A method for identifying key areas of an urban power communication network

Abstract

A method for identifying key areas in an urban power communication network, which relates to the field of power communication, solves the problems that the existing methods for identifying key areas in power communication networks fail to accurately identify key areas in power communication networks and only consider single nodes, etc. The present invention constructs an urban power communication network model; determines the damaged area; calculates the importance of stations, the importance of pole tower nodes, and the importance of edges through the entropy weight method and obtains the comprehensive importance of candidate areas; uses a combined algorithm of the black widow optimization algorithm and the slime mold algorithm to initialize the candidate areas, and calculates the fitness value and position update of the candidate areas according to the comprehensive importance of the candidate areas and other steps, and finally obtains the position of the globally optimal candidate area, which is the position of the key area. This method ensures the accuracy and effectiveness of key area identification and subsequent analysis, and provides a more effective solution for the maintenance and optimization of complex networks such as power communication networks.

Description

Technical Field

[0001] The present invention relates to the field of power communication, and particularly to a method for identifying key areas of an urban power communication network. Background Art

[0002] With the acceleration of the urbanization process, the urban power demand is continuously increasing, and the role of the power communication network in the urban power system is becoming more and more important. As an important part of the smart grid, the urban power communication network undertakes key tasks such as real-time monitoring, dispatching, load management, and fault diagnosis of urban power equipment. With the rapid development of the smart grid, the urban power communication network is no longer a simple information transmission channel, but a core platform that supports the efficient operation and stable power supply of the power system. However, with the increase in the load of the urban power communication network, the complexity of the network structure, and the threats of natural disasters and external malicious attacks, the vulnerability problems of the urban power communication network have gradually emerged. Especially in terms of the reliability of key nodes and key areas, the robustness of the power communication network has become an important factor affecting the stability of the urban power system.

[0003] In fact, the vulnerability of the urban power communication network has been exposed many times in history.

[0004] Therefore, identifying vulnerable areas of the power communication network is a necessary measure to ensure the normal operation of the smart grid. This experiment aims to more comprehensively and accurately evaluate and improve the invulnerability of the urban power communication network by constructing a complex network model including pole tower nodes and real nodes and using innovative algorithms. Summary of the Invention

[0005] The present invention provides a method for identifying key areas of an urban power communication network to solve the problems that the existing methods for identifying key areas of the power communication network fail to accurately identify the key areas in the power communication network and only consider single nodes, etc.

[0006] A method for identifying key areas of an urban power communication network, which is implemented by the following steps:

[0007] Step 1: Construct an urban power communication network model G;

[0008] Step 2: Determine the damaged area according to the urban power communication network model G constructed in Step 1;

[0009] Step 3: Calculate the importance of sites, the importance of pole tower nodes, and the importance of edges by the entropy weight method according to the damaged area determined in Step 2, and obtain the comprehensive importance of candidate areas according to the importance of sites, the importance of pole tower nodes, and the importance of edges;

[0010] Step 4: Use the combined algorithm of the Black Widow optimization algorithm and the slime mold algorithm to initialize the candidate regions, and calculate the fitness value of the candidate regions according to the comprehensive importance of the candidate regions obtained in Step 3;

[0011] Step 5: Sort the fitness values of the candidate regions in Step 4 to obtain new candidate regions, calculate the weight of position update according to the fitness of the new candidate regions, and use the position update weight to calculate the moving direction and step size of the new candidate regions in the search space;

[0012] Step 6: Update the position;

[0013] For each candidate region in the new candidate regions, use a random probability z to determine whether to randomly re-initialize the position of the candidate region. If rand < z, re-initialize the position of the candidate region; otherwise, determine the search strategy through the search ratio p*er; p is the calculated probability, which is a threshold for controlling the position update strategy;

[0014] er = min(1, max(0, 1 - diversity)), where diversity is the ratio of the standard deviation to the mean of the fitness value vector F;

[0015] If rand < p*er, use the Black Widow optimization algorithm to search for the globally optimal candidate region; otherwise, use the slime mold algorithm to search for the globally optimal candidate region; finally, the position of the globally optimal candidate region is the position of the key region.

