A glacier permafrost danger assessment method and device for plateau lifeline

Abstract

The application provides a plateau lifeline-oriented glacier permafrost risk assessment method and device, relates to the technical field of data processing, and comprises the following steps: acquiring historical detection data of a target area; the historical detection data comprises radar image data, meteorological data and geological data; influence factors are screened from the radar image data, meteorological data and geological data, and the influence factors are taken as inputs to train a preset risk prediction model to obtain a target risk prediction model; the influence factors comprise elevation, slope, maximum elevation difference, deformation rate, shear strength, pore pressure, frozen soil layer thickness and frost heaving force; current detection data of the target area is input into the target risk prediction model to determine the risk level of the target area. The application considers the influence of many factors on the railway, highway and power transmission line in the glacier area, and enhances the robustness of the safety assessment of the glacier permafrost in engineering practice.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and apparatus for assessing the hazard of glaciers and permafrost on the lifeline of plateau regions. Background Technology

[0002] In recent years, with the effects of global warming, some glaciers have begun to retreat, leading to an increase in lakes and precipitation in plateau areas. This has caused permafrost to melt, which can damage roadbeds and may even trigger geological disasters such as landslides and mudslides, damaging railways and power transmission lines. Therefore, it is crucial to understand the impact of glacial and permafrost degradation on railways, highways, and other infrastructure in plateau regions.

[0003] There are many factors that can cause geological disasters in plateau regions. One of the main reasons is the loosening of soil caused by the melting of glaciers and permafrost. This phenomenon can lead to unstable roadbeds, posing safety hazards to infrastructure, civil buildings, and industrial buildings in permafrost areas, and also posing a huge threat to human health. However, there is currently a lack of effective ways to monitor it. Summary of the Invention

[0004] The purpose of this invention is to address the current lack of effective monitoring of geological hazards in plateau regions by providing a method and apparatus for assessing the hazard of glaciers and permafrost along the lifeline of plateau regions.

[0005] The technical solution of this application embodiment is implemented as follows: The first aspect of this application provides a method for assessing the hazard of glaciers and permafrost along plateau lifelines, including: Acquire historical detection data of the target area; the historical detection data includes radar image data, meteorological data, and geological data; Influencing factors are selected from the radar image data, meteorological data, and geological data, and these influencing factors are used as inputs to train a preset risk prediction model to obtain a target risk prediction model. The influencing factors include elevation, slope, maximum elevation difference, deformation rate, shear strength, pore pressure, frozen soil thickness, and frost heave force. The current detection data of the target area is input into the target risk prediction model to determine the risk level of the target area.

[0006] Optionally, acquiring historical detection data of the target area includes: Acquire multi-time-segment radar imagery data transmitted by satellite, acquire meteorological data transmitted by weather stations, and extract geological data from DEM data.

[0007] Optionally, the step of selecting influencing factors from the radar image data, meteorological data, and geological data includes: The radar image data is processed by SAR to obtain information on the surface deformation of glaciers and permafrost.

[0008] Optionally, the step of using the influencing factor as input to train a preset risk prediction model to obtain a target risk prediction model includes training using the following logistic regression equation:

[0009] in, The risk assessment belongs to the first category. The probability of level 1 For constant terms, is the regression coefficient, and x is the influencing factor.

[0010] Optionally, inputting the current detection data of the target area into the target risk prediction model to determine the risk level of the target area includes: exist , and ,like The largest value indicates that the target area is a high-risk area. The maximum value indicates that the target area is a medium-risk area. The maximum value indicates that the target area is a low-risk area.

[0011] A second aspect of this application provides a device for assessing the hazard of glaciers and permafrost along plateau lifelines, comprising: an acquisition module, a training module, and a determination module, wherein... The acquisition module is configured to acquire historical detection data of the target area; the historical detection data includes radar image data, meteorological data, and geological data. The training module is configured to select influencing factors from the radar image data, meteorological data, and geological data, and use the influencing factors as input to train a preset risk prediction model to obtain a target risk prediction model; the influencing factors include elevation, slope, maximum elevation difference, deformation rate, shear strength, pore pressure, frozen soil layer thickness, and frost heave force. The determining module is configured to input the current detection data of the target area into the target risk prediction model to determine the risk level of the target area.

[0012] Optionally, the acquisition module is further configured to: Acquire multi-time-segment radar imagery data transmitted by satellite, acquire meteorological data transmitted by weather stations, and extract geological data from DEM data.

