Big data-based financial risk assessment and control system

Abstract

The present application relates to the technical field of financial risk management, and in particular to a big data-based financial risk assessment and control system. By means of acquiring and preprocessing multi-source financial data of an enterprise, comprising data cleaning, data fusion, and data dimensionality reduction, the system generates standardized financial data. On the basis of the standardized financial data, constructing a multi-dimensional risk assessment model, comprising a market risk assessment model, a credit risk assessment model, an operation risk assessment model and a compliance risk assessment model. Using the multi-dimensional risk assessment model to perform risk assessment, generating a risk assessment report, and according to the report, generating a financial risk management suggestion, thereby implementing real-time alert and dynamic adjustment. The present invention improves the comprehensiveness and accuracy of financial risk assessment, achieves real-time monitoring and dynamic adjustment of financial risks, and improves the integration and reliability of the system.

Description

Financial risk assessment and control system based on big data TECHNICAL FIELD

[0001] The present application belongs to the technical field of financial risk management, and specifically relates to a financial risk assessment and control system based on big data. BACKGROUND

[0002] With the rapid development of big data technology, financial risk assessment and control systems have gradually become a research hotspot in the fields of finance and enterprise management due to their ability to effectively identify and manage financial risks and improve the scientificity and accuracy of enterprise decision-making. However, existing financial risk assessment and control systems based on big data still have some deficiencies in data processing, risk assessment models, system integration, and real-time performance, which affect the efficiency and reliability of the system.

[0003] After searching, a method, system and device for financial fraud risk assessment with publication number CN112419030B were disclosed on June 27, 2023. The patent obtains financial statements and extracts financial indicator information, establishes a training sample set and a prediction sample set, uses a random forest model, an adaptive enhancement model and a guided aggregation model to predict the prediction sample set, and generates a high-risk data set for financial fraud and a risk analysis report. However, this technical solution mainly focuses on financial fraud risk assessment and has limited ability to assess a wider range of financial risk types (such as market risk, credit risk, etc.). In addition, this system mainly relies on a single data source - financial statements, and lacks the integration of external data (such as market data, public opinion data, etc.), which may result in insufficient comprehensiveness and accuracy of risk assessment.

[0004] After searching, a kind of enterprise financial process management system based on big data with publication number CN118887027B was disclosed on December 27, 2024. The patent obtains enterprise financial data, including financial statements, business data and tax data, performs dimensionality reduction processing and clustering processing, conducts security risk assessment based on clustered enterprise financial data, obtains evaluation results and performs early warning. This technical solution has certain advantages in data processing and risk assessment, but mainly focuses on the management and optimization of financial processes, and has weak real-time monitoring and dynamic adjustment capabilities for financial risks. In addition, this system lacks integration with external systems, such as bank-enterprise interface systems and market data systems, resulting in insufficient comprehensiveness and real-time performance in actual application. TECHNICAL PROBLEM

[0005] The above problems show that the existing big data-based financial risk assessment and control system still has certain deficiencies in data processing diversity, comprehensiveness of risk assessment model, system integration, and real-time performance, etc. Therefore, the present application provides a novel big data-based financial risk assessment and control system, aiming to optimize the data processing flow, improve the comprehensiveness and accuracy of risk assessment, realize real-time monitoring and dynamic adjustment of financial risk, and enhance the integration and reliability of the system, so as to meet the needs of modern enterprises for efficient, comprehensive, and real-time financial risk assessment. Technical solutions

[0006] In order to solve the deficiencies of the existing big data-based financial risk assessment and control system in data processing diversity, comprehensiveness of risk assessment model, system integration, and real-time performance, etc., the present application provides a big data-based financial risk assessment and control system, aiming to optimize the data processing flow, improve the comprehensiveness and accuracy of risk assessment, realize real-time monitoring and dynamic adjustment of financial risk, and enhance the integration and reliability of the system.

