CN117749647A - Electric power optical communication network visualization method based on improved Louvain algorithm - Google Patents
Electric power optical communication network visualization method based on improved Louvain algorithm Download PDFInfo
- Publication number
- CN117749647A CN117749647A CN202311814805.5A CN202311814805A CN117749647A CN 117749647 A CN117749647 A CN 117749647A CN 202311814805 A CN202311814805 A CN 202311814805A CN 117749647 A CN117749647 A CN 117749647A
- Authority
- CN
- China
- Prior art keywords
- node
- nodes
- community
- communities
- optical communication
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 59
- 238000004891 communication Methods 0.000 title claims description 45
- 230000003287 optical effect Effects 0.000 title claims description 43
- 238000007794 visualization technique Methods 0.000 title claims description 13
- 238000000034 method Methods 0.000 claims abstract description 24
- 230000005540 biological transmission Effects 0.000 claims description 20
- 238000007726 management method Methods 0.000 claims description 17
- 238000005516 engineering process Methods 0.000 claims description 12
- 238000010586 diagram Methods 0.000 claims description 9
- 230000000007 visual effect Effects 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 6
- 238000012800 visualization Methods 0.000 claims description 6
- 238000013079 data visualisation Methods 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 2
- 238000012423 maintenance Methods 0.000 abstract description 8
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000008030 elimination Effects 0.000 description 2
- 238000003379 elimination reaction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000002035 prolonged effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000003012 network analysis Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The method can automatically detect the community structure in the network, divide equipment nodes in the network into closely related communities, select seed nodes by introducing the degree of the nodes when dividing the communities, and set two thresholds, so that on one hand, the number of candidate nodes can be reduced, useless comparison can be avoided, and the efficiency of the algorithm can be improved; on the other hand, nodes with larger degree of failure can be eliminated, communities obtained by division are prevented from being too huge, and the quality of the community division results of the algorithm is improved, so that the relevance inside the network is revealed. The invention can obviously improve the reliability and efficiency of network operation and maintenance.
Description
Technical Field
The invention relates to the technical field of operation and maintenance management of an electric power optical communication network, in particular to an electric power optical communication network visualization method based on an improved Louvain algorithm.
Background
The effective management of the electric power optical communication network is an important guarantee for supporting the operation of the intelligent power grid, and the electric power communication is used as a solid foundation stone for the information construction of the industry, so that not only is the efficient and reliable communication capability provided, but also the powerful support is provided for the operation and management of the power grid. However, with the rapid expansion of the size of the electric power optical communication network, the difficulty of management increases, and the traditional electric power optical communication network management method relies on text and tabular data presentation, so that a manager needs to monitor and maintain the network by reading a large amount of data reports and logs. While this approach can provide detailed information, it has limitations in terms of fast positioning issues and overall network condition awareness. Especially in the face of complex network structures and large-scale data, conventional methods may lead to inefficiency and misjudgment, and it is apparent that conventional management methods have failed to meet the existing demands.
The prior art comprises the following steps: he Zhifang, zhang Xiuli, zhu Yanfang, xu Limei, liu Yu. Power grid traffic Transmission Multi-path method based on force-directed algorithm research [ J ]. Modern industry economy and informatization, 2021,11 (05). The technology adopts a traditional force-directed layout algorithm to carry out visual layout on the power grid service transmission path, but ignores the community structure of nodes in the network, which may cause inconsistent or misplacement of node positions in communities, and problems of efficiency and scalability are faced when the power grid scale is enlarged.
Therefore, the invention provides an electric power optical communication network visualization method based on an improved Louvain algorithm, which is characterized in that a network community division technology and a visualization technology are introduced to divide the electric power optical communication network into communities and visually display network topology structures and service data, so that a manager can clearly know the connection relation among transmission sites, equipment and infrastructure, and operation and maintenance management is more efficient.
