Method, device, computer device and storage medium for identifying target transformer distribution area

Abstract

The present invention discloses a method, device, computer device and storage medium for identifying a target transformer distribution area, which obtains a first signal-to-noise ratio data of a neighboring node, a network hierarchy of the neighboring node and a second signal-to-noise ratio data of a neighboring node, generates a network hierarchy coefficient set according to a comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node, modifies the first signal-to-noise ratio data using the network hierarchy coefficient set, obtains a target signal-to-noise ratio data of the neighboring node, and identifies the first transformer distribution area belonging relationship of the current node according to the Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, thereby realizing the SNR calculation function in the signal receiving and measurement process based on the communication module itself and the statistical function of the topology hierarchy of the network hierarchy of the communication module itself, and further uses the Z-score algorithm to calculate the signal-to-noise ratio amplitude value, thereby clearly determining the transformer distribution area belonging relationship of the current communication module, and improving the transformer distribution area identification rate.

Description

[Technical Field]

[0001] The present invention relates to the technical field of power line carrier communication, and in particular to a method, apparatus, computer device and storage medium for identifying a target transformer area. [Background technology]

[0002] In a power system, a transformer distribution area is the power supply range or power supply area of ​​a transformer. In the related art, the power transformer distribution area identification technology is mainly based on the zero-cross network reference characteristic information (Network Time Base, NTB) of the transformer distribution area or on the signal-to-noise ratio (SNR).

[0003] However, in the power transformer distribution area, there are circumstances such as a light load and a complex networking situation, so there is a need to improve the accuracy of the identification result by the transformer distribution area identification means in the related art. Summary of the Invention [Problem to be solved by the invention]

[0004] In the embodiments of the present specification, a method, an apparatus, a computer device and a storage medium for identifying a target transformer distribution area are provided to improve the accuracy of the identification result by the transformer distribution area identification means in the related art. [Means for solving the problem]

[0005] Embodiments herein include 1. A method for identifying a target transformer distribution area, comprising: the target transformer distribution area includes a current node and a neighboring node corresponding to the current node in the target transformer distribution area, there is a neighboring transformer distribution area corresponding to the target transformer distribution area, and there is a neighboring node corresponding to the current node in the neighboring transformer distribution area, and the method includes: obtaining first signal-to-noise ratio data of the neighboring node, a network hierarchy of the neighboring node, and second signal-to-noise ratio data of the neighboring node; generating a network hierarchy coefficient set based on a comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node, wherein elements in the network hierarchy coefficient set represent connection relationships between the neighboring node and the current node; modifying the first signal-to-noise ratio data using the network layer coefficient set to obtain target signal-to-noise ratio data for the neighboring node; and identifying a first transformer distribution area belonging relationship of the current node based on Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, respectively.

[0006] Embodiments herein include 1. A target transformer distribution area identification device, comprising: the target transformer distribution area includes a current node and a neighboring node corresponding to the current node in the target transformer distribution area, there is a neighboring transformer distribution area corresponding to the target transformer distribution area, there is a neighboring node corresponding to the current node in the neighboring transformer distribution area, and the device: an acquisition module for acquiring first signal-to-noise ratio data of the neighboring node, a network hierarchy of the neighboring node, and second signal-to-noise ratio data of the neighboring node; a comparison module that generates a network hierarchy coefficient set based on a comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node, wherein elements in the network hierarchy coefficient set represent connection relationships between the neighboring node and the current node; a correction module that corrects the first signal-to-noise ratio data using the network layer coefficient set to obtain target signal-to-noise ratio data for the neighboring node; and an identification module for identifying a first transformer distribution area affiliation of the current node based on Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, respectively.

[0007] Embodiments herein include A computer device including a memory and a processor, wherein a computer program is stored in the memory, and the processor, when executing the computer program, performs the steps of the method according to any one of the above embodiments.

[0008] Embodiments herein include A computer-readable storage medium on which a computer program is stored, The computer program provides a computer-readable storage medium that, when executed by a processor, implements the steps of the method according to any one of the above embodiments. [Effects of the Invention]

[0009] In the embodiment of the above specification, the first signal-to-noise ratio data of the neighboring node, the network hierarchy of the neighboring node, and the second signal-to-noise ratio data of the neighboring node are obtained, a network hierarchy coefficient set is generated based on the comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node, the first signal-to-noise ratio data is modified using the network hierarchy coefficient set to obtain the target signal-to-noise ratio data of the neighboring node, and the first transformer distribution area belonging relationship of the current node can be identified based on the Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, thereby realizing the SNR calculation function in the signal receiving and measurement process based on the communication module itself and the statistical function of the topology hierarchy of the communication module itself, and using the Z-score algorithm to calculate the signal-to-noise ratio amplitude value, so that the transformer distribution area belonging relationship of the current communication module can be clearly determined, and the transformer distribution area identification rate can be improved. [Brief explanation of the drawings]

[0010] [Figure 1a] FIG. 1 is a schematic diagram of NTB threshold differences according to embodiments herein. [Figure 1b] FIG. 1 is a schematic diagram of a topological relationship of a transformer distribution area according to embodiments herein. [Figure 2] FIG. 1 is a schematic diagram of a method for identifying a target transformer distribution area according to an embodiment herein. [Figure 3] FIG. 1 is a schematic diagram of a method for identifying a target transformer distribution area according to an embodiment herein. [Figure 4] FIG. 1 is a schematic diagram of a method for identifying a target transformer distribution area according to an embodiment herein. [Figure 5a] FIG. 1 is a schematic diagram of a method for identifying a target transformer distribution area according to an embodiment herein. [Figure 5b] 1 is a structural block diagram of a target transformer distribution area identification device according to an embodiment of the present specification; DETAILED DESCRIPTION OF THE INVENTION

[0011]

[0030] The following detailed description of the embodiments of the present invention is given in the accompanying drawings, in which the same or similar elements or elements having the same or similar functions are denoted by the same or similar reference numerals throughout the drawings. The embodiments described below with reference to the drawings are merely illustrative examples for explaining the present invention and should not be construed as limiting the present invention.

[0012] In a power system, a low-voltage transformer distribution area refers to the power supply range or area of ​​a single transformer. To achieve the goal of precise management and loss reduction for transformer distribution areas, power usage management departments need to understand users' transformer distribution areas and phase line attributes, and accurately and quickly obtain user information for each transformer distribution area. In particular, in transformer distribution areas with complex lines, overlapping adjacent transformer distribution areas, multiple interference sources, severe interference, and incomplete data, it is urgent to use HPLC-based transformer distribution area identification technology to significantly improve the accuracy of user data while significantly reducing the workload and labor intensity of field workers.

