Audit method based on big data

Abstract

The invention discloses an auditing method based on big data, and relates to the technical field of auditing methods, the method comprises the following steps: initializing to obtain an initial position feature and an initial importance feature of each item node in a data set; calculating a support degree given by an adjacent node of each item node, and calculating an aggregation feature of each item node according to the support degree after normalization processing; determining the initial importance feature when the similarity function is maximized as a target importance feature of the item node; according to the target importance feature of each item node in the two data sets, calculating to obtain a matching degree between the item nodes in the data sets, determining two item nodes of which the matching degree is not less than a threshold value as associated items, and auditing each data set based on each associated item; by finding out the relation between the items in the two different types of data sets, reference is provided for the auditing process, and the auditing efficiency and the auditing quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of audit methods, and more specifically, it relates to an audit method based on big data. Background Art

[0002] Audit is the review and inspection of the implementation of a certain industry's economic activities, financial revenue and expenditure, financial and economic laws and regulations. It is carried out in an organized, led and planned manner by the auditing agency and auditors from top to bottom, expanding from the audit of unit economic activities to the audit of economic activities of the entire industry, and from the audit of microeconomic activities to the audit of mesoeconomic activities or macroeconomic activities. Data is the basis of audit, and the quality of data will directly affect the quality of audit. The quality of data currently used for auditing needs to be improved. Summary of the invention

[0003] The purpose of the present invention is to provide an audit method based on big data, which can improve the quality of data used for audit work, help organizations and institutions to conduct audit work more efficiently, improve audit quality, and reduce risks while ensuring compliance.

[0004] The above technical objectives of the present invention are achieved through the following technical solutions: In the first aspect, the present application provides an audit method based on big data, comprising the following specific steps: Obtain at least two different types of data sets to be audited and preprocess the data sets, including data cleaning and abnormal data detection; Based on each preprocessed data set, the initial position features and initial importance features of each event node in the data set are obtained by random initialization using Gaussian distribution; Using the initial position features and initial importance features, calculate the support given by the adjacent nodes of each event node, and calculate the aggregated features of each event node fused with the features of the adjacent nodes based on the normalized support; The similarity function of each event node is constructed using the initial position feature and the aggregation feature, and the initial importance feature when the similarity function is maximized is determined as the target importance feature of each event node; According to the target importance characteristics of each item node in at least two data sets, the matching degree between each item node in different types of data sets is calculated, two item nodes whose matching degree is not less than a threshold are determined as related items, and each data set is audited based on each related item.

[0005] The beneficial effects of the present invention are as follows: in this scheme, firstly, the data set to be audited is preprocessed by data cleaning and abnormal data detection, and the duplicate data and abnormal data in each data set are removed, so that the data in the data set is more concise and accurate, and then the initial position features and initial importance features of each item node in the data set are obtained by random initialization of Gaussian distribution; secondly, the support given by the adjacent nodes of each item node is calculated using the initial position features and initial importance features, and the aggregate features of each item node fused with the features of the adjacent nodes are obtained according to the support after normalization, and then the similarity function of each item node is constructed using the initial position features and aggregate features, and the initial importance features when the similarity function is maximized are determined as the target importance features of each item node; finally, the matching degree between each item node in different types of data sets is calculated according to the target importance features of each item node in the two data sets, and two item nodes with matching degrees not less than a threshold are determined as related items, and related items indicate that the correlation between the two times is high, and the reference that can be provided to each other is strong, which has a promoting effect on the audit of the two data sets, and finally each data set is audited based on each related item.

[0006] In this scheme, when the data set about economic activities and the data set about financial revenue and expenditure are audited together, the expenditure of a certain event in the economic activity is a certain value, and the expenditure of a certain item in the financial revenue and expenditure is also the value. Without knowing the detailed content of the activity, the two events in the two data sets show a high degree of match. Therefore, when conducting a joint audit of different types of data sets, by finding the connection between the items in the two different types of data sets, a reference is provided for the audit process, which has a high improvement on the audit work. It not only improves the quality of data used for audit work, helps organizations and institutions to conduct audit work more efficiently, but also improves the audit quality and reduces risks while ensuring compliance.

