A Metropolitan Area Network Topology Visualization Method

Abstract

The present invention discloses a method for visualizing the topology of a metropolitan area network, comprising the following steps: step 1, pre-processing the hierarchical level of the metropolitan area network map data; step 2, performing initial Voronoi diagram segmentation on the processed metropolitan area network map to obtain a power-weighted Voronoi diagram; step 3, performing layout iteration optimization and adjustment on the power-weighted Voronoi diagram to obtain a stable Voronoi segmentation result; step 4, using constraint force to guide the layout of devices in the region to obtain the final metropolitan area network topology structure; the region refers to the subnet and sub-region in the metropolitan area network. The present invention solves the problem that the existing method lacks visual coding and visual expression, resulting in the inability to reflect the hierarchical structure of the topological network of the metropolitan area network and the relationship between devices, thereby making positioning alarm difficult and causing low operation and maintenance efficiency.

Description

Technical Field

[0001] The invention belongs to the technical field of data visualization, and in particular relates to a metropolitan area network topology visualization method. Background Art

[0002] A metropolitan area network (MAN) is a computer network that connects computers within a metropolitan area, which can be a single large city, multiple towns, or any given large area containing multiple buildings. A MAN is much larger than a local area network (LAN) but smaller than a wide area network (WAN). A MAN is composed of interconnected LANs and is usually more efficient than a WAN. Since the devices in a MAN belong to different subnets, the MAN can also be modeled as a complex hierarchical network. MAN construction and operation and maintenance engineers need to ensure the safe operation of large MANs. During the operation and maintenance process, they need to control the development trend of the MAN from a global perspective, quickly locate the fault area, the equipment that caused the fault, and the fluctuation range of the equipment, and reduce the risk and loss caused by equipment failure. Operation and maintenance work usually requires the use of information systems to monitor and locate faulty equipment.

[0003] However, since the metropolitan area network contains many subnets and devices, and there are complex hierarchical structures and connection relationships between devices, the traditional maintenance method requires a lot of manpower, is costly, and has cumbersome and inefficient work content. Operation and maintenance engineers usually need to use information systems to conduct global situational awareness of the topology of the metropolitan area network, locate alarm areas and alarm devices, and perform fixed-point maintenance. However, most information-based operation and maintenance systems use traditional force-guided algorithms to layout network topology, lacking visual coding and visual expression, which not only brings great visual confusion to operation and maintenance personnel, but also fails to reflect the hierarchical structure of the topological network of the metropolitan area network and the relationship between devices, making it very difficult to locate alarms. In addition, after the line changes, operation and maintenance personnel often need to re-learn knowledge from the messy topology, making the alarm analysis process difficult to understand and the operation and maintenance efficiency extremely low. Summary of the invention

[0004] The purpose of an embodiment of the present invention is to provide a method for visualizing the topology of a metropolitan area network, so as to solve the problem that the existing method lacks visual coding and visual expression, resulting in the inability to reflect the hierarchical structure of the topological network of the metropolitan area network and the relationship between devices, thereby making positioning alarms difficult and causing low operation and maintenance efficiency.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is a method for visualizing metropolitan area network topology, comprising the following steps:

[0006] Step 1: Preprocessing of metropolitan area network graph data level;

[0007] Step 2, perform initial Voronoi diagram segmentation on the processed metropolitan area network graph to obtain a power-weighted Voronoi diagram;

[0008] Step 3, perform layout iterative optimization and adjustment on the power-weighted Voronoi diagram to obtain a stable Voronoi segmentation result;

[0009] Step 4: Use constraints to guide the layout of devices in the area to obtain the final metropolitan area network topology; the area refers to the subnet and sub-area in the metropolitan area network.

[0010] Furthermore, the step 1 is specifically as follows:

[0011] The metropolitan area network data is converted into a graph structure G∈(V,E), where V represents the set of device vertices and E represents the set of edges between devices. The device nodes belonging to different subnets are divided into different sets, and the device nodes belonging to the same subnet are divided into the same set. In each subnet set, the subsets are divided according to the sub-areas to which the devices belong, and the devices belonging to the same sub-area are divided into the same subset. For each set, the set is weighted using the number of devices within the set. After data hierarchical preprocessing, the flattened metropolitan area network data is processed into hierarchical weighted data.

