Landslide step deformation early warning method and device, readable medium and electronic equipment

Abstract

The invention discloses a landslide step deformation early warning method. The method comprises the following steps: identifying a step inflection point of a landslide by adopting clustering analysis and a local extreme value; monitoring the dynamic change of the reservoir water level, and calculating the effective reservoir water level variation inducing landslide deformation; calculating an improved tangent angle and a displacement rate according to the characteristics of the landslide deformation stage; according to historical monitoring data, determining threshold values of a step inflection point, an effective reservoir water level variation, an improved tangent angle and a displacement rate, and establishing a reservoir step type landslide grading early warning model; according to the reservoir step type landslide grading early warning method, step deformation characteristics of landslide induced by dynamic reservoir water are analyzed, and fallback characteristics of three indexes of effective reservoir water level variation, displacement rate and improved tangent angle when the step deformation becomes stable again are analyzed; the reservoir type landslide comprehensive early warning method based on the step deformation characteristics is established, and the early warning applicability of the reservoir step type landslide is improved.

Description

Technical Field

[0001] The present application relates to the field of landslide monitoring and early warning, and in particular to a landslide step deformation early warning method, device, readable medium and electronic equipment. Background Art

[0002] The dynamic change of reservoir water level will change the characteristics of the water environment system of the slope. Through the softening effect of water-rock interaction, the thickness of the adsorbed water film of the slope soil below the reservoir water level increases significantly when immersed in water, thereby reducing the shear strength parameter value of the soil, but it does not reach the critical value of destruction, and the slope is not prone to large-scale destruction. When the reservoir water level changes weaken or stop, the slope will tend to be relatively balanced again, but because its internal environment has changed, it will show overall slow deformation, resulting in a local "deformation"-"stability" step phenomenon, and the monitoring curve shows a step-like feature. Therefore, the dynamic water storage effect is a major cause of step deformation of the reservoir bank slope. In step-type landslides, the displacement rate, acceleration, and improved tangent angle models of each step stage can easily reach the warning threshold and trigger a warning.

[0003] At present, the research on early warning and forecasting of step-type landslides in reservoirs has certain defects in actual early warning:

[0004] (1) Numerical / physical simulation methods rely on field investigations and existing data to obtain geotechnical parameters, which may be inaccurate or difficult to obtain in the actual acquisition process. In addition, this method takes a long time to calculate, which is not conducive to real-time warning.

[0005] (2) The warning threshold of the empirical method is affected by the form of reservoir water level fluctuation, slope structure, regional restrictions and other issues, resulting in the lack of objective accuracy in the selection of the reservoir water threshold that induces landslides;

[0006] (3) Each early warning model ignores the difference between step deformation and impending deformation in the development of step-type landslides, resulting in false warnings during step deformation and the lack of quantitative identification indicators for false warnings, which seriously affects the normal life and production activities in the reservoir area. Summary of the invention

[0007] In view of the above-mentioned deficiencies in the prior art, the present invention provides a landslide step deformation early warning method, device, readable medium and electronic device to solve the problems that traditional numerical / physical simulation methods are not conducive to real-time early warning, empirical methods are not objective and accurate, and various early warning models ignore the distinction between step deformation and pre-slip deformation in the development of step-type landslides.

[0008] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is as follows: In the first aspect, a landslide step deformation early warning method is provided, comprising:

[0009] S1, using cluster analysis and local extreme values ​​to identify the step turning points of landslides;

[0010] S2. Monitor the dynamic changes of reservoir water level and calculate the effective reservoir water level change that induces landslide deformation;

[0011] S3. Calculate the improved tangent angle and displacement rate according to the characteristics of the landslide deformation stage;

[0012] S4. Based on historical monitoring data, determine the thresholds of step inflection points, effective reservoir water level changes, improved tangent angles and displacement rates, and establish a graded early warning model for reservoir step-type landslides;

[0013] S5. Provide real-time warning of step deformation of the monitored landslides based on the reservoir step landslide classification warning model.

