Target station area identification method and device, computer device and storage medium

Abstract

The application discloses a target station area identification method and device, computer equipment and a storage medium. The first signal-to-noise ratio data of a nearby node, the network level of the nearby node and the second signal-to-noise ratio data of a neighbor node are acquired; and based on the comparison result of the network level of the nearby node and the network level of the current node, a network level coefficient set is generated; thus the first signal-to-noise ratio data is corrected by using the network level coefficient set to obtain the target signal-to-noise ratio data of the nearby node; and then the first station area belonging relationship of the current node can be identified according to the Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, the SNR calculation function in the signal receiving measurement process based on the communication module itself and the network level topology level statistical function of the communication module itself are realized, the signal-to-noise ratio amplitude is calculated by using the Z-score algorithm, the station area belonging relationship of the current communication module can be determined, and the station area identification rate is improved.

Description

Technical Field

[0001] This invention relates to the field of power line carrier communication, and more particularly to a method, apparatus, computer equipment, and storage medium for identifying target transformer areas. Background Technology

[0002] In a power system, a distribution area is the power supply range or region of a transformer. Related technologies primarily rely on network time-base (NTB) information for identification based on the distribution area's zero-crossing point, or on signal-to-noise ratio (SNR).

[0003] However, due to the low load and complex network conditions in power distribution areas, the accuracy of the identification results of the relevant distribution area identification methods needs to be improved. Summary of the Invention

[0004] This specification provides a target area identification method, apparatus, computer equipment, and storage medium to improve the accuracy of identification results for related art area identification methods.

[0005] This specification provides a method for identifying a target transformer area. The target transformer area includes a current node and neighboring nodes corresponding to the current node within the target transformer area. The target transformer area corresponds to neighboring transformer areas, and the current node corresponds to neighboring nodes within those neighboring transformer areas. The method includes: acquiring first signal-to-noise ratio (SNR) data of the neighboring nodes, the network level of the neighboring nodes, and second SNR data of the neighboring nodes; generating a network level coefficient set based on a comparison between the network level of the neighboring nodes and the network level of the current node; wherein the elements in the network level coefficient set represent the connection relationship between the neighboring nodes and the current node; correcting the first SNR data using the network level coefficient set to obtain target SNR data of the neighboring nodes; and identifying the first transformer area affiliation of the current node based on the Z-score calculation results corresponding to the second SNR data and the target SNR data, respectively.

[0006] This specification provides a target station area identification device, wherein the target station area includes a current node and neighboring nodes corresponding to the current node in the target station area; the target station area corresponds to neighboring station areas, and the current node corresponds to neighboring nodes in the neighboring station areas; the device includes:

[0007] The acquisition module is used to acquire the first signal-to-noise ratio data of the neighboring node, the network layer of the neighboring node, and the second signal-to-noise ratio data of the neighboring node;

[0008] The comparison module is used to generate a set of network level coefficients based on the comparison results between the network level of the neighboring node and the network level of the current node; wherein, the elements in the set of network level coefficients are used to represent the connection relationship between the neighboring node and the current node;

[0009] The correction module is used to correct the first signal-to-noise ratio data using the network hierarchy coefficient set to obtain the target signal-to-noise ratio data of the neighboring nodes;

[0010] The identification module is used to identify the first station area affiliation of the current node based on the Z score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, respectively.

[0011] This specification provides a computer device including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the above embodiments.

[0012] This specification provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the above embodiments.

[0013] In the above-described embodiment, by acquiring the first signal-to-noise ratio (SNR) data of neighboring nodes, the network hierarchy of neighboring nodes, and the second SNR data of neighboring nodes; and generating a set of network hierarchy coefficients based on the comparison results between the network hierarchy of neighboring nodes and the network hierarchy of the current node; thereby using the set of network hierarchy coefficients to correct the first SNR data to obtain the target SNR data of neighboring nodes; and further, based on the Z-score calculation results corresponding to the second SNR data and the target SNR data, identifying the first substation affiliation of the current node, realizing the SNR calculation function and the network hierarchy topology statistical function of the communication module itself in the signal reception measurement process, and using the Z-score algorithm to calculate the SNR amplitude, the substation affiliation of the current communication module can be clearly determined, improving the substation identification rate. Attached Figure Description

[0014] Figure 1a This is a schematic diagram illustrating the NTB threshold difference provided for the implementation of this specification.

[0015] Figure 1b This is a schematic diagram of the topology of the transformer substations provided for the implementation of this specification.

[0016] Figure 2 This is a schematic diagram of the target station identification method provided according to the embodiments of this specification.

[0017] Figure 3This is a schematic diagram of the target station identification method provided according to the embodiments of this specification.

[0018] Figure 4 This is a schematic diagram of the target station identification method provided according to the embodiments of this specification.

[0019] Figure 5a This is a schematic diagram of the target station identification method provided according to the embodiments of this specification.

[0020] Figure 5b This is a structural block diagram of the target station area identification device provided according to the embodiments of this specification. Detailed Implementation

[0021] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0022] In power systems, a low-voltage distribution area refers to the power supply range or region of a single transformer. To achieve refined management of distribution areas and reduce losses, power management departments need to accurately and quickly ascertain the distribution area and phase line attributes of users, particularly for complex distribution areas with overlapping lines, multiple interference sources, severe interference, and incomplete data. Therefore, there is an urgent need for a distribution area identification technology based on HPLC to significantly improve the accuracy of user data while greatly reducing the workload and labor intensity of frontline personnel.

[0023] The relevant transformer area identification technology solutions can include the following two types:

[0024] 1) Identification based on Network Time Base (NTB) characteristics of transformer substations: When the power line load is heavy, the line impedance can reach below 1 ohm, resulting in high attenuation of the carrier signal and low stability of power line data transmission; when the power line load is light, the difference in NTB between different transformer substations is small, making it impossible to make an effective transformer substation identification judgment, such as... Figure 1a As shown.