[0013] There are two types of solutions for related transformer distribution area identification technology:

[0014] 1) Identification based on the zero-cross network time base (NTB) characteristic information of the transformer distribution area: When the load on the power line is very large, the line impedance may reach 1 ohm or less, causing significant attenuation of the carrier signal and reducing the stability of data transmission over the power line. When the load on the power line is very small, as shown in Figure 1a, the difference in the zero-cross NTB between different transformer distribution areas is small, making it impossible to effectively identify and determine the transformer distribution area.

[0015] 2) Signal-to-Noise Ratio (SNR)-Based Identification: Traditionally, there has been no unified standard for SNR-based transformer distribution area identification. During transformer distribution area identification, nodes must be located in multiple network environments and at the network edge. By statistically comparing SNRs over a period of time, the node's associated transformer distribution area can be accurately identified. However, in the field, the SNR value of the correct associated transformer distribution area may be close to that of neighboring transformer distribution areas, resulting in an incorrect identification of the node's associated transformer distribution area. Furthermore, in field networks, the networking type is typically a tree-type network. As shown in Figure 1b, if the current node is a proxy node (PCO) and not at the network edge, and the SNR values ​​of the current node and its child nodes are large, using this as the SNR criterion for transformer distribution area identification will result in incorrect identification.

[0016] The embodiments of the present specification provide a flowchart of a method for identifying a target transformer distribution area. The target transformer distribution area includes a current node and a neighboring node corresponding to the current node in the target transformer distribution area, and there is a neighboring transformer distribution area corresponding to the target transformer distribution area, and there is a neighboring node corresponding to the current node in the neighboring transformer distribution area. As shown in FIG. 2, the method for identifying the target transformer distribution area may include the following steps S110 to S140.

[0017] In step S110, first signal-to-noise ratio data of the neighboring node, the network hierarchy of the neighboring node, and second signal-to-noise ratio data of the neighboring node are obtained.

[0018] Here, the target transformer distribution area includes the current node and several adjacent nodes corresponding to the current node. These adjacent nodes may be several neighboring nodes in the target transformer distribution area or several neighboring nodes in neighboring transformer distribution areas. Each of the current node and the 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 the node data of the neighboring nodes to determine first signal-to-noise ratio data of the neighboring nodes, which are characteristics of the transformer distribution area of ​​the neighboring nodes, and the network hierarchy of the neighboring nodes. For example, the terminal device of the current node can continuously receive SNR amplitude values ​​of n messages of the neighboring nodes and the network hierarchy of the neighboring nodes. The terminal device of the current node can also collect node data of the neighboring nodes to determine second signal-to-noise ratio data of the neighboring nodes. For example, the terminal device of the current node can continuously receive SNR amplitude values ​​of n messages of the neighboring nodes and the network hierarchy of the neighboring nodes.

[0019] Furthermore, data processing is performed on the node signals of adjacent nodes using big data preprocessing techniques to extract and collect original SNR data within the transformer distribution area identification period corresponding to all adjacent nodes. The big data preprocessing techniques include data cleaning, data dimensionality reduction, and data modification. In some embodiments, a transformer distribution area identification period may be set. The first signal-to-noise ratio data, the network hierarchy, and the second signal-to-noise ratio data correspond to the transformer distribution area identification period and may include data collected at certain times within the transformer distribution area identification period.

[0020] In step S120, a network hierarchy coefficient set is generated based on the comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node.

[0021] Here, the elements in the network hierarchy coefficient set represent the connection relationship between the neighboring node and the current node. Specifically, it is necessary to determine the network hierarchy relationship between the neighboring node and the current node. The network hierarchy of the neighboring node and the network hierarchy of the current node may be known, and it is also clear that the network hierarchy of the neighboring node must be smaller than the network hierarchy of the current node. Therefore, the network hierarchy of the neighboring node is compared with the network hierarchy of the current node, and a network hierarchy coefficient set is generated based on the comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node.

[0022] For example, the network hierarchy of the neighboring node may be constructed as a network hierarchy set of the neighboring node, and the elements in the network hierarchy set may be regarded as corresponding to preset coefficients. Further, based on the comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node, the preset coefficients of the elements in the network hierarchy set of the neighboring node are adjusted, and a network hierarchy coefficient set is generated based on a multiplication of the adjusted preset coefficients and the elements in the network hierarchy set of the neighboring node.

[0023] In step S130, the first signal-to-noise ratio data is modified using the network layer coefficient set to obtain target signal-to-noise ratio data of the neighboring node.

[0024] In some cases, the first signal-to-noise ratio data acquired by the terminal equipment of the current node includes some noise data, and in this embodiment, a network hierarchical coefficient set is generated by combining the preset network hierarchical relationship in the target transformer distribution area, so that the network hierarchical coefficient set is used to modify the first signal-to-noise ratio data to obtain the target signal-to-noise ratio data of the neighboring node. As an example, the network hierarchical coefficient set and the first signal-to-noise ratio data are multiplied to determine the target signal-to-noise ratio data of the neighboring node.

[0025] In step S140, a first transformer distribution area affiliation of the current node is identified 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.

[0026] Here, Z-score normalization is a data processing method. Z-score normalization converts data with different scales into Z-scores with a uniform scale for comparison. This improves data comparability but reduces data interpretability. Specifically, Z-score normalization is performed on the second signal-to-noise ratio data to obtain a Z-score matrix corresponding to the second signal-to-noise ratio data. The Z-score matrix corresponding to the target signal-to-noise ratio data corresponds to the first maximum affinity value. Z-score normalization is performed on the target signal-to-noise ratio data to obtain a Z-score matrix corresponding to the target signal-to-noise ratio data. The Z-score matrix corresponding to the second signal-to-noise ratio data corresponds to the second maximum affinity value. The first belonging affinity maximum value is compared with the second belonging affinity maximum value, and based on the comparison result, the first transformer distribution area belonging relationship of the current node is identified, such as whether the current node belongs to the target transformer distribution area or whether the current node belongs to a neighboring transformer distribution area of ​​the target transformer distribution area.

[0027] The above-mentioned method for identifying the target transformer distribution area includes obtaining the first signal-to-noise ratio data of the neighboring node, the network hierarchy of the neighboring node, and the second signal-to-noise ratio data of the neighboring node, generating a network hierarchy coefficient set based on a comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node, and then modifying 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 node, and identifying the first transformer distribution 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. Therefore, the SNR calculation function in the signal receiving and measurement process based on the communication module itself and the statistical function of the topology layer of the network layer of the communication module itself are realized, and the signal-to-noise ratio amplitude value is calculated using the Z-score algorithm, so that the transformer distribution area affiliation relationship between the current communication module and the current power line transformer distribution area (i.e., the target transformer distribution area) and the adjacent power line transformer distribution area (i.e., the neighboring transformer distribution area) can be accurately determined, and channel measurement and comprehensive evaluation can be performed for all nearby stations, thereby improving the transformer distribution area identification rate.

