A method and system for expanding urban broadband links by comprehensively deploying cost and network state

Abstract

The application relates to a city broadband link expansion method and system integrating deployment cost and network state. The method comprises the following steps: acquiring a first link utilization rate of a link group to be expanded in each preset time period and a cost function corresponding to expansion; when the first link utilization rate in the continuous multiple preset time periods exceeds a preset first threshold value, a congestion penalty function is constructed; when the first link utilization rate in the continuous multiple preset time periods is lower than a preset second threshold value, a resource waste penalty function is constructed; a result of adding the resource waste penalty function, the congestion penalty function and the cost function is determined as a target function; the target function is used for expansion link quantity calculation, and the expansion link quantity of each link group is obtained. The method can obtain a suitable expansion link quantity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet, in particular to a city broadband link expansion method and system integrating deployment cost and network state. BACKGROUND

[0002] With the rapid development of Internet technology, broadband networks have become a key infrastructure in modern society, deeply affecting the operation and development of various industries. In recent years, with the explosive growth of network users and the widespread popularity of various innovative applications, the traffic demand of broadband links has shown an unprecedented exponential growth. However, with the continuous growth of traffic demand, traditional metropolitan networks are facing the challenge of link expansion.

[0003] Currently, the expansion of broadband network links mainly relies on manual judgment, which lacks the support of data analysis-based prediction and a set of scientific expansion strategies. Therefore, in actual operation, the manual judgment expansion method may have problems such as resource waste, neglect of links that really need to be expanded, and poor expansion effect. Existing researches mostly focus on designing a new network architecture, but this usually involves high costs, technical implementation difficulties, and challenges in comprehensive deployment, so it is difficult to obtain a more appropriate number of expanded links. SUMMARY

[0004] Therefore, it is necessary to provide a city broadband link expansion method and system integrating deployment cost and network state, which can obtain a suitable number of expanded links.

[0005] A city broadband link expansion method integrating deployment cost and network state, the method comprising:

[0006] obtaining the first link utilization rate of each link group to be expanded in each preset time period and the cost function corresponding to the expansion;

[0007] when the first link utilization rate in a plurality of consecutive preset time periods exceeds a preset first threshold, constructing a congestion penalty function, and when the first link utilization rate in a plurality of consecutive preset time periods is lower than a preset second threshold, constructing a resource waste penalty function;

[0008] adding the resource waste penalty function, the congestion penalty function and the cost function to obtain a target function;

[0009] using the target function to calculate the number of expanded links to obtain the number of expanded links of each link group.

[0010] In one embodiment, the number of link expansions of each link group is calculated by a target algorithm, and the optimization process of the target algorithm comprises:

[0011] generating a plurality of candidate solutions randomly based on candidate solution generation instructions; the candidate solution generation instructions are used to instruct the target algorithm to generate a plurality of candidate solutions comprising the number of expansion links of each link group;

[0012] substituting each of the candidate solutions into the target function to obtain the fitness of each of the candidate solutions, and determining a global optimal solution among the candidate solutions based on the fitness;

[0013] performing iterative processing based on the global optimal solution and the candidate solutions until the number of iterations of the target algorithm satisfies an iteration stopping condition, and outputting an optimal solution.

[0014] In one of the embodiments, the iteration process of the target algorithm comprises:

[0015] S1, determining a global optimal solution, and determining at least one target link group from each of the candidate solutions, and updating the number of expansion links of the target link group to obtain a first updated solution;

[0016] S2, substituting the first updated solution into the target function to obtain the fitness of the first updated solution;

[0017] S3, if the fitness of the first updated solution is less than the fitness of the candidate solution, replacing the candidate solution in step S1 with the first updated solution; if the fitness of the first updated solution is less than the fitness of the global optimal solution, replacing the global optimal solution in step S1 with the first updated solution;

[0018] S4, repeating steps S1, S2 and S3 until the number of iterations of the target algorithm satisfies an iteration stopping condition, and outputting an optimal solution.

[0019] In one of the embodiments, the iteration process of the target algorithm further comprises:

[0020] obtaining a preset number of iterations for identifying a candidate solution with the maximum fitness;

[0021] when the current number of iterations of the target algorithm is a multiple of the preset number of iterations, identifying a target candidate solution with the maximum fitness among the current candidate solutions;

[0022] performing global random sampling on the target candidate solution to obtain a second updated solution;

[0023] replacing the target candidate solution among the candidate solutions with the second updated solution, and selecting a target link group based on the candidate solutions after the replacement.

[0024] In one of the embodiments, step S1 comprises:

[0025] Based on the number of expansion links in the global optimal solution, the number of expansion links of the target link group is updated, and an updated first updated solution is obtained;

[0026] And / or, the number of expansion links of the target link group is randomly updated, and an updated first updated solution is obtained.

[0027] In one of the embodiments, step S1 includes:

[0028] The number of expansion links of the target link group is updated, and an updated number of expansion links is obtained.

[0029] When the updated number of expansion links exceeds a preset expansion link threshold, based on the number of expansion links indicated by the expansion link threshold, an updated first updated solution is determined.