[0007] In order to solve the above problems, an embodiment of the present application provides a big data-based financial risk assessment and control method, comprising:

[0008] Obtaining multi-source financial data of an enterprise; wherein the multi-source financial data includes financial statement data, business data, tax data, market data, and public opinion data;

[0009] Preprocessing the multi-source financial data, including data cleaning, data fusion, and data dimension reduction, to generate standardized financial data;

[0010] Based on the standardized financial data, constructing a multi-dimensional risk assessment model; wherein the multi-dimensional risk assessment model includes a market risk assessment model, a credit risk assessment model, an operational risk assessment model, and a compliance risk assessment model;

[0011] Using the multi-dimensional risk assessment model to assess the risk of the standardized financial data, and generating a risk assessment report;

[0012] According to the risk assessment report, generating a financial risk management suggestion, and performing real-time early warning and dynamic adjustment.

[0013] As an improvement of the above scheme, the preprocessing of the multi-source financial data, including data cleaning, data fusion, and data dimension reduction, to generate standardized financial data, comprises:

[0014] Performing format unification and content verification on the financial statement data, removing duplicate data and outliers;

[0015] Fusing business data, tax data, market data, and public opinion data with financial statement data to generate comprehensive financial data;

[0016] Performing dimensionality reduction processing on the comprehensive financial data, extracting key features, and generating standardized financial data.

[0017] It should be noted that the parameters of data cleaning, data fusion, and data dimensionality reduction can be adaptively adjusted based on user demand.

[0018] As an improvement of the above-mentioned scheme, the market risk assessment model comprises:

[0019] Performing volatility analysis on market data to assess the impact of market volatility on financial data;

[0020] Performing correlation analysis on market data to assess the relevance between market data and financial data;

[0021] Based on the results of market volatility and correlation analysis, generate a market risk assessment report.

[0022] As an improvement of the above-mentioned scheme, the credit risk assessment model comprises:

[0023] Performing credit scoring on financial statement data to assess the credit status of the enterprise;

[0024] Performing transaction risk assessment on business data to assess the credit risk of the enterprise's business;

[0025] Based on the results of credit scoring and transaction risk assessment, generate a credit risk assessment report.

[0026] As an improvement of the above-mentioned scheme, the operational risk assessment model comprises:

[0027] Performing operational risk identification on business data to assess the risk points of the enterprise's internal operations;

[0028] Performing compliance checks on tax data to assess the tax risk of the enterprise;

[0029] Based on the results of operational risk identification and compliance checks, generate an operational risk assessment report.

[0030] As an improvement of the above-mentioned scheme, the compliance risk assessment model comprises:

[0031] Performing legal and regulatory matching on compliance data to assess the compliance risk of the enterprise;

[0032] Performing sentiment analysis on public opinion data to assess the impact of external public opinion on the compliance risk of the enterprise;

[0033] Based on the results of legal and regulatory matching and sentiment analysis, generate a compliance risk assessment report.

[0034] In the embodiment, the real-time early warning and dynamic adjustment include:

[0035] Based on the generated risk assessment report, a risk threshold is set, and when the risk value exceeds the threshold, real-time early warning is triggered;

[0036] According to the real-time early warning result, a financial risk management suggestion is generated, including adjusting financial strategy, optimizing business process, and strengthening internal control;

[0037] The financial risk management suggestion is pushed to the enterprise management layer to guide the enterprise to carry out risk management and decision-making.

[0038] Correspondingly, an embodiment of the present application also provides a financial risk assessment and control system based on big data, comprising a data acquisition module, a data preprocessing module, a multi-dimensional risk assessment model construction module, a risk assessment report generation module and a financial risk management suggestion module.

[0039] The data acquisition module is used to acquire multi-source financial data of an enterprise; wherein the multi-source financial data comprises financial statement data, business data, tax data, market data and public opinion data.

[0040] The data preprocessing module is used to preprocess the multi-source financial data, including data cleaning, data fusion and data dimension reduction, to generate standardized financial data.

[0041] The multi-dimensional risk assessment model construction module is used to construct a multi-dimensional risk assessment model based on the standardized financial data; wherein the multi-dimensional risk assessment model comprises a market risk assessment model, a credit risk assessment model, an operational risk assessment model and a compliance risk assessment model.

[0042] The risk assessment report generation module is used to use the multi-dimensional risk assessment model to perform risk assessment on the standardized financial data, and generate a risk assessment report.

[0043] The financial risk management suggestion module is used to generate a financial risk management suggestion according to the risk assessment report, and perform real-time early warning and dynamic adjustment.

[0044] Correspondingly, an embodiment of the present application also provides a computer terminal device, comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to realize a financial risk assessment and control method based on big data as described in the present application.

