WO2024109183A1 - 目标台区识别方法、装置、计算机设备及存储介质 - Google Patents

目标台区识别方法、装置、计算机设备及存储介质 Download PDF

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WO2024109183A1
WO2024109183A1 PCT/CN2023/112970 CN2023112970W WO2024109183A1 WO 2024109183 A1 WO2024109183 A1 WO 2024109183A1 CN 2023112970 W CN2023112970 W CN 2023112970W WO 2024109183 A1 WO2024109183 A1 WO 2024109183A1
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noise ratio
signal
network level
current node
node
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PCT/CN2023/112970
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English (en)
French (fr)
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徐鲲鹏
张春磊
李铮
代洪光
曹波
罗丹
王贤辉
肖德勇
张谦
武占侠
陈金雷
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北京智芯微电子科技有限公司
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Publication of WO2024109183A1 publication Critical patent/WO2024109183A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/10—Pre-processing; Data cleansing
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/02—Details
    • H04B3/46—Monitoring; Testing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/54—Systems for transmission via power distribution lines

Definitions

  • the present invention relates to the field of power line carrier communications, and in particular to a target station area identification method, device, computer equipment and storage medium.
  • a transformer substation is the power supply range or area of a transformer.
  • power substation identification technology is mainly based on the substation zero-crossing network benchmark feature information (Network Time Base NTB) identification, or based on the signal-to-noise ratio (SNR) identification.
  • Network Time Base NTB substation zero-crossing network benchmark feature information
  • SNR signal-to-noise ratio
  • the embodiments of this specification provide a method, apparatus, computer device and storage medium for identifying a target station area, so as to improve the accuracy of the identification result of the station area identification method in the related art.
  • the embodiment of the present specification provides a method for identifying a target area, wherein the target area includes a current node and an adjacent node corresponding to the current node in the target area; the target area corresponds to a neighboring area, and the current node corresponds to a neighboring node in the neighboring area; the method includes: obtaining first signal-to-noise ratio data of the adjacent node, the network level of the adjacent node, and second signal-to-noise ratio data of the neighboring node; generating a network level coefficient set based on a comparison result between the network level of the adjacent node and the network level of the current node; wherein the elements in the network level coefficient set are used to represent the connection relationship between the adjacent node and the current node; using the network level coefficient set to correct the first signal-to-noise ratio data to obtain the target signal-to-noise ratio data of the adjacent node; and identifying the first area affiliation of the current node according to the Z-score calculation results corresponding to the second signal
  • the embodiment of the present specification provides a target area identification device, wherein the target area includes a current node and a neighboring node corresponding to the current node in the target area; the target area corresponds to a neighboring area, and the current node corresponds to a neighboring node in the neighboring area; the device includes:
  • An acquisition module configured to acquire first signal-to-noise ratio data of the neighboring node, a network level of the neighboring node, and second signal-to-noise ratio data of the neighboring node;
  • a comparison module configured to generate a network level coefficient set based on a comparison result between the network level of the adjacent node and the network level of the current node; wherein the elements in the network level coefficient set are used to represent a connection relationship between the adjacent node and the current node;
  • a correction module used to correct the first signal-to-noise ratio data using the network level coefficient set to obtain the target signal-to-noise ratio data of the adjacent node;
  • An identification module is used to identify the first station 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 respectively.
  • An embodiment of the present specification provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the above embodiments when executing the computer program.
  • the embodiments of this specification provide a computer-readable storage medium having a computer program stored thereon.
  • the computer program is executed by a processor, the steps of the method described in any one of the above embodiments are implemented.
  • the first signal-to-noise ratio data of the adjacent node by obtaining the first signal-to-noise ratio data of the adjacent node, the network level of the adjacent node and the second signal-to-noise ratio data of the neighboring node; and based on the comparison result of the network level of the adjacent node and the network level of the current node, a network level coefficient set is generated; thereby the first signal-to-noise ratio data is corrected using the network level coefficient set to obtain the target signal-to-noise ratio data of the adjacent node; and then the first substation affiliation 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, respectively, to realize the SNR calculation function in the signal reception measurement process based on the communication module itself and the network level topology level statistical function of the communication module itself, and the Z-score algorithm is used to calculate the signal-to-noise ratio amplitude, so that the sub
  • FIG. 1 a is a schematic diagram of NTB threshold differences provided in an embodiment of the present specification.
  • FIG. 1 b is a schematic diagram of the topological relationship of the stations provided in an embodiment of this specification.
  • FIG. 2 is a schematic diagram of a target area identification method provided according to an embodiment of the present specification.
  • FIG. 3 is a schematic diagram of a target area identification method provided according to an embodiment of the present specification.
  • FIG. 4 is a schematic diagram of a target area identification method provided according to an embodiment of the present specification.
  • FIG. 5 a is a schematic diagram of a target area identification method provided according to an embodiment of the present specification.
  • FIG. 5 b is a structural block diagram of a target area identification device provided according to an embodiment of the present specification.
  • a low-voltage substation refers to the power supply range or area of a transformer.
  • the power consumption management department needs to find out the attributes of the user's substation and phase line, and accurately and quickly find out the user information of various substations.
  • a substation identification technology based on HPLC it is urgent to use a substation identification technology based on HPLC to greatly improve the accuracy of user information and greatly reduce the workload and labor intensity of grassroots personnel.
  • SNR signal-to-noise ratio
  • the embodiment of this specification provides a flow chart of a target area identification method.
  • the target area includes a current node and a neighboring node corresponding to the current node in the target area; the target area corresponds to a neighboring area, and the current node corresponds to a neighboring node in the neighboring area.
  • the target area identification method may include the following steps:
  • Step S110 Acquire first signal-to-noise ratio data of neighboring nodes, network levels of neighboring nodes, and second signal-to-noise ratio data of neighboring nodes.
  • the current node is included in the target area, and the current node corresponds to some adjacent nodes, which can be some adjacent nodes in the target area or some neighboring nodes in the neighboring area.
  • the current node and the adjacent nodes correspond to terminal devices respectively, and these terminal devices can broadcast node data in the power grid area.
  • the terminal device of the current node can collect node data of adjacent nodes to determine the area characteristics of adjacent nodes, that is, the first signal-to-noise ratio data of adjacent nodes and the network level of adjacent nodes.
  • the terminal device of the current node can continuously receive the SNR amplitude of n messages from adjacent nodes and the network level of adjacent nodes.
  • the terminal device of the current node can also collect node data of neighboring nodes to determine the second signal-to-noise ratio data of neighboring nodes.
  • the terminal device of the current node can continuously receive the SNR amplitude of n messages from neighboring nodes and the network level of adjacent nodes.
  • the node signals of the adjacent nodes are processed by big data preprocessing technology to extract the original SNR data collection of the area identification period corresponding to all adjacent nodes.
  • Big data preprocessing technology includes data cleaning, data dimension reduction and data change.
  • the area identification period can be set.
  • the first signal-to-noise ratio data, the network level and the second signal-to-noise ratio data correspond to the area identification period, and may include data collected at several time points collected within the area identification period.
  • Step S120 Generate a network level coefficient set based on the comparison result between the network level of the adjacent node and the network level of the current node.
  • the elements in the network level coefficient set are used to represent the connection relationship between the adjacent node and the current node. Specifically, it is necessary to determine the network level relationship between the adjacent node and the current node.
  • the network level of the adjacent node and the network level of the current node can be known, and it can be clear that the network level of the adjacent node needs to be smaller than the network level of the current node. Therefore, the network level of the adjacent node and the network level of the current node are compared, and the network level coefficient set is generated based on the comparison result of the network level of the adjacent node and the network level of the current node.
  • the network level of the neighboring node can be constructed as a network level set of the neighboring node, and the elements in the network level set can be regarded as corresponding to the preset coefficients. Further, based on the comparison result of the network level of the neighboring node and the network level of the current node, the preset coefficients of the elements in the network level set of the neighboring node are adjusted, and the product operation based on the adjusted preset coefficients and the elements in the network level set of the neighboring node is performed. Calculate and generate a set of network layer coefficients.
  • Step S130 Correct the first signal-to-noise ratio data using the network level coefficient set to obtain target signal-to-noise ratio data of adjacent nodes.
  • the first signal-to-noise ratio data acquired by the terminal device of the current node includes some noise data
  • a network level coefficient set is generated in combination with a preset network level relationship in the target area, so the first signal-to-noise ratio data is corrected using the network level coefficient set to obtain the target signal-to-noise ratio data of the adjacent node.
  • the target signal-to-noise ratio data of the adjacent node is determined by multiplying the network level coefficient set and the first signal-to-noise ratio data.
  • Step S140 Identify the first station 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.
  • Z-score standardization is a method of data processing. It can convert data of different magnitudes into Z-Score values of uniform measurement for comparison. Improves data comparability and weakens data interpretability.
  • the second signal-to-noise ratio data is subjected to Z-score standardization 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 belonging affinity maximum value.
  • the target signal-to-noise ratio data is subjected to Z-score standardization 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 belonging affinity maximum value. Compare the first belonging affinity maximum value with the second belonging affinity maximum value, and identify the first district belonging relationship of the current node based on the comparison result, such as the current node belongs to the target district, or the current node belongs to the neighbor district of the target district.
