CN115458099A - Person psychological image obtaining method and system based on questionnaire evaluation and electronic equipment - Google Patents
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Abstract
The application relates to a person psychological portrait acquisition method, a person psychological portrait acquisition system and electronic equipment based on questionnaire evaluation, wherein the method comprises the following steps: collecting a plurality of dimensional data of a psychological questionnaire evaluation process; and performing deep learning based on the plurality of dimensional data in the evaluation process, and performing collaborative clustering to obtain a psychological portrait of the evaluator. Compared with simple answer questionnaire evaluation, the person psychological portrait obtained finally is more accurate. Meanwhile, the technical scheme of the application has wide social benefits and can fill the blank that only important results and lack of process data in the field of psychological assessment.
Description
Technical Field
The application relates to the technical field of psychological portrait, in particular to a psychological portrait acquisition method, a psychological portrait acquisition system and electronic equipment based on questionnaire evaluation.
Background
With the continuous improvement of the living standard of people, the rapid development brings about the increase of pressure. In recent years, various domestic and foreign agitation and trade challenges are increasing, and psychological stress exceeding the past is generated on people. The world health organization indicates that the global burden imposed by psychological problems will be the second leading cause of overall human morbidity. The 22 committees of the national institutional health committee, etc. jointly issued "guidance opinions on strengthening mental health services" mentions that the mental service system is to be regarded as important.
Although the state and the ministry have made psychological service top-level design and policy guidelines, there are still a number of deficiencies from the perspective of the current psychological services in the country. The policy is imperfect, and the design and implementation of the mental service related items (papers, patents or related subjects) do not play a due role. For example, with the continuous expansion of new crown epidemic situation, people face more and more psychological stress, the number of appraisers increases rapidly, but neither the application of appraisal nor the practitioners of appraisal can meet the actual demand.
Although different manufacturers develop corresponding assessment tools, the assessment tools are mainly divided into symptomatic diagnosis, personality measurement and mental health assessment. Due to different theoretical orientations, most of the software or APP provides homogeneous functions, and only questionnaire results and simple arithmetic mean results are output to a user.
The lack of assessment means is one of the reasons for the unsatisfactory psychological assessment results. Particularly, the same group of students can make the same psychological assessment, and the psychological assessment results of different manufacturers are far from each other due to different tools. This is not a compelling question: whether the evaluation result can truly reflect the psychological condition of the evaluating person, how to evaluate the psychological evaluation result, and the like, so that more efficient psychological evaluation items or subject research is required. Professor Yang Zong Kai proposes the result of the information technology and the depth integration of education to make cross integration. The Chenli professor of Beijing university of teachers and professors proposes a new concept, a new technology and a new method which can use Internet + education to solve the problems related to mental health services. Currently, most of the existing evaluations only record results, and ignore the importance of process data in the evaluation. For example, the existing well-known questionnaire star only provides simple statistics for users, and does not consider a new method of information technology cross fusion or internet +, so that process data helpful for result evaluation cannot be obtained, and finally obtained person psychological images are not very accurate.
Disclosure of Invention
In order to obtain more accurate person psychological portrait based on questionnaire evaluation, the application provides a person psychological portrait obtaining method, a system and electronic equipment based on questionnaire evaluation.
In a first aspect, the questionnaire evaluation-based person psychological image acquisition method provided by the application adopts the following technical scheme:
a person psychological image obtaining method based on questionnaire evaluation comprises the following steps:
collecting a plurality of dimension data of a psychological questionnaire evaluation process;
and performing deep learning based on the plurality of dimensional data in the evaluation process, and performing collaborative clustering to obtain the psychological portrait of the evaluator.
By adopting the technical scheme, on the basis of common questionnaire evaluation, the psychological portrait of an evaluator is obtained by collecting a plurality of dimensional data of a psychological questionnaire evaluation process, deep learning is carried out based on the plurality of dimensional data of the psychological questionnaire evaluation process, and collaborative clustering, so that the psychological portrait of a person finally obtained is more accurate compared with simple answer questionnaire evaluation. Meanwhile, the technical scheme of the application has wide social benefits and can fill the blank that only the result is important and the process data is lacking in the field of psychological evaluation.
