Rapid gold ore prospecting method suitable for plateau desert region

Abstract

The invention discloses a gold mine rapid prospecting method suitable for a plateau desert region, and belongs to the field of gold mine prospecting, and the method comprises the steps: obtaining geological and chemical prospecting data, and selecting a prospecting region based on the geological and chemical prospecting data; acquiring a high-resolution satellite image of the prospecting area, and acquiring geological, construction and mineralization alteration characteristics of an abnormal area of the prospecting area based on the high-resolution satellite image to construct a geological data set; a gold ore metallogenic area prediction model is constructed based on the long and short term memory network and the convolutional neural network; and inputting the geological data set into the gold ore metallogenic area prediction model for calculation, and generating a gold ore metallogenic area delineation result. Compared with a traditional geological prospecting method, the method has the advantage that the prospecting cost can be remarkably reduced by utilizing the high-resolution satellite image and the deep learning model to perform gold ore metallogenic prediction. Meanwhile, due to the fact that the accuracy of the prediction result is higher, unnecessary drilling and sampling work can be reduced, and resources and time are further saved.

Description

Technical Field

[0001] The invention belongs to the field of gold prospecting, and in particular relates to a method for rapid gold prospecting suitable for plateau desert areas. Background Art

[0002] Chaizhou is located in the central and western part of Qinghai Province. Its geographical location belongs to the high-altitude alpine desert area with strong terrain cutting and residual mountains. The region is rich in gold resources, with more than 20 small and medium-sized gold deposits (points). In recent years, Qinghai Province has carried out key exploration work in this area.

[0003] The commonly used gold prospecting method is often based on 1:200,000 geochemical anomalies, followed by 1:50,000 mineral prospecting surveys, 1:10,000 geology and soil measurements, 1:2,000 rock geochemical profiles and slot drilling verification, etc. However, due to the harsh natural geographical conditions, it has the characteristics of long working cycles, large investments and low efficiency. Based on this, a rapid prospecting method for gold mines in plateau desert areas is urgently needed. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for rapid gold prospecting in plateau desert areas, comprising:

[0005] Obtaining geological and geochemical data, and selecting prospecting areas based on the geological and geochemical data;

[0006] Acquire high-resolution satellite images of the prospecting area, acquire the geological, structural, and mineralized alteration characteristics of the abnormal area in the prospecting area based on the high-resolution satellite images, determine the gold mineralization type, and construct a geological data set based on the geological, structural, and mineralized alteration characteristics of the abnormal area and the gold mineralization type;

[0007] A CNN-LSTM network is constructed based on a long short-term memory network and a convolutional neural network, and the CNN-LSTM network is trained through key features related to historical gold mineralization to obtain a gold mineralization area prediction model;

[0008] The geological data set is input into the gold mineralization area prediction model for calculation to generate a gold mineralization area delineation result.

[0009] Preferably, the process of obtaining geological and geochemical data and selecting a prospecting area based on the geological and geochemical data includes: analyzing the delineated gold anomalies based on 1:200,000 geological mapping, 1:200,000 geochemical surveys, and 1:50,000 regional geological and mineral survey results data implemented in historical working areas to establish a prospecting area.

[0010] Preferably, the process of obtaining the geological, structural and mineralized alteration characteristics of the abnormal area in the prospecting area based on the high-resolution satellite image comprises:

[0011] Conduct geochemical surveys on the prospecting area and identify gold anomalies based on the survey results;

[0012] The original gold element data and anomaly map of the gold anomaly are checked by 1:1 10,000 grass survey to obtain the distribution of geological structures;

[0013] Processing the high-resolution satellite image of the prospecting area to obtain a processed image, extracting information from the processed image based on a principal component transformation method to obtain mineralization and alteration information;

[0014] Based on the geological structure distribution and the mineralization and alteration information, the geological, structural and mineralization and alteration characteristics of the abnormal area in the prospecting area are obtained.

