Phyllostachys pubescens forest canopy information decomposition and LAI inversion method and system considering phyllostachys pubescens moth stress

Abstract

The invention relates to a phyllostachys pubescens canopy information decomposition and LAI inversion method and system considering phyllostachys pubescens stress, and belongs to the fields of forest science, ecology and remote sensing science and technology. According to the method, through combination of unmanned aerial vehicle remote sensing and a machine learning algorithm, accurate detection of phyllostachys nigra moth harm and efficient inversion of phyllostachys pubescens forest LAI are realized. According to the method, through combination of unmanned aerial vehicle remote sensing and a machine learning algorithm, accurate detection of phyllostachys nigra poison moth harm and efficient inversion of phyllostachys nigra forest LAI are realized, a scientific basis is provided for bamboo forest pest control and resource management, and the method has important ecological and economic values.

Description

Technical Field

[0001] This invention belongs to the fields of forestry, ecology and remote sensing science and technology, and specifically relates to a method and system for decomposing and retrieving canopy information and LAI inversion of moso bamboo forests that takes into account the stress of the bamboo tussock moth. Background Technology

[0002] Moso bamboo forests, as important ecological and economic resources, are severely threatened by leaf-eating pests such as the bamboo tussock moth. Traditional monitoring methods are insufficient in terms of coverage and efficiency, while the development of remote sensing technology has made large-scale, high-frequency monitoring possible. Leaf area index (LAI), as an important parameter for measuring plant physiological state, is of great significance for assessing the productivity and ecological function of moso bamboo forests. However, due to the limitations of spatial resolution of remote sensing data, the mixed pixel problem restricts the accuracy of LAI estimation. Therefore, it is urgent to study a method for decomposing moso bamboo forest canopy information and retrieving LAI that can take into account the stress of the bamboo tussock moth. This is crucial for improving the accuracy of LAI estimation, achieving sustainable development of bamboo forest resources, and effectively controlling pests and diseases. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for decomposing and retrieving the canopy information (LAI) of moso bamboo forests that takes into account the stress of the bamboo moth, so as to solve the problem of inaccurate LAI estimation of moso bamboo forests under pest stress in the prior art.

[0004] To achieve the above objectives, the technical solution of the present invention is: a method for decomposing and retrieving the canopy information (LAI) of moso bamboo forests while taking into account the stress of the bamboo tussock moth, characterized in that it achieves accurate detection of bamboo tussock moth damage and efficient inversion of the LAI of moso bamboo forests by combining UAV remote sensing and machine learning algorithms.

[0005] Furthermore, the method includes the following steps:

[0006] (1) Data acquisition and preprocessing: Data on the bamboo forest area were acquired using UAV multispectral images and field survey data, and the acquired images were preprocessed.

[0007] (2) Information extraction of moso bamboo forest and detection of tussock moth infestation in bamboo: The preprocessed data was analyzed by machine learning algorithm to extract information of moso bamboo forest area and detect tussock moth infestation in bamboo.

[0008] (3) Hyperspectral hazard response analysis and canopy information decomposition of moso bamboo forest: Based on UAV hyperspectral data, the spectral hazard response of moso bamboo leaves, branches and culms is analyzed. The continuous maximum angle cone (SMACC) algorithm is used to extract leaf endmember information, and the fully constrained least squares (FCLS) algorithm is used to decompose satellite images into pixels.

[0009] (4) Remote sensing inversion of LAI based on bamboo forest canopy abundance information: Taking bamboo forest canopy abundance information as the core variable and combining measured LAI data, an LAI estimation model based on multiple linear regression and machine learning algorithms is constructed. Through model validation and parameter optimization, the LAI of bamboo forest is inverted.

[0010] Furthermore, step (1) specifically includes:

[0011] a) Acquisition of multispectral imagery by UAVs: Remote sensing images of the study area were acquired using multispectral UAVs;

[0012] b) Field data acquisition: Conduct field surveys to obtain various bamboo forest parameters at sampling points;

[0013] c) Image data preprocessing: Preprocess the acquired image data.

[0014] Furthermore, in step (1) c), the preprocessing includes radiation correction and orthorectification.