[0014] Second, a landslide step deformation early warning device is provided, including:

[0015] Reservoir water level monitoring module, used to monitor reservoir water level changes and transmit them to the host computer;

[0016] The landslide displacement monitoring module is used to monitor the landslide displacement and transmit it to the host computer;

[0017] The host computer is used to receive water level changes and landslide displacements, identify the step inflection points of the landslide, calculate the effective reservoir water level changes that induce landslide deformation, improve the tangent angle and displacement rate, and establish a reservoir step-type landslide graded early warning model based on preset thresholds.

[0018] In a third aspect, a computer-readable medium is provided, on which a computer program is stored, which, when executed by a processing device, implements the method steps described in the first aspect.

[0019] A fourth aspect provides electronic equipment, including:

[0020] a storage device having a computer program stored thereon;

[0021] A processing device is used to execute the computer program in the storage device to implement the method steps described in the first aspect.

[0022] The beneficial effects of the present invention are:

[0023] 1. The step point selection adopts cluster analysis + local extreme value combination method for comprehensive extraction, which clearly distinguishes the step inflection point and the stable inflection point;

[0024] 2. The effective reservoir water level change is calculated by multiplying the reservoir water level change of a period of time by the effective reservoir water level coefficient. The reservoir water level change that induces landslide deformation is divided into two stages for comprehensive judgment, which improves the prediction accuracy;

[0025] 3. No need for on-site investigation and rock and soil parameters, easy to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is the flow chart of the landslide step deformation early warning method.

[0027] Figure 2 This is the deformation stage division diagram of the step-type landslide displacement curve.

[0028] Figure 3 Schematic diagram of the local extremum method.

[0029] Figure 4 This is the deformation stage division diagram of the landslide displacement time curve.

[0030] Figure 5 It is the curve diagram of landslide cumulative displacement and reservoir water level monitoring in the embodiment.

[0031] Figure 6 Landslide time-displacement curve.

[0032] Figure 7 It is the distribution diagram of effective reservoir water level change-improved tangent angle-displacement rate index and threshold line. DETAILED DESCRIPTION

[0033] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0034] like Figure 1 As shown, in one embodiment of the present invention, a landslide step deformation early warning method is provided, comprising:

[0035] S1, using cluster analysis and local extreme values ​​to identify the step turning points of landslides;

[0036] S2. Monitor the dynamic changes of reservoir water level and calculate the effective reservoir water level change that induces landslide deformation;

[0037] S3. Calculate the improved tangent angle and displacement rate according to the characteristics of the landslide deformation stage;

[0038] S4. Based on historical monitoring data, determine the thresholds of step inflection points, effective reservoir water level changes, improved tangent angles and displacement rates, and establish a graded early warning model for reservoir step-type landslides;

[0039] S5. Provide real-time warning of step deformation of the monitored landslides based on the reservoir step landslide classification warning model.

[0040] like Figure 2As shown in the figure, it is a deformation stage division diagram of the displacement curve of the step-type landslide. In the reservoir-type landslide, the dynamic change of the reservoir water level will change the characteristics of the water environment system of the slope. Through the softening effect of the water-rock action, the thickness of the adsorbed water film of the slope soil below the reservoir water level increases significantly under the immersion of water, thereby reducing the shear strength parameter value of the soil, but it does not reach the critical value of destruction, and the slope is not prone to large-scale destruction. When the reservoir water level changes weaken or stop, the slope will tend to be relatively balanced again, but because its internal environment has changed, it will show overall slow deformation, resulting in a local "deformation"-"stability" step phenomenon, and the monitoring curve shows a step-like feature.

