Regional interlocking cooperative early warning method, system and device, and storage medium

Abstract

The invention discloses a regional interlocking cooperative early warning method, system and device, and a storage medium. The method specifically comprises the following steps: S1, obtaining field production data from a controller of each oil and gas production station; s2, performing dimension reduction extraction on the field production data of each oil and gas production station by adopting a principal component analysis method to obtain key data; s3, normalizing the key data, and then performing correlation degree analysis among the key data; s4, constructing a multivariate nonlinear regression and L1 regularization constraint model based on the correlation degree, and outputting a predicted value; and S5, determining a dynamic threshold value according to the segmented mean value and the standard deviation, and giving out early warning when more than 50% of predicted values exceed the dynamic threshold value. Main component analysis is carried out on a large amount of data of each station in a field area to extract key data, correlation analysis is utilized to obtain the correlation degree of the key data, the high precision characteristic of multivariate nonlinear regression and the characteristic of lasso regression parameter screening are combined, training and fitting are carried out by utilizing a neural network, and the accuracy of the data is improved. The prediction method with the minimum deviation and the optimal coincidence rate is obtained, when data with the high correlation degree changes at the same time, early warning is generated, and prediction is not affected by a single outlier.

Description

Technical Field

[0001] The present invention relates to the field of safety technology, and in particular to a regional interlocking collaborative early warning method, system, equipment, and storage medium for oil and gas production sites. Background Art

[0002] In existing technologies, oil and gas production sites have basically achieved unmanned operation based on automatic control of RTU (Remote Terminal Unit), PLC (Programmable Logic Controller), and DCS (Distributed Control System). The central station uses data uploaded by unmanned sites to conduct daily production management of the subordinate areas through alarms, interlocking, and inspections.

[0003] Alarms and area interlocks are set based on manual experience to set alarm limits and interlock limits. When a fault occurs and reaches the threshold, the alarm or area interlock is triggered.

[0004] However, on-site production at oil and gas production sites is dynamic, and production conditions change accordingly. Using fixed, manual experience to detect production anomalies can be time-delayed. In actual production, various on-site conditions are not independent of one another, and there are inherent correlations between various production data. Understanding the interrelationships and correlations between production data, and using mathematical models to infer whether regional production conditions are abnormal, can be beneficial for early detection of production situational issues, emergency response, and early-stage management. Summary of the Invention

[0005] In response to the technical problem of untimely early warning in the existing technology, the present invention discloses a regional interlocking collaborative early warning method and system for oil and gas production sites, which effectively improves the situational awareness capability of regional production conditions, realizes early warning of important production anomalies, and reduces the incidence of safety accidents.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A regional interlocking collaborative early warning method specifically comprises the following steps:

[0008] S1: Obtain field production data from the controller of each oil and gas production site;

[0009] S2: Use principal component analysis to reduce the dimensionality of field production data from each oil and gas production site to extract key data;

[0010] S3: Normalize the key data and then analyze the correlation between the key data;

[0011] S4: Construct a multivariate nonlinear regression and L1 regularization constraint model based on the correlation degree and output the predicted value;

[0012] S5: Determine the dynamic threshold based on the segment mean and standard deviation, and issue an early warning when more than 50% of the predicted values ​​exceed the dynamic threshold.

[0013] Preferably, in said S1, the oil and gas production site includes three production platforms, a gas gathering station and a dehydration station.

[0014] Preferably, in said S1, the on-site production data includes pressure, temperature and humidity.

[0015] Preferably, in S2, the method for extracting key data is:

[0016] After matrix transformation, the field production data of the i-th oil and gas production station are zero-meaned to solve the covariance matrix and the eigenvalue λ of the i-th oil and gas production station. i , the characteristic vector C of the i-th oil and gas production site i , i = 1, 2, ..., n, where n represents the total number of oil and gas production sites; the field production data is then mapped to the feature vector space to achieve dimensionality reduction of the field production data:

[0017]

[0018] In formula (1), C i represents the covariance matrix of the i-th oil and gas production site; X i represents the on-site production data matrix of the i-th oil and gas production site; X i T represents the transposed matrix of the on-site production data matrix of the i-th oil and gas production site; m represents the number of columns of the on-site production data matrix of the i-th oil and gas production site;

[0019] P i =C i ×X i (2)

[0020] In formula (2), P i Represents the key data of the i-th oil and gas production site.

