A Matching Method for Edge Device Channels and Meter Addresses

Abstract

The present invention relates to a method for matching the channels of edge devices with the addresses of electric meters, belonging to the technical field of smart grids. It includes obtaining the channel set and the electric meter set under the transformer area from the data center of the energy controller, screening the channels to be matched to obtain the set N1 of channels to be matched; obtaining the corresponding electric meter set N2, traversing the electric meter set N2, and matching the set of channels to be matched with the set of electric meters to be matched; taking the difference of the electric meters in the electric meter set at 15-minute power points to calculate the power value; after calculating the optimal translation distance and the second-best translation distance, translating and aligning the two vectors; calculating the similarity of the vectors and outputting the similarity value; obtaining the similarity set P of a certain channel and the electric meter set N2, selecting the maximum similarity value and the second-largest similarity value in P for optimal judgment, and obtaining the optimal matching result of the channel and the electric meter. The present invention can realize the binding relationship between the transformer channel number and the electric meter address without connecting the 485 line.

Description

Technical Field

[0001] The present invention relates to a method for matching the channels of edge devices with the addresses of electric meters, belonging to the technical field of smart grids. Background Art

[0002] With the popularization of HPLC high-frequency acquisition during the upgrade of residential electric meters in low-voltage substations, the era of originally reading electric meter data through 485 lines has gradually withdrawn from the stage. At the same time, limited by the functions of the electric meter design, to implement additional functions, some intelligent sensing terminals need to be installed in the substation to collect user data through current transformers. Also due to technological upgrades and cost reduction, an intelligent sensing terminal can now collect data from multiple users, changing the previous one-to-one correspondence between electric meters and users to a one-to-many relationship between intelligent sensing terminals and users. However, how to achieve the binding relationship between the channel numbers of current transformers and the addresses of electric meters without connecting 485 lines through technical means is an urgent problem to be solved in the field of smart meters. Summary of the Invention

[0003] The purpose of the present invention is to overcome the problems existing in the prior art and provide a method for matching the channels of edge devices with the addresses of electric meters. By calculating the similarity, the address of the electric meter with the matching power consumption and channel number in the entire substation can be found, and the binding relationship between the channel number of the current transformer and the address of the electric meter can be achieved without connecting 485 lines.

[0004] To solve the above problems, the method for matching the channels of edge devices with the addresses of electric meters of the present invention includes the following steps:

[0005] Step 1: Obtain the channel set and the electric meter set in the substation from the data center of the energy controller, and screen the channels to be matched through a threshold to obtain the set N1 of channels to be matched;

[0006] Step 2: After the screening in Step 1, obtain 1 channel to be matched and its corresponding electric meter set N2. Traverse the electric meter set N2, and match the set of channels to be matched with the set of electric meters to be matched through the channel table number matching algorithm. The input of the channel table number algorithm is the set of channels to be matched and the set of electric meters to be matched, and the output is the matching result of this channel and the electric meter;

[0007] Step 3: Calculate the power consumption value of the current 15 minutes of the electric meter by taking the difference of the electric meters in the electric meter set in Step 2 at 15-minute power consumption points;

[0008] Arrange two sets of power consumption point data 15 minutes apart through the time period numbers one by one as the input of the DTW algorithm. After calculating the optimal translation distance and the second-optimal translation distance according to the DTW algorithm, perform translation alignment on the optimal translation distance vector and the second-optimal translation distance vector;

[0009] Step 4: Calculate the similarity through the two aligned vectors, calculate the similarity between the channel to be matched and its corresponding electric meter, and output the similarity value;

[0010] Step 5: After calculating the similarity set P between a certain channel and the electric meter set N2 in Steps 3 and 4, select the maximum similarity value Pmax and the second maximum similarity value P2 in P for optimal judgment to obtain the optimal matching result between the channel and the electric meter.

[0011] Further, in Step 1, the thresholds for screening the set N1 of channels to be matched are as follows:

[0012] Threshold 1: For screening the channels to be matched, the power consumption is greater than 1.5 degrees and the amount of missing data is less than 30 items.

