Rural governance-oriented grid management system

Abstract

The invention relates to the technical field of rural power supply governance, and discloses a rural governance-oriented grid management system, and the operation process of the system comprises the steps: carrying out the coarse-grained division of a power supply region, and obtaining a power supply cluster set; generating a power supply grid unit division objective function of the power supply cluster set by using the power supply grid unit division model, carrying out optimization solution on the power supply grid unit division objective function to obtain an optimal grid unit division strategy, carrying out grid unit division on a power supply area in the country, and obtaining a grid unit division result; and carrying out power supply maintenance task allocation according to the divided grid units. According to the method, the power supply grid unit division target function of the power supply cluster set is generated by using the power supply grid unit division model, grid unit division is performed on the power supply cluster set obtained by coarse-grained division, and all power supply areas in the grid units serve as power supply management areas of the grid units. And grid members are distributed for each power supply management area for power supply management, so that the rural power supply management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of rural power supply management, and in particular to a grid management system for rural management. Background Art

[0002] With the advancement of my country's urban-rural integration process, the construction and management of rural power infrastructure have become an important guarantee for achieving rural economic development and improving the quality of life of villagers. As the basic support for rural production and life, the stability, coverage and quality of power supply directly affect the economic development of rural areas and the lives of residents. In recent years, the country has vigorously promoted the transformation and construction of rural power grids, and the level of rural power grid hardware facilities has been greatly improved. However, in terms of management and maintenance, many rural power supply governance still has many problems, such as old power supply facilities, insufficient management force, and untimely response to sudden power outages. In addition, the rural geographical environment is complex, the population distribution is relatively scattered, the power supply lines are long and difficult to maintain, and the power supply guarantee capacity and management efficiency are generally low.

[0003] In order to meet the above challenges, grid management has been introduced into the field of rural power supply management, in order to refine the management area and improve management efficiency by dividing the rural power system into several small units or grids. After each grid is divided, a dedicated person or group is assigned to be responsible for the inspection, maintenance and information feedback of the power supply equipment, while ensuring that when the power equipment fails, it can be handled in the first time. Grid power supply management can not only help reduce power supply maintenance costs, but also ensure the improvement of service quality and effectively guarantee power supply stability.

[0004] However, the current grid division methods are mostly based on administrative regions or grid coverage, without fully considering the actual population density, line length and power supply demand differences in various places. For example, there may be grid areas of the same size in remote mountainous areas and densely populated villages. However, the power supply line length and population density of the former are significantly larger, resulting in uneven workload for grid workers and difficulty in efficiently maintaining some grid equipment. Therefore, a reasonable grid division method is still an urgent problem to be solved in the grid management of rural power supply. In response to this problem, the present invention proposes a grid management system for rural governance to optimize the division of rural power supply grids and balance the workload. Summary of the invention

[0005] In view of this, the present invention provides a grid management system for rural governance, which collects regional data of different power supply areas, uses the regional location distance between power supply areas for constraints, realizes the quantitative representation of regional distance constrained by normalized data, and uses the average regional distance as the cutoff distance for density calculation, and combines the membership to perform fuzzy clustering to obtain the final cluster center and power supply cluster, thereby realizing the coarse-grained division of the power supply area; the evaluation results of the divided grid units are calculated according to the population density index, the geographical distribution index and the electricity consumption density index, and the power supply cluster set is divided into grid units by using the power supply grid unit division model, and all power supply areas in the grid unit are used as the power supply management area of ​​the grid unit, and grid members are assigned to each power supply management area to carry out power supply governance, thereby improving the efficiency of rural power supply governance.

[0006] To achieve the above purpose, the present invention provides a grid management system for rural governance, comprising the following steps:

[0007] S1: Collect the regional location, transmission line length, population and power load data of different power supply areas in the village and perform coarse-grained division of the power supply areas to obtain a power supply cluster set, among which fuzzy clustering with multi-factor fusion is the implementation method of coarse-grained division;

[0008] S2: constructing a power grid unit division model, wherein the power grid unit division model selects a power cluster as a grid unit core node and an adjacent power cluster as a grid unit edge node, and divides the power cluster into a plurality of grid units;

[0009] S3: Generate a power grid unit division objective function of the power supply cluster set using the power grid unit division model, optimize and solve the power grid unit division objective function, and obtain the optimal grid unit division strategy, wherein the improved lion group algorithm is the implementation method of the model optimization solution;

[0010] S4: Divide the power supply area in the village into grid units according to the optimal grid unit division strategy, and allocate power supply maintenance tasks according to the divided grid units.

