Virtual resource allocation method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a virtual resource allocation method and device, equipment and a storage medium. The method comprises the following steps: obtaining a target dual variable, which is corresponding to a current access user and enables an optimization problem to correspond to a dual function to obtain a first target value, based on target attribute information of the current access user, the optimization problem being used for deciding a virtual resource allocated to the user; the target dual variable is obtained through prediction based on historical time sequence data in a first historical time period, the historical time sequence data comprises a historical dual variable sequence, and the historical dual variable sequence comprises a plurality of historical dual variables which are arranged according to time sequence in the first historical time period and are related to virtual resource allocation in the second historical time period; and allocating the target virtual resource to the current access user based on the target dual variable and the target related parameter of the current access user under the at least one virtual resource, accurately grasping the virtual resource allocation trend of the user through time sequence prediction, and improving the virtual resource allocation accuracy.

Description

Technical Field

[0001] This application relates to the field of data processing technologies, and in particular, to a method, apparatus, device, and storage medium for virtual resource allocation. Background Art

[0002] In the field of marketing, optimizing resource allocation to maximize benefits has always been the focus of enterprises. The traditional online operation research solution mainly uses a linear programming model to achieve online allocation of rights and interests by using the dual solution asynchronously. However, in the actual application process, a series of problems have gradually emerged. Especially in the face of a complex and changing market environment and user needs, the linear programming model is unable to handle multiple constraint conditions, resulting in a complex and inefficient solution process. Summary of the Invention

[0003] Embodiments of this application provide a method, apparatus, device, and storage medium for virtual resource allocation. By means of time series prediction, the target dual variable required for future virtual resource allocation is predicted in advance according to the historical time series data in the first historical time period. The above historical time series data includes a historical dual variable sequence, that is, the historical time series data records the situation of virtual resource allocation and the corresponding dual variable values in the past period of time (the first historical time period), accurately grasps user needs and virtual resource allocation trends, provides strong support for the current resource allocation, helps improve the predictability and flexibility of virtual resource allocation, and avoids the problem of deviation of the solution of the problem caused by the method of using the dual solution asynchronously by assuming that the adjacent sample distributions are the same in the related art, resulting in a loss of efficiency, and improves the accuracy rate and maximization effect of the operation research solution in the virtual resource allocation process. The above technical solutions are as follows: In a first aspect, embodiments of this application provide a method for virtual resource allocation, including: Obtaining the target dual variable corresponding to the current access user based on the target attribute information of the current access user; the target dual variable is the independent variable that makes the dual function corresponding to the optimization problem obtain the first target value; the optimization problem is used to decide the virtual resources allocated to the user; the target dual variable is predicted based on the historical time series data in the first historical time period; the first historical time period includes a plurality of different second historical time periods, the historical time series data includes a historical dual variable sequence, and the historical dual variable sequence includes a plurality of historical dual variables related to virtual resource allocation in the second historical time period arranged in chronological order; Allocating target virtual resources to the current access user based on the target dual variable and the target related parameters of the current access user under at least one virtual resource.

[0004] In a possible implementation manner, the method further includes: Obtain historical time-series data within the first historical time period; Predict a future dual variable sequence based on the above historical time-series data; the above future dual variable sequence includes predicted dual variables that make the dual function corresponding to the corresponding optimization problem achieve a first target value within multiple different future time periods; the time interval corresponding to the above future time period is equal to the time interval corresponding to the above second historical time period; Store the above future dual variable sequence in a database.

[0005] In a possible implementation manner, the above historical time-series data further includes at least one of the following data: a historical virtual resource cost sequence, a historical time feature sequence, and a historical traffic sequence within the above first historical time period; the obtaining of the historical time-series data within the first historical time period includes: obtaining historical data within the first historical time period; the historical data includes a plurality of historical dual variables related to virtual resource allocation within the above second historical time period and other related historical data; the other related historical data includes at least one of the following: the historical virtual resource cost, historical time information, and historical traffic information corresponding to each of the above second historical time periods; preprocess the above historical data to obtain historical time-series data.