[0030] In one of the embodiments, the method further includes:

[0031] The number of expansion links in each of the link groups is determined, and the number of original links in each of the link groups is obtained;

[0032] The first result of the multiplication between the first link utilization rate and the number of original links is calculated, and the second result of the addition between the number of expansion links and the number of original links is calculated;

[0033] Based on the quotient of the first result divided by the second result, a second link utilization rate is determined; the second link utilization rate is used to represent the average utilization rate of the links after expansion of the link group.

[0034] A city broadband link expansion system integrating deployment cost and network state, the system includes:

[0035] A data acquisition module is configured to acquire a first link utilization rate of a link group to be expanded and a cost function corresponding to expansion within each preset time period;

[0036] A first function construction module is configured to construct a congestion penalty function when the first link utilization rate within a plurality of consecutive preset time periods exceeds a preset first threshold, and to construct a resource waste penalty function when the first link utilization rate within a plurality of consecutive preset time periods is lower than a preset second threshold;

[0037] A second function construction module is configured to determine a result of the addition of the resource waste penalty function, the congestion penalty function, and the cost function as a target function;

[0038] A strategy acquisition module is configured to use the target function to calculate the number of expansion links, and to obtain the number of expansion links of each of the link groups.

[0039] The urban broadband link expansion method and system integrating deployment cost and network state can obtain the first link utilization rate of the link group to be expanded in each preset time period and the cost function corresponding to expansion, construct a congestion penalty function when the first link utilization rate in the continuous multiple preset time periods exceeds the preset first threshold, construct a resource waste penalty function when the first link utilization rate in the continuous multiple preset time periods is lower than the preset second threshold, determine the result of adding the resource waste penalty function, the congestion penalty function and the cost function as a target function, use the target function to calculate the number of expansion links, and obtain the beneficial effect of the number of expansion links of each link group. In this way, the expansion cost, congestion and resource waste can be considered comprehensively to ensure the optimal allocation of resources while minimizing the cost, and the most suitable number of expansion links is obtained. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 An application environment diagram of the urban broadband link expansion method integrating deployment cost and network state in an embodiment;

[0041] Figure 2 A flowchart of the urban broadband link expansion method integrating deployment cost and network state in an embodiment;

[0042] Figure 3 An optimization flowchart of the algorithm in an embodiment;

[0043] Figure 4 A structural block diagram of the urban broadband link expansion system integrating deployment cost and network state in an embodiment;

[0044] Figure 5 An internal structure diagram of the computer device in an embodiment. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0046] The urban broadband link expansion method integrating deployment cost and network state provided by the embodiments of the present application can be applied to, for example, Figure 1The application environment shown. Among them, the terminal interacts with the server through wired channels / wireless channels. The data storage system can store the data required by the server to process. The server obtains the first link utilization rate of each link group to be expanded in a plurality of continuous preset time periods and the cost function corresponding to the expansion; when the first link utilization rate of the server in the continuous plurality of preset time periods exceeds the preset first threshold, a congestion penalty function is constructed, and when the first link utilization rate of the server in the continuous plurality of preset time periods is lower than the preset second threshold, a resource waste penalty function is constructed; the server determines the result of adding the resource waste penalty function, the congestion penalty function and the cost function as the target function; the server uses the target function to calculate the number of expanded links to obtain the number of expanded links of each link group. Among them, the terminal can be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, Internet of Things devices, etc. The server can be a server, or a server cluster composed of multiple servers, or a cloud computing center composed of multiple servers.

[0047] In one embodiment, as shown in Figure 2 , a city broadband link expansion method integrating deployment cost and network state is provided. Taking the server in Figure 1 as an example, the method includes the following steps:

[0048] S202, obtaining the first link utilization rate of the link group to be expanded in each preset time period and the cost function corresponding to the expansion.

[0049] Among them, the number of link groups to be expanded is at least one. The link group to be expanded can be the link group to which the link in the metropolitan area network belongs, or the link group to which the link in the metropolitan area network whose traffic exceeds a certain value belongs. For example, under normal circumstances, when the link traffic exceeds 50%, expansion needs to be considered, and the links whose traffic utilization rate never exceeds 50% are removed, finally leaving 174 links, which belong to 39 link groups, and the 39 link groups are the link groups to be expanded.

[0050] The preset time period is a time period set in advance. In two continuous preset time periods, the end time node of the previous preset time period is the start time node of the next preset time period. For example, if the end time node of the previous preset time period is XX year Y month Z day M point, then the start time node of the next preset time period is XX year Y month Z day M point. The preset time period includes but is not limited to one day, one week, one month and one year.

[0051] The first link utilization rate refers to the traffic utilization rate of the link group in each preset time period. Each link group corresponds to a first link utilization rate in a preset time period. The first link utilization rate is determined based on the ratio between the actual data amount transmitted by the link group and the maximum transmission capacity. For example, the preset time period is one day, on XX year Y month Z day, the actual data amount transmitted by the link group A is M, and the maximum transmission capacity of the link group A is N, then the first link utilization rate of the link group A in the preset time period of XX year Y month Z day is (M / N) x 100%.

