A power grid technical transformation project quality evaluation method and system

Abstract

The application discloses a kind of power grid technical improvement engineering quality evaluation method and system, the method is by obtaining the historical power consumption data of target area and carries out fault analysis, the target sub-region that the fault occurrence number exceeds preset number is marked to obtain fault marking area;Power grid technical improvement area is monitored to obtain power consumption data, and fault marking area is mapped to power grid technical improvement area to obtain target technical improvement area;Power consumption data and target technical improvement area are input into target simulation model to simulate to obtain simulation data, according to simulation data to technical improvement engineering is evaluated to obtain engineering abnormal score;By obtaining the historical power consumption data of target area, mark out fault high-incidence area, and it is mapped to power grid technical improvement area.Combining current power consumption data, simulation is carried out on target technical improvement area in simulation model and engineering abnormal score is calculated, to improve engineering evaluation accuracy and engineering evaluation objectivity.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of engineering evaluation, and particularly relates to a quality evaluation method and system for power grid technical improvement engineering. BACKGROUND

[0002] The power grid production technical improvement project engineering aims to update, improve and match the existing power grid production equipment, facilities and related auxiliary facilities by using mature, advanced and applicable technology, equipment, process and material, so as to improve the safety and reliability, economy and intelligence, energy saving and environmental protection level of the power grid. The quality of the power grid technical improvement engineering is evaluated by engineering evaluation, which can effectively control the cost and economic benefit.

[0003] The existing quality evaluation of the power grid technical improvement engineering is mainly for the evaluation of the region which has been completed, the data quantity is less and the data does not have the time dimension characteristics, which leads to single data and cannot accurately evaluate the engineering. SUMMARY

[0004] In order to solve the problems in the prior art, the application provides a quality evaluation method and system for power grid technical improvement engineering.

[0005] The technical scheme of the application is as follows:

[0006] A quality evaluation method for power grid technical improvement engineering comprises:

[0007] Obtaining historical power consumption data of a target region composed of a plurality of target sub-regions, performing fault analysis on the historical power consumption data, and marking a fault marked region;

[0008] Monitoring and obtaining current power consumption data of a power grid technical improvement region, mapping the fault marked region to the power grid technical improvement region to obtain a target technical improvement region;

[0009] Inputting the current power consumption data and the power grid parameters of the target technical improvement region into a target simulation model for evaluating the quality of the power grid technical improvement engineering in the economic cost dimension, stability dimension and reliability dimension after the completion of the power grid technical improvement engineering, obtaining an evaluation result, and evaluating the power grid technical improvement engineering according to the evaluation result.

[0010] Further, the specific method for performing fault analysis on the historical power consumption data and marking a fault marked region comprises:

[0011] Marking the target sub-region whose fault occurrence number exceeds a preset number to obtain the fault marked region.

[0012] Further, the expression of the target simulation model is:

[0013]

[0014] In the formula, F is the engineering anomaly score, that is, the output of the target simulation model; e is a natural constant; v is the voltage; v0 is the rated voltage; Y is the actual technical improvement cost; Y0 is the average value of the theoretical cost of the technical improvement scheme; m is the number of fault maintenance; W j is the maintenance time after the jth fault.

[0015] Further, the specific method of evaluating the power grid technical improvement project according to the evaluation result comprises:

[0016] Comparing the engineering anomaly score with an engineering anomaly score threshold value, if the engineering anomaly score is greater than the engineering anomaly score threshold value, the power grid technical improvement project is determined to be unqualified; otherwise, the power grid technical improvement project is determined to be qualified.

[0017] Further, before the current power consumption data and the power grid parameters of the target technical improvement area are input into the target simulation model for evaluating the quality of the power grid technical improvement project in the economic cost dimension, the stability dimension and the reliability dimension of the power grid after the completion of the power grid technical improvement project, the method further comprises:

[0018] A hierarchical topology model of the power grid is constructed, and the hierarchical topology model is divided into regions according to the nodes of the power grid to obtain a hierarchical topology graph, and the nodes in the hierarchical topology graph are taken as a first population;

[0019] The first population is initialized to obtain a second population through a Logistic-Tent chaotic mapping, and the fitness of each individual in the second population is calculated; the population is a group of Staphylinus individuals, and each Staphylinus individual represents any fault position;

[0020] The Staphylinus updating operation is performed on the second population through a sine algorithm and a spiral search strategy, so that the initial population is iteratively updated, and if the iteration number meets a preset number, the iterative updating is stopped and an optimal result is output.