[0015] Preferably, the process of constructing a geological data set based on the geology, structure, mineralization and alteration characteristics of the abnormal area and the gold mineralization type also includes:

[0016] Cleaning the abnormal values ​​and missing values ​​in the geological, structural, and mineralized alteration characteristics of the abnormal area, and normalizing the cleaned data to obtain a normalized data set;

[0017] The gold mineralization type is used as a label and merged with the normalized data set to generate the geological data set.

[0018] Preferably, the process of obtaining the gold mineralization region prediction model comprises:

[0019] The data set consisting of the key features related to the history and gold mineralization is input into the long short-term memory network for calculation to obtain the extracted features, and the extracted features are used as training data to train the convolutional network. At the same time, some weights are pruned during the training process until the loss function of the model is no longer reduced, and then the output is generated to generate the gold mineralization area prediction model.

[0020] Preferably, the loss function of the gold mineralization region prediction model is expressed as:

[0021]

[0022] Among them, N represents the total number of samples, y i Represents the category to which the i-th sample belongs, y pred It is expressed as the probability of the model predicting the i-th sample, and log(·) is expressed as the logarithmic function. The predicted value y of the model pred As close as possible to the true label y i , when y i When the value of is 1, we expect y pred Close to 1, when y iWhen the value of is 0, we expect y pred Close to 0.

[0023] Preferably, after generating the gold mineralization area delineation result, the method further includes: arranging 8 drill holes based on the gold mineralization area delineation result to conduct deep verification of the corresponding ore body, and the drill holes are 75° inclined holes.

[0024] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for rapid gold prospecting suitable for plateau desert areas when executing the computer program.

[0025] On the other hand, the present invention also provides a computer-readable storage medium, which stores a computer program, characterized in that when the computer program is executed by a processor, the method for rapid gold prospecting suitable for plateau desert areas is implemented.

[0026] Compared with the prior art, the present invention has the following advantages and technical effects:

[0027] High-resolution satellite images provide a wealth of surface coverage information, including key features such as geological structure, mineralization and alteration, which are crucial for gold mineralization prediction. Satellite images allow for the observation of more subtle geological changes and signs of mineralization, thereby improving the accuracy of predictions. The CNN-LSTM network combines the advantages of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). CNN is good at processing spatial features and can extract spatial patterns related to gold mineralization from satellite images; while LSTM can process time series data, taking into account the time dependence of geological processes, which helps capture dynamic changes in the mineralization process. This fusion makes the model more efficient and accurate in processing complex geological data.

[0028] Compared with traditional geological exploration methods, the use of high-resolution satellite images and deep learning models for gold mineralization prediction can significantly reduce exploration costs. At the same time, due to the higher accuracy of the prediction results, unnecessary drilling and sampling work can be reduced, further saving resources and time. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0030] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention;

[0031] Figure 2A simplified geological mineral map of the northern area of ​​Hongliuquan in the method for rapid gold ore prospecting in plateau desert areas of the present invention;

[0032] Figure 3 A 1:25,000 geochemical combination anomaly map of the northern area of ​​Hongliuquan in the method for rapid gold ore prospecting in plateau desert areas of the present invention;

[0033] Figure 4 It is a 1:10,000 soil measurement comprehensive anomaly map of the HS-1 anomaly area in the north of Hongliuquan in the rapid gold ore prospecting method in the plateau desert area of ​​the present invention;

[0034] Figure 5 This is a profile diagram of the Hongliuquan North 0-line exploration line in the rapid gold ore prospecting method in plateau desert areas of the present invention. DETAILED DESCRIPTION

[0035] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0036] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0037] Embodiment 1

[0038] like Figure 1-5 As shown, this embodiment provides a method for rapid gold prospecting in plateau desert areas, comprising:

[0039] Formation and Identification of Gold Anomalies

[0040] Causes of formation: The formation of gold anomalies may be related to a variety of geological processes, including magmatic activity, hydrothermal action, sedimentation, etc. These processes lead to the enrichment of gold elements in certain specific areas, thus forming detectable anomalies.

