographic information data quality evaluation model based on the defined quality indexes; detecting the geographic information data by using the constructed quality evaluation model, and identifying problems and errors existing in the data; the problems of an existing geographic information data detection method in the aspects of comprehensiveness, automation, hidden problem discovery, expandability, real-time performance and the like are solved.

Description

Technical Field

[0001] The present application relates to the field of geographic information technology, and in particular to a geographic information data quality detection method and device and an electronic device. Background Art

[0002] Geographic information data plays an important role in various fields, such as navigation, map making, urban planning, etc. However, the quality of geographic information data is crucial to the accuracy and reliability of its application. Existing geographic information data detection methods have some problems and challenges in some aspects. Existing geographic information data detection methods often only focus on a single or partial quality indicator and lack comprehensiveness. The quality of geographic information data involves many aspects, such as completeness, consistency, accuracy, timeliness, etc. The detection of a single indicator is difficult to comprehensively evaluate the data quality. Traditional geographic information data detection methods require a lot of manual participation and consume a lot of time, lacking automation and efficiency. With the increase in the scale of data, traditional methods cannot meet the needs of quickly and accurately detecting the quality of geographic information data. Some geographic information data quality problems are not easily discovered by traditional methods, such as complex correlations and spatial correlations between data. Existing methods may not be able to effectively capture and identify these hidden problems. Some geographic information data detection methods are difficult to deal with data of different data types and different scales, and lack versatility and scalability. This limits the application and promotion of these methods in different scenarios. Some traditional geographic information data detection methods cannot monitor and feedback data quality problems in real time, resulting in data quality problems that may exist for a long time without being discovered and solved.

[0003] In terms of the problems of existing geographic information data detection methods in related technologies, such as comprehensiveness, automation, discovery of hidden problems, scalability and real-time performance, no effective solutions have been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a geographic information data quality detection method and device, storage medium and electronic device to at least solve the problems of existing geographic information data detection methods in the related art in terms of comprehensiveness, automation, discovery of hidden problems, scalability and real-time performance.

[0005] According to one embodiment of the present application, a method for detecting quality of geographic information data includes: Obtain geographic information data and perform preprocessing; Define quality indicators based on the characteristics of geographic information data; Based on the defined quality indicators, a geographic information data quality assessment model is constructed; The constructed quality assessment model is used to detect geographic information data and identify problems and errors in the data.

[0006] Optionally, the acquiring of geographic information data and preprocessing includes: Data cleaning, data deduplication, format unification, spatial data correction, and data format conversion; The data cleaning includes: identifying and deleting duplicate geographic information data, identifying and filling or deleting missing values ​​in the data, and identifying and processing outliers; The data deduplication includes: identifying and deleting duplicate geographic information data; The format unification includes: unifying the format of geographic information data, including coordinate system, data type, unit, and unifying the representation method of attribute values; The spatial data correction includes: correcting the spatial geometric relationship in the geographic information data and checking the topological relationship in the geographic information data; The data format conversion includes: converting the formats of geographic information data from different data sources so that they can be processed and analyzed uniformly, and converting the coordinate system of the geographic information data into a unified coordinate system.

[0007] Optionally, the definition of quality indicators based on geographic information data characteristics includes: The quality indicators include: spatial accuracy, attribute data integrity, topological relationship consistency, and data format standardization; The spatial accuracy index evaluation includes: comparing the geographic location coordinates in the geographic information data with the actual geographic location, calculating the error and deviation between the locations, and performing error analysis to evaluate the spatial accuracy; The attribute data integrity indicator evaluation includes: field level check, record level check, data comparison and verification; check the attribute fields in the geographic information data to ensure that each field has a value and there is no missing data or null value; check whether each record or spatial object contains all necessary attribute information to ensure data integrity; compare and verify the geographic information data with other data sources to ensure the integrity and consistency of the attribute data; The topological relationship consistency index evaluation includes: using topological analysis methods to detect whether there are topological errors in the data, checking whether the boundaries between adjacent elements are consistent, and checking whether the topological relationship between nodes and edges is accurate; The data format standardization index evaluation includes: checking whether the data contains metadata, whether the field name, data type, and unit of the data are standardized; evaluating whether the data uses a unified coordinate system and projection method, and checking whether the data structure conforms to the standard geographic information data format.