[0021] Further, before the iterative updating is stopped and the optimal result is output if the iteration number meets the preset number, the method further comprises:

[0022] The fault data are determined according to the optimal result, and a simulation model is constructed;

[0023] The historical training data are obtained, the historical training data and the fault data are input into the simulation model for training, and target model parameters are obtained; the historical training data comprise historical power consumption data and historical fault data;

[0024] The bias and the weight of the simulation model are updated according to the target model parameters, and a target simulation model is obtained.

[0025] Further, the fault data comprises a fault area and a fault type.

[0026] A quality evaluation system of a power grid technical improvement project, comprising a fault marking module, a data monitoring and mapping module, and an evaluation module;

[0027] The fault marking module is configured to acquire historical power consumption data of a target area composed of a plurality of target sub-areas, perform fault analysis on the historical power consumption data, and mark a fault marking area.

[0028] The data monitoring and mapping module is configured to monitor and acquire current power consumption data of a power grid technical improvement area, map the fault marking area to the power grid technical improvement area to obtain a target technical improvement area.

[0029] The evaluation module is configured to input the current power consumption data and power grid parameters of the target technical improvement area into a target simulation model for evaluating the quality of the power grid technical improvement project in terms of economic cost, stability, and reliability after completion of the power grid technical improvement project, obtain an evaluation result, and evaluate the power grid technical improvement project according to the evaluation result.

[0030] An electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to invoke and run the computer program stored in the memory to execute the method according to any one of the preceding methods.

[0031] A computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the steps of the method according to any one of the preceding methods.

[0032] Compared with the prior art, the present application has the following beneficial effects:

[0033] The present application provides a quality evaluation method and system for a power grid technical improvement project, which acquires historical power consumption data of a target area and performs fault analysis, marks target sub-areas with fault occurrence frequencies exceeding a preset number to obtain a fault marking area, monitors a power grid technical improvement area to obtain power consumption data, maps the fault marking area to the power grid technical improvement area to obtain a target technical improvement area, inputs the power consumption data and the target technical improvement area into a target simulation model to perform simulation and obtain simulation data, evaluates the technical improvement project according to the simulation data to obtain an engineering abnormality score, acquires historical power consumption data of a target area to perform fault analysis, marks a high-fault area, and maps the high-fault area to the power grid technical improvement area.

[0034] The target simulation model of the method is used for quality evaluation of the power grid technical improvement project in the economic cost dimension, the stability dimension and the reliability dimension of the power grid after the power grid technical improvement project is completed, and the evaluation result is more scientific and accurate. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 A flowchart of the quality evaluation method of the power grid technical improvement project in the embodiment;

[0036] Figure 2 A block diagram of the quality evaluation system of the power grid technical improvement project in the embodiment. DETAILED DESCRIPTION

[0037] The application will be further illustrated below in combination with the drawings and specific embodiments, and it should be understood that the embodiments are only used for illustrating the application and are not used for limiting the scope of the application, and after reading the application, various equivalent modifications of the application made by those skilled in the art all fall within the scope defined by the appended claims.

[0038] Embodiment one:

[0039] A quality evaluation method of a power grid technical improvement project of the application, as shown in the figure, comprises the following specific steps: Figure 1

[0040] S1, obtaining historical power consumption data of a target region composed of a plurality of target sub-regions, performing fault analysis on the historical power consumption data, and marking a fault marking region;

[0041] S2, monitoring and obtaining current power consumption data of the power grid technical improvement region, and mapping the fault marking region to the power grid technical improvement region to obtain a target technical improvement region;

[0042] S3, inputting the current power consumption data and the power grid parameters of the target technical improvement region into a target simulation model for quality evaluation of the power grid technical improvement project in the economic cost dimension, the stability dimension and the reliability dimension of the power grid after the power grid technical improvement project is completed, to obtain an evaluation result, and evaluating the power grid technical improvement project according to the evaluation result.