[0006] In a possible implementation manner, before predicting the future dual variable sequence based on the above historical time-series data, the method further includes: obtaining a target time-series prediction request; the target time-series prediction request carries time-series prediction constraint conditions, and the time-series prediction constraint conditions include that the total virtual resource cost allocated to users within multiple different future time periods does not exceed a preset total cost; the predicting of the future dual variable sequence based on the above historical time-series data includes: in response to the target time-series prediction request, predicting the future dual variable sequence based on the time-series prediction constraint conditions and the above historical time-series data.

[0007] In a possible implementation manner, the time-series prediction constraint conditions include that the total virtual resource cost allocated to users under a target constraint dimension within multiple different future time periods does not exceed a preset total cost; the target constraint dimension includes at least one of the following: a target user group and a target user access scenario; The predicting of the future dual variable sequence based on the above historical time-series data includes: In response to the target time-series prediction request, predicting the future dual variable sequence under the target constraint dimension based on the time-series prediction constraint conditions and the above historical time-series data under the target constraint dimension.

[0008] In a possible implementation, predicting the future dual variable sequence based on the above historical time series data includes: inputting the above historical time series data into a time series prediction model to output a future dual variable sequence; the time series prediction model is trained based on sample time series data within a second time period of the dual variable sequences within multiple known first time periods; the time interval corresponding to the first time period is equal to the time interval corresponding to the second time period, and the first time period is a time period after the second time period.

[0009] In a possible implementation, obtaining the historical time series data within the first historical time period includes: regularly obtaining, at a preset time interval, the historical time series data of historical access users belonging to the same constraint dimension within the first historical time period; the constraint dimension includes user groups and / or user access scenarios; the historical time series data is updated as the first historical time period is updated; predicting the future dual variable sequence based on the above historical time series data includes: predicting the future dual variable sequence corresponding to the historical access users under the historical constraint dimension based on the historical time series data of the historical access users belonging to the same constraint dimension; storing the above future dual variable sequence in the database includes: associatively storing the above future dual variable sequence and the historical constraint dimension identifier corresponding to the above historical access users in the database.

[0010] In a possible implementation, obtaining the target dual variable corresponding to the current access user based on the target attribute information of the current access user includes: determining the target group to which the current access user belongs based on the target attribute information of the current access user; querying in the database based on the target group identifier corresponding to the target group and / or the target access scenario identifier corresponding to the current access user to obtain the target future dual variable sequence corresponding to the current access user; determining the target dual variable corresponding to the current access user based on the current access time of the current access user and the target future dual variable sequence.

[0011] In a possible implementation, allocating target virtual resources for the current access user based on the above target dual variable and the target related parameters of the current access user under at least one virtual resource includes: searching for a second target value on the decision optimization function corresponding to the dual function based on the above target dual variable, the target related parameters of the current access user under at least one virtual resource, the target virtual resource cost, and at least one virtual resource type to obtain the target virtual resource type; the target virtual resource type is the virtual resource type corresponding to the decision optimization function taking the second target value among the at least one virtual resource types when the current access user and the target dual variable are known; allocating the corresponding target virtual resources for the current access user according to the target virtual resource type.

[0012] In a possible implementation, before allocating target virtual resources to the current accessing user based on the above-mentioned target dual variable and the target-related parameters of the current accessing user under at least one virtual resource, the method further includes: Using machine learning techniques, calculating the target-related parameters of the current accessing user under at least one virtual resource based on the target user characteristics of the current accessing user; the target user characteristics include at least one of the following: target attribute information, target transaction frequency, historical virtual resource allocation and related parameter information.

[0013] In a possible implementation, the method further includes: constructing the optimization problem based on the relevant parameters of the user under at least one virtual resource and the decision variable indicating whether to allocate a virtual resource to the user; the constraint conditions of the optimization problem include that each user is allocated only one virtual resource, and the total cost of the virtual resources allocated to at least one user does not exceed a preset total cost; based on the constraint conditions of the optimization problem, transforming the optimization problem into a dual problem to obtain the above-mentioned dual function.

[0014] In a second aspect, an embodiment of the present application provides a virtual resource allocation method, and the method includes: Obtaining the target virtual resources allocated to the current accessing user by the virtual resource allocation method provided in the first aspect or any possible implementation manner of the first aspect; displaying the target virtual resources.