[0008] Optionally, constructing a geographic information data quality assessment model based on the defined quality indicators includes: Prepare a dataset containing geographic information data and corresponding quality labels, and ensure that the dataset contains spatial geometry information, attribute data, topological relationships, and data format information; Extract and transform geographic information data, and convert spatial accuracy, attribute data integrity, topological relationship consistency, and data format standardization indicators into features that can be processed by the random forest model; Divide the data set into training set and test set, and use the holdout method to ensure that the data distribution of the training set and the test set is uniform; Train a random forest model using the training set; Use the test set to evaluate the trained random forest model and its performance; Analyze the importance of each feature in the random forest model; Optimize and adjust the model based on the evaluation results.

[0009] Optionally, the feature extraction and conversion of geographic information data to convert spatial accuracy, attribute data integrity, topological relationship consistency and data format standardization indicators into features that can be processed by the random forest model includes: Extract features for spatial accuracy, attribute data integrity, topological relationship consistency and data format standardization; convert and encode the extracted features.

[0010] Optionally, the data set is divided into a training set and a test set, and a holdout method is used to divide the data set to ensure that the data of the training set and the test set are evenly distributed, including: Import the required database, load the geographic information dataset, and define feature data and label data; Use the train_test_split function to divide the dataset into training and test sets. Control the proportion of the test set by specifying the test_size parameter, set the random seed to ensure the consistency of each division result, and perform stratified sampling according to the label y. Check the data distribution by printing the label distribution of the training set and the test set to verify whether the data set is evenly divided.

[0011] Optionally, the use of a test set to evaluate the trained random forest model to evaluate the performance of the model includes: Use the trained random forest model to predict the test set data; The model performance is evaluated by accuracy, precision, recall, and F1 score. Accuracy refers to the ratio of the number of samples predicted correctly by the model to the total number of samples, precision refers to the ratio of the number of samples predicted by the model to the total number of samples predicted to be positive, and recall refers to the ratio of the number of samples predicted by the model to the total number of samples predicted to be positive. F1 score is the harmonic mean of precision and recall, which takes precision and recall into consideration. Plot the confusion matrix to get a more intuitive understanding of the model's classification performance.

[0012] The present application also provides a geographic information data quality detection device, including: Data acquisition module: acquire geographic information data and perform preprocessing; Quality indicator definition module: defines quality indicators based on the characteristics of geographic information data; Quality assessment model building module: builds a geographic information data quality assessment model based on the defined quality indicators; Data detection module: Use the constructed quality assessment model to detect geographic information data and identify problems and errors in the data.

[0013] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned geographic information data quality detection method through the computer program.

[0014] In the embodiment of the present application, the scheme includes obtaining geographic information data and preprocessing it, defining quality indicators, building a quality assessment model, and detecting problems and errors in the data. This method combines data preprocessing, quality indicator definition, and machine learning model construction, which can effectively improve the quality and reliability of geographic information data. First, the geographic information data is preprocessed, including data cleaning, data deduplication, format unification, spatial data correction, data format conversion, data integration, and other steps to ensure the integrity and consistency of the data. Then, based on the characteristics of geographic information data, quality indicators such as spatial accuracy, attribute data integrity, topological relationship consistency, data format standardization, etc. are defined to evaluate the quality of the data. Then, a geographic information data quality assessment model is constructed based on the defined quality indicators, and machine learning algorithms such as random forests are used to evaluate and optimize the data. Finally, the evaluation model is used to detect the geographic information data, identify the problems and errors therein, and further improve the data quality. It combines data processing technology and machine learning methods, and can automatically perform quality assessment and error detection on large-scale geographic information data, improving efficiency and accuracy. At the same time, by defining quality indicators and building evaluation models, data quality can be quantitatively evaluated, and optimization and improvement can be carried out based on the evaluation results, thereby improving the reliability and accuracy of data applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 is a flow chart of a method for detecting quality of geographic information data according to an embodiment of the present application; Figure 2 It is a structural block diagram of a geographic information data quality detection device according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution 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, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal, a device terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 It is a flowchart of a geographic information data quality detection method according to an embodiment of the present application.

[0021] like Figure 1 As shown, a method for detecting the quality of geographic information data includes: S1 obtains geographic information data and performs preprocessing; S2 defines quality indicators based on the characteristics of geographic information data; S3 builds a geographic information data quality assessment model based on the defined quality indicators; S4 uses the constructed quality assessment model to detect geographic information data and identify problems and errors in the data.