[0016] Advantages of the present invention:

[0017] The recognition method of the present invention can provide strong theoretical support and technical means for the planning, maintenance and emergency handling of the power communication system by deeply studying the topological structure, service transmission requirements of the power communication network, and the importance of different nodes and links, ensuring that the power communication network can still maintain high reliability and stability under various adverse conditions and meeting the strict requirements of modern society for power supply.

[0018] In the recognition method of the present invention, by constructing a complex network model including pole tower nodes and real nodes, using intelligent optimization algorithms to identify the key regions in the network, and simulating the sequential damage of these key regions according to the importance order, comprehensively evaluating the impact of the damage behavior on the network efficiency, and at the same time, by reasonably setting the search distance mechanism to avoid the evaluation deviation caused by the excessive overlapping area of the key regions, and by adding pole tower nodes, it is also possible to more accurately estimate the cost of the lowest complete repair network based on this, ensuring the accuracy and effectiveness of the key region recognition and subsequent analysis, and providing a more effective solution for the maintenance and optimization of complex networks such as power communication networks. Description of the Drawings

[0019] Figure 1 Flow chart of a method for identifying key areas of an urban power communication network according to the present invention;

[0020] Figure 2 Schematic diagram of the concept of damage circle in the method according to the present invention;

[0021] Figure 3 Schematic diagram of a partial power communication network model in an urban area of a certain city;

[0022] Figure 4 Trend chart of damage degree (network efficiency) of the power communication network at different radii;

[0023] Figure 5 For intercepting Figure 4 Trend chart of damage degree at r = 0.08 in;

[0024] Figure 6 Comparison result chart of network efficiency between this method and existing key node identification methods. Specific implementation manner

[0025] Step 1: In the urban power network structure, it is very necessary to calculate the weight of the importance of pole and tower nodes. Pole and tower nodes play a key role in connecting and supporting the physical network architecture. However, a pole and tower node failure may only affect a small part of the lines directly connected to it, unlike a key substation node failure or a major transmission line failure that may cause a large - scale power outage or network splitting. First, this paper conducts a spatial modeling based on the power communication network in an urban area of a certain city. The urban power communication stations and poles and towers are abstracted as network nodes, and the optical cable communication links are abstracted as the edges of the network. Since the mutual transmission between services, the urban power communication network can be abstracted as a weighted undirected graph.

[0026] Each site in the urban power communication network represents 9 different types of communication sites. Based on the physical length of each communication link, it is divided into several segments by poles and towers, and corresponding pole and tower nodes are abstracted on the communication link.

[0027] Therefore, a power communication network model G has the following representation;

[0028] According to the direct mapping of the sites and pole and tower nodes containing longitude and latitude coordinate information, the urban power communication network model is defined as G(V, V’, E, W).

[0029] V is the set of sites, that is, V = {v1, v2, …, v i …v n}, v iDenote the \(i\)-th site, and the number of sites is \(n\). The attributes of a site can be expressed as: \(V_n=(ID, LO, LA)\), where \(ID\) is the node number, \(LO\) is the longitude of the location where the node is located, and \(LA\) is the latitude of the location where the node is located.

[0030] Let \(E\) be the set of edges corresponding to communication links in the power communication network, that is, \(E = \{e\) i,j | \(v\) i , \(v\) j \(\in V, i\neq j, e\) i,j = \(e\) ji}\), where \(e\) i,j represents the edge between site \(v\) i and site \(v\) j . In the set \(E\), if \(e\) i,j = 1, it means there is an edge connection between site \(v\) i and site \(v\) j . If \(e\) i,j = 0, it means there is no edge connection between site \(v\) i and site \(v\) j .

[0031] Let \(V'\) be the set of pole tower nodes. If \(e\) i,j = 1, then divide \(e\) ij = 1 into pole tower nodes. The pole tower nodes can be represented by the set: \(V'=\{V'\) i,j | \(V'\) i,j \(\in e\) i,j}\), where represents the \(k\) i -th pole tower node between site \(v\) j and site \(v\) ij ; \(k\) ij is the number of pole tower nodes on edge \(e\) i,j ;

[0032] \(k\) ij = \(L\) ij / (Ls - 1). \(L\) ij is the physical length of the edge in the network, and \(Ls\) is the segmentation length of the pole tower nodes.

