A method, system, device and storage medium for causal reasoning of abnormal indicators in power grid post-dispatch evaluation

Abstract

The present invention discloses a method, system, device and storage medium for abductive reasoning of abnormal indicators of power grid dispatch post-evaluation, and the method comprises: obtaining time series data during power grid operation, extracting abnormal indicators and various related factors according to the time series data; calculating the correlation between abnormal indicators and various related factors, and obtaining strong related factors; calculating the causal relationship between abnormal indicators and various related factors, and obtaining strong causal factors; constructing a Bayesian network based on strong related factors, strong causal factors and abnormal indicators; performing parameter learning on the Bayesian network, and performing abductive reasoning of abnormal indicators based on the Bayesian network after parameter learning. The present invention can solve the key technical problem of abnormal indicator dominant factor analysis.

Description

Technical Field

[0001] The invention relates to a causal reasoning method, system, device and storage medium for abnormal index evaluation after power grid dispatch, and belongs to the technical field of power system automation. Background Art

[0002] Grid operation indicators are an important carrier for scientifically evaluating the safe and efficient operation of the grid and guiding the grid to optimize its operation. They are also a technical means to effectively improve the lean level of grid operation. At present, many studies have been conducted on grid operation evaluation indicators. However, there is a lack of automated and intelligent evaluation methods for the changing rules of grid operation indicators, the location of abnormal indicators, and the cause analysis. Operators are often required to conduct manual analysis based on a large amount of historical data and manual experience, which is labor-intensive and inefficient, and cannot fully meet the needs of lean grid operation. This is especially true in the massive amount of data generated during grid operation. Evaluation indicators are only considered from different aspects, lack correlation analysis, and the causes of abnormal indicators are difficult to accurately locate.

[0003] The power system has the characteristics of real-time balance of power generation and consumption, real-time non-stop operation, and the diffusion of accidents. This leads to massive, rapidly growing and diverse data generated during the operation of the power system. The existing real-time indicator evaluation system is based on a certain operation indicator or a category of operation indicators of the power grid. The supporting applications corresponding to each indicator are relatively independent. However, due to the complexity of the power grid, these operation indicators cannot fully cover various risk situations in the power grid. It is necessary to use big data mining technology to extract, analyze and process massive control system data, analyze the correlation between different indicators, infer and trace the deep reasons behind the abnormal indicators, and provide power grid dispatchers with the optimal decision-making of the power grid under different conditions to improve the reliability of power supply. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a method, system, device and storage medium for causal reasoning of abnormal indicators after power grid dispatch evaluation, which can find the main causes of indicator abnormalities by constructing a Bayesian network and has strong practicality and flexibility.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention provides a method for abductive reasoning of abnormal indicators of power grid dispatch post-evaluation, comprising:

[0007] Obtain time series data during the operation of the power grid, and extract abnormal indicators and various related factors based on the time series data;

[0008] Calculate the correlation between abnormal indicators and various related factors to obtain strong correlation factors;

[0009] Calculate the causal relationship between abnormal indicators and various related factors to obtain strong causal factors;

[0010] Construct a Bayesian network based on strong correlation factors, strong causal factors, and abnormal indicators;

[0011] Parameters of the Bayesian network are learned, and abductive reasoning of abnormal indicators is performed based on the Bayesian network after parameter learning.

[0012] The abnormal index is an index of the deviation of time series data from the normal time series distribution curve.

[0013] Optionally, the time series data includes power grid equipment parameter data, power grid operation parameter data, power grid node power generation and load parameter data, and environmental parameter data.

[0014] Optionally, calculating the correlation between the abnormal index and each correlation factor to obtain the strong correlation factor includes:

[0015] Construct a dataset of abnormal indicators and various associated factors:

[0016]

[0017] In the formula, x0 is the time series data corresponding to the abnormal indicator, x i (i=1,2,…n) is the time series data corresponding to each correlation factor, x n (N) is the Nth time series data of the nth correlation factor;

[0018] According to the data set, the difference matrix sequence between the abnormal index x0 and the i-th correlation factor is obtained:

[0019] Δ 0i (l)=|x0(l)-x i (l)|, l=1,2,…,N; i=1,2,…,n

[0020] According to the difference matrix sequence, the correlation coefficient between the abnormal index x0 and the i-th correlation factor is obtained:

[0021]

[0022] In the formula, Δ min and Δ max are the minimum and maximum values ​​in the difference matrix sequence respectively, and ρ is the resolution coefficient. The correlation between the abnormal index x0 and the i-th correlation factor is obtained according to the correlation coefficient:

[0023]

[0024] Take the correlation r 0i The correlation factor x is greater than or equal to the preset correlation threshold iAs a strong correlation factor of the abnormal indicator x0.

