A method and device for online monitoring of electric energy meter series connection based on hash processing

Abstract

The present invention relates to the technical field of power grid metering, and in particular to a method and device for online monitoring of electric energy meter cross-connection based on hash processing. The present invention discloses an online monitoring method for electric energy meter cross-connection based on hash processing, which is used to monitor the cross-connection of electric energy meters in a target area. M The present invention first obtains the target area M Electricity meter continuous N The original electric energy data of the day is obtained through a series of processing to obtain the judgment data set and the swap data set with the data position swapped; the judgment data set and the swap data set are then mapped through the hash algorithm to achieve preliminary similar data classification; then several pairs of candidate electric energy meter pairs with Euclidean distance less than the distance threshold are screened out and optimized to output the final judgment result set; the present invention can realize timely and online monitoring of the target area and has a wide range of application prospects. The present invention solves the problem that the existing algorithm must have pre-connection data to perform detection.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid metering, and more specifically to: 1. an online monitoring method for connecting an electric energy meter to a household based on hash processing; 2. an online monitoring device for connecting an electric energy meter to a household based on hash processing. Background Art

[0002] There are two main types of existing technologies for analyzing and judging the connection between electricity meters and households: 1. Manual inspection; 2. Rule-based judgment algorithm.

[0003] These two methods have certain limitations in detecting abnormalities in inter-user communication:

[0004] 1. Manual inspection is inefficient and consumes a lot of human resources. When faced with large-scale electricity meters, manual inspection is even more stretched and has poor real-time performance.

[0005] 2. The rule-based judgment algorithm determines whether a cross-connection has occurred in the energy meter by analyzing the sudden changes in current and electricity. Therefore, the energy meter data before and after the cross-connection is required. Therefore, when faced with an energy meter that lacks or loses the data before the cross-connection (such as a newly installed energy meter), the existing algorithm is difficult to effectively detect.

[0006] There is a certain substation, which has several meter boxes, each of which has several single-phase meters installed inside. The above-mentioned problems still exist when the above-mentioned two methods are used to detect the inter-household connection of electricity meters in this substation. Summary of the invention

[0007] Based on this, it is necessary to provide an online monitoring method and device for electricity meter cross-connection based on hash processing to address the problem that the existing algorithm must have pre-connection data to perform detection.

[0008] The present invention is implemented by the following technical solutions:

[0009] In the first aspect, the present invention discloses an online monitoring method for electric energy meter series connection based on hash processing, which is used to monitor the M The electric energy meter is set in the electric energy meter box; each electric energy meter box has a unique box identification ID, and each electric energy meter has a unique meter identification ID.

[0010] The online monitoring method of electric energy meter series connection based on hash processing includes the following steps:

[0011] Step 1: Get M Electricity meter continuous N days of raw power data and construct N Original dataset D 1~ D N ; N≥2.

[0012] Among them, n Original dataset D n Corresponding to m The energy meter is continuously N The first n Raw power data recorded for the day; m ∈[1, M ], n ∈[1, N ].

[0013] Step 2: Traverse D 1~ D N Process and obtain N String result set Q 1~ Q N .

[0014] Among them, D n Methods for processing include:

[0015] Step 21, D n Normalize it and get n Normalized Dataset E n ;

[0016] Step 22, E n Add new power data and get n A dataset for analysis F n ;

[0017] Step 23: F n The zero line and live line data in the above table are swapped to obtain n Swap Datasets G n ;

[0018] Step 24, based on the hash processing F n , G n Conduct research and obtain the n String result set Q n .

[0019] Step 3: Take Q 1~ Q N The intersection ofR 终 .

[0020] This hash processing-based online monitoring method for electric energy meters connected to different households implements the method or process according to the embodiments of the present disclosure.

[0021] In a second aspect, the present invention discloses an online monitoring device for connecting a power meter to another household based on hash processing, which uses the online monitoring method for connecting a power meter to another household based on hash processing disclosed in the first aspect.