[0015] Furthermore, step (2) specifically includes:

[0016] a) Feature factor extraction: Extraction of feature factors including original bands, vegetation indices, and texture features;

[0017] b) Feature Optimization: Feature factors are optimized using a recursive feature elimination (RFE) algorithm combined with machine learning algorithms. The RFE algorithm optimizes features by iteratively removing the features with the lowest weights. The feature weights are calculated based on coefficients from the machine learning algorithm, and the specific formula is as follows:

[0018] w j =|β j |(j=1,2,...,n)

[0019] Preserved feature set = {f j |w j ≥threshold}

[0020] In the formula: w j β is the weight of the j-th feature. j Let be the coefficient of the j-th feature in the machine learning algorithm, n be the total number of features, and the threshold be determined through cross-validation.

[0021] c) Model establishment and evaluation: Based on the optimized feature factors, a model for information extraction and pest detection in moso bamboo forests was established using machine learning algorithms, and its accuracy was evaluated.

[0022] Furthermore, in steps b) and c) of step (2), the machine learning algorithms include Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting Tree (XGBoost).

[0023] Furthermore, step (3) specifically includes:

[0024] a) Hyperspectral data processing: Perform SG smoothing on UAV hyperspectral data;

[0025] b) Spectral hazard response analysis: Analyze the changes in spectral reflectance of leaves, branches, and culms of moso bamboo forests under different pest levels;

[0026] c) Canopy information decomposition: The continuous maximum angle cone (SMACC) algorithm is used to extract leaf endmember information, and the fully constrained least squares (FCLS) algorithm is used to decompose the satellite imagery into pixels;

[0027] The specific formula for the SMACC algorithm is as follows:

[0028]

[0029] In the formula, θ(r,e) is the spectral angle function; e k R is the new endmember spectral vector selected in the k-th iteration; remain E represents the remaining unselected pixel spectral set. k-1 The set of selected endmembers {e1,e2,…,e k-1 The iteration terminates when the minimum spectral angle between the newly added endmember and the existing endmember is less than the threshold α or when the preset endmember number limit is reached.

[0030] The specific formula for the FCLS algorithm is as follows:

[0031]

[0032] In the formula: δ is the spectral value of a pixel in a satellite remote sensing image; The end-member spectra of bamboo leaves, branches, or culms under stress level i are respectively; λ l (i), λ b (i), λ s (i) represents the abundance of leaves, branches, and culms in bamboo forests under stress level i; ε1 represents background and other influence values.

[0033] Furthermore, step (4) specifically includes:

[0034] a) Feature factor extraction and optimization: Extract the abundance information of the bamboo canopy in the bamboo forest and perform feature optimization;

[0035] b) Model construction: A LAI estimation model is constructed using multiple linear regression and machine learning algorithms;

[0036] c) Inversion accuracy evaluation: Evaluate the accuracy of the constructed LAI estimation model;

[0037] The specific formula for the multiple linear regression algorithm of the LAI estimation model is as follows:

[0038] Y = β0 + β1f1 + β2f2 + ... + β p f p +ε

[0039] In the formula: Y represents the bamboo forest area; f1, f2…f p The influencing factors of LAI in bamboo forests; β0, β1…β p ε represents the corresponding regression coefficient; ε is the error term.

[0040] The present invention also provides a system for decomposing and retrieving canopy information and LAI inversion of moso bamboo forests that takes into account the stress of the bamboo moth, including a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, it can implement the steps of the method described in any of the above.

[0041] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions executable by a processor, wherein when the processor executes the computer program instructions, it can implement the steps of any of the methods described above.

[0042] Compared with existing technologies, the present invention has the following beneficial effects: The method and system of the present invention, by combining UAV remote sensing and machine learning algorithms, realizes the accurate detection of bamboo tussock moth damage and the efficient inversion of the LAI of moso bamboo forest, providing a scientific basis for the prevention and control of bamboo forest diseases and pests and resource management, and has important ecological and economic value. Attached Figure Description

[0043] Figure 1 This is a flowchart of the method of the present invention.

[0044] Figure 2 This is a schematic diagram of multispectral image acquisition by the UAV according to the present invention.

[0045] Figure 3 This is a spectral characteristic image of the leaves, branches, and stems damaged by the bamboo tussock moth according to the present invention.

[0046] Figure 4 This is a diagram showing the results of pest level detection in bamboo forests according to the present invention.

[0047] Figure 5 This is a schematic diagram of the decomposition of canopy information according to the present invention.

[0048] Figure 6 This is a diagram showing the LAI inversion results based on UAV multispectral data from this invention.

[0049] Figure 7 This is a diagram showing the LAI inversion results based on satellite data from this invention.

