Graph theory-based adjacent cell sharing service element configuration method and system

Abstract

The application provides a kind of based on graph theory's adjacent unit sharing service element configuration method and system.Method includes: S1, the network graph of area needing to carry out service element configuration is constructed;S2, the attribute of node is set;S3, the network association attribute of node is set;S4, the neighborhood coupling number of each node is determined;S5, based on neighborhood coupling number, node configuration set is obtained, and adjacent node configuration strategy based on degree priority or adjacent node configuration strategy based on weighted degree is used;S6, the unit configuration sequence of shared service element is output.System includes: parameter setting module, data input module, data processing module, node configuration module and result generation module.The application fully considers the mutual supply contact between multiple adjacent units, solves the problem of adjacent unit common configuration service element using the way of graph theory, optimizes service element configuration space pattern, and improves service efficiency of service element.

Description

Technical Field

[0001] This invention relates to the field of resource allocation, and in particular to a method and system for allocating shared service elements based on graph theory. Background Technology

[0002] Providing public facilities for social production and residents' lives is a general material condition for the survival and development of society and the foundation for the development of all undertakings of the national economy. In modern society, the more developed the economy, the higher the demand for public facilities; well-developed public facilities play a huge role in accelerating socio-economic activities and promoting the evolution of their spatial distribution. Establishing well-developed public facilities often requires a long time and huge investment; therefore, it is generally desirable for public facilities to be shared by multiple units.

[0003] Against the backdrop of comprehensive national spatial planning, the rational allocation of public facilities has increasingly become a crucial planning issue for promoting balanced urban and rural development. On June 9, 2021, the Ministry of Natural Resources released the industry standard TD / T 1062-2021, "Technical Guidelines for Community Living Circle Planning" (hereinafter referred to as the "Guidelines"), to guide and standardize community living circle planning research and provide a basis for implementing classified guidance and differentiated management in various regions. The Guidelines propose that village and town construction should be intensive, compact, and efficiently utilize space, and advocate integrated construction and shared use. However, current public facility allocation is mainly based on the allocation of service elements under multi-objective planning. This method often ignores the mutual supply relationship of service elements between multiple units, especially when it involves the shared allocation needs of neighboring units, making it difficult to meet planning requirements. This invention addresses the needs at the unit level and combines graph theory to allocate service elements that meet the unit's need for nearby use, while emphasizing the staggered allocation and shared use of service elements between adjacent units. It proposes a graph theory-based method and system for configuring shared service elements between neighboring units. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, the purpose of this invention is to plan the shared configuration of service elements among neighboring units. This invention proposes a graph theory-based method for configuring shared service elements among neighboring units, which includes the following steps:

[0005] S1. Construct a network diagram for the areas that require service element configuration;

[0006] The region is divided into multiple sub-regions, each of which is a unit. Each unit is treated as a node in the network graph. The geographic spatial adjacency of the units is represented as an edge in the network graph. Two adjacent units indicate that there is an edge between the two nodes in the network graph, and conversely, two non-adjacent units indicate that there is no edge between the two nodes in the network graph.

[0007] S2, Set the node's attributes;

[0008] The node's attribute is the supply attribute F of the shared service element. i Attribute X of the requirement i And a comprehensive evaluation index value that characterizes the node features, where i represents the node number corresponding to the unit;

[0009] S3, Set the network association attributes of the node;

[0010] The network association attributes of nodes are divided into network association supply attributes and network association demand attributes of shared service elements, which are defined as S respectively. i and D i ;

[0011] S4. Determine the neighborhood coupling number of each node;

[0012] Based on the network association supply attribute S of each node i Network-related requirement attribute D i Calculate the neighborhood coupling number XQ of each node. i :

[0013] XQ i =S i -D i (4)

[0014] When the neighborhood coupling number XQ i When the number of neighborhood couplings is ≥0, node i does not need to be configured; when the number of neighborhood couplings is XQ i When <0, node i is the unit to be configured;

[0015] S5. Obtain the node configuration set P based on the neighborhood coupling number;

[0016] Based on the neighborhood coupling number, a neighbor node configuration strategy based on degree priority or a neighbor node configuration strategy based on weighted degree is adopted to obtain the configuration result;

[0017] The results are stored in the node configuration set P;

[0018] S6. Unit configuration order of output shared service elements

[0019] Set P stores the corresponding node numbers. The elements in set P are output in ascending order, and the output order of the node numbers is the order in which the service elements of the corresponding unit are configured. Nodes not in set P indicate that the corresponding unit does not need to be configured with service elements.

[0020] Preferably, the degree-first neighbor node configuration strategy in S5 is as follows:

[0021] S151. The degree-first neighbor node configuration strategy is implemented as follows:

[0022] S1511. Calculate the degree of each node in the network graph and set the initial value of variable j to 0;

[0023] The degree of node i is defined as the number of nodes directly edged to node i. i ;

[0024] S1512, Remove neighborhood coupling number XQ i Nodes with a value greater than or equal to 0;

[0025] S1513, according to the Degree i Neighborhood coupling numbers XQ are arranged in descending order of size. i Nodes with a value less than 0;

[0026] S1514, Degree Confirmation i The largest node is the only configured node;

[0027] When there is only one node with the highest degree, the node with the highest degree is selected as the configuration node;

[0028] When there are n (n≥2) Degrees i When the node with the largest evaluation index is selected, the evaluation index of the n nodes is compared, and the node with the largest evaluation index is determined as the unique configuration node.