[0052] The cost function is a function for calculating the cost required for link expansion, and the cost required for expansion is in a positive correlation with the expanded link. The expression form of the cost function is: , wherein Cost is the cost required for link expansion; m is the expansion cost of a unit link; x g represents the number of expanded links in the link group g; and K is the number of link groups.

[0053] S204, when the first link utilization rate in the continuous multiple preset time periods exceeds the preset first threshold value, a congestion penalty function is constructed, and when the first link utilization rate in the continuous multiple preset time periods is lower than the preset second threshold value, a resource waste penalty function is constructed.

[0054] The continuous multiple preset time periods refer to multiple preset time periods corresponding to each other. Specifically, in the multiple preset time periods, if the end time node of a previous preset time period is the start time node of a next preset time period, the two preset time periods are called continuous. For example, the preset time period is one day, and the continuous seven days are called continuous multiple preset time periods.

[0055] In some embodiments, the continuous multiple preset time periods can be a preset number of continuous preset time periods. For example, the preset number is 7, and the preset time period is one day. When the first link utilization rate in the continuous 7 days exceeds the preset first threshold value, the congestion penalty function is constructed; and when the first link utilization rate in the continuous 7 days is lower than the preset second threshold value, the resource waste penalty function is constructed.

[0056] The construction of the congestion penalty function can consider the influence of congestion when solving the objective function, so as to output a reasonable number of expanded links and effectively alleviate network congestion. The construction of the resource waste penalty function can consider the influence of resource waste when solving the objective function, so as to output a reasonable number of expanded links and effectively reduce resource waste.

[0057] The expression of the congestion penalty function is:

[0058]

[0059] Resource waste penalty function of preset time period t The expression of the resource waste penalty function of the preset time period t is:

[0060]

[0061] S g,t is the number of continuous preset time periods in which the first link utilization rate of the link group g exceeds the first threshold value, D g,t is the number of continuous preset time periods in which the first link utilization rate of the link group g is lower than the second threshold value; is a congestion penalty coefficient, used to adjust the influence of the overload degree on the total cost of expansion; is a resource waste penalty coefficient, used to control the influence of the low utilization duration on the penalty degree; U th is the first threshold value, L th is the second threshold value; t represents the preset time period t, and T represents the total number of preset time periods.

[0062] S206, determine the result of adding the resource waste penalty function, the congestion penalty function and the cost function as the objective function.

[0063] The expression of the objective function minZ is:

[0064]

[0065] a is the weight of the expansion cost, b is the weight of the congestion link penalty, c is the weight of the resource waste penalty, is the cost function, is the congestion penalty function, is the resource waste penalty function; g is the link group g, and K is the number of link groups. The objective function obtains the number of links required for expansion of each link group by minimizing the cost.

[0066] S208, use the objective function to calculate the number of expansion links to obtain the number of expansion links of each link group.

[0067] The link group expansion strategy includes the number of links required for expansion of each link group, which can also be understood as the number of links required to be newly added in the link group.

[0068] When calculating the number of expansion links, a target algorithm can be used for calculation. The target algorithm can be a discrete type of reptile search algorithm.

[0069] In some embodiments, when the number of expansion links is calculated using the objective function, a greedy algorithm, a genetic algorithm, a simulated annealing algorithm, etc. can be used to calculate the objective function, so as to obtain the number of expansion links of each link group.

[0070] In some embodiments, when the target function is used for the calculation of the number of expanded links, the solution results of the greedy algorithm, the genetic algorithm, the simulated annealing algorithm, and the discrete reptation algorithm can also be compared. The solution results of the greedy algorithm, the genetic algorithm, the simulated annealing algorithm, and the discrete reptation algorithm are shown in Table 1.

[0071] As can be seen from Table 1, the discrete reptation algorithm performs excellently in minimizing the expansion cost, with a fitness (total cost) of 362.25, which is significantly lower than those of the other three baseline models. In particular, compared with the genetic algorithm, the discrete reptation algorithm not only has a significant advantage in expansion cost, but also has a relatively small number of expanded links, further proving that the model can effectively avoid link congestion and resource waste while reducing unnecessary expansion. This indicates that the discrete reptation algorithm has strong solution set searching optimization capability in the link expansion problem, and can achieve the best balance between cost and benefit on the basis of ensuring user experience.

[0072] In order to further verify the performance of the discrete reptation algorithm in practical application, an additional constraint condition is set: under the condition of expanding 10, 15, 20, and 25 links respectively, the ability of the discrete reptation algorithm and other baseline models in selecting the optimal expanded link is compared. The purpose of the experiment is to investigate whether the model can still find the optimal expansion scheme while minimizing the expansion cost under the limitation of a given number of expanded links. Table 2 shows the experimental results.

[0073] As can be seen from Table 2, the discrete reptation algorithm can find the expansion scheme with the minimum cost under the limitation of the number of expanded links, and this scheme exhibits better performance compared with other baseline models.