[0050] Figure 8 This is a diagram showing the LAI estimation results based on canopy abundance in this invention. Detailed Implementation

[0051] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0052] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0053] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0054] This invention provides a method for decomposing and retrieving the canopy information (LAI) of moso bamboo forests while taking into account the stress of the bamboo tussock moth. By combining UAV remote sensing and machine learning algorithms, it achieves accurate detection of bamboo tussock moth damage and efficient LAI retrieval of moso bamboo forests, including the following steps:

[0055] (1) Data acquisition and preprocessing: Data on the bamboo forest area were acquired using UAV multispectral images and field survey data, and the acquired images were preprocessed.

[0056] (2) Information extraction of moso bamboo forest and detection of tussock moth infestation in bamboo: The preprocessed data was analyzed by machine learning algorithm to extract information of moso bamboo forest area and detect tussock moth infestation in bamboo.

[0057] (3) Hyperspectral hazard response analysis and canopy information decomposition of moso bamboo forest: Based on UAV hyperspectral data, the spectral hazard response of moso bamboo leaves, branches and culms is analyzed. The continuous maximum angle cone (SMACC) algorithm is used to extract leaf endmember information, and the fully constrained least squares (FCLS) algorithm is used to decompose satellite images into pixels.

[0058] (4) Remote sensing inversion of LAI based on bamboo forest canopy abundance information: Taking bamboo forest canopy abundance information as the core variable and combining measured LAI data, an LAI estimation model based on multiple linear regression and machine learning algorithms is constructed. Through model validation and parameter optimization, the LAI of bamboo forest is inverted.

[0059] The following is a detailed implementation process of the present invention.

[0060] like Figure 1 As shown, this embodiment provides a method for decomposing and retrieving the canopy information (LAI) of moso bamboo forests while considering the stress of the bamboo tussock moth, including the following steps:

[0061] (1) Data acquisition and preprocessing: Data on the bamboo forest area were acquired using UAV multispectral images and field survey data, and the acquired images were preprocessed with radiometric and geometric corrections.

[0062] (2) Information extraction of moso bamboo forest and detection of tussock moth infestation in bamboo: The preprocessed data was analyzed by machine learning algorithm to extract information of moso bamboo forest area and detect tussock moth infestation in bamboo.

[0063] (3) Hyperspectral hazard response analysis and canopy information decomposition of moso bamboo forest: Based on UAV hyperspectral data, the spectral hazard response of moso bamboo leaves, branches and culms is analyzed. The continuous maximum convex cone (SMACC) algorithm is used to extract leaf endmember information, and the fully constrained least squares (FCLS) algorithm is used to decompose the satellite image into pixels.

[0064] (4) Remote sensing inversion of LAI based on bamboo forest canopy abundance information: Taking bamboo forest canopy abundance information as the core variable and combining measured LAI data, an LAI estimation model based on multiple linear regression and machine learning algorithms is constructed. Through model validation and parameter optimization, the LAI of bamboo forest is inverted.

[0065] The following is an example Figure 4 Using the data on the damage caused by the bamboo tussock moth shown as an example, we will further explain the relevant content involved in this method.

[0066] (1) Data acquisition and preprocessing

[0067] Remote sensing data of bamboo forest areas were acquired using multispectral imagery from drones, and preprocessing such as radiometric correction and orthorectification was performed. (See schematic diagram below.) Figure 2 Simultaneously, field surveys were conducted to obtain various bamboo forest parameters at sampling points, providing a data foundation for subsequent analysis.

[0068] (2) Information extraction from moso bamboo forests and detection of tussock moth infestation in moso bamboo forests

[0069] By extracting feature factors such as original bands, vegetation indices, and texture features, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Red Edge Index (NDRE), and texture features such as mean, variance, uniformity, contrast, dissimilarity, entropy, angular binary matrix, and correlation, a recursive feature elimination (RFE) algorithm combined with machine learning algorithms was used for feature optimization. Finally, a bamboo forest information extraction and pest detection model was established using algorithms such as Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting Tree (XGBoost). This model achieved accurate extraction of bamboo forest information and effective identification of the bamboo tussock moth pest. The results are as follows: Figure 4 .

[0070] (3) Hyperspectral Hazard Response Analysis and Canopy Information Decomposition of Bambusa textilis Forest

[0071] Based on UAV hyperspectral data, the spectral reflectance of leaves, branches, and culms in moso bamboo forests was analyzed. Significant differences in spectral reflectance were found under different pest infestation levels. The results are as follows: Figure 3 The Continuous Maximum Convex Cone (SMACC) algorithm was used to extract leaf endmember information, and the Fully Constrained Least Squares (FCLS) algorithm was used to decompose satellite imagery into pixels to obtain abundance information of different land cover types, providing a foundation for LAI inversion. A schematic diagram is shown below. Figure 5 .

