CN112732891A

CN112732891A - Office course recommendation method and device, electronic equipment and medium

Info

Publication number: CN112732891A
Application number: CN202011583182.1A
Authority: CN
Inventors: 纪玉娇
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2020-12-28
Filing date: 2020-12-28
Publication date: 2021-04-30

Abstract

The application provides an office course recommendation method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring multi-dimensional office data of employees; clustering the multidimensional office data by using a preset clustering algorithm, extracting common labels according to a clustering result, and generating an office label set of the staff; mining association rules of the office label sets of the employees based on an Apriori algorithm to obtain specific strong association rules; and recommending the relevant office courses for the employees according to the strong association rule. Compared with the prior art, accurate relevance among all office behavior data of the staff can be obtained through the scheme, accurate recommendation of office courses is carried out on the staff on the basis, the staff are assisted to gradually improve office efficiency, and the staff is made to improve the effect of a closed loop.

Description

Office course recommendation method and device, electronic equipment and medium

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for recommending office courses, an electronic device, and a computer-readable storage medium.

Background

At present, most of the visual boards are constructed based on business data, intelligent analysis is carried out on a large amount of business data, and then relevant prediction or decision suggestions of office courses are provided, and then employees learn the suggested office courses to improve office efficiency and form a closed-loop effect improvement.

But there is no relevant analysis for the personal office behavior of the staff, and the staff has no grasp about the office behavior preference of the staff in work. The diversity is highlighted due to the education background, the character and various behavior differences of staff, the traditional technology for extracting the common factors based on huge business data is not suitable for individuals any more, the intelligent analysis focuses more on the diversity of behaviors and the recommended personalization, but at present, more common characteristics are extracted based on massive personal data, the quality of the personalized behaviors is judged according to the common characteristics, the innovation and theoretical basis are lacked, and practical suggestions are difficult to provide for staff office courses, so that a closed-loop effect improvement is difficult to form.

Disclosure of Invention

The application aims to provide an office course recommendation method and device, electronic equipment and a computer readable storage medium.

A first aspect of the present application provides an office course recommendation method, including:

acquiring multi-dimensional office data of employees;

clustering the multidimensional office data by using a preset clustering algorithm, extracting common labels according to a clustering result, and generating an office label set of the staff;

mining association rules of the office label set of the staff based on an Apriori algorithm to obtain specific strong association rules;

and recommending the relevant office courses for the employee according to the strong association rule.

A second aspect of the present application provides an office course recommending apparatus, including:

the acquisition module is used for acquiring the multidimensional office data of the staff;

the label generation module is used for clustering the multidimensional office data by using a preset clustering algorithm, extracting common labels according to a clustering result and generating an office label set of the staff;

the mining module is used for mining association rules of the office label set of the staff based on an Apriori algorithm to obtain specific strong association rules;

and the recommending module is used for recommending the relevant office courses to the employee according to the strong association rule.

A third aspect of the present application provides an electronic device comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the computer program when executing the computer program to perform the method of the first aspect of the application.

A fourth aspect of the present application provides a computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of the first aspect of the present application.

Compared with the prior art, the office course recommendation method, the office course recommendation device, the electronic equipment and the medium provided by the application acquire the multidimensional office data of the staff; clustering the multidimensional office data by using a preset clustering algorithm, extracting common labels according to a clustering result, and generating an office label set of the staff; mining association rules of the office label sets of the employees based on an Apriori algorithm to obtain specific strong association rules; and recommending the relevant office courses for the employees according to the strong association rule. Compared with the prior art, accurate relevance among all office behavior data of the staff can be obtained through the scheme, accurate recommendation of office courses is carried out on the staff on the basis, the staff are assisted to gradually improve office efficiency, and the staff is made to improve the effect of a closed loop.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

FIG. 1 illustrates a flow chart of a method of office course recommendation provided by some embodiments of the present application;

FIG. 2 illustrates a schematic diagram of an office course recommender provided in some embodiments of the present application;

FIG. 3 illustrates a schematic diagram of an electronic device provided by some embodiments of the present application;

FIG. 4 illustrates a schematic diagram of a computer-readable storage medium provided by some embodiments of the present application.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which this application belongs.

