Meter box identification method, meter box identification device, meter box identification system and storage medium

Abstract

The invention discloses a meter box identification method, a meter box identification device, a meter box identification system and a computer readable storage medium, and belongs to the field of meter box identification. The meter box identification method provided by the embodiment of the invention is applied to the meter box identification system. The meter box identification system comprises a plurality of meter boxes, and each meter box comprises one or more electric meters. The meter box identification method comprises the steps that signal quality parameters based on a power line dual-mode communication protocol and reported by each electric meter are acquired, and the signal quality parameters are obtained by receiving signal quality parameters of other electric meters by each electric meter; performing data preprocessing on the signal quality parameter to obtain a standard quality parameter; according to the standard quality parameters, utilizing a meter box identification algorithm to identify the meter box attribution of each electric meter; and returning to the step of acquiring the signal quality parameter based on the power line dual-mode communication protocol reported by each electric meter at a preset time interval so as to update the meter box attribution of each electric meter. Therefore, the accuracy and reliability of meter box identification can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of meter box identification, and particularly relates to a meter box identification method, a meter box identification device, a meter box identification system, and a computer-readable storage medium. Background Art

[0002] Meter box identification refers to identifying the attribution of meter boxes for different electric meters in a low-voltage distribution substation area, that is, identifying whether different electric meters physically belong to the same meter box. With the construction of a new power system, the grid connection of photovoltaic power and the access of electric vehicle charging piles, it is required that the low-voltage distribution substation area has strong holographic perception ability and intelligent regulation ability, and the role of meter box identification is becoming increasingly important. The accuracy of meter box identification has an important impact on correcting the household-transformer relationship and analyzing line loss management.

[0003] The current meter box identification methods mainly use electrical quantity data such as voltage, current, electricity consumption, and resistance to perform similarity judgment, so as to detect whether multiple electric meters belong to the same meter box. The main difficulty of this method is that the above electrical quantity data is not directly corresponding to the physical location of the electric meters. Some electrical quantity data of electric meters in different meter boxes may be relatively similar, and some electrical quantity data of electric meters in the same meter box may be quite different, resulting in a decrease in the accuracy and reliability of meter box identification. Summary of the Invention

[0004] Embodiments of the present application provide a meter box identification method, a meter box identification device, a meter box identification system, and a computer-readable storage medium to solve at least one of the above technical problems.

[0005] The meter box identification method of the embodiments of the present application is used for a meter box identification system. The meter box identification system includes multiple meter boxes, and each meter box includes one or more electric meters. The meter box identification method includes: Obtaining signal quality parameters based on a power line dual-mode communication protocol reported by each electric meter, where the signal quality parameters are obtained by each electric meter receiving the signal quality parameters of other electric meters; Performing data preprocessing on the signal quality parameters to obtain standard quality parameters; According to the standard quality parameters, using a meter box identification algorithm to identify the meter box attribution of each electric meter; Every predetermined time interval, returning to the step of obtaining the signal quality parameters based on the power line dual-mode communication protocol reported by each electric meter to update the meter box attribution of each electric meter.

[0006] In some embodiments, the signal quality parameters include wireless signal strength information and / or power line signal strength information.

[0007] In some embodiments, the data preprocessing of the signal quality parameters to obtain standard quality parameters includes: Performing default data supplementation and data format conversion on the signal quality parameters to obtain the standard quality parameters.

[0008] In some embodiments, the identifying the cabinet attribution of each electric meter according to the standard quality parameters by using a cabinet identification algorithm includes: Setting a clustering algorithm, where the clustering algorithm includes any one of DBSCAN clustering algorithm, K-Medoids clustering algorithm, and MCL clustering algorithm; Setting the clustering parameters of the clustering algorithm; Performing clustering analysis on the standard quality parameters through the clustering algorithm to obtain a clustering result; Determining the cabinet attribution of each electric meter according to the clustering result.

[0009] In some embodiments, the identifying the cabinet attribution of each electric meter according to the standard quality parameters by using a cabinet identification algorithm includes: Setting a clustering algorithm, where the clustering algorithm includes any one of DBSCAN clustering algorithm, K-Medoids clustering algorithm, and MCL clustering algorithm; Setting a set of clustering parameters of the clustering algorithm, where the set of clustering parameters includes multiple different clustering parameters; Traversing each clustering parameter in the set of clustering parameters, and performing clustering analysis on the standard quality parameters through the clustering algorithm to obtain a clustering result; Calculating the costs of multiple clustering results corresponding to multiple clustering parameters in the set of clustering parameters through a clustering cost algorithm to select the clustering result corresponding to the optimal cost; Determining the cabinet attribution of each electric meter according to the clustering result corresponding to the optimal cost.

[0010] In some embodiments, the identifying the cabinet attribution of each electric meter according to the standard quality parameters by using a cabinet identification algorithm includes: Setting multiple clustering algorithms, where multiple clustering algorithms include any multiple of DBSCAN clustering algorithm, K-Medoids clustering algorithm, and MCL clustering algorithm; Setting a set of clustering parameters for each clustering algorithm, where the set of clustering parameters includes multiple different clustering parameters; Traversing each clustering parameter in the set of clustering parameters of each clustering algorithm, and performing clustering analysis on the standard quality parameters through the clustering algorithm to obtain a clustering result; Calculate the costs of multiple clustering results corresponding to multiple clustering parameters in the clustering parameter sets of the multiple clustering algorithms through a clustering cost algorithm to select the clustering result corresponding to the optimal cost; Determine the cabinet attribution of each electricity meter according to the clustering result corresponding to the optimal cost.

[0011] In some embodiments, when the clustering algorithm is the MCL clustering algorithm, the clustering parameter is a probability conversion parameter. The clustering analysis of the standard quality parameter through the clustering algorithm to obtain a clustering result includes: Based on the probability conversion parameter, convert the standard quality parameter into a first parameter matrix; Perform normalization processing on the first parameter matrix to obtain a second parameter matrix; Set a diffusion coefficient and a shrinkage factor; Based on the diffusion coefficient and the shrinkage factor, convert the second parameter matrix into a third parameter matrix, and perform normalization processing on the third parameter matrix to obtain a fourth parameter matrix; Repeat the step of based on the diffusion coefficient and the shrinkage factor, converting the second parameter matrix into a third parameter matrix, and performing normalization processing on the third parameter matrix to obtain a fourth parameter matrix until a predetermined number of iterations is reached; Determine the clustering result according to the fourth parameter matrix.

[0012] In some embodiments, the clustering cost algorithm includes the elbow method or the silhouette coefficient method.

[0013] In some embodiments, the cabinet identification method further includes: Perform comparative analysis on multiple identification results of the cabinet attribution in multiple time periods; For each electricity meter, select the identification result of the cabinet attribution with the most occurrences in the multiple time periods as the current identification result of the cabinet attribution of the electricity meter.

[0014] The cabinet identification device according to the embodiment of the present application is used for a cabinet identification system. The cabinet identification system includes multiple cabinets, and each cabinet includes one or more electricity meters. The cabinet identification device includes: An acquisition module, configured to acquire signal quality parameters reported by each electricity meter based on a power line dual-mode communication protocol, where the signal quality parameters are obtained by each electricity meter receiving the signal quality parameters of other electricity meters; A processing module, configured to perform data preprocessing on the signal quality parameters to obtain standard quality parameters; An identification module, configured to identify the cabinet attribution of each electricity meter according to the standard quality parameter by using a meter cabinet identification algorithm; An update module, configured to return the step of obtaining the signal quality parameters reported by each electricity meter based on the power line dual-mode communication protocol at every predetermined time interval, so as to update the cabinet attribution of each electricity meter.