[0041] Identification method: The identification of gold anomalies mainly relies on geochemical exploration and geophysical exploration technology. Geochemical exploration identifies anomalies by analyzing the gold content in soil, stream sediments or rocks; while geophysical exploration uses the differences in physical properties between gold and other minerals (such as density, magnetism, resistivity, etc.) to detect anomalies.

[0042] Obtaining geological and geochemical data, and selecting prospecting areas based on the geological and geochemical data;

[0043] Acquire high-resolution satellite images of the prospecting area, acquire the geological, structural, and mineralized alteration characteristics of the abnormal area in the prospecting area based on the high-resolution satellite images, determine the gold mineralization type, and construct a geological data set based on the geological, structural, and mineralized alteration characteristics of the abnormal area and the gold mineralization type;

[0044] A CNN-LSTM network is constructed based on a long short-term memory network and a convolutional neural network, and the CNN-LSTM network is trained through key features related to historical gold mineralization to obtain a gold mineralization area prediction model;

[0045] The geological data set is input into the gold mineralization area prediction model for calculation to generate a gold mineralization area delineation result.

[0046] Furthermore, the process of obtaining geological and geochemical data and selecting a prospecting area based on the geological and geochemical data includes: analyzing the delineated gold anomalies based on 1:200,000 geological mapping, 1:200,000 geochemical survey, and 1:50,000 regional geological and mineral survey results data implemented in the historical working area to establish a prospecting area.

[0047] Furthermore, the process of obtaining the geological, structural, and mineralized alteration characteristics of the abnormal area in the prospecting area based on the high-resolution satellite image includes:

[0048] Conduct geochemical surveys on the prospecting area and identify gold anomalies based on the survey results;

[0049] The original gold element data and anomaly map of the gold anomaly are checked by 1:1 10,000 grass survey to obtain the distribution of geological structures;

[0050] 1:1 10,000-meter survey is a geological survey method, which is mainly used to conduct detailed geological survey and analysis of specific areas during mineral prospecting and detailed investigation. The following is a detailed introduction to 1:1 10,000-meter survey:

[0051] Definition: 1:1 geological survey is a small- to medium-scale geological route survey, the purpose of which is to obtain characteristic information such as geological structure, strata, igneous rock, mineralization and alteration in the area through field exploration and measurement.

[0052] Purpose: The purpose of this work is to identify the geological characteristics of the survey area, such as strata, structures, igneous rocks, etc., to study the mineralization conditions, ore-controlling factors, prospecting signs, etc. in the survey area, and to find clues or mineralized bodies on the surface in order to discover ore bodies and expand prospecting prospects.

[0053] Job content and requirements

[0054] Division of mapping units: Mapping units are mainly divided based on stratigraphic age, lithological combination, and mineralization and alteration.

[0055] Observation method: Route observation is mainly based on the crossing method and supplemented by the tracing method. Important geological bodies such as mineralized alteration outcrops, vein rocks, mineralized alteration zones, etc. are delineated as practically as possible, and fault structures regardless of their size are marked on the map.

[0056] Topographic base map: The topographic base map is a 1:50,000 or 1:100,000 topographic map enlarged into a 1:10,000 topographic map using semi-instrumental methods or using topographic features, engineering, and other landmarks to locate points.

[0057] Point and line density: There is no specific regulation on the point and line density, and the principle is to meet geological needs.

[0058] Processing the high-resolution satellite image of the prospecting area to obtain a processed image, extracting information from the processed image based on a principal component transformation method to obtain mineralization and alteration information;

[0059] The preprocessed data is integrated into a structured data set. Each row represents a sample (such as a drill hole, a profile, or a pixel), and each column represents a variable (such as the content of a certain element, the presence or absence of a certain mineral, etc.).