Disclosure of Invention
The invention provides an electric power optical communication network visualization method based on an improved Louvain algorithm, which aims to solve the problems that the existing electric power optical communication network ignores a community structure to which a node belongs, so that the positions of the nodes in the community are inconsistent or misplaced, and the efficiency and the scalability problems are encountered when the power grid scale is enlarged, so that the efficiency is low, the existing application requirements cannot be met due to misjudgment and the like.
The electric power optical communication network visualization method based on the improved Louvain algorithm is specifically realized by the following steps:
step one, acquiring service transmission equipment data of an electric power optical communication network and connection relation data between starting station equipment and terminating station equipment based on an optical communication equipment management system, wherein the acquired data set is used as a service transmission equipment data set; acquiring a service data set from a dispatching telephone service system, a video service system and a relay protection service system based on service types, and storing the service data set to obtain the data set as a service data set;
the service transmission equipment data set comprises equipment models, starting station equipment and terminating station equipment, and forms a service transmission set;
step two, according to the service transmission equipment data set obtained in the step one, adopting an improved Louvain algorithm to carry out community division of the power optical communication network, and obtaining a community division result C', wherein the specific process is as follows:
step two, using the equipment as nodes, determining the connection relation between the nodes, drawing a network diagram, and adding attribute information according to the requirement to obtain a preliminary community division result C;
secondly, compressing the community division result C obtained in the second step to obtain a new network graph G ' (V ', E '); where V' is the new node set { V } 1 ′,V 2 ′...,V r 'E' is a new edge set { E }, E 1 ′,E′ 2 ...,E′ r }；
Step two, selecting seed nodes for the new network graph G ' (V ', E ') by adopting the node-based degree, defining two thresholds as the upper limit and the lower limit of the node degree respectively, wherein the seed node set P is expressed as follows:
in the formula, deg (v s ) For node v s Is the number of degrees; g is the average degree of the node, q is the standard deviation of the degree of the node, and c and d are constants;
step two, four, for non-seed node v' o ，v′ o E, V ' -P, s is less than or equal to n-r, and then the E, V ' -P, s is distributed into corresponding new communities, when all non-seed nodes V ' o The community to which the community belongs does not change any more, and a community dividing result C' is output;
and thirdly, applying the community division result C' to a force-directed layout algorithm of attribute constraint so as to layout the network diagram in a two-dimensional or three-dimensional space, and adopting a visualization tool to visually display the layout result.
The invention has the beneficial effects that:
(1) In the method, an improved Louvain algorithm is provided, which can automatically detect community structures in a network, divide equipment nodes in the network into closely related communities, select seed nodes by introducing the degree of the utilization nodes when dividing the communities, and set two thresholds, so that on one hand, the number of candidate nodes can be reduced, useless comparison can be avoided, and the efficiency of the algorithm can be improved; on the other hand, nodes with larger degree of failure can be eliminated, communities obtained by division are prevented from being too huge, and the quality of the community division results of the algorithm is improved, so that the relevance inside the network is revealed.
(2) The force-directed layout algorithm based on attribute constraint can present nodes and links in the network on a two-dimensional plane in an intuitive mode, and the distance and the position relation between the nodes can reflect the mutual relation of the nodes, so that the structure and the characteristics of the network are clear at a glance, a manager is helped to master the network condition in all directions, the connection relation among transmission sites, equipment and infrastructure is clearly known, and planning, configuration and fault elimination are better performed; the visualized service data are provided for manager to understand more visual data according to the service type sub-module, so as to understand the service operation condition of the network, improve the decision efficiency and accuracy and understand the service condition better.
(3) In the invention, the combination of the improved Louvain algorithm and the force-directed layout algorithm based on attribute constraint has strong expandability in the aspect of network analysis, and can clearly display the community structure and node layout of the network when facing to a complex network; in addition, users can perform interactive operations such as zooming, translation, selecting nodes and the like according to requirements so as to better know the details and changes of the network.