[0021] Preferably, in S3, the method of correlation analysis is:

[0022] First, normalize the key data to obtain the normalized key data set matrix P∈R m×n , then the parent sequence P0 and each subsequence P i The correlation coefficient at the kth moment Expressed as:

[0023]

[0024] In formula (3), Represents the correlation coefficient at the kth moment; α=min i min k |P0(k)-P i (k)| represents the minimum difference between the two levels; P0(k) represents the parent sequence at the kth moment; P i (k) represents the i-th subsequence at the k-th moment; b = max i max k |P0(k)-P i (k)| represents the maximum difference between the two levels; ρ represents the resolution coefficient; i = 1, 2, ..., n-1;

[0025] Therefore, the parent sequence P0 and each subsequence P i The correlation is:

[0026]

[0027] In formula (4), r i Indicates the degree of association; M indicates the number of rows; represents the correlation coefficient at the kth moment.

[0028] Preferably, in S4, the multivariate nonlinear regression model is:

[0029]

[0030] In formula (5), represents the predicted value; β0 represents the regression coefficient of the intercept; β i represents the regression coefficient of the i-th term; Represents the jth power of the i-th key data, Represents multiple independent variables and their combinations.

[0031] Preferably, in S4, the accuracy of the multivariate nonlinear regression model is evaluated using mean absolute error and root mean square error:

[0032]

[0033] In formula (6), MAE represents mean absolute error; RMSE represents root mean square error; N represents the total number of test samples. Represents the predicted value of the test sample, y represents the observed value; MAE measures the absolute error between the predicted value and the observed value, and RMSE represents the deviation between the predicted value and the observed value. The smaller these two indicators are, the better the model fitting effect and the higher the accuracy.

[0034] Preferably, in S5, the method for determining the dynamic threshold is:

[0035] The dynamic threshold is determined by adding or subtracting the standard deviation of the segmented data:

[0036] Y.td=X.mean±λ*X.std (7)

[0037] In formula (7), Y.td represents the dynamic threshold; X.mean represents the mean of a certain range of actual values; λ represents the multiplication factor of the standard deviation, and X.std represents the standard deviation of the actual value.

[0038] The present invention also provides a regional interlocking early warning system for oil and gas production sites, comprising a data acquisition module, a data extraction module, a correlation analysis module, a prediction model construction module, a threshold determination module and an early warning module;

[0039] Among them, the data acquisition module is used to obtain on-site production data from the controller of each oil and gas production station;

[0040] The data extraction module is used to extract the on-site production data of each oil and gas production site using principal component analysis to obtain key data;

[0041] Relevance analysis module, used to perform correlation analysis on key data;

[0042] The prediction model building module is used to build a multivariate nonlinear regression model based on the correlation degree to output the prediction value;

[0043] A threshold determination module is used to dynamically determine the warning threshold;

[0044] The early warning module is used to issue an early warning when the predicted value exceeds the early warning threshold.

[0045] Preferably, the warning includes sound, light, and information push.

[0046] The present invention also provides a computer device, including a processor and a memory, wherein a computer program is stored in the memory, and when the processor executes the computer program, the above-mentioned regional interlocking collaborative warning method is implemented.

[0047] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned regional interlocking collaborative warning method is implemented.

[0048] In summary, due to the adoption of the above technical solution, compared with the prior art, the present invention has at least the following beneficial effects:

[0049] The present invention extracts key data by performing principal component analysis on a large amount of data from an on-site area, obtains the correlation degree of the key data through correlation analysis, combines the high precision characteristics of multivariate nonlinear regression and the characteristics of lasso regression parameter screening, and uses neural network training and fitting to obtain a prediction method with minimum deviation and optimal compliance rate. When data with high correlation degrees change at the same time, an early warning is generated, and a single outlier does not affect the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The figure is a flow chart of a regional interlocking collaborative early warning method for oil and gas production sites according to an exemplary embodiment of the present invention.