[0012] Furthermore, the step 2 specifically includes the following steps:

[0013] Step 21: Perform dimensionality conversion to convert all regions with devices from two-dimensional planes to three-dimensional space to obtain several planes in three-dimensional space; the obtained planes intersect with each other in three-dimensional space to generate a convex polyhedron with a lower convex hull;

[0014] Step 22: Solve the lower convex hull in the dual space to obtain the dual solution of the lower convex hull in the three-dimensional space;

[0015] Step 23: Inversely map the dual solution of the lower convex hull to obtain the solution of the lower convex hull in three-dimensional space, and then project it onto the two-dimensional plane to obtain the solution of the power-weighted Voronoi diagram.

[0016] Furthermore, the dual solution of the lower convex hull in step 22 is obtained by the surface-point duality theorem in geometry.

[0017] Furthermore, the step 3 is specifically as follows: the two steps of region position optimization and weight coefficient optimization are repeated iteratively for multiple times until the difference between the actual area and the expected area of ​​all Voronoi diagrams is less than a set threshold, at which time the final stable Voronoi segmentation result is obtained.

[0018] Furthermore, the regional location optimization is specifically as follows:

[0019] Region position optimization means moving the center of the inscribed circle of region s in the diagram to the centroid of the corresponding Voronoi diagram; when the inscribed circle of region s overlaps with another region v after moving, the weight of region s needs to be reduced: the radius of the inscribed circle Adjust to in is the minimum distance from the centroid to the boundary of the Voronoi diagram polygon, and then the power-weighted Voronoi diagram is recalculated according to the method of step S2.

[0020] Furthermore, the weight coefficient optimization is specifically as follows:

[0021] Step S31: Calculate the area of ​​the current Voronoi diagram for all regions s. current , and then according to the regional weight ω s The proportion of total weight ω total and total area total Calculate the area of ​​the desired Voronoi diagram The ratio of the current area to the expected area is used as the adjustment factor

[0022] Step S32: Use the weight adjustment coefficient to adjust the current weight, and the adjusted weight value must be smaller than the square of the distance from the region to the nearest neighbor region;

[0023] Step S33, recalculating the power-weighted Voronoi diagram according to the method of step S2;

[0024] The weight refers to the number of devices in the area.

[0025] Furthermore, the step 4 is specifically as follows:

[0026] Step 41: Treat the equipment in each area as a particle system for force-guided simulation, and set the force-guided center of the system to the center of the maximum inscribed circle of the Voronoi diagram polygon; when the horizontal and vertical coordinates of the node fall outside the Voronoi diagram, a circular centripetal force needs to be applied;

[0027] The annular centripetal force is an additional force applied to the out-of-bounds nodes, pulling the out-of-bounds nodes toward the inscribed circle of the Voronoi diagram polygon, so that they tend to be arranged on the circumference of the largest inscribed circle;

[0028] Step 42: Apply sliding friction force to the devices in the system to suppress the device nodes from scattering randomly around, so that the device nodes are distributed within the polygons of the Voronoi diagram to which they belong, and finally obtain a metropolitan area network topology structure that can reflect the structural characteristics and the connection relationship between devices.

[0029] The beneficial effects of the present invention are:

[0030] The present invention provides a fast and effective metropolitan area network topology visualization method, which can reflect the hierarchical structure characteristics and connection relationships between metropolitan area networks while ensuring the layout effect. In terms of application, the present invention can enable operation and maintenance personnel to directly locate the alarm area and specific equipment, conduct global situation perception and monitoring of the metropolitan area network, reduce the cognitive difficulty and work cost of operation and maintenance personnel, improve operation and maintenance efficiency, help ensure the stability of the metropolitan area network, and is suitable for promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 The figure is a flow chart of a method for visualizing metropolitan area network topology according to an embodiment of the present invention.

[0033] Figure 2 It is the plane Π(s) and Π(t) graphs where the two-dimensional plane regions s and t are mapped into three-dimensional space by the dimensionality conversion function Π.

[0034] Figure 3 It is a power-weighted Voronoi diagram obtained by vertically projecting the lower convex hull of a convex polyhedron produced by the intersection of planes in three-dimensional space.