In addition, the terms "first" and "second", etc. are used to distinguish different objects, rather than to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.

The embodiment of the application provides an office course recommendation method and device, an electronic device and a computer readable medium, which are described below with reference to the accompanying drawings.

Referring to fig. 1, which illustrates a flowchart of an office course recommendation method according to some embodiments of the present application, as shown, the office course recommendation method may include the following steps:

step S101: and acquiring the multidimensional office data of the staff.

The multidimensional office data can comprise basic information of staff, office behavior data and office psychological data; the basic information of the employee may include: name, gender, age, occupation, contact, school calendar, native place, etc.; office activity data (i.e., behavioral characteristics) may include: meeting behavior data, mail behavior data, audio and video behavior data, various office software use data and desktop office behavior data; the office mental data (i.e., mental features) include: whether each office action flow is fixed, whether the use time follows a certain rule and whether the efficiency is concerned more. According to the multidimensional office data, data such as staff's own office behavior data, company overall office behavior data and comprehensive love degree of the staff to each office course can be obtained through sorting.

In this step, collect the office data of staff different sources through multiple mode, accomplish the collection that gathers of basic office data, also accomplish office data warehouse and build promptly.

According to some embodiments of the present application, before step S102, the method further includes: preprocessing the collected multidimensional office data of the staff, and specifically realizing the following steps: performing data cleaning on the multidimensional office data; grading the non-numerical data to obtain a grade numerical value corresponding to the non-numerical data; and inserting corresponding values into the vacancy values in the data set by utilizing a mean fluctuation substitution method.

Specifically, for massive office data of staff, data cleaning is performed firstly, then, non-numerical data are divided into different levels, corresponding data are given based on the different levels, and accordingly assignment of relevant non-numerical indexes is completed.

Step S102: and clustering the multidimensional office data by using a preset clustering algorithm, extracting common labels according to a clustering result, and generating an office label set of the staff.

According to some embodiments of the present application, the predetermined clustering algorithm includes a K-means clustering algorithm. Specifically, step S102 may be implemented as: and utilizing a K-means clustering algorithm to assign a K value to cluster the multidimensional office data to obtain a plurality of classes.

The specific implementation scheme is as follows:

aiming at massive office data, when different categories of conference behavior data, mail behavior data, audio and video behavior data, office software use data, desktop office behavior data and the like are determined, a proper K value can be selected by using a K-Means algorithm according to prior experience of the data, and if no prior knowledge exists, a proper K value can be selected through cross validation.

After the number of k is determined, k initialized centroids need to be selected, and due to the fact that a heuristic method is adopted, position selection of the k initialized centroids has a great influence on a final clustering result and operation time of an algorithm, proper k centroids need to be selected, and preferably the centroids cannot be too close.

The input is a sample set D { x1, x 2.. xm }, the cluster number of clusters k of the clusters, the maximum number of iterations N, and the output is a cluster partition C { C1, C2.. Ck }, k samples are randomly selected from the data set D as initial k centroids { μ 1, μ 2.. mu.k } for N ═ 1, 2.. mu.k, the distances of the samples and respective centroid vectors are calculated, the samples are classified according to the distances, and then new centroids are recalculated for all sample points in each classified cluster. If all k centroid vectors have not changed, the output cluster partition C ═ C1, C2. Where Ck represents one of the classifications in the office data.

For example, for a large amount of office data of a staff, the sample set is divided into K clusters based on the distance between the office data. The method comprises the steps of enabling points in clusters to be connected together as closely as possible during initial data set, enabling distances among the clusters to be as large as possible, assuming that k is 3, randomly selecting class centroids corresponding to 3 k classes, then respectively solving the distances from all the points in all office sample data to the 3 centroids, marking the class of each sample as the class of the centroid with the minimum distance from the sample, and obtaining the class of all the sample points after first iteration by calculating the distances between the sample data and the 3 centroids. And then, respectively solving new centroids of the office data points marked as different classes, namely marking all the classes of the office data points as the classes of centroids closest to each other and solving the new centroids, and finally obtaining 3 classes of office data after continuous iteration.