[0015] For the meter cabinet identification system according to an embodiment of the present application, the meter cabinet identification system includes one or more processors and a memory, the memory stores a computer program, and when the computer program is executed by the processor, the meter cabinet identification method according to any one of the above embodiments is implemented.

[0016] For the computer-readable storage medium according to an embodiment of the present application, a computer program is stored thereon, and when the program is executed by a processor, the meter cabinet identification method according to any one of the above embodiments is implemented.

[0017] In the meter cabinet identification method, meter cabinet identification device, meter cabinet identification system and computer-readable storage medium according to an embodiment of the present application, the signal quality parameters reported by each electricity meter based on the power line dual-mode communication protocol are obtained, and data preprocessing is performed on the signal quality parameters to obtain standard quality parameters, so as to identify the cabinet attribution of each electricity meter according to the standard quality parameter. In this way, the accuracy and reliability of meter cabinet identification can be improved. In addition, at every predetermined time interval, the step of obtaining the signal quality parameters reported by each electricity meter based on the power line dual-mode communication protocol is returned to update the cabinet attribution of each electricity meter. In this way, the identification result of the cabinet attribution can be updated, and the more signal quality parameters are collected, the more accurate the identification result of the cabinet attribution is.

[0018] Some additional aspects and advantages of the embodiments of the present application will be given in the following description, some will become obvious from the following description, or will be understood through the practice of the embodiments of the present application. Description of the Drawings

[0019] The above and / or additional aspects and advantages of the present application will become obvious and easy to understand from the description of the embodiments in conjunction with the following drawings, where: Figure 1 is a schematic flowchart of a meter cabinet identification method according to some embodiments of the present application; Figure 2 is a schematic structural diagram of a meter cabinet identification system according to some embodiments of the present application; Figure 3 is a schematic diagram of the connection relationship between electricity meters inside and between meter cabinets according to some embodiments of the present application; Figure 4 is a schematic flowchart of a meter cabinet identification method according to some embodiments of the present application; Figure 5It is a schematic flow chart of the meter box identification method according to some embodiments of the present application; Figure 6 It is a schematic diagram of the working principle of the meter box identification method according to some embodiments of the present application; Figure 7 It is a schematic flow chart of the meter box identification method according to some embodiments of the present application; Figure 8 It is a schematic flow chart of the meter box identification method according to some embodiments of the present application; Figure 9 It is a schematic diagram of the working principle of the meter box identification method according to some embodiments of the present application; Figure 10 It is a schematic flow chart of the meter box identification method according to some embodiments of the present application; Figure 11 It is a schematic diagram of the working principle of the meter box identification method according to some embodiments of the present application; Figure 12 It is a schematic flow chart of the meter box identification method according to some embodiments of the present application; Figure 13 It is a schematic diagram of the RSSI information collected by the electric meter according to some embodiments of the present application; Figure 14 It is a schematic diagram of the RSSI information collected by the electric meter according to some embodiments of the present application; Figure 15 It is a schematic diagram of the standard quality parameters according to some embodiments of the present application; Figure 16 It is a schematic diagram of the first parameter matrix according to some embodiments of the present application; Figure 17 It is a schematic diagram of the distribution of RSSI information before clustering according to some embodiments of the present application; Figure 18 It is a schematic diagram of the distribution of RSSI information after clustering according to some embodiments of the present application; Figure 19 It is a schematic diagram of the modules of the meter box identification device according to some embodiments of the present application; Figure 20 It is a schematic diagram of the modules of the meter box identification device according to some embodiments of the present application; Figure 21 It is a schematic diagram of the modules of the meter box identification system according to some embodiments of the present application; Figure 22 It is a schematic diagram of the connection state between the computer-readable storage medium and the processor according to some embodiments of the present application.

[0020] Explanation of reference numerals: Meter box identification device 100, acquisition module 10, processing module 20, identification module 30, update module 40, analysis module 50, selection module 60, meter box identification system 200, transformer 201, distribution box 202, busbar switch 2021, main meter of the substation area 2022, concentrator 2023, branch box 203, branch switch 2031, outgoing line switch 2032, branch terminal 2033, meter box 204, electric meter 2041, first processor 210, memory 220, computer-readable storage medium 300, computer program 310, second processor 320. Specific embodiments

[0021] The following further describes the embodiments of the present application with reference to the accompanying drawings. The same or similar reference numerals in the drawings represent the same or similar elements or elements with the same or similar functions from beginning to end. In addition, the embodiments of the present application described below with reference to the accompanying drawings are exemplary and are only used to explain the embodiments of the present application, and should not be construed as a limitation of the present application.

[0022] As one of the sensing layer terminals of the power Internet of Things, the meter box realizes tasks such as power metering and status collection by configuring intelligent metering switches, rail meters, or main meters. The electric meters in the same meter box usually need to manually configure the communication address of the same meter box in the master control node (Master Control Node, MCO) and the main meter. After the configuration is completed, when any meter position in the meter box has situations such as addition, deletion, replacement, or address change, it will cause the actual meter information in the meter box to not match the configured information in the MCO and the main meter, resulting in incorrect summary information of the main meter. To avoid this situation, meter box identification technology is particularly necessary.

[0023] Meter box identification refers to identifying the meter box attribution of different electric meters in a low-voltage distribution substation area, that is, identifying whether different electric meters physically belong to the same meter box. With the construction of a new power system, the access of photovoltaic grid connection and electric vehicle charging piles, it is required that the low-voltage distribution substation area has strong holographic sensing ability and intelligent regulation ability, and the role of meter box identification is becoming increasingly important. The accuracy of meter box identification has an important impact on correcting the household-transformer relationship and analyzing line loss management.

[0024] The current meter box identification methods mainly use electrical quantity data such as voltage, current, electricity quantity, and resistance to make similarity judgments, so as to detect whether multiple electric meters belong to the same meter box. The main difficulty of this method is that the above electrical quantity data is not directly corresponding to the physical position of the electric meter. The electrical quantity data of some electric meters in different meter boxes may be relatively similar, and the electrical quantity data of some electric meters in the same meter box may be quite different, resulting in a reduction in the accuracy and reliability of meter box identification.

[0025] In view of this, embodiments of the present application provide a meter box identification method, a meter box identification device, a meter box identification system, and a computer-readable storage medium.

[0026] Please refer to Figure 1 and Figure 2 , the meter box identification method of the embodiments of the present application is used for the meter box identification system 200. The meter box identification system 200 includes a plurality of meter boxes 204, and each meter box 204 includes one or more electric meters 2041. The meter box identification method includes: 010: Obtain the signal quality parameters based on the power line dual-mode communication protocol reported by each electric meter 2041, where the signal quality parameters are obtained by each electric meter 2041 receiving the signal quality parameters of other electric meters 2041; 020: Perform data preprocessing on the signal quality parameters to obtain standard quality parameters; 030: According to the standard quality parameters, use the meter box identification algorithm to identify the meter box attribution of each electric meter 2041; 040: Every predetermined time period, return to the step of obtaining the signal quality parameters based on the power line dual-mode communication protocol reported by each electric meter 2041 to update the meter box attribution of each electric meter 2041.