  • the first signal-to-noise ratio data of the adjacent node, the network level of the adjacent node and the second signal-to-noise ratio data of the neighboring node are obtained; and based on the comparison result of the network level of the adjacent node and the network level of the current node, a network level coefficient set is generated; thereby, 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 adjacent node; and then, 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 first substation affiliation of the current node can be identified, and the SNR calculation function in the signal reception measurement process based on the communication module itself and the network level topology level statistics function of the communication module itself are realized, and the signal-to-noise ratio amplitude is calculated by using the Z-score algorithm, so that the substation
  • the first signal-to-noise ratio data may be generated by constructing a signal-to-noise ratio feature matrix based on initial signal-to-noise ratio amplitudes of messages from neighboring nodes as the first signal-to-noise ratio data.
  • the neighboring node data of the current node is obtained, and the number of neighboring nodes of the current node can be m.
  • the neighboring nodes of the m target stations respectively continuously receive the initial signal-to-noise ratio amplitude (SNR amplitude) of n messages and the network level of the neighboring nodes.
  • SNR amplitude initial signal-to-noise ratio amplitude of the messages of the neighboring nodes
  • a signal-to-noise ratio feature matrix i.e., SNR amplitude feature matrix
  • the signal-to-noise ratio feature matrix is a matrix of m rows and n columns.
  • SNR amplitude feature matrix The elements in the SNR amplitude feature matrix are denoted as snr i,j, which represents the jth SNR feature amplitude collected at the corresponding frequency of the neighboring nodes of the i-th target station, and the formula is as follows:
  • a network level coefficient set is generated based on a comparison result of a network level of an adjacent node with a network level of a current node. It may include: generating a network level feature matrix based on the network levels of adjacent 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 smaller than the network level of the current node, setting the level coefficient of any element to a first preset value; if the network level corresponding to any element in the network level feature matrix is not smaller than the network level of the current node, setting the level coefficient of any 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.
  • the network level feature matrix LAYER m ⁇ n is determined.
  • the network level feature matrix of the target area is a matrix with m rows and n columns.
  • the element layer i, j of the network level feature matrix represents the jth network level of the neighboring nodes of the i-th target area at the corresponding acquisition frequency. The formula is as follows:
  • the corresponding elements in the network level feature matrix are updated using the first preset value and the second preset value to obtain a network level coefficient matrix.
  • network level parameters are proposed as one of the criteria for area identification and judgment.
  • the nodes in this area are judged at the network level to avoid interference of the child nodes or nodes at the same level of the current node with the SNR statistics.
  • the area attribution judgment standard depends on the SNR value of the proxy node of the current node or the node at the same level as the proxy node.
  • the first signal-to-noise ratio data is modified using a network level coefficient set to obtain target signal-to-noise ratio data of adjacent nodes.
  • the method comprises: performing Hadamard product operation using a signal-to-noise ratio feature matrix and a network level coefficient matrix to obtain a target signal-to-noise ratio feature matrix as target signal-to-noise ratio data.
  • the SNR of the adjacent nodes of the target station area is constructed as follows:
  • network level parameters are proposed as one of the criteria for area identification and judgment.
  • the nodes in this area are judged at the network level to avoid interference of the child nodes or nodes at the same level of the current node with the SNR statistics.
  • the area attribution judgment standard depends on the SNR value of the proxy node of the current node or the node at the same level as the proxy node.
  • the second signal-to-noise ratio data is generated by constructing a neighbor signal-to-noise ratio feature matrix based on an initial signal-to-noise ratio amplitude of a message from a neighbor node as the second signal-to-noise ratio data.
  • the neighboring node data of the neighboring node is obtained, and the number of neighboring nodes of the neighboring node can be m.
  • the neighboring nodes of the m neighboring stations respectively continuously receive the initial signal-to-noise ratio amplitudes (SNR amplitudes) of n messages.
  • SNR amplitudes initial signal-to-noise ratio amplitudes
  • a neighbor signal-to-noise ratio feature matrix i.e., SNRY amplitude feature matrix
  • the neighbor signal-to-noise ratio feature matrix is a matrix of m rows and n columns.
  • snry i, j which represents the jth SNR feature amplitude collected at the corresponding frequency of the neighboring node of the i-th target station, and the formula is as follows:
  • the network belonging relationship between the current node and the target substation and the neighbor substation based on the power line parameters is determined.
  • the first substation 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, including: performing Z-score normalization 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; if the maximum value in the first judgment matrix is not less than the maximum value in the second judgment matrix, identifying that the current node belongs to the target substation; if the maximum value in the first judgment matrix is less than the maximum value in the second judgment matrix, identifying that the current node belongs to a neighboring substation.
  • the second signal-to-noise ratio data and the target signal-to-noise ratio data can be represented in matrix form.
  • the first judgment matrix has a maximum value
  • the second judgment matrix has a maximum value. Compare the maximum value in the first judgment matrix with the maximum value in the second judgment matrix.
  • the maximum value in the first judgment matrix is not less than the maximum value in the second judgment matrix, identify the current node attribute in the target area; if the maximum value in the first judgment matrix is less than the maximum value in the second judgment matrix, it is identified that the current node belongs to a neighboring area.
  • the target signal-to-noise ratio data uses a target signal-to-noise ratio feature matrix.
  • the target signal-to-noise ratio data is subjected to Z-score normalization to obtain a second judgment matrix, which may include the following steps:
  • the target signal-to-noise ratio data adopts the target signal-to-noise ratio feature matrix (i.e., the SNR amplitude feature matrix).
  • Z-score normalization is performed according to the following formula: Where z represents the standardized value, x represents the SNR amplitude of the neighboring nodes in the target area at a certain moment, ⁇ represents the average amplitude of all SNR amplitudes of the neighboring nodes in the target area within the area identification period, and ⁇ represents the standard deviation of the amplitude of all SNR amplitudes of the neighboring nodes in the target area.
  • the SNR feature amplitude matrix is normalized by Z-score to obtain the Z score matrix as shown below:
  • the Z score matrix for each element in the SNR characteristic amplitude matrix, select the element with a corresponding Z score less than the threshold ⁇ as the effective SNR amplitude of the current adjacent node in the area identification and collection period, and remove the element with a Z score greater than 0.2 from the current SNR data set corresponding to the SNR characteristic amplitude matrix; for each adjacent node in the area identification and collection period, the SNR value is statistically processed and filtered by the Z-score data set to obtain the algorithm average of the SNR characteristic amplitude in the target area identification and collection period, and the formula is as follows:
  • the target signal-to-noise ratio characteristic matrix of the target area is obtained through Z-score calculation, data screening, and calculation of arithmetic mean, and the identification SNR judgment matrix of the target area is obtained.
  • the SNR values of neighboring sites are statistically analyzed for a long time for area identification.
  • the SNR values fluctuate up and down.
  • This implementation provides a Z-score screening algorithm. After deleting nodes that are greater than the specified threshold, the arithmetic mean is obtained for the current received SNR information. The final arithmetic mean is used to measure the relationship between the current neighbor node and the area of this node.
  • the target station further includes a first central coordinator; the neighboring station further includes a second central coordinator; and the method may further include the following steps:
  • S310 Determine a first zero-crossing point standard deviation between the zero-crossing point data of the first central coordinator and the zero-crossing point data of the current node.
  • the node to be identified in the target area of the current node can receive the zero-crossing data of the first central coordinator, can also receive the zero-crossing data of the second central coordinator, and can also collect zero-crossing data. Based on the zero-crossing data of the first central coordinator and the zero-crossing data collected by the current node, the first zero-crossing standard deviation between the two is determined.
  • the first central coordinator CCO and the current node collect zero-crossing information simultaneously, and the data scale is m.
  • CCINTB1 [ ccontb1 , ccontb2 , ..., ccontbm ]
  • STANTB [ stantb1 , stantb2 , ..., stantbm ]
  • ⁇ 1 is the population mean of the target area zero-crossing point difference NTBDIFF 1
  • ⁇ 1 is the standard deviation of the zero-crossing point difference NTBDIFF 1 of this area.
  • S320 Determine a second zero-crossing point standard deviation between the zero-crossing point data of the second central coordinator and the zero-crossing point data of the current node.
  • the current node may also receive zero-crossing data from the second central coordinator, and may also collect zero-crossing data. Based on the zero-crossing data from the second central coordinator and the zero-crossing data collected by the current node, a second zero-crossing standard deviation between the two is determined.
  • the second central coordinator CCO and the current node collect zero-crossing information simultaneously, and the data scale is m.
  • CCONTB2 [ ccontb1 , ccontb2 , ..., ccontbm ]
  • NTBDIFF 2 CCONTB 2 - STANTB
  • ⁇ 2 is the overall mean of the zero-crossing point difference value NTBDIFF 2 of the neighboring stations, and ⁇ 2 is the standard deviation of the zero-crossing point difference value NTBDIFF 2 of the neighboring stations.
  • S330 Based on the comparison result of the first zero-crossing standard deviation and the second zero-crossing standard deviation, identify the second station area belonging relationship of the current node.