[0028] In some embodiments, the means for generating the first signal-to-noise ratio data may include constructing a signal-to-noise ratio feature matrix based on initial signal-to-noise ratio amplitude values ​​of messages of the neighboring nodes to provide the first signal-to-noise ratio data.

[0029] Specifically, the neighboring node data of the current node is obtained, and the number of neighboring nodes of the current node may be m. From each of, continuously receive the initial signal-to-noise ratio amplitude values ​​(SNR amplitude values) of n messages and the network hierarchy of neighboring nodes. Based on the initial signal-to-noise ratio amplitude values ​​of the messages of neighboring nodes, construct a signal-to-noise ratio feature matrix (i.e., SNR amplitude value feature matrix). Here, the signal-to-noise ratio feature matrix is ​​an m-row, n-column matrix. The element in the SNR amplitude value feature matrix is ​​denoted as snri,j, which represents the j-th SNR feature amplitude value collected at the frequency corresponding to the neighboring node in the i-th target transformer distribution area, and its formula is as follows: JPEG0007813377000001.jpg22144

[0030] In some embodiments, generating a network hierarchy coefficient set based on a comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node may include generating a network hierarchy feature matrix based on the network hierarchy of the neighboring node; comparing elements in the network hierarchy feature matrix with the network hierarchy of the current node; setting a hierarchical coefficient of any element in the network hierarchy feature matrix to a first preset value if the network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​lower than the network hierarchy of the current node; setting a hierarchical coefficient of any element in the network hierarchy feature matrix to a second preset value if the network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​equal to or higher than the network hierarchy of the current node; and updating 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.

[0031] Specifically, for the network hierarchy data of neighboring nodes in the target transformer distribution area, the network hierarchy feature matrix LAYER m×n The network hierarchy feature matrix of the target transformer distribution area is an m-by-n matrix, and the element layer of the network hierarchy feature matrix i,jrepresents the jth network hierarchy at the collection frequency corresponding to the neighboring node of the ith target transformer distribution area, and its formula is as follows: JPEG0007813377000002.jpg20145

[0032] The network hierarchy corresponding to an element in the network hierarchy feature matrix is ​​compared with the network hierarchy of the current node. If the network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​lower than the network hierarchy of the current node, the hierarchy coefficient of the element is set to a first preset value. If the network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​equal to or higher than the network hierarchy of the current node, the hierarchy coefficient of the element is set to a second preset value. For example, i,j If is less than the network hierarchy layer of the current node, then layer i,j The layer factor of is set to 1, otherwise, layer i,j The tier coefficient is set to 0, and the formula is: JPEG0007813377000003.jpg18128

[0033] Furthermore, the first preset value and the second preset value are used to update corresponding elements in the network hierarchy characteristic matrix to obtain a network hierarchy coefficient matrix. The network hierarchy coefficient matrix of the target transformer distribution area is an m-row, n-column matrix, and the elements in the network hierarchy coefficient matrix represent hierarchy coefficients corresponding to the j-th network hierarchy collected at the frequency corresponding to the adjacent node of the i-th target transformer distribution area, so that the formula JPEG0007813377000004.jpg547: Network layer weight coefficient feature matrix The result is JPEG0007813377000005.jpg410. JPEG0007813377000006.jpg26141

[0034] In the above embodiment, it is proposed to use the network hierarchy parameter as one of the criteria for identifying the transformer distribution area, and the network hierarchy determination is performed for the transformer distribution area node, thereby avoiding the interference of the child nodes or nodes at the same level of the current node on the SNR statistics. In this embodiment, the criterion for identifying the transformer 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.

[0035] In some embodiments, modifying the first signal-to-noise ratio data using the network hierarchical coefficient set to obtain target signal-to-noise ratio data of the neighboring node includes performing a Hadamard product using the signal-to-noise ratio feature matrix and the network hierarchical coefficient matrix to obtain a target signal-to-noise ratio feature matrix as the target signal-to-noise ratio data.

[0036] Specifically, based on the signal-to-noise ratio feature matrix and the network hierarchy coefficient matrix, a target signal-to-noise ratio feature matrix at the collection frequency corresponding to the neighboring node is calculated, namely: Determine JPEG0007813377000007.jpg441. Construct the adjacent node SNR of the target transformer distribution area using the following formula: JPEG0007813377000008.jpg23155

[0037] Here, SNRX i,j =snr i,j ×C i,j is.

[0038] In the above embodiment, it is proposed to use the network hierarchy parameter as one of the identification criteria of the transformer distribution area, and the network hierarchy determination is made for the transformer distribution area node, so as to avoid the interference of the child nodes or nodes at the same hierarchical level of the current node on the SNR statistics. In this embodiment, the transformer distribution area belonging criteria depends on the SNR value of the proxy node of the current node or the node at the same hierarchical level as the proxy node.

[0039] In some embodiments, the means for generating the second signal-to-noise ratio data includes constructing a neighborhood signal-to-noise ratio feature matrix based on initial signal-to-noise ratio amplitude values ​​of messages of neighboring nodes to provide the second signal-to-noise ratio data.

[0040] Specifically, the neighboring node data of the neighboring node is obtained, and the number of neighboring nodes of the neighboring node may be m. From each of , continuously receive the initial signal-to-noise ratio amplitude values ​​(SNR amplitude values) of n messages. Based on the initial signal-to-noise ratio amplitude values ​​of messages from neighboring nodes, construct a neighborhood signal-to-noise ratio feature matrix (i.e., SNRY amplitude value feature matrix). Here, the neighborhood signal-to-noise ratio feature matrix is ​​an m-row, n-column matrix. The element in the SNRY amplitude value feature matrix is ​​denoted as snryi,j, which represents the j-th SNR feature amplitude value collected at the frequency corresponding to the neighboring node in the i-th target transformer distribution area, and its formula is as follows: JPEG0007813377000009.jpg21144

[0041] Furthermore, the target signal-to-noise ratio feature matrix SNRX m,n and the neighboring signal-to-noise ratio feature matrix SNRY m,n Based on this, a network belonging relationship based on the power line parameters of the current node, the target transformer distribution area, and the neighboring transformer distribution area is determined.

[0042] In some embodiments, identifying the first transformer distribution area belonging relationship 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 includes: performing a Z-score normalization process on the second signal-to-noise ratio data to obtain a first judgment matrix; performing a Z-score normalization process on the target signal-to-noise ratio data to obtain a second judgment matrix; identifying the current node as belonging to the target transformer distribution area if the maximum value in the first judgment matrix is ​​greater than or equal to the maximum value of the second judgment matrix; and identifying the current node as belonging to a neighboring transformer distribution area if the maximum value in the first judgment matrix is ​​less than the maximum value of the second judgment matrix.

[0043] Specifically, the second signal-to-noise ratio data and the target signal-to-noise ratio data may be represented in the form of a matrix. A Z-score normalization process is performed on elements in the matrix corresponding to the second signal-to-noise ratio data to obtain a first judgment matrix. A Z-score normalization process is performed on elements in the matrix corresponding to the target signal-to-noise ratio data to obtain a second judgment matrix. The first judgment matrix has a maximum value, and the second judgment matrix has a maximum value, and 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 first judgment matrix is ​​greater than or equal to the maximum value in the second judgment matrix, the current node is identified as belonging to the target transformer distribution area. If the maximum value in the first judgment matrix is ​​less than the maximum value in the second judgment matrix, the current node is identified as belonging to the neighboring transformer distribution area.

[0044] As an example, the Z-score normalization process is performed according to the formula z=(x-μ) / δ, where z is the normalization value, x is the element in the matrix corresponding to the second signal-to-noise ratio data, μ is the average value of the SNR feature amplitude values ​​of neighboring nodes within the transformer distribution area identification period, and δ is the standard deviation of the SNR feature amplitude values ​​of neighboring nodes.

[0045] The Z-score normalization process is performed according to the formula z = (x - μ) / δ, where z is the normalization value, x is the element in the matrix corresponding to the target signal-to-noise ratio data, μ is the average value of the SNR feature amplitude values ​​of the neighboring nodes within the transformer distribution area identification period, and δ is the standard deviation of the SNR feature amplitude values ​​of the neighboring nodes.

[0046] In some embodiments, a target signal-to-noise ratio feature matrix is ​​adopted as the target signal-to-noise ratio data, as shown in Fig. 3. The step of performing Z-score normalization processing on the target signal-to-noise ratio data to obtain the second judgment matrix may include the following steps S210 to S230.

[0047] In S210, the average amplitude value and the standard deviation of the amplitude value of the signal-to-noise ratio amplitude value of the neighboring node within the transformer distribution area identification period are obtained.

[0048] In S220, a normalization process is performed based on the target signal-to-noise ratio data, the amplitude value mean value, and the amplitude value standard deviation to obtain a Z-score matrix.

[0049] In S230, the elements in the Z-score matrix are filtered and averaged according to a preset score threshold to obtain a second judgment matrix.

[0050] Specifically, the target signal-to-noise ratio feature matrix (i.e., SNR amplitude value feature matrix) is adopted as the target signal-to-noise ratio data. The Z-score normalization process is performed based on the feature data in the SNR amplitude value feature matrix of the target transformer distribution area using the formula: This is done according to JPEG0007813377000010.jpg714, where z is the normalized value, x is the SNR amplitude value of a neighboring node in the target transformer distribution area at a certain time, μ is the average amplitude value of all SNR amplitude values ​​of neighboring nodes in the target transformer distribution area within the transformer distribution area identification period, and δ is the amplitude value standard deviation of all SNR amplitude values ​​of neighboring nodes in the target transformer distribution area.

[0051] The Z-score normalization process is performed on the SNR feature amplitude value matrix to obtain a Z-score matrix as follows: JPEG0007813377000011.jpg30129

[0052] According to the Z-score matrix, for each element in the SNR feature amplitude value matrix, the element whose corresponding Z-score is less than the threshold δ is selected as the valid SNR amplitude value within the transformer distribution area identification collection period of the current neighboring node, and the element whose Z-score is greater than 0.2 is deleted from the current SNR data set corresponding to the SNR feature amplitude value matrix. For each neighboring node, within the transformer distribution area identification collection period, the SNR value is subjected to Z-score processing and the filtered data set is statistically analyzed to obtain the algorithm average number of SNR feature amplitude values ​​within the identification collection period of the target transformer distribution area, as follows: JPEG0007813377000012.jpg1048

[0053] For the target signal-to-noise ratio feature matrix of the target transformer distribution area, Z-score calculation, data screening, and arithmetic mean calculation are performed to obtain a 1-by-m matrix. Obtain the identification SNR judgment matrix for the target transformer distribution area, which is JPEG0007813377000013.jpg638. The current matrix represents the transformer distribution area affiliation results of m neighboring nodes and the current node obtained from the neighboring nodes that have undergone the transformer distribution area identification cycle.

[0054] Similarly, the discrimination SNR judgment matrix of the neighboring transformer distribution area is a matrix of 1 row and m columns, and the transformer distribution area affiliation result data set of the node in the neighboring transformer distribution area is JPEG0007813377000014.jpg640, the maximum belonging affinity value of the target transformer distribution area is max(Z), and the maximum transformer distribution area belonging value of the neighboring transformer distribution area is max(Y). If the maximum transformer distribution area belonging result value max(Z) of the target transformer distribution area is greater than or equal to max(Y), the current node belongs to the target transformer distribution area in this transformer distribution area identification cycle. Also, if the maximum transformer distribution area belonging result value max(Z) of the target transformer distribution area is less than max(Y), the current node belongs to the neighboring transformer distribution area in this transformer distribution area identification cycle.

[0055] In the above embodiment, the SNR values ​​of neighboring stations are statistically collected over a long period of time for transformer distribution area identification, and fluctuations in the SNR values ​​exist. In this embodiment, the Z-score screening algorithm is used to remove nodes exceeding a predetermined threshold, and then the arithmetic average of the currently received SNR information is obtained. The transformer distribution area relationship between the current neighboring node and the node is evaluated based on the final arithmetic average.

[0056] In some embodiments, the target transformer distribution area further includes a first central coordinator, and the neighboring transformer distribution area further includes a second central coordinator, as shown in Figure 4. The method may further include the following steps S310 to S330.

[0057] In S310, a first zero-crossing standard deviation between the zero-crossing data of the first central coordinator and the zero-crossing data of the current node is determined.

[0058] Specifically, the current node refers to a node to be identified in the target transformer distribution area. The current node may receive zero-crossing data from the first central coordinator, may receive zero-crossing data from the second central coordinator, and may collect zero-crossing data. Based on the zero-crossing data from the first central coordinator and the zero-crossing data collected by the current node, a first zero-crossing standard deviation between them is determined.