[0022] Optionally, the acquiring of geographic information data and preprocessing includes: Data cleaning, data deduplication, format unification, spatial data correction, data format conversion, and data integration; The data cleaning includes: identifying and deleting duplicate geographic information data, identifying and filling or deleting missing values ​​in the data, and identifying and processing outliers; The data deduplication includes: identifying and deleting duplicate geographic information data; The format unification includes: unifying the format of geographic information data, including coordinate system, data type, unit, and unifying the representation method of attribute values; The spatial data correction includes: correcting the spatial geometric relationship in the geographic information data and checking the topological relationship in the geographic information data; The data format conversion includes: converting the formats of geographic information data from different data sources so that they can be processed and analyzed uniformly, and converting the coordinate system of the geographic information data into a unified coordinate system.

[0023] Optionally, the definition of quality indicators based on geographic information data characteristics includes: The quality indicators include: spatial accuracy, attribute data integrity, topological relationship consistency, and data format standardization; The spatial accuracy index evaluation includes: comparing the geographic location coordinates in the geographic information data with the actual geographic location, calculating the error and deviation between the locations, and performing error analysis to evaluate the spatial accuracy; The attribute data integrity indicator evaluation includes: field level check, record level check, data comparison and verification; check the attribute fields in the geographic information data to ensure that each field has a value and there is no missing data or null value; check whether each record or spatial object contains all necessary attribute information to ensure data integrity; compare and verify the geographic information data with other data sources to ensure the integrity and consistency of the attribute data; At the field level, ensure that each attribute field has a value and there is no missing data or null values. This is done by checking the data type and content of each attribute field. Ensure that the data type is as expected and that no important information is missing; At the record level, check whether each record or spatial object contains all necessary attribute information. Each record should contain complete attribute data to ensure that there is no missing or erroneous data. It helps to ensure the integrity and availability of the dataset; Compare and verify geographic information data with other data sources to ensure the integrity and consistency of attribute data. This can be achieved through data integration and comparison to ensure the consistency of attribute data between different data sources and avoid conflicting or erroneous data; The above steps can comprehensively evaluate the integrity of attribute data in geographic information data, ensuring that the attribute information in the data set is accurate, complete and consistent. This helps improve data quality, reduce errors and inconsistencies, and thus enhance the credibility and usability of data.

[0024] The topological relationship consistency index evaluation includes: using topological analysis methods to detect whether there are topological errors in the data, checking whether the boundaries between adjacent elements are consistent, and checking whether the topological relationship between nodes and edges is accurate; Use topological analysis methods, such as topological relationship models or topological verification tools, to detect whether there are topological errors in the data. These tools can help identify problems such as overlaps, breaks, duplications, inconsistencies, etc. to ensure that the topological structure of the dataset is correct.

[0025] Make sure the boundaries between adjacent features are consistent, with no overlaps or gaps. This can be done by checking the topological relationships between the boundary lines or polygons of adjacent features. Make sure the boundary lines are properly connected, with no breaks or overlapping sections.

[0026] Ensure that the topological relationships between nodes and edges are accurate, such as whether nodes are correctly connected to edges, whether edges are correctly connected to other edges, etc. This helps ensure that the connections and relationships between spatial features in the dataset are correct.

[0027] Through the above steps, the consistency of topological relationships in geographic information data can be evaluated to ensure that the relationships and connections between spatial elements in the dataset are accurate. This helps to avoid data errors and inconsistencies and improve data quality and credibility.

[0028] The data format standardization index evaluation includes: checking whether the data contains metadata, whether the field name, data type, and unit of the data are standardized; evaluating whether the data uses a unified coordinate system and projection method, and checking whether the data structure conforms to the standard geographic information data format.

[0029] The checking whether the data contains metadata includes: ensuring that the data set contains metadata, which describes the attributes, sources, formats, quality, etc. of the data. Metadata is very important for data management and effective use, and can help users understand the meaning and context of the data.

[0030] The standardization of data field names, data types, and units includes: checking whether the data field names are clear and descriptive and follow consistent naming conventions. Ensure the standardization of data types and units so that the interpretation and analysis of data are more accurate and reliable; The assessment of whether the data uses a unified coordinate system and projection method includes: ensuring that the dataset uses a unified coordinate system and projection method to ensure the consistency of the data during spatial analysis and visualization. Different coordinate systems and projection methods may cause inconsistent or distorted data, so unified standards are crucial to the overall quality of the dataset; Checking whether the data structure conforms to the standard geographic information data format includes: ensuring that the data structure conforms to the standard geographic information data format, such as Shapefile, GeoJSON, KML, etc. This helps ensure that the data can be interoperable in different GIS software and simplifies the process of data exchange and sharing.