As described above, a large-scale network such as an optical power communication network is considered. The community structure, node layout and service data of the electric power optical communication network are visually analyzed, the network structure and the network service data are visually displayed in front of a manager, the bottleneck, weak links and optimization space in the network can be found, decision support is provided, a network management strategy is improved, and compared with the operation and maintenance management method for reading a large amount of log data and manually analyzing the data, the reliability and the efficiency of network operation and maintenance can be obviously improved.
Drawings
Fig. 1 is a flowchart of an electric power optical communication network visualization method based on an improved Louvain algorithm.
Fig. 2 is a flowchart of a Louvain algorithm modified by dividing a community of electric power optical communication networks in the present invention.
FIG. 3 shows the allocation of non-seed nodes v in the present invention o ' a flow chart.
Fig. 4 is a flow chart of implementing the power optical communication network diagram layout visualization by using the force-directed layout algorithm of attribute constraint in the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present specification. Based on the embodiments of this specification, other embodiments may be made by those of ordinary skill in the art without making any inventive effort, and are intended to be within the scope of this specification.
The embodiment is described with reference to fig. 1 to 4, and is based on an improved Louvain algorithm for visualizing an electrical optical communication network, which is characterized in that two key technologies are combined: the improved Louvain algorithm is used for efficiently and clearly identifying and dividing community structures of the power optical communication network, and the improved force-guiding layout algorithm with attribute constraint is used for visually displaying topological structures and business data of the network, so that a manager can master network conditions in all directions, and operation and maintenance management can be performed more efficiently. The implementation method is realized by the following steps:
step 1, acquiring service transmission equipment data of an electric power optical communication network and connection relation data between an initial equipment and a termination equipment based on an optical communication equipment management system, wherein the acquired data set is recorded as the service transmission equipment data set; acquiring a service data set from service systems such as a telephone service dispatching system, a video service system, a relay protection service system and the like based on service types, and storing the acquired data set as a service data set;
specifically, the service transmission device data set specifically includes: equipment model, starting station equipment and ending point equipment; forming a service transmission set m= { [1,3], [5,7], [ a, b ] }, wherein a in [ a, b ] represents an originating station device number and b represents a terminating station device number;
step 2, according to the service transmission equipment data set obtained in the step one, carrying out electric power optical communication network community division based on the proposed improved Louvain algorithm, wherein the method is specifically realized by the following steps:
first, a basic definition of a graph is given;
let G be a network graph, denoted G (V, E), where V is the set of nodes { V 1 ,V 2 ...,V n E is the set of edges { E }, E 1 ,E 2 ...,E n }。
The traditional Louvain algorithm has a plurality of problems, mainly comprising the steps that in the stage 2, the iteration is carried out until the module degree value Q is not changed any more, then the division corresponding to the maximum module degree value Q is selected as a final community division result, so that the operation time is slower, the large communities are excessively combined, the number of the small communities is more, and the network data information cannot be displayed quickly and clearly, therefore, the traditional Louvain algorithm cannot meet the requirements of the traditional Louvain algorithm on the community division quality and the algorithm efficiency for a large network such as an electric power optical communication network. Therefore, the traditional Louvain algorithm is improved, and the improvement is mainly that two thresholds are set when seed nodes are selected and are respectively used as a lower limit and an upper limit, on one hand, the nodes with smaller degree of failure can be eliminated by using the upper limit, the nodes with larger degree of failure can be eliminated by using the upper limit, the phenomenon that communities are too huge is avoided, finer community structures are difficult to find, and the dividing quality is improved; on the other hand, the number of candidate nodes of the seed node is reduced, and the running time of the algorithm is prolonged. As shown in fig. 2, the method specifically comprises the following steps:
step 21, using the equipment as nodes, determining the connection relation between the nodes, drawing a network diagram, adding attribute information according to the requirement, and executing a stage 1 of a Louvain algorithm to obtain a preliminary community division result C;
each equipment node is respectively classified into a community, and for each node v i (i=1, 2,., N), assuming that its neighbor nodes belong to N communities, node v i Sequentially adding the modules into N communities, and calculating module value change delta Q before and after the addition according to the following formula t (t=1, 2,., N), if maxΔq t > 0, then node v i Assigned to maxΔq t Corresponding community, otherwise node v i The community to which the community belongs remains unchanged;
wherein, sigma in Is the sum of all internal edge weights in community A (A e N); sigma and method for producing the same tot Is the sum of all edge weights adjacent to the nodes in community a; k (k) i Is all adjacent nodes v i Is a sum of edge weights of (a); k (k) i,in Is all slave nodes v i Edge weight sum adjacent to the node in community a; m is the sum of the weights of all the edges in the network.