[0025] Optionally, calculating the causal relationship between the abnormal indicator and each associated factor to obtain a strong causal factor includes:

[0026] For the time series data corresponding to the abnormal index and any associated factor, a linear autoregressive model of the two is constructed:

[0027]

[0028] Where, X t is the time series data corresponding to the abnormal indicator, Y t is the time series data corresponding to any associated factor; a 1j and d 1j is the fitting coefficient of the autoregressive model, ε 1t and η 1t is the residual, ∑1 and Γ1 are the residual ε 1t and η 1t The variance of

[0029] Perform joint regression on the linear autoregressive model to construct a regression model:

[0030]

[0031] In the formula, a 2j , b 2j 、c 2j and d 2j is the fitting coefficient of the regression model, ε 2t and η 2t is the time-independent prediction error, ∑2 and Γ2 are the prediction errors ε 2t and η 2t The variance of

[0032] Calculate Y t X t Granger causality:

[0033]

[0034] If F Y→X If Y is greater than the preset critical value, t Yes X t The Granger reason, there is a causal relationship;

[0035] The associated factors with causal relationships are taken as strong causal factors of abnormal indicators.

[0036] Optionally, constructing a Bayesian network based on strong correlation factors, strong causal factors, and abnormal indicators includes:

[0037] The bottom-level nodes of the Bayesian network are constructed through abnormal indicators and are recorded as leaf nodes;

[0038] Nodes constructed on leaf nodes through strong correlation factors and strong causal factors are recorded as intermediate nodes;

[0039] The node at the top level of the intermediate node is recorded as the root node;

[0040] The nodes are connected by directed edges, and the directed edges represent the relationship between the nodes.

[0041] Optionally, the parameter learning of the Bayesian network includes: discretizing the node data with continuous characteristics in the Bayesian network by using an equal division method; and using the discretized node data to perform learning by using a maximum likelihood estimation method.

[0042] Optionally, the abductive reasoning of abnormal indicators based on the Bayesian network after parameter learning includes:

[0043] definition and T q is the root node x i (i=1,2,…,m), intermediate node y j (j=1,2…,n) and the risk status of leaf node T, where a i =1,2,…,k i ; b j =1,2,…,k j ; q=1,2,…,k q ;k i , k j , k q are the number of risk states, respectively;

[0044] Calculate the root node x i The risk status is The probability of occurrence of the risk state of leaf node T is T q The probability importance of:

[0045]

[0046] In the formula, is the probability importance, is the root node x i The risk status is When the risk status of leaf node T is T q The probability of P(T = T q |x i =1) is the root node x i When the risk state of leaf node T is T q probability;

[0047] Calculate key importance based on probabilistic importance:

[0048]

[0049] Find the average of key importances:

[0050]

[0051] Sort by the average value of key importance, and the root node x with the larger value i The corresponding correlation factors are the key risk factors that cause abnormal indicators, which are output as the results of abductive reasoning.

[0052] In a second aspect, the present invention provides a causal reasoning system for abnormal indicators of power grid dispatch post-evaluation, characterized in that the system comprises:

[0053] The data extraction module is used to obtain the time series data during the operation of the power grid and extract abnormal indicators and various related factors based on the time series data;

[0054] The strong correlation factor acquisition module calculates the correlation between the abnormal index and each correlation factor to obtain the strong correlation factor;

[0055] The strong causal relationship acquisition module calculates the causal relationship between abnormal indicators and various related factors to obtain strong causal factors;

[0056] Bayesian network construction module, used to construct Bayesian networks based on strong correlation factors, strong causal factors and abnormal indicators;

[0057] The abductive reasoning module is used to learn the parameters of the Bayesian network and perform abductive reasoning of abnormal indicators based on the Bayesian network after parameter learning.

[0058] The abnormal index is an index of the deviation of time series data from the normal time series distribution curve.

[0059] In a third aspect, the present invention provides a device for causal reasoning of abnormal indicators of power grid dispatch post-evaluation, characterized in that it includes a processor and a storage medium;

[0060] The storage medium is used to store instructions;

[0061] The processor is used to operate according to the instructions to execute the steps of any of the methods described above.