[0035] Figure 4 A diagram showing the weight conflict that occurs when moving the region position.

[0036] Figure 5 A schematic diagram of the conflicts that occur when optimizing the weight coefficient.

[0037] Figure 6 This is a comparison diagram between the effects of the present invention and traditional visualization effects. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Voronoi segmentation is a layout method that recursively divides a bounded area into several centroid Voronoi figures, using the mutual inclusion relationship of polygons to reflect the hierarchical relationship. For a set of nodes with hierarchical relationships (parent-child relationships) and weights, they can be transformed into a hierarchical virtual tree, and each node on the tree is recursively divided into Voronoi figures until it is divided into leaf nodes that cannot be divided any further. The weight of a parent node is the sum of the weights of all its child nodes, and the Voronoi figure of a child node is divided by the Voronoi figure of the parent node in proportion. For the child node c of the parent node p, the area of ​​the Voronoi figure of the child node c is:

[0040]

[0041] In the metropolitan area network information operation and maintenance system, in order to reflect the hierarchy and the number of devices in different subnets, the initial segmentation will be divided into three layers: metropolitan area network-subnet-sub-area. Its goal is to tightly divide the plane figure into multiple sub-areas of unequal areas, and use the mutual inclusion relationship of polygons to reflect the hierarchical relationship, so as to facilitate the operation and maintenance personnel to troubleshoot and locate faults by area. In view of the task requirements of metropolitan area network fault location and global situation awareness in the core scenario, the use of Voronoi segmentation on the network topology to achieve steady state needs to rely on a dynamic iterative process. The static segmentation results will lead to excessive layout energy, imbalance in the aspect ratio of the Voronoi figure, and other problems. Therefore, based on the characteristics of Voronoi segmentation layout iterative optimization and force-guided layout iterative optimization, the present invention designs a force-guided layout method based on Voronoi segmentation constraints. The main ideas of the constraint-guided method include three parts: Voronoi iterative segmentation, segmentation layout optimization and adjustment, and constraint-guided layout. First, combined with the networking service attributes of the metropolitan area network, the two-dimensional canvas is tightly divided into multiple sub-areas of varying areas through dimensional transformation, dual mapping, and lower convex hull projection. After obtaining the first power-weighted Voronoi segmentation, the layout needs to be iteratively adjusted to minimize the system energy and approximate the optimal layout. Finally, the specific equipment is placed in the segmented areas to complete the overall layout. Figure 1 As shown, the following steps are included:

[0042] Step 1: Preprocessing of current graph data level

[0043] The MAN topology visualization method proposed in this paper requires hierarchical attributes and hierarchical weights as input data, while MAN device data is usually just a collection of device nodes and device edges. Therefore, it is necessary to process the flat type data into hierarchical type data in advance according to the regional attributes of the device nodes.

[0044] The metropolitan area network data is converted into a graph structure G∈(V,E), where V represents the set of device vertices and E represents the set of edges between devices. Each device vertex contains many attributes, including the subnet to which it belongs, the subregion to which it belongs, the IP address, etc. Among them, the IP address uniquely identifies the device, and the subnet to which it belongs and the subregion to which it belongs are used to process the flattened data into hierarchical data. Device nodes belonging to different subnets are divided into different sets, and device nodes belonging to the same subnet are divided into the same set. In each subnet set, subsets are further divided according to the subregion to which the device belongs, and devices belonging to the same subregion are divided into the same subset. For each set, the set is weighted using the number of devices within the set. After data hierarchical preprocessing, the flattened metropolitan area network data is processed into hierarchical weighted data.

[0045] Step 2: Initial Voronoi segmentation.