Step 22: compressing the community division result C to obtain a new network graph G ' (V ', E ');
the purpose of compressing the community division result C is to simplify the network structure, so that the following community detection steps are more efficient, and generally comprise node merging and edge reconstruction; where V' is the new node set { V } 1 ′,V 2 ′…,V r 'E' is a new edge set { E }, E 1 ′,E′ 2 …,E′ r And r.ltoreq.n.
Step 23: obtaining a seed node set P according to a formula (2) for a network graph G ' (V ', E ');
in this embodiment, seed nodes are selected based on the node degree, two thresholds are defined and respectively used as an upper limit and a lower limit of the node degree, so that the number of candidate nodes can be reduced, the iterative execution of stage 2 of the traditional Louvain algorithm is avoided, and the algorithm efficiency can be obviously improved.
Wherein deg (v) s ) Representing node v s Is the number of degrees; g represents the average degree of the node, q represents the standard deviation of the degree of the node, and c and d are constants.
Step 24: for non-seed nodes v' o (v′ o E, V' -P, s is less than or equal to n-r), and distributing the E into a corresponding new community;
as shown in fig. 3, for each non-seed node v' o Will not seed node v' o The module value change delta Q 'before and after the addition is calculated by adding the module value to T (T is less than N) communities to which the neighbor nodes belong in turn' u (u=1, 2, …, T), if the community contains seed nodes, then the community is added to set D 1 In, otherwise add to set D 2 In the above, D 1 D for community collection containing seed nodes 2 Is a community set that does not contain seed nodes. Computing set D 1 Middle DeltaQ' u The maximum value of (2) is denoted as max DeltaQ ', if max DeltaQ' > 0, the non-seed node v 'is to be formed' o Distributing the communities to communities corresponding to the max delta Q'; otherwise calculate set D 2 Middle DeltaQ' u The maximum value of (a) is denoted as max DeltaQ ', if max DeltaQ' > 0, the non-seed node v 'will be' o Distributing the communities corresponding to the max delta Q'; if the two conditions are not met, the node v 'is not a seed node' o The community to which it belongs remains unchanged.
Step 25: step 24 is repeated until all non-seed nodes v' o The community to which the community belongs does not change any more;
step 26: and outputting a community division result C', and ending the algorithm.
Step 3: applying the community division result to a force-directed layout algorithm of attribute constraint to layout the network graph in a two-dimensional or three-dimensional space, visually displaying the layout result by using a visual tool (such as D3.js), and presenting the relationship between the overall network structure and the community structure;
as shown in fig. 4, a community division result C' obtained through a Louvain algorithm is obtained by adding spring force and repulsive force constraints on adjacent communities and adjacent nodes on the basis of a traditional FR (frichterman-reingend) algorithm; here, adjacent communities refer to communities with different attributes, and adjacent nodes refer to nodes existing in communities with the same attribute;
step 31: according to the input community dividing result C ', randomly initializing the positions of all nodes in the community dividing result C';
step 32: calculating a spring force and a repulsive force between adjacent communities according to formulas (3) (7);
specifically, the spring force of adjacent communitiesActing on two attribute communities connected by edges, the calculation formula is as follows:
wherein,is the edge for calculating the spring force, d (v 1 ,v 2 ) Is the Euclidean distance, l (v 1 ,v 2 ) Is the ideal distance between seed nodes of adjacent communities, +.>Is the elastic coefficient of this edge, and the formula is as follows:
wherein deg (v) 1 ) And deg (v) 2 ) The degrees of the seed nodes of adjacent communities, respectively.