[0051] Figure 2 Schematic diagram of the structure of an oil and gas production station according to an exemplary embodiment of the present invention.

[0052] Figure 3 Schematic diagram of key data correlation analysis according to an exemplary embodiment of the present invention.

[0053] Figure 4 This is a schematic diagram of the fixed threshold detection method in the prior art.

[0054] Figure 5 FIG. 4 is a schematic diagram of dynamic threshold detection according to an exemplary embodiment of the present invention.

[0055] Figure 6 FIG. 4 is a schematic diagram of dynamic threshold detection according to an exemplary embodiment of the present invention.

[0056] Figure 7 FIG. 4 is a schematic diagram of pressure threshold prediction according to an exemplary embodiment of the present invention.

[0057] Figure 8 Schematic diagram of a regional interlocking warning system for oil and gas production sites according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present invention will be further described in detail below with reference to the examples and specific implementation methods. However, this should not be understood as limiting the scope of the present invention to the following examples, as all technologies implemented based on the present invention fall within the scope of the present invention.

[0059] In the description of the present invention, it should be understood that the terms "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0060] like Figure 1 As shown, the present invention provides a regional interlocking collaborative early warning method for oil and gas production sites, which specifically includes the following steps:

[0061] S1: Obtain field production data from the controller of each oil and gas production site.

[0062] In this embodiment, the oil and gas production site is generally a typical regional production model, that is, it includes multiple related production facilities.

[0063] like Figure 2 As shown, Huang 202 well area includes three production platforms (Huang 202H1, Huang 202H2, and Huang 202H3), a gas gathering station, and a dehydration station. Huang 202 well area is connected to Huang 203 and Huang 205 well areas. Each production platform, gas gathering station, and dehydration station is equipped with an independent controller (with a unique number), such as an RTU and PLC.

[0064] In this embodiment, a self-developed MODBUS-TCP data acquisition module is used to obtain field production data, including pressure, temperature, humidity, etc., from the RTU and PLC under the jurisdiction of the Huang 202 well area.

[0065] S2: Use principal component analysis to extract the field production data of each oil and gas production site to obtain key data.

[0066] In this embodiment, the latitude of the collected field production data is 192 points. Due to the large number of data points, the complexity of the correlation analysis will be greatly increased. Therefore, the PCA principal component analysis method will be used to perform data dimensionality reduction on the field production data of each oil and gas production station:

[0067] After matrix transformation, the field production data of the i-th oil and gas production station are zero-meaned to solve the covariance matrix and the eigenvalue λ of the i-th oil and gas production station. i (i=1,2,...,n), the characteristic vector C of the i-th oil and gas production site i(i=1,2,...,n), where n represents the total number of oil and gas production sites; the field production data is then mapped to the feature vector space to achieve dimensionality reduction of the field production data.

[0068] For example, the covariance matrix of the field production data matrix X with n rows and m columns after zero mean can be expressed as:

[0069]

[0070] In formula (1), C i represents the covariance matrix of the i-th oil and gas production site; X i represents the on-site production data matrix of the i-th oil and gas production site; X i T represents the transposed matrix of the on-site production data matrix of the i-th oil and gas production site; m represents the number of columns of the on-site production data matrix of the i-th oil and gas production site;

[0071] P i =C i ×X i (2)

[0072] In formula (2), P i It represents the key data of the i-th oil and gas production station, including the inlet and outlet pressures, as shown in Table 1.

[0073] Table 1. Key data obtained after PCA analysis

[0074]

[0075] S3: Normalize the key data and then perform correlation analysis between the key data.

[0076] In this embodiment, it is first necessary to perform strict time alignment operations on the key data of each oil and gas production site.