[0062] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the device where the storage medium is located is controlled to execute the steps of any of the above-mentioned methods.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] The present invention provides a causal reasoning method, system, device and storage medium for abnormal indicators of post-dispatch evaluation of power grids. Strong correlation factors and strong causal factors are obtained by analyzing the correlation and causal relationship of abnormal indicators, and a Bayesian network is constructed based on the strong correlation factors and strong causal factors. The Bayesian network reasoning technology is used to identify risk factors through key importance indicators, thereby finding the main causes of indicator abnormalities. The method has strong practicality and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flow chart of a method for abductive reasoning of abnormal indicators of power grid dispatch evaluation provided by an embodiment of the present invention;

[0066] Figure 2 is a flow chart of a method for obtaining strong correlation factors provided by an embodiment of the present invention;

[0067] Figure 3 It is a flow chart of a method for obtaining strong causal factors provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0069] Embodiment 1:

[0070] like Figure 1 As shown, an embodiment of the present invention provides a method for abductive reasoning of abnormal indicators of power grid dispatch post-evaluation, comprising the following steps:

[0071] (1) Obtain time series data during the operation of the power grid and extract abnormal indicators and various related factors based on the time series data;

[0072] Abnormal indicators are indicators of time series data deviating from the normal time series distribution curve.

[0073] Time series data include power grid equipment parameter data, power grid operation parameter data, power grid node power generation and load parameter data, and environmental parameter data.

[0074] (2) Calculate the correlation between the abnormal index and each correlation factor to obtain the strong correlation factor;

[0075] like Figure 2 As shown:

[0076] 2.1. Construct a data set of abnormal indicators and various related factors:

[0077]

[0078] In the formula, x0 is the time series data corresponding to the abnormal indicator, x i (i=1,2,…n) is the time series data corresponding to each correlation factor, x n (N) is the Nth time series data of the nth correlation factor;

[0079] 2.2. Obtain the difference matrix sequence between the abnormal index x0 and the i-th correlation factor according to the data set:

[0080] Δ 0i (l)=|x0(l)-x i (l)|, l=1,2,…,N; i=1,2,…,n

[0081] 2.3. Obtain the correlation coefficient between the abnormal index x0 and the i-th correlation factor according to the difference matrix sequence:

[0082]

[0083] In the formula, Δ min and Δ max are the minimum and maximum values ​​in the difference matrix sequence, respectively, and ρ is the resolution coefficient;

[0084] 2.4. Calculate the correlation between the abnormal index x0 and the i-th correlation factor according to the correlation coefficient:

[0085]

[0086] 2.5. Take the correlation r 0i Correlation factor x greater than or equal to the preset correlation threshold (generally 0.8) i As a strong correlation factor of the abnormal indicator x0.

[0087] (3) Calculate the causal relationship between abnormal indicators and various related factors to obtain strong causal factors;

[0088] like Figure 3 As shown:

[0089] 3.1. For the time series data corresponding to the abnormal index and any associated factor, a linear autoregressive model is constructed between the two:

[0090]

[0091] Where, X t is the time series data corresponding to the abnormal indicator, Y t is the time series data corresponding to any associated factor; a 1j and d 1j is the fitting coefficient of the autoregressive model, ε 1t and η 1tis the residual, ∑1 and Γ1 are the residual ε 1t and η 1t The variance of

[0092] 3.2. Perform joint regression on the linear autoregressive model to construct a regression model:

[0093]

[0094] In the formula, a 2j , b 2j 、c 2j and d 2j is the fitting coefficient of the regression model, ε 2t and η 2t is the time-independent prediction error, ∑2 and Γ2 are the prediction errors ε 2t and η 2t The variance of

[0095] 3.3. Calculate any correlation factor Y t For abnormal indicator X t Granger causality:

[0096]

[0097] If F Y→X If Y is greater than the preset critical value, t Yes X t The Granger reason, there is a causal relationship;

[0098] 3.4. Take the associated factors with causal relationships as the strong causal factors of abnormal indicators.

[0099] (4) Constructing a Bayesian network based on strong correlation factors, strong causal factors, and abnormal indicators;

[0100] 4.1. Construct the bottom-level nodes of the Bayesian network through abnormal indicators, which are recorded as leaf nodes;

[0101] 4.2. Nodes constructed on leaf nodes through strong correlation factors and strong causal factors are recorded as intermediate nodes;

[0102] 4.3. The node at the top level of the intermediate nodes is recorded as the root node;

[0103] 4.4. Nodes are connected by directed edges, which represent the relationship between nodes.

[0104] (5) Perform parameter learning on the Bayesian network and perform abductive reasoning on abnormal indicators based on the Bayesian network after parameter learning.

[0105] The parameter learning of the Bayesian network includes: discretizing the node data with continuous characteristics in the Bayesian network by using the equal division method; using the discretized node data and adopting the maximum likelihood estimation method for learning.