Preferably, the plurality of dimensional data includes one-dimensional data: the number of times of changing options, the stay time of the questions and the number of times of reviewing the questions; two-dimensional data: screen touch coordinates and three-dimensional spatial data: acceleration of the vehicle.
By adopting the technical scheme, one-dimensional, two-dimensional and three-dimensional data are collected to comprehensively carry out deep learning modeling, and finally the psychological portrait of the person to be evaluated is more accurate.
Preferably, the multiple dimension data are obtained in a silent manner, specifically including:
reading the number of questions, and setting an entering timer array, a leaving timer array and an entering time counter array for each question;
displaying the question stem content;
reading the number of options corresponding to the question, and setting an option counter array and an option change time array for each option;
displaying the option content and judging whether the option content is the last option of the current title;
if yes, starting a daemon process for silent recording, and collecting a plurality of dimensional data of the user psychological questionnaire evaluation process; otherwise, continuing to display the option content until the option of the current question is displayed completely, starting a daemon process for silent recording, and acquiring a plurality of dimensional data of the user psychological questionnaire evaluation process;
judging whether the current question is the last question or not;
if so, after obtaining the user psychological questionnaire evaluation process data of the question, storing the results of each counter and each timer so as to obtain the multiple dimensional data; otherwise, turning to the step of displaying the question stem content.
According to the method and the device, data are acquired in a silent mode, and compared with invasive data acquisition modes such as electroencephalogram, electrocardio and VR helmets or a mode that an evaluator passively (in an unnatural mode) acquires data under the monitoring of a camera, the acquired data are more real and effective, so that more accurate psychological portrait of an appraised person can be acquired finally.
Preferably, the questions are set to be one question per page, and compared with the situation that multiple questions are provided per page (estimation time is not accurate enough) in the prior art, the counter and the timer of each question can count and time more accurately, more accurate data can be acquired, and the psychological portrait of the people finally evaluated is more accurate.
Preferably, before deep learning, data integration and normalization processing are performed on the data; when the acceleration data is processed, the following method steps are adopted:
calculating a variance curve of the acceleration signal curve;
carrying out data integration treatment: carrying out segmentation processing on the variance curve according to periods to remove interference wave bands in the variance curve;
carrying out normalization treatment: the acceleration signal values are converted into 0-1, and the abrupt change state is merged into the adjacent state.
By adopting the technology to process data, the answering posture, namely whether walking answering, lying answering, hand shaking answering and the like can be further obtained, so that the answering mood can be better determined, mutation data can be eliminated, and more accurate psychological portrait of the person to be evaluated can be finally obtained.
Preferably, the collaborative clustering obtains a psychological image of the appraiser, including:
processing the data of each evaluator by adopting a kmean algorithm;
multiplying the processing result by a weight for amplifying or reducing the processing result so that the result shows the energy stage density of the acquired data;
calculating the minimum distance and the classification corresponding to the minimum distance;
dynamically adjusting the weight, and circularly and repeatedly solving the minimum distance and the classification corresponding to the minimum distance;
obtaining a corresponding weight which enables the classification corresponding to the minimum distance to be more concentrated and enables the data of the evaluator outside the classification area to be minimum as a final weight;
and obtaining a final collaborative clustering result based on the final weight, and further obtaining a psychological portrait of the appraiser.