[0033] Step 2: Determine the spatial damage form and damage method of the circle, and determine the damage area.

[0034] In this embodiment, the importance of the damage area can be expressed as a triple \(W=(W\) V , \(W\) E , \(W\) V' ), where \(W\) V =(w\) V1 , \(w\) V2 ,... \(w\) Vi ,... \(w\) Vn) is the importance of the site, W E =(w E1 , w E2 ,... w Ei ,... w En ) is the importance of the edge, W V' =(w V'1 , w V'2 ,... w V'i ,... w V'n ) is the importance of the pole and tower node.

[0035] The damage form adopted is: use a circular area to damage the power communication network model G. Let the center coordinates of the damage area be (lo0, la0) and the damage radius be r. Then the damage area can be defined as D = {(lo i , la i )|(la i , la0) 2 +(lo i , lo0) 2 ≤ r 2}), where lo and la are the longitude and latitude of the damage area respectively; lo0 and la0 are the longitude and latitude of the initial population respectively; la i and lo i are the longitude and latitude coordinates of the site v i ; that is, all sites, edges and pole and tower nodes within this area will be damaged, and the damaged sites, edges and pole and tower nodes will be removed from the model.

[0036] The damage method adopted is: obtain the location information of each site in the power communication network model G, traverse the location of each site, and calculate the Euclidean distance dv between the site and the center of the damage area. If dv ≤ r, it is considered that the site is within the critical area. Traverse all sites within the critical area and delete the site and its associated edges. Traverse each edge, calculate the Euclidean distance de between the edge and the center of the critical area. If de ≤ r, it is considered that the edge crosses the critical area, and the deleted edges and sites are added to the site deletion table remove_node and the edge deletion table removed_edges list.

[0037] Step 3: According to the damage area determined in Step 2, define the calculation rules for the importance of the damage area, and calculate the importance of the site, the importance of the pole and tower node, the importance of the edge and the comprehensive importance of the area by the entropy weight method.

[0038] In this embodiment, the importance of the sites and edges within the damage area is calculated from three aspects: topology, service, and voltage level.

[0039] Step 31, calculate the importance of the sites within the damage area from three aspects: topology, service, and site level;

[0040] Step 311. Obtain the set of topological importance TI of sites through betweenness centrality calculation, TI = (C B (v1), C B (v2),... C B (v i ),..., C B (v n ))), where C B (v i ) is the topological importance of site v i .

[0041]

[0042] Among them, v k and v j are any two sites in the network different from v i . σ jk represents the total number of shortest paths from site v j to site v k , and σ jk (v i ) represents the number of shortest paths from site v j to site v k and passing through site v i .

[0043] Step 312. Obtain the business importance vector BI of sites through calculation, BI = (BI(v1), BI(v2),.. BI(v i ),..., BI(v n )), where BI(v i ) is the business importance of site v i .

[0044]

[0045] Among them, T is the number of business types to which site v i belongs. BW is the weight of the business type. bn(v i ) is the number of each type of business of site v i , and M is the total number of business types of all sites.

[0046] Step 313. Considering the grid level factors, including 500kv substations, 220kv substations, 66kv substations, user communication stations, provincial secondary unit communication stations, municipal secondary unit communication stations, provincial company communication stations, power plants, and city company communication stations. They are divided into 9 levels according to their importance ranking, with the importance value of the most important site being 9 and the lowest importance value being 1.

[0047] In this embodiment, the importance degree W of the site is calculated based on the entropy weight method. v The specific process of the calculation is as follows:

[0048] Standardize the original data. The standardization formula is as follows: where x cd is the original data of the c-th site in the d-th index (C B (v i ), BI(v i ), voltage level). x cd * is the data after standardization of the c-th node in the d-th index, obtaining the standardized data matrix, x cd * ∈(0, 1). max(x d ) and min(x d ) are the minimum and maximum values of the d-th index.

[0049] After standardization, calculate the proportion p cd of each site under each index. The calculation formula is: Obtain the proportion matrix, where: p cd is the proportion of the c-th node under the d-th index, is the sum of the standardized values of all solutions under the d-th index.