[0011] As a further improvement method of the present invention:

[0012] Optionally, the step S1 collects data on the regional location, transmission line length, population and power load of different power supply areas in the village, including:

[0013] The regional location, transmission line length, population and power load data of different power supply areas in the village are collected. In the embodiment of the present invention, the power supply area in the village is the building in the village, and the regional data set of the collected power supply area is:

[0014]

[0015] in:

[0016] x n represents the regional data of the nth power supply area collected, and N represents the total number of power supply areas in the village;

[0017] The regional location, transmission line length, population and power load data of the nth power supply area are represented in sequence;

[0018] represents the length of the transmission line between the nth power supply area and the nearest power source, where the power source includes a power plant and a substation;

[0019] is the average daily power consumption in the power supply area;

[0020] Combined with the regional data of different power supply areas, the power supply areas are coarsely divided into K power supply clusters. Each power supply cluster has multiple power supply areas, and a power supply cluster set is obtained.

[0021] Optionally, the power supply area is coarsely divided by combining the regional data of different power supply areas, and the power supply area is divided into K power supply clusters to obtain a power supply cluster set, including:

[0022] S11: Based on the regional data of different power supply areas, calculate the regional distance between any two power supply areas; the regional distance between the nth power supply area and the qth power supply area is DIS(x n ,x q ):

[0023]

[0024] in:

[0025] Indicates the location of the area The Euclidean distance between

[0026] exp(·) represents an exponential function with a natural constant as the base;

[0027] δ(·) indicates normalization processing;

[0028] It means that for any n,q∈[1,N],n≠q, The minimum value of , i∈{2,3,4};

[0029] It means that for any n,q∈[1,N],n≠q, The maximum value of

[0030] It means that for any n,q∈[1,N],n≠q, The minimum value of

[0031] It means that for any n,q∈[1,N],n≠q, The maximum value of

[0032] S12: Calculate the average area distance AVG between any two power supply areas:

[0033]

[0034] S13: Combine the average regional distance and the regional distance between power supply areas to calculate the regional density of any power supply area, where the regional density of the nth power supply area is ρ n :

[0035]

[0036] in:

[0037] S(·) represents the indicator function;

[0038] S14: According to the regional density, the cluster center index of any power supply area is calculated, where the cluster center index of the nth power supply area is G n :

[0039] G n =τ n ρ n

[0040]

[0041] in:

[0042] τ n The cluster center distance index of the nth power supply area, DIS max Indicates the preset maximum cluster center distance indicator;

[0043] Indicates that the regional density is greater than ρ n Among all the power supply areas, the shortest area distance between the nth power supply area;

[0044] S15: Select K power supply areas with the highest cluster center index as K cluster centers, and use fuzzy clustering to cluster the power supply areas that are not cluster centers into the power supply cluster where the cluster center is located, and obtain K power supply clusters; the area data of the cluster center of the kth power supply cluster is c k ,k∈[1,K].

[0045] Optionally, constructing a power supply grid unit division model in step S2 includes:

[0046] Constructing a power grid unit division model, wherein the power grid unit division model selects a power cluster as a grid unit core node, takes an adjacent power cluster as a grid unit edge node, and divides the power cluster into a plurality of grid units, wherein the power grid unit includes a core node selection layer, a grid unit generation layer, and a grid unit evaluation layer;

[0047] The process of grid unit division using the power supply grid unit division model is as follows:

[0048] S21: The core node selection layer selects multiple power supply clusters from K power supply clusters as grid unit core nodes;

[0049] S22: The grid unit generation layer is used to calculate the distance between the power supply cluster of the non-grid unit core node and the grid unit core node, and use the power supply cluster as the grid unit edge node closest to the grid unit core node, where the distance between the kth power supply cluster and the k+1th power supply cluster is DIS(c k ,c k+1 );

[0050] S23: taking a grid unit core node and a plurality of surrounding grid unit edge nodes as a grid unit;

[0051] S24: The grid unit evaluation layer conducts a comprehensive evaluation on the rationality of the grid unit to obtain a comprehensive evaluation result of the grid unit, where the comprehensive evaluation indicators include population density index, electricity density index and regional distribution index. The comprehensive evaluation result of the rth grid unit is V r :

[0052]

[0053] in:

[0054] count(r) represents the sum of the population of all power supply areas in the rth grid cell;

[0055] area(r) represents the area of ​​the rth grid cell;

[0056] represents the population density threshold;

[0057] ε1, ε2, and ε3 represent the control parameters of population density index, regional distribution index, and electricity density index respectively;

[0058] w1, w2, w3 represent the weights of population density index, regional distribution index and electricity density index respectively;

[0059] distance(r) represents the shortest Euclidean distance from other grid cells to the rth grid cell;

[0060] Indicates the threshold of the regional distribution indicator;

[0061] μ r represents the mean value of the power load data of all power supply clusters in the rth grid unit;

[0062] σ r Represents the standard deviation of the power load data of all power supply clusters in the rth grid unit.