[0027] In the meter box identification method of the embodiments of the present application, the signal quality parameters based on the power line dual-mode communication protocol reported by each electric meter 2041 are obtained, and data preprocessing is performed on the signal quality parameters to obtain standard quality parameters, so as to identify the meter box attribution of each electric meter 2041 according to the standard quality parameters. In this way, the accuracy and reliability of meter box identification can be improved. In addition, every predetermined time period, return to the step of obtaining the signal quality parameters based on the power line dual-mode communication protocol reported by each electric meter 2041 to update the meter box attribution of each electric meter 2041. In this way, the identification result of the meter box attribution can be updated, and the more signal quality parameters are collected, the more accurate the identification result of the meter box attribution will be.

[0028] Specifically, please refer to Figure 2, the meter box identification system 200 is also the distribution substation area system. The meter box identification system 200 may include a transformer 201, a distribution box 202, a plurality of branch boxes 203, and a plurality of meter boxes 204. The transformer 201 is also a distribution transformer, which is used to convert high-voltage electric energy into low-voltage electric energy for users. The high-voltage electric energy is, for example, 10KV, and the low-voltage electric energy is, for example, 400V. The distribution box 202 includes a busbar switch 2021 and a substation area master meter 2022. The busbar switch 2021 is used to control and protect the circuit. The substation area master meter 2022 is used to measure the difference between the power supply quantity and the sold electricity quantity of the entire distribution substation area, that is, the line loss electricity quantity. Each branch box 203 includes a branch switch 2031 and a plurality of outgoing line switches 2032. Both the branch switch 2031 and the outgoing line switches 2032 are used to control and protect the circuit. Each meter box 204 includes one or more electric meters 2041. The low-voltage side outgoing line of the transformer 201 is connected to the substation area master meter 2022 through the busbar switch 2021, and the substation area master meter 2022 is respectively connected to a plurality of branch boxes 203. Each branch box 203 is connected to each electric meter 2041 of different meter boxes 204 through the branch switch 2031 and a plurality of outgoing line switches 2032.

[0029] In the embodiment of the present application, a concentrator (Central Control Office, CCO) 2023 is installed on the substation area master meter 2022, a branch terminal (Local Terminal Unit, LTU) 2033 is installed on each branch box 203, and a dual-mode communication module is installed on each electric meter 2041. The concentrator 2023, the branch terminal 2033, and the dual-mode communication module communicate with each other according to the power line dual-mode communication protocol. The power line dual-mode communication protocol supports high-speed power line carrier communication (High-speed Power Line Communications, HPLC) and high-speed wireless communication technology (High-speed Radio Frequency, HRF) communication. Among them, HPLC realizes high-speed communication based on the power line as the physical medium for signal transmission. HRF realizes high-speed communication by wirelessly transmitting signals.

[0030] The execution subject of the meter box identification method in the embodiment of the present application may be the concentrator 2023 in the distribution substation area system.

[0031] In 010, the concentrator 2023 obtains the signal quality parameters based on the power line dual-mode communication protocol reported by each electric meter 2041. Since the concentrator 2023, the branch terminal 2033, and the dual-mode communication module communicate with each other, the electric meter 2041 can directly report the signal quality parameters to the concentrator 2023 through the dual-mode communication module, or the electric meter 2041 can first report the signal quality parameters to the branch terminal 2033 through the dual-mode communication module, and then the branch terminal 2033 reports the signal quality parameters to the concentrator 2023. Specifically, it can be flexibly selected according to the actual application and is not limited here.

[0032] Among them, the signal quality parameters are obtained by each electric meter 2041 receiving the signal quality parameters of other electric meters 2041. Specifically, during the communication based on the power line dual-mode communication protocol, each electric meter 2041 sends a signal and collects the signal quality parameters transmitted by the surrounding electric meters 2041. Taking Figure 2 as an example, the distribution transformer area system includes 5 meter cabinets 204, and each meter cabinet 204 includes 3 electric meters 2041, that is, a total of 15 electric meters 2041. For the first electric meter 2041, it collects the signal quality parameters transmitted by the 2nd to 15th electric meters 2041; for the second electric meter 2041, it collects the signal quality parameters transmitted by the 1st, 3rd to 15th electric meters 2041, and so on. The signal quality parameters reported by each electric meter 2041 to the concentrator 2023 can be the signal quality parameter vector of other electric meters 2041 received by this electric meter 2041.

[0033] The signal quality parameters may include wireless signal strength information and / or power line signal strength information. That is to say, the signal quality parameters may include wireless signal strength information; or the signal quality parameters may include power line signal strength information; or the signal quality parameters may include wireless signal strength information and power line signal strength information. The wireless signal strength information is the Received Signal Strength Indication (RSSI) information. During the communication through HRF, each electric meter 2041 sends a wireless signal and measures the RSSI information of other electric meters 2041. During the communication through HPLC, each electric meter 2041 sends a wired signal and measures the power line signal strength information of other electric meters 2041.

[0034] Since the signal quality parameters based on the power line dual-mode communication protocol such as RSSI information and power line signal strength information are all related to the transmission distance, they are strongly related to the distance between the electric meters 2041. Thus, when identifying the meter cabinet based on the signal quality parameters, the meter cabinet identification result is more accurate and reliable.

[0035] In the embodiments of the present application, taking the signal quality parameter including RSSI information as an example, the RSSI information is related to the transmission distance. The mathematical relationship between the RSSI information and the transmission distance is as follows:

[0036] where d is the distance from the sending end, with the unit of meter; A is the absolute value of the power at a distance of 1 meter from the transmitting end, with the unit of dBm; and n is the path loss factor.

[0037] The connection relationship of the electricity meters 2041 inside and between the meter boxes 204 is as Figure 3 shown. Analyzing in combination with the above formula, it can be seen that the RSSIs measured by the electricity meters 2041 within the same meter box 204 are basically the same (a certain electricity meter 2041 in the meter box 204 sends a signal, and other electricity meters 2041 in the same meter box 204 receive the signal, denoted as RSSI0), and the RSSIs measured by the electricity meters 2041 within the same meter box 204 for a certain electricity meter 2041 outside the meter box 204 are basically the same (since the transmission distance and transmission environment are basically the same, the RSSIs received by the electricity meters 2041 in the meter box 204 from a certain signal source outside the meter box 204 are basically the same, denoted as RSSI1), and generally RSSI1 is greater than RSSI0 due to the different transmission distances. Therefore, the meter box can be identified with relatively high accuracy according to the difference in RSSI information.

[0038] In 020, the concentrator 2023 performs data preprocessing on the signal quality parameter to obtain a standard quality parameter. Specifically, after the concentrator 2023 collects the signal quality parameters reported by each electricity meter 2041, it can perform preprocessing operations such as default data supplementation and data format conversion on the signal quality parameter to obtain a standard quality parameter to meet the requirements of subsequent meter box identification algorithms.

[0039] In 030, the concentrator 2023 identifies the meter box attribution of each electricity meter 2041 according to the standard quality parameter by using a meter box identification algorithm. Specifically, the concentrator 2023 can perform clustering analysis on the standard quality parameter through a clustering algorithm to determine the meter box attribution of each electricity meter 2041. The clustering algorithm can include any one or more of the DBSCAN clustering algorithm, the K-Medoids clustering algorithm, and the MCL (Markov) clustering algorithm.

