Online demand management method, device, equipment, medium and product

Abstract

Embodiments of the present application disclose a method and device for managing online requirements, an electronic device, a medium and a product, and relate to the technical field of behavior management. In the case that the performance of the electronic device reaches a preset peak value, the online requirements are queued according to the average time length of the access resource consumption corresponding to each online requirement and the priority of the access resource. In the case that the average time length of the access resource consumption and the priority of the access resource are guaranteed to be optimal, the online requirements of the user are queued, so that the waiting time of the user and the priority of the access resource are both relatively optimal, thereby the total waiting time of the user can be reduced.

Description

Technical Field

[0001] This application relates to the field of behavior management technology, and in particular to a method, apparatus, device, medium and product for managing internet access needs. Background Technology

[0002] Internet access management refers to helping internet users control and manage their internet usage. In practice, there are peak periods for internet access, meaning a large number of users and high demand for internet access, which can lead to insufficient device performance.

[0003] Currently, users' internet access requests are queued according to the priority principle of accessing resources, so that the internet access requests of some users can be responded to in a timely manner. For internet access requests with lower priority, the device process will be occupied indefinitely, which will consume the device performance and result in a longer total waiting time for users.

[0004] Application content

[0005] This application provides an internet access demand management method, apparatus, device, medium, and product that can more rationally queue users' internet access demands and reduce users' waiting time.

[0006] In a first aspect, embodiments of this application provide a method for managing internet access needs, including:

[0007] When the performance of electronic devices reaches a preset peak, obtain the internet access needs of each user within a preset time period. The internet access needs include at least the resources accessed, the average duration of resource access, and the priority of resource access.

[0008] Based on the average duration of access resource consumption and the priority of access resources for each internet access request, the internet access requests are queued to obtain a target request queue, so that electronic devices can respond to each internet access request in the order of the internet access requests in the target request queue.

[0009] Secondly, embodiments of this application provide an internet access demand management device, including:

[0010] The acquisition module is used to acquire the internet access needs of each user within a preset time period when the performance of the electronic device reaches a preset peak. The internet access needs include at least the resources accessed, the average duration of resource access, and the priority of resource access.

[0011] The queuing module is used to queue up each Internet access request based on the average duration of access resource consumption and the priority of access resources, so as to obtain a target request queue, so that electronic devices can respond to each Internet access request in the order of the Internet access requests in the target request queue.

[0012] Thirdly, embodiments of this application provide an electronic device, including:

[0013] processor;

[0014] Memory is used to store computer program instructions;

[0015] When computer program instructions are executed by the processor, the method described in the first aspect is implemented.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the method described in the first aspect.

[0017] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0018] In this embodiment, when the performance of the electronic device reaches a preset peak, internet access requests are queued based on the average duration of resource consumption and the priority of the access resources. By queuing users' internet access requests while ensuring that the average duration of resource consumption and the priority of access resources are optimal, the user's waiting time and the priority of access resources are relatively optimal, thereby reducing the user's total waiting time. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating an internet access demand management method provided in this application embodiment;

[0021] Figure 2 A flowchart illustrating another method for managing internet access needs provided in this application embodiment;

[0022] Figure 3 This is a schematic diagram of a discrete subtraction process provided in an embodiment of this application;

[0023] Figure 4 This is a schematic diagram of a discrete addition process provided in an embodiment of this application;

[0024] Figure 5 A structural diagram of an Internet access demand management device provided in an embodiment of this application;

[0025] Figure 6This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0026] The features and exemplary embodiments of various aspects of this application will now be described in detail. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only configured to explain this application and are not configured to limit this application. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of this application.

[0027] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0028] To cope with the problem of a large number of internet users and high demand during peak internet usage periods, the current approach is to prioritize users' access to resources, such as a first-come, first-served system, which results in long overall waiting times for users.

[0029] Therefore, embodiments of this application provide an internet access demand management method, apparatus, device, medium, and product that can more rationally queue users' internet access demands and reduce users' waiting time.

[0030] The following describes the Internet access demand management method, apparatus, device, medium, and product provided in this application with reference to specific embodiments. Figure 1 This is a flowchart illustrating an internet access demand management method provided in an embodiment of this application. This method can be applied to servers and smart devices such as laptops and desktops.