[0046] The initial Voronoi segmentation method obtains the power-weighted Voronoi diagram in the d-dimensional space by calculating the convex hull of the d+1-dimensional space, that is, solving the power-weighted Voronoi diagram by the bottom projection method of the high-dimensional convex hull. It can be implemented by the convex hull algorithm of the three-dimensional space, which mainly includes the following steps:

[0047] Step 21: Perform dimensional conversion, convert the weighted area (that is, Note: the area here refers to the subnet or sub-area in the metropolitan area network, and the same applies below) from a two-dimensional plane to a three-dimensional space, and obtain several planes in the three-dimensional space. Figure 2 As shown, plane h0 is a plane in three-dimensional space, which extends infinitely in the x-axis and y-axis directions and z=0, that is, the two-dimensional plane where the power-weighted Voronoi diagram to be calculated is located. All regions form a set S, and the coordinates of region s in the set S on the two-dimensional plane are (x s ,y s ), whose weight is ω s The dimension-raising function Π can convert the inscribed circle of region s (the coordinates of the center of the circle (x s ,y s ),radius ) is mapped into a three-dimensional plane, such as Figure 2 As shown, the regions s and t in the two-dimensional plane are mapped into planes Π(s) and Π(t) in the three-dimensional space respectively, and the vertical projection of the intersection of these two planes is exactly the root axis of the regions s and t in the two-dimensional plane.

[0048] For each region s in the set S, the planes obtained are intersected in three-dimensional space, generating a convex polyhedron. In three-dimensional space, the lower convex hull of the convex polyhedron ( Figure 3The vertical projection of the vertices and edges of the 3D space (the gray part in the figure) to the 2D plane h0, the projection result is the corresponding polygon vertices and boundaries in the power-weighted Voronoi diagram. Since the vertical projection of the 3D space to the 2D plane only needs to discard the z-axis coordinate in mathematics, the solution of the power-weighted Voronoi diagram of the 2D plane is transformed into the solution of the lower convex hull of the 3D space.

[0049] Step 22: Solve the lower convex hull in the dual space to obtain the dual solution of the lower convex hull in three-dimensional space. To solve the lower convex hull in three-dimensional space, the surface-point duality theorem in computational geometry can be used:

[0050] Any plane in three-dimensional space can be mapped into a point Δ(h) (called a pole) in the dual space by the polar function Δ. Any point p in three-dimensional space can also be mapped into a surface Δ(p) in the dual space by the polar function Δ, and vice versa.

[0051]

[0052] In the above formula, a, b, and c represent the coefficients of the original three-dimensional space plane equation (ax+by+cz+d=0).

[0053] Since the dimension of the dual space obtained by mapping is the same as that of the original space, in order to better distinguish them, the three-dimensional space before mapping is called the normal space, and the normal space obtained by Δ mapping is called the dual space. In addition, the Δ function will preserve the relative position relationship between the points and the surfaces before and after the mapping. If in the normal space, the plane h contains a point p, then in the dual space, the plane Δ(p) also contains a point Δ(h).

[0054] Therefore, the lower convex hull in the normal space obtained after the dimensionality increase transformation can be mapped to the dual space using Δ. The intersecting planes of the lower convex hull (representing the region) are mapped to points in the dual space, the vertices of the lower convex hull are mapped to triangular faces in the dual space, and the lower convex hull (convex polyhedron) of the normal space is mapped to the convex hull (convex polygon) of the dual space. Therefore, the convex hull formed by this set of points in the dual space is the dual solution of the lower convex hull in the normal space. It is only necessary to find the convex hull in the dual space and then use Δ to convert the solution back to the normal space. At this point, the power-weighted Voronoi diagram for solving the two-dimensional plane is first converted to solving the lower convex hull in the three-dimensional space, and finally converted to solving the convex hull in the dual space. The conversion process of region s can be expressed as:

[0055] Δ(Π(s))=(x s ,y s ,x s 2 +y s 2 -ω s )

[0056] Step 23: Inversely map the dual solution of the lower convex hull to obtain the solution of the lower convex hull in three-dimensional space, and then project it onto the two-dimensional plane to obtain the solution of the power-weighted Voronoi diagram, that is, the vertex coordinate list of the Voronoi polygons belonging to each subnet and each sub-area in the metropolitan area network. The two-dimensional plane is thus tightly divided into several Voronoi figures.

[0057] Step 3: Iterate and optimize the layout.

[0058] After obtaining the initially calculated power-weighted Voronoi diagram, the layout results need to be optimized from three aspects:

[0059] (1) The aspect ratio of the sub-region polygon is close to 1 to obtain a more uniform and beautiful visual effect;

[0060] (2) The value of the global topology energy function of the metropolitan area network is minimized to obtain a more stable layout effect;

[0061] (3) The actual Voronoi diagram area of ​​each subnet and subregion in the metropolitan area network matches its weight, that is, the actual area is almost the same as the area that should be allocated according to the weight, and the difference is less than a given threshold.