[0015] In a third aspect, an embodiment of the present application provides a virtual resource allocation device, and the virtual resource allocation device includes: An obtaining module, configured to obtain the target dual variable corresponding to the current accessing user based on the target attribute information of the current accessing user; the target dual variable is the independent variable that makes the dual function corresponding to the optimization problem obtain a first target value; the optimization problem is used to decide the virtual resources allocated to the user; the target dual variable is predicted based on the historical time-series data in the first historical period; the first historical period includes a plurality of different second historical periods, the historical time-series data includes a historical dual variable sequence, and the historical dual variable sequence includes a plurality of historical dual variables related to virtual resource allocation in the second historical period arranged in chronological order; A virtual resource allocation module, configured to allocate target virtual resources to the current accessing user based on the target dual variable and the target-related parameters of the current accessing user under at least one virtual resource.

[0016] Fourthly, an embodiment of the present application provides an electronic device, including: a processor and a memory; the processor is connected to the memory; the memory is used for storing executable program codes; the processor runs a program corresponding to the executable program codes by reading the executable program codes stored in the memory, so as to execute the method provided in the first aspect or any possible implementation manner of the first aspect or the second aspect of the embodiments of the present application.

[0017] Fifthly, an embodiment of the present application provides a computer storage medium, which stores multiple instructions, and the instructions are suitable for being loaded and executed by a processor to execute the method steps provided in the first aspect or any possible implementation manner of the first aspect or the second aspect of the embodiments of the present application.

[0018] In one or more embodiments of the present application, in the first aspect, the target dual variable corresponding to the current access user is obtained based on the target attribute information of the current access user. By deeply mining the user attribute information and accurately positioning the user characteristics, a scientific basis is provided for subsequent virtual resource allocation, which helps to improve the pertinence and effectiveness of virtual resource allocation. In the second aspect, by introducing the dual variable and the optimization problem, not only is the virtual resource allocated to the user determined through the optimization problem decision, that is, the virtual resource allocation is optimized, the maximization of user-related parameters is achieved, the user experience and satisfaction are improved, the user stickiness is enhanced, and a foundation is laid for long-term development, but also the complex virtual resource allocation problem is transformed into a solvable mathematical function (dual function). By accurately solving and predicting the target dual variable that enables the dual function to obtain the first target value, it is convenient to achieve automated and intelligent decision-making, avoid the limitations of manual decision-making, and significantly improve the efficiency and accuracy of resource allocation. In the third aspect, through the method of time series prediction, the target dual variable required for future virtual resource allocation is predicted in advance based on the historical time series data in the first historical time period. The first historical time period includes multiple different second historical time periods, and the historical time series data includes a historical dual variable sequence, that is, the historical data records the situation of virtual resource allocation and the corresponding dual variable values in the past period (the first historical time period). By analyzing and mining these data, the relationship and law between virtual resource allocation and the dual variable can be discovered, the user needs and resource allocation trends can be accurately grasped, and a useful reference can be provided for future virtual resource allocation, that is, strong support can be provided for the current resource allocation, which helps to improve the predictability and flexibility of virtual resource allocation, avoid the problem of solution deviation caused by the method of assuming the same distribution of adjacent samples and asynchronously using the dual solution in related technologies, resulting in efficiency loss, and improve the accuracy rate and maximization effect of the operation research solution in the virtual resource allocation process. In the fourth aspect, accurate virtual resource allocation can be achieved based on the target dual variable corresponding to the current access user and its target-related parameters, forming a "data - decision - feedback" closed loop. For example, if the target-related parameters corresponding to the current access user are low, its virtual resource allocation strategy can be dynamically adjusted (such as increasing the coupon denomination or awarding points for high-frequency usage scenarios, etc.) to stimulate the current access user to generate transaction behavior and improve the relevant parameters. At the same time, the essence of solving the dual problem is to optimize virtual resource allocation from a global perspective. Therefore, by combining the user individual-related parameters (target-related parameters) of the current access user under at least one virtual resource and the target dual variable corresponding to the current access user obtained by solving the dual function, virtual resource allocation is performed on the current access user, taking into account both group efficiency and individual needs, which can avoid the over-concentration or waste of virtual resources, balance the global optimization and personalization corresponding to virtual resource allocation, and improve the utilization rate of virtual resources and the overall operation efficiency. Description of the Drawings