[0025] 2) Signal-to-Noise Ratio (SNR) Based Identification: Currently, there is no unified standard for SNR-based transformer substation identification. During the identification phase, it is necessary to ensure that the node is in multiple network environments and at the network end. SNR statistics are compared over a period of time to correctly identify the substation's affiliation. However, in practice, there are situations where the SNR value of the correctly affiliated substation is similar to that of adjacent substations, leading to incorrect substation affiliation identification. Furthermore, the common network topology in practice is tree-like, such as... Figure 1b As shown, the current node is a proxy node and is not located at the end of the network. If the SNR value between the current node and the sub-nodes of this area is large, and this is used as the SNR judgment condition for area identification, the area identification cannot be performed normally.

[0026] This specification provides a flowchart of a target transformer area identification method. The target transformer area includes the current node and its neighboring nodes within that target transformer area; the target transformer area has neighboring transformer areas, and the current node has neighboring nodes within those neighboring transformer areas. (See reference...) Figure 2 As shown, the target station area identification method may include the following steps:

[0027] Step S110: Obtain the first signal-to-noise ratio data of the neighboring nodes, the network level of the neighboring nodes, and the second signal-to-noise ratio data of the neighboring nodes.

[0028] The target distribution area includes the current node, which corresponds to several neighboring nodes. These neighboring nodes can be adjacent nodes within the target distribution area or neighboring nodes within neighboring distribution areas. Each current node and its neighboring nodes has a corresponding terminal device. These terminal devices can broadcast node data within the power grid area. The terminal device of the current node can collect node data from neighboring nodes to determine the distribution area characteristics of the neighboring nodes, namely, the first signal-to-noise ratio (SNR) data and the network level of the neighboring nodes. For example, the terminal device of the current node can continuously receive the SNR amplitude of n messages from neighboring nodes and the network level of the neighboring nodes. The terminal device of the current node can also collect node data from neighboring nodes to determine the second SNR data of the neighboring nodes. For example, the terminal device of the current node can continuously receive the SNR amplitude of n messages from neighboring nodes and the network level of the neighboring nodes.

[0029] Furthermore, big data preprocessing techniques are employed to process the node signals of adjacent nodes to extract the original SNR data collected for the corresponding transformer area identification period for all adjacent nodes. Big data preprocessing techniques include data cleaning, data dimensionality reduction, and data transformation. In some implementations, a transformer area identification period can be set. The first signal-to-noise ratio (SNR) data, network layer, and second SNR data correspond to the transformer area identification period and can include data collected at several time points within the transformer area identification period.

[0030] Step S120: Based on the comparison results between the network levels of neighboring nodes and the network levels of the current node, generate a set of network level coefficients.

[0031] The elements in the network hierarchy coefficient set represent the connection relationships between neighboring nodes and the current node. Specifically, it is necessary to determine the network hierarchy relationship between neighboring nodes and the current node. The network hierarchy of both the neighboring and current nodes is known, and it is clear that the network hierarchy of the neighboring node must be lower than that of the current node. Therefore, the network hierarchy of the neighboring node and the current node are compared, and the network hierarchy coefficient set is generated based on the comparison result.

[0032] For example, the network hierarchy of neighboring nodes can be constructed as a set of network hierarchy of neighboring nodes, and the elements in the set of network hierarchy can be regarded as having preset coefficients. Further, based on the comparison result between the network hierarchy of neighboring nodes and the network hierarchy of the current node, the preset coefficients of the elements in the set of network hierarchy of neighboring nodes are adjusted. Based on the product operation of the adjusted preset coefficients and the elements in the set of network hierarchy of neighboring nodes, a set of network hierarchy coefficients is generated.

[0033] Step S130: Correct the first signal-to-noise ratio data using the network hierarchy coefficient set to obtain the target signal-to-noise ratio data of the neighboring nodes.

[0034] In some cases, the first signal-to-noise ratio (SNR) data obtained by the end device of the current node includes some noise data. Furthermore, in this embodiment, a network hierarchy coefficient set is generated based on the preset network hierarchy relationship in the target area. Therefore, the first SNR data is corrected using the network hierarchy coefficient set to obtain the target SNR data for the neighboring node. For example, the target SNR data for the neighboring node is determined by multiplying the network hierarchy coefficient set and the first SNR data.

[0035] Step S140: Based on the Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, identify the first station area affiliation of the current node.

[0036] Z-score standardization is a data processing method. It transforms data of different magnitudes into Z-score values ​​for comparison, improving data comparability but reducing data interpretability. Specifically, Z-score standardization is applied to the second signal-to-noise ratio (SNR) data to obtain its corresponding Z-score matrix. The Z-score matrix corresponding to the target SNR data has a first maximum attribution affinity. The first and second maximum attribution affinity values ​​are compared, and based on the comparison result, the first substation affiliation of the current node is identified. For example, the current node belongs to the target substation, or it belongs to a neighboring substation of the target substation.

[0037] In the aforementioned target transformer area identification method, the first signal-to-noise ratio (SNR) data of neighboring nodes, the network hierarchy of neighboring nodes, and the second SNR data of neighboring nodes are acquired. A set of network hierarchy coefficients is generated based on the comparison between the network hierarchy of neighboring nodes and the network hierarchy of the current node. This set of network hierarchy coefficients is then used to correct the first SNR data, yielding the target SNR data of the neighboring nodes. Furthermore, the Z-score calculation results corresponding to the second SNR data and the target SNR data are used to identify the first transformer area affiliation of the current node. This method realizes the SNR calculation function and network hierarchy topology statistical function of the communication module itself during signal reception measurement. The Z-score algorithm is used to calculate the SNR amplitude, clearly determining the transformer area affiliation of the current communication module with the current power line transformer area (i.e., the target transformer area) and neighboring power line transformer areas (i.e., neighboring transformer areas). Channel measurement and comprehensive evaluation are performed on all nearby stations, improving the transformer area identification rate.