  • the first zero-crossing standard deviation is compared with the second zero-crossing standard deviation, and the second station area belonging relationship of the current node is identified according to the comparison result of the first zero-crossing standard deviation and the second zero-crossing standard deviation.
  • the target station area zero-crossing information standard deviation ⁇ 1 is compared with the neighboring station area zero-crossing information standard deviation ⁇ 2 , after N times of zero-crossing information collection, when ⁇ 1 ⁇ 2 , the current node should belong to the current station area, and when ⁇ 1 > ⁇ 2 , the current node belongs to the adjacent station area.
  • the accuracy of the substation identification is improved by using a substation identification method based on zero-crossing information comparison, signal-to-noise ratio and network topology relationship.
  • the method may further include the following steps: if the affiliation of the first area is consistent with the affiliation of the second area, determining the area to which the current node belongs based on the affiliation of the first area or the affiliation of the second area; or, if the affiliation of the first area is inconsistent with the affiliation of the second area, issuing an area affiliation reminder message; wherein the area affiliation reminder message is used to remind the user to verify the affiliation of the current node.
  • the first area ownership relationship and the second area ownership relationship are first determined from two perspectives, and then the first area ownership relationship and the second area ownership relationship are used for mutual verification, so the first area ownership relationship and the second area ownership relationship are compared. If the first area ownership relationship is consistent with the second area ownership relationship, indicating that the determined area ownership relationship is credible, then the area to which the current node belongs is determined based on the first area ownership relationship or the second area ownership relationship. If the first area ownership relationship and the second area ownership relationship are inconsistent, indicating that the determined area ownership relationship is untrustworthy, then an area ownership reminder message is issued to remind the user to verify the ownership relationship of the current node.
  • the accuracy of the substation identification is improved by using a substation identification method based on zero-crossing information comparison, signal-to-noise ratio and network topology relationship.
  • the method may further include the following steps: if the first zero-crossing standard deviation is less than the second zero-crossing standard deviation, determining that the current node belongs to the target station area; if the first zero-crossing standard deviation is not less than the second zero-crossing standard deviation, determining that the current node belongs to the neighboring station area.
  • the on-site substation has low load, complex networking conditions, and NTB and SNR are used as separate bases for substation identification and judgment, which cannot meet the needs of on-site operation and maintenance personnel to quickly and accurately locate the node substation.
  • the implementation method of this specification provides a method based on the statistical number of received carrier messages and the statistical SNR through the Z score, and performs substation identification based on the network level of the node sending the carrier message in the current network as a weight result, thereby providing substation identification execution efficiency and accuracy.
  • a target substation identification method is provided.
  • the target substation includes a current node and an adjacent node corresponding to the current node in the target substation; the target substation corresponds to a neighbor substation, and the current node corresponds to a neighbor node in the neighbor substation; the target substation also includes a first central coordinator; the neighbor substation also includes a second central coordinator; the method includes the following steps:
  • S402 Obtain first signal-to-noise ratio data of neighboring nodes, network levels of neighboring nodes, and second signal-to-noise ratio data of neighboring nodes.
  • a signal-to-noise ratio characteristic matrix is constructed as the first signal-to-noise ratio data.
  • a neighbor signal-to-noise ratio characteristic matrix is constructed as the second signal-to-noise ratio data.
  • S404 Generate a network level coefficient set based on a comparison result between the network level of the adjacent node and the network level of the current node.
  • the elements in the network level coefficient set are used to represent the connection relationship between the adjacent nodes and the current node.
  • a network level feature matrix is generated based on the network level of the adjacent nodes; the elements in the network level feature matrix are compared 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, the level coefficient of any element is set to the 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, the level coefficient of any element is set to the 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 a network level coefficient matrix.
  • S406 Correct the first signal-to-noise ratio data using the network level coefficient set to obtain target signal-to-noise ratio data of adjacent nodes.
  • the Hadamard product operation is performed using the signal-to-noise ratio feature matrix and the network level coefficient matrix to obtain a target signal-to-noise ratio feature matrix as the target signal-to-noise ratio data.
  • S408 Identify the first station 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.
  • the second signal-to-noise ratio data is subjected to Z-score normalization to obtain a first judgment matrix;
  • the target signal-to-noise ratio data is subjected to Z-score normalization to obtain a second judgment matrix; if the maximum value in the first judgment matrix is not less than the maximum value in the second judgment matrix, it is identified that the current node belongs to the target station area; if the maximum value in the first judgment matrix is less than the maximum value in the second judgment matrix, it is identified that the current node belongs to the neighboring station area.
  • the amplitude average value and amplitude standard deviation of the signal-to-noise ratio amplitude of the adjacent nodes within the station identification period are obtained; standardization is performed based on the target signal-to-noise ratio data, amplitude average value and amplitude standard deviation to obtain a Z score matrix; the elements in the Z score matrix are filtered and the mean value is calculated according to a preset score threshold to obtain a second judgment matrix.
  • S410 Determine a first zero-crossing point standard deviation between the zero-crossing point data of the first central coordinator and the zero-crossing point data of the current node.
  • a zero-crossing point difference between the zero-crossing point data of the first central coordinator and the zero-crossing point data of the current node is obtained; a population mean corresponding to the zero-crossing point difference is determined; and a first zero-crossing point standard deviation is determined based on the zero-crossing point difference and the population mean corresponding to the zero-crossing point difference.
  • S412 Determine a second zero-crossing point standard deviation between the zero-crossing point data of the second central coordinator and the zero-crossing point data of the current node.
  • a zero-crossing point difference between the zero-crossing point data of the second central coordinator and the zero-crossing point data of the current node is obtained; a population mean corresponding to the zero-crossing point difference is determined; and a second zero-crossing point standard deviation is determined based on the zero-crossing point difference and the population mean corresponding to the zero-crossing point difference.
  • the first zero-crossing standard deviation is smaller than the second zero-crossing standard deviation, it is determined that the current node belongs to the target station area; if the first zero-crossing standard deviation is not smaller than the second zero-crossing standard deviation, it is determined that the current node belongs to the neighboring station area.
  • S416 If the first area affiliation relationship is consistent with the second area affiliation relationship, determine the area to which the current node belongs based on the first area affiliation relationship or the second area affiliation relationship.
  • the area ownership reminder message is used to remind the user to verify the ownership relationship of the current node.
  • a method for identifying a station area includes the following steps:
  • Step 1 The concentrator communication terminal periodically collects and broadcasts zero-crossing information and broadcasts beacon frames.
  • the concentrator communication terminal sends a station area feature collection start message to the station area to be identified.
  • the concentrator communication terminal sends a station area feature notification message to the station area to be identified.
  • the concentrator communication terminal identification round is incremented by 1.
  • Step 2 The site in the area to be identified obtains the area affiliation information of all concentrator communication terminals in the current area.
  • the station to be identified collects the characteristics of the area and obtains the NTB information of the area.
  • the station to be identified collects SNR information corresponding to the received beacon frame.
  • Step 3 The substation site only receives the zero-crossing information of a single concentrator communication terminal and the SNR information corresponding to the beacon frame, and directly confirms the substation site's substation affiliation.
  • Step 4 The station receives zero-crossing information of two or more stations and SNR information corresponding to the beacon frame, and the station starts a periodic timer to collect zero-crossing error, signal-to-noise ratio, and network level information.
  • Step 5 Calculate the standard deviation of the zero-crossing point difference, and perform standardization on the signal-to-noise ratio and the network level information Z score.
  • Step 6 The station site confirms the station identification and attribution result based on the station identification related information collected in step 5, and reports the attribution result to the current networking concentrator communication terminal device.
  • Step 7 Determine whether to continue iterating based on the set iteration limit or recognition success rate. If yes, jump to step 2; otherwise, end the station area recognition.
  • the embodiment of this specification provides a target area identification device, wherein the target area includes a current node and an adjacent node corresponding to the current node in the target area; the target area corresponds to a neighboring area, and the current node corresponds to a neighboring node in the neighboring area.
  • the target area identification device includes: an acquisition module, a comparison module, a correction module, and an identification module.
  • An acquisition module configured to acquire first signal-to-noise ratio data of the neighboring node, a network level of the neighboring node, and second signal-to-noise ratio data of the neighboring node;
  • a comparison module configured to generate a network level coefficient set based on a comparison result between the network level of the adjacent node and the network level of the current node; wherein the elements in the network level coefficient set are used to represent a connection relationship between the adjacent node and the current node;
  • a correction module is used to correct the first signal-to-noise ratio data using the network level coefficient set to obtain the target signal of the adjacent node. Noise ratio data;
  • An identification module is used to identify the first station 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 respectively.
  • the device further includes: a signal-to-noise ratio characteristic matrix module, configured to construct a signal-to-noise ratio characteristic matrix based on the initial signal-to-noise ratio amplitude of the message of the neighboring node as the first signal-to-noise ratio data.
  • a signal-to-noise ratio characteristic matrix module configured to construct a signal-to-noise ratio characteristic matrix based on the initial signal-to-noise ratio amplitude of the message of the neighboring node as the first signal-to-noise ratio data.
