CN114254078A

CN114254078A - Information screening method and device

Info

Publication number: CN114254078A
Application number: CN202111580589.3A
Authority: CN
Inventors: 张梦鹿; 史晓东; 施耀一; 徐辰翀
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2021-12-22
Filing date: 2021-12-22
Publication date: 2022-03-29

Abstract

The invention provides an information screening method and device, which can be used in the financial field or other fields. The method comprises the following steps: acquiring personal information provided by a plurality of users, and extracting a plurality of key indexes from the personal information by using a natural language processing mode; determining a quantization value of the key index according to a preset post screening rule, and generating index data by using a preset index weight and the quantization value of the key index; and generating a classification result according to a similarity algorithm, a clustering algorithm and the index data, and determining a screening result corresponding to the personal information according to the classification result. According to the job seeker recruitment screening method, personal information of a plurality of authorized users is classified, so that job seekers suitable for posts are screened out, and the effects of improving recruitment efficiency and saving labor cost are achieved.

Description

Information screening method and device

Technical Field

The invention relates to the technical field of information processing, which can be used in the financial field or other fields, in particular to an information screening method and device.

Background

With the advent of the talent economic era, human resources have become the core competitiveness of strong enterprise development. At present, enterprises generally adopt campus recruitment, social recruitment, recruiting meeting, hunting head recommendation and other modes to solicit talents for themselves, and screening personal information authorized by users, such as personal resumes, is an important step in the talent selection process of the enterprises. With the development of artificial intelligence and big data analysis in recent years, the deep application of artificial intelligence in the resume screening link becomes the key point for optimizing the efficiency of human resource recruitment, and the method is very helpful for saving time cost and labor cost. Therefore, it is necessary to develop an intelligent information screening method.

Disclosure of Invention

Aiming at the problems in the prior art, the embodiments of the present invention mainly aim to provide an information screening method and apparatus, which can realize intelligent information screening and save labor cost.

In order to achieve the above object, an embodiment of the present invention provides an information screening method, where the method includes:

acquiring personal information provided by a plurality of users, and extracting a plurality of key indexes from the personal information by using a natural language processing mode;

determining a quantization value of the key index according to a preset post screening rule, and generating index data by using a preset index weight and the quantization value of the key index;

and generating a classification result according to a similarity algorithm, a clustering algorithm and the index data, and determining a screening result corresponding to the personal information according to the classification result.

Optionally, in an embodiment of the present invention, the generating index data by using a preset index weight and a quantization value of the key index includes:

obtaining a quantization value matrix according to the quantization value of the key index;

and generating an index matrix according to the quantization value matrix and a preset index weight, and taking the index matrix as the index data.

Optionally, in an embodiment of the present invention, the generating a classification result according to a similarity algorithm, a clustering algorithm, and the index data includes:

generating a similarity matrix according to a similarity algorithm and the index matrix;

and generating a classification result by using a clustering algorithm and the similarity matrix, and determining a screening result corresponding to the personal information according to the classification result.

Optionally, in an embodiment of the present invention, the generating a similarity matrix according to a similarity algorithm and the index matrix includes:

and determining the similarity between the column vectors in the index matrix according to a similarity algorithm, and obtaining the similarity matrix by using the similarity.

Optionally, in an embodiment of the present invention, the generating a classification result by using a clustering algorithm and the similarity matrix includes:

determining a Laplace matrix corresponding to the similarity matrix according to the similarity matrix;

and obtaining a preset number of eigenvectors according to the Laplace matrix, and generating the classification result by using the eigenvectors and a mean value clustering algorithm.

An embodiment of the present invention further provides an information screening apparatus, where the apparatus includes:

the key index module is used for acquiring personal information provided by a plurality of users and extracting a plurality of key indexes from the personal information by utilizing a natural language processing mode;

the index data module is used for determining the quantitative value of the key index according to a preset post screening rule and generating index data by using a preset index weight and the quantitative value of the key index;

and the screening result module is used for generating a classification result according to a similarity algorithm, a clustering algorithm and the index data and determining a screening result corresponding to the personal information according to the classification result.