[0038] In some implementations, the generation of the first signal-to-noise ratio (SNR) data may include: constructing a SNR feature matrix based on the initial SNR amplitude of the messages of neighboring nodes, and using this matrix as the first SNR data.

[0039] Specifically, the neighboring node data of the current node is obtained, and the number of neighboring nodes can be m. The neighboring nodes of the m target stations continuously receive the initial signal-to-noise ratio (SNR) amplitude of n packets and the network layer of the neighboring nodes. Based on the initial SNR amplitude of the packets from the neighboring nodes, a signal-to-noise ratio feature matrix (i.e., the SNR amplitude feature matrix) is constructed. The SNR feature matrix is ​​an m x n matrix. The elements in the SNR amplitude feature matrix are denoted as snr. i，j The value of the j-th SNR feature collected at the frequency corresponding to the neighboring nodes of the i-th target station area is represented by the following formula:

[0040]

[0041] In some implementations, generating a set of network level coefficients based on the comparison between the network levels of neighboring nodes and the network level of the current node may include: generating a network level feature matrix based on the network levels of neighboring nodes; comparing the elements in the network level feature matrix with the network level of the current node; if the network level corresponding to any element in the network level feature matrix is ​​less than the network level of the current node, then setting the level coefficient of that element to a first preset value; if the network level corresponding to any element in the network level feature matrix is ​​not less than the network level of the current node, then setting the level coefficient of that element to a second preset value; and updating the corresponding elements in the network level feature matrix using the first preset value and the second preset value to obtain a network level coefficient matrix.

[0042] Specifically, for the network-level data of neighboring nodes in the target transformer area, the network-level feature matrix LAYER is confirmed. m×n The network layer feature matrix of the target station area is an m x n matrix, and the elements of the network layer feature matrix are layer... i，j The formula represents the j-th network layer at the sampling frequency corresponding to the neighboring nodes of the i-th target area, as follows:

[0043]

[0044] Compare the network layer level corresponding to any element in the network layer feature matrix with the network layer level of the current node. If the network layer level corresponding to any element in the network layer feature matrix is ​​less than the network layer level of the current node, then set the layer coefficient of that element to a first preset value. If the network layer level corresponding to any element in the network layer feature matrix is ​​not less than the network layer level of the current node, then set the layer coefficient of that element to a second preset value. For example, when layer... i，j The network layer level less than the current node is layer. i，j The layer coefficient is set to 1; otherwise, layer... i，j The hierarchy coefficient is set to 0, as shown in the following formula:

[0045]

[0046] Furthermore, the corresponding elements in the network layer feature matrix are updated using the first and second preset values ​​to obtain the network layer coefficient matrix. The network layer coefficient matrix of the target station area is an m-by-n matrix. The elements in the network layer coefficient matrix represent the layer coefficients corresponding to the j-th network layer collected at the corresponding frequency of the neighboring nodes of the i-th target station area. Therefore, according to the following formula... The feature matrix of network layer weight coefficients can be obtained. .

[0047]

[0048] In the above implementation, network hierarchy parameters are proposed as one of the criteria for identifying and judging the distribution area. The node of this distribution area is judged by the network hierarchy to avoid interference from the child nodes or nodes at the same level of the current node on the SNR statistics. In this implementation, the criteria for judging the affiliation of this distribution area depends on the SNR value of the proxy node of the current node or the node at the same level as the proxy node.

[0049] In some implementations, the first signal-to-noise ratio (SNR) data is corrected using the network hierarchy coefficient set to obtain the target SNR data of neighboring nodes. This includes: performing a Hadamard product operation using the SNR feature matrix and the network hierarchy coefficient matrix to obtain the target SNR feature matrix, which is then used as the target SNR data.

[0050] Specifically, based on the signal-to-noise ratio (SNR) feature matrix and the network hierarchy coefficient matrix, the target SNR feature matrix at the corresponding acquisition frequency of the neighboring nodes is determined, i.e. The SNR of the nearest nodes of the target transformer area is constructed using the following formula:

[0051]

[0052] Among them, SNRX i，j =snr i，j ×C i，j .

[0053] In the above implementation, network hierarchy parameters are proposed as one of the criteria for identifying and judging the distribution area. The node of this distribution area is judged by the network hierarchy to avoid interference from the child nodes or nodes at the same level of the current node on the SNR statistics. In this implementation, the criteria for judging the affiliation of this distribution area depends on the SNR value of the proxy node of the current node or the node at the same level as the proxy node.

[0054] In some implementations, the second signal-to-noise ratio (SNR) data is generated by: constructing a neighbor SNR feature matrix based on the initial SNR amplitude of the neighbor nodes' messages, and using this matrix as the second SNR data.

[0055] Specifically, the neighboring node data of a neighboring node is obtained, and the number of neighboring nodes can be m. The neighboring nodes of the m neighboring stations continuously receive the initial signal-to-noise ratio (SNR) amplitude of n packets. Based on the initial SNR amplitude of the packets from the neighboring nodes, a neighbor SNR feature matrix (i.e., the SNRY amplitude feature matrix) is constructed. The neighbor SNR feature matrix is ​​an m x n matrix. The elements in the SNRY amplitude feature matrix are denoted as snry. i，j The value of the j-th SNR feature collected at the frequency corresponding to the neighboring nodes of the i-th target station area is represented by the following formula:

[0056]

[0057] Furthermore, based on the target signal-to-noise ratio feature matrix SNRX m，n SNRY and its neighbor signal-to-noise ratio feature matrix m，n Determine the network affiliation relationship between the current node and the target transformer area and neighboring transformer areas based on power line parameters.