[0022] The online monitoring device for electricity meter cross-connection based on hash processing includes: a data acquisition module, a cross-connection analysis module, and a result output module.

[0023] The data acquisition module is used to obtain M Electricity meter continuous N days of raw power data and construct N Original dataset D 1~ D N The user-to-user analysis module is used to traverse D 1~ D N Processing, corresponding to N String result set Q 1~ Q N The result output module is used to obtain Q 1~ Q N The intersection of R 终 .

[0024] The hash processing-based online monitoring device for electric energy meters connected to households implements the method or process according to the embodiment of the present disclosure.

[0025] In a third aspect, the present invention discloses a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the method for online monitoring of electric energy meter series connection based on hash processing disclosed in the first aspect are implemented.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. The present invention first obtains the target area M Electricity meter continuous NThe original electric energy data of the day is obtained through a series of processing to obtain a judgment data set and a swap data set with the data positions swapped; the judgment data set and the swap data set are mapped through a hash algorithm to achieve preliminary similar data classification; then several pairs of candidate electric energy meter pairs with Euclidean distances less than a distance threshold are screened out and optimized to output the final judgment result set; the present invention can realize timely and online monitoring of households in the target area, and has broad application prospects.

[0028] 2. The present invention also uses a kd tree algorithm and a nearest neighbor search algorithm when screening candidate electric energy meter pairs, which can reduce the amount of data calculation and is suitable for rapid comparison of large-scale electric energy meters, and the efficiency and accuracy are also guaranteed.

[0029] 3. Different from the existing methods, the present invention does not require data before the electric energy meter is connected to other households; therefore, the present invention is not only applicable to installed electric energy meters, but also to newly installed electric energy meters, and has a wider scope of application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0031] Figure 1 A flowchart of a method for online monitoring of electric energy meter series connection based on hash processing provided in Example 1 of the present invention;

[0032] Figure 2 for Figure 1 Flowchart of step 2 in the figure;

[0033] Figure 3 for Figure 2 Flowchart of step 24 in FIG.

[0034] Figure 4 This is a structural diagram of the electric energy meter series online monitoring device based on hash processing provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] It should be noted that when a component is referred to as being "mounted on" another component, it may be directly on the other component or there may be a central component. When a component is considered to be "set on" another component, it may be directly set on the other component or there may be a central component at the same time. When a component is considered to be "fixed to" another component, it may be directly fixed on the other component or there may be a central component at the same time.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "or / and" used herein includes any and all combinations of one or more of the related listed items.

[0038] Example 1

[0039] See also Figure 1 , Figure 1 A flowchart of a method for online monitoring of electric energy meters connected to households based on hash processing provided in Example 1 is shown.

[0040] First of all, it should be noted that the online monitoring method for electric energy meter series connection based on hash processing provided in this embodiment 1 is used to monitor the M An electric energy meter is used for household monitoring.

[0041] As mentioned in the background technology, the target area has the following characteristics:

[0042] There are several electric energy meter boxes in the target area, each of which has several electric energy meters. There are a total of M An electric energy meter.

[0043] That is to say, the electric energy meter is arranged in the electric energy meter box, wherein each electric energy meter box has a unique box identification ID, and each electric energy meter has a unique meter identification ID.

[0044] It should be noted that the energy meter is a single-phase meter; m An electric energy meter Δt For one cycle, the original electric energy data is recorded periodically; m ∈[1, M ].

[0045] The categories of raw power data include: neutral current ( I 0) and neutral voltage ( U 0) Live wire current ( I f )、Hot wire voltage( U f ).

[0046] In this embodiment 1, considering the amount of data, Δt Generally, 0.25h is taken, that is, record once every 15 minutes.

[0047] Based on the above premise, if Figure 1 As shown, the online monitoring method of electric energy meter series connection based on hash processing includes the following steps:

[0048] Step 1: Get M Electricity meter continuous N days of raw power data and construct N Original dataset D 1~ D N ; N ≥2.