By adopting the technical scheme, the psychological picture of the appraiser is obtained,
the inventor finds that when the existing algorithm carries out the collaborative clustering, the data is usually calculated by using the mean value or the variance, but the mean value can spread the characteristics of the data in the high-energy stage; the variance ignores vector characteristics such as the direction of motion, for example, in some robot algorithms or step number recording algorithms, only variance data is used, however, data of the motion sensor has positive and negative values, and the meaning of the data is lost after the data is squared, that is, the variance mode does not consider the motion characteristics of the data. The method has the advantages that the shortcomings of simple adoption of the mean value and the variance are combined, the algorithm of kmean plus weighting scaling plus minimum distance is adopted, the variance data of the motion sensor is provided, the mean value data is fused, the answering posture can be further analyzed, whether the person walks or not, whether the person lies, whether the person shakes, and the like can be further analyzed, the answering mood can be better determined, meanwhile, the weighting scaling and minimum distance method is adopted, the classification can be more concentrated, and the method is favorable for finally obtaining more accurate psychological portrait of the person to be evaluated.
Preferably, the plurality of dimensional data correspond to a plurality of status indicators, and each status indicator corresponds to a plurality of feature vectors; the method further comprises the following steps: judging whether the acquired data is matched with the characteristic vector corresponding to a certain state index, if not, judging that the acquired data is abnormal data, and rejecting the data when deep learning modeling is carried out.
By adopting the technical scheme, abnormal data can be removed in advance, and the accuracy and efficiency of deep learning modeling are improved.
In a second aspect, the questionnaire evaluation-based person psychological image acquisition system provided by the application adopts the following technical scheme: a person psychological picture acquisition system based on questionnaire evaluation comprises:
the data acquisition module is used for acquiring a plurality of dimensional data of the psychological questionnaire evaluation process;
and the psychological picture acquisition module is used for carrying out deep learning based on the plurality of dimensional data and obtaining the psychological picture of the appraiser by collaborative clustering.
In a third aspect, the electronic device provided by the present application adopts the following technical solutions: an electronic device comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and executed to implement any of the methods described above.
In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions: a computer readable storage medium storing a computer program capable of being loaded by a processor and executing a method implementing any of the foregoing methods.
In summary, the present application has the following beneficial technical effects:
by adopting the technical scheme, on the basis of ordinary questionnaire evaluation, the psychological portrait of an evaluator is obtained by acquiring a plurality of dimensional data of a psychological questionnaire evaluation process, performing deep learning based on the plurality of dimensional data of the psychological questionnaire evaluation process and performing collaborative clustering, so that the psychological portrait is more accurate compared with the simple answer questionnaire evaluation. Meanwhile, the technical scheme of the application has wide social benefits and can fill the blank that only the result is important and the process data is lacking in the field of psychological evaluation.
Drawings
FIG. 1 is a flow chart of a method of an embodiment of the present application.
Fig. 2 is a flowchart of a method for acquiring data in a silent manner in an embodiment of the present application.
FIG. 3 is a flowchart of a method for collaborative clustering to obtain a psychological picture of an evaluator in an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to figures 1-3.
The embodiment of the application discloses a person psychological portrait acquisition method based on questionnaire evaluation. Referring to fig. 1, a person psychological image obtaining method based on questionnaire evaluation includes:
s1, collecting a plurality of dimensional data of a psychological questionnaire evaluation process;
and S2, carrying out deep learning based on the plurality of dimensional data in the evaluation process, and cooperatively clustering to obtain the psychological portrait of the evaluator.
The portrait, namely the attributes and behaviors related to the psychology of the user and the answer of the questionnaire are combined through data conversion, so that the category of the user is determined.
Optionally, in step S1, the multiple pieces of dimensional data include one-dimensional data: option change times, item stay time, item review times, two-dimensional data: screen touch coordinates and three-dimensional spatial data: acceleration.
And the screen contact point coordinates are screen two-dimensional coordinate values touched by fingers in the filling process of each question. When the psychological assessment user submits an answer, the finger contacts different screen coordinates, the screen coordinates reflect the answer tracks of the user, the answer tracks of different users can be clustered, similar individuals can be found, and the problem of the commonality of the similar individuals can be found. Similarly, the same psychological analysis is carried out on individuals with the same track or the same clustering portrait, so that the intervention manpower and material resources in the later evaluation period are saved, and the efficiency of psychological service is greatly improved.
The three-dimensional data acceleration can be obtained through an acceleration sensor of the equipment.
