Power conservation monitoring method and system for meter box based on quantum phase estimation

Abstract

The application discloses a power conservation monitoring method and system for a meter box based on quantum phase estimation, and relates to the technical field of power grid operation and maintenance.The application provides a power conservation monitoring method for a meter box containing a single main meter and multiple branch meters, calculates a power conservation error through virtual grouping, and further obtains a phase angle and an amplitude vector through processing, so that phase-domain amplification detection of a power deviation is realized through a quantum phase estimation algorithm, the detection capability for a tiny power deviation is improved, and detection of whether a power abnormality occurs in the meter box is effectively realized.The application solves the problems of easy missed detection and dependence on sample training of the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid operation and maintenance, more particularly, to: 1. A power conservation monitoring method for a meter box based on quantum phase estimation; 2. A power conservation monitoring system for a meter box based on quantum phase estimation. The present application supports application in power user terminals, distribution transformer areas and low-voltage side electric energy metering systems, and can realize high-sensitivity detection of small power deviations and operation and maintenance recommendations. BACKGROUND

[0002] With the widespread application of distributed energy, smart meters and Internet of Things technology, a meter box is often composed of a main meter and multiple sub-meters for measuring the power consumption of different branches or users. In theory, the power of the main meter should be equal to the sum of the powers of the sub-meters, but in actual operation, due to factors such as line aging, insulation loss, wiring errors, electricity stealing behavior and measurement accuracy limitations, power conservation will occur between the main meter and the sub-meters.

[0003] Existing power conservation detection methods for meter boxes mainly include the following categories:

[0004] 1. Threshold determination method: by calculating the power difference between the main meter and the sub-meters, when the difference exceeds a fixed threshold (usually 5% of the total power), it is judged as abnormal. This method is simple to implement, but it is extremely insensitive to small deviations (such as 0.1%~0.3%), and is prone to missed detection.

[0005] 2. Machine learning method: use historical samples to train a classifier to identify abnormal patterns, which can improve accuracy to some extent. However, in the meter box scenario, fault samples are scarce and unevenly distributed, making it difficult for supervised learning models to generalize.

[0006] In addition, traditional methods are based on amplitude domain signal analysis, ignoring the internal correlation between time series data. With the increase in sampling frequency of the meter, the power sequence presents strong nonlinearity and multiscale characteristics, and classical algorithms face bottlenecks in sensitivity, robustness and real-time performance.

[0007] Therefore, there is an urgent need for a new power conservation monitoring method that can still have high detection sensitivity under small power deviations and does not require fault sample training. SUMMARY

[0008] In view of the problems of existing methods that are prone to missed detection and rely on sample training, a power conservation monitoring method and system for a meter box based on quantum phase estimation are provided.

[0009] The present application adopts the following technical solutions:

[0010] In a first aspect, the present application discloses a power conservation monitoring method for a meter box based on quantum phase estimation, comprising:

[0011] Step one, collecting the instantaneous power P(t) of the electric meter in the electric meter box in the current monitoring period T with time interval Δt; t represents the tth collection time in T, t∈[1, T / Δt];

[0012] Wherein, P(t) includes: the instantaneous power P total (t) of the main meter and the instantaneous power P N 1(t)~P N(t) of N sub-meters;

[0013] Step two, dividing the N sub-meters into two virtual groups A~B, and calculating the power conservation error ΔP(t);

[0014] Wherein, ΔP(t)= P total (t)-P A (t)-P B N(t); P A (t), P B N(t) represent the total power of the sub-meters in A and B respectively;

[0015] Step three, normalizing ΔP(t) to construct the normalized power error f norm (t);

[0016] Based on f norm (t), quantum state encoding is performed to obtain the amplitude vector a(t);

[0017] f norm (t) is converted into the phase angle g(t) through phase mapping; g(t)∈(-π, π];

[0018] Step four, using quantum phase estimation method to calculate the phase concentration R(T) and the abnormal point proportion η(T) based on g(t), a(t);

[0019] Step five, judging whether the power of the electric meter box is abnormal in T according to R(T), η(T);

[0020] If R(T)≥R th and η(T)≥η th , it indicates that the power of the electric meter box is abnormal in T; otherwise, it indicates that the power of the electric meter box is not abnormal in T; wherein, R th , η th represent the concentration threshold and the proportion threshold respectively.