[0058] In some implementations, the identification of the first substation affiliation of the current node is based on the Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, including: performing Z-score standardization on the second signal-to-noise ratio data to obtain a first judgment matrix; performing Z-score standardization on the target signal-to-noise ratio data to obtain a second judgment matrix; if the maximum value in the second judgment matrix is ​​not less than the maximum value in the first judgment matrix, the current node is identified as belonging to the target substation; if the maximum value in the second judgment matrix is ​​less than the maximum value in the first judgment matrix, the current node is identified as belonging to a neighboring substation.

[0059] Specifically, the second signal-to-noise ratio (SNR) data and the target SNR data can be represented in matrix form. Z-score normalization is performed on the elements of the matrix corresponding to the second SNR data to obtain the first judgment matrix. Z-score normalization is also performed on the elements of the matrix corresponding to the target SNR data to obtain the second judgment matrix. Both the first and second judgment matrices have maximum values. The maximum value in the first judgment matrix is ​​compared with the maximum value in the second judgment matrix. If the maximum value in the second judgment matrix is ​​not less than the maximum value in the first judgment matrix, the current node is identified as belonging to the target area; if the maximum value in the second judgment matrix is ​​less than the maximum value in the first judgment matrix, the current node is identified as belonging to a neighboring area.

[0060] For example, Z-score standardization is performed according to the following formula: z=(x-μ) / δ, where z represents the standardized value, x represents the element in the matrix corresponding to the second signal-to-noise ratio data, μ represents the average value of the SNR feature amplitude of neighboring nodes within the identification period of the station area, and δ represents the standard deviation of the SNR feature amplitude of neighboring nodes.

[0061] Z-score standardization is performed according to the following formula: z=(x-μ) / δ, where z represents the standardized value, x represents the element in the matrix corresponding to the target signal-to-noise ratio data, μ represents the average value of the SNR feature amplitude of the neighboring nodes within the identification period of the station area, and δ represents the standard deviation of the SNR feature amplitude of the neighboring nodes.

[0062] In some implementations, reference Figure 3As shown, the target signal-to-noise ratio (SNR) data uses the target SNR feature matrix. The target SNR data is then subjected to Z-score normalization to obtain the second judgment matrix, which may include the following steps:

[0063] S210. Obtain the average amplitude and standard deviation of the signal-to-noise ratio amplitude of neighboring nodes within the identification period of the station area.

[0064] S220. Based on the target signal-to-noise ratio data, the average amplitude, and the standard deviation of the amplitude, the Z-score matrix is ​​obtained through standardization.

[0065] S230. Filter the elements in the Z-score matrix according to the preset score threshold and calculate the mean to obtain the second judgment matrix.

[0066] Specifically, the target signal-to-noise ratio (SNR) data uses the target SNR feature matrix (i.e., the SNR amplitude feature matrix). The feature data in the target station area SNR amplitude feature matrix are Z-score standardized using the following formula: In the formula, z represents the standardized value, and x represents the snr amplitude of a neighboring node in the target area at a certain moment. This represents the average amplitude of all SNR values ​​of neighboring nodes in the target transformer area within the transformer area identification period. This represents the standard deviation of the amplitude of all SNR values ​​of neighboring nodes in the target area.

[0067] The SNR feature magnitude matrix, after Z-score normalization, yields the Z-score matrix, as shown below:

[0068]

[0069] Based on the Z-score matrix, for each element in the SNR feature amplitude matrix, elements with Z-scores less than a threshold δ are selected as the valid SNR amplitudes of the current neighboring nodes in the transformer area identification and acquisition cycle. Elements with Z-scores greater than 0.2 are removed from the current SNR dataset corresponding to the SNR feature amplitude matrix. The algorithmic average of the SNR feature amplitudes in the target transformer area identification and acquisition cycle is obtained from the dataset after Z-score processing and filtering of the SNR values ​​of each neighboring node within the transformer area identification and acquisition cycle, as shown in the following formula:

[0070] The target signal-to-noise ratio (SNR) feature matrix of the target station area, after Z-score calculation, data filtering, and arithmetic mean calculation, yields the target station area identification SNR judgment matrix, which is a 1-row, m-column matrix. The current matrix represents the distribution results of m neighboring nodes and the current node obtained after the distribution area identification cycle.

[0071] Similarly, we can obtain a 1-row, m-column matrix for neighbor substation identification SNR judgment, and the dataset of neighbor substation node attribution results is as follows. The maximum affinity of the target transformer area is max(Z), and the maximum affinity of the neighboring transformer area is max(Y). When the maximum affinity of the target transformer area is max(Z) greater than or equal to max(Y), the current node is assigned to the target transformer area in this transformer area identification period. When the maximum affinity of the target transformer area is max(Z) less than max(Y), the current node is assigned to the neighboring transformer area in this transformer area identification period.

[0072] In the above embodiments, in response to the issue of long-term statistical analysis of neighboring site SNR values ​​for transformer area identification, where SNR values ​​fluctuate, this embodiment provides a Z-score-based filtering algorithm. After deleting nodes that exceed a specified threshold, the arithmetic mean of the currently received SNR information is obtained, and the final arithmetic mean is used to measure the relationship between the current neighboring node and the current transformer area.

[0073] In some implementations, reference Figure 4 As shown, the target area also includes a first central coordinator; neighboring areas also include a second central coordinator; the method may further include the following steps:

[0074] S310. Determine the first zero-crossing standard deviation between the zero-crossing data of the first central coordinator and the zero-crossing data of the current node.