[0106] The abductive reasoning of abnormal indicators based on the Bayesian network after parameter learning includes:

[0107] definition and T q is the root node x i (i=1,2,…,m), intermediate node y j (j=1,2…,n) and the risk status of leaf node T, where a i =1,2,…,k i ; b j =1,2,…,k j ; q=1,2,…,k q ;k i , k j , k q are the number of risk states, respectively;

[0108] Calculate the root node x i The risk status is The probability of occurrence of the risk state of leaf node T is T q The probability importance of:

[0109]

[0110] In the formula, is the probability importance, is the root node x i The risk status is When the risk status of leaf node T is T q The probability of P(T = T q |x i =1) is the root node x i When the risk state of leaf node T is T q probability;

[0111] Calculate key importance based on probabilistic importance:

[0112]

[0113] Find the average of key importances:

[0114]

[0115] Sort by the average value of key importance, and the root node x with the larger value iThe corresponding correlation factors are the key risk factors that cause abnormal indicators, which are output as the results of abductive reasoning.

[0116] This embodiment uses the grey correlation method to perform correlation analysis on indicators and related factors, and uses the Granger causality analysis method to perform causal analysis on indicators and strongly related factors. The combination of the two can effectively screen out strongly related factors that cause abnormal indicators, exclude weakly related factors, and provide a basis for establishing a Bayesian structure.

[0117] The method of the present invention establishes a Bayesian network to graphically represent the correlation between indicators and related factors, and uses Bayesian network reasoning technology to identify risk factors through key importance indicators, thereby finding out the main reasons for abnormal indicators, which has strong practicality and flexibility.

[0118] Embodiment 2:

[0119] An embodiment of the present invention provides a causal reasoning system for abnormal indicators of power grid dispatch post-evaluation, the system comprising:

[0120] The data extraction module is used to obtain the time series data during the operation of the power grid and extract abnormal indicators and various related factors based on the time series data;

[0121] The strong correlation factor acquisition module calculates the correlation between the abnormal index and each correlation factor to obtain the strong correlation factor;

[0122] The strong causal relationship acquisition module calculates the causal relationship between abnormal indicators and various related factors to obtain strong causal factors;

[0123] Bayesian network construction module, used to construct Bayesian networks based on strong correlation factors, strong causal factors and abnormal indicators;

[0124] The abductive reasoning module is used to learn the parameters of the Bayesian network and perform abductive reasoning of abnormal indicators based on the Bayesian network after parameter learning.

[0125] Among them, the abnormal index is the index of time series data deviating from the normal time series distribution curve.

[0126] Embodiment three:

[0127] Based on the method for causal reasoning of abnormal indicators of power grid dispatch evaluation described in Example 1, an embodiment of the present invention provides a causal reasoning device for abnormal indicators of power grid dispatch post-evaluation, including a processor and a storage medium;

[0128] The storage medium is used to store instructions;

[0129] The processor is used to operate according to the instructions to execute the steps according to the above method.

[0130] Embodiment 4:

[0131] Based on the causal reasoning method for abnormal indicators of power grid dispatching evaluation described in Example 1, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, characterized in that when the program is executed by a processor, the device where the storage medium is located is controlled to execute the steps of the above method.

[0132] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0133] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0136] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for abductive reasoning of abnormal indicators of power grid dispatch evaluation, characterized in that: include: Acquire time series data during the operation of the power grid, and extract abnormal indicators and various related factors based on the time series data; the time series data includes power grid equipment parameter data, power grid operation parameter data, power grid node power generation and load parameter data, and environmental parameter data; Calculate the correlation between abnormal indicators and various related factors to obtain strong correlation factors; Calculate the causal relationship between abnormal indicators and various related factors to obtain strong causal factors; Construct a Bayesian network based on strong correlation factors, strong causal factors, and abnormal indicators; Perform parameter learning on the Bayesian network, and perform abductive reasoning of abnormal indicators based on the Bayesian network after parameter learning; Wherein, the abnormal index is an index of time series data deviating from the normal time series distribution curve; The parameter learning of the Bayesian network includes: discretizing the node data with continuous characteristics in the Bayesian network by using an equal division method; using the discretized node data to learn by using a maximum likelihood estimation method; The abductive reasoning of abnormal indicators based on the Bayesian network after parameter learning includes: definition and T q is the root node x i (i=1,2,…,m), intermediate node y j (j=1,2…,n) and the risk status of leaf node T, where a i =1,2,…,k i ; b j =1,2,…,k j ; q=1,2,…,k q ;k i , k j , k q are the number of risk states, respectively; Calculate the root node x i The risk status is The probability of occurrence of the risk state of leaf node T is T q The probability importance of: In the formula, is the probability importance, is the root node x i The risk status is When the risk status of leaf node T is T q The probability of P(T = T q |x i =1) is the root node x i When the risk state of leaf node T is T q probability; Calculate key importance based on probabilistic importance: Find the average of key importances: Sort by the average value of key importance, and the root nodes x with the largest average value of key importance are sorted. i The corresponding correlation factors are the key risk factors that cause abnormal indicators, which are output as the results of abductive reasoning.