In this embodiment, the multiple pieces of dimension data are obtained in a silent manner, and specifically include (as shown in fig. 2):
reading the number of questions, and setting an entering timer array, a leaving timer array and an entering time counter array for each question;
displaying the question stem content;
reading the number of options corresponding to the question, and setting an option counter array and an option change time array for each option;
displaying the option content and judging whether the option content is the last option of the current title;
if yes, starting a daemon process for silent recording, and collecting a plurality of dimensional data of the user psychological questionnaire evaluation process; otherwise, continuing to display the content of the options until the options of the current question are displayed completely, starting a daemon process for silent recording, and acquiring multiple dimensional data (mainly acquiring two-dimensional and three-dimensional data) in the psychological questionnaire evaluation process of the user;
judging whether the current question is the last question or not;
if yes, after the user psychological questionnaire evaluation process data of the question are obtained, the results of all counters and timers are stored (one-dimensional data are obtained), and then the multiple pieces of dimensional data are obtained; otherwise, turning to the step of displaying the question stem content.
Optionally, in specific implementation, the type of the topic may be determined first, and the types of single selection, multiple selection, voting, and the like may be distinguished, which may have an influence on calculating the option change times, and the like.
In other embodiments, the multiple pieces of dimensional data may also be acquired in an invasive data acquisition manner such as electroencephalogram, electrocardiogram, VR helmet, or in a passive manner (unnatural manner) by an evaluator under the monitoring of a camera.
In specific implementation, the stay time of one-time entering answer can be obtained by subtracting the numerical values of the entering timer and the leaving timer of each question; and then the total residence time of the questions entering for multiple times can be solved by combining the entering times counter array.
When counting the option change times, each option counter array and the option change time array can be used, and when a certain option is changed, the corresponding option change time array is increased by one; then the total number of changes to the title is also incremented by one.
Optionally, after the data is collected in step S1, a series of pre-processing may be performed, for example, invalid data is deleted when the original data is processed, missing fields are filled, repeated data is cleaned, then data selection is performed on the screened attributes, a data set is established, and finally, data of different types are subjected to type conversion to become data which can be processed uniformly, for example, a gender male is converted into data 1, and a gender female is converted into data 0; the answers to the words "yes" and "no" are respectively noted as 1 and 0, etc.
In order to uniformly pre-process the collected data with different dimensions, different databases may be formed for the collected data. Such as:
performing data analysis and extraction in the psychological assessment answering process, then completing data cleaning, combing and integrating data such as option change, answering time and the like, and forming an answering database (namely a one-dimensional data database) by a characteristic engineering method;
performing RPX normalization on contact coordinates in the process data, mapping relative positions, then performing statistics, summarization and analysis, and forming a contact coordinate database (namely a two-dimensional data database) for each questionnaire process data;
and (3) extracting data of the acceleration sensor in the evaluation process, removing data such as extreme values and the like, calculating variance and mean values, and forming an action database (namely a three-dimensional data database) in the questionnaire process data.
In this embodiment, the questions are set to be one question per page. In other embodiments, multiple topics per page may also be set.
Optionally, before performing the deep learning in step S2, the method further includes performing data integration and normalization processing on the data; when the acceleration data is processed, the following method steps are adopted:
calculating a variance curve of the acceleration signal curve;
and (3) carrying out data integration processing: carrying out segmentation processing on the variance curve according to periods to remove interference wave bands in the variance curve;
carrying out normalization treatment: the acceleration signal values are converted into 0-1, and the abrupt change state is merged into the adjacent state.
In this embodiment, in step S2, the collaborative clustering obtains a psychological image of the evaluator, which includes (as shown in fig. 3):
s21, processing the data of each evaluator by adopting a kmean algorithm;
s22, multiplying the processing result by a weight for amplifying or reducing the processing result so that the result shows the energy stage density of the acquired data;
s23, solving the minimum distance and the classification corresponding to the minimum distance;
s24, dynamically adjusting the weight, and circularly and repeatedly solving the minimum distance and the classification corresponding to the minimum distance;
s25, obtaining corresponding weight which enables the classification corresponding to the minimum distance to be more concentrated and enables the data (namely abnormal data at the periphery of the clustering result) of the testers outside the classification area to be minimum as final weight;
and S26, obtaining a final collaborative clustering result based on the final weight, and further obtaining a psychological portrait of the appraiser.
