Transaction relationship analysis method and device

Abstract

The present invention provides a transaction relationship analysis method and device, the method comprising: obtaining target user information and target account information; obtaining the association relationship between each user information in the target user information and each account information in the target account information within a preset period in a preset database through a relationship mining method; constructing a transaction bipartite graph based on the target user information, the target account information and the association relationship; obtaining time node parameters to be analyzed, and calculating the matching degree between the user information and the account information in the transaction bipartite graph through a Hungarian algorithm based on the time node parameters to be analyzed; and obtaining a risk status based on a comparison between the matching degree and a preset threshold.

Description

Technical Field

[0001] The present invention relates to the field of big data analysis and can be applied to the financial field and other fields, and in particular relates to a transaction relationship analysis method and device. Background Art

[0002] In the current financial field, due to the large amount of data and information hiding, supervision and analysis are difficult. For example, some insider trading occurs from time to time. Insider trading refers to insiders buying or selling securities or helping others based on inside information, which violates the "openness, fairness and justice" principles of the securities market, causing securities prices and indices to lose timeliness and objectivity, and ultimately causing the securities market to lose its role in optimizing resource allocation and serving as a barometer of the national economy. These situations clearly violate existing market principles and fairness. Therefore, the industry needs to supervise and analyze these trading behaviors to avoid or reduce the security impact of violators on the market.

[0003] In existing technologies, the use of big data is becoming increasingly diverse. Reasonable and effective analysis can effectively mine and locate the specified behaviors of specific types of user groups. However, due to the complexity and hidden factors of transaction data, traditional data mining is obviously unable to solve the problem of extracting transaction data such as the above-mentioned insider trading. In response to this problem, relevant research has been conducted both domestically and internationally. Among them, the more well-known method is the logistic regression method proposed by Scottl. Summer and John T. Sweeney. This statistical method can achieve a good insider trading identification rate. Its main conditional probability model is: Where α and β are the parameter vectors to be estimated, x is the variable of the identification system, and P is the probability of the event occurring under the condition of x.

[0004] However, the statistical probability logistic regression (Logistic) method used above cannot accurately discover the corresponding relationship between the sets of transaction parties and the causal relationship of insider trading. Therefore, in actual use, it is less effective. Therefore, this field urgently needs a transaction data analysis method that can quickly analyze the hidden relationship between the transaction parties to provide an effective reference for relevant personnel. Summary of the Invention

[0005] The purpose of the present invention is to provide a transaction relationship analysis method and device to quickly and accurately analyze the hidden relationship between the two parties in a transaction, and use this hidden relationship to provide a reference for subsequent judgment on whether there is a risk in the transaction behavior between the two parties.

[0006] To achieve the above-mentioned purpose, the present invention provides a transaction relationship analysis method, which includes: obtaining target user information and target account information; obtaining the association relationship between each user information in the target user information and each account information in the target account information within a preset period in a preset database through a relationship mining method; constructing a transaction bipartite graph based on the target user information, the target account information and the association relationship; obtaining time node parameters to be analyzed, and calculating the matching degree between the user information and the account information in the transaction bipartite graph through the Hungarian algorithm based on the time node parameters to be analyzed; and obtaining a risk status based on the comparison of the matching degree with a preset threshold.

[0007] In the above-mentioned transaction relationship analysis method, preferably, constructing a transaction bipartite graph based on the target user information, the target account information and the association relationship includes: constructing two corresponding groups of vertex sets based on the target user information and the target account information, and constructing a transaction bipartite graph with the association relationship as an edge set.

[0008] In the above-mentioned transaction relationship analysis method, preferably, obtaining the association relationship between each user information in the target user information and each account information in the target account information within a preset period in a preset database through a relationship mining method includes: obtaining the time delay from the generation time to the release time of each message within a predetermined time period in the predetermined database; taking the target user in the target user information as the starting node, connecting the account information in the target account information according to the time delay as the screening length, and constructing a cumulative relationship network sequence; extracting the risk account information that meets the conditions within the predetermined time period through preset rules, and obtaining the relationship chain between the risk account information and the target user based on the comparison between the risk account information and the cumulative relationship network sequence; generating the association relationship between the target user information and the target account information based on the relationship chains obtained in multiple predetermined time periods.