[0050] Then calculate the information entropy value H d of each site. The entropy value calculation formula is as follows:

[0051] where: is a constant, and n is the number of sites.

[0052] According to the calculated entropy value, calculate the weight w d of each index. The formula is: Finally, calculate the comprehensive score of each site through the weight and the standardized data, that is: the importance degree w vc of the c-th site = w1 * p c1 + w2 * p c2 +... w d * p cd ... + w n * p cn .

[0053] Step 32: Calculate the importance degree of the edges in the damaged area from three aspects: topology, business, and site level;

[0054] Step 321: Calculate the topological importance of edges from the perspective of betweenness centrality, the business importance of edges, and the voltage level of edges; for the connection between sites v i and v j of the edge e ij , where the betweenness centrality C B (e) is calculated as follows:

[0055]

[0056] where v m and v n are any two sites in the network. σ mn is the total number of shortest paths from site v m to site v n , and σ mn (e) is the number of shortest paths from site v m to site v n that include the edge e ij .

[0057] Step 322: Assign different weights W u to different business types u according to the criticality of the business and its impact on the network. Then the business importance B(e) of the edge can be expressed as:

[0058]

[0059] where U is the number of business types on the edge, w u is the weight coefficient of the u-th business type, and n e,u is the number of the u-th business type on the edge.

[0060] Step 323: Assign different weights according to the voltage level of the transmission line corresponding to the communication link. Assign weight W 220 = 0.6; 66 kV lines are generally used to supply power to a relatively large user group or small substations, with slightly lower importance, and assign weight W 66 = 0.3; 10 kV lines are mainly used to locally distribute power to end-users, with relatively lower importance, and assign weight W 10 = 0.1.

[0061] In this embodiment, the specific process of calculating the importance of edges based on the entropy weight method is as follows:

[0062] Standardize the original data, and the standardization formula is as follows: where x cd is the value of the c-th edge in the d-th index (C B (v i ), BI(v i), the original data of the voltage level). x cd * is the standardized data of the c-th edge on the d-th index, obtaining the standardized data matrix, x cd * ∈(0, 1). max(x d ) and min(x d ) are the minimum and maximum values of the d-th index.

[0063] After standardization, calculate the proportion p cd of each edge under each index, and the calculation formula is: Obtain the proportion matrix, where: p cd is the proportion of the c-th edge under the d-th index, is the sum of the standardized values of all solutions under the d-th index.

[0064] Then calculate the information entropy value H d of each edge, and the entropy value calculation formula is as follows: Among them: is a constant, and n is the number of stations.

[0065] According to the calculated entropy value, calculate the weight w d of each index, and the formula is: Finally, calculate the comprehensive score of each edge through the weight and the standardized data, that is: the importance of the edge. As follows.

[0066] w Ec = w 11 * p c1 + w 22 * p c2 +... w dd * p cd ...+ w nn * p cn

[0067] Step 33: Calculate the importance W ij of the tower node V′ V′i based on the importance of adjacent two stations and the importance of the edge where they are located. The formula is as follows:

[0068]

[0069] Since the importance of the edge and the station is much greater than the importance of the tower node, a larger weight will be given to the importance of the edge and the station during the calculation, and a smaller weight will be assigned to the importance of the tower node. The weights of the tower node importance, station importance, and edge importance are respectively and satisfy At the same time and Set

[0070] Step 34: Calculate the comprehensive importance of the candidate area according to the importance of the site obtained in Step 31, the importance of the edge in Step 32, and the importance of the pole and tower node in Step 33.

[0071] Assume that each site v i has an importance w Vi , and the pole and tower node V′ ij has an importance w V'i . In the entire spatial topology network, an irregular candidate area D(C, r) is generated through the Tent mapping. The center coordinates of the candidate area are represented by C, and C is the set of the central node positions of the candidate area;

[0072] where C = (c1, c2,... c ρ ..., c pop ), c ρ is the ρ-th candidate area, pop is the population size of the candidate area, c ρ ∈[lb, ub], where lb and ub are the lower and upper bounds of the candidate area respectively, lb ∈

[0073] [max(lo), min(lo)], ub ∈[max(la), min(la)]. The comprehensive importance of each candidate area D(c ρ , r) can be expressed as:

[0074]

[0075] where the weights of the pole and tower node, site, and edge are W1, W2, and W3 respectively.