In practical application, after the data are clustered by using the K-means algorithm, the clustering effect needs to be verified and evaluated, in this embodiment, the entropy-based class evaluation algorithm is used for evaluating the quality of each class, and the classes with good clustering effect are put into the clustering result; and extracting the generic labels of each type as office labels of the staff according to the clustering center of each type in the clustering result to generate an office label set of the staff.

Specifically, the generation of the office tag set of the employee includes: after the office data warehouse is built, a data analysis model needs to be built based on mass office data, firstly, an unsupervised learning algorithm (K-means clustering algorithm) is utilized to learn characteristics from label-free data, the mass office data is divided into multiple categories (such as conferences, audios and videos, signings, seals, mails, customer service, desktop office software and the like), points where most data are aggregated in the same category of data are found out in the unsupervised mode, a data model with the minimum distance between distributed data points and a nearby central point is built, aggregation points of most data of mails are built under a certain category, common characteristics of the mails of employees are further obtained, a common label system is extracted, a label set, namely a label warehouse is formed, and a data label system which is more suitable for services is generated.

Step S103: and mining association rules of the office tag set of the employee based on an Apriori algorithm to obtain specific strong association rules.

Specifically, step S103 may be implemented as: dividing office tag items in the office tag set into different item sets, determining frequent item sets in the item sets by adopting an Apriori algorithm, wherein each frequent item set k can generate 2 x (2k-1) association rules, and screening out the association rules with the confidence coefficient greater than or equal to the minimum confidence coefficient as strong association rules.

Specifically, data such as office behavior data of employees themselves, overall office behavior data of companies and comprehensive preference degrees of the employees on various office courses are fused, data association analysis is performed, and the mutual relations hidden in the data are mined. Relying on a large amount of label data (the label data are calculated in modules at an ADS layer) of a data platform ADS layer, and adopting an Apriori algorithm to determine frequent item sets of office label items such as conferences, audios and videos, signings, seals, mails, customer service services, desktop office software use and the like;

specifically, the process of determining the frequent item set by using Apriori algorithm is as follows:

let I ═ { I1, I2, … Im } be a set of m different items, such as the set of office label items mentioned above for meetings, audios and videos, tickers, stamps, mails, customer service, desktop office software usage, etc., the elements in the set being called items (items). The set of items I is called the item set (Itemset), and the set of items of length k is called the k-item set. Let task-related data D be a collection of database transactions, where each transaction T is a collection of items, such that

Each transaction having an identifier T_ID(ii) a Let A be a set of items, and transaction T contains A if and only if

Then the association rule is of the form a ═>B (wherein

And is

). There are two important metric values in the association rule metric: support and confidence. For the association rule R: A ═>B, then:

1. support (support): is the ratio of the number of transactions in the transaction set that contain both a and B to the number of all transactions. Support (a ═ B) ═ P (a ═ B) ═ count (a ═ B)/| D |.

2. Confidence (confidence): is the ratio of the number of transactions comprising a and B to the number of transactions comprising a. Configence (a ═ B) ═ P (B | a) ═ support (a ═ u B)/support (a).

And continuously iterating to generate a new candidate set, finding a candidate set with the support degree greater than or equal to a threshold value minsup (minimum support degree), generating frequent item sets, generating 2 × 2k-1 association rules by each frequent k-item set, and screening out the association rules with the confidence degrees greater than or equal to minconf (minimum confidence degree) as strong association rules.

Wherein the minimum support level represents the lowest statistical significance of the set of items. The minimum confidence indicates the lowest reliability of the association rule. If the support degree and the confidence degree reach the minimum support degree and the minimum confidence degree at the same time, the association rule is a strong association rule. The strong association rule is that: if the rule R: x ═ Y satisfies support (X ═ Y) > _ supmin (minimum support, which is used to measure the minimum importance that the rule needs to satisfy) and confidence (X ═ Y) > _ confmin (minimum confidence, which represents the minimum reliability that the association rule needs to satisfy) call the association rule X ═ Y as a strong association rule, otherwise call the association rule X ═ Y as a weak association rule.

Step S104: and recommending the relevant office courses for the employee according to the strong association rule.

Specifically, step S104 may be implemented as: sequencing all the obtained strong association rules according to the confidence degrees of the strong association rules; and recommending the relevant office courses for the employees according to the sequencing result.