Because the number of nodes in communities with different attributes is different, the rational distance l (v) 1 ,v 2 ) The following is shown:
wherein l 0 Is the basic distance between communities; n is the number of all nodes;to v respectively 1 ,v 2 The number of nodes included in the community that is the seed node; l (L) max For maximum distance of edges, depending on the size of the layout area, the calculation formula is as follows:
wherein w and h are the width and height of the layout area respectively.
Repulsive force is the force of attribute community to be repelled by others, respectively v 1 ,v 2 The formula for calculating the repulsive force between communities of seed nodes is as follows:
f r (v 1 ,v 2 )＝s(v 1 ,v 2 )·x (7)
wherein s (v) 1 ,v 2 ) To v 1 ,v 2 For the rejection strength between communities of seed nodes, x is a multi-segment function, and the formula is as follows:
wherein k is a coefficient between 0 and 1.
Step 33: calculating the spring force and repulsive force between adjacent nodes according to formulas (9) (12); neighboring nodes v within communities of the same attribute i And node v j Spring force f between a (v i ,v j ) Acting on the node with one edge connection, the calculation formula is as follows:
wherein d (v) i ,v j ) Is adjacent node v i And node v j Euclidean distance between, l (v i ,v j ) Is adjacent node v i And node v j The calculation formula is as follows:
wherein l max The maximum distance of the edges is the maximum distance of the edges, and n is the number of all nodes;for adjacent node v i And node v j The number of nodes contained in the community, +.>And->Respectively adjacent nodes v i And node v j Degree, deg max Is the maximum of all nodes, +.>For connecting node v i And v j The coefficient of elasticity of the edges of (a) is calculated as follows:
node v j Is received by node v i The repulsive force calculation formula of (2) is as follows:
f r (v i ,v j )＝s(v i ,v j )·y (12)
wherein s (v) i ,v j ) For adjacent node v i And node v j The repulsive strength between the two is that y is a multi-section function, and the formula is as follows:
wherein t is a coefficient between 0 and 1.
Step 34: calculating a new position of the node according to the forces obtained in the steps 32 and 33, and updating the position of the node;
step 35: if the node layout iteration reaches the designated times, the positions of the nodes are considered to be stable, the position of each node is output, the algorithm is ended, and a final new network layout graph is obtained;
step 4: according to the service data set obtained in the first step, invoking a graphic library Echarts.js and D3.js to perform data visualization based on a front-end bottom drawing technology SVG technology and a Canvas technology;
in this embodiment, there are ten types of service types in the optical power communication network, including a dispatch data network service, a dispatch automation service, a distribution automation service, a dispatch telephone service, an administrative telephone service, a video conference service, a relay protection service, a security automation service, a comprehensive data network service, a video service, and other services, and these service data correspond to specific service systems, including a dispatch data network service system, a dispatch automation service system, a distribution automation service system, a dispatch telephone service system, an administrative telephone service system, a video conference service system, a relay protection service system, a security automation service system, a comprehensive data network service system, a video service system, and other service systems, and the data sets in these systems are managed separately, and the information is not interconnected, and in this embodiment, the service data is acquired from these service systems and stored in the same database.
And 5, designing interface interaction operation, supporting functions of zooming, selecting nodes and the like, and realizing dynamic visual display of the power optical communication network.