[0074] In some embodiments, a preset total expansion threshold is set, and when the number of links to be expanded exceeds the total expansion threshold, link expansion is performed according to the total expansion threshold. For example, according to the number of expanded links of the output link group A, M links need to be expanded, the total expansion threshold is N, and M is greater than N. Therefore, when actually performing expansion, link group A expands N links instead of M links.

[0075] In the above method for expanding the urban broadband link by comprehensively considering the deployment cost and network status, the first link utilization rate of each link group to be expanded in each preset time period and the cost function corresponding to the expansion are obtained, a congestion penalty function is constructed when the first link utilization rate in the continuous multiple preset time periods exceeds the preset first threshold value, a resource waste penalty function is constructed when the first link utilization rate in the continuous multiple preset time periods is lower than the preset second threshold value, the result of adding the resource waste penalty function, the congestion penalty function and the cost function is determined as the target function, the target function is used for calculating the number of expansion links, and the number of expansion links of each link group is obtained. In this way, the expansion cost, congestion and resource waste can be considered comprehensively to ensure the optimal allocation of resources while minimizing the cost, and the most appropriate number of expansion links is obtained.

[0076] In one embodiment, the number of link expansions of each link group is calculated by a target algorithm, and the optimization process of the target algorithm includes:

[0077] Based on the candidate solution generation instruction, a plurality of candidate solutions are randomly generated. The candidate solution generation instruction is used to instruct the target algorithm to generate a plurality of candidate solutions including the number of expansion links of each link group.

[0078] Each candidate solution is substituted into the target function to obtain the fitness of each candidate solution, and the global optimal solution in the candidate solution is determined based on the fitness.

[0079] Based on the global optimal solution and the candidate solution, iterative processing is performed until the iteration number of the target algorithm meets the iteration stop condition, and the optimal solution is output.

[0080] Each candidate solution randomly generated based on the candidate solution generation instruction includes the number of expansion links of each link group, that is, the candidate solution includes the number of newly added links of each link group, and the number of expansion links of each link group in each candidate solution is uniformly distributed. This helps to more effectively cover the solution space and avoid the situation that some areas are over-sampled while other important areas are ignored.

[0081] The candidate solution is substituted into the target function obtained by adding the cost function, the resource waste penalty function and the congestion penalty function, and the fitness of each candidate solution can be obtained.

[0082] The global optimal solution is the candidate solution with the minimum fitness.

[0083] The iteration stop condition refers to the target number of iterations. Specifically, when the target algorithm iterates, the candidate solution and the global optimal solution are iterated multiple times until the iteration number of the target algorithm reaches the target number, it is determined that the iteration number of the target algorithm meets the iteration stop condition, and the optimal solution is output. When the optimal solution is output, it is determined that the optimization of the target algorithm is complete, and the optimized algorithm is obtained.

[0084] In this embodiment, a plurality of candidate solutions are randomly generated based on the generated instruction, each candidate solution is substituted into the target function to obtain the fitness of each candidate solution, and the global optimal solution is determined based on the fitness. The global optimal solution and the candidate solution are iteratively processed until the iteration number of the target algorithm meets the iteration stop condition, which helps to comprehensively explore the entire solution space, avoids falling into a local optimal solution too early, and increases the possibility of finding a global optimal solution.

[0085] In one embodiment, the iteration process of the target algorithm includes:

[0086] S1, determining a global optimal solution, and respectively determining at least one target link group from each candidate solution, and updating the number of expansion links of the target link group to obtain an updated first updated solution.

[0087] The number of target link groups is less than the number of link groups to be expanded. For example, 1 to K / 3 link groups to be expanded are randomly selected for expansion link number updating, and K is the number of link groups to be expanded.

[0088] The updated first updated solution includes the same content as the candidate solution, that is, the first updated solution also includes the number of expansion links of each link group. Each candidate solution corresponds to a first updated solution. Further, the candidate solution and the corresponding first updated solution may be consistent or inconsistent.

[0089] S2, substituting the first updated solution into the target function to obtain the fitness of the first updated solution.

[0090] Wherein, when solving the fitness of the first updated solution, the number of expansion links in each link group included in each first updated solution is substituted into the target function, that is, the fitness of each first updated solution can be obtained.

[0091] S3, if the fitness of the first updated solution is less than the fitness of the candidate solution, the candidate solution in step S1 is replaced by the first updated solution; if the fitness of the first updated solution is less than the fitness of the global optimal solution, the global optimal solution in step S1 is replaced by the first updated solution.

[0092] Wherein, when comparing the fitness of the first updated solution and the candidate solution, the fitness of the candidate solution is compared with the fitness of the first updated solution corresponding to the candidate solution. For example, the candidate solution X i The corresponding first updated solution is The fitness F(X i ) of the first updated solution i is compared with the fitness F(X k ) of the candidate solution X g .

[0093] The number of the first updated solutions is consistent with the number of the candidate solutions. Further, fitness of each first updated solution is calculated, and a target solution with the minimum fitness is determined from the first updated solutions. The fitness of the target solution is compared with the fitness of the global optimal solution. If the fitness of the target solution is less than the fitness of the global optimal solution, the global optimal solution is replaced by the target solution, and a replaced global optimal solution is obtained.