[0062] The first two aspects can be achieved through regional position optimization, and the third aspect can be achieved through weight coefficient optimization. The two optimization steps of regional position optimization and weight coefficient optimization are repeated iteratively for many times until the difference between the actual area and the expected area of ​​all regions is less than the set threshold, at which time the final stable Voronoi segmentation result is obtained.

[0063] Region position optimization refers to moving the center of the region's inscribed circle in the diagram to the centroid of the corresponding Voronoi diagram. When moving the region's position, since the inscribed circle of each region can only be contained in this region, it is necessary to consider Figure 4 The red circle represents the circle (inscribed circle) of area s, with a radius of The region will move to the centroid along the direction of the red arrow. If the weight is not changed, after region s moves to the centroid, the inscribed circle of region s is a blue dotted circle, which may overlap with region v. This is not allowed (the inscribed circle of a region cannot overlap with other regions), so the weight of region s needs to be reduced. Taking the minimum distance from the centroid to the boundary of the Voronoi polygon can ensure that there is no overlap with other regions, that is, to reduce the radius of the circle from the blue dotted line to Adjusted to the blue solid line In this step, the position and weight of each region are changed, but the weight change is not to get a Voronoi area that matches the weight more, but to avoid other regions appearing in the inscribed circle of the region. Since the region position and weight have changed (the weight has become smaller), the power-weighted Voronoi diagram needs to be updated.

[0064] In the metropolitan area network, the hierarchical relationship is organized according to the metropolitan area network-subnet-subregion, that is, the subnet is the parent level of the subregion, and the subregion is the child level of the subnet. The area of ​​the Voronoi cell actually allocated to each subnet and each subregion in the metropolitan area network is not proportional to the expected area, that is, the ratio between the subregion cell area and the subnet cell area is very different from the ratio between the region weight and the parent region weight. There are three main reasons:

[0065] (1) The positive correlation between the weight obtained by the number of devices and the area is nonlinear;

[0066] (2) When optimizing the regional position, both the position and weight of the region change (the area becomes smaller and the weight becomes smaller);

[0067] (3) The movement of the region to the centroid may cause the area of ​​the Voronoi polygon to change toward equilibrium.

[0068] By adjusting the weight coefficient, the area of ​​the Voronoi diagram of each region is made to match the weight as much as possible. First, the area Area of ​​the current Voronoi diagram is calculated for all regions s. current , and then according to the ratio of regional weight ω to total weight ω total and total area total Calculate the area of ​​the desired Voronoi diagram The ratio of the current area to the expected area is used as the adjustment factor

[0069] Then use the weight coefficient to adjust (increase) the current weight. However, it should be noted that the weight here refers to the number of devices in the area, which is reflected in the algorithm as the square of the radius of the inscribed circle of the area. Therefore, the adjusted weight cannot make the inscribed circle overlap with other areas. Figure 5 As shown in the figure, when the weight of region v increases to the limit, the inscribed circle of region v is a black dotted circle. If the weight is greater than this limit, the inscribed circle of region v will overlap with region s, which is contrary to the geometric meaning of the region of the power-weighted Voronoi diagram. Therefore, the adjusted weight value must be less than the square of the distance from the region to the nearest neighbor region, that is, where NN s Represents the NearestNeighbors of region s, that is, the nearest neighbors of s.

[0070] In this step, the position of the region is not changed, only the weight of the region is changed to make the actual Voronoi area of ​​the region equal to the weight. Since the area and weight of the region have changed at this time, the power-weighted Voronoi diagram needs to be updated.

[0071] After completing each round of optimization iterative calculation, in order to evaluate whether the current Voronoi diagram can better reflect the quantitative relationship of nodes in each parent-child region, the judgment condition for terminating layout optimization is introduced, that is, for each region s in the set S (region is an abstract expression of each child region), after each round of iteration, the deviation Bias between the actual area of ​​the current Voronoi diagram and the expected area according to the weight ratio is calculated. s :

[0072]

[0073] If Bias s If it is less than the threshold, it means that the layout optimization of each sub-region in the current Voronoi diagram is terminated, otherwise the optimization iteration steps will continue.