Optionally, in an embodiment of the present invention, the index data module includes:

the quantization value matrix unit is used for obtaining a quantization value matrix according to the quantization value of the key index;

and the index matrix unit is used for generating an index matrix according to the quantization value matrix and preset index weight, and taking the index matrix as the index data.

Optionally, in an embodiment of the present invention, the screening result module includes:

the similarity matrix unit is used for generating a similarity matrix according to a similarity algorithm and the index matrix;

and the screening result unit is used for generating a classification result by utilizing a clustering algorithm and the similarity matrix and determining the screening result corresponding to the personal information according to the classification result.

Optionally, in an embodiment of the present invention, the similarity matrix unit is further configured to determine a similarity between column vectors in the index matrix according to a similarity algorithm, and obtain the similarity matrix by using the similarity.

Optionally, in an embodiment of the present invention, the screening result unit includes:

the Laplace matrix subunit is used for determining a Laplace matrix corresponding to the similarity matrix according to the similarity matrix;

and the classification result subunit is used for obtaining a preset number of eigenvectors according to the Laplace matrix and generating the classification result by using the eigenvectors and a mean value clustering algorithm.

The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method when executing the program.

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

According to the job seeker recruitment screening method, personal information of a plurality of authorized users is classified, so that job seekers suitable for posts are screened out, and the effects of improving recruitment efficiency and saving labor cost are achieved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.

FIG. 1 is a flow chart of an information screening method according to an embodiment of the present invention;

FIG. 2 is a flow chart of generating index data according to an embodiment of the present invention;

FIG. 3 is a flow chart of generating classification results in an embodiment of the present invention;

FIG. 4 is a flow chart of generating classification results in another embodiment of the present invention;

FIG. 5 is a flow chart of a method of information screening in an embodiment of the present invention;

fig. 6 is a schematic structural diagram of an information screening apparatus according to an embodiment of the present invention;

FIG. 7 is a block diagram of an index data module according to an embodiment of the present invention;

FIG. 8 is a schematic structural diagram of a screening result module according to an embodiment of the present invention;

FIG. 9 is a schematic structural diagram of a screening result unit according to an embodiment of the present invention;

fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The embodiment of the invention provides an information screening method and an information screening device, which can be used in the financial field and other fields.

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Fig. 1 is a flowchart illustrating an information screening method according to an embodiment of the present invention, where an execution subject of the information screening method according to the embodiment of the present invention includes, but is not limited to, a computer. According to the job seeker recruitment screening method, personal information of a plurality of authorized users is classified, so that job seekers suitable for posts are screened out, and the effects of improving recruitment efficiency and saving labor cost are achieved. The method shown in the figure comprises the following steps:

step S1 is to acquire personal information provided by a plurality of users and extract a plurality of key indicators from the personal information by using a natural language processing method.

In which personal information provided by the user, such as a personal resume, is obtained or the user personal information is obtained from a database authorized by the user.

Further, a key index on the personal information is obtained by utilizing NLP (natural language processing), so that the comprehensive quality evaluation standard of the practitioner who wants to hire the post is selected. Specifically, taking a personal resume as an example, the key indexes include academic schools, academic specialties, project experiences, scientific research papers, patent publications, community experiences, work experiences, honor obtained, penalties received and the like.

And step S2, determining the quantization value of the key index according to the preset post screening rule, and generating index data by using the preset index weight and the quantization value of the key index.

The post screening rule is pre-constructed according to the post requirements, for example, the academic requirement is similar to or above the academic requirement, and the quantitative value of each key index and the corresponding index weight are determined. Specifically, taking the academic record as an example, the common subject, 211 subject, 985 subject, common master, 211 master and above respectively have quantization values of 1,2,3,4,5 and 6, and the lower the number, the lower the matching degree. Index weight corresponding to key index

Furthermore, a quantization value corresponding to the personal information of each user is used as a column vector to construct a quantization value matrix. And taking an index matrix obtained by multiplying the quantization value matrix and the index weight as index data.

And step S3, generating a classification result according to the similarity algorithm, the clustering algorithm and the index data, and determining a screening result corresponding to the personal information according to the classification result.