[0077] Then normalize the key data to obtain the normalized key data set matrix P∈R m×n , then the parent sequence P0 and each subsequence P i The correlation coefficient at the kth moment between (i=1,2,...,n-1) It can be expressed as:

[0078]

[0079] In formula (3), Represents the correlation coefficient at the kth moment; α=min i min k |P0(k)-P i(k)| represents the minimum difference between the two levels; P0(k) represents the parent sequence at the kth moment; P i (k) represents the i-th word series at the k-th moment; b = max i max k |P0(k)-P i (k)| represents the maximum difference between the two levels; ρ represents the resolution coefficient (usually 0.5).

[0080] In this embodiment, the mother sequence belongs to a subset of P and represents a reference sequence.

[0081] Therefore, the parent sequence P0 and each subsequence P i The correlation is:

[0082]

[0083] In formula (4), r i Indicates the degree of association; M indicates the number of rows; represents the correlation coefficient at the kth moment.

[0084] like Figure 3 As shown, the various working conditions on site are not independent of each other. There is an inevitable correlation between the key data, that is, there is a corresponding correlation between the key data; for example, the correlation between the key data with serial numbers P1 and P8 is 0.86, and the correlation between the key data with serial numbers P2 and P7 is 0.63.

[0085] S4: Based on the correlation, a multivariate nonlinear regression and L1 regularization constraint model is constructed, and the prediction value is output in combination with the existing prediction method.

[0086] In this embodiment, the data after correlation analysis can be used for multiple linear regression, using multiple correlated variables to fit the change trend of another variable. Multiple linear regression is based on the idea of ​​least squares method, using linear fitting nonlinearity, and gradually approaching the optimal solution through gradient descent. For data with small fluctuations, multiple linear regression analysis can obtain better fitting accuracy; however, for data with large fluctuations, the complexity of multiple linear regression is insufficient and the fitting accuracy is insufficient. Therefore, a multiple nonlinear regression model is used to fit the multivariate equation, as shown in the following formula:

[0087]

[0088] In formula (5),, represents the predicted value; β0 represents the regression coefficient of the intercept; β i represents the regression coefficient of the i-th term; Represents the jth power of the i-th key data, Represents multiple independent variables and their combinations, such as

[0089] In this embodiment, multivariate linear or multivariate nonlinear fitting is based on the least squares method, and least squares estimation is an unconstrained optimization problem, which is prone to overfitting and non-unique solutions; therefore, variable selection is required for multivariate regression fitting to eliminate overfitting and obtain the optimal solution.

[0090] In this example, prediction methods that are currently widely used in practice were selected for comparison, including the historical average method (HA), kernel ridge regression algorithm (Kernel Ridge), random forest algorithm (RF), extreme gradient boosting algorithm (XGBoost), and lasso regression (Least absolute shrinkage and selection operator, LASSO). The mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate the accuracy of the multivariate nonlinear regression model, as shown in Table 2.

[0091]

[0092] In formula (6), N represents the total number of test samples, represents the predicted value of the test sample, and y represents the observed value. MAE measures the absolute error between the predicted value and the observed value, and RMSE represents the deviation between the predicted value and the observed value. The smaller these two indicators are, the better the model fit and the higher the accuracy.

[0093] Table 2 Comparison of prediction performance of various algorithms using mean absolute error (MAE) and root mean square error (RMSE)

[0094]

[0095] In this embodiment, in order to further improve the accuracy of the prediction, the variable combination of the multivariate nonlinear regression model is substituted into the lasso regression, which can reduce the complexity of the nonlinear fitting and has a good fitting effect (96.51% compliance). As a shrinkage estimation method, the basic idea of ​​lasso regression is to minimize the sum of squared residuals under the constraint that the sum of the absolute values ​​of the regression coefficients is less than a constant, so as to generate certain regression coefficients that are strictly equal to 0. Although some deviation values ​​are sacrificed, the accuracy of the prediction is increased. Its parameter estimation is defined as,

[0096]

[0097] In formula (7), β Lasso represents the lasso regression coefficient; Y represents the independent variable, that is, the data to be predicted; λ is the regularization non-negative parameter; β j represents the regression coefficient; Xj represents the dependent variable, X j =(X 1j ,X 2j ,...X nj ) T ,j=1,2,...,p,X nj Indicates the jth key data.

