Expansive soil landslide monitoring method, system and equipment and storage medium

Abstract

The invention provides an expansive soil landslide monitoring method, system and device and a storage medium, and belongs to the field of geological disaster monitoring, and the method comprises the steps: obtaining measurement parameters and earth surface displacement data of an expansive soil landslide monitoring area, calculating a Spearman grade correlation coefficient between the earth surface displacement data and the measurement parameters, and calculating the Spearman grade correlation coefficient; screening data in the measurement parameters, and taking the screened measurement parameters as characteristic factors; inputting the surface displacement data and the characteristic factors into a long-short term memory (LSTM) network model for training, and adjusting and optimizing hyper-parameters of the LSTM model through a Bayesian algorithm to obtain an expansive soil monitoring model; and continuously acquiring real-time measurement parameters of the expansive soil landslide monitoring area, inputting the real-time measurement parameters into the expansive soil monitoring model to obtain a predicted value of the earth surface displacement data, and realizing expansive soil landslide monitoring according to the predicted value. The precision and reliability of landslide monitoring are obviously improved, and the disaster prevention and reduction capability is improved.

Description

Technical Field

[0001] The present invention belongs to the field of geological disaster monitoring, and particularly relates to a monitoring method, system, device and storage medium for expansive soil landslides. Background Technique

[0002] Landslides are one of the common geological disasters. Especially in expansive soil areas, due to their special water sensitivity and mechanical properties, the occurrence mechanism of landslide disasters is more complex. Expansive soil will significantly expand when encountering water and shrink when dry. This volume change leads to the complication of internal soil stress and is extremely likely to induce landslides. Global Navigation Satellite System (GNSS) technology, especially China's Beidou-3 system (BDS), with the completion of its global networking and the advantage of its built-in short message communication function, has gradually become a common method for landslide monitoring, especially playing an increasingly important role in landslide monitoring and early warning. Compared with other monitoring technologies such as total stations, GNSS, especially BDS, has significant advantages such as automation, real-time performance and high precision. Expansive soil landslides are not concentratedly distributed and are often blocked by vegetation and buildings, resulting in relatively large multipath errors and environmental errors.

[0003] Traditional landslide monitoring methods mainly rely on a single sensor to monitor parameters such as the deformation of the area. A single sensor is easily affected by environmental noise and external interference in a complex geological environment, resulting in unstable monitoring results and being unable to accurately reflect the real situation of the landslide area. And it often only monitors a single parameter, ignoring that expansive soil landslides are the result of the coupling of multiple factors. This leads to a low accuracy of early warning for expansive soil landslides, often detecting disasters only after the landslide occurs and missing the best prevention and control opportunity. Summary of the Invention

[0004] In order to solve the problem of low accuracy in monitoring expansive soil landslides, the present invention provides a monitoring method, system, device and storage medium for expansive soil landslides.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A monitoring method for expansive soil landslides specifically includes the following steps:

[0007] Obtain the environmental parameters and historical surface displacement data of the expansive soil landslide monitoring area; calculate the Spearman rank correlation coefficient between the historical surface displacement data and the environmental parameters, screen the environmental parameters according to the calculation results, and use the screened environmental parameters as characteristic factors;

[0008] Use the historical surface displacement data as the output and the characteristic factors as the input to train the Long Short-Term Memory (LSTM) model, and optimize the hyperparameters of the LSTM model through the Bayesian algorithm to obtain an expansive soil monitoring model;

[0009] Continuously obtain the real-time environmental parameters of the expansive soil landslide monitoring area, input the real-time environmental parameters into the expansive soil monitoring model to obtain the predicted value of the ground surface displacement data, and realize the expansive soil landslide monitoring according to the predicted value.

[0010] Preferably, calculate the Spearman rank correlation coefficient between the historical ground surface displacement data and the environmental parameters, specifically through the following formula:

[0011]

[0012] where d i =R(x i ) - R(y i ) is the rank difference of each pair of data, R(x) and R(y) are the two variables to be compared, i represents the i-th data of the variables to be compared sorted by numerical size; n is the number of data points, is the sum of the squares of the rank differences of all data pairs, and ρ represents the Spearman correlation coefficient.

[0013] Preferably, tuning the hyperparameters of the LSTM model by the Bayesian algorithm specifically includes the following steps:

[0014] Randomly select hyperparameter combinations, train the LSTM model and calculate the corresponding loss value;

[0015] Based on the current hyperparameter-loss pair, construct a surrogate model;

[0016] Select the next hyperparameter combination through the surrogate model and train it with the goal of minimizing the loss function of the test set until the best hyperparameter combination is found; where the hyperparameter combination specifically includes the hidden layer size, regularization parameter and learning rate.