  • the comparison module is also used to generate a network level feature matrix based on the network level of the adjacent nodes; compare 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 smaller than the network level of the current node, set the level coefficient of any element to a first preset value; if the network level corresponding to any element in the network level feature matrix is not smaller than the network level of the current node, set the level coefficient of any element to a second preset value; use the first preset value and the second preset value to update the corresponding elements in the network level feature matrix to obtain a network level coefficient matrix.
  • the correction module is further used to perform a Hadamard product operation using the signal-to-noise ratio feature matrix and the network level coefficient matrix to obtain the target signal-to-noise ratio feature matrix as the target signal-to-noise ratio data.
  • the identification module is also used 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 first judgment matrix is not less than the maximum value of the second judgment matrix, identify that the current node belongs to the target station area; if the maximum value in the first judgment matrix is less than the maximum value of the second judgment matrix, identify that the current node belongs to the neighboring station area.
  • the target signal-to-noise ratio data adopts a target signal-to-noise ratio feature matrix; the identification module is also used to obtain the amplitude average and amplitude standard deviation of the signal-to-noise ratio amplitude of the adjacent nodes within the station identification period; based on the target signal-to-noise ratio data, the amplitude average and the amplitude standard deviation, standardization is performed to obtain a Z score matrix; according to a preset score threshold, the elements in the Z score matrix are filtered and the mean is calculated to obtain the second judgment matrix.
  • the target platform area identification device includes: a processor, wherein the processor is used to execute the program modules stored in the memory, including: an acquisition module, a comparison module, a correction module, an identification module and a signal-to-noise ratio feature matrix module.
  • the specific definition of the target area identification device can be found in the above definition of the target area identification method, which will not be repeated here.
  • An embodiment of the present specification provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in any of the above embodiments are implemented.
  • An embodiment of this specification provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any of the above embodiments.
  • logic and/or steps represented in the flowchart or described in other ways 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 an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or used in conjunction with these instruction execution systems, devices or apparatuses.
  • "computer-readable medium” can be any computer-readable medium that can A device that contains, stores, communicates, propagates or transmits a program for use with or in conjunction with an instruction execution system, device or apparatus.
  • computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.
  • a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.
  • a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal
  • a dedicated integrated circuit having a suitable combination of logic gate circuits
  • PGA programmable gate array
  • FPGA field programmable gate array
  • first and second are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as “first” and “second” may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of “plurality” is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.
  • the terms “installed”, “connected”, “connected”, “fixed” and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined.
  • installed can be a fixed connection, a detachable connection, or an integral connection
  • it can be a mechanical connection or an electrical connection
  • it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined.
  • the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

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Abstract