The similarity of each column vector in the index matrix is calculated by using the conventional similarity calculation method. Specifically, each column in the index matrix represents a quantized value of a key index of each candidate. And calculating the similarity between every two columns of data, wherein the similarity can represent the similarity between two columns of data, and the similarity between every two columns of data in the index matrix is opposite to the distance between every two columns of data, namely the closer the distance is, the greater the similarity is.

Further, the way of generating the similarity matrix is to obtain the similarity matrix through continuous iteration, which is equivalent to obtaining the optimal solution of the algorithm through continuous iteration of the algorithm, that is, the optimal similarity matrix, that is, the most energy table, is the similarity relationship between every two columns of data in the index matrix, and taking the matrix at this time as the similarity matrix.

The similarity matrix can be used as input by adopting a spectral clustering algorithm, and a classification result is obtained through calculation.

Further, the specific process of the spectral clustering algorithm is as follows: and determining a corresponding Laplace matrix according to the similarity matrix, specifically, calculating the degree matrix of the similarity matrix, wherein the difference between the degree matrix and the similarity matrix is the Laplace matrix. The eigenvectors corresponding to the first c (c is the final classification number, for example, c is 2 or 3) minimum non-zero eigenvalues corresponding to the laplace matrix are obtained. And finally, using a k-means (mean value clustering) algorithm to the obtained feature vectors to finish final clustering and obtain a classification result.

Further, the number of classification results is set to two or three, which can be unqualified, undetermined and qualified. And obtaining a screening result according to the classification result, namely whether the information screening is passed or not. And if the classification result is unqualified, the personal information, such as the screening result corresponding to the personal resume, is eliminated. According to the job seeker recruitment screening method, personal information of a plurality of authorized users is classified, so that job seekers suitable for posts are screened out, and the effects of improving recruitment efficiency and saving labor cost are achieved.

As an embodiment of the present invention, as shown in fig. 2, generating the index data by using the preset index weight and the quantized value of the key index includes:

step S21, obtaining a quantization value matrix according to the quantization value of the key index;

and step S23, generating an index matrix according to the quantization value matrix and the preset index weight, and taking the index matrix as index data.

In this embodiment, as shown in fig. 3, generating a classification result according to a similarity algorithm, a clustering algorithm, and the index data includes:

step S31, generating a similarity matrix according to a similarity algorithm and an index matrix;

and step S32, generating a classification result by using a clustering algorithm and a similarity matrix, and determining a screening result corresponding to the personal information according to the classification result.

In this embodiment, generating the similarity matrix according to the similarity algorithm and the index matrix includes: and determining the similarity between the column vectors in the index matrix according to a similarity algorithm, and obtaining the similarity matrix by using the similarity.

The similarity of each column vector in the index matrix is calculated by using the conventional similarity calculation method. Specifically, each column in the index matrix represents a quantized value of a key index of each candidate.

As an embodiment of the present invention, as shown in fig. 4, the generating the classification result by using the clustering algorithm and the similarity matrix includes:

step S41, determining a Laplacian matrix corresponding to the similarity matrix according to the similarity matrix;

and step S42, obtaining a preset number of eigenvectors according to the Laplace matrix, and generating a classification result by using the eigenvectors and a mean value clustering algorithm.

Further, the specific process of the spectral clustering algorithm is as follows: and determining a corresponding Laplace matrix according to the similarity matrix, specifically, calculating the degree matrix of the similarity matrix, wherein the difference between the degree matrix and the similarity matrix is the Laplace matrix. And calculating the eigenvectors corresponding to the first c minimum non-zero eigenvalues corresponding to the Laplace matrix. And finally, using a mean value clustering algorithm to the obtained feature vectors, finishing final clustering and obtaining a classification result.

In a specific embodiment of the present invention, as shown in the information screening process shown in fig. 5, by taking a personal resume as an example, a talent database is constructed by comprehensively investigating practitioners who want to recruit posts, including information such as a study, ability, experience, and the like. And grading the comprehensive capacity of the corresponding recruiters according to the recruitment requirements of the enterprise and the talent database information, and constructing a clustering quantitative value matrix. And according to a self-adaptive neighbor method, constructing a similar matrix in clustering analysis, applying the similar matrix, and finishing classification by using a clustering algorithm, thereby finishing classification and screening of the resume of the applicant. Further, an evaluation report (classification result) is output for each resume, and a written test notification mail is automatically sent to the resume qualifier. The concrete description is as follows.