[0031] Optionally, constructing a geographic information data quality assessment model based on the defined quality indicators includes: Prepare a dataset containing geographic information data and corresponding quality labels, and ensure that the dataset contains spatial geometry information, attribute data, topological relationships, and data format information; Extract and transform geographic information data, and convert spatial accuracy, attribute data integrity, topological relationship consistency, and data format standardization indicators into features that can be processed by the random forest model; Divide the data set into training set and test set, and use the holdout method to ensure that the data distribution of the training set and the test set is uniform; Train a random forest model using the training set; Initialize the random forest regressor, train the model, make predictions on the test set, and evaluate the model performance by calculating the accuracy and confusion matrix; Use the test set to evaluate the trained random forest model and its performance; Analyze the importance of each feature in the random forest model; Optimize and adjust the model based on the evaluation results.

[0032] Optionally, the feature extraction and conversion of geographic information data to convert spatial accuracy, attribute data integrity, topological relationship consistency and data format standardization indicators into features that can be processed by the random forest model includes: Extract features for spatial accuracy, attribute data integrity, topological relationship consistency and data format standardization; convert and encode the extracted features.

[0033] Regarding feature extraction, for spatial accuracy, features related to spatial accuracy are extracted, such as coordinate accuracy, map projection, and geographic reference system; for attribute data integrity, features related to attribute data integrity are extracted, such as missing value ratio, field type, data unit, etc.; for topological relationship consistency, features related to topological relationship consistency are extracted, such as adjacent element relationships, node connection relationships, etc.; for data format standardization, features related to data format standardization are extracted, such as data format type, data structure, metadata information, etc.

[0034] Regarding transformation and encoding, the extracted features are transformed, such as standardization, normalization, discretization, etc., to ensure that the features are compared and analyzed on the same scale, and the categorical variables are encoded, such as one-hot encoding, label encoding, etc., so that the machine learning algorithm can process these features.

[0035] Optionally, the data set is divided into a training set and a test set, and a holdout method is used to divide the data set to ensure that the data of the training set and the test set are evenly distributed, including: Import the required database, load the geographic information dataset, and define feature data and label data; Use the train_test_split function to divide the dataset into training and test sets. Control the proportion of the test set by specifying the test_size parameter, set the random seed to ensure the consistency of each division result, and perform stratified sampling according to the label y. Check the data distribution by printing the label distribution of the training set and the test set to verify whether the data set is evenly divided.

[0036] Optionally, the use of a test set to evaluate the trained random forest model to evaluate the performance of the model includes: Use the trained random forest model to predict the test set data; The model performance is evaluated by accuracy, precision, recall, and F1 score. Accuracy refers to the ratio of the number of samples predicted correctly by the model to the total number of samples, precision refers to the ratio of the number of samples predicted by the model to the total number of samples predicted to be positive, and recall refers to the ratio of the number of samples predicted by the model to the total number of samples predicted to be positive. F1 score is the harmonic mean of precision and recall, which takes precision and recall into consideration. Plot the confusion matrix to get a more intuitive understanding of the model's classification performance.

[0037] like Figure 2 As shown, the embodiment of the present application also provides a geographic information data quality detection device, including: Data acquisition module 201: acquires geographic information data and performs preprocessing; Quality indicator definition module 202: defining quality indicators based on the characteristics of geographic information data; Quality assessment model building module 203: building a geographic information data quality assessment model based on the defined quality indicators; Data detection module 204: Use the constructed quality assessment model to detect geographic information data and identify problems and errors in the data.

[0038] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0039] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0040] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0041] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0042] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0043] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for detecting the quality of geographic information data, characterized in that: include: Obtain geographic information data and preprocess it; Define quality indicators based on the characteristics of geographic information data; Based on the defined quality indicators, a geographic information data quality assessment model is constructed; The constructed quality assessment model is used to detect geographic information data and identify problems and errors in the data.