[0017] Preferably, before inputting the historical ground surface displacement data and feature factors into the long short-term memory network LSTM model for training, it also includes normalizing the historical ground surface displacement data and feature factors by the Min-Max normalization method.

[0018] Preferably, after obtaining the predicted value of the ground surface displacement data, it also includes inverse normalization of the predicted value to convert the predicted value into the inversely normalized ground surface displacement data, specifically:

[0019] X orig =X norm ×(X max -X min ) + X min ;

[0020] where Xnorm is the predicted value of the surface displacement data, X max and X min are the maximum and minimum values of the surface displacement data, X orig represents the inverse-normalized surface displacement data.

[0021] Preferably, the environmental parameters include earth pressure data, temperature data, humidity data, and rainfall data.

[0022] The present invention also provides an expansive soil landslide monitoring system, specifically including:

[0023] A feature acquisition module, configured to acquire environmental parameters and historical surface displacement data of an expansive soil landslide monitoring area; calculate the Spearman rank correlation coefficient between the historical surface displacement data and the environmental parameters, screen the environmental parameters according to the calculation result, and use the screened environmental parameters as feature factors.

[0024] A model construction module, configured to use the historical surface displacement data as the output and the feature factors as the input to train a long short-term memory network (LSTM) model, and optimize the hyperparameters of the LSTM model through the Bayesian algorithm to obtain an expansive soil monitoring model.

[0025] A deformation detection module, configured to continuously acquire real-time environmental parameters of an expansive soil landslide monitoring area, input the real-time environmental parameters into the expansive soil monitoring model to obtain the predicted value of the surface displacement data, and realize the monitoring of the expansive soil landslide according to the predicted value.

[0026] The present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory, where the processor executes the computer program to implement the steps in the method for monitoring an expansive soil landslide.

[0027] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is loaded by a processor, it can execute the steps in the method for monitoring an expansive soil landslide.

[0028] The method for monitoring an expansive soil landslide provided by the present invention has the following beneficial effects:

[0029] By acquiring the environmental parameters and surface displacement data of an expansive soil landslide monitoring area, calculating the Spearman rank correlation coefficient between the surface displacement data and the measurement parameters, and screening the data in the measurement parameters according to the calculation result, the present invention can better capture the correlation between features and target variables, screen out the influencing factors with a higher correlation with the expansive soil landslide, and improve the prediction effect of the model.

[0030] Input the surface displacement data and characteristic factors into the long short-term memory network (LSTM) model for training. Considering multiple influencing factors leading to expansive soil landslides, optimize the hyperparameters of the LSTM model through the Bayesian algorithm to reduce redundant calculations and effectively avoid falling into local optimal solutions, thereby obtaining an expansive soil monitoring model. Continuously acquire the real-time environmental parameters of the expansive soil landslide monitoring area and input them into the expansive soil monitoring model to obtain the predicted values of the surface displacement data, thus realizing the monitoring of expansive soil landslides. Improve the monitoring efficiency and accuracy of expansive soil landslides, effectively conduct disaster early warnings, and reduce losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] To more clearly illustrate the embodiments of the present invention and their design schemes, the accompanying drawings required for these embodiments will be briefly introduced below. The accompanying drawings in the following description are only partial embodiments of the present invention. For those of ordinary skill in the art, other accompanying drawings can be obtained based on these drawings without creative efforts.

[0032] Figure 1 It is a flowchart of a method for monitoring expansive soil landslides according to the present invention.

[0033] Figure 2 It is the training process of the BO-LSTM model in the embodiments of the present invention.

[0034] Figure 3 It is the data processing flowchart of a monitoring system for expansive soil landslides in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] To enable those skilled in the art to better understand the technical solutions of the present invention and implement them, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be used to limit the protection scope of the present invention.

[0036] Embodiment

[0037] The present invention provides a method for monitoring expansive soil landslides, as Figure 3 shown, which specifically includes the following steps:

[0038] S1: Rainfall sensors, earth pressure sensors, and soil temperature and humidity sensors are arranged on-site to obtain rainfall data, earth pressure data, and soil temperature and humidity data. The surface displacement data is obtained through GNSS equipment.

[0039] S2: Data preprocessing.

[0040] S21: Preprocess the collected surface displacement data, rainfall data, soil temperature and humidity data, and soil pressure data. Multi-source heterogeneous data fusion performs predictions on a daily basis to obtain the monitoring data of each device for each day. For rain gauge data, the data monitored each day are added up to obtain the rainfall data for that day. For soil pressure gauges and soil temperature and humidity gauges, the average value of the data monitored each day is calculated to obtain the soil pressure data and soil temperature and humidity data for that day.