Simulation experiments are carried out by adopting the improved Louvain algorithm in the embodiment, so that the improved Louvain algorithm can improve the quality and speed of network community division compared with the traditional community division algorithm. Common measurement indexes for the community division algorithm comprise modularity and runtime; the higher the modularity value, the better the community division quality. Therefore, three data sets with different network sizes are adopted in the simulation experiment, namely Facebook1 (node number 148, edge number 1692), facebook2 (node number 1893, edge number 13835) and Hamsterster (node number 2000, edge number 16098); the conventional Louvain algorithm was used to compare with the method of this embodiment, as shown in table 1. Table 1 shows comparative data of simulation experiments performed by the improved Louvain algorithm of the present invention.
TABLE 1
Compared with the improved Louvain algorithm, the module degree value Q obtained by the traditional Louvain algorithm is smaller, and the running time is longer, mainly because the improved Louvain algorithm sets two thresholds when selecting seed nodes, and the thresholds are respectively used as a lower limit and an upper limit, on one hand, the nodes with smaller degree of failure can be eliminated by using the upper limit, the nodes with larger degree of failure can be eliminated by using the upper limit, the too huge communities are stopped, finer community structures are difficult to find, and the quality of division is improved; on the other hand, the number of candidate nodes of the seed node is reduced, and the running time of the algorithm is prolonged.
According to the method, a network community division technology and a visualization technology are introduced in management of the electric power optical communication network, a community discovery algorithm and a network layout visualization algorithm are combined, the electric power optical communication network is displayed in a graphical mode, so that the community structure, the topological structure and the connection relation of the network are intuitively revealed, a manager can clearly know the connection relation among transmission sites, equipment and infrastructure, planning, configuration and fault elimination are better carried out, and compared with a traditional method, the visual management method is beneficial to the manager to master the network situation in an all-around mode, can quickly discover and solve problems, and improves operation and maintenance efficiency. In addition, the service types of the electric power optical communication network are increasingly abundant, resulting in an increase in service data and an increase in processing pressure. Traditional manual analysis methods are difficult to meet the requirements of high efficiency and accuracy. The invention introduces data visualization and data analysis technology to better process massive business data. Through visual display of service data, a manager can intuitively observe the running state, abnormal conditions and performance indexes of the network, so that adjustment and decision can be made in time.
In summary, the network topology structure is visually displayed, and the display of the service data is fused. Therefore, a manager can see the network structure, know the service operation condition of the network, quickly find out problems and take corresponding measures. The operation management of the power optical communication network is more systematic, normalized and scientific, meets the development requirements of the intelligent power grid, and improves the operation and maintenance management efficiency of the power optical communication network.
The above examples are only preferred embodiments of the present invention, but should not be construed as limiting the scope of the present invention, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the scope of the present invention.
The specification and figures are merely exemplary illustrations of the present invention and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the invention. It will be apparent to those skilled in the art that various modifications and variations can be made without departing from the scope of the invention.