[0007] Based on the above technical solution, the present invention can also be improved as follows.

[0008] Furthermore, the above data cleaning is specifically as follows: Discretize the attribute items of each original data in the normalized data set, and transform each attribute value obtained after discretization into a preset integer range according to the size; The information gain rate of each attribute item of the original data is calculated based on the transformed attribute value, and the attribute set is constructed through each information gain rate that is not less than a preset value; Insert the corresponding data in the attribute set into the preset prefix tree, traverse each leaf node in the prefix tree to delete duplicate data, and obtain a data set after data cleaning.

[0009] The beneficial effect of adopting the above further solution is that the data cleaning process performed by calculating the information gain rate and inserting the prefix tree can reduce the time complexity of the detection process while ensuring the accuracy of the data set.

[0010] Furthermore, the above abnormal data detection is specifically as follows: The K-Means clustering algorithm is used to cluster the normalized data set, and multiple data clusters consisting of the original data in the data set are obtained; Based on multiple data clusters, a first Euclidean distance between each data in each data cluster and other data in the same cluster is calculated, as well as a second Euclidean distance between each data in each data cluster and each data in other data clusters; Calculate the outlier factor of each data in each data cluster based on the first Euclidean distance, and determine the data whose outlier factor is not less than the first threshold as local isolated data; Each data whose second Euclidean distance is not less than the second threshold is determined as global isolated data, and the original data corresponding to the local isolated data and the global isolated data is determined as abnormal data, and the abnormal data is deleted to obtain a data set after abnormal data detection.

[0011] The beneficial effect of adopting the above further scheme is: since the data to be audited is generally complex and the data volume is large, the density-based isolated points are not ideal in terms of algorithm execution efficiency and identification of global isolated points. Therefore, combined with the idea of ​​clustering algorithm, global isolated points can be identified while reducing the algorithm execution time.

[0012] Furthermore, the information gain rate of the attribute items of the above-mentioned original data is specifically: ,in: ; In the formula, Representation dataset D Medium attribute item A The information gain rate, Representation dataset D Medium attribute item A The information gain of Represents an attribute item A The value is i The number of samples, Representation dataset D The total number of samples in n Represents an attribute item A The number of values ​​​​of .

[0013] Furthermore, the outlier factor of each data in the above data clusters is specifically: ; In the formula, Representation data The outlier factor, Representation data The reachable distance, data The accessible density, Represents distance data Recent k A collection of data.

[0014] Furthermore, the support given by the adjacent nodes of each of the above event nodes is specifically: ; In the formula, Represents adjacent nodes n Give event nodes m The support of Represents an event node m The initial importance feature of Represents adjacent nodes n The initial importance characteristics of Each event node integrates the aggregate features of adjacent nodes, specifically: ; In the formula, Represents an event node m The aggregation characteristics of Represents an event node m The set of adjacent nodes of represents the support after normalization by the softmax function, Represents adjacent nodes n The initial position characteristics.

[0015] Furthermore, the above similarity function is specifically: ; In the formula, represents the objective function value, Represents an event node m The aggregation characteristics of Represents an event node m The initial position feature, corner mark T Represents a vector transpose operation; The matching degree between each event node is as follows: ,in: , ; In the formula, Represents an event node m With event nodes n The matching degree betweenT represents the vector transpose operation, Represents an event node m The target importance feature, Event Node n The target importance feature, Respectively represent event nodes m The corresponding weight matrices and bias terms are, Respectively represent event nodes n The corresponding weight matrices and bias terms respectively.