[0041] Random errors may exist in the monitoring results. To improve the training accuracy of the model, smooth the surface displacement data and choose the moving window method for processing. Specifically, it is processed through the following formula:

[0042]

[0043] where S(i) is the smoothed data value, x j is the value in the original data, k is the window size, and the window size is set to 3.

[0044] S22: In high-dimensional datasets, problems such as redundancy and multicollinearity are likely to occur, and irrelevant or invalid features will have a negative impact on the model performance. Spearman's Rank Correlation Coefficient is a non-parametric statistical method used to measure the monotonic relationship between two variables. Different from the Pearson correlation coefficient, the Spearman coefficient does not require the relationship between variables to be linear or normally distributed (for example, the distribution of rainfall is non-normal). It can capture the monotonic relationship between variables, especially when the data has complex non-linear relationships.

[0045] The calculation of the Spearman coefficient is based on the ranks (ranks) of the data, rather than the original data values. Its value ranges from -1 to +1: +1 indicates a perfect positive correlation, and all data points completely conform to a monotonically increasing relationship; -1 indicates a perfect negative correlation, and all data points completely conform to a monotonically decreasing relationship; 0 indicates no monotonic relationship. Its formula is specifically as follows:

[0046]

[0047] where d i =R(x i ) - R(y i ) is the rank difference of each pair of data points, R(x) and R(y) are the two variables to be compared, i represents the i-th data of the variable to be compared in the order of numerical size; n is the number of data points, is the sum of the squares of the rank differences of all data pairs, and ρ represents the Spearman correlation coefficient.

[0048] Through prior knowledge, it can be known that the relationship between expansive soil landslides and soil temperature is not obvious. Therefore, this characteristic factor is screened out first. The candidate input factors of the model include the sum of rainfall, earth pressure, and soil moisture for the previous 1 day, 2 days, 3 days, 7 days, and 15 days. The input factors are screened by the Spearman rank correlation coefficient. Considering the characteristic time lag delay, the relationship between the deformation quantity and the characteristic factors is analyzed, and the characteristic factor with the largest absolute value of the Spearman coefficient in each characteristic is selected as the input of the candidate model to improve the prediction accuracy of the model as much as possible.

[0049] S23: The data input into the time series prediction model includes GNSS displacement data, rainfall data, soil moisture data, and earth pressure data. The different dimensions of the data will have an adverse impact on the training and prediction accuracy of the model, such as reducing the model training speed, reducing the accuracy, causing all weight parameters to become 0 or diverging to hundreds of thousands. Therefore, all data needs to be normalized before being input into the model. The Min-Max normalization method is adopted, specifically as follows:

[0050]

[0051] where X is the original data, X min and X max are the minimum and maximum values corresponding to the original data respectively, and X norm represents the original data after normalization.

[0052] S3: Construct a BO-LSTM model, which combines Bayesian Optimization (BO for short) and Long Short-Term Memory (LSTM), as Figure 2 shown. For the Long Short-Term Memory (LSTM) model, the historical surface displacement data is used as the output, and the characteristic factors (soil moisture, earth pressure, rainfall data) are used as the input. After training, an expansive soil monitoring model is obtained.

[0053] Long Short-Term Memory (LSTM) is a special Recurrent Neural Network (RNN) that solves the problems of gradient vanishing and gradient explosion in standard RNNs when dealing with long sequence data. The LSTM network is suitable for processing time series data and can capture time-dependent relationships. The basic structure of LSTM includes multiple gating mechanisms (input gate, forget gate, output gate), which can effectively remember and forget information and adapt to long-term and short-term dependencies. The specific calculation process of LSTM includes:

[0054] Forget gate. When processing time series data, LSTM processes from left to right. Therefore, when a large amount of information is input, it is necessary to decide which information should be retained and which should be discarded. LSTM uses the Sigmoid function to achieve this goal. The Sigmoid function is specifically:

[0055]

[0056] The hidden state information of h at the previous moment (time t-1) t-1 and the data of x at this moment (time t) t are input into the Sigmoid function together. The output value is between 0 and 1, indicating whether this information should be forgotten. The closer the output value is to 0, the more likely it is to forget this information, and vice versa. This is reflected as a switch control in the forget gate. The control function f (t) decides how much of the previous memory should be forgotten, specifically:

[0057] f t = σ(W f · [h t-1 , x t + b f );

[0058] where W f , b f are the weights and biases of the forget gate respectively, and σ represents the Sigmoid activation function.