[0013] Further, in Step 2, the screening process of the set N2 of electric meters to be matched is as follows:

[0014] Find the set N1 of channels to be matched through Threshold 1, traverse the channels in N1, and then use Threshold 2 to narrow the range of electric meter matching for this channel to obtain the set N2 of electric meters to be matched; Threshold 2 is the range of electric meters with a relative error less than 20% between the additional screening item of power consumption and the channel.

[0015] Further, in Step 2, the standard for the channel meter number matching algorithm to match the set of channels to be matched and the set of electric meters to be matched is that the matching degree between the channel to be matched and the set of electric meters to be matched is above 80%.

[0016] Further, the optimal matching result between the channel and the electric meter obtained in Step 5 meets the following conditions: Pmax > 0.8; and (Pmax - P2) / Pmax > 0.2.

[0017] Further, the method for calculating the similarity described in Step 4 is Pearson similarity calculation, and the formula is as follows:

[0018]

[0019] In the formula, is the Pearson similarity, X is the optimal translation distance vector, Y is the second optimal translation distance vector, is the standard deviation, is the covariance of vector X and vector Y.

[0020] The beneficial effects of the present invention are: (1) It can reduce the on-site construction difficulty and construction steps; (2) It can achieve the matching rate of the binding relationship between the channel number and the electric meter address to reach 100% without connecting the 485 line; (3) It can implement the above functions at the edge device without the intervention of the master station, and directly send the result after completion of the matching. Description of the Drawings

[0021] Figure 1 This is a flowchart of the method for matching the edge device channel and the electricity meter address of the present invention. Detailed implementation manners

[0022] Now, the present invention will be further described in detail with reference to the accompanying drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0023] As Figure 1 shown, the method for matching the edge device channel and the electricity meter address of the present invention includes the following steps:

[0024] Step 1: Obtain the channel set and the electricity meter set under the substation area from the energy controller data center, and screen the channels to be matched through a threshold to obtain the set N1 of channels to be matched; in Step 1, the thresholds for screening the set N1 of channels to be matched are as follows:

[0025] Threshold 1: For screening the channels to be matched, the electricity consumption is greater than 1.5 degrees and the data missing amount is less than 30 pieces.

[0026] Step 2: After the screening in Step 1, obtain 1 channel to be matched and its corresponding electricity meter set N2, traverse the electricity meter set N2, and match the set of channels to be matched and the set of electricity meters to be matched through the channel table number matching algorithm. The input of the channel table number algorithm is the set of channels to be matched and the set of electricity meters to be matched, and the output is the matching result of this channel and the electricity meter;

[0027] The screening process of the set N2 of electricity meters to be matched is as follows:

[0028] Find the set N1 of channels to be matched through Threshold 1, traverse the channels in N1, and then use Threshold 2 to narrow the electricity meter matching range of this channel to obtain the set N2 of electricity meters to be matched; Threshold 2 is to increase the screening option of the electricity meter range where the relative error between the electricity consumption and the channel is less than 20%.

[0029] In Step 2, the standard for the channel table number matching algorithm to match the set of channels to be matched and the set of electricity meters to be matched is that the matching degree between the channel to be matched and the set of electricity meters to be matched is above 80%.

[0030] Step 3: Subtract the electricity meter at the 15 - minute electricity point in the electricity meter set in Step 2 to calculate the current 15 - minute electricity value of the electricity meter; 95 points can be calculated every day. If the electricity meter electricity corresponding to the channel to be matched is already for within 15 minutes, no processing is required.

[0031] Arrange two sets of power consumption data points separated by 15 minutes in sequence according to the time period number as the input of the DTW algorithm. After calculating the optimal translation distance and the second-best translation distance according to the DTW algorithm, perform translation alignment on the optimal translation distance vector and the second-best translation distance vector.