[0016] In a second aspect, the present application provides an audit system based on big data, which is applied to an audit method based on big data in any one of the first aspects, including: The first module is used to obtain at least two data sets to be audited of different types and preprocess the data sets, including data cleaning and abnormal data detection; The second module is used to obtain the initial position features and initial importance features of each event node in the data set based on each preprocessed data set using Gaussian distribution random initialization; The third module is used to calculate the support given by the adjacent nodes of each event node by using the initial position feature and the initial importance feature, and obtain the aggregated feature of each event node fused with the features of the adjacent nodes according to the normalized support calculation; The fourth module is used to construct a similarity function of each event node using the initial position feature and the aggregation feature, and determine the initial importance feature when the similarity function is maximized as the target importance feature of each event node; The fifth module is used to calculate the matching degree between each item node in different types of data sets based on the target importance characteristics of each item node in at least two data sets, determine two item nodes whose matching degree is not less than a threshold as related items, and audit each data set based on each related item.

[0017] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the methods in the first aspect when executing the computer program.

[0018] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable a computer to execute any one of the methods in the first aspect.

[0019] Compared with the prior art, the present invention has at least the following beneficial effects: In the present application, firstly, the data set to be audited is preprocessed by data cleaning and abnormal data detection, and the duplicate data and abnormal data in each data set are removed to make the data in the data set more concise and accurate. Then, the initial position features and initial importance features of each item node in the data set are obtained by random initialization of Gaussian distribution. Secondly, the support given by the adjacent nodes of each item node is calculated using the initial position features and initial importance features, and the aggregate features of each item node fused with the features of the adjacent nodes are calculated based on the support after normalization. Then, the similarity function of each item node is constructed using the initial position features and aggregate features, and the initial importance features when the similarity function is maximized are determined as the target importance features of each item node. Finally, the matching degree between each item node in different types of data sets is calculated based on the target importance features of each item node in the two data sets, and two item nodes with a matching degree not less than a threshold are determined as related items. Related items indicate that the correlation between the two times is high and the reference that can be provided to each other is strong, which is conducive to the audit of the two data sets. Finally, each data set is audited based on each related item.

[0020] In this application, when conducting a joint audit of different types of data sets, by finding the connection between each item in two different types of data sets, a reference is provided for the audit process, which has a high improvement on the audit work. It not only improves the quality of data used for audit work, helps organizations and institutions to conduct audit work more efficiently, but also improves the audit quality and reduces risks while ensuring compliance; and the data cleaning process by calculating the information gain rate and inserting the prefix tree can reduce the time complexity of the detection process while ensuring the accuracy of the data set; at the same time, because the data to be audited is generally complex and the data volume is large, the density-based isolated points are not ideal in terms of algorithm execution efficiency and identification of global isolated points. Therefore, combined with the idea of ​​clustering algorithm, global isolated points can be identified while reducing the algorithm execution time. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 A method flow chart of an audit method in an embodiment of the present invention; Figure 2 A connection diagram of an audit system in an embodiment of the present invention; Figure 3 Schematic diagram of the connection of electronic equipment in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0025] In the description of the embodiments of the present invention, "plurality" means at least 2.

[0026] Embodiment 1: In order to improve the quality of data used for audit work, help organizations and institutions to conduct audit work more efficiently, and at the same time improve audit quality and reduce risks while ensuring compliance, this embodiment provides an audit method based on big data, such as Figure 1 As shown, the specific steps include: S1, obtain at least two data sets to be audited of different types, and preprocess the data sets, including data cleaning and abnormal data detection.

[0027] Optionally, the above data cleaning is specifically as follows: S11, discretizing the attribute items of each original data in the normalized data set, and transforming each attribute value obtained after discretization into a preset integer range according to the size.

[0028] S12, calculating the information gain rate of each attribute item of the original data based on the transformed attribute value, and constructing an attribute set through each information gain rate that is not less than a preset value.

[0029] Among them, the information gain rate of the attribute items of each of the above original data is specifically: ,in: ; In the formula, Representation dataset D Medium attribute item A The information gain rate is Representation dataset D Medium attribute item A The information gain of Represents an attribute item A The value is i The number of samples, Representation dataset D The total number of samples in n Represents an attribute item A The number of values ​​​​of .