[0063] Optionally, in step S3, using the power supply grid unit division model to generate a power supply grid unit division objective function of the power supply cluster set includes:

[0064] The power grid unit division model is used to divide the power supply cluster set into grid units, and based on the comprehensive evaluation results of the grid units, the power grid unit division objective function of the power supply cluster set is generated, where the power grid unit division objective function is:

[0065]

[0066] θ=(θ1,θ2,...,θ r ,...,θ R )

[0067] in:

[0068] θ represents the grid unit division strategy, θ1,θ2,...,θ r ,...,θ R represents the regional position of the core node of the grid cell in R grid cells, θ r represents the regional position of the core node of the grid cell in the rth grid cell, and R represents the number of grid cells to be generated;

[0069] V represents the evaluation adjustment parameter;

[0070] Indicates the area position θ r The comprehensive evaluation result of the grid cell which is the core node of the grid cell.

[0071] Optionally, optimizing and solving the power supply grid unit division objective function to obtain the optimal grid unit division strategy includes:

[0072] S31: Use the power grid unit division model to select R grid unit core nodes and extract the regional positions of the grid unit core nodes as the first set of initial solutions θ 0(0):

[0073]

[0074] in:

[0075] represents the regional position of the core node of the selected r-th grid unit;

[0076] S32: Iteratively map the first group of initial solutions to obtain H groups of initial solutions, where the generation formula of the hth group of initial solutions is:

[0077]

[0078] in:

[0079] rand(0,1) represents a random number between 0 and 1;

[0080] θ 0 (h) represents the hth group of initial solutions, h∈[1,H];

[0081] I represents a vector with 1 row and R columns and all vector values ​​are 0;

[0082] S33: Set the current iteration number of each group of initial solutions to b, and the maximum iteration number to B, then the bth iteration solution of the hth group of initial solutions is θ b (h), the initial value of b is 0;

[0083] S34: Normalize each set of iterative solutions, where the iterative solution θ b The normalized result of (h) is The normalization process is to convert the regional position in the iterative solution into the nearest cluster center regional position;

[0084] S35: Substitute the normalized iterative solution into the power grid unit partition objective function to obtain the objective function value of the iterative solution, where the iterative solution The objective function value is

[0085]

[0086] The iterative solution with the highest objective function value is taken as the optimal solution obtained in the bth iteration.

[0087] S36: The iterative solution with the objective function value higher than the preset threshold is used as the position coordinate of the lioness, and the other iterative solutions are used as the position coordinates of the cubs, and the position coordinates are iterated using two lion group position update methods respectively; if is the position coordinate of the lioness, then the iteration formula is:

[0088]

[0089] in:

[0090] L represents a random number between 0 and 1;

[0091] g b (h) represents the iterative solution with the maximum objective function value in the hth group of initial solutions during the bth iteration;

[0092] Sim b (h) indicates The coordinates of the lion cubs with the highest similarity;

[0093] represents the perturbation parameter of the lioness position;

[0094] β b represents the iteration factor;

[0095] rand(0,1) represents a random number between 0 and 1;

[0096] like is the position coordinate of the lion cub, then the iteration formula is:

[0097]

[0098] in:

[0099] represents the mean of H groups of iterative solutions obtained in the b-th iteration;

[0100] represents the perturbation parameter of the lion cub position;

[0101] S37: Take the position coordinates obtained by iteration as the iterative solution, set b=b+1, return to step S34, until the maximum number of iterations is reached, and substitute the normalized iterative solution into the power grid unit division objective function, and take the iterative solution with the largest objective function value as the optimal grid unit division strategy in Represents the regional position of the core node of the rth grid unit.

[0102] Optionally, in step S4, grid unit division is performed on the power supply area in the village according to the optimal grid unit division strategy, and power supply maintenance task allocation is performed, including:

[0103] The grid unit division is performed on the power supply area in the village according to the optimal grid unit division strategy, wherein the grid unit division process of the power supply area is as follows:

[0104] S41: selecting R regional locations according to the optimal grid unit division strategy, and using the power supply cluster corresponding to the cluster center consistent with the R regional locations as the core node of the R grid units;

[0105] S42: selecting a power supply cluster as a grid unit edge node by using a power supply grid unit division model, and treating a grid unit core node and a plurality of surrounding grid unit edge nodes as a grid unit;

[0106] S43: All power supply areas within the grid unit are used as power supply management areas of the grid unit, and grid workers are assigned to each power supply management area to perform power supply management.

[0107] Optionally, in step S15, K power supply areas with the highest cluster center indexes are selected as K cluster centers, and the power supply areas that are not cluster centers are clustered into the power supply cluster where the cluster center is located by using a fuzzy clustering method, including:

[0108] S151: Initialize and generate the fuzzy membership of the power supply area of ​​the non-cluster center to the cluster center, where the fuzzy membership of the power supply area of ​​the mth non-cluster center to the kth cluster center is in m∈[1,M], M represents the total number of power supply areas outside the cluster center, k∈[1,K];

[0109] S152: Set the current clustering iteration number of fuzzy clustering to t, then the fuzzy membership The result of the tth iteration is The initial value of t is 0 and the maximum value is Max;

[0110] S153: Iterate the fuzzy membership, where the fuzzy membership The iteration formula is:

[0111]

[0112] in:

[0113] x t (m) represents the regional data of the mth non-cluster center power supply area after the tth iteration, m∈[1,M], M represents the total number of current non-cluster center power supply areas;

[0114] Represents the regional data of the kth cluster center after the tth iteration; is the cluster center selected based on the cluster center index;

[0115] U t represents the fuzzy membership matrix formed by the fuzzy membership obtained in the tth iteration, U t is a matrix with M rows and K columns, Denotes the fuzzy membership matrix U t The value of the matrix element in the mth row and kth column;

[0116] S154: Based on the iteratively obtained fuzzy membership, the cluster center is iterated, wherein the iterative formula for the kth cluster center is:

[0117]

[0118] S155: Let t=t+1, return to step S153 until the maximum number of iterations is reached, and use the K cluster centers obtained in the current iteration as the final cluster centers, calculate the fuzzy membership of the power supply areas of the non-cluster centers to the K final cluster centers, merge the power supply areas of the non-cluster centers into the power supply cluster where the final cluster center with the highest fuzzy membership is located, and obtain K power supply clusters.

[0119] In order to solve the above problems, the present invention provides a grid management system for rural governance, the system comprising:

[0120] The power supply cluster division module is used to collect the regional location, transmission line length, population and power load data of different power supply areas in the village and perform coarse-grained division of the power supply areas to obtain a power supply cluster set;

[0121] A grid unit division module is used to construct a power supply grid unit division model, select a power supply cluster as a grid unit core node, and use an adjacent power supply cluster as a grid unit edge node to divide the power supply cluster into multiple grid units;

[0122] A grid management device is used to generate a power supply grid unit division objective function of a power supply cluster set using a power supply grid unit division model, optimize and solve the power supply grid unit division objective function to obtain an optimal grid unit division strategy, divide the power supply area in the village into grid units according to the optimal grid unit division strategy, and allocate power supply maintenance tasks according to the divided grid units.

[0123] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0124] A memory storing at least one instruction;

[0125] Communication interface, enabling electronic equipment to communicate; and

[0126] A processor executes instructions stored in the memory to implement the above-mentioned grid management system for rural governance.

[0127] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned grid management system for rural governance.

[0128] Compared with the prior art, the present invention proposes a grid management system for rural governance, which has the following advantages:

[0129] First, this scheme proposes a coarse-grained division method for power supply areas. Regional data of different power supply areas are collected, and regional location distances between power supply areas are used for constraints. The closer the regional location distances, the greater the impact of differences in population, transmission line length, and power load on regional data. The regional distances constrained by normalized data are quantitatively represented, and the average regional distance is used as the cutoff distance for density calculation to obtain the density of different power supply areas. The initial cluster center of the power supply cluster is then selected, and the membership degree is iterated in combination with the membership degree of the non-cluster center to obtain the final cluster center and power supply cluster, thereby realizing the coarse-grained division of the power supply area.

[0130] At the same time, this scheme proposes a grid unit generation method, which calculates the evaluation results of the divided grid units according to the population density index, the regional distribution index and the electricity density index, and uses the power supply grid unit division model to divide the power supply cluster set into grid units. Based on the comprehensive evaluation results of the grid units, the power supply grid unit division objective function of the power supply cluster set is generated, and the position of the core node of the grid unit is solved with the optimal coverage area, power load and population density of each grid unit as the goal, so as to realize the division of grid units in the village, and all power supply areas in the grid unit are used as the power supply management area of ​​the grid unit, and grid members are assigned to each power supply management area for power supply management. In the process of solving the problem, the lion group optimization algorithm is optimized, so that the iteration of the lioness position coordinates is not only related to the historical optimal position coordinates of the lioness, but also communicates with the cubs, timely obtains and fully utilizes the effective information in the population, so as to realize a more efficient optimization mechanism, and uses the disturbance parameters to improve the diversity of the cub position and the lioness position, reduce the number of generations required for convergence, and thus improve the optimization speed and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0131] Figure 1 A schematic diagram of a flow chart of a grid management system for rural governance provided by an embodiment of the present invention;

[0132] Figure 2 A functional module diagram of a grid management system for rural power supply management provided by an embodiment of the present invention;

[0133] Figure 2In: 100 a grid management system for rural power supply management, 101 a power supply cluster division module, 102 a grid unit division module, 103 a grid management device;

[0134] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0135] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0136] The embodiment of the present application provides a grid management system for rural governance. The execution subject of the grid management system for rural governance includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the grid management system for rural governance can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0137] Embodiment 1:

[0138] A grid management system for rural governance includes the following steps:

[0139] S1: Collect the regional location, transmission line length, population and power load data of different power supply areas in the village and divide the power supply areas into coarse-grained groups to obtain a power supply cluster set.