[0049] It should be noted that, considering the amount of data, N The value should not be too large (the upper limit is usually set to 7).

[0050] Among them, n Original dataset D n Corresponding to m The energy meter is continuously N The first n Raw power data recorded for the day; n ∈[1, N ].

[0051] Specifically, D n It can be expressed as:

[0052] ;

[0053] In the formula, express D n The m A large set of raw power data, including m The energy meter is continuously N The first n 24h / day record Δt Group raw power data.

[0054] It can be expressed as:

[0055] ;

[0056] In the formula, express No. y Group raw power data, y∈[1,24h / Δt ].

[0057] It can be expressed as:

[0058] ;

[0059] In the formula, express The neutral current data in; express The neutral voltage data in; express The live wire current data in; express The live wire voltage data in.

[0060] Step 2: Traverse D 1~ D N Processing, corresponding to N String result set Q 1~ Q N .

[0061] because D 1~ D N The processing is the same as that of Figure 2 ,by D n To illustrate with an example - D n Methods for processing include:

[0062] Step 21, D n Normalize it and get n Normalized Dataset E n .

[0063] Specifically, step 21 includes:

[0064] S211, according to D n The category of the data in , and get the corresponding maximum and minimum values.

[0065] See step 1 D n The expression is:

[0066] for , whose maximum value is , the minimum value is ;

[0067] for , whose maximum value is , the minimum value is ;

[0068] for , whose maximum value is , the minimum value is ;

[0069] for , whose maximum value is , the minimum value is .

[0070] S212, based on the maximum and minimum values ​​obtained in S211, corresponds to , , , Perform normalization calculation to obtain normalized zero line current data , Normalized zero line voltage data , Normalized live wire current data , Normalized live wire voltage data .

[0071] in:

[0072] based on , ,right Perform normalization calculation to obtain normalized zero line current data ; That is to say, .

[0073] based on , ,right Perform normalization calculation to obtain the normalized zero-line voltage data ; That is to say, .

[0074] based on , ,right Perform normalization calculation to obtain normalized live wire current data ; That is to say, .

[0075] based on , ,right Perform normalization calculation to obtain the normalized live wire voltage data ; That is to say, .

[0076] S213, traverse D nAll the data of n Normalized Dataset E n .

[0077] E n It can be expressed as:

[0078] ;

[0079] In the formula, express E n The z Large group of normalized power data.

[0080] It can be expressed as:

[0081] ;

[0082] In the formula, express No. y Panel normalized power data.

[0083] It can be expressed as:

[0084] .

[0085] Step 22, E n Add new power data and get n A dataset for analysis F n .

[0086] The categories of new electric energy data include: the ratio of the normalized neutral current to the normalized live current of the electric energy meter in a single cycle, the average and variance of the normalized neutral current of the electric energy meter in a single day, the average and variance of the normalized neutral voltage of the electric energy meter in a single day, the average and variance of the normalized live current of the electric energy meter in a single day, and the average and variance of the normalized live voltage of the electric energy meter in a single day;

[0087] Specifically, step 22 includes:

[0088] S221, to Increase To form an intermediate data set , and based on Constructing an intermediate dataset .

[0089] in, express and The ratio of .

[0090] for Increase The intermediate data set formed later can be written as: .

[0091] for The constructed intermediate data set can be written as: .

[0092] S222, to Increase To form an intermediate data set .

[0093] in, represents the mean value set of electric energy data, that is, .

[0094] express The average value of the neutral current is ;

[0095] express The average value of the neutral voltage is ;

[0096] express The average value of the live wire current is ;

[0097] express The average live wire voltage data, that is .

[0098] for Increase The intermediate data set formed later can be written as: .

[0099] S223, to Increase To form an intermediate data set .

[0100] in, Represents the variance set of electric energy data, that is, .

[0101] express The variance data of the neutral current is ;

[0102] express The zero line voltage variance data is ;

[0103] express The live wire current variance data is ;

[0104] express The live line voltage variance data, that is .