本发明公开了一种目标台区识别方法、装置、计算机设备及存储介质。通过获取临近节点的第一信噪比数据、临近节点的网络层级以及邻居节点的第二信噪比数据；并基于临近节点的网络层级与当前节点的网络层级的比较结果，生成网络层级系数集合；从而利用网络层级系数集合对第一信噪比数据进行修正，得到临近节点的目标信噪比数据；进而可以根据第二信噪比数据、目标信噪比数据分别对应的Z分数计算结果，识别当前节点的第一台区归属关系，实现基于通信模块自身的信号接收测量过程中的SNR计算功能和通信模块自身网络层级拓扑层级统计功能，并采用Z-score算法计算信噪比幅值，可以明确判断当前通信模块的台区归属关系，提高台区识别率。

Description

目标台区识别方法、装置、计算机设备及存储介质 技术领域
本发明涉及技术领域电力线载波通信领域，尤其涉及一种目标台区识别方法、装置、计算机设备及存储介质。
背景技术
在电力系统中，台区是一台变压器的供电范围或区域。相关技术中，电力台区识别技术主要是基于台区过零点网络基准特征信息(Network Time Base NTB)识别，或者基于信噪比(Signal-to-Noise Ratio SNR)识别。
然而，由于电力台区存在负载低、组网情况复杂等情况，相关技术中台区识别方式的识别结果准确性有待提高。
发明内容
本说明书实施方式中提供有一种目标台区识别方法、装置、计算机设备及存储介质，以提供给相关技术中台区识别方式的识别结果准确性。
本说明书实施方式提供一种目标台区识别方法，所述目标台区包括当前节点和所述当前节点在所述目标台区中所对应的临近节点；所述目标台区对应有邻居台区，所述当前节点在所述邻居台区中对应有邻居节点；所述方法包括：获取所述临近节点的第一信噪比数据、所述临近节点的网络层级以及所述邻居节点的第二信噪比数据；基于所述临近节点的网络层级与所述当前节点的网络层级的比较结果，生成网络层级系数集合；其中，所述网络层级系数集合中的元素用于表示所述临近节点与所述当前节点之间的连接关系；利用所述网络层级系数集合对所述第一信噪比数据进行修正，得到所述临近节点的目标信噪比数据；根据所述第二信噪比数据、所述目标信噪比数据分别对应的Z分数计算结果，识别所述当前节点的第一台区归属关系。
本说明书实施方式提供一种目标台区识别装置，所述目标台区包括当前节点和所述当前节点在所述目标台区中所对应的临近节点；所述目标台区对应有邻居台区，所述当前节点在所述邻居台区中对应有邻居节点；所述装置包括：
获取模块，用于获取所述临近节点的第一信噪比数据、所述临近节点的网络层级以及所述邻居节点的第二信噪比数据；
比较模块，用于基于所述临近节点的网络层级与所述当前节点的网络层级的比较结果，生成网络层级系数集合；其中，所述网络层级系数集合中的元素用于表示所述临近节点与所述当前节点之间的连接关系；
修正模块，用于利用所述网络层级系数集合对所述第一信噪比数据进行修正，得到所述临近节点的目标信噪比数据；
识别模块，用于根据所述第二信噪比数据、所述目标信噪比数据分别对应的Z分数计算结果，识别所述当前节点的第一台区归属关系。
本说明书实施方式提供一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现上述实施方式中任一项所述的方法的步骤。
本说明书实施方式提供一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现上述实施方式中任一项所述的方法的步骤。
上述说明书实施方式中，通过获取临近节点的第一信噪比数据、临近节点的网络层级以及邻居节点的第二信噪比数据；并基于临近节点的网络层级与当前节点的网络层级的比较结果，生成网络层级系数集合；从而利用网络层级系数集合对第一信噪比数据进行修正，得到临近节点的目标信噪比数据；进而可以根据第二信噪比数据、目标信噪比数据分别对应的Z分数计算结果，识别当前节点的第一台区归属关系，实现基于通信模块自身的信号接收测量过程中的SNR计算功能和通信模块自身网络层级拓扑层级统计功能，并采用Z-score算法计算信噪比幅值，可以明确判断当前通信模块的台区归属关系，提高台区识别率。
附图说明
图1a为本说明书实施方式提供的NTB阈值差异的示意图。
图1b为本说明书实施方式提供的台区拓扑关系的示意图。
图2为根据本说明书实施方式提供的目标台区识别方法的示意图。
图3为根据本说明书实施方式提供的目标台区识别方法的示意图。
图4为根据本说明书实施方式提供的目标台区识别方法的示意图。
图5a为根据本说明书实施方式提供的目标台区识别方法的示意图。
图5b为根据本说明书实施方式提供的目标台区识别装置的结构框图。
具体实施方式
下面详细描述本发明的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本发明，而不能理解为对本发明的限制。
在电力系统中，低压台区是指一台变压器的供电范围或区域。电力用电管理部门为实现台区精细化管理和降低减损的目标，需要查清用户的台区和相线属性，准确快捷地查清各类台区的用户资料，特别针对线路复杂、台区相邻交叉、干扰源多、干扰严重、资料不全的困难台区，急需使用一种基于HPLC的台区识别技术，大幅度提高用户资料准确度，同时大大减轻基层人员的工作量和劳动强度。
相关的台区识别技术方案可以包括以下两种：
1)基于台区过零点网络基准特征信息(Network Time BaseNTB)识别：当电力线负载很重时，线路阻抗可达1欧姆以下，造成对载波信号的高衰减，电力线数据传输稳定性低；当电力线负载很轻时，不同台区之间的过零点NTB差值较小，无法做出有效台区识别判断，如图1a所示。
2)基于信噪比(Signal-to-Noise Ratio SNR)识别：基于SNR进行台区识别现阶段无统一标准，在台区识别阶段需要保证节点处于多个网络环境，并处于网络末端，通过一段时间SNR统计对比，从而正确识别台区归属，但是现场存在正确归属台区SNR值与临台区SNR值近似情况，导致节点台区归属识别错误；同时现场组网类型普遍为树形网络，如图1b所示，当前节点为代理节点(PCO)，同时节点未处于网络末端位置，如果当前节点与本台区子节点SNR值较大，以此为SNR判断条件进行台区识别，无法正常进行台区识别。
本说明书实施方式提供一种目标台区识别方法的流程图。目标台区包括当前节点和当前节点在目标台区中所对应的临近节点；目标台区对应有邻居台区，当前节点在邻居台区中对应有邻居节点。参考图2所示，该目标台区识别方法可以包括以下步骤：
步骤S110、获取临近节点的第一信噪比数据、临近节点的网络层级以及邻居节点的第二信噪比数据。
其中，在目标台区中包括当前节点，当前节点对应有一些相邻节点，这些相邻节点可以是目标台区中的一些临近节点，也可以是邻居台区中的一些邻居节点。当前节点以及相邻节点分别对应有末端设备，这些末端设备可以在电网区域内广播节点数据，当前节点的末端设备可以采集临近节点的节点数据，以确定临近节点的台区特征，即临近节点的第一信噪比数据、临近节点的网络层级。示例性地，当前节点的末端设备可以连续接收临近节点的n个报文的SNR幅值及临近节点的网络层级。当前节点的末端设备也可以采集邻居节点的节点数据，确定邻居节点的第二信噪比数据。示例性地，当前节点的末端设备可以连续接收邻居节点的n个报文的SNR幅值及临近节点的网络层级。
进一步地，采用大数据预处理技术对相邻节点的节点信号进行数据处理，以提取所有相邻节点对应的台区识别周期原始SNR数据采集。大数据预处理技术包括数据清洗、数据降维和数据变化。在一些实施方式中，可以设置台区识别周期。第一信噪比数据、网络层级以及第二信噪比数据与台区识别周期对应，可以包括在台区识别周期内采集的若干个时间点上采集到的数据。
步骤S120、基于临近节点的网络层级与当前节点的网络层级的比较结果，生成网络层级系数集合。
其中，网络层级系数集合中的元素用于表示临近节点与当前节点之间的连接关系。具体的，需要确定临近节点与当前节点之间的网络层级关系。可以已知临近节点的网络层级、当前节点的网络层级，且可以明确的是，临近节点的网络层级需要小于当前节点的网络层级。因此，比较临近节点的网络层级、当前节点的网络层级，基于临近节点的网络层级与当前节点的网络层级的比较结果，生成网络层级系数集合。
示例性地，临近节点的网络层级可以构建为临近节点的网络层级集合，网络层级集合中的元素可以看作对应有预设系数。进一步地基于临近节点的网络层级与当前节点的网络层级的比较结果，对临近节点的网络层级集合中元素的预设系数进行调整，基于调整后的预设系数和临近节点的网络层级集合中的元素进行的乘积运 算，生成网络层级系数集合。
步骤S130、利用网络层级系数集合对第一信噪比数据进行修正，得到临近节点的目标信噪比数据。
在一些情况下，当前节点的末端设备获取到的第一信噪比数据包括一些噪声数据，且本实施方式中结合目标台区中预设的网络层级关系生成了网络层级系数集合，因此利用网络层级系数集合对第一信噪比数据进行修正，得到临近节点的目标信噪比数据。示例性地，利用网络层级系数集合和第一信噪比数据进行相乘运算，确定临近节点的目标信噪比数据。
步骤S140、根据第二信噪比数据、目标信噪比数据分别对应的Z分数计算结果，识别当前节点的第一台区归属关系。
其中，Z分数(Z-Score)标准化是数据处理的一种方法。通过它能够将不同量级的数据转化为统一量度的Z-Score分值进行比较。提高了数据可比性，削弱了数据解释性。具体地，对第二信噪比数据进行Z分数标准化处理，得到第二信噪比数据对应的Z分数矩阵。目标信噪比数据对应的Z分数矩阵对应有第一归属亲和最大值。对目标信噪比数据进行Z分数标准化处理，得到目标信噪比数据对应的Z分数矩阵。第二信噪比数据对应的Z分数矩阵对应有第二归属亲和最大值。比较第一归属亲和最大值与第二归属亲和最大值，基于比较结果识别当前节点的第一台区归属关系，比如当前节点属于目标台区，或者当前节点属于目标台区的邻居台区。
上述目标台区识别方法中，通过获取临近节点的第一信噪比数据、临近节点的网络层级以及邻居节点的第二信噪比数据；并基于临近节点的网络层级与当前节点的网络层级的比较结果，生成网络层级系数集合；从而利用网络层级系数集合对第一信噪比数据进行修正，得到临近节点的目标信噪比数据；进而可以根据第二信噪比数据、目标信噪比数据分别对应的Z分数计算结果，识别当前节点的第一台区归属关系，实现基于通信模块自身的信号接收测量过程中的SNR计算功能和通信模块自身网络层级拓扑层级统计功能，并采用Z-score算法计算信噪比幅值，可以明确判断当前通信模块与当前电力线台区(即目标台区)及临近电力线台区(即邻居台区)的台区归属关系，对附近所有站点进行信道测量和综合评价，提高台区识别率。