[0098] Since the L1 regularization term uses the sum of absolute values, the loss function has non-differentiable points, which can be solved by using the coordinate axis descent method.

[0099] S5: Determine a dynamic threshold and issue an early warning when the predicted value exceeds the dynamic threshold.

[0100] In the prior art, such as Figure 4 As shown, when the fixed threshold method is used, when the on-site working conditions change during the period of 2510-2610 minutes, no early warning will be triggered, and the perception of on-site production dynamics will be weak.

[0101] Therefore, in this embodiment, Figure 5 and Figure 6 As shown in the figure, the dynamic threshold is determined by adding or subtracting 2 to 3 times the standard deviation of the segmented data mean. Local high values ​​can still be detected during the period of 2510-2610 minutes, which enhances the dynamic detection capability of production conditions.

[0102] Y.td=X.mean±λ*X.std (8)

[0103] In formula (8), Y.td represents the dynamic threshold; X.mean represents the mean of a certain range of actual values; λ represents the multiplication factor of the standard deviation, and X.std represents the standard deviation of the actual value.

[0104] In this embodiment, Figure 7 It can be seen that at the 365th data point, the pressure parameters have exceeded the normal threshold, showing abnormal high pressure. Earlier, at the 243rd data point, that is, at 7:49, the parameters P1, P4, P5, and P7 were all abnormal, which can generate a regional warning. The prediction window set by the prediction model proposed in this method is 5, which can give an early warning at 7:44, with a certain advance amount. In addition, from Figure 4 It can be seen from the P10 parameter in the figure that the fixed threshold method is not sensitive to single outlier noise when predicting trends; however, when the dynamic threshold is used, it can effectively avoid the measurement values ​​​​deviating significantly from the normal range due to the influence of the device's own status such as communication anomalies, thereby reducing a large number of invalid warnings.

[0105] Based on the above method, Figure 8As shown, the present invention also provides a regional interlocking early warning system for oil and gas production sites, including a data acquisition module, a data extraction module, a correlation analysis module, a prediction model construction module, a threshold determination module and an early warning module.

[0106] In this embodiment, the output end of the data acquisition module is connected to the input end of the data extraction module, the output end of the data extraction module is connected to the input end of the correlation analysis module, the output end of the correlation analysis module is connected to the input end of the prediction model construction module, and the output end of the prediction model construction module and the output end of the threshold determination module are respectively connected to the input end of the early warning module.

[0107] Among them, the data acquisition module is used to obtain on-site production data from the controller of each oil and gas production station, including pressure, temperature, humidity, etc.

[0108] The data extraction module is used to extract the on-site production data of each oil and gas production site using principal component analysis to obtain key data;

[0109] Relevance analysis module, used to perform correlation analysis on key data;

[0110] The prediction model building module is used to build a multivariate nonlinear regression model based on the correlation degree to output the prediction value;

[0111] A threshold determination module is used to dynamically determine the warning threshold;

[0112] The early warning module is used to issue an early warning when the predicted value exceeds the early warning threshold, including sound, light, information and other means.

[0113] The present invention also provides a computer device, including a processor and a memory, wherein a computer program is stored in the memory, and when the processor executes the computer program, the above-mentioned regional interlocking collaborative warning method is implemented.

[0114] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned regional interlocking collaborative warning method is implemented.

[0115] Those skilled in the art will appreciate that the above-mentioned embodiments are specific examples for implementing the present invention, and that in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A regional interlocking collaborative early warning method, characterized in that: The specific steps include: S1: Obtain field production data from the controller of each oil and gas production site; S2: Use principal component analysis to reduce the dimensionality of field production data from each oil and gas production site to extract key data; S3: Normalize the key data and then analyze the correlation between the key data; S4: Construct a multivariate nonlinear regression and L1 regularization constraint model based on the correlation degree and output the predicted value; S5: Determine the dynamic threshold based on the segment mean and standard deviation, and issue an early warning when more than 50% of the predicted values ​​exceed the dynamic threshold.

2. A regional interlocking collaborative early warning method according to claim 1, characterized in that: In S1, the oil and gas production site includes three production platforms, a gas gathering station and a dehydration station.