[0032] Step 4: Calculate the similarity through the two aligned vectors, calculate the similarity between the channel to be matched and its corresponding electric meter, and output the similarity value. The similarity calculation method in Step 4 is Pearson similarity calculation, and the formula is as follows:

[0033]

[0034] In the formula, is the Pearson similarity, X is the optimal translation distance vector, Y is the second-best translation distance vector, is the standard deviation, is the covariance of vector X and vector Y.

[0035] Step 5: Obtain the similarity set P between a certain channel and the electric meter set N2 through the calculations in Step 3 and Step 4. Select the maximum similarity value Pmax and the second-largest similarity value P2 in P for optimal judgment to obtain the optimal matching result between the channel and the electric meter. The optimal matching result between the channel and the electric meter obtained in Step 5 meets the following conditions: Pmax > 0.8; and (Pmax - P2) / Pmax > 0.2.

[0036] Inspired by the ideal embodiments of the present invention described above, through the above description, relevant staff can completely make various changes and modifications without departing from the technical idea of this invention. The technical scope of this invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A matching method for the edge device channel and the electricity meter address, characterized in that It includes the following steps: Step 1: Obtain the channel set and the electricity meter set under the transformer area from the energy controller data center, and screen the channels to be matched through thresholds to obtain the set N1 of channels to be matched; Step 2: After the screening in Step 1, obtain 1 channel to be matched and its corresponding electricity meter set N2. Traverse the electricity meter set N2, and match the set of channels to be matched and the set of electricity meters to be matched through the channel table number matching algorithm. The input of the channel table number matching algorithm is the set of channels to be matched and the set of electricity meters to be matched, and the output is the matching result of this channel and the electricity meter; Step 3: Take the difference of the electricity meters in the electricity meter set in Step 2 at 15-minute power points, and calculate the current 15-minute power value of the electricity meter; Arrange two sets of power point data separated by 15 minutes one by one according to the time period number as the input of the DTW algorithm. After calculating the optimal translation distance and the second-best translation distance according to the DTW algorithm, perform translation alignment on the optimal translation distance vector and the second-best translation distance vector; Step 4: Calculate the similarity through the two aligned vectors, calculate the similarity between the channel to be matched and its corresponding electricity meter, and output the similarity value; Step 5: After calculating in Step 3 and Step 4, obtain the similarity set P between a certain channel and the electricity meter set N2. Select the maximum similarity value Pmax and the second-largest similarity value P2 in P for optimal judgment to obtain the optimal matching result of the channel and the electricity meter.

2. The method for matching the edge device channel and the electricity meter address according to claim 1, wherein In Step 1, the thresholds for screening the set N1 of channels to be matched are as follows: Threshold 1: For the screening of channels to be matched, the electricity consumption is greater than 1.5 degrees and the data missing amount is less than 30 items.

3. The method for matching the edge device channel and the electricity meter address according to claim 1, characterized in that In Step 2, the screening process of the set N2 of electricity meters to be matched is as follows: Find the set N1 of channels to be matched through Threshold 1, traverse the channels in N1, and then use Threshold 2 to narrow the electricity meter matching range of this channel to obtain the set N2 of electricity meters to be matched; the Threshold 2 is to increase the screening option of the electricity meter range with a relative error between the electricity consumption and the channel less than 20%.

4. The method for matching the edge device channel and the electricity meter address according to claim 1, characterized in that: In Step 2, the standard for the channel table number matching algorithm to match the set of channels to be matched and the set of electricity meters to be matched is: the matching degree between the channel to be matched and the set of electricity meters to be matched is above 80%.

5. The matching method of the edge device channel and the electricity meter address according to claim 1, characterized in that: The optimal matching result of the channel and the electricity meter obtained in Step 5 meets the following conditions: Pmax > 0.8; and (Pmax - P2) / Pmax > 0.

2.

6. The method for matching the edge device channel and the electricity meter address according to claim 1, wherein: The method of similarity calculation described in Step 4 is Pearson similarity calculation, and the formula is as follows: In the formula, is the Pearson similarity, X is the optimal translation distance vector, and Y is the second-optimal translation distance vector. is the standard deviation, is the covariance of vector X and vector Y.

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