[0040] In 040, the concentrator 2023 returns the steps of obtaining the signal quality parameters based on the power line dual-mode communication protocol reported by each electricity meter 2041 at every predetermined time interval to update the cabinet attribution of each electricity meter 2041. Specifically, the concentrator 2023 re-obtains the signal quality parameters based on the power line dual-mode communication protocol reported by each electricity meter 2041 at regular intervals; performs data preprocessing on the signal quality parameters to obtain standard quality parameters; and identifies the cabinet attribution of each electricity meter 2041 using the cabinet identification algorithm according to the standard quality parameters. In this way, the identification result of the cabinet attribution can be updated, and the more signal quality parameters are collected, the more accurate the identification result of the cabinet attribution will be. Through the iterative update of the identification result, an optimal cabinet identification result will be obtained after each round of the cabinet identification algorithm. In addition, when the entire distribution substation area regularly performs data collection and update, secondary or multiple clustering can also be considered from aspects such as computing resource limitations, stability of the identification result, and changes in the cabinets 204 in the distribution substation area.

[0041] In summary, for the cabinet identification method of the embodiment of the present application, compared with selecting electrical quantity data such as voltage, current, electricity consumption, and resistance, signal quality parameters that can directly reflect the position (such as wireless signal strength information, power line signal strength information, etc.) are selected. Since the signal quality parameters based on the power line dual-mode communication protocol are related to the transmission distance and thus strongly related to the distance from the electricity meter 2041, using the signal quality parameters for cabinet identification can increase the accuracy of cabinet identification and greatly reduce the technical requirements for cabinet identification, improving the real-time performance and reliability of cabinet identification.

[0042] Please refer to Figure 4 , in some embodiments, performing data preprocessing on the signal quality parameters to obtain standard quality parameters (i.e., 020) includes: 021: Performing default data supplementation and data format conversion on the signal quality parameters to obtain standard quality parameters.

[0043] Specifically, default data supplementation is used to supplement the default data in the signal quality parameters with appropriate values according to the actual situation, and data format conversion is used to convert the signal quality parameters into the required format according to the subsequent cabinet identification algorithm.

[0044] Among them, there is no restriction on the order of default data supplementation and data format conversion. In the first method, the signal quality parameters can be first supplemented with default data, and then the signal quality parameters after default data supplementation can be subjected to data format conversion to obtain standard quality parameters. In the second method, the signal quality parameters can be first subjected to data format conversion, and then the signal quality parameters after data format conversion can be supplemented with default data to obtain standard quality parameters.

[0045] Taking the above second method as an example, after the concentrator 2023 collects the signal quality parameters reported by each electric meter 2041, a signal quality matrix R is constructed according to the signal quality parameters. The element Rij in the i-th row and j-th column of the signal quality matrix R represents the signal quality data received by electric meter i from electric meter j. If the electric meter 2041 does not measure the value of Rij, this value is set to the minimum value of this row of the matrix, or the minimum value of all elements of the matrix, or a certain fixed value (such as -130 dBm). The diagonal element Rii of the signal quality matrix R represents the signal quality data received by electric meter i from electric meter i, which does not actually exist. Considering that the transmission distance is very small at this time, it can be set to the maximum value of this row of the matrix, or the maximum value of all elements of the matrix, or a certain determined value. In this way, through the above data format conversion and default data supplement, standard quality parameters can be obtained to meet the requirements of subsequent meter box identification algorithms.

[0046] After the data preprocessing is completed, the concentrator 2023 can use the following Method 1, Method 2, or Method 3 to perform the meter box identification algorithm processing. Method 1 corresponds to steps 031 to 034, Method 2 corresponds to steps 041 to 045, and Method 3 corresponds to steps 051 to 055. Among them, Method 2 adds an adaptive parameter selection scheme on the basis of Method 1; Method 3 adds an adaptive clustering algorithm selection scheme on the basis of Method 2.

[0047] Method 1: Meter box identification algorithm with preset parameters Please refer to Figure 5 and Figure 6 , in some embodiments, according to the standard quality parameters, using the meter box identification algorithm to identify the meter box attribution of each electric meter 2041 (i.e., 030), including: 031: Set the clustering algorithm, and the clustering algorithm includes any one of the DBSCAN clustering algorithm, the K-Medoids clustering algorithm, and the MCL clustering algorithm; 032: Set the clustering parameters of the clustering algorithm; 033: Perform clustering analysis on the standard quality parameters through the clustering algorithm to obtain the clustering result; 034: Determine the meter box attribution of each electric meter 2041 according to the clustering result.

[0048] Specifically, the embodiment of the present application proposes to cluster the electricity meters 2041 based on any one of the DBSCAN clustering algorithm, the K-Medoids clustering algorithm, and the MCL clustering algorithm, and use the standard quality parameters to obtain the cabinet attribution of each electricity meter 2041. The above clustering algorithms rely on the configuration of some default parameters. For example, the DBSCAN clustering algorithm depends on the setting of the threshold; the K-Medoids clustering algorithm requires the number of known cabinets 204; the MCL clustering algorithm needs to convert the standard quality parameters into probability information, and the conversion process involves the selection of probability conversion parameters. In the application scenario where the distance between the cabinets 204 is large, the clustering parameters of the clustering algorithm can be statistically set according to experience to perform efficient and accurate cabinet identification.

[0049] Among them, the DBSCAN clustering algorithm and the K-Medoids clustering algorithm are classical algorithms. Therefore, the embodiment of the present application focuses on the description of the clustering process of the MCL clustering algorithm. It can be understood that the clustering processes when different clustering algorithms are selected can all refer to Figure 6 .

[0050] Please refer to Figure 7 , in some embodiments, when the clustering algorithm is the MCL clustering algorithm, the clustering parameter is the probability conversion parameter. Through the clustering algorithm, clustering analysis is performed on the standard quality parameters to obtain the clustering result (i.e., 033), including: 0331: Based on the probability conversion parameter, convert the standard quality parameters into the first parameter matrix; 0332: Normalize the first parameter matrix to obtain the second parameter matrix; 0333: Set the diffusion coefficient and the contraction factor; 0334: Based on the diffusion coefficient and the contraction factor, convert the second parameter matrix into the third parameter matrix, and normalize the third parameter matrix to obtain the fourth parameter matrix; 0335: Repeat the step of converting the second parameter matrix into the third parameter matrix based on the diffusion coefficient and the contraction factor, and normalizing the third parameter matrix to obtain the fourth parameter matrix until the predetermined number of iterations is reached; 0336: Determine the clustering result according to the fourth parameter matrix.

[0051] Specifically, when the clustering algorithm set in 031 is the MCL clustering algorithm, 032 sets the probability conversion parameter of the MCL clustering algorithm. The process of obtaining the clustering result by performing clustering analysis on the standard quality parameters through the MCL clustering algorithm is as follows: Step 1: Connection probability calculation. Convert the standard quality parameters into the probability information of whether the two electricity meters 2041 are in the same meter box 204. Specifically, the foregoing signal quality matrix R can be converted into the first parameter matrix P according to the formula Pij = 10^(Rij / (10*n)). Among them, the parameter n is a preset probability conversion parameter, representing the fading factor of the signal strength with distance, which varies with different signal types and environments.