3. A regional interlocking collaborative early warning method according to claim 1, characterized in that: In S1, the on-site production data includes pressure, temperature, and humidity.

4. A regional interlocking collaborative early warning method according to claim 1, characterized in that: In S2, the key data extraction method is: After matrix transformation, the field production data of the i-th oil and gas production station are zero-meaned to solve the covariance matrix and the eigenvalue λ of the i-th oil and gas production station. i , the characteristic vector C of the i-th oil and gas production site i , i = 1, 2, ..., n, where n represents the total number of oil and gas production sites; the field production data is then mapped to the feature vector space to achieve dimensionality reduction of the field production data: In formula (1), C i represents the covariance matrix of the i-th oil and gas production site; X i represents the on-site production data matrix of the i-th oil and gas production site; X i T represents the transposed matrix of the on-site production data matrix of the i-th oil and gas production site; m represents the number of columns of the on-site production data matrix of the i-th oil and gas production site; P i =C i ×X i (2) In formula (2), P i Represents the key data of the i-th oil and gas production site.

5. The regional interlocking collaborative early warning method according to claim 1, characterized in that: In S3, the method of correlation analysis is: First, normalize the key data to obtain the normalized key data set matrix P∈R m×n , then the parent sequence P0 and each subsequence P i The correlation coefficient at the kth moment Expressed as: In formula (3), Represents the correlation coefficient at the kth moment; α=min i min k |P0(k)-P i (k)| represents the minimum difference between the two levels; P0(k) represents the parent sequence at the kth moment; P i (k) represents the i-th subsequence at the k-th moment; b = max i max k |P0(k)-P i (k)| represents the maximum difference between the two levels; ρ represents the resolution coefficient; i = 1, 2, ..., n-1; Therefore, the parent sequence P0 and each subsequence P i The correlation is: In formula (4), r i Indicates the degree of association; M indicates the number of rows; represents the correlation coefficient at the kth moment.

6. A regional interlocking collaborative early warning method according to claim 1, characterized in that: In S4, the multivariate nonlinear regression model is: In formula (5), represents the predicted value; β0 represents the regression coefficient of the intercept; β i represents the regression coefficient of the i-th term; Represents the jth power of the i-th key data, Represents multiple independent variables and their combinations.

7. The regional interlocking coordinated early warning method according to claim 1, characterized in that: In S4, the mean absolute error and root mean square error are used to evaluate the accuracy of the multivariate nonlinear regression model: In formula (6), MAE represents mean absolute error; RMSE represents root mean square error; N represents the total number of test samples. represents the predicted value of the test sample, and y represents the observed value; MAE measures the absolute error between the predicted value and the observed value, and RMSE represents the deviation between the predicted value and the observed value. The smaller these two indicators are, the better the model fitting effect and the higher the accuracy.

8. The regional interlocking coordinated early warning method according to claim 1, characterized in that: In S5, the method for determining the dynamic threshold is: The dynamic threshold is determined by adding or subtracting the standard deviation of the segmented data: Y.td=X.mean±λ*X.std (7) In formula (7), Y.td represents the dynamic threshold; X.mean represents the mean of a certain range of actual values; λ represents the multiplication factor of the standard deviation, and X.std represents the standard deviation of the actual value.

9. A regional interlocking warning system based on the method according to any one of claims 1 to 8, characterized in that: It includes data acquisition module, data extraction module, correlation analysis module, prediction model construction module, threshold determination module and early warning module; Among them, the data acquisition module is used to obtain on-site production data from the controller of each oil and gas production station; The data extraction module is used to extract the on-site production data of each oil and gas production site using principal component analysis to obtain key data; Relevance analysis module, used to perform correlation analysis on key data; The prediction model building module is used to build a multivariate nonlinear regression model based on the correlation degree to output the prediction value; A threshold determination module is used to dynamically determine the warning threshold; The early warning module is used to issue an early warning when the predicted value exceeds the early warning threshold.

10. The regional interlocking warning system according to claim 9, characterized in that: The warning includes sound, light, and information push.

11. A computer device comprising a processor and a memory, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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