[0094] S4, repeating the step S1, the step S2 and the step S3 until the iteration number of the target algorithm meets an iteration stop condition, and outputting an optimal solution.

[0095] In the repeating of the step S1, the step S2 and the step S3, the candidate solution is updated in each iteration, and the global optimal solution can be updated or not.

[0096] In the embodiment, at least one target link group is determined from each candidate solution respectively, and the number of the expansion links of the target link group is updated, and a first updated solution is obtained. The first updated solution is substituted into the target function, and the fitness of the first updated solution is obtained. If the fitness of the first updated solution is less than the fitness of the candidate solution, the candidate solution in the step S1 is replaced by the first updated solution. If the fitness of the first updated solution is less than the fitness of the global optimal solution, the global optimal solution in the step S1 is replaced by the first updated solution. The step S1, the step S2 and the step S3 are repeated until the iteration number of the target algorithm meets the iteration stop condition. In this way, the performance decline of the traditional search algorithm in the discrete integer space can be effectively avoided, the global search capability is improved, and the target algorithm capable of outputting the accurate number of the expansion links is obtained.

[0097] In one embodiment, the iteration process of the target algorithm further includes:

[0098] A preset iteration number for identifying a candidate solution with the maximum fitness is obtained.

[0099] When the current iteration number of the target algorithm is a multiple of the preset iteration number, a target candidate solution with the maximum fitness is identified from the current candidate solution.

[0100] The target candidate solution is globally randomly sampled, and a second updated solution is obtained.

[0101] The target candidate solution in the candidate solution is replaced by the second updated solution, and the selection of the target link group is performed based on the replaced candidate solution.

[0102] The preset iteration number is not the iteration number when the target algorithm stops iteration, but the iteration number for identifying the candidate solution with the maximum fitness. For example, the preset iteration number is 500 times. After every 500 iterations, the target algorithm identifies the target candidate solution with the maximum fitness from the current candidate solution.

[0103] The global random sampling refers to randomly sampling from a target interval as the number of expansion links of each link group in the second updated solution. The target interval can be determined according to the maximum number of expansion links of the link group. For example, if the maximum number of expansion links of the link group is N, the target interval can be 0 to N.

[0104] The replaced candidate solution includes the second updated solution but does not include the target candidate solution.

[0105] In this embodiment, when the current iteration number of the target algorithm reaches a multiple of the preset iteration number, the target candidate solution with the maximum fitness is identified from the current candidate solution, global random sampling is performed on the target candidate solution to obtain a second updated solution, the target candidate solution in the candidate solution is replaced by the second updated solution, and selection of the target link group is performed based on the replaced candidate solution. This can prevent the target algorithm from falling into a local optimum in the iteration process, effectively break the trend of local convergence, introduce a new search direction for subsequent search, thereby enhancing the global search capability of the target algorithm, and enabling the target algorithm to obtain the most appropriate number of expansion links when solving the target function.

[0106] In one embodiment, the number of expansion links of the target link group is updated to obtain an updated first updated solution, including:

[0107] The number of expansion links of the target link group is updated based on the number of expansion links in the global optimal solution to obtain an updated first updated solution.

[0108] And / or, the number of expansion links of the target link group is randomly updated to obtain an updated first updated solution.

[0109] Wherein, the disturbance update of the number of expansion links of the target link group based on the number of expansion links in the global optimal solution can be understood as adjusting the number of expansion links of the target link group based on the number of expansion links in the global optimal solution. Specifically, the number of expansion links of the target link group is increased, or decreased, or only adjusted by increasing or decreasing the number of expansion links of part of the target link group based on the number of expansion links in the global optimal solution. The formula of the disturbance update is: Wherein is the number of expansion links of the target link group k in the global optimal solution, k is the number of expansion links of the target link group k in the global optimal solution, is the adjusted number of expansion links.

[0110] In the random updating of the number of expansion links of the target link group, the target interval is mainly used for random updating. The target interval can be determined according to the preset maximum number of expansion links of the link group. For example, if the maximum number of expansion links of the link group is N, the target interval can be 0 to N. In the random updating of the number of expansion links of the target link group, a value is randomly selected from 0 to N as the number of expansion links of the target link group, so as to obtain the first updated solution.

[0111] In this embodiment, the number of expansion links of the target link group is disturbed and updated based on the number of expansion links in the global optimal solution to obtain the first updated solution after updating. This helps to search around the candidate solution in detail and is conducive to finding a better local solution. The number of expansion links of the target link group is randomly updated to obtain the first updated solution after updating. This can maintain the diversity of the target algorithm and avoid falling into a local optimal solution too early.

[0112] In one embodiment, step S1 comprises:

[0113] The number of expansion links of the target link group is updated to obtain the updated number of expansion links.

[0114] When the updated number of expansion links exceeds the preset expansion link threshold, the updated first updated solution is determined based on the number of expansion links indicated by the expansion link threshold.

[0115] The expansion link threshold can also be understood as the maximum number of expansion links of the link group. When the updated number of expansion links exceeds the preset expansion link threshold, the number of expansion links indicated by the expansion link threshold is directly determined as the number of expansion links of the target link group in the first updated solution.