[0075] Specifically, the target node in the current node's area is the node to be identified. The current node can receive zero-crossing data from the first central coordinator, the second central coordinator, and can also collect zero-crossing data. The first zero-crossing standard deviation between the zero-crossing data from the first central coordinator and the zero-crossing data collected by the current node is determined.

[0076] For example, the first central coordinator (CCO) and the current node (denoted as the station STA) simultaneously collect zero-point information, with a data scale of m.

[0077]

[0078]

[0079] The zero-crossing information difference of the target area is:

[0080]

[0081]

[0082] Based on the zero-crossing difference NTBDIFF1 of the target transformer area, the sample variance of the zero-crossing information of the target transformer area is calculated. Let NTBDIFF1 be the overall mean of the zero-crossing point difference for the target transformer area. The standard deviation of the zero-crossing point difference NTBDIFF1 in this station area.

[0083]

[0084] S320. Determine the second zero-crossing standard deviation between the zero-crossing data of the second central coordinator and the zero-crossing data of the current node.

[0085] Specifically, the current node can also receive zero-crossing data from the second central coordinator, and can also collect zero-crossing data. The second zero-crossing standard deviation between the zero-crossing data from the second central coordinator and the zero-crossing data collected by the current node is determined.

[0086] For example, the second central coordinator (CCO) and the current node (denoted as the station STA) simultaneously collect zero-point information, with a data scale of m.

[0087]

[0088] The information difference between neighboring transformer stations at midnight is:

[0089]

[0090] NTBDIFF2 is the overall mean of the zero-crossing point differences between neighboring transformer substations. The standard deviation of the zero-crossing difference NTBDIFF2 between neighboring transformer stations.

[0091]

[0092] S330. Based on the comparison results of the first zero-crossing standard deviation and the second zero-crossing standard deviation, identify the second transformer area affiliation of the current node.

[0093] Specifically, the standard deviations of the first and second zero-crossing points are compared. Based on the comparison results, the affiliation of the current node with the second transformer substation is identified. For example, the standard deviations of the zero-crossing information of the target transformer substation are compared. Standard deviation of zero-point information from neighboring transformer areas After N zero-crossing information collections, when The current node should belong to this station area. The current node belongs to the Lintai District.

[0094] In the above embodiments, the accuracy of transformer area identification is improved by using a transformer area identification method based on zero-crossing information comparison, signal-to-noise ratio, and network topology.

[0095] In some implementations, the method may further include the following steps: if the affiliation of the first transformer area is consistent with the affiliation of the second transformer area, then determine the transformer area to which the current node belongs based on the affiliation of the first transformer area or the affiliation of the second transformer area; or, if the affiliation of the first transformer area is inconsistent with the affiliation of the second transformer area, then issue a transformer area affiliation reminder message; wherein, the transformer area affiliation reminder message is used to remind the user to verify the affiliation of the current node.

[0096] Specifically, in this implementation, the attribution relationships of the first and second transformer substations are first determined from two perspectives. Then, these relationships are mutually verified. Therefore, the attribution relationships of the first and second transformer substations are compared. If the first and second attribution relationships are consistent, it indicates that the determined attribution relationship is reliable, and the current node's transformer substation affiliation is determined based on either the first or second attribution relationship. If the first and second attribution relationships are inconsistent, it indicates that the determined attribution relationship is unreliable, and a transformer substation affiliation reminder message is issued to remind the user to verify the current node's attribution relationship.

[0097] In the above embodiments, the accuracy of transformer area identification is improved by using a transformer area identification method based on zero-crossing information comparison, signal-to-noise ratio, and network topology.

[0098] In some implementations, the method may further include the following steps: if the first zero-crossing standard deviation is less than the second zero-crossing standard deviation, then the current node is determined to belong to the target area; if the first zero-crossing standard deviation is not less than the second zero-crossing standard deviation, then the current node is determined to belong to the neighboring area.

[0099] In some implementations, the acquisition time of the zero-crossing data of the first central coordinator is the same as the acquisition time of the zero-crossing data of the current node; determining the first zero-crossing standard deviation between the zero-crossing data of the first central coordinator and the zero-crossing data of the current node includes: obtaining the zero-crossing difference between the zero-crossing data of the first central coordinator and the zero-crossing data of the current node; determining the population mean corresponding to the zero-crossing difference; and determining the first zero-crossing standard deviation based on the zero-crossing difference and the population mean corresponding to the zero-crossing difference.

[0100] In some implementations, field distribution areas suffer from low load, complex network topology, and the inability of using NTB and SNR as sole criteria for distribution area identification to meet the needs of field maintenance personnel for quickly and accurately locating node distribution area affiliation. This specification provides a method for distribution area identification based on the number of received carrier packets, SNR calculated using Z-scores, and the network layer of the sending carrier packet node in the current network as a weighted result, thereby improving the efficiency and accuracy of distribution area identification. Specifically, a target distribution area identification method is provided. The target distribution area includes the current node and its neighboring nodes within the target distribution area; the target distribution area corresponds to neighboring distribution areas, and the current node corresponds to neighboring nodes within those neighboring distribution areas; the target distribution area also includes a first central coordinator; the neighboring distribution areas also include a second central coordinator; the method includes the following steps:

[0101] S402. Obtain the first signal-to-noise ratio data of the neighboring node, the network level of the neighboring node, and the second signal-to-noise ratio data of the neighboring node.

[0102] Specifically, a signal-to-noise ratio (SNR) feature matrix is ​​constructed based on the initial SNR amplitude of packets from neighboring nodes, serving as the first SNR data. A neighbor SNR feature matrix is ​​constructed based on the initial SNR amplitude of packets from neighboring nodes, serving as the second SNR data.

[0103] S404. Based on the comparison results between the network hierarchy of neighboring nodes and the network hierarchy of the current node, generate a set of network hierarchy coefficients.