The method aims at the office behaviors with strong relevance discovered by the algorithm, if the relevance between several office behaviors is discovered, the employees with the relevance behaviors have certain characteristics, the office behaviors preferred by the employees are extracted for recommending the relevant practical courses, the playing rate of the relevant recommended courses is effectively improved on the premise of ensuring that the employees are loved as much as possible, the employees are loved and effective, and a closed-loop employee effect improvement is created.

For example, assume that there are several strong association rules and their confidence (confidence) is given: configence is 50%; configence is 80%; confidence is 60%.

And after sorting according to the confidence degree, recommending the office courses related to the strong association rule of 80% to the employee.

According to some embodiments of the present application, the method may further include:

and presenting the strong association rule and the office course recommended according to the strong association rule to the staff in a visual interface form. For example, the recommendation is presented to the employee in a Web page form, so that the employee obtains a more intuitive recommendation result.

Based on the application, the staff can visually see the change trend of personal core office data and the time distribution condition of each office behavior, so that the staff can be helped to better distribute the time; the staff can check the relevance between each item of office behavior data of the staff, and in the process, the difference between the staff and similar staff is found, and existing problems are found in time.

According to the office course recommendation method provided by the embodiment of the application, the multidimensional office data of the staff are obtained; clustering the multidimensional office data by using a preset clustering algorithm, extracting common labels according to a clustering result, and generating an office label set of the staff; mining association rules of the office label sets of the employees based on an Apriori algorithm to obtain specific strong association rules; and recommending the relevant office courses for the employees according to the strong association rule. Compared with the prior art, accurate relevance among all office behavior data of the staff can be obtained through the scheme, accurate recommendation of office courses is carried out on the staff on the basis, the staff are assisted to gradually improve office efficiency, and the staff is made to improve the effect of a closed loop.

In the above embodiment, an office course recommendation method is provided, and correspondingly, an office course recommendation device is also provided. The office course recommending device provided by the embodiment of the application can implement the office course recommending method, and the office course recommending device can be implemented through software, hardware or a combination of software and hardware. For example, the office course recommending apparatus may include integrated or separate functional modules or units to perform the corresponding steps of the above-mentioned methods. Referring to fig. 2, a schematic diagram of an office course recommendation device according to some embodiments of the present application is shown. Since the apparatus embodiments are substantially similar to the method embodiments, they are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for relevant points. The device embodiments described below are merely illustrative.

As shown in fig. 2, the office course recommending apparatus 10 may include:

the acquisition module 101 is used for acquiring multidimensional office data of the staff;

the label generation module 102 is configured to cluster the multidimensional office data by using a preset clustering algorithm, extract a common label according to a clustering result, and generate an office label set of the employee;

the mining module 103 is configured to mine association rules of the office tag sets of the employees based on an Apriori algorithm to obtain specific strong association rules;

and the recommending module 104 is configured to recommend the relevant office courses to the employee according to the strong association rule.

In some implementations of embodiments of the present application, the apparatus 10 further comprises:

and the preprocessing module is used for preprocessing the multidimensional office data of the staff before the label generating module utilizes a preset clustering algorithm to cluster the multidimensional office data.

In some implementations of embodiments of the present application, the preprocessing module is specifically configured to:

performing data cleaning on the multidimensional office data;

grading the non-numerical data to obtain a grade numerical value corresponding to the non-numerical data;

and inserting corresponding values into the vacancy values in the data set by utilizing a mean fluctuation substitution method.

In some implementations of embodiments of the present application, the preset clustering algorithm includes a K-means clustering algorithm.

Correspondingly, the tag generation module 102 is specifically configured to:

utilizing a K-means clustering algorithm to designate a K value to cluster the multidimensional office data to obtain a plurality of classes;

evaluating the quality of each class by using an entropy-based class evaluation algorithm, and putting the classes with good clustering effect into a clustering result;

and extracting the generic labels of each type as office labels of the staff according to the clustering center of each type in the clustering result to generate an office label set of the staff.