[0030] S13, inserting each corresponding data in the attribute set into a preset prefix tree, traversing each leaf node in the prefix tree to delete duplicate data, and obtaining a data set after data cleaning.

[0031] Specifically, the improvements in the characteristics and structure of the preset prefix tree are as follows: 1) Non-leaf nodes are used as index split items and do not store data information.

[0032] 2) Only leaf nodes store data, and can store multiple pieces of data.

[0033] 3) There are n attributes as index items of data, with n+2 layers, where the first layer is the root node and the last layer stores sample data information.

[0034] Among them, when detecting duplicate data in the prefix tree, first, traverse the data in each leaf node in turn; secondly, compare each sample point with the data in the leaf node where it is located. If the similarity between the two data is greater than the given threshold, it will be output to the similar data set. After comparing with other data, the data will be marked as compared data and will not be compared again during the next traversal. For example, if A and B are in the same leaf node, A will be compared with B. If their similarity is greater than the given threshold, A will be output to the similar data set and deleted from the leaf node where it is located. There is only one piece of data in the leaf nodes where C, D, and G are located, so no comparison is required, that is, these single existing data are not duplicate data.

[0035] Specifically, after the prefix tree is improved, similar data can be quickly gathered in the same leaf node, reducing the calculation process, and then traversing the leaf nodes, calculating the similarity between data within the leaf nodes, completing the detection of duplicate data, and improving the efficiency of duplicate data detection.

[0036] Optionally, the above abnormal data detection is specifically as follows: S14, using the K-Means clustering algorithm to perform clustering processing on the normalized data set, and obtain multiple data clusters consisting of each original data in the data set.

[0037] S15, based on the multiple data clusters, calculating a first Euclidean distance between each data in each data cluster and other data in the same cluster, and a second Euclidean distance between each data in each data cluster and each data in other data clusters.

[0038] S16, calculating the outlier factor of each data in each data cluster based on the first Euclidean distance, and determining the data whose outlier factor is not less than the first threshold as local isolated data.

[0039] Among them, the outlier factor of each data in the above data clusters is specifically: ; In the formula, Representation data The outlier factor, Representation data The reachable distance, data The accessible density, Represents distance data Recent k A collection of data.

[0040] S17, determining each data whose second Euclidean distance is not less than the second threshold as global isolated data, and determining the original data corresponding to the local isolated data and the global isolated data as abnormal data, and deleting the abnormal data to obtain a data set after abnormal data detection.

[0041] Specifically, since the data to be audited is generally complex and the data volume is large, density-based isolated points are not ideal in terms of algorithm execution efficiency and identification of global isolated points. Therefore, combined with the idea of ​​clustering algorithm, global isolated points can be identified while reducing the algorithm execution time.

[0042] S2, based on each preprocessed data set, uses Gaussian distribution to randomly initialize and obtain the initial position features and initial importance features of each event node in the data set.

[0043] Among them, the support of adjacent nodes to the current node is reflected by the importance feature, and the support of the same node to other adjacent nodes is also different; therefore, the position feature reflects the environmental information of the adjacent nodes, while the importance feature reflects the unique support relationship. Compared with the position feature, the importance feature has stronger discriminability.

[0044] S3, using the initial position features and initial importance features, calculate the support given by the adjacent nodes of each event node, and calculate the aggregated features of each event node by integrating the features of the adjacent nodes based on the normalized support.

[0045] Among them, the support given by the adjacent nodes of each of the above event nodes is specifically: ; In the formula, Represents adjacent nodes n Give event nodes m The support of Represents an event node m The initial importance feature of Represents adjacent nodes n The initial importance characteristics of

[0046] Furthermore, each event node integrates the aggregated features of the adjacent node features, specifically: ; In the formula, Represents an event node m The aggregation characteristics of Represents an event node m The set of adjacent nodes of represents the support after normalization by the softmax function, Represents adjacent nodes n The initial position characteristics.