[0041] Therefore, dynamic water storage is a major cause of step deformation of reservoir bank slope. In step-type landslides, the displacement rate, acceleration and improved tangent angle models of each step stage can easily reach the warning threshold and trigger the warning. However, when the reservoir water level changes weaken or stop, the landslide may return to equilibrium and enter a new low-speed deformation stage. The key to the early warning of step-type landslides lies in analyzing the relationship and difference between the step stage and the pre-slip stage, and selecting early warning criteria with good universality.

[0042] Starting from the causes and deformation results of the step deformation of the reservoir bank slope, this application selects the displacement rate, improved tangent angle and effective reservoir water level change as the key early warning criteria for quantitative analysis, aiming to avoid the limitations of a single indicator criterion, and uses the local extreme value method and cluster analysis method to superimpose and identify the "step inflection point" and "stable inflection point", which are used as breakpoints to divide the "step stage" of rapid deformation and the "slow-speed deformation stage" of deformation decline.

[0043] When the deformation enters a step inflection point and reaches the relevant threshold, an early warning is issued. After a certain period of time, if the landslide deformation slows down, a stable inflection point appears, and each criterion meets the corresponding conditions, the early warning event is lifted. If the deformation continues to increase and each indicator reaches the threshold and no new stable inflection point appears, the early warning information will continue to be issued and the relevant early warning plan will be activated until the landslide occurs. The specific methods include:

[0044] S11. K value cluster analysis is used for the landslide displacement rate characteristics. The threshold is set to initially distinguish the step stage and the low-speed deformation stage. The expression of K value clustering is:

[0045]

[0046] Among them, K is the displacement rate, x i is the value corresponding to the i-th displacement rate K, S2 is the data variance, the K value is greater than the threshold as the step stage, and the K value is less than the threshold as the low-speed deformation stage;

[0047] S12, drawing a time-displacement curve according to the monitoring data, and identifying a step inflection point in the time-displacement curve using a local partial derivative;

[0048] S12 includes:

[0049] S121, draw a time-displacement curve according to the monitoring data, perform least square fitting processing along the x-axis from left to right in the test interval, and calculate the first-order partial derivatives of the center point to the left and right sides respectively;

[0050] The test interval is obtained through GMM clustering, and its expression is:

[0051] BIC = kln(n)-2ln(L)

[0052] Among them, BIC is the length of the test interval, kln(n) is the penalty term, k is the number of parameters, n is the number of samples, and L is the maximum value of the likelihood function of the estimated model;

[0053] S122. If the absolute value of the first-order partial derivative of the left side of the center point with respect to time is less than 0.1, and the absolute value of the first-order partial derivative of the right side of the center point with respect to time is greater than 0.1, the center point is determined to be a turning point;

[0054] S123. Calculate the second-order partial derivatives of the turning point with respect to time on both sides. If the absolute values ​​of the second-order partial derivatives on both sides of the turning point are less than 0.1, the turning point is a step inflection point.

[0055] The K-value cluster analysis method cannot completely distinguish the deformation stages. On the one hand, the surge data caused by equipment abnormalities will be directly classified as the step stage. On the other hand, the step point cannot be clearly identified as a step inflection point or a stable inflection point. Therefore, the local extreme value method is introduced to distinguish and judge. The core is to use local partial derivatives in the time-displacement curve drawn by the monitoring data to determine the local maximum and minimum values. The schematic diagram of the local extreme value method is shown in the figure. Figure 3 shown.

[0056] The reservoir water level changes that induce landslide deformation can be divided into two stages: the first stage is the early reservoir water level changes. In this stage, the slope below the reservoir water level has a softening effect. The softening effect makes the thickness of the adsorbed water film of the slope soil below the reservoir water level significantly increase under the immersion of water, thereby reducing the shear strength parameter value of the soil body and playing a role in nurturing landslides. The second stage is the critical reservoir water level change stage. The reservoir water level changes in this stage directly trigger the occurrence of landslides. Since a change does not necessarily lead to the occurrence of a landslide, and only part of each change has an effect on the occurrence of a landslide, the accumulated reservoir water change obviously cannot be used as a critical threshold. Therefore, the effective reservoir water level change H that induces landslide deformation is obtained by multiplying the reservoir water level changes for a period of time by the effective reservoir water level coefficient. effect The expression is:

[0057] H effect =h i +ah (i-1) +a 2 h (i-2) +a 3 h (i-3) ......a n h (i-n)

[0058] Among them, a is the effective reservoir water level coefficient, h i is the absolute value of the reservoir water level change on that day, h i-1 is the absolute value of the reservoir water level change on the previous day, h i-n It is the absolute value of the reservoir water level change in the previous n days, and n is the number of days the reservoir water level change lasts.

[0059] like Figure 4 The figure shows the deformation stage division diagram of the landslide displacement time curve. Figure 4 As shown in the figure, the inverse trigonometric function can be used to improve the tangent angle to reflect the landslide deformation stage. i and displacement rate v i The expression is:

[0060]

[0061] Where v is the constant value of displacement rate in the low-speed deformation stage.

[0062] In this embodiment, a landslide step deformation early warning device is exemplarily provided, comprising:

[0063] Reservoir water level monitoring module, used to monitor reservoir water level changes and transmit them to the host computer;

[0064] The landslide displacement monitoring module is used to monitor the landslide displacement and transmit it to the host computer;

[0065] The host computer is used to receive water level changes and landslide displacements, identify the step inflection points of the landslide, calculate the effective reservoir water level changes that induce landslide deformation, improve the tangent angle and displacement rate, and establish a reservoir step-type landslide graded early warning model based on preset thresholds.

[0066] Optionally, the landslide step deformation warning device may further include a storage module for storing the monitoring reservoir water level change and the landslide displacement, and the host computer establishes a reservoir step landslide graded warning model based on the stored historical data of the monitoring reservoir water level change and the landslide displacement.

[0067] In this embodiment, a computer-readable medium is exemplified, on which a computer program is stored. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two, but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program used by an instruction execution system, device, or device or used in combination with it. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0068] In some embodiments, any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol) can be used for communication, and can be interconnected with any form or medium of digital data communication (e.g., communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), internets (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

[0069] The computer-readable medium may be included in the above-mentioned electronic device; or may exist independently without being assembled into the electronic device.

[0070] An exemplary electronic device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. The electronic device includes:

[0071] a storage device having a computer program stored thereon;

[0072] A processing device, used to execute the computer program in the storage device, used to implement the method steps described in any one of claims 1-6.

[0073] In a specific embodiment of the present application, based on the monitoring data from 2018 to 2022, the correlation analysis between the landslide deformation event and the duration T of the reservoir water level change was performed using SPSS software. The duration T of the reservoir water level impact was taken as 3 days. The landslide cumulative displacement and reservoir water level monitoring curves are shown in Figure 2. Figure 5 As shown in the figure, the deformation history of Linbang landslide can be divided into three stages in this application. This study focuses on the third stage: from April 2018 to the present, the landslide has multiple sliding surfaces. The deformation of the rear edge of the landslide has intensified under the action of continuous reservoir water level fluctuations, and there are multiple step deformations. Taking typical equipment monitoring data as an example, the information of each inflection point is determined by combining the local extreme value method and cluster analysis method as shown in Table 1. A total of 14 inflection points and 204 points in 7 step stages are obtained, with a recognition rate of 97.14%, which is basically consistent with the actual manual screening results, indicating that K value clustering superimposed on local extreme value analysis has a good recognition ability for step point identification.