[0074] Step 4: Constraints guide layout

[0075] Through Voronoi segmentation, the canvas is divided into two layers of nested polygons. The larger polygons in the upper layer represent subnets, and different subnets are separated by thicker white lines; the smaller polygons in the lower layer represent subregions within the subnet, and different subregions are separated by thinner white lines. The color of the polygon is related to the number of devices it contains. The more devices there are, the lighter the color of the polygon is, and vice versa. After Voronoi segmentation, it is necessary to layout specific equipment in the polygon according to the area. The layout of equipment in the area adopts a constrained force-guided layout to reasonably layout the equipment in the polygon, maintain the internal networking structure, and avoid crossing the boundary. In order to obtain a more concise and beautiful device network topology, three constraints are added to the force guidance: central force, centripetal force, and friction.

[0076] First, treat the devices in each area as a particle system for force-guided simulation, and set the force-guided center of the system to the center of the largest inscribed circle of the polygon. The purpose of this is to make the devices in the area move closer to the center of the inscribed circle to avoid too many nodes "going out of bounds". Node "going out of bounds" is defined as a node whose horizontal and vertical coordinates fall outside the area. After applying centripetal force, some nodes will still go out of bounds.

[0077] For out-of-bounds nodes, a circular centripetal force is applied. The circular centripetal force is an additional force applied to out-of-bounds nodes, pulling the out-of-bounds nodes toward the inscribed circle of the Voronoi polygon, so that they tend to be arranged on the circumference of the largest inscribed circle.

[0078] Finally, in order to better ensure that the topological structure of the equipment in the area is not destroyed by the additional force, reduce cross-edges, and balance the layout, sliding friction is applied in the system. Friction refers to adding sliding friction in the physical sense to each node, increasing the speed attenuation of the node, and effectively suppressing the phenomenon of nodes flying around irregularly, so as to obtain a more uniform and beautiful node distribution at the end of force guidance, and the device nodes can be distributed in the polygonal area to which they belong, and reflect the structural characteristics. In engineering implementation, the addition of sliding friction only requires adding and configuring the "velocity Decay" parameter in the force simulator. The larger the value of this parameter, the greater the friction applied in the system.

[0079] After applying the above force-guided constraints, the distribution of device nodes inside the area is approximately circular, and the layout effect is beautiful; the visual distinction of device nodes in different areas is high and the number of out-of-bounds nodes is significantly reduced, which can reflect the structural characteristics and connection relationships.

[0080] The fast and effective metropolitan area network topology visualization method provided by the present invention can reflect the hierarchical structure characteristics and connection relationships between metropolitan area networks while ensuring the layout effect. Figure 6 The results of the metropolitan area network topology visualization method proposed in this patent are compared with those of the traditional force-directed node-link graph and the traditional Voronoi segmentation.

[0081] As can be seen from the figure, the layout of the device is messy when using the traditional force-guided node-link graph visualization, which cannot reflect the hierarchical relationship and is difficult to operate and maintain; the traditional Voronoi segmentation will produce polygons with extreme aspect ratios, and the layout effect is poor. The metropolitan area network visualization method invented in this paper has the following advantages over the traditional visualization method:

[0082] (1) The canvas is divided according to the hierarchical relationship of metropolitan area network devices, combining business attributes with visualization methods.

[0083] (2) Use Voronoi polygons to tightly divide the canvas to achieve better layout results and visual effects.

[0084] (3) Use weighted Voronoi segmentation to reflect the number of devices in each level area and reduce the cognitive cost of operation and maintenance personnel.

[0085] (4) The aspect ratio of extreme polygons is optimized to achieve the best layout effect.

[0086] In terms of application, the present invention enables operation and maintenance personnel to directly locate alarm areas and specific equipment, conduct global situation awareness and monitoring of the metropolitan area network, reduce the cognitive difficulty and work costs of operation and maintenance personnel, improve operation and maintenance efficiency, help ensure the stability of the metropolitan area network, and is suitable for promotion and application.