[0060] Since the magnitude and units of different variables may be different, the data needs to be standardized. The method of standardization is usually to convert each variable into a distribution with a mean of 0 and a variance of 1. Use the standardized data to calculate the covariance matrix. The covariance matrix reflects the linear relationship and interdependence between variables. Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors. The eigenvalue represents the variance of the data in different directions, while the eigenvector indicates these directions. Usually, the first few principal components with larger eigenvalues ​​are selected as the new variable basis, which explain most of the variation in the data. Analyze the relationship between the principal components and the original variables to determine the geological significance or mineralization alteration information represented by each principal component. This can be achieved by observing the principal component loading (i.e., the projection of the original variable on the principal component). For example, a principal component may mainly reflect the information of silicification alteration, while another principal component may be related to pyritization.

[0061] Based on the geological structure distribution and the mineralization and alteration information, the geological, structural and mineralization and alteration characteristics of the abnormal area in the prospecting area are obtained.

[0062] Furthermore, the process of constructing a geological data set based on the geology, structure, mineralization and alteration characteristics of the abnormal area and the gold mineralization type also includes:

[0063] Cleaning the abnormal values ​​and missing values ​​in the geological, structural, and mineralized alteration characteristics of the abnormal area, and normalizing the cleaned data to obtain a normalized data set;

[0064] The gold mineralization type is used as a label and merged with the normalized data set to generate the geological data set.

[0065] Furthermore, the process of obtaining the gold mineralization area prediction model includes:

[0066] The data set consisting of the key features related to the history and gold mineralization is input into the long short-term memory network for calculation to obtain the extracted features, and the extracted features are used as training data to train the convolutional network. At the same time, some weights are pruned during the training process until the loss function of the model is no longer reduced, and then the output is generated to generate the gold mineralization area prediction model.

[0067] Collect historical geological, geochemical, geophysical and other multi-source data related to gold mineralization. These data may include rock sample analysis results, soil measurement data, stream sediment measurement data and remote sensing images.

[0068] The collected data are cleaned and preprocessed to remove outliers and missing values, and necessary format conversion and standardization are performed to ensure the quality and consistency of the data.

[0069] Long short-term memory (LSTM) networks are used to process time series data and extract key features related to gold mineralization. These features may include the evolution of geological structures, the time series of mineralization and alteration information, etc.

[0070] The spatial data is processed by convolutional neural network (CNN) to extract features in images or spatial distribution. These features may include information such as strata, lithology, and structure in geological maps.

[0071] The features extracted by LSTM are input into CNN as training data for training. CNN can further learn the spatial distribution patterns and mutual relationships of these features.

[0072] During the training process, an adaptive learning rate optimizer (such as Adam) is used to dynamically adjust the learning rate to improve training efficiency.

[0073] At the same time, regularization techniques (such as Dropout and L1 / L2 regularization) are applied to prevent overfitting and improve the generalization ability of the model.

[0074] During the training process, the model weights are checked regularly and those that contribute less to the model performance are pruned. This helps reduce the complexity of the model and improves training speed and prediction efficiency.

[0075] The performance of the model is evaluated through methods such as cross-validation, and the model is adjusted and optimized based on the evaluation results.

[0076] When the loss function of the model no longer decreases significantly, the training is stopped and the final gold mineralization area prediction model is output.

[0077] Furthermore, the loss function of the gold mineralization area prediction model is expressed as:

[0078]

[0079] Among them, N represents the total number of samples, y i Represents the category to which the i-th sample belongs, y pred It is expressed as the probability of the model predicting the i-th sample, and log(·) is expressed as the logarithmic function. The predicted value y of the model pred As close as possible to the true label y i , when y i When the value of is 1, we expect y pred Close to 1, when y i When the value of is 0, we expect y pred Close to 0.

[0080] The final CNN-LSTM network consists of 1 convolution layer, 1 pooling layer, 3 recurrent layers and 2 fully connected layers, wherein the convolution layer uses 256 2*1 convolution kernels to convolve the signal to obtain 256 1X128-dimensional feature maps; the pooling layer performs dimensionality reduction processing on the output of the convolution layer to reduce the size of the feature map, reducing the merged one-dimensional signal of size 1X128 to 1X64; each recurrent layer includes three layers of long short-term memory networks, which are used to process the one-dimensional signal after convolution to mine the timing information of the received signal; the output of the network is a 1X2-dimensional unique hot encoding form, SM code corresponds to

[10] , AL code corresponds to

[01] ; the output layer uses Soft-max as the activation function, and the remaining networks all use ReLU as the activation function.