[0074] Table 1 Detailed information of step points in monitoring data

[0075]

[0076] In this embodiment, the variation law of the effective reservoir water level when the seven step deformations fall back to the stable development stage is obtained by solving the variation of the effective reservoir water level that induces landslide deformation. The information of the variation of the effective reservoir water level three days before the start of the seven step deformations is analyzed, and the data is arithmetic averaged by mathematical statistical methods. It is found that when the variation of the effective reservoir water level exceeds 4.69m, 5.59m, and 7.74m for three consecutive days, the landslide is likely to initiate step deformation, so the Heffect threshold of 4.69m is taken. During the transition from step deformation to the constant speed stage, the variation of the effective reservoir water level shows an overall downward trend for three consecutive days, and the variation of the effective reservoir water level on the third day is less than the average value of 1.42m in the low-speed deformation stage. Therefore, one of the judgment conditions for step deformation is H effect <1.42m.

[0077] Based on the deformation intervals in the monitoring curve of the measuring point, the average displacement rate of each step stage and the constant speed stage is obtained by arithmetic average, and the displacement rate of the low-speed deformation stage is 0.92mm / d, and the displacement rate of the step stage is 3.87mm / d. It shows that when the displacement curve has a step deformation, its displacement rate remains above 3.87mm / d, so the displacement rate threshold of the step deformation is 3.78mm / d.

[0078] According to the improved tangent angle definition, the landslide deformation is divided into three stages: α<45° is the initial deformation stage, α=45° is the slow deformation stage, α>45° is the accelerated deformation stage, and the accelerated deformation stage can be further refined into three stages: initial acceleration stage (45-80°), medium acceleration stage (80-85°) and accelerated stage (>85°). In this way, the landslide deformation can be divided into 4 warning levels. When α<45°, no warning is triggered. The corresponding thresholds of each deformation stage are calculated. The specific division is shown in the following table:

[0079] Table 2 Improved tangent angle-displacement rate warning and deformation stage correspondence table

[0080]

[0081] When the step deformation falls back, the displacement rate, effective reservoir water level change and curve characteristics show the following rules: ① a stable inflection point appears. ② the effective reservoir water level change H effect <1.42m. ③ Average displacement rate <3.78mm / d. Therefore, when the above three conditions are met at the same time, it is considered to be a temporary step deformation and the warning is lifted. Finally, combined with the above multi-criteria indicators and the macroscopic deformation signs of Linbang landslide, combined with the 4-level warning mechanism and Figure 6 The landslide time-displacement curve is shown in Figure 2, and the reservoir step landslide graded early warning model is established as shown in Table 3.

[0082] Table 3 Corresponding table of reservoir step landslide classification warning model

[0083]

[0084]

[0085] Apply each early warning criterion to the entire monitoring period, and use three criterion thresholds to make threshold demarcation lines, namely, "v = 3.87 mm / d, H effect =4.69m, α=45°” to make an overall evaluation of the third stage of early warning work of Linbang landslide history, such as Figure 7 .from Figure 7 It can be seen that there have been 7 step deformations in the landslide since 2018. By comparing the warning intervals, it can be seen that the single criterion has reached the warning threshold in all 7 step intervals, including April 30, 2020 (H effect , v, α trigger orange, yellow, and yellow warnings respectively) and August 9, 2021 (H effectWhen the three criteria (i.e., v, and α) trigger orange, yellow, and orange warnings respectively, exceed the warning threshold at the same time but do not reach the red warning threshold, the landslide has a certain risk of damage and a warning should be issued. At other times, only one criterion or two criteria reach the threshold, indicating that the landslide has multiple step deformations but the overall instability risk is low and is in slow creep deformation. After the external environment changes subside, the step deformation will turn to a stable state.

[0086] On the other hand, at the end of the 7 step deformations, the three indicators all weakened, and an obvious stable inflection point appeared in the monitoring curve. At the end, they all met the quantitative conditions for lifting the warning, and the production and life in the reservoir area were restored in time. Therefore, the multi-indicator warning criterion can make up for the disadvantage that a single indicator is difficult to reflect the disaster-pregnant process of landslides, and the quantitative lifting of the warning conditions can make up for the shortcomings of lifting the step deformation warning by empirical methods in the past.