[0029] S1515. Record the determined node index i, put the node index i into the j-th element of set P, and set the node corresponding to F. i The value of increases by 1, and j = j + 1;

[0030] S1516. Based on the network graph with the added shared service elements, redetermine the neighborhood coupling number of all nodes in the network graph, and return to step S1512. Repeat S1512-S1516 until all nodes in the network meet the shared configuration requirements, then stop the loop.

[0031] Preferably, the neighbor node configuration strategy based on weighted degree in S5 is as follows:

[0032] S152. The service element configuration strategy based on weighted degree is implemented as follows:

[0033] S1521, Calculate the weighted degree WDegree of node i. i And set the initial value of variable j to 0;

[0034] The weight w between node i and its neighboring node j ij The feature evaluation index value x equal to node i i The feature evaluation index value x of node j j The product of:

[0035] w ij =w ji =x i *x j

[0036] If there is no edge connecting node i and node j, then w ij =w ji =0,

[0037] The weighted degree WDegree of node i i Defined as:

[0038]

[0039] Where n is the number of nodes;

[0040] S1522, Remove nodes with a neighborhood coupling number ≥ 0;

[0041] S1523, All nodes are ranked according to their weighted degree WDegree i Sort in descending order from largest to smallest;

[0042] S1524, Weighted degree WDegree i The largest node is determined to be the only configured node;

[0043] S1525. Record the determined node index i, put the node index i into the j-th element of set P, and set the node corresponding to F. i The value of increases by 1, and j = j + 1;

[0044] S1526. Based on the network graph with the added shared service elements, redetermine the neighborhood coupling number of all nodes in the network graph, and return to step S1522. Repeat S1522-S1526 until all nodes in the network meet the shared configuration requirements, then stop the loop.

[0045] Preferably, the attribute of the node in S2 is the supply attribute F of the shared service element. i Attribute X of the requirement i And a comprehensive evaluation index value characterizing the node features, where i represents the node number corresponding to the unit, specifically:

[0046] S121. Determine the service elements that need to be configured, and set the supply attribute F of the corresponding node according to the number of service elements included in each unit. i ;

[0047] S122. Set the demand attribute X for each node's service element according to the demand for the service element. i ;

[0048] S123. Set comprehensive evaluation index values;

[0049] The comprehensive evaluation index value is used to characterize the node characteristics. The comprehensive evaluation index value of the unit corresponding to node i can be obtained directly from the database, or the comprehensive evaluation method can be used to obtain the comprehensive evaluation index value of the unit corresponding to node i based on the unit evaluation index.

[0050] The unit evaluation indicators include four dimensions: population, economy, space, and morphology. The population dimension includes indicators such as total population, population density, labor force, and elderly population, reflecting the unit's population vitality. The economic dimension includes industrial development level, energy consumption level, and income level, reflecting the unit's economic vitality. The spatial dimension includes spatial expansion suitability, spatial expansion performance, and spatial expansion ecological and environmental benefits, reflecting the unit's potential and impact on spatial expansion. The morphological dimension includes morphological dimension, clustering dimension, and correlation dimension, reflecting the unit's morphological characteristics. The unit evaluation indicators for each dimension are used to construct the comprehensive unit evaluation indicators for that dimension through a comprehensive evaluation method. The comprehensive evaluation indicators for each dimension are then used to construct the overall comprehensive unit evaluation indicators through a further comprehensive evaluation method.

[0051] The comprehensive evaluation method determines the evaluation index and its weights for the characteristics of the descriptive unit based on one or a combination of Delphi method, factor pair comparison method, and analytic hierarchy process, and finally obtains a comprehensive evaluation index value for the characteristics of the descriptive unit.

[0052] Preferably, the network association attributes of nodes in S3 are divided into network association supply attributes and network association demand attributes of shared service elements, which are defined as S respectively. i and D i Specifically:

[0053] S131. Based on the shareable service elements of adjacent nodes, the network association supply attribute S of each node. i It should be equal to the supply attribute F of this unit. i Supply attribute F of adjacent spatial units j The sum, mathematically represented as follows:

[0054]

[0055] Among them, F ij (j≠i) represents the current status characteristics of shared service elements between node i and node j, where n is the number of nodes, and F ij The possible values ​​are as follows:

[0056]

[0057] S132, Network Association Requirement Attributes of Each Node (D) i The expression is as follows:

[0058] D i =X i (3)

[0059] Among them, X i The required attributes of the elements serving the i-node.

[0060] Preferably, dividing the region into multiple sub-regions specifically involves using village-level administrative region spatial data files to divide the region into sub-regions, i.e., the unit is an administrative village group.