[0081] It can be understood that the final CNN-LSTM network mainly includes 1 convolution layer, 1 pooling layer, 3 recurrent layers and 2 fully connected layers. Except for the last fully connected layer with only 2 nodes, which uses the normalized exponential function (Soft-max function) as the activation function, the rest of the network uses a linear rectification function. The output of the network is a 1×2-dimensional One-Hot encoding form, and the input layer is a 2×128 input vector.

[0082] In order to use storage efficiently, the network needs to be quantized in combination with pruning. Quantization is learning a reduced bit precision without affecting network performance. It is considered to be one of the most effective ways to meet the low memory requirements of resource-constrained devices. It is used to store weights and subsequent matrix calculations in a compact format such as float-16, int-32 or even lower for neural networks in the forward, backward and update processes. This efficient use of storage makes it possible to load large models into on-chip SRAM cache instead of off-chip DRAM memory. Post-training quantization converts weights from floating-point representation to integer representation with 8 bits of precision. At inference time, activations are also converted to int8 format and further calculations are performed using integer-based weights.

[0083] Furthermore, after generating the gold mineralization area delineation result, the method further includes: arranging 8 drill holes based on the gold mineralization area delineation result to conduct deep verification of the corresponding ore body, and the drill holes are 75° inclined holes.

[0084] Specifically:

[0085] Step 1: Collect the results of 1:200,000 geological mapping, 1:200,000 geochemical survey, 1:50,000 regional geological and mineral survey implemented by previous researchers in the work area, analyze the identified gold anomalies, select the areas with better fit between 1:200,000 As32 single gold anomaly and 1:50,000 HS56 gold anomaly, and establish the prospecting area.

[0086] Step 2: Carry out 1:25,000 geochemical survey work in the selected prospecting area, using stream sediments and rock debris residual slope deposits as sampling media, and sampling and intercepting grain sizes of -10 mesh to +60 mesh. And make sampling point maps of Au and other elements, single element anomaly maps, original data maps, etc., circle gold anomalies, and circle 7 gold anomalies, namely Hs-1 to Hs-7 anomalies.

[0087] Step 3: Using the original gold data and anomaly maps as carriers, the 7 gold anomalies identified in step 2 were inspected using a 1:1 survey to understand the distribution of strata, structures, and igneous rocks, collect mineralization clues such as mineralization and alteration, and use the block method to collect reconnaissance chemical samples. Preliminary clues to gold mineralization were found in the Hs-1 and Hs-2 anomaly areas. The main body of the Hs-1 anomaly area is quartz veins, and the main body of the Hs-2 anomaly area is northwest-trending tectonic alteration belts.

[0088] Step 4: Special tracing and mapping of quartz veins and structural deformation zones in the Hs-1 and Hs-2 abnormal areas was carried out, 135 quartz veins and 5 structural alteration zones were delineated, and a large-scale geological map was drawn. The structural alteration zone was traced along the strike using a 1:2000 rock geochemical profile, and the extension and distribution of the alteration zone and its mineralization enrichment characteristics were more accurately controlled.

[0089] Step 5: Through surface tracing, surface trenching, stripping and other prospecting projects were used to expose and control quartz veins and alteration zones. A total of 45 gold deposits or mineralized bodies were accurately circled and connected using groove samples. The true thickness, grade, occurrence and other characteristics of the ore (mineralization) bodies were accurately controlled, and the trenching project was laid out on the sampling plan.

[0090] Step 6: Based on the scale and occurrence of the ore body revealed by the exploration trench, 8 drill holes were selected to conduct deep verification of the corresponding ore body. The drill holes were arranged at a 75° inclination, and gold deposits or mineralized bodies were delineated in the drill holes. The purpose of gold prospecting was achieved.

[0091] On the other hand, this embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, the method for rapid gold prospecting suitable for plateau desert areas is implemented.

