Power failure data acquisition method and device based on power consumption behavior analysis

Abstract

The invention discloses a power failure data acquisition method and device based on power consumption behavior analysis, and the method comprises the steps: obtaining original data collected by an intelligent metering terminal, carrying out the data preprocessing of the original data based on a cubic spline algorithm, and obtaining a preprocessing database; processing the preprocessing database based on a golden section algorithm to obtain power utilization behavior characteristics of the transformer area and the user; and taking the user satisfaction of each transformer area as a target function, processing the power utilization behavior characteristics of the transformer areas and the users based on a dwarf-cat-weasel algorithm, and determining a power failure data scheme. According to the analysis method, the influence of the acquisition process on the normal power consumption behavior of the user is reduced to the minimum, the influence of the user transformer relation data acquisition on the power consumption experience of the user is avoided, the data acquisition efficiency is improved, and the labor cost of user transformer verification is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power engineering, and in particular to a method and device for collecting power outage data based on power consumption behavior analysis. Background Art

[0002] The sorting out of household-transformer relationship has always been a major problem for power supply companies. The power outage method can solve the problem of household-transformer relationship troubleshooting. However, facing the current pressure of high-quality service and power supply reliability, the comprehensive power outage troubleshooting method is no longer applicable, and the data analysis method has high requirements for data collection. At present, the metering terminal equipment in the substation area is complicated, and some equipment cannot realize data collection or the data time is inconsistent with the actual time.

[0003] In addition, with the widespread popularization of intelligent metering terminal data collection equipment, the data collection work of power outage time in the substation and users has been further simplified, promoting the application of data analysis methods in the field of household-to-transformer relationship identification. However, due to uncertain factors such as communication failures and equipment failures, it is very easy to have bad data that is difficult to find during data collection. Summary of the invention

[0004] The purpose of the present invention is to provide a power outage data collection method and device based on power consumption behavior analysis, which can improve data collection efficiency and reduce the labor cost of household transformer verification, and solve the problems existing in the background technology.

[0005] To achieve the above objectives, the present invention provides, on one hand, a method for collecting power outage data based on power consumption behavior analysis, comprising:

[0006] Acquire raw data collected by the smart metering terminal, perform data preprocessing on the raw data based on a cubic spline algorithm, and obtain a preprocessing database;

[0007] The pre-processing database is processed based on the golden section algorithm to obtain the characteristics of power consumption behavior of the substation and users;

[0008] Taking the user satisfaction of each substation as the objective function, the power consumption behavior characteristics of the substation and users are processed based on the dwarf mongoose algorithm to determine the power outage data plan.

[0009] As a further solution of the present invention, the obtaining of raw data collected by the smart metering terminal and the preprocessing of the raw data based on a cubic spline algorithm include:

[0010] Obtain the time series data of each user's electricity consumption;

[0011] For missing data, two cubic spline interpolation functions S are obtained using adjacent data points in the time dimension and user dimension. j (x) and S i (x);

[0012] Take S j (x) and S i The mean value of (x) is used as interpolation supplementary data;

[0013] Based on the processed data, establish the mapping relationship F i k (t) between each electricity consumption and time.

[0014] As a further solution of the present invention, the method includes obtaining the original data collected by the intelligent metering terminal, and performing data preprocessing on the original data based on the cubic spline algorithm, including:

[0015] Input the electricity consumption data of k users in the transformer area with m×n dimensions

[0016] Screen out the missing value sequences, and respectively construct cubic polynomials S j , u ij ), (x j+1 , u ij+1 ), and (x i , u ij )(x i+1 , u i+1j ) for each missing value sequence adjacent data points (x j (x) and S i (x), such that S j (x j ) = u i,j , S j (x j+1 ) = u i,j+1 , S j '(x j ) = S j '(x j+1 ), S j ”(x j ) = S j ”(x j+1 ), S i (x i ) = u i,j , S i (x i+1 ) = u i+1,j , S i '(x i ) = S i '(x i+1 ), S i ”(x i ) = S i ”(x i+1 );

[0017] Using the interpolation conditions, a system of linear equations consisting of 8n - 8 equations is obtained, and the cubic polynomials S j (x) and S i (x) respectively corresponding to the unknown constants a, b, c, and d are obtained by solving the corresponding system of linear equations;

[0018] For any input value x, search to find the maximum value x i in the data points such that x i < x, and calculate the value of the interpolation function f(x) through S i (x);

[0019] Calculate the historical power change rate of the same season and the same time period for users of the same category using historical data of the same category of users, find the upper and lower limits of the change rate respectively, and mark and delete the data that exceeds the change rate as abnormal data.

[0020] As a further solution of the present invention, the preprocessed database is processed based on the golden section algorithm to obtain the power consumption behavior characteristics of the transformer area and users, including:

[0021] Input the accuracy tol of the golden section algorithm, including the time lower limit L, the time upper limit U, the golden section coefficient T, and the number of users U n ;

[0022] Use the golden section method to determine the values of t1 and t2;

[0023] Find the power consumption F i k (t1) and F i k (t2) corresponding to the user at t1 and t2 times respectively according to the input data;

[0024] Iteratively calculate until the difference between the time upper limit U and the time lower limit L is less than the accuracy tol of the golden section algorithm, and output the peak power consumption period and the low power consumption period of the current user;

[0025] Iteratively calculate until the operation of all the number of users U n is completed, and output the power consumption time range characteristics of all users.

[0026] As a further solution of the present invention, the preprocessed database is processed based on the golden section algorithm to obtain the power consumption behavior characteristics of the transformer area and users, including:

[0027] Step 1: Given the power consumption time ranges L = 0 and U = 24 for each user on weekdays, holidays, and rest days, determine the accuracy tol and the golden section coefficient T = 0.618, and the number of users U n ;

[0028] Step 2: Let k = 1, if k < U nThen execute step 3; otherwise, execute step 11.

[0029] Step 3: Based on the input data, find the electricity consumption of user i in the k-th substation area at times t1 and t2, denoted as F i k (t1) and F i k (t2), where t1 < t2.

[0030] Step 4: Determine whether U - L is greater than the accuracy tol. If so, proceed to step 5; otherwise, execute step 10.

[0031] Step 5: Determine whether to calculate the low - electricity - consumption period. If so, execute step 6; otherwise, execute step 8.

[0032] Step 6: If F i k (t1) < F i k (t + 1), then U = t2, t2 = t1, F i k (t2) = F i k (t1), t1 = L+(1 - T)(U - L), and recalculate F i k (t1); otherwise, execute step 7.

[0033] Step 7: L = t1, t1 = t2, F i k (t1) = F i k (t2), t2 = L+T(U - L), and recalculate F i k (t2).

[0034] Step 8: If F i k (t1) > F i k (t + 1), then U = t2, t2 = t1, F i k (t2) = F i k (t1), t1 = L+(1 - T)(U - L), and recalculate F i k (t1); otherwise, execute step 9.

[0035] Step 9: L = t1, t1 = t2, F i k (t1) = F i k(t2), where t2 = L + T(U - L), and recalculate F i k (t2);

[0036] Step 10: Output the low - electricity - consumption period t l or the peak - electricity - consumption period t u , k = k + 1;

[0037] Step 11: Output the peak - electricity - consumption periods and low - electricity - consumption periods of the users in multiple working days, holidays, and rest days.

[0038] As a further solution of the present invention, taking the user satisfaction of each sub - station area as the objective function, based on the dwarf mongoose algorithm to process the electricity - consumption behavior characteristics of the sub - station area and users, and determining the power - outage data plan, including:

[0039] Step 101: Taking the user satisfaction of each sub - station area as the objective function, establish the following model:

[0040]

[0041] Where, is a 0 - 1 variable. When is 1, it means that user i in the operation - distribution system belongs to sub - station area k, t k represents the data - collection time of the users corresponding to the kth sub - station area, represents the low - electricity - consumption period corresponding to the ith user in the kth sub - station area, represents the peak - electricity - consumption period corresponding to the ith user in the kth sub - station area, t k ∈[0, 24];

[0042] Step 102: Initialize the parameters and population of the dwarf mongoose optimization algorithm;

[0043] Step 103: Taking formula (1) as the objective function, calculate the fitness function value of the population;

[0044] Step 104: Calculate the probability that an individual in the alpha group becomes the leader according to formula (2). In formula (2), f i is the objective - function value of the ith individual, and N is the population size;

[0045]

[0046] Step 105: Determine the candidate positions of the food. The specific calculation formula is shown in formula (3), where X i+1 is the new position of the food source, X i represents the current position of the female leader, phi is a random number distributed in [-1, 1], peep takes the value of 2, X randRepresents a random individual in the alpha group;

[0047] X i+1 = X i + phi × peep × (X i - X rand ) (3),

[0048] Step 106: Update the foraging result of the alpha group according to formula (3);

[0049] Step 107: If the foraging result of the alpha group is inferior to the food candidate position, execute Step 108, otherwise execute Step 109;

[0050] Step 108: Update the position of the alpha group members using formula (4),

[0051] X new = Lb + rand × (Ub - Lb) (4),

[0052] where X new represents the updated position of the alpha group individuals, rand is a random number, Ub represents the upper limit of the individual position, which is taken as 24 in this problem, and Lb represents the lower limit of the individual position, which is taken as 0 in this problem;

[0053] Step 109: The new alpha group continues to forage according to formula (3) and searches for the sleep hill according to formula (5),

[0054]

[0055] where X sm is the position of the sleep hill, is the calculation formula of the direction vector for the meerkat to move to the sleep hill as shown in formula (6), represents the calculation formula of the average value of the sleep hill as shown in formula (7), CF represents the calculation formula of the population movement ability as shown in formula (8);

[0056]

[0057]

[0058]

[0059] Step 110: Determine whether the iteration condition is satisfied. If it is satisfied, execute Step 103, otherwise output the power outage time of users in each substation area.

[0060] On the other hand, the present invention provides a power outage data acquisition device based on power consumption behavior analysis, including:

[0061] A preprocessing module that obtains the original data collected by the intelligent metering terminal, performs data preprocessing on the original data based on the cubic spline algorithm, and obtains a preprocessing database;

[0062] A golden section algorithm module that processes the preprocessing database based on the golden section algorithm to obtain the power consumption behavior characteristics of the substation area and users;

[0063] A dwarf mongoose algorithm module that takes the user satisfaction of each substation area as the objective function, processes the power consumption behavior characteristics of the substation area and users based on the dwarf mongoose algorithm, and determines the power outage data plan.

[0064] Compared with the prior art, the beneficial effects of the present invention are:

[0065] The present invention provides a power outage data collection method and device based on power consumption behavior analysis. The method includes processing missing value data using the cubic spline algorithm, obtaining the peak and valley power consumption periods of each user in the substation area using the golden section algorithm, and planning the power outage data collection plan for each substation area with the user satisfaction as the goal using the dwarf mongoose algorithm. Through the above analysis method, the present application minimizes the impact of the collection process on the normal power consumption behavior of users, avoids the impact of the household-substation relationship data collection on the user power consumption experience, improves the data collection efficiency, and reduces the labor cost of household-substation verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a flowchart of a power outage data collection method based on power consumption behavior analysis in an embodiment;

[0067] Figure 2 is a flowchart of a power outage data collection method based on power consumption behavior analysis in another embodiment;

[0068] Figure 3 is a schematic diagram of a power outage data collection device based on power consumption behavior analysis. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0069] In order to enable those skilled in the art to better understand the technical solutions in one or more of this specification, the following will clearly and completely describe the technical solutions in one or more embodiments of this specification with reference to the accompanying drawings in one or more embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments in one or more of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this document.

[0070] It should be noted that, without conflict, one or more embodiments in this specification and the features in the embodiments may be combined with each other. The following will describe in detail one or more embodiments of this specification with reference to the accompanying drawings and in combination with the embodiments.

[0071] See Figure 1 、 2 , a power outage data collection method based on electricity consumption behavior analysis, including:

[0072] S102. Obtain the original data collected by the intelligent metering terminal, and perform data preprocessing on the original data based on the cubic spline algorithm to obtain a preprocessing database;

[0073] S104. Process the preprocessing database based on the golden section algorithm to obtain the electricity consumption behavior characteristics of the transformer substation area and users;

[0074] S106. Take the user satisfaction of each transformer substation area as the objective function, and process the electricity consumption behavior characteristics of the transformer substation area and users based on the dwarf mongoose algorithm to determine the power outage data scheme.

[0075] Through the above analysis method, this application minimizes the impact of the collection process on the normal electricity consumption behavior of users, avoids the impact of the collection of household-transformer relationship data on the electricity consumption experience of users, improves the data collection efficiency, and reduces the labor cost of household-transformer verification.

[0076] In one embodiment, obtaining the original data collected by the intelligent metering terminal and performing data preprocessing on the original data based on the cubic spline algorithm (i.e., S102) includes:

[0077] Obtain the time series data of the electricity consumption of each user;

[0078] For the missing data, use the adjacent data points in the time dimension and the user dimension to obtain two cubic spline interpolation functions S j (x) and S i (x);

[0079] Take the mean value of S j (x) and S i (x) as the interpolation supplementary data;

[0080] Based on the processed data, establish the mapping relationship F i k (t) between each electricity consumption and time.

[0081] In one embodiment, obtaining the original data collected by the intelligent metering terminal and performing data preprocessing on the original data based on the cubic spline algorithm (i.e., S102) includes:

[0082] Input the electricity consumption data of k users in the transformer substation area with dimensions of m×n

[0083] Filter out the missing value sequences, and for each adjacent data point (x j , u ij ), (x j+1 , u ij+1 ), and (x i , u ij ) (x i+1 , u i+1j ) of each missing value sequence, construct cubic polynomials S j (x) and S i (x) such that S j (x j ) = u i,j , S j (x j+1 ) = u i,j+1 , S j '(x j ) = S j '(x j+1 ), S j ”(x j ) = S j ”(x j+1 ), S i (x i ) = u i,j , S i (x i+1 ) = u i+1,j , S i '(x i ) = S i '(x i+1 ), S i ”(x i ) = S i ”(x i+1 );

[0084] Using the interpolation conditions, obtain a system of linear equations consisting of 8n - 8 equations, and obtain the unknown constants a, b, c, and d corresponding to the cubic polynomials S j (x) and S i (x) respectively by solving the corresponding system of linear equations;

[0085] For any input value x, search for the maximum value x i in the data points such that x i < x, and calculate the value of the interpolation function f(x) through S i (x);

[0086] Calculate the past electricity change rate in the same season and at the same time period using the historical data of users in the same category, find the upper and lower limits of the change rate respectively, and mark and delete the data that exceeds the change rate as abnormal data.

[0087] In one embodiment, the preprocessed database is processed based on the golden section algorithm to obtain the power consumption behavior characteristics of the substation area and users (i.e., S104), including:

[0088] Input the accuracy tol of the golden section algorithm, including the time lower limit L, the time upper limit U, the golden section coefficient T, and the number of users U n ;

[0089] Use the golden section method to determine the values of t1 and t2;

[0090] Find the corresponding power consumption F of the user at time t1 and time t2 respectively according to the input data i k (t1), F i k (t2);

[0091] Iteratively calculate until the difference between the time upper limit U and the time lower limit L is less than the accuracy tol of the golden section algorithm, and output the peak and valley periods of the current user's power consumption;

[0092] Iteratively calculate until all the number of users U n of operations is completed, and output the power consumption time range characteristics of all users.

[0093] In one embodiment, the preprocessed database is processed based on the golden section algorithm to obtain the power consumption behavior characteristics of the substation area and users (i.e., S104), including:

[0094] Step 1: Given the power consumption time ranges of each user on weekdays, holidays, and rest days, L = 0, U = 24, determine the accuracy tol and the golden section coefficient T = 0.618, and the number of users U n ;

[0095] Step 2: Let k = 1. If k < U n then execute Step 3, otherwise execute Step 11;

[0096] Step 3: Find the power consumption F of the k-th substation area user i at time t1 and time t2 according to the input data i k (t1), F i k (t2), and t1 < t2;

[0097] Step 4: Determine whether U - L is greater than the accuracy tol. If so, perform Step 5, otherwise execute Step 10;

[0098] Step 5: Determine whether to calculate the valley period of power consumption. If so, execute Step 6, otherwise execute Step 8;

[0099] Step 6: If Fi k (t1)<F i k (t + 1), then U = t2, t2 = t1, F i k (t2) = F i k (t1), t1 = L + (1 - T)(U - L), and recalculate F i k (t1), otherwise execute step 7;

[0100] Step 7: L = t1, t1 = t2, F i k (t1) = F i k (t2), t2 = L + T(U - L), and recalculate F i k (t2);

[0101] Step 8: If F i k (t1) > F i k (t + 1), then U = t2, t2 = t1, F i k (t2) = F i k (t1), t1 = L + (1 - T)(U - L), and recalculate F i k (t1), otherwise execute step 9;

[0102] Step 9: L = t1, t1 = t2, F i k (t1) = F i k (t2), t2 = L + T(U - L), and recalculate F i k (t2);

[0103] Step 10: Output the low - electricity - consumption period t l or the high - electricity - consumption period t u , k = k + 1;

[0104] Step 11: Output the high - electricity - consumption periods and low - electricity - consumption periods of the users on multiple working days, holidays and rest days.

[0105] In one embodiment, taking the user satisfaction of each transformer substation as the objective function, the power outage data scheme is determined by processing the power consumption behavior characteristics of the transformer substation and users based on the dwarf mongoose algorithm, including:

[0106] Step 101: Establish the following model with the user satisfaction of each substation area as the objective function:

[0107]

[0108] where, is a 0-1 variable, when is 1, it means that user i in the operation and distribution system belongs to substation area k, and t k represents the data acquisition time corresponding to the users in the k-th substation area, represents the low electricity consumption period corresponding to the i-th user in the k-th substation area, represents the high electricity consumption period corresponding to the i-th user in the k-th substation area, t k ∈[0,24];

[0109] Step 102: Initialize the parameters and population of the dwarf mongoose optimization algorithm;

[0110] Step 103: Calculate the fitness function value of the population with formula (1) as the objective function;

[0111] Step 104: Calculate the probability of an individual in the alpha group becoming the leader according to formula (2). In formula (2), f i is the objective function value of the i-th individual, and N is the population size;

[0112]

[0113] Step 105: Determine the candidate position of the food. The specific calculation formula is shown in formula (3), where X i+1 is the new position of the food source, X i represents the current position of the female leader, phi is a random number distributed in [-1,1], peep takes the value of 2, and X rand represents a random individual in the alpha group;

[0114] X i+ 1 =X i +phi×peep×(X i -X rand ) (3),

[0115] Step 106: Update the foraging result of the alpha group according to formula (3);

[0116] Step 107: If the foraging result of the alpha group is worse than the candidate position of the food, execute Step 108, otherwise execute Step 109;

[0117] Step 108: Update the position of the alpha group members using formula (4),

[0118] Xnew = Lb + rand × (Ub - Lb) (4),

[0119] where X new represents the position of an individual in the updated alpha group, rand is a random number, Ub represents the upper limit of the individual position, which is 24 in this problem, and Lb represents the lower limit of the individual position, which is 0 in this problem;

[0120] Step 109: The new alpha group continues to forage according to formula (3) and searches for the sleep hill according to formula (5),

[0121]

[0122] where X sm is the position of the sleep hill, is the calculation formula for the direction vector of the meerkat moving to the sleep hill, as shown in formula (6), represents the average value of the sleep hill, and the calculation formula is as shown in formula (7), CF represents the calculation formula for the population movement ability, as shown in formula (8);

[0123]

[0124]

[0125]

[0126] Step 110: Determine whether the iteration condition is satisfied. If it is satisfied, execute Step 103; otherwise, output the power outage time of each substation area user.

[0127] To further avoid the impact of customer - transformer relationship data collection on users' power consumption experience and improve the application of data analysis methods in customer - transformer relationships, the present invention combines the cubic spline algorithm and the golden section algorithm to obtain the peak and trough power consumption periods of each user in the substation area, and then uses the dwarf meerkat algorithm to plan the power outage data collection scheme for each substation area with the goal of user satisfaction. This minimizes the impact of the collection process on users' normal power consumption behavior, avoids the impact of customer - transformer relationship data collection on users' power consumption experience, improves the data collection efficiency, and reduces the labor cost of customer - transformer verification.

[0128] See Figure 3 , corresponding to the embodiment of the power outage data collection method based on power consumption behavior analysis described above, this application also provides an embodiment of a power outage data collection device based on power consumption behavior analysis. The device includes:

[0129] A pre - processing module 301, which acquires the original data collected by the intelligent metering terminal, performs data pre - processing on the original data based on the cubic spline algorithm, and obtains a pre - processed database;

[0130] The golden section algorithm module 302 processes the preprocessing database based on the golden section algorithm to obtain the power consumption behavior characteristics of the substation area and users;

[0131] The dwarf mongoose algorithm module 303 takes the user satisfaction of each substation area as the objective function, processes the power consumption behavior characteristics of the substation area and users based on the dwarf mongoose algorithm, and determines the power outage data plan.

[0132] It should be noted that the embodiments of the power outage data acquisition device based on power consumption behavior analysis in this specification and the embodiments of the power outage data acquisition method based on power consumption behavior analysis in this specification are based on the same inventive concept. Therefore, for the specific implementation of this embodiment, reference may be made to the corresponding implementation of the power outage data acquisition method based on power consumption behavior analysis described above, and repeated parts will not be elaborated.

[0133] The above are only one or more embodiments of this specification and are not used to limit one or more of this specification. For those skilled in the art, one or more of this specification can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of one or more of this specification shall be included within the scope of the claims of one or more of this specification.

Claims

1. A power outage data collection method based on electricity consumption behavior analysis, characterized in that Including: Obtain the original data collected by the intelligent metering terminal, and perform data preprocessing on the original data based on the cubic spline algorithm to obtain a preprocessing database; Process the preprocessing database based on the golden section algorithm to obtain the power consumption behavior characteristics of the substation area and users; Taking the user satisfaction of each substation area as the objective function, process the power consumption behavior characteristics of the substation area and users based on the dwarf meerkat algorithm to determine the power outage data plan.

2. The power outage data collection method based on electricity consumption behavior analysis according to claim 1, wherein The obtaining of the original data collected by the intelligent metering terminal and the data preprocessing of the original data based on the cubic spline algorithm include: Obtain the time series data of the power consumption of each user; For missing value data, two cubic spline interpolation functions Sj(x) and S i (x) are obtained using adjacent data points in the time dimension and user dimension; Take S j (x) and S i The mean value of (x) and S is used as the interpolation supplementary data; Based on the processed data, establish the mapping relationship F between each electricity consumption and time i k (t) 3. The power outage data collection method based on electricity consumption behavior analysis according to claim 1, wherein The obtaining of the original data collected by the intelligent metering terminal and the data preprocessing of the original data based on the cubic spline algorithm include: Input the power consumption data of k users in the m×n dimensional substation area Filter out the missing value sequences, and for each adjacent data point (x j , u ij ), (x j+1 , u ij+1 ), and (x i , u ij )(x i+1 , u i+1j ) of each missing value sequence, construct cubic polynomials S j (x) and S i (x) such that S j (x j ) = u i,j , S j (x j+1 ) = u i,j+1 , S j '(x j ) = S j '(x j+1 ), S j ”(x j ) = S j ”(x j+1 ), S i (x i ) = u i,j , S i (x i+1 ) = u i+1,j , S i '(x i ) = S i '(x i+1 ), S i ”(x i ) = S i ”(x i+1 ); Using the interpolation conditions, a system of linear equations consisting of 8n - 8 equations is obtained, and the cubic polynomials S j (x) and S i (x) respectively corresponding to the unknown constants a, b, c, d are obtained by solving the corresponding system of linear equations; For any input value x, search to find the maximum value x among the data points i , such that x i < x, and calculate the value of the interpolation function f(x) through S i (x); Use the historical data of users in the same category to calculate the past electricity change rate in the same season and at the same time period, find the upper and lower limits of the change rate respectively, and mark and delete the data that exceeds the change rate as abnormal data.