[0021] The electric meter box power conservation monitoring method based on quantum phase estimation realizes the method or process according to the embodiments of the present disclosure.

[0022] In a second aspect, the present application discloses a power conservation monitoring system for an electric meter box based on quantum phase estimation, which uses the power conservation monitoring method for an electric meter box based on quantum phase estimation disclosed in the first aspect.

[0023] The power conservation monitoring system for an electric meter box based on quantum phase estimation comprises a data acquisition module, a virtual grouping module, a quantization module, a quantum phase estimation module, and an anomaly determination module.

[0024] The data acquisition module is configured to acquire the instantaneous power P(t) of the electric meters in the electric meter box at a time interval Δt within a current monitoring period T. norm The quantization module is configured to normalize ΔP(t) to construct a normalized power error f norm (t); encode f norm (t) into a phase angle g(t) through phase mapping. The quantum phase estimation module is configured to calculate a phase concentration R(T) and an anomaly point proportion η(T) based on g(t) and a(t) using the quantum phase estimation method.

[0025] The power conservation monitoring system for an electric meter box based on quantum phase estimation implements the method or process according to the embodiments of the present application.

[0026] In a third aspect, the present application discloses a computer program product comprising a computer program. The computer program, when executed by a processor, implements the steps of the power conservation monitoring method for an electric meter box based on quantum phase estimation disclosed in the second aspect.

[0027] Compared with the prior art, the present application has the following beneficial effects:

[0028] 1. The present application provides a power conservation monitoring method for an electric meter box comprising a single main meter and multiple branch meters. The power conservation error is calculated through virtual grouping, and the phase angle and amplitude vector are further processed, so that the phase domain amplification detection of power deviation is realized through the quantum phase estimation method, the detection capability for slight power deviation is improved, and the detection of whether the power of the electric meter box is abnormal is effectively realized.

[0029] 2. After the power anomaly of the electric meter box is monitored, the related contribution deviation is introduced to reflect the power fluctuation of the branch meters, and the phase domain amplification detection of power fluctuation is realized through the quantum phase estimation method, the detection capability for slight power fluctuation is improved, which is helpful for locating the abnormal electric meter and determining the maintenance priority.

[0030] 3、The quantum phase estimation method adopted by the present application has core logic that is compatible with quantum computing architecture at the mathematical level, supports migration to quantum platforms for operation, and does not need to reconfigure the core mathematical framework, and is expected to provide a potential high-performance quantum-enhanced solution for power monitoring of meter boxes. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0032] Figure 1 The flowchart of the meter box power conservation monitoring method based on quantum phase estimation provided for Embodiment 1 of the present application;

[0033] Figure 2 The method flowchart for determining the maintenance priority by combining the relative contribution bias and the quantum phase estimation method provided for Embodiment 1 of the present application. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0035] It should be noted that when a component is referred to as "mounted on" another component, it can be directly on the other component or there can be a middle component. When a component is referred to as "disposed on" another component, it can be directly disposed on the other component or there can be a middle component. When a component is referred to as "fixed on" another component, it can be directly fixed on the other component or there can be a middle component.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0037] Embodiment 1

[0038] Referring to Figure 1The flow chart of the quantum phase estimation based power conservation monitoring method of the meter box is shown in FIG. 1.

[0039] As mentioned in the background, the present application is directed to a meter box containing 1 master meter and N sub-meters. In order to facilitate the understanding of the method, a specific example is provided in Embodiment 1, i.e. a meter box containing 1 master meter and 4 sub-meters (including: meter1~meter4).

[0040] The quantum phase estimation based power conservation monitoring method of the meter box includes the following steps:

[0041] Step 1: Collect the instantaneous power P(t) of the meter box at time interval Δt in the current monitoring period T; t represents the tth collection time in T, t∈[1, T / Δt].

[0042] Wherein, P(t) includes: the instantaneous power P total (t) of the master meter, and the instantaneous power P1(t)~P N (t) of the N sub-meters. These are the original data for subsequent monitoring.

[0043] Generally, Δt is recorded in minutes (recommended to take 5min~15min), and T is recorded in hours (recommended to take 1h~2h). In Embodiment 1, Δt takes 15min, T takes 2h, and then t∈[1,8].

[0044] Referring to Table 1, the results of sampling the meter box in the specific example 8 times in T=1h are shown.

[0045] Table 1 Instantaneous power of the meter box

[0046]

[0047] Step 2: Divide the N sub-meters into 2 virtual groups A~B, and calculate the power conservation error ΔP(t).