[0019] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 It is a schematic structural diagram of a virtual resource allocation system provided by an exemplary embodiment of the present application; Figure 2 It is a schematic flowchart of a virtual resource allocation method provided by an exemplary embodiment of the present application; Figure 3 It is a schematic flowchart of constructing a dual function provided by an exemplary embodiment of the present application; Figure 4 It is a schematic flowchart of implementing operational research solution in a virtual resource allocation scenario provided by an exemplary embodiment of the present application; Figure 5 It is a schematic flowchart of implementing operational research solution in another virtual resource allocation scenario provided by an exemplary embodiment of the present application; Figure 6 It is a schematic structural diagram of a virtual resource allocation device provided by an exemplary embodiment of the present application; Figure 7 It is a schematic structural diagram of an electronic device provided by an exemplary embodiment of the present application. Detailed implementation manners

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application.

[0022] The terms "first", "second", "third", etc. in this specification, the claims and the above drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0023] Next, please refer to Figure 1 , which is a schematic structural diagram of a virtual resource allocation system provided by an exemplary embodiment of the present application. As Figure 1 shown, the virtual resource allocation system may include: a terminal 110 and a server 120. Among them: The terminal 110 includes user terminals corresponding to one or more users. A corresponding user version of the application can be installed on the terminal 110. In this application, the user can register and log in to the corresponding account. Based on the above user version of the application, the user can perform online transaction access, receive, and write off virtual resources through the corresponding terminal 110, and send the corresponding target access operations, target attribute information, etc. to the server 120.

[0024] Understandably, the above terminal 110 can be a mobile phone, a tablet computer, a desktop computer, a laptop computer, a notebook computer, an Ultra-mobile Personal Computer (UMPC), a handheld computer, a netbook, a Personal Digital Assistant (PDA), a wearable electronic device, etc. The embodiments of the present application do not make any limitations in this regard.

[0025] The server 120 can be the server corresponding to the user version of the application used by the user in the terminal 110 for online transactions, and is used to perform corresponding resource allocation and other services after the user logs in and accesses the user version of the application. The server 120 can be a hardware server, a virtual server, a cloud server, etc. The embodiments of the present application do not make any limitations in this regard.

[0026] Specifically, the server 120 can first obtain the target dual variable corresponding to the current access user based on the target attribute information of the current access user. The above target dual variable is the independent variable that makes the dual function corresponding to the optimization problem obtain the first target value. The above optimization problem is used to decide the virtual resources allocated to the user. The above target dual variable is predicted based on the historical time series data within the first historical time period. The above first historical time period includes a plurality of different second historical time periods. The above historical time series data includes a historical dual variable sequence. The above historical dual variable sequence includes a plurality of historical dual variables related to the virtual resource allocation within the above second historical time period arranged in chronological order. Then, based on the above target dual variable and the target related parameters of the current access user under at least one virtual resource, the server 120 allocates target virtual resources to the current access user.

[0027] The network can be a medium that provides a communication link between the terminal 110 and the server 120, or can also be the Internet including network devices and transmission media, which is not limited thereto. The transmission medium can be a wired link, such as but not limited to coaxial cable, optical fiber, and Digital Subscriber Line (DSL), etc., or a wireless link, such as but not limited to Wireless Fidelity (WIFI), Bluetooth, and mobile device network, etc.

[0028] Understandably,Figure 1 The numbers of the terminals 110 and the server 120 in the virtual resource allocation system shown are only for illustration. In a specific implementation, the virtual resource allocation system may include any number of terminals 110 and servers 120, and the embodiments of the present application do not make specific limitations thereto. For example but not limited to, the terminal 110 may be a user terminal cluster composed of multiple user terminals, and the server 120 may be a server cluster composed of multiple servers.