[0061] The present invention further discloses a graph theory-based neighbor-unit shared service element configuration system, which includes the following modules:

[0062] The module includes a parameter setting module, a data input module, a data processing module, a node configuration module, and a result generation module, specifically:

[0063] The parameter setting module is used to set the parameters required for unit configuration, including unit division data, service element type, and evaluation indicators describing unit characteristics.

[0064] The data input module is used to select the input file based on the parameters in the parameter setting module;

[0065] The data processing module is used to construct a network diagram for the region that requires service element configuration and to set various attributes of nodes and edges; the various attributes of nodes and edges are obtained from the parameter setting module and the data input module.

[0066] The node configuration module is used to select different sub-configuration modules for node configuration in the network graph constructed by the data processing module. The node configuration module includes two sub-configuration modules: a first sub-configuration module based on a degree-first neighbor node configuration strategy and a second sub-configuration module based on a weighted degree neighbor node configuration strategy.

[0067] The results generation module obtains the unit configuration results through the node configuration module.

[0068] Preferably, the parameter setting module specifically includes a service element setting unit and an evaluation index setting unit for descriptive features;

[0069] The service element setting unit is used to set the type of service element;

[0070] The evaluation index setting unit for the characteristics of the description unit is used to set the evaluation index for the characteristics of the description unit, specifically including population dimension data sub-unit, economic dimension data sub-unit, spatial dimension data sub-unit and morphological dimension data sub-unit.

[0071] The population dimension data sub-units include the setting of indicators for total population, population density, working-age population, and elderly population;

[0072] The economic dimension data sub-units include the setting of indicators for industrial development level, energy consumption level, and income level;

[0073] The spatial dimension data sub-unit includes the setting of indicators for spatial expansion suitability, spatial expansion performance, and spatial expansion ecological and environmental benefits;

[0074] The morphological dimension data sub-unit includes the setting of morphological dimension, aggregation dimension, and correlation dimension indicators.

[0075] Preferably, the data input module searches for keywords in the file name to perform fuzzy or exact matching of the input file; the input file includes the selected unit division data file, the current spatial location distribution data file of the selected service element type, and the evaluation index data file describing the characteristics of the selected unit.

[0076] Preferably, the data processing module includes a unit feature evaluation index integration module, a network construction module, and a service element processing module;

[0077] The unit feature evaluation index comprehensive module is used to determine the evaluation index and its weight by using one or a combination of methods such as Delphi method, factor pair comparison method, and analytic hierarchy process based on the selected unit feature evaluation index, and finally calculate a comprehensive evaluation index value describing the unit feature.

[0078] The network construction module is used to construct a network graph to describe the relationship between units. The network graph is constructed based on the input unit partitioning data. Units in the network graph are used as nodes, and the geographic spatial adjacency of units is used as edges.

[0079] The service element processing module is used to calculate the supply attributes, demand attributes, network-related supply attributes and demand attributes of the corresponding nodes of each unit based on the matching calculation of the current spatial location distribution of service elements and the spatial range of the units, and to determine the neighborhood coupling number of each unit node.

[0080] Compared with the prior art, the present invention has the following beneficial effects:

[0081] (1) By fully considering the mutual supply relationship of service elements among multiple neighboring units, the role of service elements can be better utilized;

[0082] (2) Determine the configuration order of units so that each service element configuration is the optimal configuration under the current conditions, and the service elements can be balanced in configuration among units and can be shared and utilized to the maximum extent.

[0083] (3) The problem of jointly configuring service elements in neighboring units is solved by using graph theory. The implementation method is simple and highly practical.

[0084] (4) Optimize the spatial pattern of service element allocation and improve the service efficiency of service elements. Attached Figure Description

[0085] Figure 1 This is a flowchart of the graph theory-based neighbor unit shared service element configuration method of the present invention;

[0086] Figure 2 This is a schematic diagram of village and group nodes according to an embodiment of the present invention;

[0087] Figure 3 This is a schematic diagram of the network edges formed by the spatial adjacency relationship of villages and groups in an embodiment of the present invention;

[0088] Figure 4 This is a flowchart of the neighbor node configuration strategy based on degree priority on the basis of the neighborhood coupling number in this invention;

[0089] Figure 5 This is a flowchart of the neighbor node configuration strategy based on weighted degree on the basis of the neighborhood coupling number in this invention;

[0090] Figure 6 This is an embodiment of the invention regarding the initial degree of network village groups in Shizhu County. i Schematic diagram;

[0091] Figure 7 This is a schematic diagram of the output results of the degree-first neighboring village group configuration strategy in an embodiment of the present invention;

[0092] Figure 8 This is the initial weighted degree InDegree of the network village group in Shizhu County, according to an embodiment of the present invention. i Schematic diagram;

[0093] Figure 9 This is a schematic diagram of the output results of the facility configuration strategy based on weighted degree according to an embodiment of the present invention;

[0094] Figure 10 This is a flowchart of the neighboring unit shared configuration system according to an embodiment of the present invention. Detailed Implementation

[0095] To facilitate understanding and implementation of the invention's content and technical solutions, the invention will be described in more detail below with reference to the accompanying drawings and embodiments. It should be understood that those skilled in the art can implement the invention without requiring some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the invention by illustrating examples. Therefore, the specific embodiments described herein are only used to explain the invention and do not limit the invention.

[0096] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0097] According to the present invention, a graph theory-based method for configuring neighboring cell shared service elements is provided, such as... Figure 1 As shown, the method includes the following steps:

[0098] S1. Construct a network diagram for the regions that require service element configuration; the specific implementation includes the following sub-steps:

[0099] To construct a network graph for regions requiring service element configuration, it is necessary to build the nodes and edges of the network graph separately. The region is divided into multiple sub-regions, each sub-region being a unit. Each unit is treated as a node in the network graph, and the geographic spatial adjacency of units is represented as edges in the network graph. Two adjacent units indicate that an edge exists between the two nodes in the network graph, while two non-adjacent units indicate that no edge exists between the two nodes. The region can be divided according to administrative regions.

[0100] This embodiment takes Shizhu County, Chongqing Municipality, as an example. Shizhu County is located in the upper reaches of the Yangtze River, in eastern Chongqing. It has a total population of 389,000 and a total area of ​​3,014 square kilometers, comprising 236 village-level administrative districts. Before 2021, Shizhu County was a county-level administrative region characterized by poverty, ethnic minorities, and typical mountainous terrain, with a significant shortage of some basic service elements. Therefore, choosing Shizhu County as the research subject is representative. A graph theory-based method for configuring shared service elements among neighboring units is used to maximize the fulfillment of the need for nearby shared configuration among units.

[0101] Shizhu County, Chongqing Municipality, is the region for which service element allocation is to be studied. A network diagram needs to be constructed, requiring the separate construction of nodes and edges. In this embodiment, village-level administrative region spatial data files are used, with administrative village groups as sub-regions, i.e., units are administrative village groups. In other words, in this embodiment, the nodes of the network diagram represent village groups, such as... Figure 2As shown, villages are treated as nodes in the network graph, and the geographical adjacency of administrative regions is represented as edges in the network graph. Two adjacent villages indicate the existence of an edge between the two nodes in the network graph; conversely, no edge exists between them. Figure 3 As shown.

[0102] S2, Set the node's attributes;

[0103] The shared service elements of this invention include, but are not limited to, kindergartens, primary schools, hospitals, public toilets, and any service elements that meet the basic living needs of the public.

[0104] This invention sets the attributes of nodes as supply attributes, demand attributes, and comprehensive evaluation index values ​​that characterize the features of shared service elements.

[0105] S121. Determine the service elements that need to be configured, and set the supply attributes of the corresponding nodes according to the number of service elements included in each unit, defined as F. i , where i represents the node number corresponding to the unit.

[0106] S122. Set the demand attribute X for each node's service elements based on the demand for service elements. i .

[0107] S123. Set comprehensive evaluation index values;

[0108] The comprehensive evaluation index value is used to characterize the node characteristics. The unit comprehensive evaluation index value corresponding to node i can be obtained directly from the database, or the unit comprehensive evaluation index corresponding to node i can be obtained by using the comprehensive evaluation method based on the unit evaluation index.

[0109] Typically, unit evaluation indicators are categorized into four dimensions: population, economy, space, and morphology. The population dimension includes indicators such as total population, population density, labor force, and elderly population, reflecting the unit's population vitality. The economic dimension includes indicators such as industrial development level, energy consumption level, and income level, reflecting the unit's economic vitality. The spatial dimension includes indicators such as spatial expansion suitability, spatial expansion performance, and spatial expansion ecological and environmental benefits, reflecting the unit's potential and impact of spatial expansion. The morphological dimension includes morphological dimension, clustering dimension, and correlation dimension, reflecting the unit's morphological characteristics. The unit evaluation indicators for each dimension can be used to construct a comprehensive unit evaluation indicator for that dimension using existing comprehensive evaluation methods. These comprehensive evaluation indicators can then be further used to construct a comprehensive unit evaluation indicator. Comprehensive evaluation methods may include one or more of the following: the Delphi method, the pairwise comparison method, and the analytic hierarchy process (AHP), to determine the evaluation indicators describing the unit's characteristics and their weights, ultimately yielding a comprehensive evaluation indicator value describing the unit's characteristics.

[0110] Since the unit in this embodiment is an administrative village group, the attributes of the corresponding node are set as the supply attributes and demand attributes of the village group service elements, as well as the comprehensive evaluation index value describing the characteristics of the village group.

[0111] Assuming that the service element to be configured in this embodiment is a primary school, and village group A has no primary school, then the node number of village A in the network graph is 1, then F1 = 0; village group B has a primary school, then the node number of village B in the network graph is 2, then F2 = 1.

[0112] Village group demand attribute X i It can be set according to actual needs. Typically, based on the requirement that neighboring villages and groups can centrally establish a primary school, kindergarten, health clinic, and other service elements, the village / group's demand attribute X is determined. i All are set to a constant of 1, X1 = 1, X2 = 1, indicating that the demand for a primary school in villages A and B is one.

[0113] S3, Set the network association attributes of the node;

[0114] The network association attributes of nodes are divided into network association supply attributes and network association demand attributes of shared service elements, which are defined as S respectively. i and D i .

[0115] S131. Based on the shareable service elements of adjacent nodes, the network association supply attribute S of each node. i It should be equal to the supply attribute F of this node. i Supply attribute F of spatially adjacent nodes j The sum, mathematically represented as follows:

[0116]

[0117] Among them, F ij (j≠i) represents the current status characteristics of shared service elements between node i and node j, where n is the number of nodes, and F ij The possible values ​​are as follows:

[0118]

[0119] S132, Network Association Requirement Attributes of Each Node (D) i The expression is as follows:

[0120] D i =X i (3)

[0121] In this embodiment, based on the shareable service elements of neighboring villages, the network-related supply attribute S of each village group is determined. iIt should be obtained according to formula (1). For example, the supply attribute F1 of primary school in village A is 0; the supply attribute F2 of primary school in village B is 1; and the supply attribute F3 of primary school in village C is 1. Village A is only adjacent to villages B and C, so according to formula (1), the network association supply attribute S1 of village A is 2.

[0122] Because in this embodiment, the village group's demand attribute X i The value is set to a constant of 1; therefore, in the network association requirement attribute of village group A, D1 = X1 = 1.

[0123] S4. Determine the neighborhood coupling number of each node;

[0124] Based on the network association supply attribute S of each node i Network-related requirement attribute D i Calculate the neighborhood coupling number XQ of each node. i :

[0125] XQ i =S i -D i (4)

[0126] When the neighborhood coupling number XQ i When the number of neighborhood couplings is ≥0, node i does not need to be configured; when the number of neighborhood couplings is XQ i When <0, node i is the unit to be configured.

[0127] For example, for village group A,

[0128] XQ1=S1-D1=1

[0129] Since the neighborhood coupling number XQ1 of village group A is greater than or equal to 0, village group A does not need to be configured as a unit.

[0130] S5. Obtain the node configuration set based on the neighborhood coupling number;

[0131] The node configuration set P stores the configuration results.

[0132] Based on the neighborhood coupling number, this invention designs two configuration strategies: a degree-first neighbor node configuration strategy and a weighted degree neighbor node configuration strategy. These two methods are parallel schemes, and the specific steps are as follows: Figure 5 :

[0133] S151. The degree-first neighbor node configuration strategy is implemented as follows:

[0134] S1511. Calculate the degree of each node in the network graph and set the initial value of variable j to 0 (j is an integer).

[0135] In a network structure, degree can be quantified as the number of nodes directly connected to it. The degree of node i is defined as the number of nodes directly connected to node i by an edge. i .

[0136] S1512, Remove neighborhood coupling number XQ i Nodes with a value greater than or equal to 0;

[0137] S1513, according to the Degree i Neighborhood coupling numbers XQ are arranged in descending order of size. i Nodes with a value less than 0.

[0138] S1514, Degree Confirmation i The largest node is the only configured node.

[0139] When there is only one node with the highest degree, select the node with the highest degree as the configuration node;

[0140] When there are n (n≥2) Degrees i When the node with the largest evaluation index is selected, the node with the largest evaluation index is compared with the evaluation index of the n nodes, and the node with the largest evaluation index is selected as the unique configuration node.

[0141] S1515. Record the determined node index i, put the node index i into the j-th element of set P, and set the node corresponding to F. i The value of F increases by 1, and j = j + 1. i An increase in the value indicates that a shared service element has been configured for node i.

[0142] S1516. Based on the network graph with the added shared service elements, redetermine the neighborhood coupling number of all nodes in the network graph, and return to step S1512. Repeat S1512-S1516 until all nodes in the network meet the shared configuration requirements.

[0143] In this embodiment, because of the network association requirement attribute D of the village group i Both are 1, therefore when the supply attribute F is increased... i Afterwards, the neighborhood coupling number of the village group and its adjacent village groups will be greater than 0, therefore the village group index i will not appear repeatedly in the set P. If the village group's required attribute D... i If the value is greater than 1, then village group number i may appear repeatedly in set P, but the number of repetitions will not exceed the number of repetitions of the required attribute D. i In this embodiment, a network graph with node degree is shown as follows: Figure 6 As shown, the degree of the node. i For specific numerical values, Figure 6 For ease of display, the relative degree of a node is shown by its node size. Figure 7This is a schematic diagram illustrating the output of the degree-first neighboring village group configuration strategy. Figure 7 The villages and groups that need to be configured are selected sequentially according to the configuration order. Figure 7 The configuration order is not displayed; only the units corresponding to the last node that needs to be configured are shown.

[0144] S152. The service element configuration strategy based on weighted degree is implemented as follows:

[0145] S1521, Calculate the weighted degree WDegree of node i. i And set the initial value of variable j to 0 (j is an integer).

[0146] Using the comprehensive evaluation index value describing the characteristics of the unit as the weight of the directed edges in the weighted graph of the network graph, the weighted degree of a node represents the comprehensive service capability of the node's configured service elements to its neighboring nodes. The weight w between node i and its neighboring node j is defined. ij The feature evaluation index value x equal to node i i The feature evaluation index value x of node j j The product of:

[0147] w ij =w ji =x i *x j

[0148] If there is no edge connecting node i and node j, then w ij =w ji =0,

[0149] The weighted degree WDegree of node i i Defined as:

[0150]

[0151] Where n is the number of nodes;

[0152] S1522, Remove nodes with a neighborhood coupling number ≥ 0.

[0153] S1523, All nodes are ranked according to their weighted degree WDegree i Sort in descending order from largest to smallest.

[0154] S1524, Weighted degree WDegree i The largest node is determined as the unique configuration node.

[0155] S1525. Record the determined node index i, put the node index i into the j-th element of set P, and set the node corresponding to F. i The value of increases by 1, and j = j + 1.

[0156] S1526. Based on the network graph with the added shared service elements, redetermine the neighborhood coupling number of all nodes in the network graph, and return to step S1522. Repeat S1522-S1526 until all nodes in the network meet the shared configuration requirements.

[0157] In this embodiment, the initial WDegree of the village group node is calculated. i The results are as follows Figure 8 As shown, the spatial configuration results of service elements for each village group obtained based on the weighted service element configuration strategy are as follows: Figure 9 As shown.

[0158] S6. Output the unit configuration order of shared service elements;

[0159] Set P is a node configuration set obtained based on the neighborhood coupling number, storing the corresponding node indices. Elements in set P are output in ascending order; the output order of the node indices represents the configuration order of the corresponding units. The earlier the node indices are output, the earlier the service element needs to be configured. Nodes not in set P indicate that the corresponding unit does not need to be configured with service elements and can be represented by NULL.

[0160] To implement the graph theory-based neighbor-sharing configuration method, this embodiment also provides a graph theory-based neighbor-sharing configuration system, which includes the following modules: a parameter setting module 210, a data input module 220, a data processing module 230, a node configuration module 240, and a result generation module 250, specifically:

[0161] The parameter setting module 210 is used to set the parameters required for unit configuration, including unit division data, service element type, evaluation indicators (or comprehensive evaluation indicators) describing unit characteristics, etc.

[0162] Data input module 220 is used to select an input file according to the parameters in the parameter setting module;

[0163] Data processing module 230 is used to construct a network diagram for the area where service elements need to be configured and to set various attributes of nodes and edges; the various attributes of nodes and edges are obtained according to parameter setting module 210 and data input module 220;

[0164] The node configuration module 240 is used to configure the nodes of the sub-configuration module for the network graph constructed by the data processing module.

[0165] The sub-configuration module includes: a first sub-configuration module based on a degree-first neighbor node configuration strategy and a second sub-configuration module based on a weighted degree neighbor node configuration strategy;

[0166] The result generation module 250 obtains the unit configuration result through the node configuration module 240.

[0167] The parameter setting module 210 specifically includes a service element setting unit and an evaluation index setting unit for descriptive features;

[0168] The service element setting unit is used to set the type of service element;

[0169] The evaluation index setting unit for describing unit characteristics is used to set the evaluation index for describing unit characteristics, specifically including population dimension data sub-units, economic dimension data sub-units, spatial dimension data sub-units, and morphological dimension data sub-units.

[0170] The population dimension data sub-units include the setting of indicators such as total population, population density, labor force, and elderly population;

[0171] The economic dimension data sub-units include the setting of indicators such as industrial development level, energy consumption level, and income level;

[0172] The spatial dimension data sub-unit includes the setting of indicators such as spatial expansion suitability, spatial expansion performance, and spatial expansion ecological and environmental benefits;

[0173] The morphological dimension data sub-unit includes the setting of indicators such as morphological dimension, aggregation dimension, and correlation dimension.

[0174] The data input module 220 obtains the initial data required for configuration from external input units by searching for keywords in the file name, either through fuzzy matching or exact matching. The data input file should include the selected unit division data file, the current spatial location distribution data file of the selected service element type, and the evaluation index data file of the selected descriptive unit characteristics.

[0175] Data processing module 230 includes a unit feature evaluation index integration module, a network construction module, and a service element processing module;

[0176] The unit feature evaluation index comprehensive module uses one or more methods such as Delphi method, factor pair comparison method, and analytic hierarchy process to determine the evaluation index and its weights that describe the unit feature based on the selected unit feature evaluation index, and finally calculates a comprehensive evaluation index value that describes the unit feature.

[0177] The network construction module is used to build a network to describe the relationships between units. It constructs a network graph based on the input unit partitioning data. Units in the network graph are treated as nodes, and the geographic spatial adjacency relationships of the units are treated as edges.

[0178] The service element processing module is used to calculate the supply attributes, demand attributes, network association supply attributes and demand attributes of the corresponding nodes of each unit based on the matching calculation of the spatial distribution of the current service elements and the spatial range of the units, and to determine the neighborhood coupling number of each node. The results of the service element processing module are the same as the results of steps S3 and S4.

[0179] The node configuration module 240 determines the service element space configuration and its configuration order by selecting different node configuration strategies for the first sub-configuration module and the second sub-configuration module through iteration.

[0180] Finally, it should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A graph theory-based method for configuring shared service elements among neighboring units, characterized in that... Includes the following steps: S1. Construct a network diagram for the areas that require service element configuration; The region is divided into multiple sub-regions, each of which is a unit. Each unit is treated as a node in the network graph. The geographic spatial adjacency of the units is represented as an edge in the network graph. Two adjacent units indicate that there is an edge between the two nodes in the network graph, and conversely, two non-adjacent units indicate that there is no edge between the two nodes in the network graph. S2, Set the node's attributes; The attributes of nodes are the supply attributes of shared service elements. Demand attributes And a comprehensive evaluation index value that characterizes the node features, where i represents the node number corresponding to the unit; S3, Set the network association attributes of the node; The network association attributes of nodes are divided into network association supply attributes and network association demand attributes of shared service elements, which are defined as follows: and ; In S3, the network association attributes of nodes are divided into network association supply attributes and network association demand attributes of shared service elements, defined as follows: and Specifically: S131. Based on the service elements that can be shared by adjacent nodes, the network association supply attributes of each node. It should be equal to the supply attribute of this unit. Supply attributes of adjacent spatial units The sum, mathematically represented as follows: = + (1) ； in, (j≠i) represents the current status characteristics of shared service elements between node i and node j, where n is the number of nodes. The possible values ​​are as follows: (2) ； S132, Network Association Requirement Attributes of Each Node The expression is as follows: (3) ； in, The required attributes of elements serving the i-node; S4. Determine the neighborhood coupling number of each node; Based on the network association supply attributes of each node Network-related requirements attributes Calculate the neighborhood coupling number of each node. : (4) ； When the neighborhood coupling number When the number of neighbor couplings is ≥0, node i does not need to be configured; when the number of neighbor couplings is ≥0, node i does not need to be configured. When the value is 0, node i is the unit to be configured; S5. Obtain the node configuration set P based on the neighborhood coupling number; Based on the neighborhood coupling number, a neighbor node configuration strategy based on degree priority or a neighbor node configuration strategy based on weighted degree is adopted to obtain the configuration result; The results are stored in the node configuration set P; S6. Unit configuration order of output shared service elements Set P stores the corresponding node numbers. The elements in set P are output in ascending order, and the output order of the node numbers is the order in which the service elements of the corresponding unit are configured. Nodes not in set P indicate that the corresponding unit does not need to be configured with service elements.

2. The graph theory-based method for configuring shared service elements among neighboring units according to claim 1, characterized in that: The degree-first neighbor node configuration strategy in S5 is as follows: S151. The degree-first neighbor node configuration strategy is implemented as follows: S1511. Calculate the degree of each node in the network graph and set the initial value of variable j to 0; The degree of node i is defined as the number of nodes directly edged to node i. ; S1512, Remove neighborhood coupling number Nodes with a value greater than or equal to 0; S1513, according to Neighborhood coupling numbers are arranged in descending order of size. Nodes with a value of 0; S1514, Confirm The largest node is the only configured node; When there is only one node with the highest degree, the node with the highest degree is selected as the configuration node; When there are n When the node with the largest evaluation index is n≥2, compare the evaluation index values ​​of the n nodes and determine the node with the largest evaluation index as the unique configuration node. S1515. Record the determined node index i, put the node index i into the j-th element of set P, and set the node corresponding to... The value of is increased by 1, and j = j + 1; S1516. Based on the network graph with the added shared service elements, redetermine the neighborhood coupling number of all nodes in the network graph, and return to step S1512. Repeat S1512-S1516 until all nodes in the network meet the shared configuration requirements, then stop the loop.

3. The graph theory-based method for configuring shared service elements among neighboring units according to claim 1, characterized in that: The neighbor node configuration strategy based on weighted degree in S5 is as follows: S152. The service element configuration strategy based on weighted degree is implemented as follows: S1521, Calculate the weighted degree of node i. And set the initial value of variable j to 0; Weights between node i and its neighboring node j The feature evaluation index value equal to node i The feature evaluation index value of node j The product of: ； If there is no edge connecting node i and node j, then , Weighted degree of node i Defined as: ； Where n is the number of nodes; S1522, Remove nodes with a neighborhood coupling number ≥ 0; S1523, All nodes are ranked according to weighted degree Sort in descending order from largest to smallest; S1524, Weighting The largest node is determined to be the only configured node; S1525. Record the determined node index i, put the node index i into the j-th element of set P, and set the node corresponding to... The value of is increased by 1, and j = j + 1; S1526. Based on the network graph with the added shared service elements, redetermine the neighborhood coupling number of all nodes in the network graph, and return to step S1522. Repeat S1522-S1526 until all nodes in the network meet the shared configuration requirements, then stop the loop.