[0048] Wherein, ΔP(t)= P total (t)-P A (t)-P B (t); P A (t) and P B (t) represent the total power of A and B respectively.

[0049] Specifically, the total number of sub-meters in A is Num A , and the total number of sub-meters in B is Num B . Num A , Num B satisfy the following requirements: Num A +Num B=N. Then there are:

[0050] If N is even, Num A = Num B =N / 2.

[0051] If N is odd, Num A = 1+N / 2, Num B =(N / 2)-1, or Num A =(N / 2)-1, Num B =1+N / 2.

[0052] It should be noted that A and B contain the meter table which is theoretically random. However, since the meter table in the meter box is actually numbered, it is recommended to divide it in order of the serial number for the convenience of subsequent maintenance: if N is even, A contains the first N / 2 meter table, and B contains the first N / 2 meter table. If N is odd, A contains the first (N+1) / 2 meter table, and B contains the first (N+1) / 2 meter table, or A contains the first (N-1) / 2 meter table, and B contains the first (N-1) / 2 meter table.

[0053] Then, in a specific example: meter1 and meter2 form A, and meter3 and meter4 form B.

[0054] Therefore, ΔP(t) = [3.0, 0.5, 3.0, 2.0, 2.0, 2.5, 2.0, 0.5].

[0055] Step three, normalize ΔP(t) to construct normalized power error f norm (t).

[0056] Based on f norm (t), quantum state encoding is performed to obtain amplitude vector a(t).

[0057] f norm (t) is converted into phase angle g(t) through phase mapping; g(t) ∈ (-π, π].

[0058] Step three is actually a pre-operation of quantum phase estimation method - first normalize to unify the quantization interval, and then realize the standardized quantum state through phase mapping and quantum state encoding.

[0059] Specifically,

[0060] ①, normalize ΔP(t) to construct normalized power error f norm (t) includes:

[0061] S301, taking the maximum value in ΔP(t) as a normalization coefficient α(T);

[0062] S302, dividing ΔP(t) by α(T) to obtain f norm (t).

[0063] Therefore, the expression of f norm (t) is:

[0064] ;

[0065] Wherein, max(.) represents the maximum value function.

[0066] Then, in a specific example: α(T)=3.0; f norm (t)=[1.0,1 / 6,1.0,2 / 3,2 / 3,5 / 6,2 / 3,1 / 6].

[0067] For convenience of representation, here taking 4 digits after the decimal point for representation:

[0068] f norm (t)=[1.0,0.1667,1.0,0.6667,0.6667,0.8333,0.6667,0.1667].

[0069] 2、Based on f norm (t) to encode quantum state to obtain amplitude vector a(t) includes:

[0070] S303, calculating the sum of squares of f norm (1)~ f norm (T / Δt), and taking square root to obtain L2 norm L2(T);

[0071] S304, dividing f norm (t) by L2(T) to obtain a(t).

[0072] Therefore, the expression of a(t) is:

[0073] .

[0074] It should be noted that, This is to meet the basic requirements of quantum state.

[0075] Then, in a specific example:

[0076] L2(T)=2.0207;

[0077] a(t) = [0.4949, 0.0825, 0.4949, 0.3299, 0.3299, 0.4124, 0.3299, 0.0825].

[0078] iii. f norm (t) is converted to phase angle g(t) by phase mapping including:

[0079] S305, f norm (t) is multiplied by 2π to convert to original angle O norm (t); O norm (t) ∈ [-2π, π].

[0080] S306, O norm (t) is corrected to (-π, π] to get g(t);

[0081] wherein, if -π < O norm (t) ≤ π, then g(t) = O norm (t).

[0082] if -2π ≤ O norm (t) ≤ -π, then g(t) = O norm (t) + 2π.

[0083] if π < O norm (t) ≤ 2π, then g(t) = O norm (t) - 2π.

[0084] By the above operation, f norm (t) is converted to phase domain, so that abnormal deviation corresponds to significant phase shift.

[0085] Then, in a specific example:

[0086] O norm (t) = [2π, π / 3, 2π, 4π / 3, 4π / 3, 5π / 3, 4π / 3, π / 3]

[0087] g(t) = [0.0, π / 3, 0.0, -2π / 3, -2π / 3, -π / 3, -2π / 3, π / 3].