[0052] Step 2: Normalize the first parameter matrix P to obtain the second parameter matrix Q. Specifically, the normalization process can be carried out according to the formula Qij = Pij / (∑jPij).

[0053] Step 3: Set the diffusion coefficient r and the contraction factor a. Usually, r = 2, and the value of a is greater than 1.

[0054] Step 4: Perform the following matrix operations based on the diffusion coefficient r and the contraction factor a, Q′=(Q^r).^a, to convert the second parameter matrix Q into the third parameter matrix Q′. Among them, ^r represents the matrix power operation, and.^a represents the operation on the matrix elements. Then, normalize the third parameter matrix Q′ to obtain the fourth parameter matrix Q″. This normalization operation is the same as that in Step 2.

[0055] Step 5: Use the fourth parameter matrix Q″ as the second parameter matrix Q, and repeat Step 4 until the predetermined number of iterations N is reached.

[0056] Step 6: Decode and cluster according to the fourth parameter matrix Q″ to obtain the clustering result. After the above operations, in the fourth parameter matrix Q″, the high values (close to 1) in a certain column will be concentrated on specific rows, and the electricity meters j with Q″ij close to 1 in the same row belong to the same class.

[0057] Method 2: Meter box identification algorithm with adaptive parameter selection Please refer to Figure 8 and Figure 9 , in some embodiments, according to the standard quality parameters, use the meter box identification algorithm to identify the meter box attribution (i.e., 030) of each electricity meter 2041, including: 041: Set the clustering algorithm, and the clustering algorithm includes any one of the DBSCAN clustering algorithm, the K-Medoids clustering algorithm, and the MCL clustering algorithm; 042: Set the clustering parameter set of the clustering algorithm, and the clustering parameter set includes various different clustering parameters; 043: Traverse each clustering parameter in the clustering parameter set, and perform clustering analysis on the standard quality parameters through the clustering algorithm to obtain the clustering result; 044: Calculate the cost for multiple clustering results corresponding to multiple clustering parameters in the clustering parameter set through a clustering cost algorithm to select the clustering result corresponding to the optimal cost; 045: Determine the cabinet attribution of each electricity meter 2041 according to the clustering result corresponding to the optimal cost.

[0058] Specifically, as mentioned above, clustering algorithms such as DBSCAN clustering algorithm, K-Medoids clustering algorithm, and MCL clustering algorithm rely on the configuration of some default parameters. For example, the DBSCAN clustering algorithm depends on the setting of the threshold; the K-Medoids clustering algorithm requires knowing the number of cabinets 204; the MCL clustering algorithm needs to convert the standard quality parameters into probability information, and the conversion process involves the selection of probability conversion parameters. When these clustering algorithms are applied to the identification of cabinets in different distribution substations, they cannot adaptively select the clustering parameters of the clustering algorithm, resulting in good cabinet identification results in some distribution substations and poor cabinet identification results in some distribution substations. The embodiment of the present application proposes a scheme for adaptive parameter selection to improve the universality of the clustering algorithm in different distribution substations.

[0059] It has been found through research that clustering algorithms such as DBSCAN clustering algorithm, K-Medoids clustering algorithm, and MCL clustering algorithm need to traverse some parameters to perform accurate clustering when the signal strength discrimination between cabinets 204 decreases due to the small distance between cabinets 204. Please refer to Figure 9 In the embodiment of the present application, first, set the clustering algorithm. For example, the set clustering algorithm is the K-Medoids clustering algorithm; then, set the clustering parameter set of the K-Medoids clustering algorithm, and the clustering parameter set includes a variety of different clustering parameters; then, substitute each clustering parameter in the clustering parameter set, and perform clustering analysis on the standard quality parameters through the K-Medoids clustering algorithm to obtain the clustering result; after that, calculate the cost for multiple clustering results corresponding to multiple clustering parameters in the clustering parameter set through the clustering cost algorithm to select the clustering result corresponding to the optimal cost, and the clustering parameter corresponding to the optimal cost is also the adaptively selected clustering parameter; finally, determine the cabinet attribution of each electricity meter 2041 according to the clustering result corresponding to the optimal cost.

[0060] In some embodiments, the clustering cost algorithm includes the Elbow Method or the Silhouette Coefficient method.

[0061] Specifically, the basic idea of the Elbow Method is to select appropriate clustering parameters by observing the sum of squared errors (SSE) under different clustering parameters.

[0062]

[0063] Among them, represents the i-th category, and p is a sample point in the centroid of the i-th category (when using the k-medoids clustering algorithm represents the centroid, and when using other clustering algorithms it is calculated according to the mean of all samples in

[0064] Try different clustering parameters. Taking the K-Medoids clustering algorithm as an example (other clustering algorithms are similar and have similar rules with the change of clustering parameters). First, try different numbers of clusters k, and then calculate the SSE for each k value. Finally, plot the k values and the corresponding SSEs into a graph to observe the changing trend of SSE with the k value. In an ideal situation, as the k value increases, the SSE will gradually decrease. When the k value increases to a certain value, the rate of decrease of SSE will significantly slow down, and this turning point is the so-called "elbow". Generally, the k value at the "elbow" can be selected as the optimal clustering parameter. Based on the optimal clustering parameter, the clustering result obtained by clustering the standard quality parameters through the clustering algorithm is the clustering result corresponding to the optimal cost.

[0065] The silhouette coefficient method mainly reflects the changes within and between clusters, combines the cohesion and separation of clustering, and is used to evaluate the clustering effect. Its value is between [-1, 1], and the larger the value, the better the clustering effect. The calculation formula of the silhouette coefficient is:

[0066] Among them, is the average distance from the sample to other samples within the same category ; , is the number of samples in the category where is located; is the average distance from the sample to the samples in the nearest different category. The calculation method is similar to but needs to traverse other categories to obtain multiple values (where represents the average distance from the sample to all points within the category and represents the category where the sample

[0067] After calculating the silhouette coefficients of all samples, the average silhouette coefficient can be obtained by calculating the average value. The average silhouette coefficient can comprehensively evaluate the clustering effect, and the clustering parameter corresponding to the maximum average silhouette coefficient is selected as the optimal clustering parameter. Based on the optimal clustering parameter, the clustering result obtained by clustering the standard quality parameters through the clustering algorithm is the clustering result corresponding to the optimal cost.

[0068] It should be noted that the process of "performing clustering analysis on the standard quality parameters through the clustering algorithm to obtain the clustering result" in step 043 is the same as that in step 033, and it can also include steps 0331 to 0336, which will not be elaborated here.

[0069] In addition, in some embodiments, when the distance between the meter boxes 204 is large, for example, greater than a predetermined distance, steps 031 to 034 can be used to determine the meter box attribution of each electric meter 2041; when the distance between the meter boxes 204 is small, for example, less than a predetermined distance, steps 041 to 045 can be used to determine the meter box attribution of each electric meter 2041.