[0047] S4, constructs the similarity function of each event node using the initial position feature and the aggregation feature, and determines the initial importance feature when the similarity function is maximized as the target importance feature of each event node; the maximum value of the similarity function is 100%, that is, 1.

[0048] Specifically, the similarity function is: ; In the formula, represents the objective function value, Represents an event node m The aggregation characteristics of Represents an event node m The initial position feature, corner mark T Represents a vector transpose operation.

[0049] S5, based on the target importance characteristics of each item node in at least two data sets, calculate the matching degree between each item node in different types of data sets, determine two item nodes whose matching degree is not less than a threshold as related items, and audit each data set based on each related item.

[0050] Specifically, when conducting a joint audit of different types of data sets, by finding the connections between the various items in the two different types of data sets, a reference can be provided for the audit process, which has a significant impact on the audit work. It not only improves the quality of data used for audit work, helps organizations and institutions to conduct audit work more efficiently, but also improves the quality of audits and reduces risks while ensuring compliance.

[0051] Among them, the matching degree between each event node is specifically: ,in: , ; In the formula, Represents an event node m With event nodes n The matching degree between T represents the vector transpose operation, Represents an event node m The target importance feature, Event Node n The target importance feature, Respectively represent event nodes m The corresponding weight matrices and bias terms are, Respectively represent event nodes n The corresponding weight matrices and bias terms respectively.

[0052] Embodiment 2: The present application embodiment provides an audit system based on big data, which is applied to an audit method based on big data in any one of Embodiment 1, such as Figure 2 As shown, including: The first module is used to obtain at least two data sets to be audited of different types and preprocess the data sets, wherein the preprocessing includes data cleaning and abnormal data detection.

[0053] The second module is used to obtain the initial position features and initial importance features of each event node in the data set based on each preprocessed data set using Gaussian distribution random initialization.

[0054] The third module is used to calculate the support given by the adjacent nodes of each event node using the initial position features and the initial importance features, and obtain the aggregated features of each event node by integrating the features of the adjacent nodes based on the normalized support calculations.

[0055] The fourth module is used to construct a similarity function for each event node using the initial position feature and the aggregation feature, and determine the initial importance feature when the similarity function is maximized as the target importance feature of each event node.

[0056] The fifth module is used to calculate the matching degree between each item node in different types of data sets based on the target importance characteristics of each item node in at least two data sets, determine two item nodes whose matching degree is not less than a threshold as related items, and audit each data set based on each related item.

[0057] Embodiment 3: This embodiment of the present application provides an electronic device, such as Figure 3 As shown, it includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any method in Embodiment 1 is implemented.

[0058] Embodiment 4: The embodiment of the present application provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable a computer to execute any method in Embodiment 1.

[0059] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An audit method based on big data, characterized in that: The specific steps include: Acquire at least two data sets to be audited of different types, and preprocess the data sets, wherein the preprocessing includes data cleaning and abnormal data detection; Based on each preprocessed data set, the initial position features and initial importance features of each event node in the data set are obtained by random initialization using Gaussian distribution; Using the initial position feature and the initial importance feature, calculate the support given by the adjacent nodes of each event node, and calculate the aggregated feature of each event node by fusing the features of the adjacent nodes according to the normalized support; Constructing a similarity function for each event node using the initial position feature and the aggregation feature, and determining the initial importance feature when the similarity function is maximized as the target importance feature of each event node; Based on the target importance characteristics of each item node in at least two of the data sets, the matching degree between the item nodes in different types of data sets is calculated, two item nodes whose matching degree is not less than a threshold are determined as related items, and each data set is audited based on each of the related items.

2. According to the big data audit method described in claim 1, it is characterized in that: The data cleaning is specifically as follows: Discretize the attribute items of each original data in the normalized data set, and transform each attribute value obtained after discretization into a preset integer range according to the size; The information gain rate of each attribute item of the original data is calculated based on the transformed attribute value, and the attribute set is constructed through each information gain rate that is not less than a preset value; Insert the corresponding data in the attribute set into the preset prefix tree, traverse each leaf node in the prefix tree to delete duplicate data, and obtain a data set after data cleaning.