[0104] The elements in the network hierarchy coefficient set represent the connection relationship between neighboring nodes and the current node. Specifically, a network hierarchy feature matrix is ​​generated based on the network hierarchy of neighboring nodes; the elements in the network hierarchy feature matrix are compared with the network hierarchy of the current node; if the network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​less than the network hierarchy of the current node, the hierarchy coefficient of that element is set to a first preset value; if the network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​not less than the network hierarchy of the current node, the hierarchy coefficient of that element is set to a second preset value; the corresponding elements in the network hierarchy feature matrix are updated using the first and second preset values ​​to obtain the network hierarchy coefficient matrix.

[0105] S406. Correct the first signal-to-noise ratio data using the network hierarchy coefficient set to obtain the target signal-to-noise ratio data of the neighboring nodes.

[0106] Specifically, the target signal-to-noise ratio (SNR) feature matrix and the network layer coefficient matrix are used to perform Hadamard product operations to obtain the target SNR feature matrix, which is then used as the target SNR data.

[0107] S408. Based on the Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, identify the first station area affiliation of the current node.

[0108] Specifically, the second signal-to-noise ratio data is subjected to Z-score normalization to obtain the first judgment matrix; the target signal-to-noise ratio data is subjected to Z-score normalization to obtain the second judgment matrix; if the maximum value in the second judgment matrix is ​​not less than the maximum value in the first judgment matrix, the current node is identified as belonging to the target area; if the maximum value in the second judgment matrix is ​​less than the maximum value in the first judgment matrix, the current node is identified as belonging to the neighboring area.

[0109] Furthermore, the average amplitude and standard deviation of the signal-to-noise ratio amplitude of neighboring nodes within the identification period of the station area are obtained; based on the target signal-to-noise ratio data, the average amplitude and the standard deviation of the amplitude, standardization processing is performed to obtain the Z-score matrix; the elements in the Z-score matrix are filtered and the mean is calculated according to the preset score threshold to obtain the second judgment matrix.

[0110] S410. Determine the first zero-crossing standard deviation between the zero-crossing data of the first central coordinator and the zero-crossing data of the current node.

[0111] Specifically, the zero-crossing difference between the zero-crossing data of the first central coordinator and the zero-crossing data of the current node is obtained; the population mean corresponding to the zero-crossing difference is determined; and the first zero-crossing standard deviation is determined based on the zero-crossing difference and the population mean corresponding to the zero-crossing difference.

[0112] S412. Determine the second zero-crossing standard deviation between the zero-crossing data of the second central coordinator and the zero-crossing data of the current node.

[0113] Specifically, the zero-crossing difference between the zero-crossing data of the second central coordinator and the zero-crossing data of the current node is obtained; the population mean corresponding to the zero-crossing difference is determined; and the second zero-crossing standard deviation is determined based on the zero-crossing difference and the population mean corresponding to the zero-crossing difference.

[0114] S414. Based on the comparison results of the first zero-crossing standard deviation and the second zero-crossing standard deviation, identify the second transformer area affiliation of the current node.

[0115] Specifically, if the standard deviation of the first zero-crossing point is less than the standard deviation of the second zero-crossing point, the current node is determined to belong to the target transformer area; if the standard deviation of the first zero-crossing point is not less than the standard deviation of the second zero-crossing point, the current node is determined to belong to the neighboring transformer area.

[0116] S416. If the affiliation of the first transformer area is consistent with that of the second transformer area, then determine the transformer area to which the current node belongs based on either the affiliation of the first transformer area or the affiliation of the second transformer area.

[0117] S418. If the affiliation of the first and second transformer areas is inconsistent, a transformer area affiliation reminder message will be issued.

[0118] Among them, the area affiliation reminder message is used to remind users to verify the affiliation of the current node.

[0119] In some implementations, reference Figure 5a As shown, a method for identifying transformer substations does not include the following steps:

[0120] Step 1: The concentrator communication terminal periodically collects and broadcasts zero-crossing information and broadcasts beacon frames.

[0121] The concentrator communication terminal sends a feature collection start message to the site to be identified.

[0122] The concentrator communication terminal sends a station feature notification message to the station to be identified.

[0123] The concentrator communication terminal identification round is incremented by 1.

[0124] Step 2: The stations to be identified obtain the station affiliation information of all concentrator communication terminals in the current area.

[0125] The site to be identified performs area feature collection to obtain the area NTB information.

[0126] The site to be identified collects the SNR information corresponding to the received beacon frames.

[0127] Step 3: The substation only receives the zero-crossing information of a single concentrator communication terminal and the SNR information corresponding to the beacon frame, directly confirming the substation's substation affiliation.

[0128] Step 4: The station receives zero-crossing information and SNR information corresponding to beacon frames from two or more stations. The station starts a periodic timer to collect zero-crossing error, signal-to-noise ratio, and network layer information.

[0129] Step 5: Calculate the standard deviation of the zero-crossing difference, and standardize the signal-to-noise ratio and network hierarchy information Z-score.

[0130] Step 6: Based on the information collected in Step 5, the transformer substation confirms the substation identification and attribution results, and reports the attribution results to the current network concentrator communication terminal equipment.

[0131] Step 7: Based on the set iteration limit or recognition success rate, determine whether to continue iterating. If to continue, go to step 2; otherwise, end the station area recognition.

[0132] This specification provides a target station area identification device, wherein the target station area includes a current node and neighboring nodes corresponding to the current node in the target station area; the target station area corresponds to neighboring station areas, and the current node corresponds to a neighboring node in the neighboring station areas. (Reference) Figure 5b As shown, the target area identification device includes: an acquisition module, a comparison module, a correction module, and an identification module.