2. The method for abductive reasoning of abnormal indicators of power grid dispatch post-evaluation according to claim 1 is characterized in that: The calculation of the correlation between the abnormal index and each correlation factor to obtain the strong correlation factor includes: Construct a dataset of abnormal indicators and various associated factors: In the formula, x0 is the time series data corresponding to the abnormal indicator, x i (i=1,2,…n) is the time series data corresponding to each correlation factor, x n (N) is the Nth time series data of the nth correlation factor; According to the data set, the difference matrix sequence between the abnormal index x0 and the i-th correlation factor is obtained: Δ 0i (l)=|x0(l)-x i (l)|,l=1,2,…,N;i=1,2,…,n According to the difference matrix sequence, the correlation coefficient between the abnormal index x0 and the i-th correlation factor is obtained: In the formula, Δ min and Δ max are the minimum and maximum values ​​in the difference matrix sequence, respectively, and ρ is the resolution coefficient; According to the correlation coefficient, the correlation between the abnormal index x0 and the i-th correlation factor is obtained: Take the correlation r 0i The correlation factor x is greater than or equal to the preset correlation threshold i As a strong correlation factor of the abnormal indicator x0.

3. The method for abductive reasoning of abnormal indicators of power grid dispatch post-evaluation according to claim 1 is characterized in that: The calculation of the causal relationship between the abnormal index and each associated factor to obtain the strong causal factors includes: For the time series data corresponding to the abnormal index and any associated factor, a linear autoregressive model of the two is constructed: Where, X t is the time series data corresponding to the abnormal indicator, Y t is the time series data corresponding to any associated factor; a 1j and d 1j is the fitting coefficient of the autoregressive model, ε 1t and η 1t is the residual, ∑1 and Γ1 are the residual ε 1t and η 1t The variance of Perform joint regression on the linear autoregressive model to construct a regression model: In the formula, a 2j 、b 2j 、c 2j and d 2j is the fitting coefficient of the regression model, ε 2t and η 2t is the time-independent prediction error, Σ2 and Γ2 are the prediction errors ε 2t and η 2t The variance of Calculate Y t X t Granger causality: If F Y→X If Y is greater than the preset critical value, t Yes X t The Granger reason, there is a causal relationship; The associated factors with causal relationships are taken as strong causal factors of abnormal indicators.

4. The method for abductive reasoning of abnormal indicators of power grid dispatch post-evaluation according to claim 1 is characterized in that: The Bayesian network constructed based on strong correlation factors, strong causal factors and abnormal indicators includes: The bottom-level nodes of the Bayesian network are constructed through abnormal indicators and are recorded as leaf nodes; Nodes constructed on leaf nodes through strong correlation factors and strong causal factors are recorded as intermediate nodes; The node at the top level of the intermediate node is recorded as the root node; The nodes are connected by directed edges, and the directed edges represent the relationship between the nodes.

5. A post-dispatch evaluation abnormal index abduction reasoning system for power grid, characterized in that: The system is used to execute the steps of the method for abductive reasoning of abnormal indicators of power grid dispatch post-evaluation according to any one of claims 1 to 4, and the system includes: The data extraction module is used to obtain the time series data during the operation of the power grid and extract abnormal indicators and various related factors based on the time series data; The strong correlation factor acquisition module calculates the correlation between the abnormal index and each correlation factor to obtain the strong correlation factor; The strong causal relationship acquisition module calculates the causal relationship between abnormal indicators and various related factors to obtain strong causal factors; Bayesian network construction module, used to construct Bayesian networks based on strong correlation factors, strong causal factors and abnormal indicators; The abductive reasoning module is used to learn the parameters of the Bayesian network and perform abductive reasoning of abnormal indicators based on the Bayesian network after parameter learning; The abnormal index is an index of the deviation of time series data from the normal time series distribution curve.

6. A device for causal reasoning of abnormal indicators after power grid dispatch evaluation, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the device where the storage medium is located is controlled to execute the steps of the method according to any one of claims 1 to 4.

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