[0045] Correspondingly, an embodiment of the present application also provides a computer readable storage medium, which comprises a stored computer program, wherein the computer readable storage medium controls a device where the computer readable storage medium is located to execute a big data-based financial risk assessment and control method according to the present application when the computer program runs. Advantages

[0046] The present application provides a big data-based financial risk assessment and control system, which acquires multi-source financial data and performs preprocessing, constructs a multi-dimensional risk assessment model, generates a risk assessment report and financial risk management suggestions, and performs real-time early warning and dynamic adjustment, thereby improving the comprehensiveness and accuracy of financial risk assessment, realizing real-time monitoring and dynamic adjustment of financial risk, and improving the integration and reliability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0047] Fig. 1 is a flowchart of a big data-based financial risk assessment and control method according to an embodiment of the present application;

[0048] As shown in Fig. 1, the present embodiment comprises steps 101 to 105, and each step is as follows:

[0049] Step 101: acquiring multi-source financial data of an enterprise; wherein the multi-source financial data comprises financial statement data, business data, tax data, market data and public opinion data.

[0050] Step 102: preprocessing the multi-source financial data, including data cleaning, data fusion and data dimension reduction, to generate standardized financial data.

[0051] Step 103: constructing a multi-dimensional risk assessment model based on the standardized financial data; wherein the multi-dimensional risk assessment model comprises a market risk assessment model, a credit risk assessment model, an operational risk assessment model and a compliance risk assessment model.

[0052] Step 104: using the multi-dimensional risk assessment model to perform risk assessment on the standardized financial data to generate a risk assessment report.

[0053] Step 105: generating financial risk management suggestions according to the risk assessment report, and performing real-time early warning and dynamic adjustment.

[0054] Fig. 2 is a structural diagram of a big data-based financial risk assessment and control system according to an embodiment of the present application;

[0055] As shown in Fig. 2, it comprises a data acquisition module 10, a data preprocessing module 20, a multi-dimensional risk assessment model construction module 30, a risk assessment report generation module 40 and a financial risk management suggestion module 50.

[0056] The data acquisition module 10 is configured to acquire multi-source financial data of an enterprise, wherein the multi-source financial data comprises financial statement data, business data, tax data, market data and public opinion data.

[0057] The data preprocessing module 20 is configured to preprocess the multi-source financial data, including data cleaning, data fusion and data dimension reduction, to generate standardized financial data.

[0058] The multi-dimensional risk assessment model construction module 30 is configured to construct a multi-dimensional risk assessment model based on the standardized financial data, wherein the multi-dimensional risk assessment model comprises a market risk assessment model, a credit risk assessment model, an operational risk assessment model and a compliance risk assessment model.

[0059] The risk assessment report generation module 40 is configured to perform risk assessment on the standardized financial data using the multi-dimensional risk assessment model, to generate a risk assessment report.

[0060] The financial risk management suggestion module 50 is configured to generate financial risk management suggestions according to the risk assessment report, and to perform real-time early warning and dynamic adjustment.

[0061] FIG. 3 is a schematic diagram of a data preprocessing process according to an embodiment of the present application;

[0062] As shown in FIG. 3, the data preprocessing includes steps 301 to 303, which are specifically as follows:

[0063] Step 301: Perform format unification and content verification on the financial statement data, and remove duplicate data and abnormal values.

[0064] Step 302: Fuse the business data, tax data, market data and public opinion data with the financial statement data to generate comprehensive financial data.

[0065] Step 303: Perform dimension reduction processing on the comprehensive financial data, extract key features, and generate standardized financial data.

[0066] FIG. 4 is a schematic diagram of a market risk assessment model according to an embodiment of the present application;

[0067] As shown in FIG. 4, the market risk assessment model includes steps 401 to 403, which are specifically as follows:

[0068] Step 401: Perform volatility analysis on the market data to assess the impact of market volatility on the financial data.

[0069] Step 402: Perform correlation analysis on the market data to assess the relevance between the market data and the financial data.

[0070] Step 403: generating a market risk assessment report based on the market volatility and correlation analysis results.

[0071] FIG. 5 is a flowchart of a credit risk assessment model according to an embodiment of the present application;

[0072] As shown in FIG. 5, the credit risk assessment model includes steps 501 to 503, which are as follows:

[0073] Step 501: credit scoring on financial statement data to assess the credit status of the enterprise.