[0009] In the above-mentioned transaction relationship analysis method, preferably, taking the target user in the target user information as the starting node, connecting the account information in the target account information according to the delay as the screening length, and constructing a cumulative relationship network sequence includes: obtaining a length parameter according to the delay, taking the target user in the target user information as the starting node, and constructing an induced subgraph by associating the account information in the target account information through the length parameter; and constructing a cumulative relationship network sequence by incremental updating based on the induced subgraphs constructed in multiple predetermined time periods.

[0010] In the above-mentioned transaction relationship analysis method, preferably, risk account information that meets the conditions within a predetermined time period is extracted through preset rules, and based on the comparison between the risk account information and the cumulative relationship network sequence, the relationship chain between the risk account information and the target user is obtained, which includes: extracting one or more risk account information that meets the preset rules within a predetermined time period in a predetermined database; comparing the risk account information with the induced subgraph of the corresponding time period in the cumulative relationship network sequence; when the risk account information appears in the induced subgraph, obtaining the relationship chain between the risk account information and the target user.

[0011] The present invention also provides a transaction relationship analysis device, which includes: a data acquisition module, a model construction module and an analysis module; the data acquisition module is used to obtain target user information and target account information; the model construction module is used to obtain the association relationship between each user information in the target user information and each account information in the target account information within a preset period in a preset database through a relationship mining method; and, construct a transaction bipartite graph based on the target user information, the target account information and the association relationship; the analysis module is used to obtain time node parameters to be analyzed, and calculate the matching degree between the user information and the account information in the transaction bipartite graph through the Hungarian algorithm based on the time node parameters to be analyzed; and, obtain the risk status based on the comparison of the matching degree with a preset threshold.

[0012] In the above-mentioned transaction relationship analysis device, preferably, the model construction module includes a construction unit, and the construction unit is used to construct two corresponding groups of vertex sets according to the target user information and the target account information, and construct a transaction bipartite graph with the association relationship as the edge set.

[0013] In the above-mentioned transaction relationship analysis device, preferably, the model construction module includes a relationship extraction unit, which is used to obtain the delay from the generation time to the release time of each message within a predetermined time period in a predetermined database; taking the target user in the target user information as the starting node, connecting the account information in the target account information according to the delay as the screening length, and constructing a cumulative relationship network sequence; extracting the risk account information that meets the conditions within the predetermined time period through preset rules, and obtaining the relationship chain between the risk account information and the target user based on the comparison between the risk account information and the cumulative relationship network sequence; generating the association relationship between the target user information and the target account information based on the relationship chain obtained in multiple predetermined time periods.

[0014] The present invention also 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 the above method when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium storing a computer program for executing the above method.

[0016] The beneficial technical effect of the present invention is that the transaction relationship analysis method and device provided by the present invention can quickly locate risky users and accounts, and provide data reference and support to staff in a timely and effective manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention. In the drawings:

[0018] Figure 1A A flowchart of a transaction relationship analysis method provided by one embodiment of the present invention;

[0019] Figure 1B A schematic diagram illustrating the principle of constructing a bipartite graph provided by an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of a process for association management analysis provided by an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of the process of constructing a relationship network sequence provided by an embodiment of the present invention;

[0022] Figure 4 A schematic diagram of the process of building a relationship chain according to an embodiment of the present invention;

[0023] Figure 5 A schematic diagram of the structure of a transaction relationship analysis device provided by an embodiment of the present invention;

[0024] Figure 6 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following describes the embodiments of the present invention in detail with reference to the accompanying drawings and examples, so that the present invention can fully understand how to apply technical means to solve technical problems and achieve technical effects, and thus implement the invention accordingly. It should be noted that, as long as no conflict exists, the various embodiments of the present invention and the various features therein can be combined with each other, and the resulting technical solutions are all within the scope of protection of the present invention.

[0026] Additionally, the steps shown in the flowcharts of the accompanying drawings may be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that shown.

[0027] Please refer to Figure 1A As shown, the present invention provides a transaction relationship analysis method provided by the present invention, the method comprising:

[0028] S101 obtains target user information and target account information;

[0029] S102 obtains, in a preset database, by using a relationship mining method, association relationships between each user information in the target user information and each account information in the target account information within a preset period;

[0030] S103 constructs a transaction bipartite graph based on the target user information, the target account information and the association relationship;

[0031] S104 obtains parameters of the time node to be analyzed, and calculates the matching degree between the user information and the account information in the transaction bipartite graph using the Hungarian algorithm according to the parameters of the time node to be analyzed;

[0032] S105 obtains a risk status based on a comparison between the matching degree and a preset threshold.