[0140] The S1 step collects data on the regional location, transmission line length, population and power load of different power supply areas in the village, including:

[0141] The regional location, transmission line length, population and power load data of different power supply areas in the village are collected. In the embodiment of the present invention, the power supply area in the village is the building in the village, and the regional data set of the collected power supply area is:

[0142]

[0143] in:

[0144] x n represents the regional data of the nth power supply area collected, and N represents the total number of power supply areas in the village;

[0145] The regional location, transmission line length, population and power load data of the nth power supply area are represented in sequence;

[0146] represents the length of the transmission line between the nth power supply area and the nearest power source, where the power source includes a power plant and a substation;

[0147] is the average daily power consumption in the power supply area;

[0148] Combined with the regional data of different power supply areas, the power supply areas are coarsely divided into K power supply clusters. Each power supply cluster has multiple power supply areas, and a power supply cluster set is obtained.

[0149] The power supply area is coarsely divided by combining the regional data of different power supply areas, and the power supply area is divided into K power supply clusters to obtain a power supply cluster set, including:

[0150] S11: Based on the regional data of different power supply areas, calculate the regional distance between any two power supply areas; the regional distance between the nth power supply area and the qth power supply area is DIS(x n ,x q ):

[0151]

[0152] in:

[0153] Indicates the location of the area The Euclidean distance between

[0154] exp(·) represents an exponential function with a natural constant as the base;

[0155] δ(·) indicates normalization processing;

[0156] It means that for any n,q∈[1,N],n≠q, The minimum value of , i∈{2,3,4};

[0157] It means that for any n,q∈[1,N],n≠q, The maximum value of

[0158] It means that for any n,q∈[1,N],n≠q, The minimum value of

[0159] It means that for any n,q∈[1,N],n≠q, The maximum value of

[0160] S12: Calculate the average area distance AVG between any two power supply areas:

[0161]

[0162] S13: Combine the average regional distance and the regional distance between power supply areas to calculate the regional density of any power supply area, where the regional density of the nth power supply area is ρ n :

[0163]

[0164] in:

[0165] S(·) represents the indicator function;

[0166] S14: According to the regional density, the cluster center index of any power supply area is calculated, where the cluster center index of the nth power supply area is G n :

[0167] G n =τ n ρ n

[0168]

[0169] in:

[0170] τ n The cluster center distance index of the nth power supply area, DIS max Indicates the preset maximum cluster center distance indicator;

[0171] Indicates that the regional density is greater than ρ n Among all the power supply areas, the shortest area distance between the nth power supply area;

[0172] S15: Select K power supply areas with the highest cluster center index as K cluster centers, and use fuzzy clustering to cluster the power supply areas that are not cluster centers into the power supply cluster where the cluster center is located, and obtain K power supply clusters; the area data of the cluster center of the kth power supply cluster is c k ,k∈[1,K].

[0173] S2: construct a power supply grid unit division model, wherein the power supply grid unit division model selects a power supply cluster as a grid unit core node and an adjacent power supply cluster as a grid unit edge node, and divides the power supply cluster into a plurality of grid units.

[0174] The power supply grid unit division model is constructed in step S2, including:

[0175] Constructing a power grid unit division model, wherein the power grid unit division model selects a power cluster as a grid unit core node, takes an adjacent power cluster as a grid unit edge node, and divides the power cluster into a plurality of grid units, wherein the power grid unit includes a core node selection layer, a grid unit generation layer, and a grid unit evaluation layer;

[0176] The process of grid unit division using the power supply grid unit division model is as follows:

[0177] S21: The core node selection layer selects multiple power supply clusters from K power supply clusters as grid unit core nodes;

[0178] S22: The grid unit generation layer is used to calculate the distance between the power supply cluster of the non-grid unit core node and the grid unit core node, and use the power supply cluster as the grid unit edge node closest to the grid unit core node, where the distance between the kth power supply cluster and the k+1th power supply cluster is DIS(c k ,c k+1 );

[0179] S23: taking a grid unit core node and a plurality of surrounding grid unit edge nodes as a grid unit;

[0180] S24: The grid unit evaluation layer conducts a comprehensive evaluation on the rationality of the grid unit to obtain a comprehensive evaluation result of the grid unit, where the comprehensive evaluation indicators include population density index, electricity density index and regional distribution index. The comprehensive evaluation result of the rth grid unit is V r :

[0181]

[0182] in:

[0183] count(r) represents the sum of the population of all power supply areas in the rth grid cell;

[0184] area(r) represents the area of ​​the rth grid cell;

[0185] represents the population density threshold;

[0186] ε1, ε2, and ε3 represent the control parameters of population density index, regional distribution index, and electricity density index respectively;

[0187] w1, w2, w3 represent the weights of population density index, regional distribution index and electricity density index respectively;

[0188] distance(r) represents the shortest Euclidean distance from other grid cells to the rth grid cell;

[0189] Indicates the threshold of the regional distribution indicator;

[0190] μ r represents the mean value of the power load data of all power supply clusters in the rth grid unit;

[0191] σ r Represents the standard deviation of the power load data of all power supply clusters in the rth grid unit.