[0059] For example, the first central coordinator (CCO) and the current node (called a transformer distribution area station (STA)) simultaneously collect zero-crossing information, and the data size is m. JPEG0007813377000015.jpg7128JPEG0007813377000016.jpg7128

[0060] The zero crossing information difference of the target transformer distribution area is as follows: JPEG0007813377000017.jpg7128JPEG0007813377000018.jpg7128

[0061] Based on the zero-crossing difference NTBDIFF1 of the target transformer distribution area, calculate the sample variance of the zero-crossing information of the target transformer distribution area, where μ1 is the overall average value of the zero-crossing difference NTBDIFF1 of the target transformer distribution area, and σ1 is the standard deviation of the zero-crossing difference NTBDIFF1 of the transformer distribution area. JPEG0007813377000019.jpg20128

[0062] In S320, a second zero-crossing standard deviation between the zero-crossing data of the second central coordinator and the zero-crossing data of the current node is determined.

[0063] Specifically, the current node may receive the zero-crossing data of the second central coordinator, and may further collect the zero-crossing data, and determine a second zero-crossing standard deviation between the zero-crossing data of the second central coordinator and the zero-crossing data collected by the current node based on the zero-crossing data of the second central coordinator and the zero-crossing data collected by the current node.

[0064] As an example, the second central coordinator CCO and the current node (called the transformer distribution area station STA) simultaneously collect zero-crossing information, and the data scale is m. JPEG0007813377000020.jpg8133

[0065] The zero crossing information difference of the neighboring transformer distribution area is as follows: JPEG0007813377000021.jpg7128

[0066] μ2 is the overall average value of the zero-crossing difference NTBDIFF2 in the neighboring transformer distribution area, and σ2 is the standard deviation of the zero-crossing difference NTBDIFF2 in the neighboring transformer distribution area. JPEG0007813377000022.jpg20128

[0067] In S330, the second transformer distribution area affiliation of the current node is identified based on the comparison result between the first zero crossing standard deviation and the second zero crossing standard deviation.

[0068] Specifically, the first zero-crossing standard deviation is compared with the second zero-crossing standard deviation, and the second transformer distribution area affiliation of the current node is identified based on the comparison result. For example, the zero-crossing information standard deviation σ1 of the target transformer distribution area is compared with the zero-crossing information standard deviation σ2 of the neighboring transformer distribution area, and the zero-crossing information is collected N times. If σ1<σ2, the current node belongs to the transformer distribution area, and if σ1>σ2, the current node belongs to the neighboring transformer distribution area.

[0069] In the above embodiment, the transformer distribution area identification method based on the comparison of zero-crossing information, signal-to-noise ratio and network topology relationship improves the accuracy of transformer distribution area identification.

[0070] In some embodiments, the method may further include a step of determining the transformer distribution area to which the current node belongs based on the first transformer distribution area affiliation relationship or the second transformer distribution area affiliation relationship if the first transformer distribution area affiliation relationship and the second transformer distribution area affiliation relationship match, and sending a transformer distribution area affiliation reminder message if the first transformer distribution area affiliation relationship and the second transformer distribution area affiliation relationship do not match, wherein the transformer distribution area affiliation reminder message is for prompting a user to check the affiliation of the current node.

[0071] Specifically, in this embodiment, the first and second transformer distribution area affiliations are first determined from two perspectives, and then cross-validation is performed using the first and second transformer distribution area affiliations to compare the first and second transformer distribution area affiliations. If the first and second transformer distribution area affiliations are consistent, it indicates that the determined transformer distribution area affiliations are reliable, and the transformer distribution area to which the current node belongs is determined based on the first or second transformer distribution area affiliations. If the first and second transformer distribution area affiliations are inconsistent, it indicates that the determined transformer distribution area affiliations are unreliable, and a transformer distribution area affiliation reminder message is sent to prompt the user to check the current node's affiliation.

[0072] In the above embodiment, the transformer distribution area identification method based on the comparison of zero-crossing information, signal-to-noise ratio and network topology relationship improves the accuracy of transformer distribution area identification.

[0073] In some embodiments, the method may further include determining that the current node belongs to the target transformer distribution area if the first zero crossing standard deviation is less than the second zero crossing standard deviation, and determining that the current node belongs to the neighboring transformer distribution area if the first zero crossing standard deviation is greater than or equal to the second zero crossing standard deviation.

[0074] In some embodiments, the collection time of the zero-crossing data of the first central coordinator and the collection time of the zero-crossing data of the current node are the same, and the step of 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 the steps of obtaining a zero-crossing difference between the zero-crossing data of the first central coordinator and the zero-crossing data of the current node, determining a global average value corresponding to the zero-crossing difference, and determining the first zero-crossing standard deviation based on the zero-crossing difference and the global average value corresponding to the zero-crossing difference.

[0075] In some embodiments, in an on-site transformer distribution area, the load is light and the networking situation is complex. Therefore, if either NTB or SNR is used alone as the basis for determining the transformer distribution area, a maintenance person on-site cannot efficiently and accurately identify the transformer distribution area to which a node belongs. In embodiments herein, the efficiency and accuracy of the execution of transformer distribution area identification are improved by receiving carrier messages, counting the number of received carrier messages, collating SNRs using Z-scores, and using the network hierarchy in the current network of the node transmitting the carrier message as a weighting result to identify the transformer distribution area. Specifically, a method for identifying a target transformer distribution area is provided. The target transformer distribution area includes a current node and a neighboring node corresponding to the current node in the target transformer distribution area, the target transformer distribution area corresponds to a neighboring transformer distribution area, there is a neighboring node in the neighboring transformer distribution area corresponding to the current node, the target transformer distribution area further includes a first central coordinator, and the neighboring transformer distribution area further includes a second central coordinator, and the method includes the following steps S402 to S418.

[0076] At S402, first signal-to-noise ratio data of a neighboring node, a network hierarchy of the neighboring node, and second signal-to-noise ratio data of a neighboring node are obtained.

[0077] Specifically, a signal-to-noise ratio feature matrix is ​​constructed based on the initial signal-to-noise ratio amplitude value of the message of the neighboring node, and is used as the first signal-to-noise ratio data.A neighboring signal-to-noise ratio feature matrix is ​​constructed based on the initial signal-to-noise ratio amplitude value of the message of the neighboring node, and is used as the second signal-to-noise ratio data.

[0078] In S404, a network hierarchy coefficient set is generated based on the comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node.

[0079] Here, 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 the neighboring nodes, and 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 ​​lower than the network hierarchy of the current node, the hierarchy coefficient of the element is set to a first preset value. If the network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​higher than the network hierarchy of the current node, the hierarchy coefficient of the element is set to a second preset value. Corresponding elements in the network hierarchy feature matrix are updated using the first and second preset values ​​to obtain a network hierarchy coefficient matrix.

[0080] In S406, the first signal-to-noise ratio data is modified using the network layer coefficient set to obtain target signal-to-noise ratio data of the neighboring node.