[0043] Embodiment two:

[0044] The embodiment is further designed on the basis of embodiment one, and the specific method for performing fault analysis on the historical power consumption data and marking the fault marking region in this embodiment comprises:

[0045] Marking the target sub-region whose fault occurrence frequency exceeds the preset frequency to obtain the fault marking region. For example, the target region has had more than 10 power failures in the past year, and the preset frequency is 5 times, so the region will be automatically marked as a fault marking region. This method is more efficient and accurate than manual inspection, which helps to reduce data processing and improve analysis efficiency. ​

[0046] Example Three:

[0047] This example is further designed on the basis of Example One, in which the target simulation model relies on historical data to predict the behavior of the power grid, such as load forecasting models, fault detection models, etc. built using machine learning algorithms. This model can simulate the operating state of the power grid under different conditions, including load changes, fault occurrence and recovery, etc. By inputting the power grid parameters and electricity consumption data before and after the technical improvement, the model can output the operating state and performance indicators of the power grid after the implementation of the technical improvement project, such as voltage stability, current distribution, fault rate, etc. These simulation data can be used to evaluate the effect of the technical improvement project and give the corresponding engineering anomaly score.

[0048] The expression of the target simulation model in this example is:

[0049]

[0050] In the formula, F is the engineering anomaly score, which is the output of the target simulation model; e is the natural constant; v is the voltage; v0 is the rated voltage; Y is the actual technical improvement cost; Y0 is the average value of the theoretical cost of the technical improvement scheme; m is the number of fault repairs; W j is the repair time after the jth fault.

[0051] The larger the engineering anomaly score output by the target simulation model in this example, the worse the effect of the current technical improvement project, and the voltage in the simulation data can effectively evaluate the stability of the power grid, is directly proportional to F, and by controlling the variable method, the smaller the value of , the smaller F represents that the power grid is more stable at this time, and the engineering anomaly score is also lower; when the technical improvement scheme of the target area power grid is determined, there are often multiple technical improvement schemes, each corresponding to a cost, and the average value of the above cost is calculated, which can effectively evaluate the economy of the power grid when compared with the actual cost, the smaller indicates that the economy of the power grid at this time is the highest, and the engineering anomaly score at this time is also smaller, which means lower cost; the longer the average repair time of the power grid fault, the poorer the reliability of the technical improvement project of the power grid, and the higher the engineering anomaly score at this time. The minimum time constant of power grid repair is half an hour, that is, 0.5 hours, so 0.5 hours is taken as the repair standard value.

[0052] In this expression, is the economic cost coefficient, is the stability coefficient, The reliability coefficient is the ratio of the stability coefficient to the economic cost coefficient. When the economic cost coefficient becomes smaller, the stability coefficient becomes larger, and the reliability coefficient becomes larger, it indicates that the economic cost of the power grid technical improvement is low, and the stability and reliability are poor. When the economic cost coefficient becomes smaller, the stability coefficient becomes smaller, and the reliability coefficient becomes smaller, it indicates that the economic cost of the power grid technical improvement is low, and the stability and reliability are good. When the economic cost coefficient becomes larger, the stability coefficient becomes smaller, and the reliability coefficient becomes smaller, it indicates that the economic cost of the power grid technical improvement is high, and the stability and reliability are good. When the economic cost coefficient becomes larger, the stability coefficient becomes larger, and the reliability coefficient becomes larger, it indicates that the economic cost of the power grid technical improvement is high, and the stability and reliability are poor.

[0053] Embodiment Four

[0054] The embodiment is further designed on the basis of Embodiment Three, and the specific method for evaluating the power grid technical improvement project according to the evaluation result in this embodiment includes:

[0055] The engineering abnormal score is compared with the engineering abnormal score threshold value. If the engineering abnormal score is greater than the engineering abnormal score threshold value, the power grid technical improvement project is determined to be unqualified. Otherwise, the power grid technical improvement project is determined to be qualified.