1) And constructing a post talent database. Obtaining key factors on the resume through NLP, obtaining other published authorized information through data mining, selecting comprehensive quality evaluation standards of practitioners intending to recruit posts, wherein the comprehensive quality evaluation standards comprise m indexes such as academic colleges, academic specialties, project experiences, scientific research papers, patent publications, community experiences, work experiences, acquired honor, penalties and the like, and then determining the weight v of each index according to enterprise needs_i. (which is an initial value, is known)

2) For each index, different quantization standards can be set according to the matching degree with the position, for example, for academic institutions, the method can be divided into the following general subjects, 211 subject, 985 subject, 211 subject, and above, which are quantized to 1,2,3,4,5,6, respectively, and the lower the number, the lower the matching degree. Similarly, other indices are also quantized according to the standard from 1.

The n applicants are divided according to the information in the talent database and aiming at the m indexes respectively to obtain an m X n dimensional original matrix Z, and the matrix X can be obtained by adding a weight factor and used as an input matrix X of the clustering algorithm, as shown in formula (1).

x_ij＝z_ijv_j (1)

3) And constructing a similarity matrix by using a self-adaptive neighbor method. The similarity matrix is a core matrix of a clustering algorithm, and the solving process is shown in formula (2):

wherein x is_iAn ith column vector representing X representing the m index quantized scores of the ith person, X_jIs x_iS is a similarity matrix whose elements S_ijRepresenting the similarity between the ith person and the jth person, calculating the similarity matrix is an optimization problem, and can solve an optimal solution, where it is considered that the closer the distance between two persons is, the greater the similarity is (because the indexes of each person have been quantified previously), and the greater the possibility that two persons are classified into one class is.

The degree of S is expressed, and a second term regular term in formula (2) is introduced to avoid an extreme case that the similarity of a certain neighbor is zero when the similarity of the neighbor is 1

Further, the function of the regularization term is to make the algorithm more accurate, and avoid that when the similarity of some two columns reaches the maximum (i.e. 1), the similarity between the two columns and other columns is 0 (no similarity). This condition affects the final algorithm accuracy, so a regularization term is added. And gamma is a coefficient of the regular term, and the value of gamma is continuously updated iteratively according to the formula (2) to finally obtain the value of gamma.

4) After the similarity matrix is solved according to the spectral clustering algorithm, the algorithm model can be operated according to the steps of the clustering algorithm to obtain the final clustering result. The results can be simply classified into 2 categories, one category being qualified and one category being unqualified (the results can also be classified into 3 categories, one category being qualified, one category being to be verified and one category being unqualified as required).

5) And classifying all the resumes according to the classification result to obtain an evaluation report of each resume, and automatically sending a stroke test notification mail to the qualified resume according to the result.

The spectral clustering algorithm comprises the following steps:

obtaining a similarity matrix S, and obtaining a Laplace matrix L according to S_S＝D_S-S, wherein D_SIs a degree matrix, only the elements on the diagonal are not 0, and

solving a Laplace matrix L_SAnd c is the final classification number, for example, c is 2, and the final clustering is completed by using a k-means algorithm for the feature vector corresponding to the minimum non-zero eigenvalue of the corresponding first c (c is the final classification number), so as to obtain a classification result.

According to the method and the system, personal information of a plurality of authorized users is classified, the academic calendar, the profession, the working experience, the project experience and other aspects of the candidate are evaluated, the matching degree of the candidate and the post is comprehensively evaluated, so that job seekers suitable for the post are screened out, and the effects of improving recruitment efficiency and saving labor cost are achieved.

Fig. 6 is a schematic structural diagram of an information screening apparatus according to an embodiment of the present invention, where the apparatus includes:

a key index module 10, configured to obtain personal information provided by multiple users, and extract multiple key indexes from the personal information by using a natural language processing manner;

the index data module 20 is configured to determine a quantization value of the key index according to a preset post screening rule, and generate index data by using a preset index weight and the quantization value of the key index;

and the screening result module 30 is configured to generate a classification result according to a similarity algorithm, a clustering algorithm, and the index data, and determine a screening result corresponding to the personal information according to the classification result.