[0133] The acquisition module is used to acquire the first signal-to-noise ratio data of the neighboring node, the network layer of the neighboring node, and the second signal-to-noise ratio data of the neighboring node;

[0134] The comparison module is used to generate a set of network level coefficients based on the comparison results between the network level of the neighboring node and the network level of the current node; wherein, the elements in the set of network level coefficients are used to represent the connection relationship between the neighboring node and the current node;

[0135] The correction module is used to correct the first signal-to-noise ratio data using the network hierarchy coefficient set to obtain the target signal-to-noise ratio data of the neighboring nodes;

[0136] The identification module is used to identify the first station area affiliation of the current node based on the Z score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, respectively.

[0137] In some embodiments, the apparatus further includes a signal-to-noise ratio feature matrix module, used to construct a signal-to-noise ratio feature matrix based on the initial signal-to-noise ratio amplitude of the messages of the neighboring nodes, as the first signal-to-noise ratio data.

[0138] In some implementations, the comparison module is further configured to: generate a network hierarchy feature matrix based on the network hierarchy of the neighboring nodes; compare the elements in the network hierarchy feature matrix with the network hierarchy of the current node; if the network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​less than the network hierarchy of the current node, then set the hierarchy coefficient of the element to a first preset value; if the network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​not less than the network hierarchy of the current node, then set the hierarchy coefficient of the element to a second preset value; and update the corresponding elements in the network hierarchy feature matrix using the first preset value and the second preset value to obtain a network hierarchy coefficient matrix.

[0139] In some implementations, the correction module is further configured to perform a Hadamard product operation using the signal-to-noise ratio feature matrix and the network layer coefficient matrix to obtain a target signal-to-noise ratio feature matrix, which is then used as the target signal-to-noise ratio data.

[0140] In some implementations, the identification module is further configured to perform Z-score normalization on the second signal-to-noise ratio data to obtain a first judgment matrix; perform Z-score normalization on the target signal-to-noise ratio data to obtain a second judgment matrix; if the maximum value in the second judgment matrix is ​​not less than the maximum value in the first judgment matrix, the current node is identified as belonging to the target substation area; if the maximum value in the second judgment matrix is ​​less than the maximum value in the first judgment matrix, the current node is identified as belonging to the neighboring substation area.

[0141] In some implementations, the target signal-to-noise ratio (SNR) data is a target SNR feature matrix; the identification module is further configured to obtain the average amplitude and standard deviation of the SNR amplitude of the neighboring nodes within the identification period of the station area; perform standardization processing based on the target SNR data, the average amplitude, and the standard deviation of the amplitude to obtain a Z-score matrix; and filter the elements in the Z-score matrix and calculate the mean according to a preset score threshold to obtain the second judgment matrix.

[0142] For specific limitations on the target area identification device, please refer to the limitations on the target area identification method mentioned above, which will not be repeated here.

[0143] This specification provides a computer device including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the above embodiments.

[0144] This specification provides a computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0145] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0146] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0147] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0148] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0149] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0150] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for identifying target transformer areas, characterized in that, The target area includes the current node and the neighboring nodes corresponding to the current node in the target area; The target transformer area corresponds to neighboring transformer areas, and the current node corresponds to a neighboring node in the neighboring transformer areas; the method includes: Acquire the first signal-to-noise ratio data of the neighboring node, the network level of the neighboring node, and the second signal-to-noise ratio data of the neighboring node; Based on the comparison results between the network hierarchy of the neighboring node and the network hierarchy of the current node, a set of network hierarchy coefficients is generated; wherein, the elements in the set of network hierarchy coefficients are used to represent the connection relationship between the neighboring node and the current node; The first signal-to-noise ratio data is corrected using the network hierarchy coefficient set to obtain the target signal-to-noise ratio data of the neighboring nodes; Based on the Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, the first station area affiliation relationship of the current node is identified.

2. The method according to claim 1, characterized in that, The method for generating the first signal-to-noise ratio data includes: Based on the initial signal-to-noise ratio (SNR) amplitude of the messages from the neighboring nodes, a signal-to-noise ratio feature matrix is ​​constructed as the first SNR data.

3. The method according to claim 2, characterized in that, The comparison result between the network hierarchy of the neighboring nodes and the network hierarchy of the current node generates a set of network hierarchy coefficients, including: Generate a network hierarchical feature matrix based on the neighboring node network hierarchy; Compare the elements in the network layer feature matrix with the network layer of the current node; If the network level corresponding to any element in the network level feature matrix is ​​less than the network level of the current node, then the level coefficient of any element is set to a first preset value. If the network level corresponding to any element in the network level feature matrix is ​​not less than the network level of the current node, then the level coefficient of any element is set to a second preset value. The corresponding elements in the network layer feature matrix are updated using the first preset value and the second preset value to obtain the network layer coefficient matrix.

4. The method according to claim 3, characterized in that, The step of correcting the first signal-to-noise ratio (SNR) data using the network hierarchy coefficient set to obtain the target SNR data of the neighboring nodes includes: The target signal-to-noise ratio (SNR) feature matrix is ​​obtained by performing a Hadamard product operation on the SNR feature matrix and the network layer coefficient matrix, and is used as the target SNR data.

5. The method according to claim 1, characterized in that, The second signal-to-noise ratio data is generated in the following ways: Based on the initial signal-to-noise ratio (SNR) amplitude of the neighboring nodes' messages, a neighbor SNR feature matrix is ​​constructed as the second SNR data.

6. The method according to claim 1, characterized in that, The step of identifying the first station area affiliation of the current node based on the Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data includes: The second signal-to-noise ratio data is subjected to Z-score normalization to obtain the first judgment matrix; The target signal-to-noise ratio data is subjected to Z-score normalization to obtain the second judgment matrix; If the maximum value in the second judgment matrix is ​​not less than the maximum value in the first judgment matrix, the current node is identified as belonging to the target station area; If the maximum value in the second judgment matrix is ​​less than the maximum value in the first judgment matrix, the current node is identified as belonging to the neighboring station area.