[0105] for Increase The intermediate data set formed later can be written as: .

[0106] S224, traverse E n ,based on Build F n .

[0107] F n It can be expressed as: .

[0108] Step 23: F n The zero line and live line data in the above table are swapped to obtain n Swap Datasets G n .

[0109] Specifically, step 23 includes:

[0110] S231, will Swap the positions of the neutral and live wire data in .

[0111] in, express The intermediate data set obtained by swapping the positions can be expressed as:

[0112] ;

[0113] In the formula, express The intermediate data set obtained by swapping the positions can be expressed as:

[0114] ;

[0115] express The intermediate data set obtained by swapping the positions can be expressed as:

[0116] ;

[0117] express The intermediate data set obtained by swapping the positions can be expressed as:

[0118] ;

[0119] express Increase The intermediate data set formed after that can be expressed as:

[0120] ;

[0121] express The intermediate data set obtained by swapping the positions can be expressed as:

[0122] ;

[0123] express The intermediate data set obtained by swapping the positions can be expressed as:

[0124] .

[0125] Based on the above position swap, Adjust to , so that the live wire and the neutral wire can be compared in the subsequent analysis.

[0126] S232, traverse E n ,based on Build G n .

[0127] G n It can be expressed as: .

[0128] Step 24, based on the hash processing F n , G n Conduct research and obtain the n String result set Q n .

[0129] Among them, see Figure 3, step 24 comprises:

[0130] S241, using hash algorithm F n Map to X n In the hash bucket, G n Map to Y n hash buckets, and filter out the ones that are mapped at the same time F n , G n of Z n Hash buckets.

[0131] S241 is the specific application process of the hash function, which specifically includes:

[0132] S2411, Definition K Hash functions H 1~ H K .

[0133] in, ; k ∈[1, K ];

[0134] In the formula, H k Indicates k A hash function; a k Represents a hash vector, whose specifications are the same as or The transpose of is the same; b k Indicates the hash coefficient; w Indicates the width of the hash bucket; X express H k The independent variable;

[0135] It should be noted that H k Can X Converted into one dimension; due to K Hash functions H 1~ H K ,So H 1~ H K right X After processing, it can be formed K Dimensional data - that is, K Dimensional hash table.

[0136] S2412, will As X Substitute H 1~ H K , get the m indivual K Dimensional Hash Table 1 ;

[0137] in, ;

[0138] Traversal , corresponding to M indivual K Dimensional Hash Table 1 ;

[0139] right Divide and divide the hash buckets belonging to the same specification K The dimension hash table is placed in the corresponding hash bucket, and a total of X n Hash buckets.

[0140] S2412 is designed to be based on The data sequence is constructed X n Hash buckets.

[0141] S2413, will As X Substitute H 1~ H K , get the m indivual K Dimensional Hash Table 2 ;

[0142] in, ;

[0143] Traversal , a total of M indivual K Dimensional Hash Table 2 ;

[0144] right Divide and divide the hash buckets belonging to the same specification K The two-dimensional hash table is placed in the corresponding hash bucket, and a total of Y n Hash buckets.

[0145] S2413 is designed to be based on The data sequence is constructed Y n Hash buckets.

[0146] S2414, based on S2412 X n hash buckets, S2413 Y n hash buckets, filter out the K Dimensional hash table 1, K Dimensional hash table Z n hash buckets - that is, they are mapped simultaneously F n , G n of Z n Hash buckets .

[0147] This allows the electricity meters to be preliminarily classified to reduce the number of subsequent comparisons.

[0148] S242, for the F n , G n Process and filter out the Euclidean distance that is less than the distance threshold Several candidate pairs of electric energy meters.

[0149] S242 is the process of conducting preliminary research and judgment, which specifically includes:

[0150] S2421, based on the kd algorithm U f All K The first dimension hash table is node-processed to obtain f kd trees;

[0151] in, U f Indicates that S2414 obtains Z n The first in the hash bucket f hash buckets; f ∈[1, Z n ].