[0029] Next, in combination with Figure 1 , an embodiment of the present application provides a virtual resource allocation method. Specifically, please refer to Figure 2 , which is a schematic flowchart of a virtual resource allocation method provided by an exemplary embodiment of the present application. As Figure 2 shown, the virtual resource allocation method includes the following steps: S201, obtaining a target dual variable corresponding to the current access user based on the target attribute information of the current access user, where the target dual variable is an independent variable that makes the dual function corresponding to the optimization problem obtain a first target value, the optimization problem is used to determine the virtual resources allocated to the user, and the target dual variable is predicted based on the historical time series data within the first historical time period.

[0030] Specifically, the above-mentioned current access user may be, but not limited to, a user currently accessing the target application. The above-mentioned target application may be, but not limited to, an application for online transactions such as shopping and financial management. The above-mentioned target application may be a separate online transaction software installed on the terminal, or a small program integrated in other software, etc., and the embodiments of this specification do not make limitations thereto. The above-mentioned target attribute information includes data characteristics related to the current access user, such as, but not limited to, basic information such as the occupation and native place of the current access user, historical transaction records, etc. These information are used to distinguish the group category to which the current access user belongs. The above-mentioned group category may be divided according to basic attributes such as the age group, occupation, or location of the user, or may be divided according to the transaction preferences or transaction habits of the user. For example, a green food transaction group (i.e., users who like to purchase green food online), etc., can be specifically set according to actual needs, and the embodiments of this specification do not make limitations thereto.

[0031] The purpose of the above optimization problem is to decide how to allocate virtual resources to users currently accessing the target application, so as to make all user-related parameters meet preset conditions, such as but not limited to maximizing or minimizing the sum of all user-related parameters or making all user-related parameters consistent, etc. The above-mentioned related parameters can include but are not limited to the write-off rate, access rate, transaction volume, etc. related to virtual resources, and can be specifically set according to actual needs. The above write-off rate can be the ratio of the use or conversion of the allocated virtual resources by users, that is, the virtual resource utilization rate or conversion rate, or can also be the improvement ratio of the user conversion rate (such as but not limited to the order placement probability) in the case of allocating virtual resources, etc. The embodiments of this specification do not make limitations in this regard. The larger the sum of the user write-off rates, the higher the total transaction rate of users in the target application, that is, the larger the sum of the corresponding benefits generated by users in the case of allocating virtual resources. The above virtual resources can include but are not limited to subsidies, coupons or virtual points, etc. Correspondingly, the virtual resource quantity can include but is not limited to the subsidy amount, the denomination of the coupon or the number of virtual points, etc. This specification does not make limitations in this regard.

[0032] The above dual function is another function corresponding to the function of the original optimization problem. It can understand the original optimization problem from different perspectives, and solving the dual function is often more efficient than directly solving the original optimization problem. The dual variable is a variable corresponding to the variable of the original optimization problem and is used to form the dual problem. Solving the dual variable corresponding to the dual function in the dual problem can find the objective solution of the original optimization problem.

[0033] Optionally, the target dual variable corresponding to the current access user may be the independent variable value that enables the dual function corresponding to the optimization problem in the target group and / or target access scenario corresponding to the current access user to obtain a first target value (such as but not limited to the minimum value, maximum value, preset value, etc.). The optimization problem corresponding to the target group of the current access user is used to decide the virtual resources allocated to each user in the target group to enable the relevant parameters of all users corresponding to the target group to meet preset conditions, such as but not limited to maximizing or minimizing the sum of relevant parameters of all users corresponding to the target group or making the relevant parameters of all users consistent, etc.; the optimization problem corresponding to the target access scenario of the current access user is used to decide the virtual resources allocated to each user in the target access scenario to enable the relevant parameters of all users in the target access scenario to meet preset conditions, such as but not limited to maximizing or minimizing the sum of relevant parameters of all users in the target access scenario or making the relevant parameters of all users consistent, etc.; the optimization problem corresponding to the target group and target access scenario of the current access user is used to decide the virtual resources allocated to each user in the target group in the target access scenario to enable the relevant parameters of all users corresponding to the target group in the target access scenario to meet preset conditions, such as but not limited to maximizing or minimizing the sum of relevant parameters of all users corresponding to the target group in the target access scenario or making the relevant parameters of all users consistent, etc. The target group may be, but is not limited to, obtained by grouping based on target attribute information such as the basic user information of the current access user (such as but not limited to region, consumption preference, etc.) or trading habits (such as high consumption, low consumption, etc.), and may include, for example but not limited to, young user groups, elderly user groups, user groups in area A, user groups in area B, high-consumption groups, etc.

[0034] The above first historical time period includes a plurality of different second historical time periods, and the plurality of second historical time periods do not overlap and are continuous. The first historical time period is the time period before the current access time. Different current time periods to which the current access time belongs correspond to different time ranges of the historical time series data used for predicting the target dual variable, that is, the first historical time period is different. For example but not limited to, when the current time period is the T time period, the above first historical time period should be a time period before the T time period, that is, the (T - 1) time period. When the current time period is the (T + 1) time period, the above first historical time period should be a time period before the (T + 1) time period, that is, the T time period. The time lengths of the current time period and the first historical time period corresponding to each other are equal. The current time period includes a plurality of current time sub-periods with equal and continuous non-overlapping time lengths (i.e., time intervals), and the time interval corresponding to the current time sub-period is equal to the time interval corresponding to the second historical time period.

[0035] Optionally, the target dual variable corresponding to the current access user may, but is not limited to, first predicting a future dual variable sequence corresponding to a future time period (i.e., the current time period) based on historical time series data within a first historical time period, and then querying from the future dual variable sequence based on the current time sub-period (i.e., the future time sub-period) to which the current access time belongs. That is, the target dual variable may be the future dual variable corresponding to the current time sub-period (i.e., the future time sub-period) in the future dual variable sequence.

[0036] Optionally, the target dual variable corresponding to the current access user may also, but is not limited to, first predicting a future dual variable sequence corresponding to a future time period (i.e., the current time period) based on historical time series data within a first historical time period, and then performing a weighted sum on multiple future dual variables in the future dual variable sequence based on the current time sub-period (i.e., the future time sub-period) to which the current access time belongs.

[0037] Optionally, the target dual variable corresponding to the current access user may also, but is not limited to, first predicting a future dual variable sequence corresponding to a future time period (i.e., the current time period) based on historical time series data within a first historical time period, and then searching for a first objective value (such as, but not limited to, a minimum value or a maximum value or a preset value, etc.) on the dual function corresponding to the optimization problem based on multiple future dual variables in the future dual variable sequence. Alternatively, searching for the first objective value on the dual function corresponding to the optimization problem based on multiple future dual variables in the future dual variable sequence, the target-related parameters of the current access user under at least one virtual resource, the target virtual resource cost, and at least one virtual resource type. That is, the above target dual variable is the future dual variable in the future dual variable sequence that makes the dual function corresponding to the optimization problem in the current time period obtain the first objective value. The target-related parameters may, but are not limited to, include the target write-off rate or target access rate or target transaction volume, etc., of the current access user under at least one virtual resource.

[0038] Optionally, the target dual variable corresponding to the current access user may also, but is not limited to, directly predicting the independent variable that makes the dual function corresponding to the optimization problem obtain the first objective value in the future time period (i.e., the current time period) based on historical time series data within a first historical time period.

[0039] The above historical time series data includes a historical dual variable sequence, and the historical dual variable sequence includes multiple historical dual variables related to virtual resource allocation within a second historical time period arranged in chronological order.

[0040] Optionally, the above historical time-series data may also include, but is not limited to, at least one of the following data: the historical virtual resource cost sequence, the historical time feature sequence, and the historical traffic sequence within the above first historical time period. The above historical virtual resource cost sequence may include, but is not limited to, the historical virtual resource costs that can be allocated to at least one historical access user in multiple second historical time periods arranged in chronological order (i.e., the total virtual resource cost that can be divided among all historical access users within the second historical time period). The above historical time feature sequence refers to a sequence composed of a series of time feature values extracted from each second historical time period within the first historical time period. These time features are usually used to predict future trends or behaviors, such as, but not limited to, timestamps, periodic features (such as hours, days, weeks, months, etc.), and special time attribute features (such as weekends, holidays, Double Eleven, etc.). The above historical traffic sequence can reflect the consumption speed of historical virtual resource costs in each second historical time period, such as, but not limited to, the historical access traffic corresponding to each of the multiple second historical time periods.