7. The method according to claim 6, characterized in that, The target signal-to-noise ratio (SNR) data uses a target SNR feature matrix; the Z-score normalization process is applied to the target SNR data to obtain a second judgment matrix, including: Obtain the average amplitude and standard deviation of the signal-to-noise ratio amplitude of the neighboring nodes within the identification period of the station area; Based on the target signal-to-noise ratio data, the average amplitude, and the standard deviation of the amplitude, a Z-score matrix is ​​obtained through standardization. The second judgment matrix is ​​obtained by filtering the elements in the Z-score matrix and calculating the mean based on a preset score threshold.

8. The method according to any one of claims 1 to 7, characterized in that, The target area further includes a first central coordinator; the neighboring areas further include a second central coordinator; the method further includes: Determine the first zero-crossing standard deviation between the zero-crossing data of the first central coordinator and the zero-crossing data of the current node; Determine the second zero-crossing standard deviation between the zero-crossing data of the second central coordinator and the zero-crossing data of the current node; Based on the comparison results of the first zero-crossing standard deviation and the second zero-crossing standard deviation, the second station area affiliation relationship of the current node is identified.

9. The method according to claim 8, characterized in that, The method further includes: If the affiliation of the first transformer area is consistent with that of the second transformer area, then the transformer area to which the current node belongs is determined based on either the affiliation of the first transformer area or the affiliation of the second transformer area. If the affiliation of the first transformer area is inconsistent with that of the second transformer area, a transformer area affiliation reminder message is issued; wherein, the transformer area affiliation reminder message is used to remind the user to verify the affiliation of the current node.

10. The method according to claim 8, characterized in that, The step of identifying the second transformer area affiliation of the current node based on the comparison result of the first zero-crossing standard deviation and the second zero-crossing standard deviation includes: If the first zero-crossing standard deviation is less than the second zero-crossing standard deviation, then the current node is determined to belong to the target area. If the first zero-crossing standard deviation is not less than the second zero-crossing standard deviation, then the current node is determined to belong to the neighboring station area.

11. The method according to claim 8, characterized in that, The acquisition time of the zero-crossing data of the first central coordinator is the same as the acquisition time of the zero-crossing data of the current node; determining the first zero-crossing standard deviation between the zero-crossing data of the first central coordinator and the zero-crossing data of the current node includes: Obtain the zero-crossing difference between the zero-crossing data of the first central coordinator and the zero-crossing data of the current node; Determine the population mean corresponding to the zero-crossing point difference; The first zero-crossing standard deviation is determined based on the zero-crossing difference and the population mean corresponding to the zero-crossing difference.

12. A target station area identification device, characterized in that, The target area includes the current node and the neighboring nodes corresponding to the current node in the target area; The target transformer area corresponds to neighboring transformer areas, and the current node corresponds to a neighboring node in the neighboring transformer areas; the device includes: The acquisition module is used to acquire the first signal-to-noise ratio data of the neighboring node, the network layer of the neighboring node, and the second signal-to-noise ratio data of the neighboring node; The comparison module is used to generate a set of network level coefficients based on the comparison results between the network level of the neighboring node and the network level of the current node; wherein, the elements in the set of network level coefficients are used to represent the connection relationship between the neighboring node and the current node; The correction module is used to correct the first signal-to-noise ratio data using the network hierarchy coefficient set to obtain the target signal-to-noise ratio data of the neighboring nodes; The identification module is used to identify the first station area affiliation of the current node based on the Z score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, respectively.

13. The apparatus according to claim 12, characterized in that, The device further includes: The signal-to-noise ratio feature matrix module is used to construct a signal-to-noise ratio feature matrix based on the initial signal-to-noise ratio amplitude of the messages of the neighboring nodes, which serves as the first signal-to-noise ratio data.

14. The apparatus according to claim 13, characterized in that, The comparison module is further configured to generate a network layer feature matrix based on the network layer of the neighboring nodes; and compare the elements in the network layer feature matrix with the network layer of the current node. If the network level corresponding to any element in the network level feature matrix is ​​less than the network level of the current node, then the level coefficient of the element is set to a first preset value; if the network level corresponding to any element in the network level feature matrix is ​​not less than the network level of the current node, then the level coefficient of the element is set to a second preset value; the corresponding elements in the network level feature matrix are updated using the first preset value and the second preset value to obtain the network level coefficient matrix.

15. The apparatus according to claim 14, characterized in that, The correction module is further configured to perform a Hadamard product operation using the signal-to-noise ratio feature matrix and the network layer coefficient matrix to obtain a target signal-to-noise ratio feature matrix, which is used as the target signal-to-noise ratio data.

16. The apparatus according to claim 12, characterized in that, The identification module is further configured to perform Z-score normalization on the second signal-to-noise ratio data to obtain a first judgment matrix; perform Z-score normalization on the target signal-to-noise ratio data to obtain a second judgment matrix; if the maximum value in the second judgment matrix is ​​not less than the maximum value in the first judgment matrix, the current node is identified as belonging to the target station area; If the maximum value in the second judgment matrix is ​​less than the maximum value in the first judgment matrix, the current node is identified as belonging to the neighboring station area.

17. The apparatus according to claim 16, characterized in that, The target signal-to-noise ratio (SNR) data uses a target SNR feature matrix; the identification module is further used to obtain the average amplitude and standard deviation of the SNR amplitude of the neighboring nodes within the identification period of the station area; based on the target SNR data, the average amplitude, and the standard deviation of the amplitude, a Z-score matrix is ​​obtained by standardization; the elements in the Z-score matrix are filtered and the mean is calculated according to a preset score threshold to obtain the second judgment matrix.

18. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 11.

19. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 11.

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