[0059] Input gate. The information from the previous text is selectively input into the input gate. The task at this layer is to decide which information needs to be updated and by how much.

[0060] i t = σ(W i · [h t-1 , x t + b i );

[0061] c t = tanh(W c [h (t-1) , x t + b c );

[0062] C t = i t * c t + f (t) * C t-1 ;

[0063] where W i and W crepresents the corresponding weight, b i and b c represents the corresponding bias, c t represents the current cell state value, and tanh is the tanh function. Similar to the output of the forget gate, the output value of the input gate is between 0 and 1, indicating whether to forget the information of the candidate memory cell value. The closer the output value is to 0, the more likely it is to forget this information, and vice versa; C t is the output value of the current memory cell. Calculate the impact of the current input on memory.

[0064] Output gate, after the screening of the first two gates is completed, finally through the output gate,

[0065] determines which information needs to be output, and the output o t value is used as an intermediate variable and calculated by the following formula:

[0066] o t =σ(W o ·[h t-1 , x t +b o );

[0067] In the formula, W o is the weight matrix of the output gate that adjusts the connection vector dimension, and b o is the bias vector in the RNN. The output value o t and the memory cell C t determine the value of the current hidden state h t as shown in the following formula:

[0068] h t =o t *tanh(C t );

[0069] Combine the output gate and the updated memory cell to generate the final hidden state.

[0070] Apply Bayesian optimization to tune the hyperparameters of the LSTM, including the hidden layer size, regularization parameter, learning rate, etc., to avoid the uncertainty brought by manual hyperparameter tuning and improve the performance of the model. After generating the initial hyperparameter combination by the Bayesian algorithm, train the model. Use the LSTM to train the model and test the prediction accuracy of the model. If the accuracy requirement is not met, a new set of hyperparameters will be generated by the Bayesian optimization algorithm after updating, and repeat the iteration until the accuracy requirement is met to obtain the expansive soil monitoring model.

[0071] S4: Load the trained model into the server, input the real-time environmental data into the expansive soil monitoring model to obtain the predicted values of the ground displacement data, and realize the monitoring of expansive soil landslides. Perform inverse normalization on the predicted values to convert the normalized data back to the original dimensional data range in order to obtain the actual ground displacement prediction data. Specifically:

[0072] X orig = X norm × (X max - X min ) + X min ;

[0073] Wherein, X norm is the predicted value of the GNSS displacement data, X max and X min are the maximum and minimum values in the GNSS displacement data, and X orig represents the inverse-normalized GNSS displacement data.

[0074] The present invention also provides an expansive soil landslide monitoring system, which specifically includes:

[0075] A data acquisition module, including a deformation monitoring module and a multi-source sensor module; the deformation monitoring module is used to obtain the ground displacement data according to the GNSS receiver, GNSS antenna, 4G antenna, pole of the fixed antenna and the corresponding connecting wires arranged in the monitoring area. The multi-source sensor module: is used to obtain the rainfall data, soil pressure data, soil temperature and humidity data in real time, and the soil pressure sensors, temperature and humidity sensors and rain sensors are distributed at multiple key positions in the landslide monitoring area.

[0076] A transmission and communication module (4G DTU): is used to receive the signals transmitted by each sensor, perform preliminary signal processing and fusion, and use 4G wireless communication technology to transmit the received signals to the designated server for visualization and data analysis.

[0077] A deformation quantity prediction module is used to input the data of the transmission module into the expansive soil monitoring model to obtain the predicted values of the landslide displacement deformation quantity, and realize the monitoring of expansive soil landslides according to the predicted values.

[0078] A power management module: uses a solar panel for charging, connects a storage battery, and uses a single-channel controller, which is commonly used in solar street lights for power management.

[0079] A centralized management module: uses a distribution box to centrally place the GNSS receiver, DTU, single-channel controller, and storage battery, and can be locked to perform waterproof and safety management.

[0080] Each module in the above-mentioned expansive soil landslide monitoring system can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of the processor in a computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0081] The present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps in an embodiment of an expansive soil landslide monitoring method. For the specific implementation method, reference can be made to the method embodiment, which will not be elaborated here.

[0082] Furthermore, the present invention also provides a non-transitory computer-readable storage medium containing instructions, on which a computer program is stored. For example, a memory containing instructions, and the above instructions can be executed by the processor of the computer device to complete the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc. When the computer program is executed by the processor, it can implement the steps in an embodiment of an expansive soil landslide monitoring method. For the specific implementation method, reference can be made to the method embodiment, which will not be elaborated here.