[0152] No. f A kd tree has several tree nodes, which are related to U f of K The dimensional hash table is one-to-one.

[0153] This step will be completed by K The one-dimensional hash table is converted into a kd tree, which facilitates the subsequent use of the nearest neighbor search algorithm for node screening and reduces the amount of data calculation.

[0154] S2422, will U f one of K Dimensional hash table 2 as the target node a , and use the kd tree nearest neighbor search algorithm to f A kd tree is used for node screening;

[0155] Traversal U f All K Dimensional hash table 2, obtaining several pairs of candidate electric energy meter pairs;

[0156] Among them, if a With f A tree node on a kd tree b The Euclidean distance between them is less than the distance threshold ( It can be adjusted according to the actual situation. It is recommended to take 1), then a , b The corresponding two electric energy meters are regarded as a pair of candidate electric energy meters.

[0157] because K The first dimension hash table is based on Obtained, K The second dimension hash table is based on So one is more biased towards the live data and the other is more biased towards the neutral data. a , b Performing Euclidean distance is equivalent to comparing the data of one table (mainly live wire data) with the data of another table (mainly neutral wire data). In this way, if there is a series of households, the two or more electricity meters involved in the series of households may have certain similarities in the neutral wire and live wire data. Therefore, this phenomenon is converted into a threshold judgment problem based on Euclidean distance.

[0158] Specifically, a , b The calculation formula of the Euclidean distance between is:

[0159] ;

[0160] In the formula, Δ d ab express a , b The Euclidean distance of X a express a Corresponding K Dimensional hash table two; Xb express b Corresponding K Dimensional hash table one.

[0161] It should be noted that the nearest neighbor search algorithm can effectively reduce the amount of data calculation by selecting tree nodes on the kd tree for judgment and avoiding calculations on all tree nodes.

[0162] S243, firstly, deduplicate several candidate electric energy meter pairs according to the meter identification ID; then, screen the deduplicated candidate electric energy meter pairs according to the box identification ID, and remove the electric energy meters that are not in the same meter box to obtain Q n .

[0163] Since S242 is judged based on the Euclidean distance, it is easy for the candidate electric energy meter pair to be the same electric energy meter, or the candidate electric energy meter pair to appear in different meter boxes, so these situations need to be eliminated.

[0164] Step 3: Take Q 1~ Q N The intersection of R 终 ;

[0165] Right now .

[0166] Through the continuous N The above processing is performed on the original power data of the day, and a result set of household connections is obtained every day. N Some connections had already appeared days ago, and N If it is not processed in the day, then Q 1~ Q N There will be these cross-account information, so Q 1~ Q N By taking the intersection, you can filter out these linked account information.

[0167] Example 2

[0168] This embodiment 2 simultaneously provides an electric energy meter cross-household online monitoring device based on hash processing, which uses the electric energy meter cross-household online monitoring method based on hash processing disclosed in embodiment 1.

[0169] See also Figure 4 The online monitoring device for the electric energy meter series connection based on hash processing includes: a data acquisition module, a series connection judgment module, and a result output module.

[0170] The data acquisition module is used to obtain M Electricity meter continuousN days of raw power data and construct N Original dataset D 1~ D N .

[0171] The user-to-user analysis module is used to traverse D 1~ D N Processing, corresponding to N String result set Q 1~ Q N .

[0172] Among them, the cross-user analysis module includes: normalization submodule, data addition submodule, data swap submodule, and data analysis submodule; the normalization submodule is used to D n Normalize it and get n Normalized Dataset E n The data addition submodule is used to E n Add new power data and get n A dataset for analysis F n The data skewing submodule is used to F n The zero line and live line data in the above table are swapped to obtain n Swap Datasets G n The data analysis submodule is used to analyze F n , G n Conduct research and obtain the n String result set Q n .

[0173] The result output module is used to obtain Q 1~ Q N The intersection of R 终 .

[0174] Since the hash processing-based online monitoring device for electric energy meters connected to households uses the hash processing-based online monitoring method for electric energy meters connected to households in Example 1, it has the same effect as Example 1 and will not be repeated here.

[0175] Example 3

[0176] This embodiment 3 discloses a computer device, including a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of the method for online monitoring of electric energy meters connected to households based on hash processing disclosed in embodiment 1 are implemented.

[0177] This embodiment 3 also discloses a readable storage medium, in which computer program instructions are stored. When the computer program instructions are read and executed by a processor, the steps of the online monitoring method for electric energy meter series connection based on hash processing disclosed in embodiment 1 are executed.

[0178] This embodiment 3 also discloses a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the method for online monitoring of electric energy meter series connection based on hash processing disclosed in embodiment 1 are implemented.

[0179] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for online monitoring of electric energy meter series connection based on hash processing, which is used to monitor the M Each electric energy meter is used for household monitoring; The electric energy meter is arranged in the electric energy meter box; each electric energy meter box has a unique box identification ID, and each electric energy meter has a unique meter identification ID; the method is characterized in that it comprises the following steps: Step 1: Get M Electricity meter continuous N days of raw power data and construct N Original dataset D 1~ D N ; N ≥2; among them, n Original dataset D n Corresponding to m The energy meter is continuously N The first n Raw power data recorded for the day; m ∈[1, M ], n ∈[1, N ]; Step 2: Traverse D 1~ D N Processing, corresponding to N String result set Q 1~ Q N ; Among them, D n Methods for processing include: Step 21, D n Normalize it and get n Normalized Dataset E n ; Step 22, E n Add new power data and get n A dataset for analysis F n ; Step 23: F n The zero line and live line data in the above table are swapped to obtain n Swap Datasets G n ; Step 24, based on the hash processing F n , G n Conduct research and obtain the n String result set Q n ; Step 3: Take Q 1~ Q N The intersection of R 终 ; Step 24 includes: S241, using hash algorithm F n Map to X n In the hash bucket, G n Map to Y n hash buckets, and filter out the ones that are mapped at the same time F n , G n of Z n hash buckets; S242, for the F n , G n Process and filter out the Euclidean distance that is less than the distance threshold Several candidate pairs of electric energy meters; S243, firstly, deduplicate several candidate electric energy meter pairs according to the meter identification ID; then, screen the deduplicated candidate electric energy meter pairs according to the box identification ID, and remove the electric energy meters that are not in the same meter box to obtain Q n ; S241 includes: S2411, Definition K Hash functions H 1~ H K ; in, ; k ∈[1, K ]; In the formula, H k Indicates k A hash function; a k Represents a hash vector, whose specifications are the same as or The transpose of is the same; b k Indicates the hash coefficient; w Indicates the width of the hash bucket; X express H k The independent variable; S2412, will As X Substitute H 1~ H K , get the m indivual K Dimensional Hash Table 1 ; in, ; Traversal , corresponding to M indivual K Dimensional Hash Table 1 ; right Divide and divide the hash buckets belonging to the same specification K The dimension hash table is placed in the corresponding hash bucket, and a total of X n hash buckets; S2413, will As X Substitute H 1~ H K , get the m indivual K Dimensional Hash Table 2 ; in, ; Traversal , a total of M indivual K Dimensional Hash Table 2 ; right Divide and divide the hash buckets belonging to the same specification K The two-dimensional hash table is placed in the corresponding hash bucket, and a total of Y n hash buckets; S2414, based on S2412 X n hash buckets, S2413 Y n hash buckets, filter out the K Dimensional hash table 1, K Dimensional hash table Z n Hash buckets ; S242 includes: S2421, based on the kd algorithm U f All K The first dimension hash table is node-processed to obtain f kd trees; in, U f Indicates that S2414 obtains Z n The first in the hash bucket f hash buckets; f ∈[1, Z n ]; No. f A kd tree has several tree nodes, which are related to U f of K The dimensional hash table is one-to-one; S2422, will U f one of K Dimensional hash table 2 as the target node a , and use the kd tree nearest neighbor search algorithm to f A kd tree is used for node screening; Traversal U f All K Dimensional hash table 2, obtaining several pairs of candidate electric energy meter pairs; Among them, if a With f A tree node on a kd tree b The Euclidean distance between them is less than the distance threshold ,but a , b The corresponding two electric energy meters are regarded as a pair of candidate electric energy meters.

2. The method for online monitoring of electric energy meter series connection based on hash processing according to claim 1 is characterized in that: The electric energy meter is a single-phase meter; m An electric energy meter Δt For one cycle, the original electric energy data is recorded periodically; The categories of raw power data include: neutral line current, neutral line voltage, live line current, live line voltage; The categories of new electric energy data include: the ratio of the normalized neutral line current to the normalized live line current of the electric energy meter in a single cycle, the mean and variance of the normalized neutral line current of the electric energy meter in a single day, the mean and variance of the normalized neutral line voltage of the electric energy meter in a single day, the mean and variance of the normalized live line current of the electric energy meter in a single day, and the mean and variance of the normalized live line voltage of the electric energy meter in a single day.

3. The method for online monitoring of electric energy meter series connection based on hash processing according to claim 2 is characterized in that: In step one, ； ； ； In the formula, express D n The m A large set of raw power data, including m The energy meter is continuously N The first n 24h / day record Δt Group raw power data; express No. y Group raw power data, y ∈[1,24h / Δt ]; express The neutral current data in; express The neutral voltage data in; express The live wire current data in; express The live wire voltage data in.

4. The method for online monitoring of electric energy meter series connection based on hash processing according to claim 3 is characterized in that: Step 21 includes: S211, according to D n The category of the data in the table, get the corresponding maximum and minimum values; S212, based on the maximum and minimum values ​​obtained in S211, corresponds to , , , Perform normalization calculation to obtain normalized zero line current data , Normalized zero line voltage data , Normalized live wire current data , Normalized live wire voltage data ; S213, traverse D n All the data of n Normalized Dataset E n ; ； ； ； In the formula, express E n The z Large group normalized power data; express No. y Panel normalized power data.

5. The method for online monitoring of electric energy meter series connection based on hash processing according to claim 4 is characterized in that: Step 22 includes: S221, to Increase To form an intermediate data set , and based on Constructing an intermediate dataset ; in, express and The ratio of ; ； ； S222, to Increase To form an intermediate data set ; ; in, represents the mean value set of electric energy data; ; express The average value data of the neutral line current; express The average value data of the neutral voltage; express The average value data of live wire current; express The average live wire voltage data; S223, to Increase To form an intermediate data set ; ; in, represents the variance set of electric energy data; ; express The variance data of the neutral current; express The zero line voltage variance data; express The live wire current variance data; express Live line voltage variance data; S224, traverse E n ,based on Build F n ; .

6. The method for online monitoring of electric energy meter series connection based on hash processing according to claim 5 is characterized in that: Step 23 includes: S231, will Swap the positions of the neutral and live wire data in ; in, express Intermediate data set obtained by swapping positions; ; express Intermediate data set obtained by swapping positions; ; express Intermediate data set obtained by swapping positions; ; express Intermediate data set obtained by swapping positions; ; express Increase The intermediate data set formed later; ; express Intermediate data set obtained by swapping positions; ; express Intermediate data set obtained by swapping positions; ; S232, traverse E n ,based on Build G n ; .

7. An online monitoring device for electric energy meter series connection based on hash processing, characterized in that: It uses the online monitoring method for electric energy meter series connection based on hash processing as described in any one of claims 1 to 6; The electric energy meter series online monitoring device based on hash processing comprises: The data acquisition module is used to obtain M Electricity meter continuous N days of raw power data and construct N Original dataset D 1~ D N ; The user-to-user analysis module is used to traverse D 1~ D N Processing, corresponding to N String result set Q 1~ Q N ; as well as The result output module is used to obtain Q 1~ Q N The intersection of R 终 .

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