In some implementations of the embodiments of the present application, the mining module 103 is specifically configured to:

and determining frequent item sets in the office tag items in the office tag sets by adopting an Apriori algorithm, wherein each frequent item set k can generate 2(2k-1) association rules, and screening out the association rules with the confidence degrees larger than or equal to the minimum confidence degree as strong association rules.

In some implementations of the embodiments of the present application, the recommending module 104 is specifically configured to:

sequencing all the obtained strong association rules according to the confidence degrees of the strong association rules;

and recommending the relevant office courses for the employees according to the sequencing result.

In some implementations of embodiments of the present application, the multidimensional office data includes basic information, office behavior data, and office mental data; wherein the content of the first and second substances,

the basic information includes: name, gender, age, occupation, contact, school calendar and native place;

the office activity data includes: meeting behavior data, mail behavior data, audio and video behavior data, various office software use data and desktop office behavior data;

the office psychological data includes: whether each office action flow is fixed, whether the use time follows a certain rule and whether the efficiency is concerned more.

and the display module is used for presenting the strong association rule and the office courses recommended according to the strong association rule to the staff in a visual interface form.

The office course recommending device 10 provided in the embodiment of the present application has the same beneficial effects as the office course recommending method provided in the previous embodiment of the present application based on the same inventive concept.

The embodiment of the present application further provides an electronic device corresponding to the office course recommendation method provided in the foregoing embodiment, where the electronic device may be a mobile phone, a notebook computer, a tablet computer, a desktop computer, or the like, so as to execute the office course recommendation method.

Please refer to fig. 3, which illustrates a schematic diagram of an electronic device according to some embodiments of the present application. As shown in fig. 3, the electronic device 20 includes: the system comprises a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the computer program to execute the office course recommendation method provided by any of the foregoing embodiments of the present application.

The Memory 201 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.

Bus 202 can be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 201 is used for storing a program, and the processor 200 executes the program after receiving an execution instruction, and the office course recommendation method disclosed in any of the foregoing embodiments of the present application may be applied to the processor 200, or implemented by the processor 200.

The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the method in combination with the hardware thereof.

The electronic equipment provided by the embodiment of the application and the office course recommending method provided by the embodiment of the application have the same inventive concept and have the same beneficial effects as the method adopted, operated or realized by the electronic equipment.

Referring to fig. 4, the computer readable storage medium is an optical disc 30, and a computer program (i.e., a program product) is stored thereon, and when being executed by a processor, the computer program executes the office course recommendation method according to any of the foregoing embodiments.

It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory, or other optical and magnetic storage media, which are not described in detail herein.

The computer-readable storage medium provided by the above-mentioned embodiment of the present application and the office course recommendation method provided by the embodiment of the present application have the same beneficial effects as the method adopted, executed or implemented by the application program stored in the computer-readable storage medium.

It should be noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present disclosure, and the present disclosure should be construed as being covered by the claims and the specification.

Claims

1. An office course recommendation method, comprising:

acquiring multi-dimensional office data of employees;

2. The method of claim 1, wherein before clustering the multi-dimensional office data using the predetermined clustering algorithm, further comprising:

preprocessing the multidimensional office data of the staff;

the pretreatment comprises the following steps:

performing data cleaning on the multidimensional office data;

3. The method according to claim 1, wherein the multidimensional office data comprises basic information, office behavioral data, and office psychological data; wherein the content of the first and second substances,

4. The method of claim 3, wherein the predetermined clustering algorithm comprises a K-means clustering algorithm;

5. The method according to claim 1, wherein the mining association rules for the employee's office tag sets based on Apriori algorithm to obtain specific strong association rules comprises:

and determining frequent item sets in the office tag items in the office tag sets by adopting an Apriori algorithm, generating 2 × 2k-1 association rules for each frequent item set k, and screening out the association rules with the confidence degrees larger than or equal to the minimum confidence degree as strong association rules.

6. The method of claim 5, wherein the making relevant office course recommendations for the employee according to the strong association rules comprises:

7. The method of claim 1, further comprising:

and presenting the strong association rule and the office course recommended according to the strong association rule to the staff in a visual interface form.

8. An office course recommendation apparatus, comprising:

9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method according to any of claims 1 to 7.

10. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of any one of claims 1 to 7.