4. The graph theory-based method for configuring shared service elements among neighboring units according to claim 1, characterized in that: The attributes of the nodes in S2 are the supply attributes of shared service elements. Demand attributes And a comprehensive evaluation index value characterizing the node features, where i represents the node number corresponding to the unit, specifically: S121. Determine the service elements that need to be configured, and set the supply attributes of the corresponding nodes according to the number of service elements included in each unit. ; S122. Set the demand attributes of each node's service elements according to the demand for the service elements. ; S123. Set comprehensive evaluation index values; The comprehensive evaluation index value is used to characterize the node characteristics. The comprehensive evaluation index value of the unit corresponding to node i can be obtained directly from the database, or the comprehensive evaluation method can be used to obtain the comprehensive evaluation index value of the unit corresponding to node i based on the unit evaluation index. The unit evaluation indicators include four dimensions: population, economy, space, and morphology. The population dimension includes indicators such as total population, population density, labor force, and elderly population, reflecting the unit's population vitality. The economic dimension includes industrial development level, energy consumption level, and income level, reflecting the unit's economic vitality. The spatial dimension includes spatial expansion suitability, spatial expansion performance, and spatial expansion ecological and environmental benefits, reflecting the unit's potential and impact on spatial expansion. The morphological dimension includes morphological dimension, clustering dimension, and correlation dimension, reflecting the unit's morphological characteristics. The unit evaluation indicators for each dimension are used to construct the comprehensive unit evaluation indicators for that dimension through a comprehensive evaluation method. The comprehensive evaluation indicators for each dimension are then used to construct the overall comprehensive unit evaluation indicators through a further comprehensive evaluation method. The comprehensive evaluation method determines the evaluation index and its weights for the characteristics of the descriptive unit based on one or a combination of Delphi method, factor pair comparison method, and analytic hierarchy process, and finally obtains a comprehensive evaluation index value for the characteristics of the descriptive unit.

5. The graph theory-based method for configuring shared service elements among neighboring units according to claim 1, characterized in that: The division of the region into multiple sub-regions specifically involves using village-level administrative region spatial data files to divide the region into sub-regions, with each unit being an administrative village group.

6. A graph-based neighbor-unit shared service element configuration system for the graph-based neighbor-unit shared service element configuration method of claim 1, characterized in that: Includes the following modules: The module includes a parameter setting module, a data input module, a data processing module, a node configuration module, and a result generation module, specifically: The parameter setting module is used to set the parameters required for unit configuration, including unit division data, service element type, and comprehensive evaluation index value describing unit characteristics; The data input module is used to select the input file based on the parameters in the parameter setting module; The data processing module is used to construct a network diagram for the region that requires service element configuration and to set various attributes of nodes and edges; the various attributes of nodes and edges are obtained from the parameter setting module and the data input module. The node configuration module is used to select different sub-configuration modules for node configuration in the network graph constructed by the data processing module. The node configuration module includes two sub-configuration modules: a first sub-configuration module based on a degree-first neighbor node configuration strategy and a second sub-configuration module based on a weighted degree neighbor node configuration strategy. The results generation module obtains the unit configuration results through the node configuration module.

7. The graph theory-based neighbor-unit shared service element configuration system according to claim 6, characterized in that: The parameter setting module specifically includes a service element setting unit and an evaluation index setting unit for descriptive features; The service element setting unit is used to set the type of service element; The evaluation index setting unit for the characteristics of the description unit is used to set the comprehensive evaluation index value of the characteristics of the description unit, specifically including population dimension data sub-unit, economic dimension data sub-unit, spatial dimension data sub-unit and morphological dimension data sub-unit. The population dimension data sub-units include the setting of indicators for total population, population density, working-age population, and elderly population; The economic dimension data sub-units include the setting of indicators for industrial development level, energy consumption level, and income level; The spatial dimension data sub-unit includes the setting of indicators for spatial expansion suitability, spatial expansion performance, and spatial expansion ecological and environmental benefits; The morphological dimension data sub-unit includes the setting of morphological dimension, aggregation dimension, and correlation dimension indicators.

8. The graph theory-based neighbor-unit shared service element configuration system according to claim 6, characterized in that: The data input module searches for keywords in the file name to perform fuzzy or exact matching of the input file; the input file includes the selected unit division data file, the current spatial location distribution data file of the selected service element type, and the comprehensive evaluation index value data file of the selected descriptive unit characteristics.

9. The graph theory-based neighbor-unit shared service element configuration system according to claim 6, characterized in that: The data processing module includes a unit feature evaluation index integration module, a network construction module, and a service element processing module; The unit feature evaluation index comprehensive module is used to determine the comprehensive evaluation index value and its weight by using one or a combination of methods such as Delphi method, factor pair comparison method, and analytic hierarchy process based on the selected unit feature comprehensive evaluation index value, and finally calculate a comprehensive evaluation index value describing the unit feature. The network construction module is used to construct a network graph to describe the relationship between units. The network graph is constructed based on the input unit partitioning data. Units in the network graph are used as nodes, and the geographic spatial adjacency of units is used as edges. The service element processing module is used to calculate the supply attributes, demand attributes, network-related supply attributes and demand attributes of the corresponding nodes of each unit based on the matching calculation of the current spatial location distribution of service elements and the spatial range of the units, and to determine the neighborhood coupling number of each unit node.

Images