[0192] S3: Generate a power supply grid unit division objective function of the power supply cluster set using the power supply grid unit division model, optimize and solve the power supply grid unit division objective function, and obtain an optimal grid unit division strategy.

[0193] In the step S3, the power supply grid unit division model is used to generate the power supply grid unit division objective function of the power supply cluster set, including:

[0194] The power grid unit division model is used to divide the power supply cluster set into grid units, and based on the comprehensive evaluation results of the grid units, the power grid unit division objective function of the power supply cluster set is generated, where the power grid unit division objective function is:

[0195]

[0196] θ=(θ1,θ2,...,θ r ,...,θ R )

[0197] in:

[0198] θ represents the grid unit division strategy, θ1,θ2,...,θ r ,...,θ R represents the regional position of the core node of the grid cell in R grid cells, θ r represents the regional position of the core node of the grid cell in the rth grid cell, and R represents the number of grid cells to be generated;

[0199] V represents the evaluation adjustment parameter;

[0200] Indicates the area position θ r The comprehensive evaluation result of the grid cell which is the core node of the grid cell.

[0201] Optionally, optimizing and solving the power supply grid unit division objective function to obtain the optimal grid unit division strategy includes:

[0202] S31: Use the power grid unit division model to select R grid unit core nodes and extract the regional positions of the grid unit core nodes as the first set of initial solutions θ 0 (0):

[0203]

[0204] in:

[0205] represents the regional position of the core node of the selected r-th grid unit;

[0206] S32: Iteratively map the first group of initial solutions to obtain H groups of initial solutions, where the generation formula of the hth group of initial solutions is:

[0207]

[0208] in:

[0209] rand(0,1) represents a random number between 0 and 1;

[0210] θ 0 (h) represents the hth group of initial solutions, h∈[1,H];

[0211] I represents a vector with 1 row and R columns and all vector values ​​are 0;

[0212] S33: Set the current iteration number of each group of initial solutions to b, and the maximum iteration number to B, then the bth iteration solution of the hth group of initial solutions is θ b (h), the initial value of b is 0;

[0213] S34: Normalize each set of iterative solutions, where the iterative solution θ b The normalized result of (h) is The normalization process is to convert the regional position in the iterative solution into the nearest cluster center regional position;

[0214] S35: Substitute the normalized iterative solution into the power grid unit partition objective function to obtain the objective function value of the iterative solution, where the iterative solution The objective function value is

[0215]

[0216] The iterative solution with the highest objective function value is taken as the optimal solution obtained in the bth iteration.

[0217] S36: The iterative solution with the objective function value higher than the preset threshold is used as the position coordinate of the lioness, and the other iterative solutions are used as the position coordinates of the cubs, and the position coordinates are iterated using two lion group position update methods respectively; if is the position coordinate of the lioness, then the iteration formula is:

[0218]

[0219] in:

[0220] g b (h) represents the iterative solution with the maximum objective function value in the hth group of initial solutions during the bth iteration;

[0221] Sim b (h) indicates The coordinates of the lion cub's position with the highest similarity; the similarity is calculated using the cosine similarity algorithm;

[0222] represents the perturbation parameter of the lioness position;

[0223] β b represents the iteration factor;

[0224] rand(0,1) represents a random number between 0 and 1;

[0225] L represents a random number between 0 and 1;

[0226] like is the position coordinate of the lion cub, then the iteration formula is:

[0227]

[0228] in:

[0229] represents the mean of H groups of iterative solutions obtained in the b-th iteration;

[0230] S37: Take the position coordinates obtained by iteration as the iterative solution, set b=b+1, return to step S34, until the maximum number of iterations is reached, and substitute the normalized iterative solution into the power grid unit division objective function, and take the iterative solution with the largest objective function value as the optimal grid unit division strategy in Represents the regional position of the core node of the rth grid unit.

[0231] S4: Divide the power supply area in the village into grid units according to the optimal grid unit division strategy, and allocate power supply maintenance tasks according to the divided grid units.

[0232] In the step S4, the power supply area in the village is divided into grid units according to the optimal grid unit division strategy, and power supply maintenance tasks are allocated, including:

[0233] The grid unit division is performed on the power supply area in the village according to the optimal grid unit division strategy, wherein the grid unit division process of the power supply area is as follows:

[0234] S41: selecting R regional locations according to the optimal grid unit division strategy, and using the power supply cluster corresponding to the cluster center consistent with the R regional locations as the core node of the R grid units;

[0235] S42: selecting a power supply cluster as a grid unit edge node by using a power supply grid unit division model, and treating a grid unit core node and a plurality of surrounding grid unit edge nodes as a grid unit;

[0236] S43: All power supply areas within the grid unit are used as power supply management areas of the grid unit, and grid workers are assigned to each power supply management area to perform power supply management.