Claims (6)
1. The electric power optical communication network visualization method based on the improved Louvain algorithm is characterized by comprising the following steps of: the method is realized by the following steps:
step one, acquiring service transmission equipment data of an electric power optical communication network and connection relation data between starting station equipment and terminating station equipment based on an optical communication equipment management system, wherein the acquired data set is used as a service transmission equipment data set; acquiring a service data set from a dispatching telephone service system, a video service system and a relay protection service system based on service types, and storing the service data set to obtain the data set as a service data set;
the service transmission equipment data set comprises equipment models, starting station equipment and terminating station equipment, and forms a service transmission set;
step two, according to the service transmission equipment data set obtained in the step one, adopting an improved Louvain algorithm to carry out community division of the power optical communication network, and obtaining a community division result C', wherein the specific process is as follows:
step two, using the equipment as nodes, determining the connection relation between the nodes, drawing a network diagram, and adding attribute information according to the requirement to obtain a preliminary community division result C;
secondly, compressing the community division result C obtained in the second step to obtain a new network graph G ' (V ', E '); where V' is the new node set { V } 1 ′,V′ 2 ...,V′ r E 'is a new edge set { E' 1 ,E′ 2 ...,E′ r }；
Step two, selecting seed nodes for the new network graph G ' (V ', E ') by adopting the node-based degree, defining two thresholds as the upper limit and the lower limit of the node degree respectively, wherein the seed node set P is expressed as follows:
in the formula, deg (v s ) For node v s Is the number of degrees; g is the average degree of the node, q is the standard deviation of the degree of the node, and c and d are constants;
step two, four, for non-seed node v' o ，v′ o E, V ' -P, s is less than or equal to n-r, and then the E, V ' -P, s is distributed into corresponding new communities, when all non-seed nodes V ' o The community to which the community belongs does not change any more, and a community dividing result C' is output;
and thirdly, applying the community division result C' to a force-directed layout algorithm of attribute constraint so as to layout the network diagram in a two-dimensional or three-dimensional space, and adopting a visualization tool to visually display the layout result.
2. The improved Louvain algorithm-based power optical communication network visualization method according to claim 1, wherein the method comprises the following steps: and after the third step, the method further comprises a fourth step of calling a graphic library to perform data visualization based on the front-end bottom drawing technology and the Canvas technology according to the service data set obtained in the first step.
3. The improved Louvain algorithm-based power optical communication network visualization method according to claim 1, wherein the method comprises the following steps: and step five, designing a visual interface for interactive operation, supporting functions of zooming and selecting nodes, and realizing dynamic visual display of the power optical communication network.
4. The improved Louvain algorithm-based power optical communication network visualization method according to claim 1, wherein the method comprises the following steps: the specific process of the first step is as follows:
the equipment is used as nodes, the connection relation between the nodes is determined to draw a network diagram, attribute information is added according to the requirement, each node is respectively classified into a community, for each node, the neighbor node of each node is set to belong to N communities, and node v is set i Sequentially adding the modules into N communities, and calculating the module value variation delta Q before and after the addition t (t=1, 2,., N.) if the modularity value varies the mostmaxΔQ t > 0, then node v i Assigned to maxΔq t Corresponding community, otherwise node v i The community is kept unchanged, and a preliminary community division result C is obtained;
in Sigma in Is the sum of all internal edge weights in community A (A E N); sigma (sigma) tot The sum of all edge weights adjacent to the nodes in the community A is calculated; k (k) i For all adjacent nodes v i Is a sum of edge weights of (a); k (k) i,in For all slave nodes v i Edge weight sum adjacent to the node in community a; m is the sum of the weights of all the edges in the network.
5. The improved Louvain algorithm-based power optical communication network visualization method according to claim 1, wherein the method comprises the following steps: the specific process of the second step is as follows:
will not seed node v' o Sequentially added to T communities to which its neighbor nodes belong, T is less than N; calculating the module value change delta Q 'before and after adding' u U=1, 2, T, if the community includes a seed node therein, then the community is added to set D 1 In, otherwise add to set D 2 In, calculate set D 1 Module degree variation Δq' u If max Δq '> 0, the non-seed node v' o Distributing the communities to communities corresponding to the max delta Q'; otherwise calculate set D 2 Middle DeltaQ' u The maximum value of (a) is denoted as max DeltaQ ', if max DeltaQ' > 0, the non-seed node v 'will be' o Distributing the communities corresponding to the max delta Q'; if the two conditions are not met, the node v 'is not a seed node' o The community to which it belongs remains unchanged.