[0056] The above-mentioned qualified indicates that a power grid technical improvement project aims to improve the power supply stability and efficiency of a target area. After system evaluation, the power grid technical improvement project obtains an engineering abnormal score higher than the score threshold value, indicating that the power supply stability and efficiency of the area after technical improvement are significantly improved, and the failure rate is significantly reduced. Therefore, the project is determined to be a normal scheme, and the power grid enterprise can continue to promote similar technical improvement projects according to the plan, and consider promoting this scheme as a successful case in other areas. The above-mentioned unqualified indicates that another power grid technical improvement project aims to optimize the power grid structure and reduce line loss. After the project is implemented and evaluated by the system, the engineering abnormal score obtained by the project is lower than the score threshold value, indicating that the technical improvement effect is not as expected, and the line loss is still high. Therefore, the project is determined to be an abnormal scheme, which indicates that the technical improvement project may be design defects, construction problems or inaccurate evaluation standards, etc., and the technical improvement strategy is adjusted accordingly to avoid resource waste.

[0057] By scoring the power grid technical improvement project and comparing it with the score threshold value, it can be quickly and accurately determined whether the technical improvement project achieves the expected effect, thereby avoiding the tedious evaluation process and the subjectivity of human judgment, and improving the efficiency and accuracy of decision-making. Dividing the technical improvement project into normal schemes and abnormal schemes helps the power grid enterprise to reasonably allocate resources according to the actual situation of the project. Qualified represents good technical improvement effect, which can continue to be executed according to the original plan or promoted as a successful case. Unqualified needs to be analyzed to adjust the strategy to avoid wasting resources on projects with poor effects.

[0058] Embodiment Five

[0059] The embodiment is further designed on the basis of embodiment one, and in the embodiment, the current power consumption data and the power grid parameters of the target technical improvement area are input into the target simulation model for power grid technical improvement project quality evaluation in the economic cost dimension, the stability dimension and the reliability dimension after the completion of the power grid technical improvement project, and the input further includes:

[0060] A hierarchical topology model of the power grid is constructed, the hierarchical topology model is divided into regions according to the nodes of the power grid to obtain a hierarchical topology graph, and the nodes in the hierarchical topology graph are taken as the first population;

[0061] The first population is initialized to obtain a second population through a Logistic-Tent chaotic mapping, and the fitness of each individual in the second population is calculated; the population is a group of scarab individuals, and each scarab individual represents any fault location;

[0062] The scarab updating operation is performed on the second population through a sine algorithm and a spiral search strategy, so that the initial population is iteratively updated, and if the iteration number meets the preset number, the iteration and updating are stopped and the optimal result is output.

[0063] In the example, the hierarchical topology model of the power grid is constructed, which can clearly show the structure hierarchy and node connection relationship of the power grid, the hierarchical topology model is divided into regions according to the nodes of the power grid to obtain a hierarchical topology graph, which is helpful to more accurately locate the region where the fault occurs, reduces the search range and improves the positioning efficiency.

[0064] The scarab algorithm used in the example is a meta-heuristic optimization algorithm that simulates the behavior of scarabs in nature. The algorithm is inspired by the behaviors of scarabs in finding food, rolling dung balls, and finding breeding sites, and searches for the optimal solution of the problem by simulating these behaviors. Each "scarab" represents a candidate solution, and they move and interact in the solution space to find and improve solutions, and the ultimate goal is to find the best or near-optimal solution to the problem; the scarab algorithm updates the candidate set of solutions by simulating four main behaviors of scarabs: rolling, breeding, foraging and acquisition: the rolling behavior simulates the scarab rolling the dung ball along a straight line until it changes direction when it encounters an obstacle; the breeding behavior simulates the process of finding a safe breeding site, where the boundary of the breeding area is dynamically adjusted with iterations; the foraging behavior simulates the foraging of small scarabs in the dynamically updated foraging area; the acquisition behavior simulates that some scarabs may acquire the dung balls of other scarabs. Through the iterative execution of these behaviors, the algorithm continuously updates the positions of individuals in the population to explore new solutions or improve existing solutions, gradually approaching the optimal solution of the problem.