在一些实施方式中，第一信噪比数据的生成方式，可以包括：基于临近节点的报文的初始信噪比幅值，构建信噪比特征矩阵，作为第一信噪比数据。
具体地，获取当前节点的临近节点数据，当前节点的临近节点的数量可以是m个。m个目标台区的临近节点分别连续接收n个报文的初始信噪比幅值(SNR幅值)及临近节点的网络层级。基于临近节点的报文的初始信噪比幅值，构建信噪比特征矩阵(即SNR幅值特征矩阵)。其中，信噪比特征矩阵为m行n列的矩阵。SNR幅值特征矩阵中的元素记为snri，j表示第i个目标台区的临近节点对应频率下采集的第j个snr特征幅值，公式如下：
在一些实施方式中，基于临近节点的网络层级与当前节点的网络层级的比较结果，生成网络层级系数集合， 可以包括：基于临近节点网络层级生成网络层级特征矩阵；将网络层级特征矩阵中的元素与当前节点的网络层级进行比较；若网络层级特征矩阵中任一元素对应的网络层级小于当前节点的网络层级，则设置任一元素的层级系数为第一预设值；若网络层级特征矩阵中的任一元素对应的网络层级不小于当前节点的网络层级，则设置任一元素的层级系数为第二预设值；利用第一预设值、第二预设值对网络层级特征矩阵中的对应元素进行更新，得到网络层级系数矩阵。
具体地，针对目标台区中临近节点的网络层级数据，确认网络层级特征矩阵LAYERm×n。目标台区的网络层级特征矩阵为m行n列的矩阵，网络层级特征矩阵的元素layeri，j表示第i个目标台区的临近节点对应采集频率下的第j个网络层级，公式如下：
比较网络层级特征矩阵中的元素对应的网络层级与当前节点的网络层级；若网络层级特征矩阵中任一元素对应的网络层级小于当前节点的网络层级，则设置任一元素的层级系数为第一预设值。若网络层级特征矩阵中的任一元素对应的网络层级不小于当前节点的网络层级，则设置任一元素的层级系数为第二预设值。示例性地，当layeri，j小于当前节点的网络层级layer，layeri，j的层级系数设置为1，否则layeri，j的层级系数设置为0，公式如下：
进一步的，利用第一预设值、第二预设值对网络层级特征矩阵中的对应元素进行更新，得到网络层级系数矩阵。目标台区的网络层级系数矩阵为m行n列的矩阵，网络层级系数矩阵中的元素表示第i个目标台区的邻近节点对应频率下采集的第j个网络层级对应的层级系数，由此根据如下公式Cn×m＝C(LAYERm×n，layer)可以得到网络层级权重系数特征矩阵Cm×n。
上述实施方式中，提出网络层级参数作为台区识别判断标准之一，本台区节点经过网络层级判断，避免当前节点的子节点或同层级节点对SNR统计的干扰，本实施方式中本台区归属判断标准依赖于当前节点的代理节点或与代理节点同层级的节点的SNR值。
在一些实施方式中，利用网络层级系数集合对第一信噪比数据进行修正，得到临近节点的目标信噪比数据， 包括：利用信噪比特征矩阵、网络层级系数矩阵进行哈达马乘积运算，得到目标信噪比特征矩阵，作为目标信噪比数据。
具体地，根据信噪比特征矩阵与网络层级系数矩阵，确认临近节点对应采集频率下目标信噪比特征矩阵，即SNRXm×n＝SNRm×n×Cn×m。构建目标台区的临近节点SNR，公式如下：
其中，SNRXi，j＝snri，j×Ci，j。
上述实施方式中，提出网络层级参数作为台区识别判断标准之一，本台区节点经过网络层级判断，避免当前节点的子节点或同层级节点对SNR统计的干扰，本实施方式中本台区归属判断标准依赖于当前节点的代理节点或与代理节点同层级的节点的SNR值。
在一些实施方式中，第二信噪比数据的生成方式，包括：基于邻居节点的报文的初始信噪比幅值，构建邻居信噪比特征矩阵，作为第二信噪比数据。
具体地，获取邻居节点的临近节点数据，邻居节点的临近节点的数量可以是m个。m个邻居台区的邻居节点分别连续接收n个报文的初始信噪比幅值(SNR幅值)。基于邻居节点的报文的初始信噪比幅值，构建邻居信噪比特征矩阵(即SNRY幅值特征矩阵)。其中，邻居信噪比特征矩阵为m行n列的矩阵。SNRY幅值特征矩阵中的元素记为snryi，j表示第i个目标台区的临近节点对应频率下采集的第j个snr特征幅值，公式如下：
进一步的，根据目标信噪比特征矩阵SNRXm，n和邻居信噪比特征矩阵SNRYm，n，确定当前节点与目标台区、邻居台区基于电力线参数的网络归属关系。
在一些实施方式中，根据第二信噪比数据、目标信噪比数据分别对应的Z分数计算结果，识别当前节点的第一台区归属关系，包括：对第二信噪比数据进行Z分数标准化处理，得到第一判断矩阵；对目标信噪比数据进行Z分数标准化处理，得到第二判断矩阵；若第一判断矩阵中的最大值不小于第二判断矩阵的最大值，识别当前节点属于目标台区；若第一判断矩阵中的最大值小于第二判断矩阵的最大值，识别当前节点属于邻居台区。
具体地，第二信噪比数据、目标信噪比数据可以采用矩阵形式进行表示。针对第二信噪比数据对应的矩阵中元素进行Z-score标准化处理，得到第一判断矩阵。针对目标信噪比数据对应的矩阵中元素进行Z-score标准化处理，得到第二判断矩阵。第一判断矩阵具有最大值，第二判断矩阵具有最大值，比较第一判断矩阵中的最大值与第二判断矩阵的最大值。若第一判断矩阵中的最大值不小于第二判断矩阵的最大值，识别当前节点属 于目标台区；若第一判断矩阵中的最大值小于第二判断矩阵的最大值，识别当前节点属于邻居台区。
示例性地，按照如下公式进行Z-score标准化处理：z＝(x-μ)/δ，式中，z表示标准化数值，x表示第二信噪比数据对应的矩阵中元素，μ表示在台区识别周期内邻居节点的snr特征幅值的平均值，δ表示邻居节点的snr特征幅值的标准差。
按照如下公式进行Z-score标准化处理：z＝(x-μ)/δ，式中，z表示标准化数值，x表示目标信噪比数据对应的矩阵中元素，μ表示在台区识别周期内，临近节点的snr特征幅值的平均值，δ表示临近节点的snr特征幅值的标准差。
在一些实施方式中，参考图3所示，目标信噪比数据采用目标信噪比特征矩阵。对目标信噪比数据进行Z分数标准化处理，得到第二判断矩阵，可以包括以下步骤：
S210、获取临近节点的信噪比幅值在台区识别周期内的幅值平均值、幅值标准差。
S220、基于目标信噪比数据、幅值平均值、幅值标准差进行标准化处理，得到Z分数矩阵。
S230、根据预设分数阈值对Z分数矩阵中的元素过滤处理以及均值计算，得到第二判断矩阵。
具体地，目标信噪比数据采用目标信噪比特征矩阵(即SNR幅值特征矩阵)。基于目标台区SNR幅值特征矩阵中的特征数据按如下公式进行Z-score标准化处理：式中，z表示标准化数值，x表示目标台区中临近节点某一时刻snr幅值，μ表示在台区识别周期内目标台区中临近节点所有snr幅值的幅值平均值，δ表示目标台区中临近节点所有snr幅值的幅值标准差。
SNR特征幅值矩阵，通过Z-score标准化处理，得到Z分数矩阵，如下所示：
依据Z分数矩阵，针对SNR特征幅值矩阵中的各元素，选择对应Z分数小于阈值δ的元素作为当前临近节点在台区识别采集周期中有效的SNR幅值，将Z分数大于0.2的元素从SNR特征幅值矩阵对应的当前SNR数据集合中移除；针对每个临近节点在台区识别采集周期内统计SNR值经过Z-score处理过滤后的数据集合得到目标台区识别采集周期内SNR特征幅值的算法平均数，公式如下：
目标台区的目标信噪比特征矩阵，经过Z-score计算、数据筛选、计算算数平均数，得到，目标台区的识别SNR判断矩阵，目标台区识别SNR判断矩阵为1行m列的矩阵，Z＝[z1，z2，…，zm]；当前矩阵表示临近节点经过台区识别周期得到m个临近节点与当前节点的台区归属结果。
同样的，可以得到邻居台区识别SNR判断矩阵为1行m列的矩阵，邻居台区节点台区归属结果数据集为 Y＝[y1，y2，…，ym]，在目标台区归属亲和最大值为max(Z)，邻居台区台区归属最大值为max(Y)；当目标台区台区归属结果最大值max(Z)大于或等于max(Y)，当前节点在本次台区识别周期内，归属为目标台区；目标台区台区归属结果最大值max(Z)小于max(Y)，当前节点在本次台区识别周期内，台区归属为邻居台区。
上述实施方式中，针对台区识别长时间统计邻居站点SNR值，SNR值存在上下波动情况，本实施方式提供了一种基于Z-score分数筛选算法，删除大于规定阈值的节点后，再针对当前接收SNR信息获取算数平均数，以最终算数平均数衡量当前邻居节点与本节点台区关系。
在一些实施方式中，参考图4所示，目标台区还包括第一中央协调器；邻居台区还包括第二中央协调器；该方法还可以包括以下步骤：
S310、确定第一中央协调器的过零点数据与当前节点的过零点数据之间的第一过零点标准差。
具体地，当前节点目标台区中的待识别节点。当前节点可以接收第一中央协调器的过零点数据，也可以接收第二中央协调器的过零点数据，还可以采集过零点数据。基于第一中央协调器的过零点数据与当前节点采集的过零点数据确定两者之间的第一过零点标准差。
示例性地，第一中央协调器CCO与当前节点(记为台区站点STA)同时采集过零点信息，数据规模为m。
CCINTB1＝[ccontb1，ccontb2，…，ccontbm]
STANTB＝[stantb1，stantb2，…，stantbm]
目标台区过零点信息差值为：
NTBDIFF1＝CCONTB1-STANTB
NTBDIFF1＝[ntbdiff1，ntbdiff2，…，ntbdiffm]
基于目标台区过零点差值NTBDIFF1，计算目标台区过零点信息样本方差。μ1为目标台区过零点差值NTBDIFF1的总体均值，σ1为本台区过零点差值NTBDIFF1的标准差。
S320、确定第二中央协调器的过零点数据与当前节点的过零点数据之间的第二过零点标准差。
具体地，当前节点也可以接收第二中央协调器的过零点数据，还可以采集过零点数据。基于第二中央协调器的过零点数据与当前节点采集的过零点数据确定两者之间的第二过零点标准差。
示例性地，第二中央协调器CCO与当前节点(记为台区站点STA)同时采集过零点信息，数据规模为m。
CCONTB2＝[ccontb1，ccontb2，…，ccontbm]
邻居台区过零点信息差值为：
NTBDIFF2＝CCONTB2-STANTB
μ2为邻居台区过零点差值NTBDIFF2的总体均值，σ2为邻居台区过零点差值NTBDIFF2的标准差。
S330、基于第一过零点标准差与第二过零点标准差的比较结果，识别当前节点的第二台区归属关系。