[0088] For convenience of representation, here 4 digits after the decimal point are represented:

[0089] g(t) = [0.0, 1.0472, 0.0, -2.0944, -2.0944, -1.0472, -2.0944, 1.0472].

[0090] Step four, calculate the phase concentration R(T) and the abnormal point proportion η(T) based on g(t) and a(t) by quantum phase estimation method.

[0091] Step four is to realize the micro deviation amplification detection by quantum phase estimation (QPE) method.

[0092] Specifically,

[0093] ①, the calculation process of the phase concentration R(T) is as follows:

[0094] S401, calculate the phase factor phase_factor(t) based on g(t);

[0095] Wherein, phase_factor(t) = e ig(t) =cosg(t)+ising(t), which is a complex number.

[0096] S402, calculate the product of a(t) and phase_factor(t), and take the average as R(T).

[0097] Therefore, the expression of R(T) is:

[0098] .

[0099] It should be noted that the closer R(T) is to 1, the more concentrated the phase distribution is, and the more significant the abnormal situation is.

[0100] Then, in specific examples:

[0101] phase_factor(t) = [1.0+0.0i, 0.5+0.866i, 1.0+0.0i, -0.5-0.866i, -0.5-0.866i, 0.5-0.866i, -0.5-0.866i, 0.5+0.866i];

[0102] R(T) = 0.4693.

[0103] ②, the calculation process of the abnormal point proportion η(T) is as follows:

[0104] S403, count the total number Number(T) of collection time points T that satisfy ; wherein, g th represents the phase threshold, generally 0.2 radian.

[0105] S404, divide Number(T) by T / Δt to get η(T).

[0106] Therefore, the expression of η(T) is:

[0107] .

[0108] Then, in a specific example:

[0109] Number(T)=6; η(T)=0.75.

[0110] Step five, judging whether the power of the electric meter box is abnormal in T according to R(T) and η(T);

[0111] If R(T)≥R th and η(T)≥η th , it indicates that the power of the electric meter box is abnormal in T;

[0112] Otherwise, it indicates that the power of the electric meter box is not abnormal in T;

[0113] Wherein, R th , η th respectively represent the concentration threshold and the proportion threshold. Generally, R th takes 0.3, and η th takes 0.3.

[0114] Then, in a specific example:

[0115] R(T)=0.4693≥0.3 (satisfies), η(T)=0.75≥0.3 (satisfies), and it is determined that the power of the electric meter box is abnormal in T.

[0116] Of course, after judging that the power is abnormal, the maintenance personnel needs to carry out maintenance. The present application can also provide the maintenance personnel with a basis for maintenance - if the power of the electric meter box is abnormal in T, the priority of the meter maintenance is determined by combining the relative contribution deviation and the quantum phase estimation method.

[0117] Specifically, the priority of the meter maintenance is determined by combining the relative contribution deviation and the quantum phase estimation method, including:

[0118] I. Introducing the relative contribution deviation for processing:

[0119] S501, calculating the relative contribution deviation Dev n (t) of the nth meter.

[0120] Wherein, the calculation process of Dev n (t) is as follows:

[0121] S5011, dividing P n (t) by the total power P sum (t) of the N meters at t to obtain the instantaneous relative contribution proportion r n (t) of the nth meter.

[0122] wherein, r n (t) reflects the proportion of the nth sub-table in the total power at time t.

[0123] S5012, calculate the mean of r n (t) as the reference value Mean n (t).

[0124] wherein, Mean n (t) is used to measure the fluctuation deviation of r n (t).

[0125] S5013, subtract Mean n (t) from r n (t) to obtain Dev n (t).

[0126] Therefore, the expression of Dev n (t) is:

[0127] ;

[0128] Dev n (t) represents the degree of deviation from the reference value. It should be noted that the instantaneous relative contribution ratio of the normal sub-table should be small fluctuations around its mean value, and the deviation of the abnormal sub-table will present significant fluctuations.

[0129] Then, in specific examples:

[0130] P sum (t) = [18.0, 21.5, 17.5, 21.0, 19.5, 20.0, 21.5, 20.5];

[0131] Take meter1 as an example:

[0132] r1(t) = [0.2778, 0.3256, 0.3714, 0.3095, 0.3333, 0.3500, 0.3488, 0.3902];

[0133] Mean1(t) = 0.3383;

[0134] Dev1(t) = [-0.0606, -0.0128, 0.0331, -0.0288, -0.0050, 0.0117, 0.0105, 0.0519];

[0135] meter2~meter3 are not repeated here.

[0136] II, introduce quantum phase estimation method for subsequent processing:

[0137] S502, normalize κ n (t) to construct normalized contribution deviation κ n (t);

[0138] Based on κ n (t), encode quantum state to obtain amplitude vector a' n (t);

[0139] Convert κ n (t) into phase angle g' n (t) through phase mapping; g' n (t) ∈ (-π, π];

[0140] S503, calculate phase concentration R' n (T) and abnormal point proportion η' n (T) based on g' n (t) and a' n (t) by quantum phase estimation method.

[0141] The calculation principles of S501-S503 are the same as S401-S404 above, only the data is different, so here will not be repeated, only briefly described:

[0142] 1, the expression of κ n (t) is:

[0143] .

[0144] 2, the expression of a' n (t) is:

[0145] .

[0146] 3, convert κ n (t) into phase angle g' n (t) through phase mapping, including:

[0147] S5025, multiply κ n (t) by 2π to convert into original angle O n (t); O n (t) ∈ [-2π, π];

[0148] S5026, correct O n (t) to (-π, π] to obtain g' n (t);

[0149] Wherein, if -π < O n (t) ≤ π, g' n (t) = O n (t).

[0150] if -2π≤O n (t)≤-π, then g' n (t)= O n (t)+2π.

[0151] if π n (t)≤2π, then g' n (t)= O n (t)-2π.

[0152] 4, R' n (T) is expressed as:

[0153] .

[0154] 5, η' n (T) is expressed as:

[0155] ;

[0156] wherein, Number' n (T) represents the total number of collection time points that the nth sub-table satisfies .

[0157] In specific examples:

[0158] Take meter1 as an example:

[0159] K1(t)=[-1.0,-0.2107,0.5463,-0.4758,-0.0827,0.1925,0.1733,0.8570];

[0160] a'1(t)=[-0.6485,-0.1366,0.3543,-0.3086,-0.0536,0.1248,0.1124,0.5558];

[0161] g'1(t)=[0.0,-1.3237,-2.8504,-2.9897,-0.5195,1.2096,1.0890,-0.8984];

[0162] R'1(T)=0.1203; η'1(T)=0.875.

[0163] meter2~meter3 are not described again:

[0164] R'2(T)=0.1470; η'2(T)=0.75.

[0165] R'3(T) = 0.1155; η'3(T) = 0.875.

[0166] R'4(T) = 0.0271; η'4(T) = 0.675.

[0167] III, combined R' n (T), η' n (T) analyzes N sub-meters to determine the priority of sub-meter maintenance.

[0168] It should be noted that the higher η' n (T), the greater the probability of abnormality; the higher R' n (T), the more likely the abnormality is due to hardware failure. Therefore, R' n (T), η' n (T) are high.

[0169] In specific examples:

[0170] η'1(T) ~ η'4(T) is close to and high, indicating that meter1 ~ meter4 all have abnormalities;

[0171] R'1(T), R'2(T), R'3(T) are significantly higher than R'4(T), indicating that the relative contribution of meter1 ~ meter3 is more concentrated in the phase distribution, and there are fluctuation type and unstable abnormal characteristics, and it is speculated that the power transmission loss is unstable due to line aging, so that the instantaneous power ratio repeatedly fluctuates around the reference value; the relative contribution of meter4 is dispersed in the phase distribution, showing a stable offset characteristic, and it is more likely that there is a long-term fixed power metering deviation (such as slight loosening of the connection, metering accuracy drift), rather than a short-term fluctuation problem. In summary, the current abnormality is more inclined to line aging hazards of meter1 ~ meter3 and stable metering deviation of meter4, and line detection of meter1 ~ meter3 should be prioritized.

[0172] It should be noted that since the quantum phase estimation (QPE) core logic used in the present application is compatible with the quantum computing architecture at the mathematical level, all models (such as quantum state encoding, phase mapping, concentration calculation, etc.) follow the mathematical specifications of quantum state evolution. In the future, if quantum computers make breakthroughs in fault tolerance and scale, the core process of the present application can be migrated to a quantum platform for operation, without the need to reconfigure the core mathematical framework, and is expected to provide a potential high-performance quantum enhancement solution for power meter box power monitoring.

[0173] Example 2

[0174] The embodiment 2 provides a power conservation monitoring system for an electric meter box based on quantum phase estimation, which uses the power conservation monitoring method for the electric meter box based on quantum phase estimation provided in the embodiment 1.

[0175] Referring to Figure 2 , the power conservation monitoring system for the electric meter box based on quantum phase estimation comprises a data acquisition module, a virtual grouping module, a quantization module, a quantum phase estimation module and an abnormality judgment module.

[0176] The data acquisition module is configured to acquire the instantaneous power P(t) of the electric meter in the electric meter box at a time interval Δt in a current monitoring period T. The virtual grouping module is configured to divide N electric meters into two virtual groups A and B and calculate a power conservation error ΔP(t). The quantization module is configured to normalize ΔP(t) to construct a normalized power error f norm (t); encode a quantum state based on f norm (t) to obtain an amplitude vector a(t); and convert f norm (t) into a phase angle g(t) through phase mapping. The quantum phase estimation module is configured to calculate a phase concentration R(T) and an abnormal point proportion η(T) based on g(t) and a(t) by using the quantum phase estimation method. The abnormality judgment module is configured to judge whether a power abnormality occurs in the electric meter box in T according to R(T) and η(T).

[0177] Since the system uses the power conservation monitoring method for the electric meter box based on quantum phase estimation in the embodiment 1, the system also has the same effect, which is not repeated here.

[0178] Embodiment 3

[0179] The embodiment 3 discloses a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the power conservation monitoring method for the electric meter box based on quantum phase estimation disclosed in the embodiment 1 when executing the computer program.

[0180] The computer device can be a mobile terminal or a fixed terminal. The former is, for example, a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Portable Application Description), a PMP (Portable Media Player), a vehicle terminal (for example, a vehicle navigation terminal) and the like. The latter is, for example, a digital TV, a desktop computer and the like.

[0181] The embodiment 3 also discloses a readable storage medium, which stores computer program instructions, and the computer program instructions are read and run by a processor to execute the steps of the power conservation monitoring method for electric meter box based on quantum phase estimation disclosed in the embodiment 1.

[0182] The readable storage medium can include, but is not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0183] The embodiment 3 also discloses a computer program product, which includes a computer program. The computer program is executed by a processor to implement the steps of the power conservation monitoring method for electric meter box based on quantum phase estimation disclosed in the embodiment 1.

[0184] It should be noted that the computer program for executing the above-mentioned computer program can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, python, and conventional procedural programming languages such as "C" language or similar programming languages. The computer program can be completely executed on a user computer, partially executed on a user computer, partially executed on a user computer and partially executed on a remote computer, or completely executed on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN).

[0185] The above-mentioned embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method for power conservation monitoring of an electrical metering cabinet based on quantum phase estimation, characterized by, Comprising: Step one, collecting the instantaneous power P(t) of the electric meter in the electric meter box in the current monitoring period T with time interval Δt; t represents the tth collection time in T, t∈[1, T / Δt]; Wherein, P(t) includes: main table instantaneous power P total (t), N sub-table instantaneous power P1(t)~P N (t); Step two, dividing the N electric meters into two virtual groups A~B and calculating the power conservation error ΔP(t); wherein ΔP(t) = P total (t) - P A (t) - P B (t); P A (t), P B (t) respectively represent the partial total power of A, B; Step three, ΔP(t) is normalized to construct a normalized power error f norm (t); Based on f norm (t) performing quantum state encoding to obtain an amplitude vector a(t); f norm (t) converting to a phase angle g(t) by phase mapping; g(t) e (-π, π]; Step four, calculating the phase concentration R(T) and the abnormal point proportion η(T) based on g(t), a(t) by quantum phase estimation method; Step five, judging whether the electric meter box has power abnormality in T according to R(T), η(T): If R(T) ≥ R th and η(T) ≥ η th , it indicates that power anomaly occurs in the electric meter box within T; otherwise, it indicates that power anomaly does not occur in the electric meter box within T; wherein, R th and η th respectively represent the concentration threshold and the proportion threshold.

2. The quantum phase estimation based power meter box power conservation monitoring method of claim 1, wherein, In step two, the total number of sub-tables in A is Num A , and the total number of sub-tables in B is Num B ; Wherein, if N is even, Num A = Num B =N / 2; If N is odd, Num A = 1+N / 2, Num B = (N / 2)-1, or Num A = (N / 2)-1, Num B = 1+N / 2.

3. The quantum phase estimation based power meter box power conservation monitoring method of claim 1, wherein, In step three, f norm The expression for (t) is: ； In the formula, max(.) represents the maximum value function; The expression of a(t) is: ； f norm (t) converting to a phase angle g(t) by phase mapping comprises: S305, f norm (t) multiplied by 2π to convert to original angle O norm (t); O norm (t) e [-2π, π] S306, O norm (t) modifying to (-π, π] to get g(t); wherein, if -π < O norm (t)≤π, then g(t) = O norm (t) if -2π < O < π, then g(t) = O norm if -π < t < π, then g(t) = t norm if t > π, then g(t) = t - 2π if n < 0 norm (t) < 2π, then g(t) = 0 norm (t) - 2π.

4. The quantum phase estimation based power meter box power conservation monitoring method of claim 1, wherein, In step four, the expression of R(T) is: ； The expression of η(T) is: ； In the formula, Number(T) represents the total number of collection time instants in T that satisfy g th represents a phase threshold value.

5. The quantum phase estimation based power meter box power conservation monitoring method of claim 2, wherein, Step five also includes: If the electric meter box has power abnormality in T, determine the electric meter maintenance priority by combining the relative contribution deviation and the quantum phase estimation method.

6. The quantum phase estimation based power meter box power conservation monitoring method of claim 5, wherein, Determine the electric meter maintenance priority by combining the relative contribution deviation and the quantum phase estimation method includes: S501, calculate the relative contribution deviation Dev of the nth partial table n (t); wherein Dev n The expression for (t) is: ； where P n (t) represents the instantaneous power of the nth partial table at t; P sum (t) represents the total power of the N partial tables at t; S502, to Dev n (t) is normalized to construct a normalized contribution bias K n (t); Based on K n (t) performing quantum state encoding to obtain an amplitude vector a' n (t); Kappa n (t) is converted into a phase angle g' n (t); g' n (t) ∈ (-π, π] S503, employing quantum phase estimation method based on g' n (t), a' n (t) calculating phase concentration R' n (T) and abnormal point proportion η' n (T); S504, combine R' n (T), η' n (T) analyzes the N sub-tables to determine sub-table repair priority.

7. The quantum phase estimation based power meter box power conservation monitoring method of claim 1, wherein, In S502, K n The expression of (t) is: ； In the formula, max(.) represents the maximum value function; a' n The expression of (t) is: ； The κ n (t) is converted into a phase angle g' by phase mapping n (t) comprises: S5025, κ n (t) multiplied by 2π to convert to original angle O n (t); O n (t) e [-2π, π] S5026, O n (t) modified to (-π, π] to get g' n (t); wherein, if -π < O n (t) ≤ π, then g' n (t) = 0 n (t). if -2π < O < π, then g'(t) = O n if -π < O < π, then g'(t) = O n if -2π < O < -π, then g'(t) = O n if -π < O < 0, then g'(t) if n < 0 n (t) = 2π - g n (t) = 0 n (t) = 2π - g 8. The quantum phase estimation based power meter box power conservation monitoring method of claim 1, wherein, In S503, R' n The expression of (T) is: ； η' n The expression for (T) is: ； In the formula, Number n (T) represents that the nth partial table meets the total number of collection time points; g th represents a phase threshold value.

9. A quantum phase estimation based power kiosk power conservation monitoring system, characterized by, It adopts the electric meter box power conservation monitoring method based on quantum phase estimation as claimed in any one of claims 1-8; It comprises: A data collection module for collecting the instantaneous power P(t) of the electric meter in the electric meter box in the current monitoring period T with time interval Δt; A virtual grouping module for dividing the N electric meters into two virtual groups A~B and calculating the power conservation error ΔP(t); quantization module configured to normalize ΔP(t) to construct a normalized power error f norm (t); perform quantum state encoding on f norm (t) to obtain an amplitude vector a(t); convert f norm (t) into a phase angle g(t) through phase mapping; A quantum phase estimation module for calculating the phase concentration R(T) and the abnormal point proportion η(T) based on g(t), a(t) by quantum phase estimation method; and An abnormality judgment module for judging whether the electric meter box has power abnormality in T according to R(T), η(T).

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the electric meter box power conservation monitoring method based on quantum phase estimation as claimed in any one of claims 1-8.

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