[0033] Constructing a transaction bipartite graph based on the target user information, the target account information, and the association relationship includes: constructing two corresponding vertex sets based on the target user information and the target account information, and constructing a transaction bipartite graph with the association relationship as the edge set. In the above embodiment, the bipartite graph is also called a bipartite graph. Let G = (V, E) be an undirected graph. If the vertex V can be divided into two mutually disjoint subsets (A, B), and the two vertices i and j associated with each edge (i, j) in the graph belong to these two different vertex sets (i in A, j in B), the bipartite graph is a graph that does not contain any odd cycles. The specific principles and descriptions can be obtained through existing technologies and will not be described in detail in this invention.

[0034] In actual work, Figure 1B As shown in Figure 2, the specific principle for constructing a bipartite transaction graph between the two parties is as follows: the vertex sets represent the target user company executives and the transaction accounts, and the edge sets represent the relationship between the two. The connection reason for each edge in the graph, i.e., the causal relationship, is given, and its probability serves as a weight. Starting from the perspective of maximizing illegal benefits, an optimization goal is constructed.

[0035]

[0036] Among them, x i The value is 0 or 1, indicating the matching relationship between the target user and the target transaction account in the potential transaction case; i represents the probability of each edge; i, p i, x i The value range is rational numbers; thus, the constructed bipartite transaction graph is used to discover the maximum set relationship of illegal related transactions, thereby determining the risk situation.

[0037] Please refer to Figure 2 As shown, in one embodiment of the present invention, the above step S102 of obtaining the association relationship between each user information in the target user information and each account information in the target account information within a preset period by using a relationship mining method in a preset database may include:

[0038] S201 obtains the time delay from the generation time to the release time of each message in the predetermined time period in the predetermined database;

[0039] S202 takes the target user in the target user information as the starting node, connects the account information in the target account information according to the delay as the screening length, and constructs a cumulative relationship network sequence;

[0040] S203 extracts risk account information that meets the conditions within a predetermined time period according to preset rules, and obtains the relationship chain between the risk account information and the target user based on the comparison between the risk account information and the accumulated relationship network sequence;

[0041] S204 generates an association relationship between the target user information and the target account information based on the relationship chain obtained within a plurality of predetermined time periods.

[0042] Please refer to Figure 3 As shown, in the above embodiment, step S202 takes the target user in the target user information as the starting node, connects the account information in the target account information according to the delay as the screening length, and constructs the cumulative relationship network sequence, which may include:

[0043] S301 obtains a length parameter according to the time delay, takes the target user in the target user information as the starting node, and constructs an induced subgraph by associating the account information in the target account information with the length parameter;

[0044] S302 constructs a cumulative relationship network sequence by incremental updating based on the induced subgraphs constructed in multiple predetermined time periods.

[0045] Please refer to Figure 4 As shown, in the above embodiment, step S203 extracts risk account information that meets the conditions within a predetermined time period according to preset rules, and obtains the relationship chain between the risk account information and the target user based on the comparison between the risk account information and the accumulated relationship network sequence, including:

[0046] S401 extracts one or more risk account information that meets preset rules within a predetermined time period from a predetermined database;

[0047] S402 compares the risk account information with the induced subgraph of the corresponding time period in the cumulative relationship network sequence;

[0048] S403: When the risky account information appears in the induced subgraph, a relationship chain between the risky account information and the target user is obtained.

[0049] Specifically, in actual work, the principles of the above embodiments in actual application are as follows:

[0050] 1. Relationship mining method, that is, finding possible edges, for example, building a network from the perspectives of people-transactions and people-events, starting with the target user as the starting node set, and using communication data to incrementally build edge sets; specifically,

[0051] (1) For a message, the time window from the formation of the message to the release of the message is set as l, and its length is set as l. The cumulative relationship network sequence {G i} l , where G i represents the induced subgraph consisting of all reachable nodes from the target user in the previous i days, the sequence {G i} l Daily incremental updates.

[0052] (2) For each i, find the k accounts with the largest returns on day i, and check whether they appear in G in turn. i If it appears, BFS searches for the target user's node that can be reached, and considers that there is potential insider trading between the two.