具体地，比较第一过零点标准差与第二过零点标准差，根据一过零点标准差与第二过零点标准差的比较结果，识别当前节点的第二台区归属关系。示例性地，比较目标台区过零点信息标准差σ1与邻居台区过零点信息标准差σ2，经过N次过零点信息采集，当σ1＜σ2，当前节点应归属本台区，当σ1＞σ2，当前节点归属为临台区。
上述实施方式中，通过基于过零点信息比较、信噪比及网络拓扑关系的台区识别方法，以提高台区识别的准确度。
在一些实施方式中，该方法还可以包括以下步骤：若第一台区归属关系与第二台区归属关系一致，则基于第一台区归属关系或者第二台区归属关系，确定当前节点的所属台区；或者，若第一台区归属关系与第二台区归属关系不一致，则发出台区归属提醒消息；其中，台区归属提醒消息用于提醒用户核实当前节点的归属关系。
具体地，本实施方式中，首先分别从两个角度确定第一台区归属关系与第二台区归属关系，然后利用第一台区归属关系与第二台区归属关系进行相互验证，因此，比较第一台区归属关系与第二台区归属关系。若第一台区归属关系与第二台区归属关系一致，表明确定的台区归属关系是可信的，则基于第一台区归属关系或者第二台区归属关系，确定当前节点的所属台区。若第一台区归属关系与第二台区归属关系不一致，表明确定的台区归属关系是不可信的，则发出台区归属提醒消息；以提醒用户核实当前节点的归属关系。
上述实施方式中，通过基于过零点信息比较、信噪比及网络拓扑关系的台区识别方法，以提高台区识别的准确度。
在一些实施方式中，该方法还可以包括以下步骤：若第一过零点标准差小于第二过零点标准差，则确定当前节点属于目标台区；若第一过零点标准差不小于第二过零点标准差，则确定当前节点属于邻居台区。
在一些实施方式中，第一中央协调器的过零点数据的采集时间与当前节点的过零点数据的采集时间相同；确定第一中央协调器的过零点数据与当前节点的过零点数据之间的第一过零点标准差，包括：获取第一中央协调器的过零点数据与当前节点的过零点数据之间的过零点差值；确定过零点差值对应的总体均值；基于过零点差值、过零点差值对应的总体均值确定第一过零点标准差。
在一些实施方式中，现场台区存在负载低、组网情况复杂、NTB与SNR作为单独进行台区识别判断依据，无法满足现场运维人员快速、准确定位节点台区归属的缺点。本说明书实施方式提供一种基于接收载波报文统计个数并通过Z分数统计SNR，并根据发送载波报文节点在当前网络的网络层级作为权重结果进行台区识别，从而提供台区识别执行效率和准确性。具体地，提供一种目标台区识别方法。目标台区包括当前节点和当前节点在目标台区中所对应的临近节点；目标台区对应有邻居台区，当前节点在邻居台区中对应有邻居节点；目标台区还包括第一中央协调器；邻居台区还包括第二中央协调器；该方法包括以下步骤：
S402、获取临近节点的第一信噪比数据、临近节点的网络层级以及邻居节点的第二信噪比数据。
具体地，基于临近节点的报文的初始信噪比幅值，构建信噪比特征矩阵，作为第一信噪比数据。基于邻居节点的报文的初始信噪比幅值，构建邻居信噪比特征矩阵，作为第二信噪比数据。
S404、基于临近节点的网络层级与当前节点的网络层级的比较结果，生成网络层级系数集合。
其中，网络层级系数集合中的元素用于表示临近节点与当前节点之间的连接关系。具体地，基于临近节点网络层级生成网络层级特征矩阵；将网络层级特征矩阵中的元素与当前节点的网络层级进行比较；若网络层级特征矩阵中任一元素对应的网络层级小于当前节点的网络层级，则设置任一元素的层级系数为第一预设值；若网络层级特征矩阵中的任一元素对应的网络层级不小于当前节点的网络层级，则设置任一元素的层级系数为第二预设值；利用第一预设值、第二预设值对网络层级特征矩阵中的对应元素进行更新，得到网络层级系数矩阵。
S406、利用网络层级系数集合对第一信噪比数据进行修正，得到临近节点的目标信噪比数据。
具体地，利用信噪比特征矩阵、网络层级系数矩阵进行哈达马乘积运算，得到目标信噪比特征矩阵，作为目标信噪比数据。
S408、根据第二信噪比数据、目标信噪比数据分别对应的Z分数计算结果，识别当前节点的第一台区归属关系。
具体地，对第二信噪比数据进行Z分数标准化处理，得到第一判断矩阵；对目标信噪比数据进行Z分数标准化处理，得到第二判断矩阵；若第一判断矩阵中的最大值不小于第二判断矩阵的最大值，识别当前节点属于目标台区；若第一判断矩阵中的最大值小于第二判断矩阵的最大值，识别当前节点属于邻居台区。
进一步地，获取临近节点的信噪比幅值在台区识别周期内的幅值平均值、幅值标准差；基于目标信噪比数据、幅值平均值、幅值标准差进行标准化处理，得到Z分数矩阵；根据预设分数阈值对Z分数矩阵中的元素过滤处理以及均值计算，得到第二判断矩阵。
S410、确定第一中央协调器的过零点数据与当前节点的过零点数据之间的第一过零点标准差。
具体地，获取第一中央协调器的过零点数据与当前节点的过零点数据之间的过零点差值；确定过零点差值对应的总体均值；基于过零点差值、过零点差值对应的总体均值确定第一过零点标准差。
S412、确定第二中央协调器的过零点数据与当前节点的过零点数据之间的第二过零点标准差。
具体地，获取第二中央协调器的过零点数据与当前节点的过零点数据之间的过零点差值；确定该过零点差值对应的总体均值；基于该过零点差值、该过零点差值对应的总体均值确定第二过零点标准差。
S414、基于第一过零点标准差与第二过零点标准差的比较结果，识别当前节点的第二台区归属关系。
具体地，若第一过零点标准差小于第二过零点标准差，则确定当前节点属于目标台区；若第一过零点标准差不小于第二过零点标准差，则确定当前节点属于邻居台区。
S416、若第一台区归属关系与第二台区归属关系一致，则基于第一台区归属关系或者第二台区归属关系，确定当前节点的所属台区。
S418、若第一台区归属关系与第二台区归属关系不一致，则发出台区归属提醒消息。
其中，台区归属提醒消息用于提醒用户核实当前节点的归属关系。
在一些实施方式中，参考图5a所示，提供一种台区识别方法没包括如下步骤：
步骤1：集中器通信终端周期性采集和广播过零点信息，广播信标帧。
集中器通信终端向待识别台区站点发送台区特征采集启动报文。
集中器通信终端向待识别台区站点发送台区特征告知报文。
集中器通信终端识别轮次加1。
步骤2：待识别台区站点分别获取当前区域内所有集中器通信终端台区归属信息。
待识别站点进行台区特征采集，获取台区NTB信息。
待识别站点采集接收信标帧对应的SNR信息。
步骤3：台区站点仅接收到单一集中器通信终端过零点信息和信标帧对应的SNR信息，直接确认台区站点的台区归属。
步骤4：台区站点接收两个及两个以上台区过零点信息及信标帧对应的SNR信息，站点启动周期性定时器，采集过零点误差、信噪比、网络层级信息。
步骤5：计算过零点差值标准差，信噪比、网络层级信息Z分数标准化处理。
步骤6：台区站点基于步骤5中采集台区识别相关信息确认台区识别归属结果，并将归属结果上报当前组网集中器通信终端设备。
步骤7：根据设定的迭代次数限值，或者识别成功率，判断是否继续迭代，如继续，跳转到步骤2，否则结束台区识别。
本说明书实施方式提供一种目标台区识别装置，所述目标台区包括当前节点和所述当前节点在所述目标台区中所对应的临近节点；所述目标台区对应有邻居台区，所述当前节点在所述邻居台区中对应有邻居节点。参考图5b所示，目标台区识别装置包括：获取模块、比较模块、修正模块、识别模块。
获取模块，用于获取所述临近节点的第一信噪比数据、所述临近节点的网络层级以及所述邻居节点的第二信噪比数据；
比较模块，用于基于所述临近节点的网络层级与所述当前节点的网络层级的比较结果，生成网络层级系数集合；其中，所述网络层级系数集合中的元素用于表示所述临近节点与所述当前节点之间的连接关系；
修正模块，用于利用所述网络层级系数集合对所述第一信噪比数据进行修正，得到所述临近节点的目标信 噪比数据；
识别模块，用于根据所述第二信噪比数据、所述目标信噪比数据分别对应的Z分数计算结果，识别所述当前节点的第一台区归属关系。
在一些实施方式中，所述装置还包括：信噪比特征矩阵模块，用于基于所述临近节点的报文的初始信噪比幅值，构建信噪比特征矩阵，作为所述第一信噪比数据。
在一些实施方式中，所述比较模块，还用于基于所述临近节点网络层级生成网络层级特征矩阵；将所述网络层级特征矩阵中的元素与所述当前节点的网络层级进行比较；若所述网络层级特征矩阵中任一元素对应的网络层级小于所述当前节点的网络层级，则设置所述任一元素的层级系数为第一预设值；若所述网络层级特征矩阵中的任一元素对应的网络层级不小于所述当前节点的网络层级，则设置所述任一元素的层级系数为第二预设值；利用所述第一预设值、所述第二预设值对所述网络层级特征矩阵中的对应元素进行更新，得到网络层级系数矩阵。
在一些实施方式中，所述修正模块，还用于利用所述信噪比特征矩阵、所述网络层级系数矩阵进行哈达马乘积运算，得到所述目标信噪比特征矩阵，作为所述目标信噪比数据。
在一些实施方式中，所述识别模块，还用于对所述第二信噪比数据进行Z分数标准化处理，得到第一判断矩阵；对所述目标信噪比数据进行Z分数标准化处理，得到第二判断矩阵；若所述第一判断矩阵中的最大值不小于所述第二判断矩阵的最大值，识别所述当前节点属于所述目标台区；若所述第一判断矩阵中的最大值小于所述第二判断矩阵的最大值，识别所述当前节点属于所述邻居台区。
在一些实施方式中，所述目标信噪比数据采用目标信噪比特征矩阵；所述识别模块，还用于获取所述临近节点的信噪比幅值在台区识别周期内的幅值平均值、幅值标准差；基于所述目标信噪比数据、所述幅值平均值、所述幅值标准差进行标准化处理，得到Z分数矩阵；根据预设分数阈值对所述Z分数矩阵中的元素过滤处理以及均值计算，得到所述第二判断矩阵。
在另一个实施示例中，上述目标台区识别装置包括：处理器，其中所述处理器用于执行存在存储器的上述程序模块，包括：获取模块、比较模块、修正模块、识别模块和信噪比特征矩阵模块。
关于目标台区识别装置的具体限定可以参见上文中对于目标台区识别方法的限定，在此不再赘述。
本说明书实施方式提供一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现上述任一实施方式所述的方法的步骤。
本说明书实施方式提供一种计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现上述任一实施方式所述的方法的步骤。