[0072] (4) LAI remote sensing inversion based on bamboo canopy abundance information in moso bamboo forest

[0073] This invention integrates bamboo forest canopy abundance information into LAI (Leaf Area Inversion) remote sensing retrieval. Specifically, using UAV hyperspectral data, it delves into the spectral characteristics of bamboo forest leaves under the stress of the bamboo tussock moth, meticulously constructs a ground object spectral library, and rigorously selects leaf endmembers under different pest levels. Based on this, pixel decomposition technology is used to accurately retrieve the abundance information of various ground objects in satellite data, successfully obtaining the abundance data of the bamboo forest canopy under different pest levels. This abundance data provides a detailed characterization of the composition of the bamboo forest canopy, offering high-precision input for LAI retrieval.

[0074] Furthermore, based on this abundance information, this study ingeniously constructed a multiple linear regression and machine learning estimation model to fully explore the intrinsic relationship between abundance data and LAI. Rigorous experimental verification showed that the XGBoost model based on canopy abundance data performed excellently, indicating that this model can accurately capture the impact of insect pest stress and changes in bamboo forest structure on LAI, effectively improving the inversion accuracy. Simultaneously, a comparative analysis of the results obtained from UAV multispectral inversion was conducted... Figure 6 The results of the Sentinel-2A satellite data inversion are as follows: Figure 7 The results of the canopy abundance information inversion are as follows: Figure 8Multiple LAI models were compared, and the RF and XGBoost models based on canopy abundance data showed the highest accuracy. LAI models constructed based on bamboo forest canopy abundance information all demonstrated excellent inversion results. Comparative analysis of the inversion results of LAI models from three data sources revealed that the LAI model based on bamboo forest canopy abundance data performed slightly better than the model based on Sentinel-2A data, and significantly better than the model based on UAV multispectral data. This fully demonstrates that incorporating canopy abundance information into LAI estimation models can significantly improve the accuracy of bamboo forest LAI estimation at the remote sensing scale, strongly promoting the development of remote sensing inversion technology for bamboo forest LAI under insect pest stress, and providing solid data support for further in-depth research on the ecophysiological changes of bamboo forests.

[0075] The present invention also provides a system for decomposing and retrieving canopy information and LAI inversion of moso bamboo forests that takes into account the stress of the bamboo moth, including a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, it can implement the steps of the method described in any of the above.

[0076] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions executable by a processor, wherein when the processor executes the computer program instructions, it can implement the steps of any of the methods described above.

[0077] 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.

[0078] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for canopy information decomposition and LAI inversion in moso bamboo forests considering the stress of the bamboo tussock moth, characterized in that, By combining UAV remote sensing and machine learning algorithms, we can achieve accurate detection of bamboo tussock moth damage and efficient inversion of the range of influence areas (LAI) in moso bamboo forests.

2. The method for decomposing and retrieving canopy information and LAI inversion of moso bamboo forests considering the stress of the bamboo tussock moth, as described in claim 1, is characterized in that... The method includes the following steps: (1) Data acquisition and preprocessing: Data on the bamboo forest area were acquired using UAV multispectral images and field survey data, and the acquired images were preprocessed. (2) Information extraction of moso bamboo forest and detection of tussock moth infestation in bamboo: The preprocessed data was analyzed by machine learning algorithm to extract information of moso bamboo forest area and detect tussock moth infestation in bamboo. (3) Hyperspectral hazard response analysis and canopy information decomposition of moso bamboo forest: Based on UAV hyperspectral data, the spectral hazard response of moso bamboo leaves, branches and culms is analyzed. The continuous maximum angle cone (SMACC) algorithm is used to extract leaf endmember information, and the fully constrained least squares (FCLS) algorithm is used to decompose satellite images into pixels. (4) Remote sensing inversion of LAI based on bamboo forest canopy abundance information: Taking bamboo forest canopy abundance information as the core variable and combining measured LAI data, an LAI estimation model based on multiple linear regression and machine learning algorithms is constructed. Through model validation and parameter optimization, the LAI of bamboo forest is inverted.

3. The method for decomposing and retrieving canopy information and LAI inversion of moso bamboo forests considering the stress of the bamboo tussock moth, as described in claim 2, is characterized in that... Step (1) specifically includes: a) Acquisition of multispectral imagery by UAVs: Remote sensing images of the study area were acquired using multispectral UAVs; b) Field data acquisition: Conduct field surveys to obtain various bamboo forest parameters at sampling points; c) Image data preprocessing: Preprocess the acquired image data.