[0065] In this example, the first population is initialized by the Logistic-Tent chaotic mapping to obtain the second population. The above operation can enhance the diversity of the population, make the initial solution space more extensive and uniform, and is conducive to subsequent optimization search. The fitness of each individual in the second population is calculated, which can evaluate the advantages and disadvantages (fitness) of each fault location, provide guidance for subsequent Scarab updating operation, and enable the algorithm to converge to the optimal solution faster. The Scarab updating operation is performed on the second population, and the individual position in the population is updated iteratively, so that the algorithm can continuously approach the optimal solution, and finally output the optimal result, realizing accurate positioning of the fault location.

[0066] In this example, the sine algorithm is mainly applied in the rolling behavior of the Scarab optimization algorithm to balance the global and local search capabilities. The spiral search strategy is applied in the breeding, foraging and acquisition behaviors to help the algorithm jump out of the local optimal solution in the later stage. The two strategies jointly act on the iterative updating process of the initial population to improve the performance of the Scarab optimization algorithm.

[0067] Embodiment six:

[0068] The embodiment is further designed on the basis of embodiment five, and before stopping the iterative updating and outputting the optimal result when the number of iterations meets the preset number of times, the embodiment further includes:

[0069] determining fault data according to the optimal result and constructing a simulation model;

[0070] obtaining historical training data, inputting the historical training data and the fault data into the simulation model for training to obtain target model parameters; the historical training data includes historical power consumption data and historical fault data;

[0071] updating the bias and weight of the simulation model according to the target model parameters to obtain a target simulation model, so as to improve the accuracy of simulation data and ensure the scientificity and effectiveness of the technical improvement engineering decision.

[0072] According to the optimal result obtained by the Scarab algorithm, the fault data can be accurately determined, and the fault data can include the fault area and the fault type. This step not only provides accurate basic information for subsequent simulation model construction, but also enriches the diversity of training data, so that the model can learn the fault characteristics more comprehensively in the training process. By obtaining historical training data and inputting these data and fault data into the simulation model for training, more accurate target model parameters can be obtained. The historical power consumption data and historical fault data in the historical training data provide rich learning samples for the model, which helps the model to better capture the power grid operation rules and fault occurrence mechanism.

[0073] In this example, after obtaining the target model parameters, the biases and weights of the simulation model were updated, thus obtaining the target simulation model. By adjusting the model's internal parameters, the model can make more accurate predictions and judgments when faced with new fault data. The construction of the target simulation model improves the accuracy and efficiency of fault location. Because the dung beetle algorithm enriches the fault data, the training data becomes richer and more comprehensive, further improving the accuracy and reliability of the target simulation model.

[0074] In this example, the available electricity consumption data for the target area after the power grid upgrade is insufficient to fully reflect the upgrade's effects. A simulation model is used to model the electricity consumption in the target area over a future period following the upgrade, enabling accurate prediction and assessment of changes in electricity demand and providing a basis for power grid planning and dispatch. In this process, a machine learning model is employed as the core of the simulation model. This model fully utilizes historical data and fault characteristics for training, improving the accuracy and generalization ability of predictions, thus better adapting to the complexity and uncertainty of power grid upgrade projects.

[0075] Example 7:

[0076] This embodiment is a further design based on Embodiment Six, in which the fault data includes the fault area and the fault type.

[0077] Example 8:

[0078] This invention provides a quality assessment system for power grid technical upgrading projects, such as... Figure 2 As shown, it includes a fault marking module, a data monitoring and mapping module, and an evaluation module;

[0079] The fault marking module is used to acquire historical electricity consumption data of a target area consisting of multiple target sub-areas, perform fault analysis on the historical electricity consumption data, and mark fault-marked areas.

[0080] The data monitoring and mapping module is used to monitor and obtain the current electricity consumption data of the power grid technical transformation area, and map the fault-marked area to the power grid technical transformation area to obtain the target technical transformation area;

[0081] The evaluation module is used to input current electricity consumption data and power grid parameters of the target technical renovation area into the target simulation model, which is used to evaluate the quality of the power grid technical renovation project based on the dimensions of economic cost, stability and reliability after the completion of the power grid technical renovation project, to obtain the evaluation results, and to evaluate the power grid technical renovation project based on the evaluation results.

[0082] Example 9:

[0083] An electronic device comprises a memory and a processor, the memory stores a computer program, and the processor is configured to invoke and run the computer program stored in the memory to execute the method according to any one of the above embodiments.