[0074] Step 502: transaction risk assessment on business data to assess the credit risk of the business of the enterprise.

[0075] Step 503: generating a credit risk assessment report based on the credit scoring and transaction risk assessment results.

[0076] FIG. 6 is a schematic diagram of a terminal device according to an embodiment of the present application;

[0077] As shown in FIG. 6, the terminal device according to the embodiment includes a processor 601, a memory 602, and a computer program stored in the memory 602 and executable on the processor 601. When the processor 601 executes the computer program, the steps of the above-mentioned financial risk assessment and control method based on big data in the embodiments are implemented, for example, all the steps of the financial risk assessment and control method based on big data shown in FIG. 1. Alternatively, when the processor 601 executes the computer program, the functions of the modules in the above-mentioned system embodiments are implemented, for example, all the modules of the financial risk assessment and control system based on big data shown in FIG. 2. Embodiments of the present application

[0078] The present application provides a financial risk assessment and control system based on big data, which is characterized by the acquisition, preprocessing, and construction of multi-dimensional risk assessment models of multi-source data, as well as real-time early warning and dynamic adjustment mechanisms, thereby achieving comprehensive monitoring and efficient management of enterprise financial risks. The specific embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0079] In this embodiment, the system first acquires multi-source financial data of the enterprise through the data acquisition module 10, which includes financial statement data, business data, tax data, market data, and public opinion data. Financial statement data mainly comes from the balance sheet, income statement, and cash flow statement of the enterprise; business data covers transaction records, contract information, and other information in the daily operation of the enterprise; tax data includes the enterprise's tax declaration records and related tax compliance files; market data involves macroeconomic indicators, industry trends, and competitor analysis; public opinion data comes from social media, news reports, and public evaluation of the enterprise's brand. The diversity of these data provides a rich foundation for subsequent risk assessment.

[0080] After acquiring multi-source financial data, the data preprocessing module 20 cleans, fuses, and reduces the dimension of the data to generate standardized financial data. As shown in FIG. 3, the data preprocessing process includes steps 301 to 303. In step 301, the financial statement data is unified in format and content checked to remove duplicate data and outliers. For example, for the total assets field in the balance sheet, if the value of a certain record is found to deviate significantly from the normal range, it will be marked as an outlier and removed. In addition, due to differences in data formats from different sources, a unified standard is used for conversion to ensure seamless integration of all data. In step 302, business data, tax data, market data, and public opinion data are fused with financial statement data to generate comprehensive financial data. This process is achieved through data correlation algorithms, such as using the enterprise's unique identifier to match and integrate data from different sources. In step 303, the comprehensive financial data is reduced in dimension to extract key features and generate standardized financial data. The dimension reduction method can use principal component analysis (PCA) or linear discriminant analysis (LDA) to reduce the dimension of the data while retaining the most important information. For example, assuming that the original data contains 100 characteristic variables, after PCA processing, only the first 10 principal components are retained as the key features of the standardized data.

[0081] After the data preprocessing is completed, the multi-dimensional risk assessment model construction module 30 constructs a multi-dimensional risk assessment model based on the standardized financial data. The model includes a market risk assessment model, a credit risk assessment model, an operational risk assessment model, and a compliance risk assessment model. As shown in FIG. 4, the flow of the market risk assessment model includes steps 401 to 403. In step 401, volatility analysis is performed on the market data to assess the impact of market volatility on the financial data. Volatility analysis can be achieved by calculating the standard deviation or the GARCH model. For example, if the standard deviation of the revenue data of a certain enterprise in the past year is 5%, it indicates that the revenue volatility of the enterprise is high. In step 402, correlation analysis is performed on the market data to assess the correlation between the market data and the financial data. Correlation analysis can use the Pearson correlation coefficient formula: r = Σ[(X_i - X̄)(Y_i - Ȳ)] / √[Σ(X_i - X̄)²Σ(Y_i - Ȳ)²], where X and Y represent market data and financial data, respectively, and X̄ and Ȳ are their means. If the calculation result is close to 1, it indicates that the two are highly positively correlated. In step 403, based on the results of market volatility and correlation analysis, a market risk assessment report is generated. For example, if the revenue volatility of a certain enterprise is high and highly correlated with market data, its market risk level is rated as high.