[0081] Specifically, the Hadamard product is calculated 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 target signal-to-noise ratio data.

[0082] In S408, a first transformer distribution area affiliation of the current node is identified 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.

[0083] Specifically, a Z-score normalization process is performed on the second signal-to-noise ratio data to obtain a first judgment matrix, and a Z-score normalization process is performed on the target signal-to-noise ratio data to obtain a second judgment matrix. If the maximum value in the first judgment matrix is ​​greater than or equal to the maximum value in the second judgment matrix, the current node is identified as belonging to the target transformer distribution area; if the maximum value in the first judgment matrix is ​​less than the maximum value in the second judgment matrix, the current node is identified as belonging to the neighboring transformer distribution area.

[0084] Furthermore, the amplitude value average value and amplitude value standard deviation of the signal-to-noise ratio amplitude value of the neighboring node within the transformer distribution area identification period are obtained, and a normalization process is performed according to the target signal-to-noise ratio data, the amplitude value average value, and the amplitude value standard deviation to obtain a Z-score matrix, and a filtering process and an average calculation are performed on the elements in the Z-score matrix according to a preset score threshold to obtain a second judgment matrix.

[0085] In S410, a first zero-crossing standard deviation between the zero-crossing data of the first central coordinator and the zero-crossing data of the current node is determined.

[0086] 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 overall average value 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 overall average value corresponding to the zero-crossing difference.

[0087] In S412, a second zero-crossing standard deviation between the zero-crossing data of the second central coordinator and the zero-crossing data of the current node is determined.

[0088] 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 overall average value 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 overall average value corresponding to the zero-crossing difference.

[0089] In S414, the second transformer distribution area affiliation of the current node is identified based on the comparison result between the first zero crossing standard deviation and the second zero crossing standard deviation.

[0090] Specifically, if the first zero cross standard deviation is less than the second zero cross standard deviation, it is determined that the current node belongs to the target transformer distribution area, and if the first zero cross standard deviation is greater than or equal to the second zero cross standard deviation, it is determined that the current node belongs to the neighboring transformer distribution area.

[0091] In S416, if the first transformer distribution area affiliation relationship and the second transformer distribution area affiliation relationship match, the transformer distribution area to which the current node belongs is determined based on the first transformer distribution area affiliation relationship or the second transformer distribution area affiliation relationship.

[0092] In S418, if the first transformer distribution area affiliation relationship and the second transformer distribution area affiliation relationship do not match, a transformer distribution area affiliation reminder message is sent.

[0093] Here, the transformer distribution area affiliation reminder message is used to prompt the user to check the current node affiliation.

[0094] In some embodiments, as shown in FIG. 5a, a transformer distribution area identification method is provided, which includes the following steps 1 to 7.

[0095] In step 1, the concentrator communication terminal periodically collects and broadcasts zero-crossing information and broadcasts a beacon frame.

[0096] The concentrator communication terminal transmits a transformer distribution area feature collection start message to the transformer distribution area station to be identified.

[0097] The concentrator communication terminal transmits a transformer distribution area characteristic notification message to the transformer distribution area station to be identified.

[0098] The number of identification rounds of the concentrator communication terminal increases by one.

[0099] In step 2, the transformer distribution area station to be identified respectively obtains the transformer distribution area affiliation information of all concentrator communication terminals within the current area.

[0100] The identified station collects the transformer distribution area features and obtains the transformer distribution area NTB information.

[0101] The station to be identified collects and receives SNR information corresponding to the beacon frames.

[0102] In step 3, if the transformer distribution area station only receives the zero crossing information and the SNR information corresponding to the beacon frame from a single concentrator communication terminal, it directly confirms the transformer distribution area belonging to the transformer distribution area station.

[0103] In step 4, if the transformer distribution area station receives the zero-crossing information of two or more transformer distribution areas and the SNR information corresponding to the beacon frame, the station starts a periodic timer and collects the zero-crossing error, the signal-to-noise ratio, and the network hierarchy information.

[0104] In step 5, the zero-crossing difference standard deviation is calculated, and Z-score normalization processing is performed on the signal-to-noise ratio and network hierarchy information.

[0105] In step 6, the transformer distribution area station confirms the transformer distribution area identification affiliation result based on the transformer distribution area identification related information collected in step 5, and reports the affiliation result to the current networking concentrator communication terminal equipment.

[0106] In step 7, it is determined whether to continue the iteration based on the set limit value of the number of iterations or the identification success rate. If it continues, proceed to step 2; if it does not continue, end the transformer distribution area identification.

[0107] In an embodiment herein, an apparatus for identifying a target transformer distribution area is provided, the target transformer distribution area includes a current node and a neighboring node corresponding to the current node in the target transformer distribution area, there is a neighboring transformer distribution area corresponding to the target transformer distribution area, and there is a neighboring node corresponding to the current node in the neighboring transformer distribution area. As shown in Fig. 5b, the apparatus for identifying a target transformer distribution area includes an acquisition module, a comparison module, a correction module, and an identification module.

[0108] The acquisition module is used to acquire first signal-to-noise ratio data of the neighboring node, a network hierarchy of the neighboring node, and second signal-to-noise ratio data of the neighboring node.

[0109] The comparison module is used to generate a network hierarchy coefficient set based on the comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node, where the elements in the network hierarchy coefficient set are for representing the connection relationship between the neighboring node and the current node.

[0110] A correction module is used to correct the first signal-to-noise ratio data using the network layer coefficient set to obtain target signal-to-noise ratio data of the neighboring node.

[0111] The identification module is used to identify a first transformer distribution area affiliation of the current node based on Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, respectively.

[0112] In some embodiments, the device further includes a signal-to-noise ratio feature matrix module that constructs a signal-to-noise ratio feature matrix based on an initial signal-to-noise ratio amplitude value of a message of the neighboring node, and sets the signal-to-noise ratio feature matrix as the first signal-to-noise ratio data.

[0113] In some embodiments, the comparison module is further configured to generate a network hierarchy feature matrix based on the network hierarchy of the neighboring node, compare elements in the network hierarchy feature matrix with the network hierarchy of the current node, and if the network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​lower than the network hierarchy of the current node, set the hierarchy coefficient of the any element as a first preset value, and if the network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​equal to or higher than the network hierarchy of the current node, set the hierarchy coefficient of the any element as a second preset value, and update 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.

[0114] In some embodiments, the correction module is further configured to perform a Hadamard product using the signal-to-noise ratio feature matrix and the network layer coefficient matrix to obtain the target signal-to-noise ratio feature matrix, which is the target signal-to-noise ratio data.