[0031] like Figure 1 As shown, this method for managing internet access needs may include the following steps:

[0032] S110. When the performance of the electronic device reaches a preset peak, obtain the internet access needs of each user within a preset time period.

[0033] Internet access requirements include at least the resources accessed, the average duration of resource access, and the priority of resource access.

[0034] S120. Based on the average duration of access resource consumption corresponding to each Internet access demand and the priority of access resources, queue each Internet access demand to obtain a target demand queue, so that the electronic device responds to each Internet access demand in the order of the Internet access demands in the target demand queue.

[0035] In this embodiment, when the performance of the electronic device reaches a preset peak, internet access requests are queued based on the average duration of resource consumption and the priority of the access resources. By queuing users' internet access requests while ensuring that the average duration of resource consumption and the priority of access resources are optimal, the user's waiting time and the priority of access resources are relatively optimal, thereby reducing the user's total waiting time.

[0036] The above steps are explained in detail below:

[0037] In S110, the performance of an electronic device reaching a preset peak can be achieved by the electronic device's memory, bandwidth, central processing unit (CPU), etc., reaching a preset peak.

[0038] The preset time period could be a specific period starting from when the electronic device's performance reaches a preset peak. When the electronic device's performance reaches the preset peak, the internet access needs of each user through that electronic device within the preset time period can be obtained. A single user can have one or more internet access needs within the same preset time period. Each internet access need can correspond to a specific access resource.

[0039] In this application embodiment, the Internet access requirement may include, but is not limited to, the user's identity identifier, access resources, the average duration of access resource consumption, and the priority of access resources.

[0040] The average time consumed in accessing resources can be determined based on the number of times a user accesses the same resource within a certain time period and the time consumed in each access. For example, if user A accesses resource C a total of three times within time period B, and the time consumed in each access to resource C is t1, t2 and t3 respectively, then the average time consumed in accessing resource C is (t1+t2+t3) / 3.

[0041] In this embodiment, to facilitate queuing of internet access requests, a time stamp can be added to resource C based on the average time consumed to access it. The time stamp can be represented by an integer greater than or equal to 0; the smaller the time stamp, the shorter the average time consumed to access resource C. The average time consumed to access resource C is the corresponding time stamp.

[0042] The priority of accessing resources can also be represented by an integer greater than or equal to 0, with smaller values ​​indicating higher priority.

[0043] In S120, based on the average duration of resource consumption required for each internet access request and the resource priority, each internet access request can be queued to obtain a target request queue. In this way, the total waiting time of users can be optimized, that is, the total waiting time of users can be reduced.

[0044] For example, for each internet access request, the average duration of resource consumption and resource priority can be summed to obtain a cumulative sum. The internet access requests can then be queued based on this cumulative sum. For instance, the smaller the cumulative sum, the higher the internet access request is ranked, meaning the electronic device will prioritize processing that request.

[0045] For example, other algorithms, such as adaptive search differential algorithms, can be used to queue up various internet access requests by combining the average time consumed to access resources with the priority of accessing resources.

[0046] This application embodiment queues each internet access request while ensuring the average duration of access resource consumption and the optimal resource priority. This makes the user's total waiting time and resource priority relatively optimal, thereby shortening the user's total waiting time and improving the management efficiency of internet access requests.

[0047] Taking the use of an adaptive search differential algorithm, combining the average time consumed by accessing resources with the priority of accessing resources to queue internet access requests as an example, such as... Figure 2 As shown, this method for managing internet access needs may include the following steps:

[0048] S210. When the performance of the electronic device reaches a preset peak, obtain the internet access needs of each user within a preset time period.

[0049] S220. Randomly generate a population based on the quantity and dimensions of internet access needs.

[0050] The initialization parameters of the population include the maximum number of iterations, the initial mutation factor, and the initial crossover factor.

[0051] S230. Perform mutation operations on the base individuals in the population to obtain the mutated individuals corresponding to the base individuals.

[0052] S240. Perform a crossover operation on each base individual and the variant individuals corresponding to the base individual to obtain the offspring individuals of the base individual.

[0053] S250. Use the evaluation function to determine the evaluation function values ​​of the base individual and the offspring individuals corresponding to the base individual.

[0054] The evaluation function is associated with the average duration of resource access and the priority of resource access.

[0055] S260. Determine the next generation population based on the evaluation function values ​​of the base individual and its corresponding offspring.

[0056] S270. When the number of iterations reaches the maximum number of iterations or the evaluation function value meets the stability condition, the internet access demand ranking result corresponding to the last generation population is determined as the target demand queue.

[0057] The process of S210 can be found in the above embodiments, and will not be repeated here for the sake of brevity.

[0058] The other steps described above are explained in detail below:

[0059] In S220, the population size represents the number of internet access requests, and each individual in the population (also called a base individual) corresponds to one internet access request. Each internet access request can be generated by a D-dimensional natural number vector NNV = {1, 2, ..., D}. The access resource, the average duration of accessing the resource, and the priority of accessing the resource each correspond to different integers in the natural number vector NNV.

[0060] For example, each base individual X in the population m =random_shuffle m (NNV)={X m1 ,X m2 ,…,X mD} Here, `random_shuffle(x)` is a C++ function that randomly rearranges all the integer elements in the vector `x`. That is, for each base individual `X` in the population... m The time is obtained by randomly rearranging all elements in the natural number vector NNV.

[0061] Population initialization parameters can include, for example, the maximum number of iterations G. max The initial mutation factor F0 and the initial crossover factor CR0 can be set according to actual needs.

[0062] The mutation factor is used to perform a mutation operation on the base individuals in the population to obtain mutated individuals of the base individuals. The crossover factor is used to perform a crossover operation on the base individuals and their corresponding mutated individuals to obtain the offspring individuals of the base individuals. In the embodiments of this application, the mutation factor and the crossover factor can change with the number of iterations of the population, thereby improving the global search performance of the adaptive search difference algorithm.

[0063] In S230, the mutation operation is performed on the base individual X in the population. m Generate a variant individual V m Essentially, the mutation operation is a combination of discrete addition and discrete subtraction operations on real vectors, as shown below:

[0064]

[0065] Where m = 1, 2, ..., NP, m ≠ r1 ≠ r2 ≠ r3, and NP is the size of the population. During the population iteration process, the size of the population, i.e., NP, remains unchanged. r1, r2, and r3 are three distinct indices randomly selected from the range [1, NP].

[0066] X r2 ! X r3 Representing the base individual That is, the base individual X r2 The element in the base individual X r3 In this embodiment of the application, the address index can be... Let it be denoted as the difference vector d m ,Right now Difference vector d m The determination process can be found in [reference]. Figure 3 Based on individual X r2 Taking element "3" as an example, element 3 in the base individual X r3 The address index in is 2, therefore, d m1 =2. Difference vector d m The process of determining other elements is similar to d. m1 The process of determining it is similar.

[0067] With F g For example, if the value is 1, the mutant individual V m It can be achieved by analyzing the base individual X r1 Sum of difference vectors d m It is obtained by performing discrete addition. For example, That is, the difference vector d m The address index in the base individual X r1 By selecting each element from the list, we obtain the mutated individual V. m .

[0068] like Figure 4As shown, for the difference vector d m The address index "2" in the base individual X r1 The element corresponding to address index "2" is "5", therefore, the mutated individual V m The first element V m1 =5, mutant individual V m The process of determining other elements is similar to that of V. m1 Similarly, from this, we can obtain the mutant individual V. m .

[0069] For example,

[0070]

[0071] Among them, rand m (0,1) represents a value randomly selected between (0,1), F g The target crossover factor. The function `random`. F (x) represents F in individual x. g % of the elements are randomly rearranged.

[0072] In S240, for each iteration, after the mutation operation is completed, a crossover operation is performed on the base individual and its corresponding mutated individual to obtain the offspring of the base individual. The crossover operation involves recombinating the base individual X. m and variant individual V m The offspring individual U is obtained. m .

[0073] In S250, after the crossover operation is completed, to ensure that the population size remains unchanged, the evaluation function can be used to determine the base individual X. m and offspring individual U m The evaluation function value is related to the average duration of resource access and the priority of resource access.

[0074] The evaluation function value is used to evaluate the base individual X. m and offspring individual U m The process involves identifying individuals to enter the next generation of the population in order to achieve population renewal.

[0075] In S260, for example, the individual with the smaller evaluation function value can be selected to enter the next generation of the population. For example, the base individual X... m The evaluation function value is greater than the offspring individual U. m Then you can choose the offspring individual U. m Enter the next generation of the population. Through continuous iteration, we can ensure that the final population is optimal, that is, the demand permutation result is optimal.

[0076] This application utilizes an adaptive search differential algorithm to obtain various forms of discrete mutation operations by combining different base individuals and differential vectors, while ensuring the optimal priority of accessed resources and access duration. This queues up the internet access needs of different users, making the total waiting time of users relatively optimal and shortening the waiting time of users.

[0077] In some embodiments, the above-described S230 may include the following steps:

[0078] Two different base individual indices are randomly selected from the base individual indices of the population and denoted as the first base individual index and the second base individual index, respectively.

[0079] Discrete subtraction is performed on the first base individual corresponding to the first base individual index and the second base individual corresponding to the second base individual index to obtain the difference vector;

[0080] Based on the difference vector and the target mutation factor, a mutation operation is performed on the third basic individual in the population to obtain the mutated individual corresponding to the third basic individual. The basic individual index of the third basic individual is different from the indexes of the first and second basic individuals. The target mutation factor is associated with the current iteration number and the initial mutation factor of the population.

[0081] In the embodiments of this application, the first base individual index and the second base individual index are different base individual indices randomly selected from the base individual indices of the population.

[0082] Discrete subtraction is performed on the first primitive individual corresponding to the first primitive individual index and the second primitive individual corresponding to the second primitive individual index to obtain the difference vector. The process of discrete subtraction can be found in [reference needed]. Figure 3 For the sake of brevity, details will not be elaborated here.

[0083] Once the difference vector is obtained, the third basic individual in the population can be mutated based on the difference vector and the target mutation factor to obtain the mutated individual corresponding to the third basic individual.

[0084] The target mutation factor is associated with the current iteration number and the initial mutation factor of the population. With a fixed initial mutation factor, the target mutation factor can change with the current iteration number, thereby avoiding getting stuck in local optima during the search process and improving the global search performance of the algorithm.

[0085] The third basic individual is an individual in the population, and its index is different from both the first and second basic individual indices. In this embodiment, the third basic individual changes with the number of iterations; that is, different mutation strategies can be used at different search stages. Different mutation strategies result in different third basic individuals, thus improving the local search performance of the adaptive search difference algorithm.

[0086] For example, if the current iteration number of the population is less than q1 percent of the maximum iteration number, the third base individual includes the current base individual in the population;

[0087] If the current iteration number of the population is greater than or equal to q1 percent of the maximum iteration number and less than q2 percent of the maximum iteration number, the third base individual includes the base individual after the current base individual in the population is corrected to be biased towards the best base individual.

[0088] If the current iteration number of the population is greater than or equal to q2 percent of the maximum iteration number and less than q3 percent of the maximum iteration number, the third basic individual includes a randomly selected basic individual in the population.

[0089] If the current iteration number of the population is greater than or equal to q3 percent of the maximum iteration number, but less than the maximum iteration number, the third basic individual includes the best basic individual in the population.

[0090] The values ​​of q1, q2, and q3 can be set according to actual needs. For example, in some embodiments, q1 = 25, q2 = 50, and q3 = 75.

[0091] For example, when g < 0.25G max In this case, the PDDE / current / 1 mutation strategy can be used, that is, the third basic individual is the current basic individual in the population. For example, when g=1, the current basic individual can be the first basic individual in the population, when g=2, the current basic individual can be the second basic individual in the population, and so on.

[0092] For example, when 0.25G max ≤g<0.5G max In this case, a PDDE / current-to-best / 1 mutation strategy can be adopted, that is, the third base individual is the base individual after the current base individual is modified to be the best base individual. The best base individual is the base individual with the smallest evaluation function value in the population corresponding to the current iteration number.

[0093] For example, when 0.5G max ≤g<0.75G maxIn this case, the PDDE / rand / 1 mutation strategy can be adopted, that is, the third basic individual is a basic individual randomly selected from the population.

[0094] For example, 0.75G max ≤g <G max In this case, the PDDE / best / 1 mutation strategy can be adopted, that is, the third basic individual is the best basic individual in the population.

[0095] In other words, the mutation strategies for different search stages can be seen as follows:

[0096]

[0097] For details on the variations of different search strategies, please refer to the above embodiments, where X r2 and X r3 They can be denoted as the first basic individual and the second basic individual, respectively. That is, r2 and r3 are the indices of the first basic individual and the second basic individual, respectively.

[0098] In the above formula, when g < 0.25G max At that time, the third basic individual is X. i When 0.25G max ≤g<0.5G max At that time, the third basic individual is X. i and X best When 0.5G max ≤g<0.75G max At that time, the third basic individual is X. r1 When 0.75G max ≤g <G max At that time, the third basic individual is X. best Various forms of mutation operations can be obtained by combining different base individuals and difference vectors.

[0099] This application's embodiments divide the iteration process into four stages, with different mutation strategies employed in each stage. This allows for mutation searches to proceed in a better direction during the computation process, improving the local search performance of the adaptive search differential algorithm and ultimately enhancing the overall performance of the algorithm. Consequently, it enables more reasonable queuing of various internet access demands, reducing user waiting time.

[0100] In some embodiments, before "performing a mutation operation on the third primitive individual in the population based on the difference vector and the target mutation factor to obtain a mutated individual corresponding to the third primitive individual", the target mutation factor can be determined by the following method:

[0101] Determine the first ratio between the current iteration number and the maximum iteration number;

[0102] The target variation factor is determined based on the initial variation factor and the first ratio.

[0103] For example, Among them, F g Let F0 be the target mutation factor, F0 be the initial mutation factor, and g be the current iteration number. As the iteration number increases, the target mutation factor F... g The value of F0 gradually changes to 1.

[0104] The embodiments of this application improve the mutation factor, thereby avoiding the problem of the algorithm getting stuck in local optima and premature convergence, and improving the global search performance of the algorithm.

[0105] In some embodiments, S240 may include the following steps:

[0106] For each base individual, a value between (0,1) is randomly generated;

[0107] Recombining the base individual and the variant individuals corresponding to the base individual yields the first generation of offspring individuals;

[0108] If the value is less than the target crossover factor, the first offspring individual is determined as an offspring of the base individual;

[0109] If the value is greater than or equal to the target crossover factor, recombine the first offspring individual and the base individual to obtain the offspring individuals of the base individual;

[0110] The target crossover factor is associated with the current iteration number and the initial crossover factor of the population.

[0111] For example, the base individual and the corresponding variant individual can be recombine to obtain the first offspring individual. When the randomly generated value is less than the target crossover factor, the first offspring individual can be directly identified as the offspring of the base individual. When the randomly generated value is greater than or equal to the target crossover factor, the first offspring individual and the base individual need to be recombine, and the recombination result is taken as the offspring of the base individual.

[0112] For example, the process of generating offspring individuals can be seen as follows:

[0113]

[0114] Where m = 1, 2, ..., NP, CR g For the target cross factor, This represents the discrete crossover operator.

[0115] For example, before "recombining the base individual and the variant individuals corresponding to the base individual to obtain the first offspring individuals", the target crossover factor CR can be determined in the following way.g :

[0116] Determine the second ratio between the current iteration count and the maximum iteration count;

[0117] The target cross factor is determined based on the initial cross factor and the second ratio.

[0118] For example, As the number of iterations increases, the target cross-factor CR g It gradually changes from CR0 to 1.

[0119] In the early stages of the iteration in this application, random is used according to a certain probability. F (x) and This generates mutated and offspring individuals, increasing their diversity and resulting in better global search performance. With algorithm iteration, more mutated and offspring individuals are generated through pointer-based mutation operations and... Generation improves the local search performance of the algorithm. This allows for a more optimal population and a more optimal demand queue, thus reducing user waiting time.

[0120] This application's embodiments introduce an adaptive search strategy when queuing internet access requests. This strategy involves combining different base individuals and difference vectors to obtain various forms of discrete mutation operations, avoiding local optimization and premature convergence, and improving the algorithm's global and local search performance. This results in a final request queue that optimizes both the priority and access duration of accessed resources, while also optimizing the total waiting time for users.

[0121] Based on the same inventive concept, this application also provides an internet access demand management device, which is described below in conjunction with... Figure 5 The Internet access demand management device provided in the embodiments of this application will be described in detail.

[0122] Figure 5 This is a structural diagram of an Internet access demand management device provided in an embodiment of this application.

[0123] like Figure 5 As shown, the Internet access demand management device may include:

[0124] The acquisition module 501 is used to acquire the internet access needs of each user within a preset time period when the performance of the electronic device reaches a preset peak. The internet access needs include at least the resources accessed, the average duration of resource access, and the priority of resource access.

[0125] The queuing module 502 is used to queue up each Internet access request according to the average duration of access resource consumption and the priority of access resources, so as to obtain a target request queue, so that the electronic device responds to each Internet access request in the order of the Internet access requests in the target request queue.

[0126] In this embodiment, when the performance of the electronic device reaches a preset peak, internet access requests are queued based on the average duration of resource consumption and the priority of the access resources. By queuing users' internet access requests while ensuring that the average duration of resource consumption and the priority of access resources are optimal, the user's waiting time and the priority of access resources are relatively optimal, thereby reducing the user's total waiting time.

[0127] In some embodiments, the queuing module 502 includes:

[0128] The random unit is used to randomly generate a population based on the quantity and dimensions of internet access demand. The initialization parameters of the population include the maximum number of iterations, the initial mutation factor, and the initial crossover factor.

[0129] Mutation units are used to perform mutation operations on the base individuals in the population to obtain mutated individuals corresponding to the base individuals;

[0130] Crossover units are used to perform crossover operations on each base individual and the variant individuals corresponding to the base individual to obtain the offspring individuals of the base individual;

[0131] The first determining unit is used to determine the evaluation function values ​​of the base individual and the offspring individual corresponding to the base individual using the evaluation function. The evaluation function is related to the average duration of resource access and the priority of resource access.

[0132] The second determining unit is used to determine the next generation population based on the evaluation function values ​​of the base individual and the offspring individuals corresponding to the base individual;

[0133] The third determining unit is used to determine the internet access demand ranking result corresponding to the last generation population as the target demand queue when the number of iterations reaches the maximum number of iterations or the evaluation function value meets the stability condition.

[0134] In some embodiments, the mutation unit is specifically used for:

[0135] Two different base individual indices are randomly selected from the base individual indices of the population and denoted as the first base individual index and the second base individual index, respectively.

[0136] Discrete subtraction is performed on the first base individual corresponding to the first base individual index and the second base individual corresponding to the second base individual index to obtain the difference vector;

[0137] Based on the difference vector and the target mutation factor, a mutation operation is performed on the third basic individual in the population to obtain the mutated individual corresponding to the third basic individual. The basic individual index of the third basic individual is different from the indexes of the first and second basic individuals. The target mutation factor is associated with the current iteration number and the initial mutation factor of the population.

[0138] In some embodiments, when the current iteration number of the population is less than q1 percent of the maximum iteration number, the third base individual includes the current base individual in the population;

[0139] If the current iteration number of the population is greater than or equal to q1 percent of the maximum iteration number and less than q2 percent of the maximum iteration number, the third base individual includes the base individual after the current base individual in the population is corrected to be biased towards the best base individual.

[0140] If the current iteration number of the population is greater than or equal to q2 percent of the maximum iteration number and less than q3 percent of the maximum iteration number, the third basic individual includes a randomly selected basic individual in the population.

[0141] If the current iteration number of the population is greater than or equal to q3 percent of the maximum iteration number, but less than the maximum iteration number, the third basic individual includes the best basic individual in the population;

[0142] The optimal base individual is the base individual with the smallest evaluation function value in the population corresponding to the current iteration number.

[0143] In some embodiments, the Internet access demand management device may further include:

[0144] The fourth determining unit is used to determine the first ratio of the current iteration number to the maximum iteration number before the mutation unit performs mutation operation on the third base individual according to the difference vector and the target mutation factor to obtain the mutated individual corresponding to the third base individual.

[0145] The target variation factor is determined based on the initial variation factor and the first ratio.

[0146] In some embodiments, the cross unit is specifically used for:

[0147] For each base individual, a value between (0,1) is randomly generated;

[0148] Recombining the base individual and the variant individuals corresponding to the base individual yields the first generation of offspring individuals;

[0149] If the value is less than the target crossover factor, the first offspring individual is determined as an offspring of the base individual;

[0150] If the value is greater than or equal to the target crossover factor, recombine the first offspring individual and the base individual to obtain the offspring individuals of the base individual;

[0151] The target crossover factor is associated with the current iteration number and the initial crossover factor of the population.

[0152] In some embodiments, the fourth determining unit is further configured to determine a second ratio of the current iteration number to the maximum iteration number before the crossover unit recombines the base individual and the variant individual corresponding to the base individual to obtain the first offspring individual;

[0153] The target cross factor is determined based on the initial cross factor and the second ratio.

[0154] Figure 5 Each module in the illustrated device has the ability to implement Figure 1 and Figure 2 The functions of each step and the corresponding technical effects are described in detail here for the sake of brevity.

[0155] Based on the same inventive concept, this application also provides an electronic device. The following, in conjunction with... Figure 6 The electronic devices provided in the embodiments of this application will be described in detail.

[0156] like Figure 6 As shown, the electronic device may include a processor 610 and a memory 620 for storing computer program instructions.

[0157] The processor 610 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that may be configured to implement the embodiments of this application.

[0158] Memory 620 may include mass storage for data or instructions. For example, and not limitingly, memory 620 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. In one instance, memory 620 may include removable or non-removable (or fixed) media, or memory 620 may be non-volatile solid-state memory. In one instance, memory 620 may be read-only memory (ROM). In one instance, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0159] The processor 610 reads and executes computer program instructions stored in the memory 620 to achieve... Figure 1 and Figure 2 The method in the illustrated embodiment achieves... Figure 1 and Figure 2 The corresponding technical effects achieved by the methods in the illustrated embodiments are described briefly and will not be elaborated further here.

[0160] In one example, the electronic device may also include a communication interface 630 and a bus 640. Wherein, as... Figure 6 As shown, the processor 610, memory 620, and communication interface 630 are connected via bus 640 and communicate with each other.

[0161] The communication interface 630 is mainly used to realize communication between various modules, devices and / or equipment in the embodiments of this application.

[0162] Bus 640 includes hardware, software, or both, that couples components of an electronic device together. For example, and not as a limitation, bus 640 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 640 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0163] When the electronic device reaches a preset peak performance, it can obtain the internet access needs of each user within a preset time period and then execute the internet access demand management method in this embodiment, thereby achieving a combination of... Figure 1 and Figure 2 The described method for managing internet access needs and Figure 5 The device described is for managing internet access needs.

[0164] Furthermore, in conjunction with the internet access demand management methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the internet access demand management methods in the above embodiments.

[0165] This application provides a computer program product, including a computer program that is executed by at least one processor to implement the various processes of the above-described Internet access demand management method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0166] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0167] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0168] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0169] The aspects of embodiments of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to create a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0170] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for managing internet access needs, characterized in that, include: When the performance of the electronic device reaches a preset peak, the internet access needs of each user within a preset time period are obtained. The internet access needs include at least the access resources, the average duration of the access resources consumed, and the priority of the access resources. Based on the average duration of access resource consumption corresponding to each access request and the priority of the access resource, each access request is queued to obtain a target request queue, so that the electronic device responds to each access request in the order of the access requests in the target request queue. The step of queuing the internet access requests according to the average duration of access resource consumption and the priority of the access resources to obtain a target request queue, so that the electronic device responds to each internet access request in the order of the internet access requests in the target request queue, includes: Based on the number of internet access requests and the dimensions of each internet access request, a population is randomly generated. The initialization parameters of the population include the maximum number of iterations, the initial mutation factor, and the initial crossover factor. Perform a mutation operation on the base individuals in the population to obtain mutated individuals corresponding to the base individuals; Perform a crossover operation on each base individual and the variant individuals corresponding to the base individual to obtain the offspring individuals of the base individual; The evaluation function values ​​of the base individual and the corresponding offspring individual are determined by using an evaluation function, wherein the evaluation function is related to the average duration of the access resource consumption and the priority of the access resource; The next generation population is determined based on the evaluation function values ​​of the base individual and its corresponding offspring. If the number of iterations reaches the maximum number of iterations or the evaluation function value meets the stability condition, the internet access demand ranking result corresponding to the last generation population is determined as the target demand queue.

2. The method according to claim 1, characterized in that, The step of performing a mutation operation on the base individuals in the population to obtain mutated individuals corresponding to the base individuals includes: Two different base individual indices are randomly selected from the base individual indices of the population and denoted as the first base individual index and the second base individual index, respectively. Discrete subtraction is performed on the first base individual corresponding to the first base individual index and the second base individual corresponding to the second base individual index to obtain the difference vector; Based on the difference vector and the target mutation factor, a mutation operation is performed on the third basic individual in the population to obtain a mutated individual corresponding to the third basic individual. The basic individual index of the third basic individual is different from both the first basic individual index and the second basic individual index. The target mutation factor is associated with the current iteration number of the population and the initial mutation factor.

3. The method according to claim 2, characterized in that, If the current iteration number of the population is less than q1 percent of the maximum iteration number, the third basic individual includes the current basic individual in the population; If the current iteration number of the population is greater than or equal to q1 percent of the maximum iteration number and less than q2 percent of the maximum iteration number, the third base individual includes the base individual after the current base individual in the population is corrected to be biased towards the best base individual. If the current iteration number of the population is greater than or equal to q2 percent of the maximum iteration number and less than q3 percent of the maximum iteration number, the third basic individual includes a base individual randomly selected from the population. If the current iteration number of the population is greater than or equal to q3 percent of the maximum iteration number, but less than the maximum iteration number, the third basic individual includes the best basic individual in the population; The optimal base individual is the base individual with the smallest evaluation function value in the population corresponding to the current iteration number.

4. The method according to claim 2 or 3, characterized in that, Before performing a mutation operation on the third primitive individual in the population based on the difference vector and the target mutation factor to obtain the mutated individual corresponding to the third primitive individual, the method further includes: Determine a first ratio between the current iteration number and the maximum iteration number; The target mutation factor is determined based on the initial mutation factor and the first ratio.

5. The method according to claim 1, characterized in that, The step of performing a crossover operation on each base individual and the corresponding variant individuals to obtain the offspring individuals of the base individuals includes: For the base individual, a value in the range (0,1) is randomly generated; The base individual and the variant individuals corresponding to the base individual are recombined to obtain the first generation of offspring individuals; If the value is less than the target crossover factor, the first offspring individual is determined as an offspring of the base individual; If the value is greater than or equal to the target crossover factor, the first offspring individual and the base individual are recombinated to obtain the offspring individuals of the base individual; The target crossover factor is associated with the current iteration number of the population and the initial crossover factor.

6. The method according to claim 5, characterized in that, Before recombining the base individual and the variant individuals corresponding to the base individual to obtain the first offspring individual, the method further includes: Determine a second ratio between the current iteration number and the maximum iteration number; The target cross factor is determined based on the initial cross factor and the second ratio.

7. An internet access demand management device, characterized in that, include: The acquisition module is used to acquire the internet access needs of each user within a preset time period when the performance of the electronic device reaches a preset peak. The internet access needs include at least the access resources, the average duration of the access resources consumed, and the priority of the access resources. The queuing module is used to queue the internet access requests according to the average duration of access resource consumption corresponding to each internet access request and the priority of the access resources, so as to obtain a target request queue, so that the electronic device responds to each internet access request in the order of the internet access requests in the target request queue. The queuing module includes: A random unit is used to randomly generate a population based on the number of internet access requests and the dimensions of each internet access request. The initialization parameters of the population include the maximum number of iterations, the initial mutation factor, and the initial crossover factor. A mutation unit is used to perform mutation operations on the base individuals in the population to obtain mutated individuals corresponding to the base individuals; A crossover unit is used to perform a crossover operation on each of the base individuals and the variant individuals corresponding to the base individuals to obtain the offspring individuals of the base individuals; The first determining unit is used to determine the evaluation function values ​​of the base individual and the offspring individual corresponding to the base individual using an evaluation function, wherein the evaluation function is associated with the average duration of the access resource consumption and the priority of the access resource; The second determining unit is used to determine the next generation population based on the evaluation function values ​​of the base individual and the offspring individuals corresponding to the base individual; The third determining unit is used to determine the internet access demand ranking result corresponding to the last generation population as the target demand queue when the number of iterations reaches the maximum number of iterations or the evaluation function value meets the stability condition.

8. An electronic device, characterized in that, include: processor; Memory is used to store computer program instructions; When the computer program instructions are executed by the processor, the method as described in any one of claims 1-6 is implemented.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, the method as described in any one of claims 1-6 is implemented.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method as described in any one of claims 1-6.

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