[0082] As shown in FIG. 5, the flow of the credit risk assessment model includes steps 501 to 503. In step 501, credit scoring is performed on the financial statement data to assess the credit status of the enterprise. Credit scoring can be achieved by the Z-score model, whose formula is: Z = 1.2X_1 + 1.4X_2 + 3.3X_3 + 0.6X_4 + 1.0X_5, where X_1 to X_5 represent operating capital / total assets, retained earnings / total assets, pre-tax profit / total assets, stock market value / total liabilities, and sales revenue / total assets, respectively. If the Z value is less than 1.8, it indicates that the enterprise has high credit risk. In step 502, transaction risk assessment is performed on the business data to assess the credit risk of the enterprise's business. For example, by analyzing the default rate and delinquency rate in historical transaction records, the transaction risk index of the enterprise is calculated. In step 503, based on the credit scoring and transaction risk assessment results, a credit risk assessment report is generated. For example, if the Z value of a certain enterprise is 1.5 and the transaction risk index is high, its credit risk level is rated as high.

[0083] The operational risk assessment model assesses the risk points of internal operations and tax risks by identifying operational risks from business data and conducting compliance checks on tax data. For example, in operational risk identification, potential operational errors or fraudulent activities are identified by analyzing weak links in business processes. In compliance checks, tax data is automatically audited using a rule engine to ensure compliance with relevant laws and regulations. Based on the above analysis results, an operational risk assessment report is generated.

[0084] The compliance risk assessment model assesses the compliance risks of the enterprise and the impact of external public opinion on it by matching compliance data with laws and regulations and conducting sentiment analysis on public opinion data. For example, in the legal regulation matching, natural language processing technology is used to compare the compliance data of the enterprise with the latest laws and regulations to identify potential compliance loopholes. In sentiment analysis, text mining technology is used to classify public opinion data into positive, negative, and neutral sentiment to assess public attitudes towards the enterprise brand. Based on the above analysis results, a compliance risk assessment report is generated.

[0085] The risk assessment report generation module 40 uses multi-dimensional risk assessment models to assess the standardized financial data and generate a risk assessment report. This report not only contains the risk assessment results of each dimension, but also calculates the comprehensive risk index of the enterprise through weighted average method. For example, assuming that the weights of market risk, credit risk, operational risk and compliance risk are 0.3, 0.3, 0.2 and 0.2 respectively, and their corresponding risk scores are 80, 70, 60 and 50, then the comprehensive risk index is: 0.3x80 + 0.3x70 + 0.2x60 + 0.2x50 = 67. According to the comprehensive risk index, the risk level of the enterprise is divided into low, medium and high levels.

[0086] The financial risk management suggestion module 50 generates financial risk management suggestions based on the risk assessment report and conducts real-time early warning and dynamic adjustment. As shown in Figure 1, in step 105, risk thresholds are set based on the generated risk assessment report, and real-time early warning is triggered when the risk value exceeds the threshold. For example, if the comprehensive risk index of an enterprise exceeds 70, the system automatically sends a warning notice to the enterprise management. Based on the real-time early warning results, financial risk management suggestions are generated, including adjusting financial strategies, optimizing business processes, and strengthening internal controls. For example, for enterprises with high market risk, it is recommended to diversify investment portfolios to reduce the impact of fluctuations in a single market; for enterprises with high credit risk, it is recommended to strengthen customer credit audits and shorten the account period. These suggestions are pushed to the enterprise management through the system to guide their risk management and decision-making.

[0087] The application further provides a computer terminal device as shown in Figure 6, which comprises a processor 601, a memory 602, and a computer program stored in the memory 602 and executable on the processor 601. The processor 601 implements all steps of the above-mentioned financial risk assessment and control method based on big data when executing the computer program. In addition, the application further provides a computer readable storage medium, which stores a computer program that controls the device to execute the above-mentioned method when running.

[0088] In summary, the application realizes comprehensive monitoring and efficient management of enterprise financial risks through acquisition and preprocessing of multi-source data, construction of multi-dimensional risk assessment model, generation of risk assessment report, and real-time early warning and dynamic adjustment mechanism. The integration and reliability of the system have been significantly improved, providing scientific decision support for enterprise management.