[0092] On the other hand, this embodiment also provides a computer-readable storage medium, which stores a computer program, and is characterized in that when the computer program is executed by a processor, it implements the method for rapid gold prospecting suitable for plateau desert areas.

[0093] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for rapid gold prospecting in plateau desert areas, characterized in that: include: Obtaining geological and geochemical data, and selecting prospecting areas based on the geological and geochemical data; Acquire high-resolution satellite images of the prospecting area, acquire the geological, structural, and mineralized alteration characteristics of the abnormal area in the prospecting area based on the high-resolution satellite images, determine the gold mineralization type, and construct a geological data set based on the geological, structural, and mineralized alteration characteristics of the abnormal area and the gold mineralization type; A CNN-LSTM network is constructed based on a long short-term memory network and a convolutional neural network, and the CNN-LSTM network is trained through key features related to historical gold mineralization to obtain a gold mineralization area prediction model; The geological data set is input into the gold mineralization area prediction model for calculation to generate a gold mineralization area delineation result.

2. A method for rapid gold prospecting in plateau desert areas according to claim 1, characterized in that: The process of obtaining geological and geochemical data and selecting a prospecting area based on the geological and geochemical data includes: analyzing the delineated gold anomalies based on 1:200,000 geological mapping, 1:200,000 geochemical survey, and 1:50,000 regional geological and mineral survey results data implemented in the historical work area to establish a prospecting area.

3. A method for rapid gold prospecting in plateau desert areas according to claim 1, characterized in that: The process of obtaining the geological, structural and mineralized alteration characteristics of the abnormal area in the prospecting area based on the high-resolution satellite image includes: Conduct geochemical surveys on the prospecting area and identify gold anomalies based on the survey results; The original gold element data and anomaly map of the gold anomaly are checked by 1:1 10,000 grass survey to obtain the distribution of geological structures; Processing the high-resolution satellite image of the prospecting area to obtain a processed image, extracting information from the processed image based on a principal component transformation method to obtain mineralization and alteration information; Based on the geological structure distribution and the mineralization and alteration information, the geological, structural and mineralization and alteration characteristics of the abnormal area in the prospecting area are obtained.

4. A method for rapid gold prospecting in plateau desert areas according to claim 1, characterized in that: The process of constructing a geological data set based on the geology, structure, mineralization and alteration characteristics of the anomalous area and the gold mineralization type also includes: Cleaning the abnormal values ​​and missing values ​​in the geological, structural, and mineralized alteration characteristics of the abnormal area, and normalizing the cleaned data to obtain a normalized data set; The gold mineralization type is used as a label and merged with the normalized data set to generate the geological data set.

5. The method for rapid gold prospecting in plateau desert areas according to claim 1 is characterized in that: The process of obtaining the gold mineralization region prediction model comprises: The data set consisting of the key features related to the history and gold mineralization is input into the long short-term memory network for calculation to obtain the extracted features, and the extracted features are used as training data to train the convolutional network. At the same time, some weights are pruned during the training process until the loss function of the model is no longer reduced, and then the output is generated to generate the gold mineralization area prediction model.

6. A method for rapid gold prospecting in plateau desert areas according to claim 1, characterized in that: The loss function of the gold mineralization region prediction model is expressed as: Among them, N represents the total number of samples, y i Represents the category to which the i-th sample belongs, y pred It is expressed as the probability of the model predicting the i-th sample, log(·) is expressed as the logarithmic function; the predicted value y of the model pred As close as possible to the true label y i , when y i When the value of is 1, we expect y pred Close to 1, when y i When the value of is 0, we expect y pred Close to 0.

7. The method for rapid gold prospecting in plateau desert areas according to claim 1 is characterized in that: After generating the gold mineralization area delineation result, the method further includes: arranging 8 drill holes based on the gold mineralization area delineation result to conduct deep verification of the corresponding ore body, and the drill holes are 75° inclined holes.

8. An electronic device comprising a memory, a processor, and a computing program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method described in any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method described in any one of claims 1 to 7 is implemented.

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