需要说明的是，在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，“计算机可读介质”可以是任何可以 包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
应当理解，本发明的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。例如，如果用硬件来实现，和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本发明的描述中，“多个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。
在本发明中，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”、“固定”等术语应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或成一体；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通或两个元件的相互作用关系，除非另有明确的限定。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本发明中的具体含义。
尽管上面已经示出和描述了本发明的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本发明的限制，本领域的普通技术人员在本发明的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (19)

  1. 一种目标台区识别方法，其特征在于，所述目标台区包括当前节点和所述当前节点在所述目标台区中所对应的临近节点；所述目标台区对应有邻居台区，所述当前节点在所述邻居台区中对应有邻居节点；所述方法包括：
    获取所述临近节点的第一信噪比数据、所述临近节点的网络层级以及所述邻居节点的第二信噪比数据；
    基于所述临近节点的网络层级与所述当前节点的网络层级的比较结果，生成网络层级系数集合；其中，所述网络层级系数集合中的元素用于表示所述临近节点与所述当前节点之间的连接关系；
    利用所述网络层级系数集合对所述第一信噪比数据进行修正，得到所述临近节点的目标信噪比数据；
    根据所述第二信噪比数据、所述目标信噪比数据分别对应的Z分数计算结果，识别所述当前节点的第一台区归属关系。
  2. 根据权利要求1所述的方法，其特征在于，所述第一信噪比数据的生成方式，包括：
    基于所述临近节点的报文的初始信噪比幅值，构建信噪比特征矩阵，作为所述第一信噪比数据。
  3. 根据权利要求2所述的方法，其特征在于，所述基于所述临近节点的网络层级与所述当前节点的网络层级的比较结果，生成网络层级系数集合，包括：
    基于所述临近节点网络层级生成网络层级特征矩阵；
    将所述网络层级特征矩阵中的元素与所述当前节点的网络层级进行比较；
    若所述网络层级特征矩阵中任一元素对应的网络层级小于所述当前节点的网络层级，则设置所述任一元素的层级系数为第一预设值；
    若所述网络层级特征矩阵中的任一元素对应的网络层级不小于所述当前节点的网络层级，则设置所述任一元素的层级系数为第二预设值；
    利用所述第一预设值、所述第二预设值对所述网络层级特征矩阵中的对应元素进行更新，得到网络层级系数矩阵。
  4. 根据权利要求3所述的方法，其特征在于，所述利用所述网络层级系数集合对所述第一信噪比数据进行修正，得到所述临近节点的目标信噪比数据，包括：
    利用所述信噪比特征矩阵、所述网络层级系数矩阵进行哈达马乘积运算，得到所述目标信噪比特征矩阵，作为所述目标信噪比数据。
  5. 根据权利要求1所述的方法，其特征在于，所述第二信噪比数据的生成方式，包括：
    基于所述邻居节点的报文的初始信噪比幅值，构建邻居信噪比特征矩阵，作为所述第二信噪比数据。
  6. 根据权利要求1所述的方法，其特征在于，所述根据所述第二信噪比数据、所述目标信噪比数据分别对应的Z分数计算结果，识别所述当前节点的第一台区归属关系，包括：
    对所述第二信噪比数据进行Z分数标准化处理，得到第一判断矩阵；
    对所述目标信噪比数据进行Z分数标准化处理，得到第二判断矩阵；
    若所述第一判断矩阵中的最大值不小于所述第二判断矩阵的最大值，识别所述当前节点属于所述目标台区；
    若所述第一判断矩阵中的最大值小于所述第二判断矩阵的最大值，识别所述当前节点属于所述邻居台区。
  7. 根据权利要求6所述的方法，其特征在于，所述目标信噪比数据采用目标信噪比特征矩阵；所述对所述目标信噪比数据进行Z分数标准化处理，得到第二判断矩阵，包括：
    获取所述临近节点的信噪比幅值在台区识别周期内的幅值平均值、幅值标准差；
    基于所述目标信噪比数据、所述幅值平均值、所述幅值标准差进行标准化处理，得到Z分数矩阵；
    根据预设分数阈值对所述Z分数矩阵中的元素过滤处理以及均值计算，得到所述第二判断矩阵。
  8. 根据权利要求1至7任一项所述的方法，其特征在于，所述目标台区还包括第一中央协调器；所述邻居台区还包括第二中央协调器；所述方法还包括：
    确定所述第一中央协调器的过零点数据与所述当前节点的过零点数据之间的第一过零点标准差；
    确定所述第二中央协调器的过零点数据与所述当前节点的过零点数据之间的第二过零点标准差；
    基于所述第一过零点标准差与所述第二过零点标准差的比较结果，识别所述当前节点的第二台区归属关系。
  9. 根据权利要求8所述的方法，其特征在于，所述方法还包括：
    若所述第一台区归属关系与所述第二台区归属关系一致，则基于所述第一台区归属关系或者所述第二台区归属关系，确定所述当前节点的所属台区；
    若所述第一台区归属关系与所述第二台区归属关系不一致，则发出台区归属提醒消息；其中，所述台区归属提醒消息用于提醒用户核实所述当前节点的归属关系。
  10. 根据权利要求8所述的方法，其特征在于，所述基于所述第一过零点标准差与所述第二过零点标准差的比较结果，识别所述当前节点的第二台区归属关系，包括：
    若所述第一过零点标准差小于所述第二过零点标准差，则确定所述当前节点属于所述目标台区；
    若所述第一过零点标准差不小于所述第二过零点标准差，则确定所述当前节点属于所述邻居台区。
  11. 根据权利要求8所述的方法，其特征在于，所述第一中央协调器的过零点数据的采集时间与所述当前节点的过零点数据的采集时间相同；所述确定所述第一中央协调器的过零点数据与所述当前节点的过零点数据之间的第一过零点标准差，包括：
    获取所述第一中央协调器的过零点数据与所述当前节点的过零点数据之间的过零点差值；
    确定所述过零点差值对应的总体均值；
    基于所述过零点差值、所述过零点差值对应的总体均值确定所述第一过零点标准差。
  12. 一种目标台区识别装置，其特征在于，所述目标台区包括当前节点和所述当前节点在所述目标台区中 所对应的临近节点；所述目标台区对应有邻居台区，所述当前节点在所述邻居台区中对应有邻居节点；所述装置包括：
    获取模块，用于获取所述临近节点的第一信噪比数据、所述临近节点的网络层级以及所述邻居节点的第二信噪比数据；
    比较模块，用于基于所述临近节点的网络层级与所述当前节点的网络层级的比较结果，生成网络层级系数集合；其中，所述网络层级系数集合中的元素用于表示所述临近节点与所述当前节点之间的连接关系；
    修正模块，用于利用所述网络层级系数集合对所述第一信噪比数据进行修正，得到所述临近节点的目标信噪比数据；
    识别模块，用于根据所述第二信噪比数据、所述目标信噪比数据分别对应的Z分数计算结果，识别所述当前节点的第一台区归属关系。
  13. 根据权利要求12所述的装置，其特征在于，所述装置还包括：
    信噪比特征矩阵模块，用于基于所述临近节点的报文的初始信噪比幅值，构建信噪比特征矩阵，作为所述第一信噪比数据。
  14. 根据权利要求13所述的装置，其特征在于，所述比较模块，还用于基于所述临近节点网络层级生成网络层级特征矩阵；将所述网络层级特征矩阵中的元素与所述当前节点的网络层级进行比较；若所述网络层级特征矩阵中任一元素对应的网络层级小于所述当前节点的网络层级，则设置所述任一元素的层级系数为第一预设值；若所述网络层级特征矩阵中的任一元素对应的网络层级不小于所述当前节点的网络层级，则设置所述任一元素的层级系数为第二预设值；利用所述第一预设值、所述第二预设值对所述网络层级特征矩阵中的对应元素进行更新，得到网络层级系数矩阵。
  15. 根据权利要求14所述的装置，其特征在于，所述修正模块，还用于利用所述信噪比特征矩阵、所述网络层级系数矩阵进行哈达马乘积运算，得到所述目标信噪比特征矩阵，作为所述目标信噪比数据。
  16. 根据权利要求12所述的装置，其特征在于，所述识别模块，还用于对所述第二信噪比数据进行Z分数标准化处理，得到第一判断矩阵；对所述目标信噪比数据进行Z分数标准化处理，得到第二判断矩阵；若所述第一判断矩阵中的最大值不小于所述第二判断矩阵的最大值，识别所述当前节点属于所述目标台区；若所述第一判断矩阵中的最大值小于所述第二判断矩阵的最大值，识别所述当前节点属于所述邻居台区。
  17. 根据权利要求16所述的装置，其特征在于，所述目标信噪比数据采用目标信噪比特征矩阵；所述识别模块，还用于获取所述临近节点的信噪比幅值在台区识别周期内的幅值平均值、幅值标准差；基于所述目标信噪比数据、所述幅值平均值、所述幅值标准差进行标准化处理，得到Z分数矩阵；根据预设分数阈值对所述Z分数矩阵中的元素过滤处理以及均值计算，得到所述第二判断矩阵。
  18. 一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，其特征在于，所述处理器执行所述计算机程序时实现权利要求1至11中任一项所述的方法的步骤。
  19. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实 现权利要求1至11中任一项所述的方法的步骤。
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