[0089] The above description shows and describes several preferred embodiments of the application, but as previously described, it should be understood that the application is not limited to the forms disclosed herein, should not be considered as excluding other embodiments, and can be used in various other combinations, modifications and environments, and can be modified within the scope of the inventive concept described herein by the above-mentioned teaching or related technical or knowledge. Any modification and change made by those skilled in the art without departing from the spirit and scope of the application shall be within the protection scope of the claims of the application.

Claims

1. A big data based financial risk assessment and control method, characterized in that, The method comprises the following steps: acquiring multi-source financial data of an enterprise, the multi-source financial data comprising financial statement data, business data, tax data, market data, and public opinion data; preprocessing the multi-source financial data, including data cleaning, data fusion, and data dimension reduction, to generate standardized financial data; constructing a multi-dimensional risk assessment model based on the standardized financial data, the multi-dimensional risk assessment model comprising a market risk assessment model, a credit risk assessment model, an operational risk assessment model, and a compliance risk assessment model; conducting risk assessment on the standardized financial data using the multi-dimensional risk assessment model to generate a risk assessment report; generating financial risk management suggestions according to the risk assessment report, and conducting real-time early warning and dynamic adjustment.

2. The method of claim 1, wherein, The preprocessing of the multi-source financial data comprises: format unification and content verification of the financial statement data to remove duplicate data and outliers; fusing the business data, tax data, market data, and public opinion data with the financial statement data to generate comprehensive financial data; dimension reduction processing of the comprehensive financial data to extract key features and generate standardized financial data.

3. The method of claim 1, wherein, The market risk assessment model comprises: volatility analysis of the market data to assess the impact of market volatility on the financial data; correlation analysis of the market data to assess the relevance between the market data and the financial data; generating a market risk assessment report based on the results of market volatility and correlation analysis.

4. The method of claim 1, wherein, The credit risk assessment model comprises: credit scoring of the financial statement data to assess the credit status of the enterprise; transaction risk assessment of the business data to assess the credit risk of the enterprise's business; generating a credit risk assessment report based on the results of credit scoring and transaction risk assessment.

5. The method of claim 1, wherein, The operational risk assessment model comprises: operational risk identification of the business data to assess the risk points of the enterprise's internal operations; compliance checking of the tax data to assess the tax risk of the enterprise; generating an operational risk assessment report based on the results of operational risk identification and compliance checking.

6. The method of claim 1, wherein, The compliance risk assessment model comprises: legal and regulatory matching of the compliance data to assess the compliance risk of the enterprise; sentiment analysis of the public opinion data to assess the impact of external public opinion on the compliance risk of the enterprise; generating a compliance risk assessment report based on the results of legal and regulatory matching and sentiment analysis.

7. The method of claim 1, wherein, The real-time early warning and dynamic adjustment comprises: setting a risk threshold based on the generated risk assessment report, and triggering real-time early warning when the risk value exceeds the threshold; generating financial risk management suggestions according to the results of real-time early warning, including adjusting financial strategies, optimizing business processes, and strengthening internal controls; pushing the financial risk management suggestions to the management of the enterprise.

8. A big data based financial risk assessment and control system, characterized in that, The method comprises the following steps: a data acquisition module (10) for acquiring multi-source financial data of an enterprise, the multi-source financial data comprising financial statement data, business data, tax data, market data, and public opinion data; a data preprocessing module (20) for preprocessing the multi-source financial data, including data cleaning, data fusion, and data dimension reduction, to generate standardized financial data; A multi-dimensional risk assessment model construction module (30) is configured to construct a multi-dimensional risk assessment model based on the standardized financial data, wherein the multi-dimensional risk assessment model comprises a market risk assessment model, a credit risk assessment model, an operational risk assessment model and a compliance risk assessment model; A risk assessment report generation module (40) is configured to generate a risk assessment report by performing risk assessment on the standardized financial data using the multi-dimensional risk assessment model; A financial risk management suggestion module (50) is configured to generate a financial risk management suggestion according to the risk assessment report, and to perform real-time early warning and dynamic adjustment.

9. A computer terminal device, characterized by The computer readable storage medium comprises a stored computer program, wherein the computer program, when executed, controls a device in which the computer readable storage medium is located to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored computer program, wherein the computer program, when executed, controls a device in which the computer readable storage medium is located to perform the method according to any one of claims 1 to 7.

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