[0070] Method 3: Clustering by fusing multiple algorithms Please refer to Figure 10 and Figure 11 , in some embodiments, according to the standard quality parameters, using the meter box identification algorithm to identify the meter box attribution of each electric meter 2041 (i.e., 030), including: 051: Set multiple clustering algorithms, and the multiple clustering algorithms include any combination of DBSCAN clustering algorithm, K-Medoids clustering algorithm, and MCL clustering algorithm; 052: Set the clustering parameter set for each clustering algorithm, and the clustering parameter set includes multiple different clustering parameters; 053: Traverse each clustering parameter in the clustering parameter set of each clustering algorithm, and perform clustering analysis on the standard quality parameters through the clustering algorithm to obtain the clustering result; 054: Calculate the cost of the multiple clustering results corresponding to the multiple clustering parameters in the clustering parameter sets of multiple clustering algorithms through the clustering cost algorithm to select the clustering result corresponding to the optimal cost; 055: Determine the meter box attribution of each electric meter 2041 according to the clustering result corresponding to the optimal cost.

[0071] Specifically, the embodiments of the present application use multiple clustering algorithms for meter box identification, and propose an adaptive parameter selection and algorithm selection strategy to optimize the meter box identification algorithm and the meter box identification process, and improve the accuracy and robustness of meter box identification.

[0072] Based on Method 1, the cost of the clustering results corresponding to different clustering parameters of the DBSCAN clustering algorithm, K-Medoids clustering algorithm, and MCL clustering algorithm is calculated respectively. The cost calculation method can be the elbow method, silhouette coefficient method in Method 2, or other methods that can calculate the cost of the clustering results. The optimal clustering result is selected according to the cost size of different clustering algorithms.

[0073] Taking the elbow method as an example, assuming that the MCL clustering algorithm and K-Medoids clustering algorithm are used for clustering analysis and the clustering results are obtained, then the cost of the MCL clustering algorithm can be calculated according to the elbow method calculation formula and the cost of the K-Medoids clustering algorithm respectively. Then, the smaller value between and is selected, and the corresponding clustering result is used as the final clustering result. It should be noted that the process of "performing clustering analysis on the standard quality parameters through a clustering algorithm to obtain a clustering result" in step 053 is the same as that in step 033, and it can also include steps 0331 to 0336, which will not be elaborated here.

[0074] Please refer to

[0075] In some embodiments, the meter box identification method further includes: Figure 12 050: Comparing and analyzing the multiple identification results of the meter box attribution in multiple time periods; 060: For each electricity meter 2041, selecting the identification result of the meter box attribution that appears the most times in multiple time periods as the current identification result of the meter box attribution of the electricity meter 2041. Specifically, due to the fact that the situation of the meter box to which the electricity meter 2041 belongs is fixed for a long time in actual applications, the multiple identification results in multiple time periods can be compared, and each electricity meter 2041 selects the meter box 204 with the most occurrences of the identification result for classification. In this way, the fine-tuning of the clustering result is realized, and the accuracy of the clustering result is improved.

[0076] For example, in the 10-meter box identification processes corresponding to 10 time periods, the identification result of a certain electricity meter 2041 belongs to the first meter box 204 six times, belongs to the second meter box 204 two times, and belongs to the third meter box 204 once. And the identification result of this electricity meter 2041 in the current 10th time belongs to the second meter box 204. Then, it is finally determined that the current identification result of this electricity meter 2041 belongs to the first meter box 204.

[0077]

[0078] ​In summary, the meter box identification method according to the embodiments of the present application is as follows: (1) In terms of the overall solution, first, perform meter box identification clustering analysis using the signal quality parameters received by the dual-mode communication module. Secondly, propose the MCL clustering algorithm. Then, adaptively select the optimal clustering parameters and the optimal clustering algorithm through the method of cost calculation. Finally, iterate and update the meter box identification result through data in different time periods. The specific implementation details are as follows. (2) Based on multi-dimensional information such as the RSSI information or the power line signal strength information measured by the dual-mode communication module, perform meter box identification. (3) Propose a meter box identification algorithm based on the MCL clustering algorithm, and select parameters through preset parameters or an adaptive algorithm. (4) Propose a meter box identification algorithm based on the DBSCAN clustering algorithm and the K-Medoids clustering algorithm, and select parameters through preset parameters or an adaptive algorithm. (5) Iteratively update the meter box identification result. Since the topological structure of the meter box 204 is basically the same for a long time, the clustering algorithm analysis can be performed regularly, and then each electricity meter 2041 is classified into the meter box 204 with the most occurrences. In this way, the fine-tuning of the clustering result is realized, and the accuracy of the clustering result is improved.

[0079] The meter box identification method according to the embodiments of the present application has at least the following advantages: (1) Performing meter box identification based on the signal quality parameters of the dual-mode communication module has higher clustering accuracy compared to directly using electrical parameters such as voltage, current, power consumption, and resistance. (2) Propose the MCL clustering algorithm, which has a novel theory and robust results, and fits the meter box identification algorithm. (3) Propose an adaptive clustering parameter selection algorithm, which can solve the problem of unknown parameters in traditional clustering algorithms. (4) Propose an iterative update scheme for the clustering result, which improves the accuracy of the clustering result.

[0080] The working process of the meter box identification method according to the embodiments of the present application will be described below with specific examples. Taking the aforementioned method one as an example, the measurement data of the 3rd substation area of Evergrande Oasis is used for meter box identification.

[0081] First, collect signal quality parameters. The RSSI data collected by each electricity meter 2041 is as Figure 13 and Figure 14 shown. Among them, Figure 13 is the RSSI data of other electricity meters 2041 measured by the 118th electricity meter 2041, and Figure 14 is the RSSI data of other electricity meters 2041 measured by the 79th electricity meter 2041. Then, perform data preprocessing on the signal quality parameters. As Figure 15 shown, perform missing data supplementation, and set the RSSI data of the unmeasured electricity meter 2041 to the minimum value. Figure 15The RSSI data measured by the first 20 electricity meters 2041 is shown. Additionally, data format conversion is performed to construct the RSSI data in the form of a signal quality matrix R. Then, according to the MCL clustering algorithm, the signal quality matrix R is converted into a first parameter matrix P. That is, according to the formula Pij = 10^(Rij / (10*n)), the signal quality matrix R is converted into the first parameter matrix P. Figure 16 The first parameter matrix P corresponding to the first 20 electricity meters 2041 is shown. Finally, according to the first parameter matrix P, the meter box identification algorithm is used for meter box identification. Figure 17 It is a distribution schematic diagram of the RSSI data before clustering. Figure 18 It is a distribution schematic diagram of the data rearranged according to the meter box attribution after clustering. From Figure 17 and Figure 18 It can be seen that after clustering, the RSSI data is rearranged, that is, the RSSI data of the same meter box 204 is clustered together. The contour map is clustered in blocks near the diagonal according to the meter box attribution. Figure 18 There are 12 rectangular regions in the diagonal direction in

[0082] Please refer to Figure 19 , the meter box identification device 100 of the embodiment of the present application is used for the meter box identification system 200. The meter box identification system 200 includes a plurality of meter boxes 204, and each meter box 204 includes one or more electricity meters 2041. The meter box identification device 100 includes an acquisition module 10, a processing module 20, an identification module 30, and an update module 40. The acquisition module 10 is used to acquire the signal quality parameters based on the power line dual-mode communication protocol reported by each electricity meter 2041, where the signal quality parameters are obtained by each electricity meter 2041 receiving the signal quality parameters of other electricity meters 2041. The processing module 20 is used to perform data preprocessing on the signal quality parameters to obtain standard quality parameters. The identification module 30 is used to identify the meter box attribution of each electricity meter 2041 according to the standard quality parameters by using the meter box identification algorithm. The update module 40 is used to return to the step of acquiring the signal quality parameters based on the power line dual-mode communication protocol reported by each electricity meter 2041 every predetermined time period to update the meter box attribution of each electricity meter 2041.