[0087] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0088] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A method for visualizing metropolitan area network topology, characterized in that: The following steps are involved: Step 1: Preprocessing of metropolitan area network graph data level; Step 2, perform initial Voronoi diagram segmentation on the processed metropolitan area network graph to obtain a power-weighted Voronoi diagram; Step 3, perform layout iterative optimization and adjustment on the power-weighted Voronoi diagram to obtain a stable Voronoi segmentation result; Step 4: Use constraint force to guide the layout of devices in the area to obtain the final metropolitan area network topology; the area refers to the subnet and sub-area in the metropolitan area network; The step 2 specifically includes the following steps: Step 21: Perform dimensionality conversion to convert all regions with devices from two-dimensional planes to three-dimensional space to obtain several planes in three-dimensional space; the obtained planes intersect with each other in three-dimensional space to generate a convex polyhedron with a lower convex hull; Step 22: Solve the lower convex hull in the dual space to obtain the dual solution of the lower convex hull in the three-dimensional space; Step 23: Inversely map the dual solution of the lower convex hull to obtain the solution of the lower convex hull in three-dimensional space, and then project it onto the two-dimensional plane to obtain the solution of the power-weighted Voronoi diagram; The step 3 is specifically: repeating the two steps of region position optimization and weight coefficient optimization through multiple iterations until the difference between the actual area and the expected area of ​​all Voronoi diagrams is less than a set threshold, at which time the final stable Voronoi segmentation result is obtained.

2. A method for visualizing metropolitan area network topology according to claim 1, characterized in that: The step 1 is specifically as follows: Convert the metropolitan area network data into a graph structure G∈(V,E), where V represents the device vertex set and E represents the edge set between devices; divide the device nodes belonging to different subnets into different sets, and the device nodes belonging to the same subnet into the same set; In each subnet set, subsets are divided according to the sub-areas to which the devices belong. Devices belonging to the same sub-area are divided into the same subset. For each set, the set is weighted using the number of devices within the set. After data hierarchical preprocessing, the flattened metropolitan area network data is processed into hierarchical weighted data.

3. A method for visualizing metropolitan area network topology according to claim 1, characterized in that: The dual solution of the lower convex hull in step 22 is obtained by the surface-point duality theorem in geometry.

4. A method for visualizing metropolitan area network topology according to claim 1, characterized in that: The regional location optimization is specifically as follows: Region position optimization means moving the center of the inscribed circle of region s in the diagram to the centroid of the corresponding Voronoi diagram; when the inscribed circle of region s overlaps with another region v after moving, the weight of region s needs to be reduced: the radius of the inscribed circle Adjust to in is the minimum distance from the centroid to the boundary of the Voronoi diagram polygon, and then the power-weighted Voronoi diagram is recalculated according to the method of step S2.

5. A method for visualizing metropolitan area network topology according to claim 1, characterized in that: The weight coefficient optimization is specifically as follows: Step S31: Calculate the area of ​​the current Voronoi diagram Are for all regions s acurrent , and then according to the regional weight ω s The proportion of total weight ω total and total area total Calculate the area of ​​the desired Voronoi diagram The ratio of the current area to the expected area is used as the adjustment factor Step S32: Use the weight adjustment coefficient to adjust the current weight, and the adjusted weight value must be smaller than the square of the distance from the region to the nearest neighbor region; Step S33, recalculating the power-weighted Voronoi diagram according to the method of step S2; The weight refers to the number of devices in the area.

6. A method for visualizing metropolitan area network topology according to claim 1, characterized in that: The step 4 is specifically as follows: Step 41: Treat the equipment in each area as a particle system for force-guided simulation, and set the force-guided center of the system to the center of the maximum inscribed circle of the Voronoi diagram polygon; when the horizontal and vertical coordinates of the node fall outside the Voronoi diagram, a circular centripetal force needs to be applied; The annular centripetal force is an additional force applied to the out-of-bounds nodes, pulling the out-of-bounds nodes toward the inscribed circle of the Voronoi diagram polygon, so that they tend to be arranged on the circumference of the largest inscribed circle; Step 42: Apply sliding friction force to the devices in the system to suppress the device nodes from scattering randomly around, so that the device nodes are distributed within the polygons of the Voronoi diagram to which they belong, and finally obtain a metropolitan area network topology structure that can reflect the structural characteristics and the connection relationship between devices.

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