4. A power outage data collection method based on electricity consumption behavior analysis according to claim 1, characterized in that, The processing of the preprocessing database based on the golden section algorithm to obtain the power consumption behavior characteristics of the substation area and users includes: Input the accuracy tol of the golden section algorithm, including the time lower limit L, the time upper limit U, the golden section coefficient T, and the number of users U n ; Use the golden section method to determine the values of t1 and t2; Find the corresponding electricity consumption F of the user at time t1 and time t2 respectively according to the input data i k (t1), F i k (t2); Iteratively calculate until the difference between the time upper limit U and the time lower limit L is less than the golden section algorithm precision tol, and output the current user's peak power consumption period and valley period; Iteratively calculate until the operation for all user numbers U is completed, and output the power consumption time range characteristics of all users. n ​ 5. A power outage data collection method based on electricity consumption behavior analysis according to claim 1, characterized in that, The processing of the preprocessing database based on the golden section algorithm to obtain the power consumption behavior characteristics of the substation area and users includes: Step 1: Given the power consumption time range of each user on weekdays, holidays, and rest days as L = 0 and U = 24, determine the precision tol and the golden ratio coefficient T = 0.618, and the number of users U n ; Step 2: Let k = 1. If k < U n then execute Step 3; otherwise, execute Step 11. Step 3: Find the electricity consumption of user i in the k-th substation area at time t1 and t2 based on the input data, denoted as F i k (t1) and F i k (t2), where t1 < t2; Step 4: Judge whether U - L is greater than the precision tol. If so, go to step 5; otherwise, execute step 10; Step 5: Judge whether to calculate the valley power consumption period. If so, execute step 6; otherwise, execute step 8; Step 6: If F i k (t1) < F i k (t + 1), then U = t2, t2 = t1, F i k (t2) = F i k (t1), t1 = L + (1 - T)(U - L), and recalculate F i k (t1), otherwise execute Step 7; Step 7: L = t1, t1 = t2, F i k (t1) = F i k (t2), t2 = L + T(U - L), and recalculate F i k (t2); Step 8: If F i k (t1) > F i k (t + 1), then U = t2, t2 = t1, F i k (t2) = F i k (t1), t1 = L + (1 - T)(U - L), and recalculate F i k (t1), otherwise execute Step 9; Step 9: L = t1, t1 = t2, F i k (t1) = F i k (t2), t2 = L + T(U - L), and recalculate F i k (t2); Step 10: Output the off-peak electricity consumption period t l or the peak period t u , k = k + 1; Step 11: Output the peak power consumption period and valley power consumption period of all users on multiple working days, holidays and rest days.

6. The power outage data collection method based on electricity consumption behavior analysis according to claim 1, characterized in that The taking the user satisfaction of each substation area as the objective function and processing the power consumption behavior characteristics of the substation area and users based on the dwarf meerkat algorithm to determine the power outage data plan includes: Step 101: Taking the user satisfaction of each substation area as the objective function, establish the following model: Among them, is a 0-1 variable. When is 1, it means that user i in the operation and distribution system belongs to substation area k, and t k represents the data collection time of the users corresponding to the kth substation area, represents the low electricity consumption period corresponding to the ith user in the kth substation area, represents the high electricity consumption period corresponding to the ith user in the kth substation area, t k ∈[0, 24]; Step 102: Initialize the parameters and population of the dwarf meerkat optimization algorithm; Step 103: Taking formula (1) as the objective function, calculate the fitness function value of the population; Step 104: Calculate the probability of an individual in the alpha group becoming the leader according to formula (2), where f in formula (2) i is the objective function value of the i-th individual, and N is the population size; Step 105: Determine the food candidate location. The specific calculation formula is shown in Equation (3), where X i+1 is the new location of the food source, X i represents the current location of the female leader, phi is a random number distributed in [-1, 1], peep takes a value of 2, and X rand represents a random individual in the alpha group; X i+1 = X i + phi × peep × (X i - X rand ) (3) Step 106: Update the foraging results of the alpha group according to formula (3); Step 107: If the foraging result of the alpha group is inferior to the food candidate position, execute step 108; otherwise, execute step 109; Step 108: Update the positions of the alpha group members using formula (4), X new = Lb + rand × (Ub - Lb) (4), Where X new represents the position of the individuals in the updated alpha group, rand is a random number, Ub represents the upper limit of the individual position which is taken as 24 in this problem, and Lb represents the lower limit of the individual position which is taken as 0 in this problem; Step 109: The new alpha group continues to forage according to formula (3) and searches for the sleeping mound according to formula (5), Where X sm is the position of the sleep hillock, is the direction vector calculation formula for the mongoose to move to the sleep hillock as shown in Equation (6), represents the average value calculation formula of the sleep hillock as shown in Equation (7), and CF represents the population movement ability calculation formula as shown in Equation (8); Step 110: Judge whether the iteration condition is satisfied. If so, execute step 103; otherwise, output the power outage time of each substation area user.

7. A power outage data collection device based on power consumption behavior analysis, characterized in that Including: A preprocessing module that obtains the original data collected by the intelligent metering terminal and performs data preprocessing on the original data based on the cubic spline algorithm to obtain a preprocessing database; A golden section algorithm module that processes the preprocessing database based on the golden section algorithm to obtain the power consumption behavior characteristics of the substation area and users; The dwarf mongoose algorithm module takes the user satisfaction of each substation area as the objective function, processes the characteristics of the substation area and user electricity consumption behavior based on the dwarf mongoose algorithm, and determines the power outage data plan.

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