[0237] Embodiment 2:

[0238] like Figure 2 As shown, it is a functional module diagram of a grid management system for rural governance provided by an embodiment of the present invention, which can implement the grid management system for rural governance in Example 1.

[0239] The grid management system 100 for rural governance of the present invention can be installed in an electronic device. According to the functions to be implemented, the grid management system for rural governance can include a power supply cluster division module 101, a grid unit division module 102 and a grid management device 103. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0240] The power supply cluster division module 101 is used to collect the regional location, transmission line length, population and power load data of different power supply areas in the village and perform coarse-grained division of the power supply areas to obtain a power supply cluster set;

[0241] A grid unit division module 102 is used to construct a power supply grid unit division model, select a power supply cluster as a grid unit core node, and use an adjacent power supply cluster as a grid unit edge node, and divide the power supply cluster into a plurality of grid units;

[0242] The grid management device 103 is used to generate a power supply grid unit division objective function of a power supply cluster set using a power supply grid unit division model, optimize and solve the power supply grid unit division objective function, obtain an optimal grid unit division strategy, divide the power supply area in the village into grid units according to the optimal grid unit division strategy, and allocate power supply maintenance tasks according to the divided grid units.

[0243] In detail, each module in the grid management system 100 for rural governance in the embodiment of the present invention is used in the same manner as described above. Figure 1 The technical means are the same as the grid management system for rural governance described in, and can produce the same technical effects, so I will not go into details here.

[0244] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0245] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising" or any other variants thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0246] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0247] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A grid management system for rural governance, characterized by: The system operation process includes: S1: Collect the regional location, transmission line length, population and power load data of different power supply areas in the village and perform coarse-grained division of the power supply areas to obtain a power supply cluster set; S2: constructing a power grid unit division model, wherein the power grid unit division model selects a power cluster as a grid unit core node and an adjacent power cluster as a grid unit edge node, and divides the power cluster into a plurality of grid units; S3: Generate a power grid unit division objective function of the power supply cluster set using the power grid unit division model, optimize and solve the power grid unit division objective function, and obtain the optimal grid unit division strategy; S4: Divide the power supply area in the village into grid units according to the optimal grid unit division strategy, and allocate power supply maintenance tasks according to the divided grid units.

2. A grid management system for rural governance as claimed in claim 1, characterized in that: The S1 step includes: The regional location, transmission line length, population and power load data of different power supply areas in the village are collected. The regional data set of the power supply area collected is: in: x n represents the regional data of the nth power supply area collected, and N represents the total number of power supply areas in the village; The regional location, transmission line length, population and power load data of the nth power supply area are represented in sequence; It represents the length of the transmission line between the nth power supply area and the nearest power source, where the power source includes the power plant and the substation.

3. A grid management system for rural governance as claimed in claim 2, characterized in that: The power supply area is coarsely divided by combining the regional data of different power supply areas, and the power supply area is divided into K power supply clusters to obtain a power supply cluster set, including: S11: Based on the regional data of different power supply areas, calculate the regional distance between any two power supply areas; the regional distance between the nth power supply area and the qth power supply area is DIS(x n ,x q ); S12: Calculate and obtain the average area distance AVG between any two power supply areas; S13: Combine the average regional distance and the regional distance between power supply areas to calculate the regional density of any power supply area, where the regional density of the nth power supply area is ρ n ; S14: According to the regional density, the cluster center index of any power supply area is calculated, where the cluster center index of the nth power supply area is G n ; S15: Select K power supply areas with the highest cluster center index as K cluster centers, and use fuzzy clustering to cluster the power supply areas that are not cluster centers into the power supply cluster where the cluster center is located, and obtain K power supply clusters; the area data of the cluster center of the kth power supply cluster is c k ,k∈[1,K].

4. A grid management system for rural governance as claimed in claim 1, characterized in that: The S2 step includes: Constructing a power grid unit division model, wherein the power grid unit division model selects a power cluster as a grid unit core node, takes an adjacent power cluster as a grid unit edge node, and divides the power cluster into a plurality of grid units, wherein the power grid unit includes a core node selection layer, a grid unit generation layer, and a grid unit evaluation layer; The process of grid unit division using the power supply grid unit division model is as follows: S21: The core node selection layer selects multiple power supply clusters from K power supply clusters as grid unit core nodes; S22: The grid unit generation layer is used to calculate the distance between the power supply cluster of the non-grid unit core node and the grid unit core node, and use the power supply cluster as the grid unit edge node closest to the grid unit core node, where the distance between the kth power supply cluster and the k+1th power supply cluster is DIS(c k ,c k+1 ); S23: taking a grid unit core node and a plurality of surrounding grid unit edge nodes as a grid unit; S24: The grid unit evaluation layer conducts a comprehensive evaluation on the rationality of the grid unit to obtain a comprehensive evaluation result of the grid unit, where the comprehensive evaluation indicators include population density index, electricity density index and regional distribution index. The comprehensive evaluation result of the rth grid unit is V r .