4. The method for decomposing and retrieving canopy information and LAI inversion of moso bamboo forests considering the stress of the bamboo tussock moth, as described in claim 3, is characterized in that... In step c), the preprocessing includes radiometric correction and orthorectification.

5. The method for decomposing and retrieving canopy information and LAI inversion of moso bamboo forests considering the stress of the bamboo tussock moth, as described in claim 2, is characterized in that... Step (2) specifically includes: a) Feature factor extraction: Extraction of feature factors including original bands, vegetation indices, and texture features; b) Feature Optimization: Feature factors are optimized using a recursive feature elimination (RFE) algorithm combined with machine learning algorithms. The RFE algorithm optimizes features by iteratively removing the features with the lowest weights. The feature weights are calculated based on coefficients from the machine learning algorithm, and the specific formula is as follows: w j =|β j |(j=1,2,…,n) Preserved feature set = {f j |w j ≥threshold} In the formula: w j β is the weight of the j-th feature. j Let be the coefficient of the j-th feature in the machine learning algorithm, n be the total number of features, and the threshold be determined through cross-validation. c) Model establishment and evaluation: Based on the optimized feature factors, a model for information extraction and pest detection in moso bamboo forests was established using machine learning algorithms, and its accuracy was evaluated.

6. The method for decomposing and retrieving canopy information and LAI inversion of moso bamboo forests considering the stress of the bamboo tussock moth, as described in claim 5, is characterized in that... In steps (b) and (c) of step (2), the machine learning algorithms include Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting Tree (XGBoost).

7. A method for decomposing and retrieving canopy information and LAI inversion of moso bamboo forests considering the stress of the bamboo tussock moth, as described in claim 2, is characterized in that... Step (3) specifically includes: a) Hyperspectral data processing: Perform SG smoothing on UAV hyperspectral data; b) Spectral hazard response analysis: Analyze the changes in spectral reflectance of leaves, branches, and culms of moso bamboo forests under different pest levels; c) Canopy information decomposition: The continuous maximum angle cone (SMACC) algorithm is used to extract leaf endmember information, and the fully constrained least squares (FCLS) algorithm is used to decompose the satellite imagery into pixels; The specific formula for the SMACC algorithm is as follows: In the formula, θ(r,e) is the spectral angle function; e k R is the new endmember spectral vector selected in the k-th iteration; remain E represents the remaining unselected pixel spectral set. k-1 The set of selected endmembers {e1,e2,…,e k-1 The iteration terminates when the minimum spectral angle between the newly added endmember and the existing endmember is less than the threshold α or when the preset endmember number limit is reached. The specific formula for the FCLS algorithm is as follows: In the formula: δ is the spectral value of a pixel in a satellite remote sensing image; The end-member spectra of bamboo leaves, branches, or culms under stress level i are respectively; λ l (i), λ b (i), λ s (i) represents the abundance of leaves, branches, and culms in bamboo forests under stress level i; ε1 represents background and other influence values.

8. A method for decomposing and retrieving canopy information and LAI inversion of moso bamboo forests considering the stress of the bamboo tussock moth, as described in claim 2, is characterized in that... Step (4) specifically includes: a) Feature factor extraction and optimization: Extract the abundance information of the bamboo canopy in the bamboo forest and perform feature optimization; b) Model construction: A LAI estimation model is constructed using multiple linear regression and machine learning algorithms; c) Inversion accuracy evaluation: Evaluate the accuracy of the constructed LAI estimation model; The specific formula for the multiple linear regression algorithm of the LAI estimation model is as follows: Y=β0+β1f1+β2f2+...+β p f p +e In the formula: Y represents the bamboo forest area; f1, f2…f p The influencing factors of LAI in bamboo forests; β0, β1…β p ε represents the corresponding regression coefficient; ε is the error term.

9. A system for decomposing and retrieving canopy information and LAI inversion in moso bamboo forests considering the stress of the bamboo tussock moth, characterized in that, It includes a memory, a processor, and computer program instructions stored in the memory and executable by the processor, which, when executed by the processor, enable the implementation of the steps of the method as described in any one of claims 1-8.

10. A computer-readable storage medium having stored thereon computer program instructions executable by a processor, wherein when the processor executes the computer program instructions, it is able to implement the steps of the method as described in any one of claims 1-8.

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