[0116] Further, when the updated number of expansion links is less than or equal to the preset expansion link threshold, the updated first updated solution is determined according to the updated number of expansion links.

[0117] Further, in the updating of the number of expansion links of the target link group, at least one of the disturbance updating and the random updating can be used for updating.

[0118] In this embodiment, when the updated number of expansion links exceeds the preset expansion link threshold, the updated first updated solution is determined based on the number of expansion links indicated by the expansion link threshold. This can effectively control the amount of resource usage and avoid the problem of resource depletion due to excessive expansion.

[0119] In one embodiment, the urban broadband link expansion method considering deployment cost and network state further comprises:

[0120] The number of expansion links in each link group is determined, and the number of original links in each link group is obtained.

[0121] A first result of multiplication between the first link utilization rate and the number of original links is calculated, and a second result of addition between the number of expansion links and the number of original links is calculated.

[0122] Based on the quotient of the first result divided by the second result, a second link utilization rate is determined; the second link utilization rate is used to represent the average utilization rate of the links after expansion of the link group.

[0123] The original link refers to the link in the link group before expansion. The number of original links refers to the number of links in the link group before expansion.

[0124] The first link utilization rate refers to the traffic utilization rate of the original link in the link group in each preset time period. Each link group corresponds to a first link utilization rate in a preset time period.

[0125] The second link utilization rate of the link group g in the preset time period t after expansion is The calculation formula is: . Wherein, n g is the number of original links in the link group g, x g is the number of expansion links in the link group g, and u g,t is the first link utilization rate of the link group g in the preset time period t before expansion.

[0126] In this embodiment, the second link utilization rate is determined based on the quotient of the first result divided by the second result, so that the change of the link utilization rate after expansion can be intuitively understood.

[0127] The application further provides an application scenario of the comprehensive deployment cost and network state city broadband link expansion method. Specifically, the comprehensive deployment cost and network state city broadband link expansion method is applied in the application scenario as follows:

[0128] The optimization process of the target algorithm is as follows Figure 3The server generates a plurality of candidate solutions based on the candidate solution, wherein each candidate solution includes the number of expansion links of each link group. The server substitutes each candidate solution into the objective function to obtain the fitness of each candidate solution, and determines a global optimal solution in the candidate solutions based on the fitness. The server determines at least one target link group from each candidate solution, and updates the number of expansion links of the target link group based on the number of expansion links in the global optimal solution, and / or randomly updates the number of expansion links of the target link group. The update can be a local update with a 70% probability, and the random update can be a global random sampling with a 30% probability. When the updated number of expansion links exceeds a preset expansion link threshold, the server corrects the boundary based on the number of expansion links indicated by the expansion link threshold to determine a first updated solution. The server substitutes the first updated solution into the objective function to obtain the fitness of the first updated solution. If the fitness of the first updated solution is less than the fitness of the candidate solution, the server replaces the candidate solution with the first updated solution to obtain a replaced candidate solution; if the fitness of the first updated solution is less than the fitness of the global optimal solution, the server replaces the global optimal solution with the first updated solution to obtain a replaced global optimal solution. The server identifies a target candidate solution with the maximum fitness in the current candidate solution when the current iteration number of the target algorithm reaches a multiple of the preset iteration number, and performs global random sampling on the target candidate solution to obtain a second updated solution. The server replaces the target candidate solution in the candidate solution with the second updated solution, and selects the target link group based on the replaced candidate solution until the iteration number of the target algorithm satisfies the iteration stop condition, and outputs an optimal solution to obtain an optimized target algorithm.

[0129] The server obtains the first link utilization rate of the link group to be expanded in each preset time period and the cost function corresponding to the expansion. When the first link utilization rate in the continuous multiple preset time periods exceeds a preset first threshold, the server constructs a congestion penalty function; when the first link utilization rate in the continuous multiple preset time periods is lower than a preset second threshold, the server constructs a resource waste penalty function. The server adds the resource waste penalty function, the congestion penalty function and the cost function to construct an objective function. The server uses the optimized target algorithm to solve the objective function to obtain the number of expansion links of each link group.

[0130] The server determines the number of expansion links in each link group, and obtains the number of original links in each link group. The server calculates a first result obtained by multiplying the first link utilization rate and the number of original links, and calculates a second result obtained by adding the number of expansion links and the number of original links. The server determines a second link utilization rate based on the quotient of the first result divided by the second result. The second link utilization rate represents the average utilization rate of the links after expansion of the link group.

[0131] It should be understood that although each step in the flowchart involved in the embodiments described above is shown in sequence according to the arrow, the steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, the execution of the steps has no strict order limitation, and the steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps.

[0132] Based on the same inventive concept, the embodiments of the present application also provide a comprehensive deployment cost and network state urban broadband link expansion system for implementing the above-mentioned comprehensive deployment cost and network state urban broadband link expansion method. The problem-solving implementation scheme provided by the system is similar to the implementation scheme described in the above method, so the specific limitations in one or more comprehensive deployment cost and network state urban broadband link expansion system embodiments provided below can refer to the limitations of the comprehensive deployment cost and network state urban broadband link expansion method described above, and will not be repeated here.

[0133] In one embodiment, as shown in Figure 4 a comprehensive deployment cost and network state urban broadband link expansion system is provided, comprising:

[0134] A data acquisition module is configured to acquire first link utilization rates of link groups to be expanded in each preset time period and a cost function corresponding to expansion.

[0135] A first function construction module is configured to construct a congestion penalty function when the first link utilization rate in the continuous multiple preset time periods exceeds a preset first threshold value, and construct a resource waste penalty function when the first link utilization rate in the continuous multiple preset time periods is lower than a preset second threshold value.

[0136] A second function construction module is configured to determine a result of adding the resource waste penalty function, the congestion penalty function and the cost function as a target function.

[0137] A strategy acquisition module is configured to use the target function to calculate the number of expanded links to obtain the number of expanded links of each link group.

[0138] In one of the embodiments, the city broadband link expansion system integrating deployment cost and network status is further configured to: generate a plurality of candidate solutions randomly based on candidate solution generation instructions, wherein the candidate solution generation instructions are used to instruct the target algorithm to generate a plurality of candidate solutions including the number of expansion links of each link group; substitute each candidate solution into the target function to obtain the fitness of each candidate solution, and determine the global optimal solution among the candidate solutions based on the fitness; and perform iterative processing based on the global optimal solution and the candidate solutions until the iteration number of the target algorithm meets the iteration stop condition, and output the optimal solution.

[0139] In one of the embodiments, the city broadband link expansion system integrating deployment cost and network status is further configured to: S1, determine the global optimal solution, and determine at least one target link group from each candidate solution respectively, and update the number of expansion links of the target link group to obtain the first updated solution after the update; S2, substitute the first updated solution into the target function to obtain the fitness of the first updated solution; S3, if the fitness of the first updated solution is less than the fitness of the candidate solution, replace the candidate solution in step S1 with the first updated solution to obtain the candidate solution after the replacement; if the fitness of the first updated solution is less than the fitness of the global optimal solution, replace the global optimal solution in step S1 with the first updated solution to obtain the global optimal solution after the replacement; and S4, repeat steps S1, S2 and S3 until the iteration number of the target algorithm meets the iteration stop condition, and output the optimal solution.

[0140] In one of the embodiments, the city broadband link expansion system integrating deployment cost and network status is further configured to: obtain a preset iteration number for identifying the candidate solution with the maximum fitness; identify the target candidate solution with the maximum fitness in the current candidate solution when the current iteration number of the target algorithm is a multiple of the preset iteration number; perform global random sampling on the target candidate solution to obtain a second updated solution; replace the target candidate solution in the candidate solution with the second updated solution, and select the target link group based on the candidate solution after the replacement.

[0141] In one of the embodiments, the city broadband link expansion system integrating deployment cost and network status is further configured to: perform perturbation update on the number of expansion links of the target link group based on the number of expansion links in the global optimal solution to obtain the first updated solution after the update; and / or, perform random update on the number of expansion links of the target link group to obtain the first updated solution after the update.

[0142] In one of the embodiments, the city broadband link expansion system integrating deployment cost and network status is further configured to: update the number of expansion links of the target link group to obtain the updated number of expansion links; and when the updated number of expansion links exceeds a preset expansion link threshold, determine the first updated solution after the update based on the number of expansion links indicated by the expansion link threshold.

[0143] In one of the embodiments, the urban broadband link expansion system integrating deployment cost and network status is further configured to: determine the number of expansion links in each link group, and obtain the number of original links in each link group; calculate a first result of multiplication between the first link utilization rate and the number of original links, and calculate a second result of addition between the number of expansion links and the number of original links; determine the second link utilization rate based on a quotient of the first result divided by the second result; and the second link utilization rate is used to represent the average utilization rate of links in the link group after expansion.

[0144] The modules in the urban broadband link expansion system integrating deployment cost and network status described above can be implemented by software, hardware, or a combination thereof, in whole or in part. The modules described above can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the modules.

[0145] In one embodiment, a computer device, which can be a server, has an internal structure diagram as shown in Figure 5 The computer device includes a processor, a memory, and a network interface connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store a first link utilization rate, a cost function, a preset time period, a first threshold value, a second threshold value, a congestion penalty function, a resource waste penalty function, an objective function, and an expansion link number. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement an urban broadband link expansion method integrating deployment cost and network status.

[0146] Those skilled in the art can understand that Figure 5 The structure shown in the above

[0147] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0148] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0149] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for expanding urban broadband links based on comprehensive deployment costs and network status, characterized in that: The method comprises: Obtaining a first link utilization rate of the link group to be expanded and a cost function corresponding to the expansion in each preset time period; When the utilization rate of the first link exceeds a preset first threshold value in a plurality of consecutive preset time periods, a congestion penalty function is constructed; when the utilization rate of the first link is lower than a preset second threshold value in a plurality of consecutive preset time periods, a resource waste penalty function is constructed; Determine a result of weighted summation of the resource waste penalty function, the congestion penalty function, and the cost function as an objective function; Calculating the number of expansion links using the objective function to obtain the number of expansion links for each link group; Congestion penalty function The expression is: Resource waste penalty function for preset time period t The expression is: Among them, S g,t is the number of consecutive preset time periods in which the utilization of the first link of link group g exceeds the first threshold, D g,t is the number of consecutive preset time periods in which the utilization of the first link of link group g is lower than the second threshold; is the congestion penalty coefficient, which is used to adjust the impact of overload on the total cost of expansion; is the resource waste penalty coefficient, which is used to control the impact of low utilization duration on the degree of penalty; U th is the first threshold, L th is the second threshold; t represents the preset time period t, T represents the total number of preset time periods, u g,t is the first link utilization rate of link group g in the preset time period t before capacity expansion, and n is the number of consecutive preset time periods.

2. The method according to claim 1, characterized in that The link expansion quantity of each link group is calculated by a target algorithm. The optimization process of the target algorithm includes: Based on a candidate solution generation instruction, a plurality of candidate solutions are randomly generated; the candidate solution generation instruction is used to instruct the target algorithm to generate a plurality of candidate solutions including the number of expansion links of each link group; Substituting each of the candidate solutions into the objective function, respectively, to obtain the fitness of each of the candidate solutions, and determining a global optimal solution among the candidate solutions based on the fitness; Iterative processing is performed based on the global optimal solution and the candidate solutions until the number of iterations of the target algorithm meets the iteration stopping condition, and the optimal solution is output.

3. The method according to claim 2, characterized in that The iterative process of the target algorithm includes: S1. Determine a global optimal solution, determine at least one target link group from each candidate solution, and update the number of expansion links of the target link group to obtain an updated first updated solution; S2. Substituting the first updated solution into the objective function to obtain the fitness of the first updated solution; S3. If the fitness of the first updated solution is less than the fitness of the candidate solution, the candidate solution in step S1 is replaced by the first updated solution; if the fitness of the first updated solution is less than the fitness of the global optimal solution, the global optimal solution in step S1 is replaced by the first updated solution; S4. Repeat steps S1, S2, and S3 until the number of iterations of the target algorithm meets the iteration stop condition and output the optimal solution.

4. The method according to claim 3, characterized in that The iterative process of the target algorithm also includes: Obtaining a preset number of iterations for identifying a candidate solution with the maximum fitness; When the current number of iterations of the target algorithm is a multiple of the preset number of iterations, identifying a target candidate solution with the greatest fitness among the current candidate solutions; Performing global random sampling on the target candidate solution to obtain a second updated solution; The target candidate solution among the candidate solutions is replaced with the second updated solution, and a target link group is selected based on the replaced candidate solution.

5. The method according to claim 3, characterized in that Step S1 includes: Based on the number of expansion links in the global optimal solution, performing a disturbance update on the number of expansion links in the target link group to obtain an updated first updated solution; And / or, randomly updating the number of expansion links of the target link group to obtain an updated first updated solution.

6. The method according to claim 3, characterized in that Step S1 includes: Updating the number of expansion links of the target link group to obtain an updated number of expansion links; When the updated number of expansion links exceeds a preset expansion link threshold, an updated first update solution is determined based on the number of expansion links indicated by the expansion link threshold.

7. The method according to claim 1, characterized in that The method further comprises: Determining the number of expanded links in each of the link groups, and obtaining the number of original links in each of the link groups; Calculating a first result of multiplying the first link utilization rate by the number of the original links, and calculating a second result of adding the number of the expanded links to the number of the original links; A second link utilization is determined based on a quotient of the first result divided by the second result; the second link utilization is used to represent an average utilization of the links of the link group after the expansion.

8. A system for expanding urban broadband links based on comprehensive deployment costs and network status, characterized in that: The system comprises: A data acquisition module, configured to acquire the first link utilization of the link group to be expanded within each preset time period and a cost function corresponding to the expansion; A first function construction module is configured to construct a congestion penalty function when the utilization of the first link exceeds a preset first threshold value within a plurality of consecutive preset time periods, and to construct a resource waste penalty function when the utilization of the first link is lower than a preset second threshold value within a plurality of consecutive preset time periods; A second function construction module is used to determine a result of weighted summation of the resource waste penalty function, the congestion penalty function and the cost function as an objective function; a strategy acquisition module, configured to calculate the number of expansion links using the objective function to obtain the number of expansion links for each link group; Congestion penalty function The expression is: Resource waste penalty function for preset time period t The expression is: Among them, S g,t is the number of consecutive preset time periods in which the utilization of the first link of link group g exceeds the first threshold, D g,t is the number of consecutive preset time periods in which the utilization of the first link of link group g is lower than the second threshold; is the congestion penalty coefficient, which is used to adjust the impact of overload on the total cost of expansion; is the resource waste penalty coefficient, which is used to control the impact of low utilization duration on the degree of penalty; U th is the first threshold, L th is the second threshold; t represents the preset time period t, T represents the total number of preset time periods, u g,t is the first link utilization rate of link group g in the preset time period t before capacity expansion, and n is the number of consecutive preset time periods.

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