[0053] 2. Construct a bipartite graph of the target user and target account sets and their potential relationships. The edge weights represent the degree of suspicion. The initial suspicion is set to 1 and is dynamically updated daily based on the income changes within the time window. Let the bipartite graph on day i be H i .

[0054] 3. For each i, call the Kuhn-Munkres algorithm to calculate H i When the suspicion degree in the match exceeds the threshold, the corresponding relationship is considered to be suspected of insider trading.

[0055] Finally, the complexity analysis is carried out, and the relationship mining algorithm is used for each G i Do incremental updates, by sorting the account numbers, and update G in linear time i , the time complexity is O(N log N), where N is the update data and G iThe time complexity of the Kuhn-Munkres algorithm is O(mn), where n and m are the number of vertices and edges in the bipartite graph, respectively. Therefore, the time complexity is O(N log N + mn). Due to the characteristics of the insider trading problem, there are not many edges in the bipartite graph, so the algorithm actually runs very fast. Compared with existing technologies, it has not only higher accuracy but also greatly improved efficiency. It can quickly locate risky users and accounts, and provide timely and effective data reference and support to staff.

[0056] Please refer to Figure 5 As shown, the present invention also provides a transaction relationship analysis device, which includes: a data acquisition module, a model construction module and an analysis module; the data acquisition module is used to obtain target user information and target account information; the model construction module is used to obtain the association relationship between each user information in the target user information and each account information in the target account information within a preset period in a preset database through a relationship mining method; and, construct a transaction bipartite graph based on the target user information, the target account information and the association relationship; the analysis module is used to obtain time node parameters to be analyzed, and calculate the matching degree between the user information and the account information in the transaction bipartite graph through the Hungarian algorithm based on the time node parameters to be analyzed; and, obtain the risk status based on the comparison between the matching degree and a preset threshold.

[0057] In the above embodiment, the model construction module may include a construction unit, which is used to construct two corresponding vertex sets based on the target user information and the target account information, and to construct a transaction bipartite graph with the association relationship as the edge set. Furthermore, the model construction module also includes a relationship extraction unit, which is used to obtain the time delay from the generation time to the release time of each message within a predetermined time period in a predetermined database; using the target user in the target user information as the starting node, connecting the account information in the target account information according to the delay as the screening length, and constructing a cumulative relationship network sequence; extracting the risk account information that meets the conditions within the predetermined time period according to preset rules, and obtaining the relationship chain between the risk account information and the target user based on the comparison of the risk account information and the cumulative relationship network sequence; generating the association relationship between the target user information and the target account information based on the relationship chain obtained within multiple predetermined time periods.

[0058] The present invention also 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 the above method when executing the computer program.

[0059] The present invention also provides a computer-readable storage medium storing a computer program for executing the above method.

[0060] like Figure 6 As shown, the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processing unit 130, a display 160, and a power supply 170. It is worth noting that the electronic device 600 does not necessarily have to include Figure 6 In addition, the electronic device 600 may also include all components shown in Figure 6 For components not shown, reference may be made to the prior art.

[0061] like Figure 6 As shown, the central processing unit 100 is sometimes also referred to as a controller or an operation control unit, and may include a microprocessor or other processor device and / or logic device. The central processing unit 100 receives inputs and controls the operations of various components of the electronic device 600 .

[0062] Memory 140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information and may also store programs that execute the relevant information. The CPU 100 may execute the programs stored in memory 140 to implement information storage or processing.

[0063] The input unit 120 provides input to the CPU 100. The input unit 120 may be, for example, a keypad or touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0064] The memory 140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), or a SIM card. Alternatively, it may be a memory that retains information even when power is off, can be selectively erased, and is provided with more data. Examples of such memory are sometimes referred to as EPROMs. The memory 140 may also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142 for storing application programs and function programs or processes for executing the operations of the electronic device 600 via the central processing unit 100.

[0065] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 144 of the memory 140 may include various driver programs for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0066] The communication module 110 is a transmitter / receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter / receiver) 110 is coupled to the central processor 100 to provide input signals and receive output signals, which may be the same as in a conventional mobile communication terminal.