[0084] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps according to any one of the above embodiments.

[0085] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for quality evaluation of power grid technical improvement project, characterized in that, The method comprises the following steps: acquiring historical power consumption data of a target region composed of a plurality of target sub-regions, performing fault analysis on the historical power consumption data, and marking a fault marked region; monitoring and acquiring current power consumption data of a power grid technical improvement region, mapping the fault marked region to the power grid technical improvement region to obtain a target technical improvement region; inputting the current power consumption data and power grid parameters of the target technical improvement region into a target simulation model for evaluating the quality of a power grid technical improvement project in terms of economic cost, stability and reliability of the power grid after completion of the power grid technical improvement project, obtaining an evaluation result, and evaluating the power grid technical improvement project according to the evaluation result.

2. The method of quality assessment of power grid technical reformation project according to claim 1, characterized in that, The specific method for performing fault analysis on the historical power consumption data and marking a fault marked region comprises: marking a target sub-region with a fault occurrence number exceeding a preset number to obtain a fault marked region.

3. The method of claim 1, wherein the quality of the power grid modification project is evaluated based on the following items: The expression of the target simulation model is: ​ In the formula, F is the engineering anomaly score, that is, the output of the target simulation model; e is a natural constant; v is the voltage; v0 is the rated voltage; Y is the actual technical transformation cost; Y0 is the average value of the theoretical cost of the technical transformation scheme; m is the number of fault maintenance; W j is the maintenance time after the jth fault.

4. The method of quality assessment of power grid technical reformation project according to claim 3, characterized in that, The specific method for evaluating the power grid technical improvement project according to the evaluation result comprises: comparing the project anomaly score with a project anomaly score threshold value, determining that the power grid technical improvement project is unqualified if the project anomaly score is greater than the project anomaly score threshold value, and otherwise determining that the power grid technical improvement project is qualified.

5. The method of claim 1, wherein, Before the current power consumption data and the power grid parameters of the target technical improvement region are input into the target simulation model for evaluating the quality of the power grid technical improvement project in terms of economic cost, stability and reliability of the power grid after completion of the power grid technical improvement project, the method further comprises the following steps: constructing a hierarchical topology model of the power grid, dividing the hierarchical topology model according to power grid nodes to obtain a hierarchical topology graph, and taking nodes in the hierarchical topology graph as a first population; initializing the first population by a Logistic-Tent chaotic mapping to obtain a second population, and calculating the fitness of each individual in the second population; the population is a group of scarab individuals, and each scarab individual represents any fault position; performing scarab updating operation on the second population by a sine algorithm and a spiral search strategy, so that the initial population is iteratively updated, and the iteration is stopped and the optimal result is output if the iteration number meets a preset number.

6. The method of quality assessment of power grid technical reformation project according to claim 5, characterized in that, Before the iteration is stopped and the optimal result is output if the iteration number meets the preset number, the method further comprises the following steps: determining fault data according to the optimal result and constructing a simulation model; acquiring historical training data, inputting the historical training data and the fault data into the simulation model for training, and obtaining target model parameters; the historical training data comprises historical power consumption data and historical fault data; updating the bias and weight of the simulation model according to the target model parameters to obtain a target simulation model.

7. The method of quality assessment of power grid technical reformation project according to claim 6, characterized in that, The fault data comprises a fault region and a fault type.

8. A quality assessment system for power grid technical upgrading projects, characterized in that, The method comprises a fault marking module, a data monitoring and mapping module and an evaluation module; the fault marking module is configured to acquire historical power consumption data of a target region composed of a plurality of target sub-regions, perform fault analysis on the historical power consumption data, and mark a fault marked region; The data monitoring and mapping module is configured to monitor and acquire current power consumption data of a power grid technical improvement area, and map the fault marked area to the power grid technical improvement area to obtain a target technical improvement area. The evaluation module is configured to input the current power consumption data and power grid parameters of the target technical improvement area into a target simulation model for evaluating power grid technical improvement project quality in terms of power grid economic cost, stability and reliability after completion of the power grid technical improvement project, to obtain an evaluation result, and evaluate the power grid technical improvement project according to the evaluation result.

9. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the steps of the method according to any one of claims 1 to 7.

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