The talent database is constructed by comprehensively investigating the employees who want to recruit the post, and comprises information such as a study, ability and experience. And grading the comprehensive capacity of the corresponding recruiters according to the recruitment requirements of the enterprise and the talent database information, and constructing a clustering quantitative value matrix. And according to a self-adaptive neighbor method, constructing a similar matrix in clustering analysis, applying the similar matrix, and finishing classification by using a clustering algorithm, thereby finishing classification and screening of the resume of the applicant.

As an embodiment of the present invention, as shown in fig. 7, the index data module 20 includes:

a quantization value matrix unit 21, configured to obtain a quantization value matrix according to the quantization value of the key indicator;

and an index matrix unit 22, configured to generate an index matrix according to the quantization value matrix and a preset index weight, and use the index matrix as the index data.

In this embodiment, as shown in fig. 8, the screening result module 30 includes:

a similarity matrix unit 31, configured to generate a similarity matrix according to a similarity algorithm and the index matrix;

and a screening result unit 32, configured to generate a classification result by using a clustering algorithm and the similarity matrix, and determine a screening result corresponding to the personal information according to the classification result.

In this embodiment, the similarity matrix unit is further configured to determine a similarity between column vectors in the index matrix according to a similarity algorithm, and obtain the similarity matrix by using the similarity.

In the present embodiment, as shown in fig. 9, the screening result unit 32 includes:

a laplacian matrix subunit 321, configured to determine, according to the similarity matrix, a laplacian matrix corresponding to the similarity matrix;

and a classification result subunit 322, configured to obtain a preset number of eigenvectors according to the laplacian matrix, and generate the classification result by using the eigenvectors and a mean value clustering algorithm.

Based on the same application concept as the information screening method, the invention also provides the information screening device. Because the principle of solving the problems of the information screening device is similar to that of the information screening method, the implementation of the information screening device can refer to the implementation of the information screening method, and repeated parts are not repeated.

As shown in fig. 10, the electronic device 600 may further include: communication module 110, input unit 120, audio processing unit 130, display 160, power supply 170. It is noted that the electronic device 600 does not necessarily include all of the components shown in FIG. 10; furthermore, the electronic device 600 may also comprise components not shown in fig. 10, which may be referred to in the prior art.

As shown in fig. 10, the central processor 100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling the operation of the various components of the electronic device 600.

The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.

The input unit 120 provides input to the cpu 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display an object to be displayed, such as an image or a character. The display may be, for example, an LCD display, but is not limited thereto.

The memory 140 may be a solid state memory such as Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142, and the application/function storage section 142 is used to store application programs and function programs or a flow for executing the operation of the electronic device 600 by the central processing unit 100.

The memory 140 may also include a data store 143, the data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage portion 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging application, address book application, etc.).

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 an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.

Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. 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 to implement general telecommunications functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, so that recording on the local can be enabled through a microphone 132, and so that sound stored on the local can be played through a speaker 131.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. An information screening method, comprising:

2. The method according to claim 1, wherein the generating index data using the preset index weight and the quantized value of the key index comprises:

3. The method of claim 2, wherein generating the classification result based on the similarity algorithm, the clustering algorithm, and the indicator data comprises:

4. The method of claim 3, wherein generating a similarity matrix based on the similarity algorithm and the indicator matrix comprises:

5. The method of claim 3, wherein the generating the classification result using the clustering algorithm and the similarity matrix comprises:

6. An information screening apparatus, comprising:

7. The apparatus of claim 6, wherein the metric data module comprises:

8. The apparatus of claim 7, wherein the screening results module comprises:

9. The apparatus according to claim 8, wherein the similarity matrix unit is further configured to determine a similarity between column vectors in the index matrix according to a similarity algorithm, and obtain the similarity matrix using the similarity.

10. The apparatus of claim 8, wherein the screening result unit comprises:

11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 5 when executing the computer program.

12. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 5.