[0083] In some embodiments, the signal quality parameters include wireless signal strength information and / or power line signal strength information.

[0084] In some embodiments, the processing module 20 is specifically used to perform default data supplementation and data format conversion on the signal quality parameters to obtain standard quality parameters.

[0085] In some embodiments, the recognition module 30 is specifically configured to: set a clustering algorithm, where the clustering algorithm includes any one of DBSCAN clustering algorithm, K-Medoids clustering algorithm, and MCL clustering algorithm; set the clustering parameters of the clustering algorithm; perform clustering analysis on the standard quality parameters through the clustering algorithm to obtain a clustering result; and determine the cabinet attribution of each electricity meter 2041 according to the clustering result.

[0086] In some embodiments, the recognition module 30 is specifically configured to: set a clustering algorithm, where the clustering algorithm includes any one of DBSCAN clustering algorithm, K-Medoids clustering algorithm, and MCL clustering algorithm; set a set of clustering parameters for the clustering algorithm, where the set of clustering parameters includes multiple different clustering parameters; traverse each clustering parameter in the set of clustering parameters, perform clustering analysis on the standard quality parameters through the clustering algorithm to obtain a clustering result; calculate the costs of the multiple clustering results corresponding to the multiple clustering parameters in the set of clustering parameters through a clustering cost algorithm to select the clustering result corresponding to the optimal cost; and determine the cabinet attribution of each electricity meter 2041 according to the clustering result corresponding to the optimal cost.

[0087] In some embodiments, the recognition module 30 is specifically configured to: set multiple clustering algorithms, where the multiple clustering algorithms include any multiple of DBSCAN clustering algorithm, K-Medoids clustering algorithm, and MCL clustering algorithm; set a set of clustering parameters for each clustering algorithm, where the set of clustering parameters includes multiple different clustering parameters; traverse each clustering parameter in the set of clustering parameters for each clustering algorithm, perform clustering analysis on the standard quality parameters through the clustering algorithm to obtain a clustering result; calculate the costs of the multiple clustering results corresponding to the multiple clustering parameters in the sets of clustering parameters for the multiple clustering algorithms through a clustering cost algorithm to select the clustering result corresponding to the optimal cost; and determine the cabinet attribution of each electricity meter 2041 according to the clustering result corresponding to the optimal cost.

[0088] In some embodiments, when the clustering algorithm is the MCL clustering algorithm, the clustering parameter is a probability conversion parameter. The recognition module 30 is specifically configured to: based on the probability conversion parameter, convert the standard quality parameters into a first parameter matrix; perform normalization processing on the first parameter matrix to obtain a second parameter matrix; set a diffusion coefficient and a contraction factor; based on the diffusion coefficient and the contraction factor, convert the second parameter matrix into a third parameter matrix and perform normalization processing on the third parameter matrix to obtain a fourth parameter matrix; repeat the step of based on the diffusion coefficient and the contraction factor, converting the second parameter matrix into a third parameter matrix and performing normalization processing on the third parameter matrix to obtain a fourth parameter matrix until a predetermined number of iterations is reached; and determine the clustering result according to the fourth parameter matrix.

[0089] In some embodiments, the clustering cost algorithm includes the elbow method or the silhouette coefficient method.

[0090] Please refer to Figure 20 In some embodiments, the meter box identification device 100 further includes an analysis module 50 and a selection module 60. The analysis module 50 is configured to perform a comparative analysis on multiple identification results of the meter box attribution in multiple time periods. The selection module 60 is configured to, for each electric meter 2041, select the identification result of the meter box attribution that appears the most times in multiple time periods as the current identification result of the meter box attribution of the electric meter 2041.

[0091] It should be noted that the explanations of the meter box identification method in the foregoing embodiments are equally applicable to the meter box identification device 100 in the embodiments of the present application, and will not be elaborated herein.

[0092] Please refer to Figure 21 The meter box identification system 200 according to the embodiment of the present application includes one or more processors (i.e., the first processor 210) and a memory 220, and the memory 220 stores a computer program. When the computer program is executed by the first processor 210, the meter box identification method according to any of the foregoing embodiments is implemented.

[0093] For example, when the computer program is executed by the first processor 210, the following meter box identification method is implemented: 010: Obtain the signal quality parameters reported by each electric meter 2041 based on the power line dual-mode communication protocol, where the signal quality parameters are obtained by each electric meter 2041 receiving the signal quality parameters of other electric meters 2041; 020: Perform data preprocessing on the signal quality parameters to obtain standard quality parameters; 030: Identify the meter box attribution of each electric meter 2041 according to the standard quality parameters by using a meter box identification algorithm; 040: Return to step 010 of obtaining the signal quality parameters reported by each electric meter 2041 based on the power line dual-mode communication protocol at each interval of a predetermined time period to update the meter box attribution of each electric meter 2041.

[0094] For another example, when the computer program is executed by the first processor 210, the following meter box identification method is implemented: 031: Set a clustering algorithm, where the clustering algorithm includes any one of the DBSCAN clustering algorithm, the K-Medoids clustering algorithm, and the MCL clustering algorithm; 032: Set the clustering parameters of the clustering algorithm; 033: Perform clustering analysis on the standard quality parameters by using the clustering algorithm to obtain a clustering result; 034: Determine the meter box attribution of each electric meter 2041 according to the clustering result.

[0095] It should be noted that the explanations of the meter box identification method in the foregoing embodiments are equally applicable to the meter box identification system 200 of the embodiments of the present application, and will not be elaborated herein.

[0096] Please refer to Figure 22 , the computer-readable storage medium 300 of the embodiments of the present application stores a computer program 310 thereon. When the program is executed by a processor (i.e., the second processor 320), the meter box identification method of any of the foregoing embodiments is implemented.

[0097] For example, when the program is executed by the second processor 320, the following meter box identification method is implemented: 010: Obtain the signal quality parameters based on the power line dual-mode communication protocol reported by each electricity meter 2041, where the signal quality parameters are obtained by each electricity meter 2041 receiving the signal quality parameters of other electricity meters 2041; 020: Perform data preprocessing on the signal quality parameters to obtain standard quality parameters; 030: According to the standard quality parameters, use the meter box identification algorithm to identify the meter box attribution of each electricity meter 2041; 040: At every predetermined time interval, return to the step of obtaining the signal quality parameters based on the power line dual-mode communication protocol reported by each electricity meter 2041 to update the meter box attribution of each electricity meter 2041.

[0098] Again, for example, when the program is executed by the second processor 320, the following meter box identification method is implemented: 031: Set a clustering algorithm, where the clustering algorithm includes any one of the DBSCAN clustering algorithm, the K-Medoids clustering algorithm, and the MCL clustering algorithm; 032: Set the clustering parameters of the clustering algorithm; 033: Perform clustering analysis on the standard quality parameters through the clustering algorithm to obtain a clustering result; 034: Determine the meter box attribution of each electricity meter 2041 according to the clustering result.

[0099] It should be noted that the explanations of the meter box identification method in the foregoing embodiments are equally applicable to the computer-readable storage medium 300 of the embodiments of the present application, and will not be elaborated herein.