[0067] Based on different communication technologies, multiple communication modules 110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module. The communication module (transmitter / receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132, thereby implementing common telecommunication functions. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 130 is also coupled to the central processing unit 100, enabling local recording via the microphone 132 and playback of stored audio via the speaker 131.

[0068] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0070] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0072] The specific embodiments 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 embodiment 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. A transaction relationship analysis method, characterized in that: The method comprises: Obtain target user information and target account information; Obtaining, in a preset database, by using a relationship mining method, an association relationship between each user information in the target user information and each account information in the target account information within a preset period; Constructing a transaction bipartite graph based on the target user information, the target account information, and the association relationship; Obtaining a time node parameter to be analyzed, and calculating a matching degree between the user information and the account information in the transaction bipartite graph using a Hungarian algorithm according to the time node parameter to be analyzed; Obtaining a risk status based on a comparison between the matching degree and a preset threshold; Obtaining the association relationship between each user information in the target user information and each account information in the target account information within a preset period by using a relationship mining method in a preset database includes: Obtain the time delay from the creation time to the release time of each message in the predetermined time period in the predetermined database; Taking the target user in the target user information as a starting node, connecting the account information in the target account information according to the time delay as a screening length to construct a cumulative relationship network sequence; Extracting risk account information that meets the conditions within a predetermined time period according to preset rules, and obtaining the relationship chain between the risk account information and the target user based on the comparison between the risk account information and the accumulated relationship network sequence; generating an association relationship between the target user information and the target account information based on the relationship chain obtained within a plurality of predetermined time periods; Taking the target user in the target user information as a starting node, connecting the account information in the target account information according to the time delay as a screening length, and constructing a cumulative relationship network sequence includes: Obtaining a length parameter according to the time delay, taking the target user in the target user information as a starting node, and constructing an induced subgraph by associating the account information in the target account information with the length parameter; Based on the induced subgraphs constructed over multiple predetermined time periods, a cumulative relationship network sequence is constructed through incremental updating.

2. The transaction relationship analysis method according to claim 1, characterized in that: Constructing a transaction bipartite graph based on the target user information, the target account information, and the association relationship includes: constructing two corresponding vertex sets based on the target user information and the target account information, and constructing a transaction bipartite graph with the association relationship as an edge set.

3. The transaction relationship analysis method according to claim 2, characterized in that: Extracting risk account information that meets the conditions within a predetermined time period according to preset rules, and obtaining a relationship chain between the risk account information and the target user based on the comparison between the risk account information and the accumulated relationship network sequence includes: Extracting one or more risk account information that meets preset rules from a predetermined database within a predetermined time period; Comparing the risk account information with the induced subgraph of the corresponding time period in the cumulative relationship network sequence; When the risky account information appears in the induced subgraph, a relationship chain between the risky account information and the target user is obtained.

4. A transaction relationship analysis device, characterized in that: The device comprises: a data acquisition module, a model building module and an analysis module; The data acquisition module is used to obtain target user information and target account information; The model building module is used to obtain the association relationship between each user information in the target user information and each account information in the target account information within a preset period by using a relationship mining method in a preset database; and constructing a transaction bipartite graph based on the target user information, the target account information, and the association relationship; The analysis module is used to obtain time node parameters to be analyzed, calculate the matching degree between the user information and the account information in the transaction bipartite graph using the Hungarian algorithm based on the time node parameters to be analyzed; and obtain the risk status based on the comparison between the matching degree and a preset threshold.

5. The transaction relationship analysis device according to claim 4, characterized in that: The model construction module includes a construction unit, which is used to construct two corresponding vertex sets according to the target user information and the target account information, and to construct a transaction bipartite graph with the association relationship as an edge set.

6. The transaction relationship analysis device according to claim 5, characterized in that: The model building module includes a relationship extraction unit, which is used to obtain the time delay from the generation time to the release time of each message in a predetermined time period in a predetermined database; Taking the target user in the target user information as the starting node, the account information in the target account information is connected according to the time delay as the screening length to construct a cumulative relationship network sequence; the risk account information that meets the conditions within a predetermined time period is extracted according to preset rules, and the relationship chain between the risk account information and the target user is obtained based on the comparison between the risk account information and the cumulative relationship network sequence; based on the relationship chains obtained within multiple predetermined time periods, the association relationship between the target user information and the target account information is generated.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 3 is implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program for executing the method according to any one of claims 1 to 3.

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