6. The improved Louvain algorithm-based power optical communication network visualization method according to claim 1, wherein the method comprises the following steps: thirdly, adding spring force and repulsive force constraint to adjacent communities and adjacent nodes according to community division results C', and obtaining a new network diagram layout; the specific process is as follows:
step three, randomly initializing positions of all nodes in a community division result C', and calculating spring force and repulsive force between adjacent communities;
spring force between adjacent communitiesThe calculation formula of (2) is as follows:
in the method, in the process of the invention,to calculate the edge of the spring force, d (v 1 ,v 2 ) Is the Euclidean distance, l (v 1 ,v 2 ) Ideal distance for seed nodes of adjacent communities, +.>Is edge->Is a coefficient of elasticity of (a);
step three, calculating the spring force and repulsive force between adjacent nodes;
neighboring nodes v within communities of the same attribute i And node v j Spring force f between a (v i ,v j ) Acting on the node with one edge connection, the calculation formula is as follows:
wherein d (v) i ,v j ) Is adjacent node v i And node v j Euclidean distance between, l (v i ,v j ) For adjacent node v i And node v j Is a desired distance from the center of the lens;
and step three, calculating a new position of the node according to the spring force and the repulsive force between the adjacent communities obtained in the step three and the spring force and the repulsive force between the adjacent nodes obtained in the step two, and updating the position of the node to obtain a new network diagram layout graph.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311814805.5A CN117749647A (en) | 2023-12-27 | 2023-12-27 | Electric power optical communication network visualization method based on improved Louvain algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311814805.5A CN117749647A (en) | 2023-12-27 | 2023-12-27 | Electric power optical communication network visualization method based on improved Louvain algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117749647A true CN117749647A (en) | 2024-03-22 |
Family
ID=90252619
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311814805.5A Pending CN117749647A (en) | 2023-12-27 | 2023-12-27 | Electric power optical communication network visualization method based on improved Louvain algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117749647A (en) |
-
2023
- 2023-12-27 CN CN202311814805.5A patent/CN117749647A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10761687B2 (en) | User interface that facilitates node pinning for monitoring and analysis of performance in a computing environment | |
| CN111885040A (en) | Distributed network situation perception method, system, server and node equipment | |
| CN103838803A (en) | Social network community discovery method based on node Jaccard similarity | |
| CN112765143B (en) | Low-voltage Internet of things distribution network data processing method based on edge computing technology | |
| CN107611962B (en) | Power grid system branch searching method and system and electronic equipment | |
| CN101183370A (en) | Topological modelling approach based on class definition and relationship definition | |
| CN113807520A (en) | Knowledge graph alignment model training method based on graph neural network | |
| CN113688490A (en) | Network co-construction sharing processing method, device, equipment and storage medium | |
| CN115225508A (en) | Internet target range scene construction method | |
| CN106547622B (en) | Network resource situation presentation data optimization processing method based on computing module | |
| CN113706333A (en) | Method and system for automatically generating topology island of power distribution network | |
| CN115499286A (en) | Hierarchical management system for industrial control network assets | |
| CN105069710A (en) | Data processing method and system for intelligent power grid | |
| CN113810234B (en) | Method and device for processing micro-service link topology and readable storage medium | |
| KR20160032281A (en) | Method for wieght lightening of big capacity design data | |
| CN115439015B (en) | Local area power grid data management method, device and equipment based on data middleboxes | |
| CN117749647A (en) | Electric power optical communication network visualization method based on improved Louvain algorithm | |
| CN114756301B (en) | Log processing method, device and system | |
| CN110008597A (en) | Building Information Model triangulation methodology and device based on parallel computation frame | |
| CN111177227B (en) | Power data self-service analysis system and decision application migration method | |
| CN116523328A (en) | Intelligent decision-making method for cooperation of aviation equipment and manufacturing industry chain | |
| CN115935080A (en) | Social network flow data oriented MPICH parallel computing-based maximum cluster enumeration method | |
| CN115884235A (en) | 5G network digital twin modeling method and device, computer equipment and storage medium | |
| CN113761293A (en) | Graph data strong-connectivity component mining method, device, equipment and storage medium | |
| CN114004585A (en) | Power consumer data management system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |