Urban broadband link capacity expansion method and system integrating deployment cost and network state

Abstract

The invention relates to an urban broadband link capacity expansion method and system integrating deployment cost and a 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 rates in a plurality of continuous preset time periods exceed a preset first threshold value, constructing a congestion penalty function, and when the first link utilization rates in a plurality of continuous preset time periods are lower than a preset second threshold value, constructing a resource waste penalty function; determining a result obtained by adding the resource waste penalty function, the congestion penalty function and the cost function as a target function; and the target function is used to calculate the capacity expansion link number, and the capacity expansion link number of each link group is obtained. By adopting the method, a proper number of expansion links can be obtained.

Description

Technical Field

[0001] The present application relates to the field of Internet technology, and in particular to a method and system for expanding urban broadband links based on comprehensive deployment costs and network status. Background Art

[0002] With the rapid development of internet technology, broadband networks have become critical infrastructure in modern society, profoundly impacting the operations and development of various industries. In recent years, with the explosive growth in the number of network users and the widespread adoption of various innovative applications, traffic demand on broadband links has experienced unprecedented exponential growth. However, this continuous increase in traffic demand has posed challenges to traditional metropolitan area networks (MANs).

[0003] Currently, broadband network link expansion relies primarily on manual judgment, a method that lacks predictive support based on data analysis and a scientific expansion strategy. Consequently, in practice, this manual approach can lead to resource waste, neglect of links truly in need of expansion, and poor expansion results. Existing research has largely focused on designing entirely new network architectures, but this often comes with high costs, technical difficulties, and challenges in full deployment, making it difficult to determine the optimal number of expansion links. Summary of the Invention

[0004] Based on this, it is necessary to provide a method and system for expanding urban broadband links that takes into account deployment costs and network status and can obtain a suitable number of expansion links in response to the above technical problems.

[0005] A method for expanding urban broadband links based on comprehensive deployment costs and network status, the method comprising:

[0006] 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;

[0007] 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;

[0008] Determine a result of adding the resource waste penalty function, the congestion penalty function, and the cost function as an objective function;

[0009] The objective function is used to calculate the number of expansion links to obtain the number of expansion links of each link group.

[0010] In one embodiment, the link expansion quantity of each link group is calculated by a target algorithm, and the optimization process of the target algorithm includes:

[0011] 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;

[0012] 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;

[0013] 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.

[0014] In one embodiment, the iterative process of the target algorithm includes:

[0015] 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;

[0016] S2. Substituting the first updated solution into the objective 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, 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;

[0018] 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.

[0019] In one embodiment, the iterative process of the target algorithm further includes:

[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 greatest fitness among the current candidate solutions;

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

[0023] 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.

[0024] In one embodiment, step S1 includes:

[0025] 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;

[0026] And / or, randomly updating the number of expansion links of the target link group to obtain an updated first updated solution.

[0027] In one embodiment, step S1 includes:

[0028] Updating the number of expansion links of the target link group to obtain an updated number of expansion links;

[0029] 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.

[0030] In one embodiment, the method further comprises:

[0031] 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;

[0032] 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;

[0033] 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.

[0034] A system for expanding urban broadband links based on comprehensive deployment costs and network status, comprising:

[0035] 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;

[0036] 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;

[0037] A second function construction module is used to determine a result of adding the resource waste penalty function, the congestion penalty function and the cost function as an objective function;

[0038] The strategy acquisition module is used to calculate the number of expansion links using the objective function to obtain the number of expansion links of each link group.

[0039] The above-mentioned urban broadband link expansion method and system that comprehensively considers deployment cost and network status 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 multiple consecutive preset time periods exceeds the preset first threshold, a congestion penalty function is constructed. When the first link utilization rate in multiple consecutive preset time periods is lower than the preset second threshold, a resource waste penalty function is constructed. The result of adding the resource waste penalty function, the congestion penalty function and the cost function is determined as the objective function. The objective function is used to calculate the number of expansion links to obtain the number of expansion links of each link group. The beneficial effect is that this can comprehensively consider the expansion cost, congestion situation and resource waste situation, ensure that while minimizing the cost, the optimal configuration of resources is achieved, and obtain the most appropriate number of expansion links. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A diagram illustrating an application environment of a method for expanding urban broadband links that integrates deployment costs and network status in one embodiment;

[0041] Figure 2 A flowchart of a method for expanding urban broadband links considering comprehensive deployment costs and network status in one embodiment;

[0042] Figure 3 Schematic diagram of an optimization process of an algorithm in one embodiment;

[0043] Figure 4 A structural block diagram of a city broadband link expansion system that integrates deployment costs and network status in one embodiment;

[0044] Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0046] The embodiment of the present application provides a method for expanding urban broadband links based on comprehensive deployment costs and network status, which can be applied to Figure 1In the application environment shown. The terminal interacts with the server through a wired channel / wireless channel. The data storage system can store data that the server needs to process. The server obtains the first link utilization rate of each link group to be expanded within multiple consecutive preset time periods and the cost function corresponding to the expansion; when the first link utilization rate within multiple consecutive preset time periods exceeds a preset first threshold, the server constructs a congestion penalty function, and when the first link utilization rate within multiple consecutive preset time periods is lower than a preset second threshold, the server constructs a resource waste penalty function; the server determines the result of adding the resource waste penalty function, the congestion penalty function and the cost function as the objective function; the server uses the objective function to calculate the number of expansion links to obtain the number of expansion links for each link group. The terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices, etc. The server can be a single server, or a server cluster composed of multiple servers, or a cloud computing center composed of multiple servers.

[0047] In one embodiment, Figure 2 As shown in the figure, a method for expanding urban broadband links based on comprehensive deployment cost and network status is provided. Figure 1 The following steps are used as an example to illustrate the server in the example:

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

[0049] The number of link groups to be expanded is at least one. A link group to be expanded can be a link group within a metropolitan area network (MAN), or a link group within a MAN where link traffic exceeds a specific value. For example, expansion is typically considered only when link traffic exceeds 50%. Excluding links whose traffic utilization never exceeds 50%, 174 links remain, belonging to 39 link groups. These 39 link groups are the link groups to be expanded.

[0050] A preset time period is a pre-set time period. For two consecutive preset time periods, the end time of the previous preset time period is the start time of the next preset time period. For example, if the preset time period is one day, and the end time of the previous preset time period is M:00 on Y / M / XYYYZ, then the start time of the next preset time period is M:00 on Y / M / XYYYZ. Preset time periods include, but are 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 within each preset time period. Each link group corresponds to a first link utilization rate within a preset time period. The first link utilization rate is determined based on the ratio between the actual amount of data transmitted by the link group and its maximum transmission capacity. For example, if the preset time period is one day, and on Y / Y / XX, the actual amount of data transmitted by link group A is M, and the maximum transmission capacity of link group A is N, then the first link utilization rate of link group A during the preset time period of Y / Y / XX is (M / N) × 100%.

[0052] The cost function is used to calculate the cost required for link expansion. The cost required for expansion is proportional to the number of links expanded. The cost function is expressed as: , where Cost is the cost required for link expansion; m is the expansion cost per unit link; x g Indicates the number of expanded links in link group g; K is the number of link groups.

[0053] S204: When the utilization of the first link exceeds a preset first threshold in multiple consecutive preset time periods, a congestion penalty function is constructed; when the utilization of the first link is lower than a preset second threshold in multiple consecutive preset time periods, a resource waste penalty function is constructed.

[0054] The term "consecutive multiple preset time periods" refers to a set of consecutive preset time periods. Specifically, if the end time of a previous preset time period is the start time of the next preset time period, these are considered consecutive preset time periods. For example, if the preset time period is one day, then seven consecutive days are considered consecutive multiple preset time periods.

[0055] In some embodiments, the plurality of consecutive preset time periods may be a preset number of consecutive preset time periods. For example, if the preset number is 7 and the preset time period is one day, then when the utilization of the first link exceeds a preset first threshold for 7 consecutive days, a congestion penalty function is constructed; and when the utilization of the first link is below a preset second threshold for 7 consecutive days, a resource waste penalty function is constructed.

[0056] Constructing a congestion penalty function allows you to take into account the impact of congestion when solving the objective function, thereby outputting a reasonable number of expansion links and effectively alleviating network congestion. Constructing a resource waste penalty function allows you to take into account the impact of resource waste when solving the objective function, thereby outputting a reasonable number of expansion links and effectively reducing resource waste.

[0057] Congestion penalty function The expression is:

[0058] Resource waste penalty function for preset time period t The expression is:

[0059] 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, and T represents the total number of preset time periods.

[0060] S206 , determining a result of adding the resource waste penalty function, the congestion penalty function, and the cost function as an objective function.

[0061] Among them, the expression of the objective function minZ is:

[0062] a is the weight of the expansion cost, b is the weight of the congested link penalty, and 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 minimizes the cost and obtains the number of links required to expand the capacity of each link group.

[0063] S208 , using the objective function to calculate the number of expansion links, to obtain the number of expansion links of each link group.

[0064] The link group expansion strategy includes the number of links required to be expanded in each link group, which can also be understood as the number of links that need to be newly added in the link group.

[0065] When calculating the number of expansion links, a target algorithm may be used for calculation, wherein the target algorithm may be a discrete reptile search algorithm.

[0066] In some embodiments, when using the objective function to calculate the number of expansion links, a greedy algorithm, a genetic algorithm, a simulated annealing algorithm, etc. may be used to calculate the objective function to obtain the number of expansion links of each link group.

[0067] In some embodiments, when using the objective function to calculate the number of expansion links, the results of the greedy algorithm, genetic algorithm, simulated annealing algorithm, and discrete reptile search algorithm can also be compared. The results of the greedy algorithm, genetic algorithm, simulated annealing algorithm, and discrete reptile search algorithm are shown in Table 1.

[0068] Table 1 shows that the discrete reptile search algorithm excels in minimizing expansion costs, with a fitness (total cost) of 362.25, significantly lower than the other three baseline models. Compared to the genetic algorithm, the discrete reptile search algorithm not only offers a significant advantage in expansion costs, but also requires a relatively small number of expansion links, further demonstrating that the model can effectively avoid link congestion and resource waste while reducing unnecessary expansion. This demonstrates that the discrete reptile search algorithm possesses strong solution set search and optimization capabilities for link expansion problems, achieving an optimal balance between cost and benefit while ensuring user experience.

[0069] To further validate the performance of the discrete reptile search algorithm in real-world applications, an additional constraint was imposed: the ability of the discrete reptile search algorithm to select the optimal expansion links was compared with other baseline models when expanding 10, 15, 20, and 25 links. The goal of the experiment was to examine whether the model could still find the optimal expansion solution while minimizing the expansion cost under the given limit on the number of expansion links. Table 2 shows the experimental results.

[0070] As can be seen from Table 2, the discrete reptile search algorithm can find the expansion solution with the lowest cost when the number of expansion links is limited, and this solution shows better performance than other baseline models.

[0071] In some embodiments, a preset total capacity expansion threshold is used. When the number of links requiring expansion exceeds the threshold, link expansion is performed based on the threshold. For example, if the outputted number of expansion links for link group A indicates that M links require expansion and the threshold is N, where M is greater than N, then during actual expansion, link group A will be expanded to N links instead of M.

[0072] In the above-mentioned urban broadband link expansion method that comprehensively considers deployment cost and network status, by 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, when the first link utilization rate in multiple consecutive preset time periods exceeds the preset first threshold, a congestion penalty function is constructed, and when the first link utilization rate in multiple consecutive preset time periods is lower than the preset second threshold, a resource waste penalty function is constructed. The result of adding the resource waste penalty function, the congestion penalty function and the cost function is determined as the objective function, and the objective function is used to calculate the number of expansion links to obtain the number of expansion links for each link group. In this way, the expansion cost, congestion situation and resource waste situation can be comprehensively considered to ensure that the optimal configuration of resources is achieved while minimizing the cost, and the most appropriate number of expansion links is obtained.

[0073] In one embodiment, the link expansion quantity of each link group is calculated by a target algorithm. The optimization process of the target algorithm includes:

[0074] 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.

[0075] Substitute each candidate solution into the objective function to obtain the fitness of each candidate solution, and determine the global optimal solution among the candidate solutions based on the fitness.

[0076] Iterate based on the global optimal solution and candidate solutions until the number of iterations of the target algorithm meets the iteration stop condition and outputs the optimal solution.

[0077] Each candidate solution randomly generated based on the candidate solution generation instruction includes the number of expanded links for each link group. This means that the candidate solution includes the number of newly added links for each link group, and the number of expanded links for each link group is evenly distributed within each candidate solution. This helps to more effectively cover the solution space and avoid oversampling certain areas while neglecting other important areas.

[0078] By substituting the candidate solution into the objective function obtained by adding the cost function, resource waste penalty function, and congestion penalty function, the fitness of each candidate solution can be obtained.

[0079] The global optimal solution is the candidate solution with the smallest fitness among the candidate solutions.

[0080] The iterative stopping condition refers to the target number of iterations to stop. Specifically, when the target algorithm is iterating, multiple iterations are performed based on the candidate solutions and the global optimal solution until the target algorithm reaches the target number of iterations. This determines that the target algorithm's number of iterations meets the iterative stopping condition, and the optimal solution is output. When the optimal solution is output, the target algorithm is considered optimized, resulting in the optimized algorithm.

[0081] In this embodiment, multiple candidate solutions are randomly generated based on the candidate solution generation instruction, each candidate solution is substituted into the objective function respectively, the fitness of each candidate solution is obtained, and the global optimal solution among the candidate solutions is determined 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 stop condition. This helps to fully explore the entire solution space, avoid falling into a local optimal solution too early, and increase the possibility of discovering the global optimal solution.

[0082] In one embodiment, the iterative process of the target algorithm includes:

[0083] 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.

[0084] 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 are randomly selected from the link groups to be expanded to update the number of expansion links, where K is the number of link groups to be expanded.

[0085] The updated first updated solution contains the same content as the candidate solution, i.e., the first updated solution also includes the number of expanded links in each link group. Each candidate solution corresponds to a first updated solution. Furthermore, the candidate solution may or may not be consistent with the corresponding first updated solution.

[0086] S2. Substitute the first updated solution into the objective function to obtain the fitness of the first updated solution.

[0087] When calculating the fitness of the first updated solution, the number of expanded links in each link group included in each first updated solution is substituted into the objective function, so that the fitness of each first updated solution can be calculated.

[0088] 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.

[0089] 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. i The corresponding first updated solution is , then the first update solution Fitness and candidate solution X i The fitness F(X i ) for size comparison.

[0090] The number of first updated solutions is consistent with the number of candidate solutions. Furthermore, the fitness of each first updated solution is calculated, and the 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 that of the global optimal solution, the global optimal solution is replaced with the target solution to obtain the replaced global optimal solution.

[0091] S4. Repeat steps S1, S2, and S3 until the number of iterations of the target algorithm meets the iteration stop condition and outputs the optimal solution.

[0092] When repeating step S1, step S2, and step S3, the candidate solution will be updated in each iteration, and the global optimal solution may or may not be updated.

[0093] In this embodiment, at least one target link group is determined from each candidate solution, and the number of expansion links of the target link group is updated to obtain an updated first update solution. The first update solution is substituted into the objective function to obtain the fitness of the first update solution. If the fitness of the first update solution is less than the fitness of the candidate solution, the candidate solution in step S1 is replaced by the first update solution. If the fitness of the first update solution is less than the fitness of the global optimal solution, the global optimal solution in step S1 is replaced by the first update solution. Steps S1, S2 and S3 are repeated until the number of iterations of the target algorithm meets the iteration stop condition. This can effectively avoid the performance degradation of the traditional search algorithm in the discrete integer space, improve the global search capability, and thus obtain a target algorithm that can accurately output the number of expansion links.

[0094] In one embodiment, the iterative process of the target algorithm further includes:

[0095] Gets the preset number of iterations used to identify the candidate solution with the maximum fitness.

[0096] When the current iteration number of the target algorithm is a multiple of the preset iteration number, a target candidate solution with the greatest fitness is identified from the current candidate solutions.

[0097] Perform global random sampling on the target candidate solution to obtain the second updated solution.

[0098] The target candidate solution in the candidate solutions is replaced with the second updated solution, and the target link group is selected based on the replaced candidate solution.

[0099] The preset number of iterations is not the number of iterations at which the target algorithm stops, but the number of iterations it takes to identify the candidate solution with the highest fitness. For example, if the preset number of iterations is 500, then the target algorithm will identify the target candidate solution with the highest fitness from the current candidate solutions after every 500 iterations.

[0100] Global random sampling involves randomly selecting a value from a target interval to serve as the number of expansion links for each link group in the second updated solution. The target interval can be determined based on the maximum number of expansion links for the link group. For example, if the maximum number of expansion links for the link group is N, the target interval can be 0 to N.

[0101] The replaced candidate solutions include the second updated solution but do not include the target candidate solution.

[0102] In this embodiment, when the current number of iterations of the target algorithm reaches a multiple of the preset number of iterations, the target candidate solution with the largest fitness is identified from the current candidate solutions, the target candidate solution is globally randomly sampled to obtain a second updated solution, the target candidate solution in the candidate solution is replaced with the second updated solution, and the target link group is selected based on the replaced candidate solution. This can prevent the target algorithm from falling into a local optimum during the iteration process, effectively break the trend of local convergence, introduce a new search direction for subsequent searches, 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 objective function.

[0103] In one embodiment, updating the number of expansion links in the target link group to obtain an updated first updated solution includes:

[0104] Based on the number of expansion links in the global optimal solution, the number of expansion links in the target link group is disturbed and updated to obtain an updated first updated solution.

[0105] And / or, randomly update the number of expansion links of the target link group to obtain an updated first updated solution.

[0106] Among them, based on the number of expansion links in the global optimal solution, the perturbation update of the number of expansion links of the target link group 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, based on the number of expansion links in the global optimal solution, the number of expansion links of the target link group is increased or decreased, or only the number of expansion links of some target link groups is adjusted by increasing or decreasing the operation. The formula for perturbation update is expressed as: ,in is the number of expanded links in target link group k in candidate solution i after disturbance update; Best_P k is the number of expansion links of target link group k in the global optimal solution, The number of expansion links to be adjusted.

[0107] When randomly updating the number of expansion links in the target link group, the update is primarily performed based on a target interval. The target interval can be determined based on a preset maximum number of expansion links in the link group. For example, if the maximum number of expansion links in the link group is N, the target interval can be 0 to N. When randomly updating the number of expansion links in the target link group, a value is randomly selected from 0 to N as the number of expansion links in the target link group, thereby obtaining a first updated solution.

[0108] In this embodiment, the number of expansion links in the target link group is perturbed and updated based on the number of expansion links in the global optimal solution to obtain a first updated solution. This facilitates a detailed search near candidate solutions and helps find a more optimal local solution. The first updated solution is obtained by randomly updating the number of expansion links in the target link group, thus maintaining the diversity of the target algorithm and avoiding premature entrapment in a local optimal solution.

[0109] In one embodiment, step S1 includes:

[0110] The number of expansion links of the target link group is updated to obtain an updated number of expansion links.

[0111] 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.

[0112] The expansion link threshold can also be understood as the maximum number of expansion links in 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 in the target link group in the first updated solution.

[0113] Furthermore, when the updated number of expansion links is less than or equal to a preset expansion link threshold, an updated first update solution is determined according to the updated number of expansion links.

[0114] Furthermore, when the number of expansion links of the target link group is updated, the update can be performed by at least one of a disturbance update and a random update.

[0115] In this embodiment, when the number of expanded links after the update exceeds a preset expanded link threshold, the updated first updated solution is determined based on the number of expanded links indicated by the expanded link threshold. This can effectively control resource usage and avoid the problem of resource exhaustion due to excessive expansion.

[0116] In one embodiment, the method for expanding urban broadband links based on comprehensive deployment costs and network status further includes:

[0117] The number of expanded links in each link group is determined, and the number of original links in each link group is obtained.

[0118] A first result of multiplying the first link utilization rate and the number of original links is calculated, and a second result of adding the number of expanded links and the number of original links is calculated.

[0119] 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 characterize an average utilization of the links of the link group after capacity expansion.

[0120] The original links refer to the links in the link group before capacity expansion, and the number of original links refers to the number of links in the link group before capacity expansion.

[0121] The first link utilization refers to the traffic utilization of the original link in the link group in each preset time period. Each link group corresponds to a first link utilization in a preset time period.

[0122] The second link utilization rate of link group g within the preset time period t after expansion The calculation formula is: Among them, n g is the number of original links in link group g, x g is the number of expansion links in link group g, u g,t is the first link utilization of link group g in the preset time period t before capacity expansion.

[0123] In this embodiment, the second link utilization is determined based on the quotient of the first result divided by the second result, so that changes in the link utilization after expansion can be intuitively understood.

[0124] This application also provides an application scenario, which applies the above-mentioned urban broadband link expansion method based on comprehensive deployment cost and network status. Specifically, the application of the urban broadband link expansion method based on comprehensive deployment cost and network status in this application scenario is as follows:

[0125] The optimization process of the target algorithm is as follows Figure 3As shown. Specifically, the server randomly generates multiple candidate solutions based on the candidate solution generation instruction. Each candidate solution includes the number of expansion links of each link group. The server substitutes each candidate solution into the objective function respectively, obtains the fitness of each candidate solution, and determines the global optimal solution among the candidate solutions based on the fitness. The server determines at least one target link group from each candidate solution respectively, and based on the number of expansion links in the global optimal solution, performs a perturbation update on the number of expansion links of the target link group, and / or randomly updates the number of expansion links of the target link group. The perturbation update can be a local perturbation update with a probability of 70%, and the random update can be a global random sampling with a probability of 30%. When the updated number of expansion links exceeds the preset expansion link threshold, the server performs boundary correction based on the number of expansion links indicated by the expansion link threshold to determine the updated 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 that of the candidate solution, the server replaces the candidate solution with the first updated solution to obtain the replaced candidate solution; if the fitness of the first updated solution is less than that of the global optimal solution, the server replaces the global optimal solution with the first updated solution to obtain the replaced global optimal solution. When the current number of iterations of the target algorithm reaches a multiple of the preset number of iterations, the server identifies the target candidate solution with the highest fitness among the current candidate solutions, performs global random sampling on the target candidate solutions to obtain a second updated solution, replaces the target candidate solution among the candidate solutions with the second updated solution, and selects the target link group based on the replaced candidate solution if the iteration stopping condition is not met, until the number of iterations of the target algorithm meets the iteration stopping condition, outputs the optimal solution, and obtains the optimized target algorithm.

[0126] The server obtains the first link utilization of the link group to be expanded within each preset time period and the corresponding cost function for the expansion. When the first link utilization exceeds a preset first threshold for multiple consecutive preset time periods, the server constructs a congestion penalty function. When the first link utilization falls below a preset second threshold for multiple consecutive preset time periods, the server constructs a resource waste penalty function. The server constructs an objective function based on the sum of the resource waste penalty function, the congestion penalty function, and the cost function. The server solves the objective function using an optimized objective algorithm to determine the number of links to be expanded for each link group.

[0127] The server determines the number of expanded links in each link group and obtains the number of original links in each link group. The server calculates a first result of multiplying the first link utilization by the number of original links, and calculates a second result of adding the number of expanded links to the number of original links. The server determines a second link utilization based on the quotient of the first result divided by the second result. The second link utilization represents the average utilization of the links in the link group after expansion.

[0128] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0129] Based on the same inventive concept, the embodiments of the present application also provide a system for expanding urban broadband links based on comprehensive deployment costs and network status, which is used to implement the above-mentioned method for expanding urban broadband links based on comprehensive deployment costs and network status. The implementation solution provided by this system is similar to the implementation solution described in the above-mentioned method. Therefore, the specific limitations of one or more embodiments of the system for expanding urban broadband links based on comprehensive deployment costs and network status provided below can be found in the above-mentioned limitations of the method for expanding urban broadband links based on comprehensive deployment costs and network status, and will not be repeated here.