2. The method according to claim 1, characterized in that The obtaining of geographic information data and preprocessing includes: Data cleaning, data deduplication, format unification, spatial data correction, and data format conversion; The data cleaning includes: identifying and deleting duplicate geographic information data, identifying and filling or deleting missing values ​​in the data, and identifying and processing outliers; The data deduplication includes: identifying and deleting duplicate geographic information data; The format unification includes: unifying the format of geographic information data, including coordinate system, data type, unit, and unifying the representation method of attribute values; The spatial data correction includes: correcting the spatial geometric relationship in the geographic information data and checking the topological relationship in the geographic information data; The data format conversion includes: converting the formats of geographic information data from different data sources so that they can be processed and analyzed uniformly, and converting the coordinate system of the geographic information data into a unified coordinate system.

3. The method according to claim 2, characterized in that The quality indicators defined based on the characteristics of geographic information data include: The quality indicators include: spatial accuracy, attribute data integrity, topological relationship consistency, and data format standardization; The spatial accuracy index evaluation includes: comparing the geographic location coordinates in the geographic information data with the actual geographic location, calculating the error and deviation between the locations, and performing error analysis to evaluate the spatial accuracy; The attribute data integrity indicator evaluation includes: field level check, record level check, data comparison and verification; check the attribute fields in the geographic information data to ensure that each field has a value and there is no missing data or null value; check whether each record or spatial object contains all necessary attribute information to ensure data integrity; compare and verify the geographic information data with other data sources to ensure the integrity and consistency of the attribute data; The topological relationship consistency index evaluation includes: using topological analysis methods to detect whether there are topological errors in the data, checking whether the boundaries between adjacent elements are consistent, and checking whether the topological relationship between nodes and edges is accurate; The data format standardization index evaluation includes: checking whether the data contains metadata, whether the field name, data type, and unit of the data are standardized; evaluating whether the data uses a unified coordinate system and projection method, and checking whether the data structure conforms to the standard geographic information data format.

4. The method according to claim 3, characterized in that The geographic information data quality assessment model is constructed based on the defined quality indicators, including: Prepare a dataset containing geographic information data and corresponding quality labels, and ensure that the dataset contains spatial geometry information, attribute data, topological relationships, and data format information; Extract and transform geographic information data, and convert spatial accuracy, attribute data integrity, topological relationship consistency, and data format standardization indicators into features that can be processed by the random forest model; Divide the data set into training set and test set, and use the holdout method to ensure that the data distribution of the training set and the test set is uniform; Train a random forest model using the training set; Use the test set to evaluate the trained random forest model and its performance; Analyze the importance of each feature in the random forest model; Optimize and adjust the model based on the evaluation results.

5. The method according to claim 4, characterized in that The feature extraction and conversion of geographic information data converts spatial accuracy, attribute data integrity, topological relationship consistency and data format standardization indicators into features that can be processed by the random forest model, including: Extract features for spatial accuracy, attribute data integrity, topological relationship consistency and data format standardization; convert and encode the extracted features.

6. The method according to claim 4, characterized in that The data set is divided into a training set and a test set, and the holdout method is used to ensure that the data of the training set and the test set are evenly distributed, including: Import the required database, load the geographic information dataset, and define feature data and label data; Use the train_test_split function to divide the dataset into training and test sets. Control the proportion of the test set by specifying the test_size parameter, set the random seed to ensure the consistency of each division result, and perform stratified sampling according to the label y. Check the data distribution by printing the label distribution of the training set and the test set to verify whether the data set is evenly divided.

7. The method according to claim 6, characterized in that The test set is used to evaluate the trained random forest model and the performance of the model, including: Use the trained random forest model to predict the test set data; The model performance is evaluated by accuracy, precision, recall, and F1 score. Accuracy refers to the ratio of the number of samples predicted correctly by the model to the total number of samples, precision refers to the ratio of the number of samples predicted by the model to the total number of samples predicted to be positive, and recall refers to the ratio of the number of samples predicted by the model to the total number of samples predicted to be positive. F1 score is the harmonic mean of precision and recall, which takes precision and recall into consideration. Plot the confusion matrix to get a more intuitive understanding of the model's classification performance.

8. A geographic information data quality detection device, characterized in that: include: Data acquisition module: acquire geographic information data and perform preprocessing; Quality indicator definition module: defines quality indicators based on the characteristics of geographic information data; Quality assessment model building module: builds a geographic information data quality assessment model based on the defined quality indicators; Data detection module: Use the constructed quality assessment model to detect geographic information data and identify problems and errors in the data.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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