[0115] In some embodiments, the identification module is further used for: performing a Z-score normalization process on the second signal-to-noise ratio data to obtain a first judgment matrix; performing a Z-score normalization process on the target signal-to-noise ratio data to obtain a second judgment matrix; and identifying the current node as belonging to the target transformer distribution area if the maximum value in the first judgment matrix is ​​greater than or equal to the maximum value of the second judgment matrix; and identifying the current node as belonging to the neighboring transformer distribution area if the maximum value in the first judgment matrix is ​​less than the maximum value of the second judgment matrix.

[0116] In some embodiments, the target signal-to-noise ratio data is a target signal-to-noise ratio feature matrix, and the identification module further obtains the amplitude value average value and amplitude value standard deviation of the signal-to-noise ratio amplitude value of the neighboring node within a transformer distribution area identification period, performs a normalization process according to the target signal-to-noise ratio data, the amplitude value average value, and the amplitude value standard deviation to obtain a Z-score matrix, and performs a filtering process and average calculation on the elements in the Z-score matrix according to a preset score threshold to obtain the second judgment matrix.

[0117] In another embodiment, the above-mentioned target transformer distribution area identification device includes a processor, wherein the processor is used to execute the above-mentioned program modules stored in a memory, including an acquisition module, a comparison module, a correction module, an identification module, and a signal-to-noise ratio feature matrix module.

[0118] Regarding specific limitations regarding the device for identifying the target transformer distribution area, reference may be made to the limitations regarding the method for identifying the target transformer distribution area described above, and detailed description thereof will not be given here.

[0119] An embodiment of the present specification provides a computer device including a memory having a computer program stored therein, and a processor that executes the computer program to implement the steps of the method described in any one of the above embodiments.

[0120] An embodiment of the present specification provides a computer-readable storage medium having a computer program stored thereon, the computer program being characterized in that, when executed by a processor, the computer program implements the steps of the method described in any one of the above embodiments.

[0121] It should be noted that the logic and / or steps represented in flowcharts or otherwise described herein, e.g., considered as sequences of executable instructions to implement logical functions, may be tangibly embodied in any computer-readable medium for or for use in combination with an instruction execution system, apparatus, or device (e.g., a computer-based system including a system of processors or other systems capable of fetching and executing instructions from an instruction execution system, apparatus, or device). As used herein, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. More specific examples of computer-readable media (a non-exhaustive list) include electrical connections having one or more wires (electronic devices), portable computer cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disk read-only memory (CD-ROM). The computer readable medium may also be paper or other suitable medium upon which the program may be printed, such that the program may be obtained electronically and stored in computer memory, for example, by optically scanning the paper or other medium and then programming, decrypting, or otherwise processing as needed.

[0122] It should be understood that various portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, when implemented in hardware, as in other embodiments, the hardware may be implemented using any of the techniques known in the art, such as a discrete logic circuit having logic gate circuits for implementing logical functions of data signals, an application specific integrated circuit having an appropriate combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), or a combination thereof.

[0123] In the description herein, references to terms such as "one embodiment," "some embodiments," "examples," "embodiments," or "some examples" mean that at least one embodiment or example of the present invention includes the particular feature, structure, material, or characteristic described with reference to that embodiment or example. Exemplary references to the above terms in this specification do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0124] Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not understood to indicate or imply relative importance or the number of technical features depicted. Thus, a feature qualified as "first" or "second" can explicitly or implicitly include at least one feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise expressly and specifically limited.

[0125] In the present invention, unless otherwise clearly specified and limited, the terms "attach," "couple," "connect," "fixed," etc. should be understood in a broad sense, and may refer to a fixed connection, a detachable connection, or an integral connection, a mechanical connection, an electrical connection, a direct connection, an indirect connection via an intermediate medium, an internal communication between two components, or an interactive relationship between two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0126] Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are illustrative and are not limiting of the present invention, and that those skilled in the art can change, modify, substitute, and vary the above embodiments within the scope of the present invention.

Claims

1. 1. A method for identifying a target transformer distribution area, comprising: It is assumed that the target transformer distribution area includes a current node and a neighboring node in the target transformer distribution area that can communicate with the current node via power line carrier communication, and a neighboring transformer distribution area is a transformer distribution area that is neighboring to the target transformer distribution area, and there is a neighboring node in the neighboring transformer distribution area that can communicate with the current node via power line carrier communication, and the method includes: obtaining first signal-to-noise ratio data broadcast from the neighboring node, a network hierarchy of the neighboring node, and second signal-to-noise ratio data broadcast from the neighboring node; generating a network hierarchy coefficient set based on a comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node, wherein elements in the network hierarchy coefficient set represent connection relationships between the neighboring node and the current node; modifying the first signal-to-noise ratio data using the network layer coefficient set to obtain target signal-to-noise ratio data for the neighboring node; and identifying a first transformer distribution area affiliation relationship of the current node, indicating whether the current node belongs to the target transformer distribution area or the neighboring transformer distribution area, based on Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, respectively.

2. Obtaining the first signal-to-noise ratio data broadcast from the neighboring node includes:

2. The method of claim 1, further comprising: constructing a signal-to-noise ratio feature matrix as the first signal-to-noise ratio data based on initial signal-to-noise ratio amplitude values ​​of messages broadcast from the neighboring nodes.

3. The step of generating a set of network hierarchy coefficients based on a comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node includes: generating a network hierarchy feature matrix based on the network hierarchy of the neighboring nodes; comparing elements in the network hierarchy feature matrix with the network hierarchy of the current node; If a network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​smaller than the network hierarchy of the current node, setting a hierarchy coefficient of the any element as a first preset value; If a network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​equal to or higher than the network hierarchy of the current node, setting a hierarchy coefficient of the any element as a second preset value; and updating 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.

4. the step of modifying the first signal-to-noise ratio data using the network layer coefficient set to obtain target signal-to-noise ratio data for the neighboring node comprises:

4. The method of claim 3, further comprising: performing a Hadamard product 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.

5. Obtaining the second signal-to-noise ratio data broadcasted from the neighboring node comprises:

2. The method of claim 1, further comprising: constructing a neighborhood signal-to-noise ratio feature matrix as the second signal-to-noise ratio data based on initial signal-to-noise ratio amplitude values ​​of messages broadcast from the neighboring nodes.

6. The step of identifying a first transformer distribution area affiliation relationship of the current node, indicating whether the current node belongs to the target transformer distribution area or the neighboring transformer distribution area, based on Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, respectively, comprises: performing a Z-score normalization process on the second signal-to-noise ratio data to obtain a first judgment matrix; performing Z-score normalization on the target signal-to-noise ratio data to obtain a second judgment matrix; Identifying the current node as belonging to the target transformer distribution area if the maximum value in the first judgment matrix is ​​greater than or equal to the maximum value of the second judgment matrix; and identifying the current node as belonging to the neighboring transformer distribution area if the maximum value in the first judgment matrix is ​​less than the maximum value in the second judgment matrix.

7. The step of adopting the target signal-to-noise ratio feature matrix as the target signal-to-noise ratio data, performing Z-score normalization processing on the target signal-to-noise ratio data, and obtaining a second judgment matrix includes: obtaining an amplitude value average value and an amplitude value standard deviation of the signal-to-noise ratio amplitude value of the neighboring node within a transformer distribution area identification period; performing a normalization process based on the target signal-to-noise ratio data, the amplitude mean value, and the amplitude standard deviation to obtain a Z-score matrix; and filtering and averaging the elements in the Z-score matrix according to a preset score threshold to obtain the second judgment matrix.

8. The target transformer distribution area further includes a first central coordinator, and the neighboring transformer distribution area further includes a second central coordinator, and the method includes: calculating a sample variance of the difference between the zero-crossing data collected by the first central coordinator and the zero-crossing data collected by the current node as a first zero-crossing standard deviation; calculating a sample variance of the difference between the zero-crossing data collected by the second central coordinator and the zero-crossing data collected by the current node as a second zero-crossing standard deviation; The method according to any one of claims 1 to 7, further comprising: identifying a second transformer distribution area belonging relationship of the current node, indicating whether the current node belongs to the target transformer distribution area or the neighboring transformer distribution area, based on a comparison result between the first zero crossing standard deviation and the second zero crossing standard deviation.

9. When the transformer distribution area to which the current node belongs indicated by the first transformer distribution area affiliation relationship and the transformer distribution area to which the current node belongs indicated by the second transformer distribution area affiliation relationship match, determining the transformer distribution area to which the current node belongs based on the first transformer distribution area affiliation relationship or the second transformer distribution area affiliation relationship; 9. The method of claim 8, further comprising: if the transformer distribution area to which the current node belongs indicated by the first transformer distribution area affiliation relationship does not match the transformer distribution area to which the current node belongs indicated by the second transformer distribution area affiliation relationship, transmitting a transformer distribution area affiliation reminder message, the transformer distribution area affiliation reminder message being intended to prompt a user to check the affiliation of the current node.

10. the step of identifying a second transformer distribution area belonging relationship of the current node, indicating whether the current node belongs to the target transformer distribution area or the neighboring transformer distribution area, based on a comparison result between the first zero crossing standard deviation and the second zero crossing standard deviation, comprising: determining that the current node belongs to the target transformer distribution area if the first zero crossing standard deviation is smaller than the second zero crossing standard deviation; and determining that the current node belongs to the neighboring transformer distribution area if the first zero crossing standard deviation is greater than or equal to the second zero crossing standard deviation.

11. The collection time of the zero-crossing data of the first central coordinator and the collection time of the zero-crossing data of the current node are the same, and the step of determining a 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 a zero-crossing difference between the zero-crossing data of the first central coordinator and the zero-crossing data of the current node; determining an overall average value corresponding to the zero-crossing difference; and determining the first zero-crossing standard deviation based on the zero-crossing difference and an overall mean value corresponding to the zero-crossing difference.

12. 1. A target transformer distribution area identification device, comprising: It is assumed that the target transformer distribution area includes a current node and a neighboring node in the target transformer distribution area that can communicate with the current node via power line carrier communication, and a neighboring transformer distribution area is a transformer distribution area that is adjacent to the target transformer distribution area, and there is a neighboring node in the neighboring transformer distribution area that can communicate with the current node via power line carrier communication, and the device: an acquisition module that acquires first signal-to-noise ratio data broadcast from the neighboring node, a network hierarchy of the neighboring node, and second signal-to-noise ratio data broadcast from the neighboring node; a comparison module that generates a network hierarchy coefficient set based on a comparison result between the network hierarchy of the neighboring node and the network hierarchy of the current node, wherein elements in the network hierarchy coefficient set represent connection relationships between the neighboring node and the current node; a correction module that corrects the first signal-to-noise ratio data using the network layer coefficient set to obtain target signal-to-noise ratio data for the neighboring node; and an identification module for identifying a first transformer distribution area affiliation relationship of the current node, indicating whether the current node belongs to the target transformer distribution area or the neighboring transformer distribution area, based on Z-score calculation results corresponding to the second signal-to-noise ratio data and the target signal-to-noise ratio data, respectively.

13. 13. The apparatus of claim 12, further comprising a signal-to-noise ratio feature matrix module that constructs a signal-to-noise ratio feature matrix based on an initial signal-to-noise ratio amplitude value of a message broadcast from the neighboring node, as the first signal-to-noise ratio data.

14. The comparison module further comprises: generating a network hierarchy feature matrix based on the network hierarchy of the neighboring nodes; comparing elements in the network hierarchy feature matrix with the network hierarchy of the current node; If a network hierarchy corresponding to any element in the network hierarchy feature matrix is ​​smaller than the network hierarchy of the current node, setting a hierarchy coefficient of the any element as a first preset value; If a network layer corresponding to any element in the network layer feature matrix is ​​equal to or higher than the network layer of the current node, setting a layer coefficient of the any element as a second preset value; 14. The apparatus of claim 13, further comprising: updating 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.

15. The correction module further comprises:

15. The device according to claim 14, wherein a Hadamard product is performed 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 identification module further comprises: performing a Z-score normalization process 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 first judgment matrix is ​​equal to or greater than the maximum value in the second judgment matrix, identify the current node as belonging to the target transformer distribution area; 16. The apparatus of claim 15, wherein if the maximum value in the first judgment matrix is ​​less than the maximum value in the second judgment matrix, the current node is identified as belonging to the neighboring transformer distribution area.

17. The target signal-to-noise ratio data is the target signal-to-noise ratio feature matrix, and the identification module further comprises: obtain an amplitude value average value and an amplitude value standard deviation of the signal-to-noise ratio amplitude value of the neighboring node within a transformer distribution area identification period; performing a normalization process based on the target signal-to-noise ratio data, the amplitude value average value, and the amplitude value standard deviation to obtain a Z-score matrix; 17. The apparatus of claim 16, further comprising: filtering and averaging the elements in the Z-score matrix according to a preset score threshold to obtain the second judgment matrix.

18. A computer device including a memory in which a computer program is stored and a processor, A computer device characterized in that said processor, when executing said computer program, implements the steps of the method according to any one of claims 1 to 7.

19. A non-volatile computer-readable storage medium on which a computer program is stored, A non-volatile computer-readable storage medium, characterized in that the computer program implements the steps of the method according to any one of claims 1 to 7 when executed by a processor.

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