[0083] Those skilled in the art should understand that the embodiments of the present invention can provide a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented 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.

[0084] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0085] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more processes and / or blocks Figure 1 in one or more processes and / or blocks Figure 1 specified in the function.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more processes and / or blocks Figure 1 in one or more processes and / or blocks Figure 1 specified in the function.

[0087] It should be noted that the above-described specific embodiments can enable those skilled in the art to more comprehensively understand the present invention, but do not limit the present invention in any way. Therefore, although this specification and the embodiments have described the present invention in detail, those skilled in the art should understand that the present invention can still be modified or equivalently replaced; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered by the protection scope of the patent of the present invention. Any reference signs in the claims should not be construed as limiting the claimed claims. Any simple variations or equivalent replacements of the technical solutions that can be obviously obtained by any person skilled in the art within the technical scope disclosed by the present invention fall within the protection scope of the present invention.

Claims

1. A method for monitoring expansive soil landslide, characterized in that: The following steps are involved: Obtain environmental parameters and historical surface displacement data in the expansive soil landslide monitoring area; Calculating the Spearman rank correlation coefficient between the historical surface displacement data and the environmental parameters, screening the environmental parameters according to the calculation results, and using the screened environmental parameters as characteristic factors; The historical surface displacement data is used as output and the characteristic factor is used as input to train a long short-term memory network LSTM model, and the hyperparameters of the LSTM model are tuned by a Bayesian algorithm to obtain an expansive soil monitoring model; Real-time environmental parameters of the expansive soil landslide monitoring area are continuously acquired, the real-time environmental parameters are input into the expansive soil monitoring model, the predicted values ​​of the surface displacement data are obtained, and the expansive soil landslide monitoring is realized according to the predicted values.

2. The method for monitoring expansive soil landslide according to claim 1, characterized in that: The Spearman rank correlation coefficient between the historical surface displacement data and the environmental parameters is calculated using the following formula: Among them, d i =R(x i )-R(y i ) is the rank difference of each pair of data, R(x) and R(y) are the two variables to be compared, i represents the i-th data of the variable to be compared in numerical order; n is the number of data points, is the sum of squares of the rank differences of all data pairs, and ρ represents the Spearman correlation coefficient.

3. The method for monitoring expansive soil landslide according to claim 1, characterized in that: The hyperparameters of the LSTM model are tuned by the Bayesian algorithm, specifically comprising the following steps: Randomly select a hyperparameter combination, train the LSTM model and calculate the corresponding loss value; Build a proxy model based on the current hyperparameter-loss pair; The next hyperparameter combination is selected through the proxy model and trained to minimize the loss function of the test set until the best hyperparameter combination is found; wherein the hyperparameter combination specifically includes the hidden layer size, regularization parameter and learning rate.

4. The method for monitoring expansive soil landslide according to claim 1, characterized in that: Before the historical surface displacement data and characteristic factors are input into the long short-term memory network LSTM model for training, the historical surface displacement data and characteristic factors are normalized using a Min-Max normalization method.

5. The method for monitoring expansive soil landslide according to claim 1, characterized in that: After obtaining the predicted value of the surface displacement data, the method further includes inverse normalization of the predicted value, and converting the predicted value into inverse normalized surface displacement data, specifically: X orig =X norm ×(X max -X min )+X min ; Among them, X norm is the predicted value of the surface displacement data, X max and X min is the maximum and minimum value of the surface displacement data, X orig Represents the inverse normalized surface displacement data.

6. The method for monitoring expansive soil landslide according to claim 1, characterized in that: The environmental parameters include soil pressure data, temperature data, humidity data and rainfall data.

7. An expansive soil landslide monitoring system, characterized in that: include: Feature acquisition module, used to obtain environmental parameters and historical surface displacement data of the expansive soil landslide monitoring area; Calculating the Spearman rank correlation coefficient between the historical surface displacement data and the environmental parameters, screening the environmental parameters according to the calculation results, and using the screened environmental parameters as characteristic factors; A model building module is used to use the historical surface displacement data as output and the characteristic factors as input to train a long short-term memory network LSTM model, and to tune the hyperparameters of the LSTM model through a Bayesian algorithm to obtain an expansive soil monitoring model; The deformation detection module is used to continuously obtain real-time environmental parameters of the expansive soil landslide monitoring area, input the real-time environmental parameters into the expansive soil monitoring model, obtain the predicted value of the surface displacement data, and realize the expansive soil landslide monitoring according to the predicted value.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is loaded into a processor, it can execute the steps of the method according to any one of claims 1 to 6.

Images