The weight can be adjusted from small to large by dynamically adjusting the weight; a preferred range may be 0.6-1.5.
In other embodiments, the mean or variance may also be used to calculate the final collaborative clustering result, so as to obtain the psychological picture of the assessor.
Optionally, the multiple pieces of dimensional data correspond to multiple state indexes, and each state index corresponds to multiple feature vectors; the step S2 of performing deep learning based on the multiple dimensional data of the evaluation process further includes: the dimensional data correspond to a plurality of state indexes, and each state index corresponds to a plurality of feature vectors; the method further comprises the following steps: and judging whether the acquired data is matched with the characteristic vector corresponding to a certain state index, if not, judging that the acquired data is abnormal data, and rejecting the data when performing deep learning modeling.
Specifically, if a set of feature vectors is matched, the data corresponds to the category corresponding to the associated feature vector. If the data are not matched with all current categories, the data are classified as abnormal data, manual screening can be reserved, and the manual marking result is used as sensitive data in future deep learning modeling. For example, for the answer duration index, according to the answer duration ratio, the extremely short answer ratio, the distraction state ratio and the like, through data clustering, and synthesizing the feature vectors tested by different users, the one-dimensional duration data can obtain a plurality of groups of feature vectors. When the newly collected time length data is matched with the feature vector, 4 types of answer time length feature vectors belonging to normal, excessive, insufficient and abnormal answers can be obtained. For the option change indexes, after clustering and matching the feature vectors, 4 types of option change types including earlier stage examination question change, middle stage deduction change, later stage wrong selection change and abnormity can be obtained by combining the one-dimensional duration feature vector and the two-dimensional contact coordinate feature vector. Similarly, after clustering and matching the feature vectors of the three-dimensional space-time data of the acceleration sensor, 4 kinds of feature vectors of normality, anxiety, distraction and abnormality and corresponding indexes can be obtained.
The embodiment of the application also discloses a person psychological image obtaining system based on questionnaire evaluation. A system for obtaining a psychological portrait of a person based on questionnaire evaluation comprises:
the data acquisition module is used for acquiring a plurality of dimensional data of the psychological questionnaire evaluation process;
and the psychological portrait acquisition module is used for carrying out deep learning based on the plurality of dimensional data and obtaining the psychological portrait of the appraiser through collaborative clustering.
It will be clear to those skilled in the art that, for convenience and simplicity of description, the above division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above described functions.
The embodiment of the application also discloses the electronic equipment. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and executed to implement any of the methods described above.
The electronic device may be a mobile phone, a tablet, or other electronic device, and the electronic device includes but is not limited to a processor and a memory, for example, the electronic device may further include an input/output device, a network access device, a bus, and the like.
A processor in the present application may include one or more processing cores. The processor executes or executes the instructions, programs, code sets, or instruction sets stored in the memory, calls data stored in the memory, performs various functions of the present application, and processes the data. The Processor may be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It is understood that the electronic devices for implementing the above processor functions may be other devices, and the embodiments of the present application are not limited in particular.
The memory may be an internal storage unit of the electronic device, for example, a hard disk or a memory of the electronic device, or an external storage device of the electronic device, for example, a plug-in hard disk, a smart card (SMC), a secure digital card (SD) or a flash memory card (FC) provided on the electronic device, and the memory may also be a combination of the internal storage unit of the electronic device and the external storage device, and the memory is used for storing a computer program and other programs and data required by the electronic device, and may also be used for temporarily storing data that has been output or will be output, which is not limited in this application.