[0076] Step 4: Initialize using the combined Black Widow Optimization Algorithm and Slime Mould Algorithm (BW0A - SMA); at the initial stage of key area recognition, the initialization of the candidate area has an important impact on the search performance.

[0077] For the solution space B(c ρ , r), under the condition of a given initial radius r = 0.08, set the starting position parameter c0 = (144.01, 44.54) of the candidate area center in the network space and the candidate area population size pop = 150, and set the random probability z = 0.03. The candidate area D(C, r) follows the following rules:

[0078]

[0079] where A is a control parameter to make the position distribution of each candidate area more random and diverse, c fis the position of the f-th candidate region.

[0080] Step Five: Calculate the fitness value of the candidate region;

[0081] Calculate the fitness value of the candidate region through the comprehensive importance of the candidate region calculated in Step Three. Set the fitness function F(c ρ , r) = -W(c ρ , r). And sort D(C, r) according to the fitness value to obtain the new region DI(C, r); the specific process is as follows:

[0082] Calculate the importance value W(c ρ , r) of each candidate region D(c ρ , r). For each candidate region D(c ρ , r), calculate its fitness value through the fitness function F(c ρ , r) = -W(c ρ , r), and sort D(C, r) according to the fitness value to obtain the new candidate region DI(C, r). The lower the fitness value, the higher the criticality of the candidate region. Therefore, the algorithm will preferentially select candidate regions with low fitness values.

[0083] Step Six: Obtain the new candidate region DI(C, r) according to the fitness sorting in Step Five, and update the weight WI based on the position calculated from the fitness of the new candidate region DI(C, r). Guide the moving direction and step size of the candidate region in the search space.

[0084] Step 61: For DI(C, r), in the θ-th iteration (θ ∈ (0, MaxIter - 1)), first calculate the fitness difference sf of DI(C, r), sf = fitness_best - fitness_worst, where fitness_worst is the worst fitness value of DI(C, r), fitness_worst = max(F(c ρ , r)), fitness_best is the best fitness value of DI(C, r), fitness_best = min(F(c ρ , r)).

[0085] Step 62: For DI(C, r), during the search process, according to the fitness difference sf, use WI to guide the moving direction and step size of the candidate region in the search space.

[0086]

[0087] In the formula, fitnessI is the fitness value; α is a parameter to control the weight adjustment amplitude; ∈ is a constant to prevent the denominator from being zero.

[0088] Step 7: Location update;

[0089] For each candidate region D(cρ, r), it is determined by the random probability z whether to randomly re-initialize the position of the candidate region. If rand < z, the individual position is re-initialized;

[0090] Otherwise, if rand ≥ z, the search strategy is determined by the search ratio p*er;

[0091] When z ≤ rand < p*er, for the candidate region with fitness close to the global optimum, the black widow optimization algorithm is used for search. The algorithm location update formula is as follows:

[0092]

[0093] When rand ≥ p*er, the slime mold algorithm is used for global search,

[0094]

[0095] where c(θ + 1) is the position of the candidate region after the (θ + 1)-th iteration, c b (θ) is the optimal position of the current fitness value, m is a random number between [0.4, 0.9], β is a random number within [-1, 1], c A (θ), c B (θ) are the positions of the randomly selected A and B candidate regions. c(θ) is the position of the current candidate region, vb is a random number [-a, a], a is a constant, rand, rand1 are random numbers in (0, 1).

[0096] In this embodiment, p is the calculation probability, which is a threshold for controlling the position update strategy, p = tanh(|fitness_best - fitnessI|), where fitness_best is the current global optimal fitness value, and the tanh function maps the fitness difference to the interval (0, 1). er = min(1, max(0, 1 - diversity)), where σ(F(c ρ , r)) is the standard deviation of the fitness value vector F, and μ(F(c ρ , r)) is the mean of the fitness value vector F.

[0097] After the location update, the position of the globally optimal candidate region obtained by the algorithm search is the position of the key region.

[0098] In this embodiment, it also includes detecting the effect of the key region recognition algorithm in the urban power communication network, defining evaluation indicators, network efficiency, and analyzing the network after damaging the key region.