5. A grid management system for rural governance as claimed in claim 4, characterized in that: The S3 step includes: The power grid unit division model is used to divide the power supply cluster set into grid units, and based on the comprehensive evaluation results of the grid units, the power grid unit division objective function of the power supply cluster set is generated, where the power grid unit division objective function is: θ=(θ1,θ2,...,θ r ,...,θ R ) in: θ represents the grid unit division strategy, θ1,θ2,...,θ r ,...,θ R represents the regional position of the core node of the grid cell in R grid cells, θ r represents the regional position of the core node of the grid cell in the rth grid cell, and R represents the number of grid cells to be generated; V represents the evaluation adjustment parameter; Indicates the area position θ r The comprehensive evaluation result of the grid cell which is the core node of the grid cell.

6. A grid management system for rural governance as claimed in claim 5, characterized in that: The optimization and solution of the power supply grid unit division objective function to obtain the optimal grid unit division strategy includes: S31: Use the power grid unit division model to select R grid unit core nodes and extract the regional locations of the grid unit core nodes as the first set of initial solutions θ 0 (0); S32: iteratively map the first group of initial solutions to obtain H groups of initial solutions; S33: Set the current iteration number of each group of initial solutions to b, and the maximum iteration number to B, then the bth iteration solution of the hth group of initial solutions is θ b (h), the initial value of b is 0; S34: Normalize each set of iterative solutions, where the iterative solution θ b The normalized result of (h) is The normalization process is to convert the regional position in the iterative solution into the nearest cluster center regional position; S35: Substitute the normalized iterative solution into the power grid unit partition objective function to obtain the objective function value of the iterative solution, where the iterative solution The objective function value is The iterative solution with the highest objective function value is taken as the optimal solution obtained in the bth iteration. S36: taking the iterative solution with the objective function value higher than the preset threshold as the position coordinates of the lioness, and taking the other iterative solutions as the position coordinates of the cubs, and respectively adopting two lion group position update methods to iterate the position coordinates; S37: Take the position coordinates obtained by iteration as the iterative solution, set b=b+1, return to step S34, until the maximum number of iterations is reached, and substitute the normalized iterative solution into the power grid unit division objective function, and take the iterative solution with the largest objective function value as the optimal grid unit division strategy in Represents the regional position of the core node of the rth grid unit.

7. A grid management system for rural governance as claimed in claim 6, characterized in that: The S4 step includes: The grid unit division is performed on the power supply area in the village according to the optimal grid unit division strategy, and the grid unit division process of the power supply area is as follows: S41: selecting R regional locations according to the optimal grid unit division strategy, and using the power supply cluster corresponding to the cluster center consistent with the R regional locations as the core node of the R grid units; S42: selecting a power supply cluster as a grid unit edge node by using a power supply grid unit division model, and treating a grid unit core node and a plurality of surrounding grid unit edge nodes as a grid unit; S43: All power supply areas within the grid unit are used as power supply management areas of the grid unit, and grid workers are assigned to each power supply management area to perform power supply management.

8. A grid management system for rural governance as claimed in claim 3, characterized in that: The step S15 comprises: S151: Initialize and generate the fuzzy membership of the power supply area of ​​the non-cluster center to the cluster center, where the fuzzy membership of the power supply area of ​​the mth non-cluster center to the kth cluster center is in m∈[1,M], M represents the total number of power supply areas outside the cluster center, k∈[1,K]; S152: Set the current clustering iteration number of fuzzy clustering to t, then the fuzzy membership The result of the tth iteration is The initial value of t is 0 and the maximum value is Max; S153: iterating the fuzzy membership; S154: iterating the cluster center based on the iteratively obtained fuzzy membership; S155: Let t=t+1, return to step S153 until the maximum number of iterations is reached, and use the K cluster centers obtained in the current iteration as the final cluster centers, calculate the fuzzy membership of the power supply areas of the non-cluster centers to the K final cluster centers, merge the power supply areas of the non-cluster centers into the power supply cluster where the final cluster center with the highest fuzzy membership is located, and obtain K power supply clusters.

9. A grid management system for rural governance, characterized in that: The system comprises: The power supply cluster division module is used to collect the regional location, transmission line length, population and power load data of different power supply areas in the village and perform coarse-grained division of the power supply areas to obtain a power supply cluster set; A grid unit division module is used to construct a power supply grid unit division model, select a power supply cluster as a grid unit core node, and use an adjacent power supply cluster as a grid unit edge node to divide the power supply cluster into multiple grid units; A grid management device is used to generate a power supply grid unit division objective function of a power supply cluster set using a power supply grid unit division model, optimize and solve the power supply grid unit division objective function to obtain an optimal grid unit division strategy, divide the power supply area in the village into grid units according to the optimal grid unit division strategy, and allocate power supply maintenance tasks according to the divided grid units, so as to realize a grid management system for rural governance as described in any one of claims 1 to 8.

Images