3. According to the big data audit method described in claim 1, it is characterized in that: The abnormal data detection is specifically as follows: The K-Means clustering algorithm is used to cluster the normalized data set, and multiple data clusters consisting of the original data in the data set are obtained; Based on the plurality of data clusters, a first Euclidean distance between each data in each data cluster and other data in the same cluster is calculated, as well as a second Euclidean distance between each data in each data cluster and each data in other data clusters; Calculate the outlier factor of each data in each data cluster based on the first Euclidean distance, and determine the data whose outlier factor is not less than a first threshold as local isolated data; Each data whose second Euclidean distance is not less than a second threshold is determined as global isolated data, and the original data corresponding to the local isolated data and the global isolated data is determined as abnormal data, and the abnormal data is deleted to obtain a data set after abnormal data detection.

4. According to the big data audit method described in claim 2, it is characterized in that: The information gain rate of the attribute items of each original data is as follows: ,in: ; In the formula, Representation dataset D Medium attribute item A The information gain rate is Representation dataset D Medium attribute item A The information gain of Represents an attribute item A The value is i The number of samples, Representation dataset D The total number of samples in n Represents an attribute item A The number of values ​​​​of .

5. The audit method based on big data according to claim 3 is characterized in that: The outlier factor of each data in each data cluster is: ； In the formula, Representation data The outlier factor, Representation data The reachable distance, data The accessible density, Represents distance data Recent k A collection of data.

6. The audit method based on big data according to claim 1 is characterized in that: The support given by the adjacent nodes of each event node is as follows: ; In the formula, Represents adjacent nodes n Give event nodes m The support of Represents an event node m The initial importance feature of Represents adjacent nodes n The initial importance characteristics of Each event node integrates the aggregate features of adjacent nodes, specifically: ; In the formula, Represents an event node m The aggregation characteristics of Represents an event node m The set of adjacent nodes of represents the support after normalization by the softmax function, Represents adjacent nodes n The initial position characteristics.

7. The audit method based on big data according to claim 1 is characterized in that: The similarity function is specifically: ; In the formula, represents the objective function value, Represents an event node m The aggregation characteristics of Represents an event node m The initial position feature, corner mark T Represents a vector transpose operation; The matching degree between each event node is as follows: ,in: ， ； In the formula, Represents an event node m With event nodes n The matching degree between T represents the vector transpose operation, Represents an event node m The target importance feature, Event Node n The target importance feature, Respectively represent event nodes m The corresponding weight matrices and bias terms are, Respectively represent event nodes n The corresponding weight matrices and bias terms respectively.

8. An audit system based on big data, applied to an audit method based on big data as claimed in any one of claims 1 to 7, characterized in that: include: The first module is used to obtain at least two data sets to be audited of different types and preprocess the data sets, wherein the preprocessing includes data cleaning and abnormal data detection; The second module is used to obtain the initial position features and initial importance features of each event node in the data set based on each preprocessed data set by using Gaussian distribution random initialization; The third module is used to calculate the support given by the adjacent nodes of each event node by using the initial position feature and the initial importance feature, and obtain the aggregated feature of each event node fused with the features of the adjacent nodes according to the normalized support calculation; The fourth module is used to construct a similarity function of each event node using the initial position feature and the aggregation feature, and determine the initial importance feature when the similarity function is maximized as the target importance feature of each event node; The fifth module is used to calculate the matching degree between each item node in different types of data sets based on the target importance characteristics of each item node in at least two of the data sets, determine two item nodes whose matching degree is not less than a threshold as related items, and audit each data set based on each of the related items.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the method according to any one of claims 1 to 7 is implemented when the processor executes the computer program.

10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, which enable a computer to execute the method of any one of claims 1-7.

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