[0130] In one embodiment, Figure 4 As shown, a system for expanding urban broadband links based on comprehensive deployment costs and network status is provided, including:

[0131] The data acquisition module is used to obtain 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.

[0132] The first function construction module is used to construct a congestion penalty function when the first link utilization exceeds a preset first threshold in multiple consecutive preset time periods, and to construct a resource waste penalty function when the first link utilization is lower than a preset second threshold in multiple consecutive preset time periods.

[0133] The second function construction module is used to determine the result of adding the resource waste penalty function, the congestion penalty function and the cost function as the objective function.

[0134] The strategy acquisition module is used to calculate the number of expansion links using the objective function to obtain the number of expansion links of each link group.

[0135] In one embodiment, the urban broadband link expansion system considering comprehensive deployment cost and network status is further used to: randomly generate multiple candidate solutions based on a candidate solution generation instruction; the candidate solution generation instruction is used to instruct the target algorithm to generate multiple candidate solutions including the number of expanded links of each link group; substitute each candidate solution into the objective function respectively to obtain the fitness of each candidate solution, and determine the global optimal solution among the candidate solutions based on the fitness; perform iterative processing based on the global optimal solution and the candidate solutions until the number of iterations of the target algorithm meets the iteration stop condition, and output the optimal solution.

[0136] In one embodiment, the urban broadband link expansion system that comprehensively considers deployment cost and network status is also used to: S1, determine the global optimal solution, and 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, substitute 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, replace the candidate solution in step S1 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, replace the global optimal solution in step S1 with the first updated solution to obtain a replaced global optimal solution; S4, repeat steps S1, S2 and S3 until the number of iterations of the target algorithm meets the iteration stop condition and outputs the optimal solution.

[0137] In one of the embodiments, the urban broadband link expansion system that comprehensively considers deployment costs and network status is also used to: obtain a preset number of iterations for identifying a candidate solution with the greatest fitness; when the current number of iterations of the target algorithm is a multiple of the preset number of iterations, identify a target candidate solution with the greatest fitness among the current candidate solutions; perform global random sampling on the target candidate solution to obtain a second updated solution; replace the target candidate solution among the candidate solutions with the second updated solution, and select a target link group based on the replaced candidate solution.

[0138] In one embodiment, the urban broadband link expansion system based on comprehensive deployment cost and network status is further used to: based on the number of expansion links in the global optimal solution, perform a perturbation update on the number of expansion links in the target link group to obtain an updated first updated solution; and / or, perform a random update on the number of expansion links in the target link group to obtain an updated first updated solution.

[0139] In one embodiment, the urban broadband link expansion system that comprehensively considers deployment costs and network status is further used to: update the number of expansion links in 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, determine an updated first update solution based on the number of expansion links indicated by the expansion link threshold.

[0140] In one embodiment, the urban broadband link expansion system that comprehensively considers deployment costs and network status is further used to: determine the number of expanded links in each link group and obtain the number of original links in each link group; calculate a first result of multiplying the first link utilization and the number of original links, and calculate a second result of adding the number of expanded links and the number of original links; determine a second link utilization based on the quotient of the first result divided by the second result; the second link utilization is used to characterize the average link utilization of the link group after expansion.

[0141] Each module in the aforementioned urban broadband link expansion system, which integrates deployment costs and network status, can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device's memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0142] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory and a network interface connected via a system bus. The processor of the computer device is used 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 operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the first link utilization, the cost function, the preset time period, the first threshold, the second threshold, the congestion penalty function, the resource waste penalty function, the objective function and the number of expansion links. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for expanding urban broadband links that comprehensively considers deployment costs and network status is implemented.

[0143] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0144] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this 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 memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0145] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0146] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by 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 adding the resource waste penalty function, the congestion penalty function, and the cost function as an objective function; The objective function is used to calculate the number of expansion links to obtain the number of expansion links of each link group.

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 adding the resource waste penalty function, the congestion penalty function and the cost function as an objective function; The strategy acquisition module is used to calculate the number of expansion links using the objective function to obtain the number of expansion links of each link group.

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