[0099] During the process of area damage, record the network efficiency and output data related to the network efficiency.

[0100] Network efficiency calculation method:

[0101]

[0102] where d ij is the shortest path between sites v i and v j .

[0103] Specific implementation method 2. Combining Figures 2 to 6 to illustrate this implementation method. This implementation method is an embodiment of a method for identifying key areas of an urban power communication network described in Specific implementation method 1: To illustrate the process of the key area identification method based on the damaged area, Figure 2 is an example of area damage. The black nodes are sites, the gray nodes are pole tower nodes, and the dashed area is the damage circle, that is, the damaged area. The anti-damage analysis of a part of the power optical cable network in an urban area of a certain city in Jilin Province is used as an example to illustrate:

[0104] 1. Construct a power communication network model;

[0105] Abstract the communication sites in each level of substation, communication station and each power plant in a part of the urban power communication network in a certain city in Jilin Province as sites, and the displayed geographical position coordinates of each communication site correspond to the positions in the model space; abstract the connections between each communication site as undirected edges; based on the physical length of each communication link, divide it into several segments with pole towers and abstract the corresponding pole tower nodes on the communication link.

[0106] Construct a power communication network model G(V, V’, E, W) of a part of the urban area of a certain city in Jilin Province, as Figure 3 shown, Figure 3 is a schematic diagram of a part of the power communication network model in an urban area of a certain city. This network contains 13 sites, 17 optical cables and 171 pole tower nodes. After statistically analyzing the length L of each link and the number of pole tower nodes si on the link, Ls = 0.22 is calculated.

[0107] 2. Calculate the site importance, edge importance and pole tower node importance according to Step 3 in Specific implementation method 1, and comprehensively consider these three factors to calculate the comprehensive importance of the candidate area.

[0108] 3. Set the method parameters according to Step 4 in Specific implementation method 1, set the population size pop = 150, and set the starting position parameter c0 = (144.01, 44.54) of the candidate area center in the network space.

[0109] 4. Read the network and coordinate information of the power communication network, establish a corresponding power communication network model. Each slime mold represents an expected damage center in the actual space. Conduct circular damage simulation on the position of each individual to calculate the fitness F(c ρ ,r), and then iteratively calculate the individual movement direction and step size in space by calculating and updating the weight WI. Each iteration determines the search strategy according to the search ratio p*er, so as to update the position, making the individual gradually approach the key area of the power communication network, and finally obtaining the position of the key area.

[0110] 5. Analysis of the key area of the power communication network; Set the initial five radii r = 0.05, 0.08, 0.1, 0.12, 0.15, select the optimal radius for experiments. Through experiments on the five initial radii, select the top k individuals among the individuals, calculate the decline of their network efficiency, and then select the optimal radius r for the key area identification experiment to obtain the change trend of the damage degree of the power communication network under different radii, as Figure 4 shown.

[0111] At the same time, it is found that when r < 0.08, the damage degree is relatively low. Compared with the original network efficiency of 0.5021, when r = 0.05, the network efficiency is only slightly less than and close to 0.5 after damaging the key area. Although the key area in the whole network can be found, due to the small radius, the impact on the whole network is relatively small. When r > 0.08, as the radius increases, the damage effect weakens. When r ≥ 0.12, due to the too large radius, most of the network is likely to fail when damaging the network, resulting in a change in the network properties, and there will be a situation where even if the damage range is too large, the network efficiency will be higher.

[0112] From Figure 5 it shows the change of the network efficiency with the ranking of the key area after damaging the area at a radius of 0.08. Observing the chart, it can be seen that when the key area is not damaged, the network efficiency is close to 0.5 and at a relatively high level. Once this key area is damaged, the network efficiency will immediately drop to about 0.47, indicating that the area ranked first plays a crucial role in the network efficiency. In contrast, when damaging other ranked areas, the change range of the network efficiency is relatively small.