[0087] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A landslide step deformation early warning method, characterized in that: include: S1, using cluster analysis and local extreme values ​​to identify the step turning points of landslides; S2. Monitor the dynamic changes of reservoir water level and calculate the effective reservoir water level change that induces landslide deformation; S3. Calculate the improved tangent angle and displacement rate according to the characteristics of the landslide deformation stage; S4. Based on historical monitoring data, determine the thresholds of step inflection points, effective reservoir water level changes, improved tangent angles and displacement rates, and establish a graded early warning model for reservoir step-type landslides; S5. Provide real-time warning of step deformation of the monitored landslides based on the reservoir step landslide classification warning model.

2. The landslide step deformation early warning method according to claim 1 is characterized in that: S1 includes: S11. K value cluster analysis is used for the landslide displacement rate characteristics. The threshold is set to initially distinguish the step stage and the low-speed deformation stage. The expression of K value clustering is: Among them, K is the displacement rate, x i is the value corresponding to the i-th displacement rate K, S2 is the data variance, the K value is greater than the threshold as the step stage, and the K value is less than the threshold as the low-speed deformation stage; S12. Draw a time-displacement curve based on the monitoring data, and use local partial derivatives in the time-displacement curve to identify step inflection points.

3. The landslide step deformation early warning method according to claim 2 is characterized in that: S12 includes: S121, draw a time-displacement curve according to the monitoring data, perform least square fitting processing along the x-axis from left to right in the test interval, and calculate the first-order partial derivatives of the center point to the left and right sides respectively; S122. If the absolute value of the first-order partial derivative of the left side of the center point with respect to time is less than 0.1, and the absolute value of the first-order partial derivative of the right side of the center point with respect to time is greater than 0.1, the center point is determined to be a turning point; S123. Calculate the second-order partial derivatives of the turning point with respect to time on both sides. If the absolute values ​​of the second-order partial derivatives on both sides of the turning point are less than 0.1, the turning point is a step inflection point.

4. The landslide step deformation early warning method according to claim 2 is characterized in that: In S121, the test interval is obtained through GMM clustering, and its expression is: BIC = kln(n)-2ln(L) Among them, BIC is the length of the test interval, kln(n) is the penalty term, k is the number of parameters, n is the number of samples, and L is the maximum value of the likelihood function of the estimated model.

5. The landslide step deformation early warning method according to claim 1 is characterized in that: The effective reservoir water level change H in S2 that induces landslide deformation effect The expression is: H effect =h i +ah (i-1) +a 2 h (i-2) +a 3 h (i-3) ......a n h (i-n) Among them, a is the effective reservoir water level coefficient, h i is the absolute value of the reservoir water level change on that day, h i-1 is the absolute value of the reservoir water level change on the previous day, h i-n It is the absolute value of the reservoir water level change in the previous n days, and n is the number of days the reservoir water level change lasts.

6. The landslide step deformation early warning method according to claim 1 is characterized in that: In S3, the tangent angle a is improved i and displacement rate v i The expression is: Where v is the constant value of displacement rate in the low-speed deformation stage.

7. A landslide step deformation early warning device, used to execute the landslide step deformation early warning method according to any one of claims 1 to 6, characterized in that: include: Reservoir water level monitoring module, used to monitor reservoir water level changes and transmit them to the host computer; The landslide displacement monitoring module is used to monitor the landslide displacement and transmit it to the host computer; The host computer is used to receive water level changes and landslide displacements, identify the step inflection points of the landslide, calculate the effective reservoir water level changes that induce landslide deformation, improve the tangent angle and displacement rate, and establish a reservoir step-type landslide graded early warning model based on preset thresholds.

8. A computer readable medium having a computer program stored thereon, characterized in that When the computer program is executed by a processing device, the method steps described in any one of claims 1 to 6 are implemented.

9. An electronic device, characterized in that include: a storage device having a computer program stored thereon; A processing device, used to execute the computer program in the storage device, used to implement the method steps described in any one of claims 1-6.

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