[0100] In summary, in the meter box identification method, meter box identification device 100, meter box identification system 200, and computer-readable storage medium 300 according to the embodiments of the present application, signal quality parameters based on the power line dual-mode communication protocol reported by each electricity meter 2041 are obtained, and the signal quality parameters are preprocessed to obtain standard quality parameters, so as to identify the meter box attribution of each electricity meter 2041 according to the standard quality parameters. In this way, the accuracy and reliability of meter box identification can be improved. In addition, every predetermined time period, the step of obtaining the signal quality parameters based on the power line dual-mode communication protocol reported by each electricity meter 2041 is returned to update the meter box attribution of each electricity meter 2041. In this way, the identification result of the meter box attribution can be updated, and the more signal quality parameters are collected, the more accurate the identification result of the meter box attribution will be.

[0101] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0102] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process, and the scope of the preferred embodiments of the present application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0103] The logic and / or steps represented in the flowchart or otherwise described herein can, for example, be considered as a definable sequence of executable instructions for implementing logical functions, and can be embodied specifically in any computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a computer-readable storage medium can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection part having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable storage medium can even be paper or other suitable media on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0104] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0105] Those of ordinary skill in the art can understand that all or part of the steps carried out in implementing the above-described embodiment methods can be completed by a program instructing relevant hardware. The said program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment. In addition, in each of the embodiments of the present application, each functional unit can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in a module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disc, etc.

[0106] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present application. The scope of the present application is defined by the claims and their equivalents.

Claims

1. A meter box identification method, characterized in that: Used in a meter box identification system, the meter box identification system includes a plurality of meter boxes, each of the meter boxes includes one or more electric meters, and the meter box identification method includes: Acquire a signal quality parameter based on the power line dual-mode communication protocol reported by each of the electric meters, wherein the signal quality parameter is obtained by each of the electric meters receiving the signal quality parameters of other electric meters; Performing data preprocessing on the signal quality parameters to obtain standard quality parameters; According to the standard quality parameters, using a meter box identification algorithm to identify the meter box to which each of the electric meters belongs; At every predetermined time period, the step of obtaining the signal quality parameter based on the power line dual-mode communication protocol reported by each of the electric meters is returned to update the meter box affiliation of each of the electric meters.

2. The meter box identification method according to claim 1, characterized in that: The signal quality parameter includes wireless signal strength information and / or power line signal strength information.

3. The meter box identification method according to claim 1, characterized in that: The performing data preprocessing on the signal quality parameter to obtain a standard quality parameter includes: Default data supplement and data format conversion are performed on the signal quality parameters to obtain the standard quality parameters.

4. The meter box identification method according to claim 1, characterized in that: The method of identifying the meter box of each electric meter by using a meter box identification algorithm according to the standard quality parameter includes: Setting a clustering algorithm, wherein the clustering algorithm includes any one of a DBSCAN clustering algorithm, a K-Medoids clustering algorithm, and an MCL clustering algorithm; Setting clustering parameters of the clustering algorithm; Performing cluster analysis on the standard quality parameters by using the clustering algorithm to obtain clustering results; The meter box affiliation of each of the electric meters is determined according to the clustering result.

5. The meter box identification method according to claim 1, characterized in that: The method of identifying the meter box of each electric meter by using a meter box identification algorithm according to the standard quality parameter includes: Setting a clustering algorithm, wherein the clustering algorithm includes any one of a DBSCAN clustering algorithm, a K-Medoids clustering algorithm, and an MCL clustering algorithm; Setting a clustering parameter set of the clustering algorithm, wherein the clustering parameter set includes a plurality of different clustering parameters; Traversing each of the clustering parameters in the clustering parameter set, performing clustering analysis on the standard quality parameters by using the clustering algorithm to obtain a clustering result; Performing cost calculation on the plurality of clustering results corresponding to the plurality of clustering parameters in the clustering parameter set by using a clustering cost algorithm, so as to select the clustering result corresponding to the optimal cost; The meter box affiliation of each of the electric meters is determined according to the clustering result corresponding to the optimal cost.

6. The meter box identification method according to claim 1, characterized in that: The method of identifying the meter box of each electric meter by using a meter box identification algorithm according to the standard quality parameter includes: Setting a plurality of clustering algorithms, wherein the plurality of clustering algorithms include any plurality of DBSCAN clustering algorithms, K-Medoids clustering algorithms, and MCL clustering algorithms; Setting a clustering parameter set for each of the clustering algorithms, wherein the clustering parameter set includes a plurality of different clustering parameters; Traversing each of the clustering parameters in the clustering parameter set of each of the clustering algorithms, performing clustering analysis on the standard quality parameters through the clustering algorithm to obtain a clustering result; Performing cost calculation on a plurality of clustering results corresponding to a plurality of clustering parameters in a plurality of clustering parameter sets of the clustering algorithms by using a clustering cost algorithm, so as to select the clustering result corresponding to the optimal cost; The meter box affiliation of each of the electric meters is determined according to the clustering result corresponding to the optimal cost.

7. The meter box identification method according to any one of claims 4 to 6, characterized in that: When the clustering algorithm is an MCL clustering algorithm, the clustering parameter is a probability conversion parameter, and the clustering analysis of the standard quality parameter by the clustering algorithm to obtain a clustering result includes: Based on the probability conversion parameter, converting the standard quality parameter into a first parameter matrix; Normalizing the first parameter matrix to obtain a second parameter matrix; Set the diffusion coefficient and shrinkage factor; Based on the diffusion coefficient and the shrinkage factor, the second parameter matrix is ​​converted into a third parameter matrix, and the third parameter matrix is ​​normalized to obtain a fourth parameter matrix; Repeating the steps of converting the second parameter matrix into a third parameter matrix based on the diffusion coefficient and the contraction factor, and normalizing the third parameter matrix to obtain a fourth parameter matrix until a predetermined number of iterations is reached; The clustering result is determined according to the fourth parameter matrix.

8. The meter box identification method according to claim 5 or 6, characterized in that: The clustering cost algorithm includes an elbow method or a silhouette coefficient method.

9. The meter box identification method according to claim 1, characterized in that: The meter box identification method further includes: Compare and analyze multiple identification results of meter box ownership in multiple time periods; For each of the electric meters, the identification result of the meter box affiliation that appears most frequently in the multiple time periods is selected as the current identification result of the meter box affiliation of the electric meter.

10. A meter box identification device, characterized in that: Used in a meter box identification system, the meter box identification system includes a plurality of meter boxes, each of which includes one or more electric meters, and the meter box identification device includes: An acquisition module, used for acquiring the signal quality parameters based on the power line dual-mode communication protocol reported by each of the electric meters, wherein the signal quality parameters are obtained by each of the electric meters receiving the signal quality parameters of other electric meters; A processing module, used for performing data preprocessing on the signal quality parameters to obtain standard quality parameters; An identification module, used to identify the meter box of each electric meter using a meter box identification algorithm according to the standard quality parameter; The updating module is used to return to the step of obtaining the signal quality parameters based on the power line dual-mode communication protocol reported by each of the electric meters at intervals of a predetermined time period, so as to update the meter box affiliation of each of the electric meters.

11. A meter box identification system, characterized in that: The meter box identification system includes one or more processors and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the meter box identification method according to any one of claims 1 to 9 is implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the meter box identification method described in any one of claims 1 to 9 is implemented.

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