[0013] Optionally, the training module is specifically configured as follows: The radar image data is processed by SAR to obtain information on the surface deformation of glaciers and permafrost.

[0014] A third aspect of this application provides an electronic device, including a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements the glacier and permafrost hazard assessment method for plateau lifelines described in the first aspect.

[0015] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0016] Compared with the prior art, the beneficial effects of the technical solution provided in this application are: This invention provides a method and system for assessing the hazard of glaciers and permafrost along high-altitude lifelines. It acquires historical survey data of the target area and filters influencing factors from radar imagery, meteorological, and geological data. These influencing factors are used as input to train a pre-set risk prediction model, resulting in a target risk prediction model. Current survey data of the target area is then input into this model to determine the risk level of the target area. This invention considers the impact of numerous factors on railways, highways, and power transmission lines in glacier areas, enhancing the robustness of glacier and permafrost safety assessments in engineering practice and providing technical support for glacier and permafrost safety assessments in engineering projects. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a method for assessing the hazard of glaciers and permafrost along plateau lifelines, provided in this application embodiment; Figure 2 A schematic diagram of the structure of a glacier and permafrost hazard assessment device for high-altitude lifelines provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0019] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0020] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0021] The accompanying drawings show some block diagrams and / or flowcharts. It should be understood that some blocks or combinations thereof in the block diagrams and / or flowcharts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when executed by the processor, these instructions can create means for implementing the functions / operations described in these block diagrams and / or flowcharts.

[0022] In some embodiments, please refer to Figure 1 , Figure 1 A flowchart illustrating the glacier and permafrost hazard assessment method for plateau lifelines provided in this application embodiment; the glacier and permafrost hazard assessment method for plateau lifelines provided in this application embodiment includes: S110, acquire historical detection data of the target area; historical detection data includes radar image data, meteorological data and geological data.

[0023] Here, historical exploration data can include not only radar imagery data, meteorological data, and geological data, but also data on landslides and collapses of icebergs and permafrost within the target area.

[0024] In some embodiments, S110, acquiring historical detection data of the target area includes; Acquire multi-time-segment radar imagery data transmitted by satellite, acquire meteorological data transmitted by weather stations, and extract geological data from DEM data.

[0025] For example, multi-time-period radar imagery data of the study area can be obtained from the Sentinel-1A satellite. Based on the wired or wireless connection with the weather station, meteorological data transmitted by the weather station can be obtained. Topographic data can be obtained from the DEM data. Subsequently, data such as soil and tower construction characteristics can also be obtained.

[0026] S120 selects influencing factors from radar image data, meteorological data, and geological data, and uses these influencing factors as inputs to train a preset risk prediction model to obtain a target risk prediction model. The influencing factors include elevation, slope, maximum elevation difference, deformation rate, shear strength, pore pressure, frozen soil thickness, and frost heave force.

[0027] Influencing factors are those factors that affect the state of glaciers and permafrost in the target area.

[0028] In some embodiments, S120, influencing factors are selected from radar image data, meteorological data, and geological data, including: SAR (Surveyor Airborne Radar) image data is processed to obtain surface deformation information of glaciers and permafrost. In this embodiment, some influencing factors can be calculated, while others can be obtained directly.

[0029] In one example, S120 may include the following steps: S121. Select interferometric pairs that meet the spatiotemporal baseline conditions. Here, the spatial baseline threshold is 150m, and the temporal baseline threshold is 2 years. The interferometric pairs are divided into two smaller baseline sets. Register all interferometric pairs onto the same coordinate system for easier subsequent calculations.

[0030] S122. The selected interferometric pairs are processed using the two-track differential interferometry method, and SRTM data is used as an external DEM to eliminate topographic phase information. To reduce computation and suppress noise, a multi-view operation with 2 views in the range direction and 10 views in the azimuth direction is performed on the interferogram. Subsequently, a least-squares-based filtering method is used to further eliminate the influence of noise in the multi-view differential interferogram, and the phase unwrapping is performed using the minimum cost flow method.

[0031] S123. Use least squares fitting to eliminate the trend phase effect caused by orbital errors: ; in The coordinates of a pixel in the range and azimuth directions in the SAR coordinate system. These are the parameters of the undetermined model. To uniformly extract the phase values ​​from the differential interferogram, the sampling interval can be... Pixels located in low-coherence and deformed regions were discarded during the selection process. The above equation was solved using iterative least squares, and reliable results were obtained with approximately two iterations. Finally, the trend phase of all pixels caused by orbital errors was calculated using the obtained model parameters and removed from the differential interferogram.

[0032] S124. To avoid calculation errors caused by low coherence points, high coherence points are selected in the differential interferogram for calculation. Here, the average coherence value and minimum coherence value of all SAR images are used to select the target. That is, the average coherence value of the selected high coherence points is greater than 0.5, and the minimum coherence value of all differential interferograms is greater than 0.3 (the corresponding pixels in the differential image with a value less than this are considered decoherent).

[0033] S125. Solve the selected high coherence points point by point using the SBAS method to separate the elevation residuals, atmospheric delay phase, and the time series results of surface deformation in the radar line of sight.

[0034] In some embodiments, S120, the influencing factor is used as input to train a preset risk prediction model to obtain a target risk prediction model, including training using the following logistic regression equation:

[0035] in, The risk assessment belongs to the first category. The probability of level 1 For constant terms, is the regression coefficient, and x is the influencing factor.

[0036] In another example, S120, influencing factors are selected from radar imagery data, meteorological data, and geological data, including: S126. In plateau regions, the melting of subgrade soil causes water to shift from the ice phase to the liquid phase, reducing soil saturation. The constitutive model for seepage in unsaturated soil follows Darcy's law, and the formula for calculating the shear strength of unsaturated soil is as follows: ; in, Indicates effective cohesion. Indicates the effective internal friction angle. Indicates normal stress, This represents the matrix suction. The pore pressure is then calculated.

[0037] S127. The limit equilibrium method is used to assess the stability of the roadbed, which calculates the safety factor of the roadbed based on the Mohr-Coulomb criterion. One or more possible sliding surfaces are assumed; these surfaces can be circular arcs or other shapes, usually determined according to geological conditions and slope morphology. The slope is then divided into potential sliding bodies and stable bodies; the sliding body is the part that may slip. The expression for the Mohr-Coulomb criterion is: ; in, It is shear stress. It is cohesion. It is normal stress. This is the internal friction angle. On the sliding surface, the shear stress equals the shear strength; therefore, the shear strength on the sliding surface can be calculated using the Mohr-Coulomb criterion. Safety factor. Defined as the ratio of the shear strength on the sliding surface to the actual shear stress. For the limit equilibrium state, the safety factor can be expressed as: ; For circular slip surfaces, the Swedish circular arc method can be used. If the calculated safety factor is greater than 1, the slope is stable on the assumed slip surface. A factor less than 1 indicates potential instability.

[0038] S128. For the problem of tower slope stability, determine the soil moisture content of the study area ( ) and density ( ), of which the frost heave rate is: ; in Since the frost heave is a constant, the frost heave force can be estimated by multiplying the frost heave rate by the soil's own weight. ; A drop in temperature will exacerbate frost heave, so the frost heave coefficient needs to be adjusted according to temperature changes.

[0039] S129. The freezing strength between frozen soil and foundation was determined using linear regression, with cohesion selected as the regression parameter. and internal friction angle .

[0040] S1210, Calculate the thickness of the frozen soil layer: ; Indicates the depth of the frozen layer. It is a coefficient that depends on soil properties. It is an extreme low temperature. The soil thermal conductivity is used to analyze the stability of the tower using the finite element method. First, the shear strength is defined by the formula above, with an elastic modulus of 0.5 and a Poisson's ratio of 0.3. The upper boundary is set as the roadbed surface, and the lower boundary as the tower depth. The load is given by the frost heave force of the frozen soil. Then, a thermo-mechanical coupling analysis is performed to simulate the impact of ground temperature changes on the frozen soil. Using ABAQUS, numerical simulations are conducted to calculate the strain and stress distribution of the tower under different frost heave forces, frozen soil thicknesses, and low temperatures. Areas exhibiting stress concentration or significant frost heave deformation are identified as high-risk areas.

[0041] S1211. A backpropagation (BP) neural network is used to simulate and calculate the maximum displacement, maximum stress, and road surface deformation index of the transmission tower. Soil shear strength, pore pressure, frozen soil thickness, vegetation normalization coefficient, elevation, slope, tower foundation load, and transmission tower type are selected as influencing factors. The maximum displacement, maximum stress, and road surface deformation index of the tower are used as the output layer. The number of hidden layer neurons is determined according to the following formula: ; in The optimal number of hidden layer neurons. The number of input neurons, The number of output neurons, for Any integer between 0 and 1. The optimal number of neurons is achieved when the mean squared error is minimized. Mean squared error: ; in, For the true value, For predicted values, This is the mean.

[0042] S130: Input the current detection data of the target area into the target risk prediction model to determine the risk level of the target area.

[0043] By using the quantitative results of the above-mentioned influencing factors, such as elevation, slope, maximum elevation difference, deformation rate, shear strength, pore pressure, permafrost thickness, and frost heave force, as independent variables and the glacier and permafrost hazard assessment as the dependent variable, the risk level of the target area can be determined.

[0044] In some embodiments, S130, the current detection data of the target area is input into the target risk prediction model to determine the risk level of the target area, including: exist , and ,like The largest value indicates that the target area is a high-risk area. The largest value indicates that the target area is a medium-risk area. The highest value indicates that the target area is a low-risk area.

[0045] In this embodiment, the permafrost hazard can be quantified according to Table 1 based on the obtained maximum tower displacement, maximum stress, and pavement deformation index. If the maximum tower displacement threshold is... The maximum stress threshold is The maximum threshold for roadside deformation is .

[0046] Table 1

[0047] This embodiment acquires historical detection data of the target area and filters influencing factors from radar imagery, meteorological, and geological data. These influencing factors are used as input to train a preset risk prediction model, resulting in a target risk prediction model. The current detection data of the target area is then input into this model to determine the risk level of the target area. This invention considers the impact of numerous factors on railways, highways, and power transmission lines in glacier areas, enhancing the robustness of glacier and permafrost safety assessments in engineering practice and providing technical support for glacier and permafrost safety assessments in engineering projects.

[0048] In some embodiments, please refer to Figure 2 , Figure 2 This is a schematic diagram of the structure of the glacier and permafrost hazard assessment system for plateau lifelines provided in this application embodiment; the glacier and permafrost hazard assessment device 200 for plateau lifelines provided in this application embodiment includes: an acquisition module 210, a training module 220, and a determination module 230, wherein, The acquisition module 210 is configured to acquire historical detection data of the target area; the historical detection data includes radar image data, meteorological data, and geological data. Training module 220 is configured to select influencing factors from radar image data, meteorological data, and geological data, and use these influencing factors as input to train a preset risk prediction model to obtain a target risk prediction model. The influencing factors include elevation, slope, maximum elevation difference, deformation rate, shear strength, pore pressure, frozen soil thickness, and frost heave force. The determination module 230 is configured to input the current detection data of the target area into the target risk prediction model to determine the risk level of the target area.

[0049] In some embodiments, the acquisition module 210 is further configured to: Acquire multi-time-segment radar imagery data transmitted by satellite, acquire meteorological data transmitted by weather stations, and extract geological data from DEM data.

[0050] In some embodiments, the training module 220 is specifically configured as follows: SAR is used to process radar image data to obtain information on surface deformation of glaciers and permafrost.

[0051] In some embodiments, the training module 220 is specifically configured to perform training using the following logistic regression equation:

[0052] in, The risk assessment belongs to the first category. The probability of level 1 For constant terms, is the regression coefficient, and x is the influencing factor.

[0053] In some embodiments, the determining module 230 is specifically configured as follows: exist , and ,like The largest value indicates that the target area is a high-risk area. The largest value indicates that the target area is a medium-risk area. The highest value indicates that the target area is a low-risk area.

[0054] The glacier and permafrost hazard assessment device for plateau lifelines provided in this application embodiment is based on the above-mentioned glacier and permafrost hazard assessment method for plateau lifelines. To avoid repetition, it will not be described again here.

[0055] It should be noted that the glacier and permafrost hazard assessment device for plateau lifelines provided in this application embodiment and the glacier and permafrost hazard assessment method for plateau lifelines provided in this application embodiment are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned glacier and permafrost hazard assessment method for plateau lifelines, and the repeated parts will not be described again.

[0056] In some embodiments, please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 300 provided in this embodiment includes a processor 310 and a memory 320; the memory 320 stores a computer program, wherein the computer program, when executed by the processor, implements the aforementioned method for assessing the hazard of glaciers and permafrost along the lifeline of plateau regions.

[0057] Specifically, processor 310 may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. Processor 310 may also include onboard memory for caching purposes. Processor 310 may be a single processing unit or multiple processing units for performing different actions of the method flow according to embodiments of this application.

[0058] Memory 320 may be any medium capable of containing, storing, transmitting, propagating, or transmitting instructions. For example, memory 320 may include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, instruments, or propagation media. Specific examples of memory 320 include: magnetic storage devices such as magnetic tape or hard disk drives (HDDs); optical storage devices such as optical discs (CD-ROMs); and may also be random access memory (RAM) or flash memory; and / or wired / wireless communication links.

[0059] This application also provides a computer-readable medium storing a computer program that, when executed by a processor, implements the aforementioned method for assessing the hazard of glaciers and permafrost along plateau lifelines. This computer-readable medium may be included in the apparatus, device / system described in the above embodiments; or it may exist independently and not assembled into the apparatus, device / system. The aforementioned computer-readable medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0060] According to embodiments of this application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, optical fiber, radio frequency signals, etc., or any suitable combination thereof.

[0061] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application. Therefore, the scope of this application should not be limited to the above embodiments, but should be defined not only by the appended claims, but also by the equivalents of the appended claims.

Claims

1. A method for assessing the hazard of glaciers and permafrost along the lifeline of plateau regions, characterized in that, include: Acquire historical detection data of the target area; the historical detection data includes radar image data, meteorological data, and geological data; Influencing factors are selected from the radar image data, meteorological data, and geological data, and these influencing factors are used as inputs to train a preset risk prediction model to obtain a target risk prediction model. The influencing factors include elevation, slope, maximum elevation difference, deformation rate, shear strength, pore pressure, frozen soil thickness, and frost heave force. The current detection data of the target area is input into the target risk prediction model to determine the risk level of the target area.

2. The glacier and permafrost hazard assessment method for plateau lifelines according to claim 1, characterized in that, The acquisition of historical detection data of the target area includes: Acquire multi-time-segment radar imagery data transmitted by satellite, acquire meteorological data transmitted by weather stations, and extract geological data from DEM data.

3. The glacier and permafrost hazard assessment method for plateau lifelines according to claim 2, characterized in that, The process of selecting influencing factors from the radar image data, meteorological data, and geological data includes: The radar image data is processed by SAR to obtain information on the surface deformation of glaciers and permafrost.

4. The glacier and permafrost hazard assessment method for plateau lifelines according to claim 1, characterized in that, The step of using the influencing factors as input to train a preset risk prediction model to obtain a target risk prediction model includes training using the following logistic regression equation: in, The risk assessment belongs to the first category. The probability of level 1 For constant terms, is the regression coefficient, and x is the influencing factor.

5. The glacier and permafrost hazard assessment method for plateau lifelines according to claim 4, characterized in that, The step of inputting the current detection data of the target area into the target risk prediction model to determine the risk level of the target area includes: exist , and ,like The largest value indicates that the target area is a high-risk area. The maximum value indicates that the target area is a medium-risk area. The maximum value indicates that the target area is a low-risk area.

6. A device for assessing the hazard of glaciers and permafrost on high-altitude lifelines, characterized in that, include: The module consists of an acquisition module, a training module, and a determination module, among which... The acquisition module is configured to acquire historical detection data of the target area; the historical detection data includes radar image data, meteorological data, and geological data. The training module is configured to select influencing factors from the radar image data, meteorological data, and geological data, and use the influencing factors as input to train a preset risk prediction model to obtain a target risk prediction model; the influencing factors include elevation, slope, maximum elevation difference, deformation rate, shear strength, pore pressure, frozen soil layer thickness, and frost heave force. The determining module is configured to input the current detection data of the target area into the target risk prediction model to determine the risk level of the target area.

7. The glacier and permafrost hazard assessment device for plateau lifelines according to claim 6, characterized in that, The acquisition module is further configured to: Acquire multi-time-segment radar imagery data transmitted by satellite, acquire meteorological data transmitted by weather stations, and extract geological data from DEM data.

8. The glacier and permafrost hazard assessment device for plateau lifelines according to claim 6, characterized in that, The training module is specifically configured as follows: The radar image data is processed by SAR to obtain information on the surface deformation of glaciers and permafrost.

9. An electronic device comprising a processor and a memory; said memory having a storage for a computer program, wherein, When executed by the processor, the computer program implements the glacier and permafrost hazard assessment method for plateau lifelines as described in any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method of any one of claims 1 to 5.

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