The embodiment of the application also discloses a computer readable storage medium. A computer readable storage medium storing a computer program that can be loaded by a processor and executed to implement any of the above methods.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, and the computer program may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM), among others.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: all equivalent variations made according to the methods and principles of the present application should be covered by the protection scope of the present application.
Claims (10)
1. A person psychological image obtaining method based on questionnaire evaluation is characterized by comprising the following steps:
collecting a plurality of dimensional data of a psychological questionnaire evaluation process;
and performing deep learning based on the plurality of dimensional data in the evaluation process, and performing collaborative clustering to obtain a psychological portrait of the evaluator.
2. The method of claim 1, wherein said plurality of dimensional data includes one-dimensional data: the number of times of changing options, the stay time of the questions and the number of times of reviewing the questions; two-dimensional data: screen touch coordinates and three-dimensional spatial data: acceleration.
3. The questionnaire evaluation-based person psychological image obtaining method according to claim 1 or 2, wherein the plurality of dimensional data are obtained in a silent manner, and specifically comprises:
reading the number of questions, and setting an entering timer array, a leaving timer array and an entering time counter array for each question;
displaying the question stem content;
reading the number of options corresponding to the question, and setting an option counter array and an option change time array for each option;
displaying the option content and judging whether the option content is the last option of the current title;
if yes, starting a daemon process for silent recording, and collecting a plurality of dimensional data of the user psychological questionnaire evaluation process; otherwise, continuing to display the option content until the option of the current question is displayed completely, starting a daemon process for silent recording, and acquiring a plurality of dimensional data of the user psychological questionnaire evaluation process;
judging whether the current question is the last question or not;
if so, after obtaining the user psychological questionnaire evaluation process data of the question, storing the results of each counter and each timer so as to obtain the multiple dimensional data; otherwise, turning to the step of displaying the question stem content.
4. The method for acquiring a psychological portrait of a person based on questionnaire evaluation according to claim 3, wherein the questions are set to one question per page.
5. The questionnaire evaluation-based person psychological image obtaining method according to claim 2, wherein before deep learning, data integration and normalization are performed on the data; when the acceleration data is processed, the following method steps are adopted:
calculating a variance curve of the acceleration signal curve;
carrying out data integration treatment: carrying out segmentation processing on the variance curve according to periods to remove interference wave bands in the variance curve;
and (3) carrying out normalization treatment: the acceleration signal values are converted into 0-1, and the abrupt change state is merged into the adjacent state.
6. The method for obtaining psychological portrait of person based on questionnaire evaluation of claim 1, wherein said collaborative clustering obtains psychological portrait of person to be evaluated, comprising:
processing the data of each evaluator by adopting a kmean algorithm;
multiplying the processing result by a weight for amplifying or reducing the processing result to enable the result to show the energy stage density of the acquired data;
calculating the minimum distance and the classification corresponding to the minimum distance;
dynamically adjusting the weight, and circularly and repeatedly solving the minimum distance and the classification corresponding to the minimum distance;
obtaining a corresponding weight which enables the classification corresponding to the minimum distance to be more concentrated and enables the data of the evaluator outside the classification area to be minimum as a final weight;
and obtaining a final collaborative clustering result based on the final weight, and further obtaining a psychological portrait of the appraiser.
7. The method of claim 1, wherein the dimensional data correspond to state indexes, and each state index corresponds to a feature vector; the method further comprises the following steps: judging whether the acquired data is matched with the characteristic vector corresponding to a certain state index, if not, judging that the acquired data is abnormal data, and rejecting the data when deep learning modeling is carried out.
8. A system for obtaining a psychological portrait of a person based on questionnaire evaluation, comprising:
the data acquisition module is used for acquiring a plurality of dimensional data in the psychological questionnaire evaluation process;
and the psychological portrait acquisition module is used for carrying out deep learning based on the plurality of dimensional data and obtaining the psychological portrait of the appraiser through collaborative clustering.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and executed to perform the method of any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored which can be loaded by a processor and which executes a computer program implementing the method of any of claims 1 to 7.
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