[0113] Table 1

[0114]

[0115] As shown in Table 1, after finding the key area, number the pole tower nodes in sequence according to the pole tower node number after the site to obtain Nodes within the critical area, which have important connection functions in the network. For example, Node 1 is connected to multiple nodes. 1,2 ,e 1,3 ,e 1,4 ,e 1,12 , and its removal will destroy the connection structure of the network, thereby affecting network efficiency. By observing node information, the sites corresponding to these removed nodes have different business importance levels and site grades. For example, the business importance level corresponding to v1 is 0.5, and the site grade is 220kv. These attributes indicate that nodes may play an important role in network business transmission and structural support, and their damage will have a greater impact on the network, meeting the characteristics of nodes within the critical area.

[0116] The comparison results of the method described in this embodiment with the existing critical area recognition methods centered around critical node identification are as Figure 6 shown. It can be seen from the figure that whether it is the network efficiency (dashed line) after the damage area is identified by the comparison experiment method when the radius = 0.08 or the network efficiency (solid line) after the critical area of this article is damaged when the radius = 0.08, the overall network efficiency shows an upward trend after the damage area. From the experimental results, after the critical area found by this article's algorithm is damaged, the network efficiency is 10% higher than that of the comparison algorithm. This is because the sites v1 and edges e 1,2 ,e 1,3 ,e 1,4 ,e 1,12 in the network undertake important functions such as business transmission, information exchange, and network structure support. After the critical area found by the comparison algorithm is damaged, due to its relatively low importance in the network (only judged based on node importance), the network efficiency after damage is 0.50, and the impact on the overall network efficiency is relatively small, and the decline in network efficiency is not as large as that of the critical area found by this article's algorithm. This further proves the effectiveness of the method of the present invention in finding the critical area, and it can find a critical area that has a greater and more comprehensive impact on network efficiency.

[0117] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope described in this specification.

[0118] The above-described embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent should be subject to the appended claims.

Claims

1. A method for identifying key areas of an urban power communication network, characterized in that: This method is implemented by the following steps: Step 1: Construct an urban power communication network model G; Step 2: Determine the damage area according to the urban power communication network model G constructed in Step 1; Step 3: According to the damage area determined in Step 2, calculate the importance of stations, the importance of pole and tower nodes, and the importance of edges by the entropy weight method, and obtain the comprehensive importance of the candidate area according to the importance of stations, the importance of pole and tower nodes, and the importance of edges; Step 4: Use the combined algorithm of the black widow optimization algorithm and the slime mold algorithm to initialize the candidate area, and calculate the fitness value of the candidate area according to the comprehensive importance of the candidate area obtained in Step 3; Step 5: Sort the fitness values of the candidate areas described in Step 4 to obtain new candidate areas, calculate the weight of position update according to the fitness of the new candidate areas, and use the position update weight to calculate the moving direction and step size of the new candidate areas in the search space; Step 6: Position update; For each candidate area in the new candidate areas, use a random probability z to determine whether to randomly re-initialize the position of the candidate area. If rand < z, re-initialize the position of the candidate area; otherwise, determine the search strategy by the search ratio p*er; p is the calculation probability, which is a threshold for controlling the position update strategy; er = min(1, max(0, 1 - diversity)), where diversity is the ratio of the standard deviation to the mean of the fitness value vector F; If rand < p*er, use the black widow optimization algorithm to search for the globally optimal candidate area; otherwise, use the slime mold algorithm to search for the globally optimal candidate area; rand is a random number taken from (0, 1); finally, the position of the globally optimal candidate area obtained is the position of the key area.

2. The key area identification method for the urban power communication network according to claim 1, characterized in that: In Step 1, according to the topology structure and spatial information of the power communication network, construct a power communication network model G(V, V’, E, W); where: V = {v1, v2, …, v i , …, v n} is the set of sites of the power communication network model G, and the number of nodes is n; E = {e i,j | v i , v j ∈ V, i ≠ j, e i,j = e ji} is the set of edges corresponding to communication links in the power communication network, e i,j represents the edge between the sites v i and the site v j in the power communication network model G; if e i,j = 1 in the set E, then there is an edge connection between the sites v i and the site v j ; if e i,j = 0, then there is no edge connection between the sites v i and the site v j ; V’ = {V' i,j | V' i,j ∈ e i,j} is the set of pole and tower nodes, where: The said k ij is the number of pole tower nodes on the edge e i,j ; k ij = L ij / (Ls - 1); Ls is the segmentation length of the pole tower nodes, and L ij is the physical length of the edge in the network.

3. The key area identification method for an urban power communication network according to claim 1, wherein: In Step 2, the specific process of determining the damage area is: Use a circular area to damage the power communication network model G. Set the center coordinates of the damaged area as (lo0, la0) and the damage radius as r. Then the damaged area is defined as D = {(lo i , la i ) | (la i , la0) 2 + (lo i , lo0) 2 ≤ r 2},where lo and la are the longitude and latitude of the candidate region respectively; lo0 and la0 are the longitude and latitude of the initial population respectively; la i and lo i are the longitude and latitude coordinates of site v i .

4. A method for identifying key areas of an urban power communication network according to claim 3, characterized in that: In Step 2, the damage method used is: obtain the position information of each station in the power communication network model G, traverse the positions of each station, and calculate the Euclidean distance dv between the station and the center of the damage area; If dv ≤ r, it is considered that the station is located within the key area; traverse all stations located within the key area, delete the stations and their associated edges; traverse each edge and calculate the Euclidean distance de between the edge and the center of the damage area; If de ≤ r, it is considered that the edge crosses the key area, and add the deleted edges and stations to the station deletion table remove_node and the edge deletion table removed_edges list; 5. The key area identification method for an urban power communication network according to claim 4, wherein: In Step 3, generate an irregular candidate area D(C, r) through the Tent mapping. The center coordinates of the candidate area are represented by C, which is the set of the central node positions of the candidate area; C = (c1, c2,... c ρ ..., c pop ), c ρ is the ρ-th candidate region, pop is the size of the candidate region population, c ρ ∈ [lb, ub], lb ∈ [max(lo), min(lo)], ub ∈ [max(la), min(la)], where lb and ub are the lower and upper bounds of the candidate region respectively; the formula for the comprehensive importance of each candidate region D(c ρ , r) is: Among them, W1, W2, and W3 are the pole tower node, site, and edge weights respectively; W V is the importance of the site, and W E is the importance of the edge, and W V' is the importance of the pole tower node.

6. The key area identification method for an urban power communication network according to claim 5, wherein: In Step 4, it is set that the candidate area D(C, r) follows the following rules: where A is a control parameter that makes the position distribution of each candidate region more random and diverse, and c f is the position of the f-th candidate region; For each candidate region D(c ρ ,r), its fitness value is calculated by the fitness function F(c ρ ,r) = -W(c ρ ,r).

7. A method for identifying key areas of an urban power communication network according to claim 6, characterized in that: The specific process of Step 5 is: Step 5-1: Sort the fitness values described in Step 5 to obtain new candidate areas DI(C, r), and calculate the position update weight WI according to the fitness values of the new candidate areas DI(C, r); Step Five Two: For the new candidate region DI(C, r), in the θ-th iteration, first calculate the fitness difference sf of DI(C, r), sf = fitness_best - fitness_worst; where fitness_worst is the worst fitness value of DI(C, r), fitness_worst = max(F(c ρ , r)), fitness_best is the best fitness value of DI(C, r), fitness_best = min(F(c ρ , r)); θ ∈ (0, MaxIter - 1); Step Five Three. According to the fitness difference sf, use the weight WI for position update to guide the moving direction and step size of the candidate region in the search space, which is expressed by the following formula: In the formula, α is a parameter to control the weight adjustment amplitude; ∈ is a constant.

8. A method for identifying key areas of an urban power communication network according to claim 7, characterized in that: In Step Six, when rand < p*er, for the candidate region with fitness close to the global optimum, use the black widow optimization algorithm for search, which is expressed by the following formula: When rand ≥ p*er, use the slime mold algorithm for global search, which is expressed by the following formula: Among them, c(θ + 1) is the position of the candidate region after the (θ + 1)-th iteration, and c b (θ) is the optimal position of the current fitness value, m is a random number between [0.4, 0.9], β is a random number within [-1, 1], and c A (θ), c B (θ) are the positions of the randomly selected A-th and B-th candidate regions, c(θ) is the position of the current candidate region, vb is a random number [-a, a], and a is a constant.

Images