Intelligent landslide early warning method fusing slope dynamic adjustment and I-D threshold evolution

Abstract

The invention discloses a landslide intelligent early warning method fusing slope dynamic adjustment and I-D threshold evolution, and belongs to the technical field of geological disaster early warning, and the method comprises the following steps: S1, data collection and analysis; s2, constructing a rainfall data processing and I-D threshold model; s3, adjusting a threshold value; s4, generating an early warning threshold value; and S5, carrying out real-time monitoring and early warning. According to the intelligent landslide early warning method fusing slope dynamic adjustment and I-D threshold evolution, key geological parameters are quantified through a dynamic adjustment mechanism, and the early warning precision is remarkably improved; meanwhile, a real-time early warning platform based on multi-source data fusion is constructed, scientific and accurate graded early warning is achieved, geological environment changes are adapted, and the reliability of an early warning result is effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of geological disaster early warning technology, specifically involving a landslide intelligent early warning method that integrates slope dynamic adjustment and ID threshold evolution. Background Technology

[0002] Geological disaster early warning is conducted by the natural resources department in conjunction with meteorological and other departments. It is divided into meteorological risk level forecasts and short-term warnings, with four levels: blue, yellow, orange, and red. The higher the level, the greater the risk, and corresponding to different prevention measures. By predicting the possibility and risk of disasters caused by rainfall, especially geological disasters such as landslides induced by rainfall, it helps to prevent and mitigate disasters and ensure safety.

[0003] However, existing technologies have the following shortcomings: traditional geological disaster early warning models use a uniform threshold and do not consider the geological differences of different slopes, resulting in low early warning accuracy; traditional models are static models and cannot adapt to changes in the geological environment; existing technologies have problems such as incomplete data cleaning and insufficient fusion of multi-source data, which makes the reliability of early warning results low.

[0004] Therefore, a new method is urgently needed. Summary of the Invention

[0005] The purpose of this invention is to provide a landslide intelligent early warning method that integrates slope dynamic adjustment and ID threshold evolution. This method quantifies key geological parameters through a dynamic adjustment mechanism, which significantly improves the accuracy of early warning. At the same time, it constructs a real-time early warning platform that integrates multi-source data to achieve scientific and accurate hierarchical early warning, adapts to changes in the geological environment, and effectively improves the reliability of early warning results.

[0006] To achieve the above objectives, this invention provides a landslide intelligent early warning method that integrates slope dynamic adjustment and ID threshold evolution, comprising the following steps:

[0007] S1. Collect landslide event data and rainfall data;

[0008] S2. Based on the landslide event data and rainfall data in S1, a threshold curve is fitted by fitting a power function curve; a baseline threshold is obtained using a statistical model, and rainfall warning levels are classified according to landslide classification.

[0009] S3. Based on the baseline threshold in S2 and the geological environment data collected in S1, establish a numerical simulation model, analyze the relationship between changes in geological environment factors and the adjustment amount of rainfall threshold, and obtain the threshold adjustment rules.

[0010] S4. Based on the threshold adjustment rules of S3 and the baseline threshold of S2, the baseline threshold is adjusted and calculated to obtain the dynamic threshold, and the dynamic threshold is saved to the threshold database.

[0011] S5 compares real-time rainfall data with the dynamic thresholds stored in S4 in real time, triggers risk level determination, and generates an early warning.

[0012] Preferably, in S1, the landslide event data includes the time of occurrence, geographical location, and altitude; the rainfall data includes the rainfall intensity and duration.

[0013] Preferably, S1 also includes collecting geological environmental data, including meteorological and hydrological data, topography, stratigraphy and lithology, geological structure, neotectonic movements and earthquakes, and human engineering activities.

[0014] Preferably, in S3, the geological environmental factors include: slope, overburden thickness, internal friction angle, cohesion, and permeability coefficient.

[0015] The preferred formula for adjusting the slope is:

[0016] y = -6.24x + 157.2(R) 2 =0.9955);

[0017] Where x is the slope change value, and y is the threshold adjustment amount;

[0018] The formula for adjusting the thickness of the cover layer is as follows:

[0019] y = 45x - 18.18653 (R) 2 =0.932);

[0020] The formula for adjusting the internal friction angle is:

[0021] y = 1.8x - 45(R) 2 =0.925);

[0022] The formula for adjusting cohesion is:

[0023] F si =0.047x + 0.268(R) 2 =0.99999);

[0024] F smin =0.045x - 0.02(R) 2 =0.99958);

[0025] Among them, F si F is the initial stability coefficient; smin This represents the lowest stability coefficient. Preferably, in S3, the threshold adjustment rule includes:

[0026] The slope adjustment rules are expressed as follows:

[0027] ΔR α = -0.02097(α-25)×Rs ;

[0028] Where, ΔR α R is the threshold adjustment amount corresponding to the slope. s The baseline threshold is used; the overburden thickness adjustment rule is expressed as follows:

[0029] ΔR d =0.1056(d-1.5)×R s +12;

[0030] Where, ΔR d This is the threshold adjustment amount corresponding to the thickness;

[0031] The internal friction angle adjustment rule is expressed as follows:

[0032]

[0033] in, This represents the threshold adjustment amount corresponding to the angle;

[0034] The cohesion adjustment rule is expressed as follows:

[0035] ΔR c =0.02(c-15)×R s ;

[0036] Where, ΔR c This is the threshold adjustment amount corresponding to the cohesion.

[0037] The slope height adjustment rule is expressed as follows:

[0038] ΔR h =0.035(h-30)×R s ;

[0039] Where, ΔR h This represents the threshold adjustment amount corresponding to the slope height;

[0040] The permeability coefficient adjustment rule is expressed as follows:

[0041]

[0042] Where, ΔR k This represents the threshold adjustment amount corresponding to the permeability coefficient.

[0043] Preferably, S5 also includes closed-loop feedback of early warning information to S4 for dynamic threshold updates, and feedback to S3 for optimizing threshold adjustment rules.

[0044] This invention also provides a landslide intelligent early warning system that integrates slope dynamic adjustment and ID threshold evolution, including:

[0045] The data acquisition module collects landslide event data and rainfall data;

[0046] A baseline threshold construction module, connected to the data acquisition module, performs threshold curve fitting by fitting a power function curve based on landslide event data and rainfall data; obtains the baseline threshold using a statistical model; and classifies rainfall warning levels according to landslide classification.

[0047] The threshold adjustment module is connected to the benchmark threshold construction module. Based on the benchmark threshold and geological environment data, a numerical simulation model is established to analyze the relationship between changes in geological environment factors and the amount of rainfall threshold adjustment, and to obtain the threshold adjustment rules.

[0048] The warning threshold is generated and connected to the threshold adjustment module. Based on the threshold adjustment rules and the baseline threshold, the baseline threshold is adjusted and calculated to obtain the dynamic threshold, and the dynamic threshold is saved to the threshold database.

[0049] The dynamic early warning module is connected to the early warning threshold generation module and is used to compare real-time rainfall data with dynamic thresholds in real time, trigger risk level determination, and generate early warnings.

[0050] Therefore, the landslide intelligent early warning method that integrates slope dynamic adjustment and ID threshold evolution, as described above, has the following advantages compared with the prior art:

[0051] (1) The dynamic adjustment mechanism of “one slope, one threshold” in this invention overcomes the problem of low early warning accuracy caused by the traditional model’s unified threshold not taking into account geological differences by quantifying key geological parameters such as slope and soil properties, thereby achieving a significant improvement in early warning accuracy.

[0052] (2) This invention adopts the technical means of constructing a real-time early warning platform that integrates multi-source data fusion, and integrates meteorological monitoring, geological information and expert evaluation systems. It overcomes the problems of incomplete data cleaning and insufficient multi-source data fusion in existing systems, thereby achieving the technical effect of realizing scientific and accurate hierarchical early warning, adapting to changes in the geological environment, and improving the reliability of early warning results.

[0053] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0054] Figure 1 This is a flowchart of an embodiment of the landslide intelligent early warning method integrating slope dynamic adjustment and ID threshold evolution of the present invention;

[0055] Figure 2 This is a system architecture diagram of an embodiment of the landslide intelligent early warning method integrating slope dynamic adjustment and ID threshold evolution of the present invention;

[0056] Figure 3 This is a flowchart illustrating the threshold adjustment process of an embodiment of the landslide intelligent early warning method integrating slope dynamic adjustment and ID threshold evolution of the present invention.

[0057] Figure 4 This is a data graph showing the change in stability coefficients in an embodiment of the landslide intelligent early warning method that integrates slope dynamic adjustment and ID threshold evolution according to the present invention. Figure 4 In this context, 'a' represents different slopes; Figure 4 In this context, 'b' represents different capping layer thicknesses; Figure 4 In this context, 'c' represents different cohesive forces; Figure 4 In this context, d represents different internal friction angles; Figure 4 In this context, 'e' represents different permeability coefficients; Figure 4 In this context, 'f' represents different elevation differences;

[0058] Figure 5 This is a flowchart illustrating the rainfall early warning process of an embodiment of the landslide intelligent early warning method integrating slope dynamic adjustment and ID threshold evolution according to the present invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Unless otherwise defined, the technical or scientific terms used in the present invention should have the ordinary meaning understood by those skilled in the art.

[0060] Example 1

[0061] like Figures 1-5 As shown, the landslide intelligent early warning method of the present invention, which integrates slope dynamic adjustment and ID threshold evolution, includes the following steps:

[0062] S1. Collect landslide event data from the past 20 years in the study area, including occurrence time, geographical location, and altitude, to create a landslide disaster data archive. Ensure accurate recording of the geographical location and occurrence time of each landslide event. The main sources of landslide event data are authoritative project results such as 1:50,000 detailed geological hazard surveys and 1:10,000 geological hazard investigations in key towns.

[0063] Collect rainfall data and obtain hourly rainfall data from the nearest rain gauge station around the landslide event for the 6 days prior to the landslide to obtain key indicators such as rainfall intensity and duration.

[0064] Collect geological environment data, covering engineering geological conditions data of the study area, including meteorological and hydrological data, topography, stratigraphy and lithology, geological structure, neotectonic movement and earthquakes, and human engineering activities.

[0065] S2. Based on the landslide event data and rainfall data in S1, a semi-logarithmic coordinate scatter plot is plotted in a double logarithmic coordinate system. Threshold curves are fitted by fitting power function curves. The baseline threshold is determined using a statistical model. Based on the landslide classification, rainfall warning levels are divided to obtain the baseline threshold curve.

[0066] In this step, the rainfall data is processed, and Lagrange interpolation is used to handle missing values. Based on the distribution of known data points, a polynomial is constructed, expressed as:

[0067] y = a0 + a1x + a2x 2 +…+a n-1 x n-1 ;

[0068] Given n known points (x) in the plane n ,y n Substituting into the polynomial, we obtain the Lagrange expression:

[0069]

[0070] This allows the polynomial curve to pass through these known points. By substituting the time points corresponding to the missing values ​​into the Lagrange expression, the approximate monitoring value corresponding to that moment can be obtained, filling data gaps and ensuring data integrity. Outliers are cleaned up using MATLAB. After deleting values ​​that are outside the reasonable range, the interpolation process is repeated.

[0071] The overall data was randomly classified in a 7:3 ratio, with 70% used for ID threshold calculation and 30% used for result verification; multiple geological disasters occurring on the same day were counted as one event.

[0072] Based on preprocessed landslide event data and rainfall data, a power function curve was fitted using MATLAB in a double logarithmic coordinate system. An ID curve baseline threshold was constructed based on a statistical model, and the threshold curve was validated by combining it with historical landslide event distributions. The statistical model is expressed as follows:

[0073] I = c + α × D β ;

[0074] Where I represents precipitation intensity; D represents precipitation duration; and α, β, and c are statistical parameters.

[0075] When fitting the power function curve, the least squares method is used to optimize α, β, and c, and the fitting error is reduced through multiple iterations.

[0076] S3. Statistically, the average value of continuous factors and the mode of discrete factors are taken to establish a benchmark slope. Among them, continuous factors include cover thickness, elevation difference, and slope; discrete factors include whether the slope is cut and vegetation cover type. In this way, a representative benchmark slope is established by comprehensively considering various topographic factors.

[0077] A generalized slope model plan view was drawn using AutoCAD 2025 and imported into GeoStuido 2022 in DWG format. Using the SEEP / W module, Darcy's law for two-dimensional saturated and unsaturated soil seepage was applied, considering parameters such as time, horizontal and vertical permeability coefficients, to study the seepage field variations. The two-dimensional saturated and unsaturated soil seepage is represented as follows:

[0078]

[0079] Where (x, y) are spatial coordinates; k x k is the permeability coefficient in the x-direction. y y is the permeability coefficient in the y direction; Q is the source-sink term; m w ρ is the parameter relating volumetric water content change to hydraulic head change. w ρ is the density of water; g is the acceleration due to gravity; t is time; h is the total head.

[0080] Stability coefficients were calculated using the Morgenstern-Price method of the SLOPE / W module;

[0081] Numerical simulation models were established based on different adjustment factors to obtain the results of stability coefficient changes. The relationship between changes in geological environmental factors and rainfall threshold adjustment was analyzed to determine the threshold adjustment rules. The geological environmental factors include:

[0082] The slope, specifically:

[0083] Under continuous rainfall, the steeper the slope, the lower the critical rainfall required for instability. For example, the critical rainfall for a 25-degree slope is 198 mm, while for a 35-degree slope it is only 140 mm. Using a 25-degree slope as a baseline, the slope change value and the threshold adjustment value are negatively correlated. The fitted adjustment rule formula is as follows:

[0084] y = -6.24x + 157.2(R) 2 =0.9955);

[0085] Where x is the slope change value, and y is the threshold adjustment amount;

[0086] The thickness of the cover layer is as follows:

[0087] During continuous rainfall, the greater the cover layer thickness, the higher the critical rainfall for slope instability. For example, the critical rainfall is 116 mm when the cover layer thickness is 1.5 m, and increases to 198 mm when the thickness reaches 3.5 m. Using a cover layer thickness of 1.5 m as a baseline, the thickness variation is positively correlated with the threshold adjustment value, and the fitting rule formula is as follows:

[0088] y = 45x - 18.18653 (R) 2 =0.932);

[0089] The internal friction angle is as follows:

[0090] The pattern is unique when the internal friction angle is less than 25 degrees; therefore, only data with an internal friction angle greater than or equal to 25 degrees are used for fitting. Based on the baseline of (corresponding critical rainfall of 277mm), the angle change value is positively correlated with the threshold adjustment value, and the fitted adjustment rule formula is as follows:

[0091] y = 1.8x - 45(R) 2 =0.925);

[0092] Cohesion, specifically:

[0093] Changes in cohesion only alter the initial stability coefficient F of the slope. si and minimum stability coefficient F smin Both are highly linearly correlated with cohesion, and the relationship is expressed as:

[0094] F si =0.047x + 0.268(R) 2 =0.99999);

[0095] F smin =0.045x - 0.02(R) 2 =0.99958);

[0096] The greater the cohesion, the higher the stability margin of the slope, and the greater the critical rainfall threshold required to induce slope instability.

[0097] Permeability coefficient, specifically:

[0098] The permeability coefficient does not change the initial stability coefficient of a slope, but it does affect the slope instability process under rainfall conditions. When the permeability coefficient is large (k≥5×10⁻⁷ m / s), the slope is prone to complete saturation, and the stability coefficient decreases the most. When the permeability coefficient is small (k≤2.5×10⁻⁶ m / s), the slope saturates slowly, the stability coefficient decreases only slightly, and the slope is less sensitive to rainfall. In other words, the larger the permeability coefficient, the more sensitive the slope is to rainfall, and the smaller the critical rainfall threshold, meaning that even a small amount of rainfall can trigger slope instability.

[0099] Threshold adjustment rules include:

[0100] The slope adjustment rules are expressed as follows:

[0101] ΔR α = -0.02097(α-25)×R s ;

[0102] Where, ΔR α R is the threshold adjustment amount corresponding to the slope. s The baseline threshold;

[0103] The rules for adjusting the thickness of the cover layer are expressed as follows:

[0104] ΔR d =0.1056(d-1.5)×R s +12;

[0105] Where, ΔR d This is the threshold adjustment amount corresponding to the thickness;

[0106] The internal friction angle adjustment rule is expressed as follows:

[0107]

[0108] in, This represents the threshold adjustment amount corresponding to the angle;

[0109] The cohesion adjustment rule is expressed as follows:

[0110] ΔR c =0.02(c-15)×R s ;

[0111] Where, ΔR c This is the threshold adjustment amount corresponding to the cohesion.

[0112] The slope height adjustment rule is expressed as follows:

[0113] ΔR h =0.035(h-30)×R s ;

[0114] Where, ΔR h This represents the threshold adjustment amount corresponding to the slope height;

[0115] The permeability coefficient adjustment rule is expressed as follows:

[0116]

[0117] Where, ΔR k This is the threshold adjustment amount corresponding to the permeability coefficient;

[0118] The corrected threshold R is expressed as:

[0119]

[0120] The baseline rainfall threshold is corrected according to the threshold adjustment rules to make the warning threshold highly match the actual geological environment and improve the accuracy of the warning.

[0121] S4. Based on the threshold adjustment rules determined in S3, the baseline threshold is adjusted and calculated to obtain the dynamic threshold; the dynamic threshold is then assigned to the corresponding landslide unit or region. Simultaneously, a threshold database is established to store the dynamic thresholds for different landslide units or regions. The database can be regularly updated with dynamic thresholds based on new monitoring data and research findings, ensuring that the early warning system operates based on accurate dynamic thresholds, thereby improving the accuracy and timeliness of early warnings.

[0122] S5. Establish a data interface with the meteorological bureau to acquire rainfall data in real time at high frequency using the meteorological bureau's API. Clean the rainfall data and standardize its format, remove outliers, and ensure data accuracy; integrate the data according to time series, organizing the scattered rainfall data into continuous and ordered time series data;

[0123] The processed rainfall data is compared in real time with the dynamic threshold in S4. Based on the comparison results, preliminary early warning information is generated, and the final early warning information is determined after expert consultation and then disseminated through multiple terminals.

[0124] After the early warning information is issued, we will continuously monitor the rainfall and landslide status and adjust the early warning information according to the new data.

[0125] Therefore, this invention adopts the above-mentioned landslide intelligent early warning method that integrates slope dynamic adjustment and ID threshold evolution. This method quantifies key geological parameters through a dynamic adjustment mechanism, which significantly improves the accuracy of early warning. At the same time, it constructs a real-time early warning platform that integrates multi-source data to achieve scientific and accurate hierarchical early warning, adapts to changes in the geological environment, and effectively improves the reliability of early warning results.

[0126] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A landslide intelligent early warning method integrating slope dynamic adjustment and ID threshold evolution, characterized in that, Includes the following steps: S1. Collect landslide event data and rainfall data; S2. Based on the landslide event data and rainfall data in S1, a threshold curve is fitted by fitting a power function curve; a baseline threshold is obtained using a statistical model, and rainfall warning levels are classified according to landslide classification. S3. Based on the baseline threshold in S2 and the geological environment data collected in S1, establish a numerical simulation model, analyze the relationship between changes in geological environment factors and the adjustment amount of rainfall threshold, and obtain the threshold adjustment rules. S4. Based on the threshold adjustment rules of S3 and the baseline threshold of S2, the baseline threshold is adjusted and calculated to obtain the dynamic threshold, and the dynamic threshold is saved to the threshold database. S5 compares real-time rainfall data with the dynamic thresholds stored in S4 in real time, triggers risk level determination, and generates an early warning.

2. The landslide intelligent early warning method integrating slope dynamic adjustment and ID threshold evolution according to claim 1, characterized in that, In S1, landslide event data includes the time of occurrence, geographical location, and altitude; rainfall data includes rainfall intensity and duration.

3. The landslide intelligent early warning method integrating slope dynamic adjustment and ID threshold evolution according to claim 1, characterized in that, S1 also includes the collection of geological environmental data, which includes meteorological and hydrological data, topography, stratigraphy and lithology, geological structure, neotectonic movements and earthquakes, and human engineering activities.

4. The landslide intelligent early warning method integrating slope dynamic adjustment and ID threshold evolution according to claim 1, characterized in that, In S3, geological environmental factors include: slope, overburden thickness, internal friction angle, cohesion, and permeability coefficient.

5. The landslide intelligent early warning method integrating slope dynamic adjustment and ID threshold evolution according to claim 4, characterized in that, The formula for adjusting the slope is: y=-6.24x+157.2(R 2 =0.9955); Where x is the slope change value, and y is the threshold adjustment amount; The formula for adjusting the thickness of the cover layer is as follows: y=45x-18.18653(R 2 =0.932); The formula for adjusting the internal friction angle is: y=1.8x-45(R 2 (0.925) The formula for adjusting cohesion is: F si =0.047x+0.268(R 2 (=0.99999); F smin =0.045x-0.02(R 2 =0.99958); Among them, F si F is the initial stability coefficient; smin This represents the lowest stability coefficient.

6. The landslide intelligent early warning method integrating slope dynamic adjustment and ID threshold evolution according to claim 1, characterized in that, In S3, the threshold adjustment rules include: The slope adjustment rules are expressed as follows: ΔR α =-0.02097(α-25)×R s ; Where, ΔR α R is the threshold adjustment amount corresponding to the slope. s The baseline threshold; The rules for adjusting the thickness of the cover layer are expressed as follows: ΔR d =0.1056(d-1.5)×R s +12; Where, ΔR d This is the threshold adjustment amount corresponding to the thickness; The internal friction angle adjustment rule is expressed as follows: in, This represents the threshold adjustment amount corresponding to the angle; The cohesion adjustment rule is expressed as follows: ΔR c =0.02(c-15)×R s ; Where, ΔR c This is the threshold adjustment amount corresponding to the cohesion. The slope height adjustment rule is expressed as follows: ΔR h =0.035(h-30)×R s ; Where, ΔR h This represents the threshold adjustment amount corresponding to the slope height; The permeability coefficient adjustment rule is expressed as follows: Where, ΔR k This represents the threshold adjustment amount corresponding to the permeability coefficient.

7. The landslide intelligent early warning method integrating slope dynamic adjustment and ID threshold evolution according to claim 1, characterized in that, S5 also includes closed-loop feedback of early warning information to S4 for dynamic threshold updates, and feedback to S3 for optimizing threshold adjustment rules.

8. A landslide intelligent early warning system integrating slope dynamic adjustment and ID threshold evolution, applied to the landslide intelligent early warning method integrating slope dynamic adjustment and ID threshold evolution as described in any one of claims 1-7, characterized in that, include: The data acquisition module collects landslide event data and rainfall data; A baseline threshold construction module, connected to the data acquisition module, performs threshold curve fitting by fitting a power function curve based on landslide event data and rainfall data; obtains the baseline threshold using a statistical model; and classifies rainfall warning levels according to landslide classification. The threshold adjustment module is connected to the benchmark threshold construction module. Based on the benchmark threshold and geological environment data, a numerical simulation model is established to analyze the relationship between changes in geological environment factors and the amount of rainfall threshold adjustment, and to obtain the threshold adjustment rules. The warning threshold is generated and connected to the threshold adjustment module. Based on the threshold adjustment rules and the baseline threshold, the baseline threshold is adjusted and calculated to obtain the dynamic threshold, and the dynamic threshold is saved to the threshold database. The dynamic early warning module is connected to the early warning threshold generation module and is used to compare real-time rainfall data with dynamic thresholds in real time, trigger risk level determination, and generate early warnings.

9. A computer device, characterized in that, include: A processor configured to be coupled to a memory, read and execute instructions and / or program code in the memory to perform the method as described in any one of claims 1-7.

10. A computer-readable medium, characterized in that, The computer-readable medium stores computer program code that, when executed on a computer, causes the computer to perform the method as described in any one of claims 1-7.

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