[0041] Optionally, the above historical time-series data may also include, but is not limited to, a historical dual intermediate function sequence, which consists of historical dual intermediate functions arranged in chronological order within multiple second historical time periods. The above historical dual intermediate function may include, but is not limited to, historical dual variables, historical relevant parameters of historical access users under at least one virtual resource, and virtual resource costs within the corresponding second historical time period. For example, but not limited to, it may be , where refers to the dual intermediate function within the t time period, refers to the relevant parameter corresponding to access user i when allocating virtual resource j, refers to the dual variable corresponding to access user i within the t time period, refers to the virtual resource cost corresponding to access user i when allocating virtual resource j, where j is greater than or equal to 1 and less than or equal to the total number of preset virtual resource types, and C refers to the total number of users.

[0042] Optionally, the above historical dual variable may be the independent variable that makes the dual function corresponding to the optimization problem within the corresponding second historical time period achieve the first target value, or the dual variable actually used during virtual resource allocation within the second historical time period, or the historical target dual variable that makes the dual function corresponding to the optimization problem achieve the first target value within the first historical time period discretized according to a preset time step (such as, but not limited to, 0.005, 0.006, etc.), and the above historical target dual variable is within the above historical dual variable sequence, etc. This embodiment of the present specification does not make any limitations in this regard.

[0043] Optionally, the target dual variable corresponding to the current access user described above may also, but is not limited to, first predict the future dual intermediate function sequence within the future time period (i.e., the current time period) based on the historical dual intermediate function sequence within the first historical time period, and discretize the historical target dual variables corresponding to the historical dual functions that achieve the first target value within the first historical time period according to a preset time step (such as, but not limited to, 0.005, 0.006, etc.) to obtain multiple historical discrete dual variables. Then, determine the target dual intermediate function corresponding to the future time sub-segment to which the current access time belongs in the future dual intermediate function sequence, and based on this target dual intermediate function and the total target virtual resource cost corresponding to the future time sub-segment to which the current access time belongs, determine the target dual function corresponding to the optimization problem within the future time sub-segment to which the current access time belongs. Finally, search for the first target value on the above target dual function based on the multiple historical discrete dual variables to obtain it.

[0044] The above at least one type of virtual resource may refer to the virtual resources corresponding to at least one type of virtual resource quantity under the same type of virtual resource, such as, but not limited to, coupons with a discount of 5 for orders over 30, coupons with a discount of 6 for orders over 30, etc., or may refer to the virtual resources corresponding to a certain type of virtual resource quantity under at least one type of virtual resource, such as, but not limited to, a subsidy of 3, coupons with a discount of 7 for orders over 30, coupons with a discount of 3 for orders over 30, etc. The relevant parameters of the historical access user under at least one type of virtual resource refer to the various virtual resource utilization rates or conversion rates corresponding to the historical access user under the condition of allocating various virtual resources. The virtual resource cost of the historical access user under at least one type of virtual resource refers to the cost required to allocate a certain type of virtual resource to the historical access user. The total historical virtual resource cost corresponding to the above second historical time period refers to the total cost budget value preset for allocating virtual resources to all historical access users within the second historical time period.

[0045] Next, please continue to refer to Figure 2 , as Figure 2 shown, after obtaining the target dual variable corresponding to the current access user based on the target attribute information of the current access user in S201, the virtual resource allocation method may also, but is not limited to, include: S202, allocate target virtual resources to the current access user based on the target dual variable and the target relevant parameters of the current access user under at least one type of virtual resource.

[0046] In some possible embodiments, before the above S202 allocates target virtual resources for the current access user based on the target dual variable and the target related parameters of the current access user under at least one virtual resource, the virtual resource allocation method may further include, but is not limited to: using machine learning technology to calculate the target related parameters of the current access user under at least one virtual resource based on the target user characteristics of the current access user, where the target user characteristics include at least one of the following: target attribute information, target transaction frequency, historical virtual resource allocation and related parameter information.

[0047] The above target related parameters may include, but are not limited to, the target write-off rate, target access rate, target transaction volume, etc. of the current access user under at least one virtual resource. The above target write-off rate can be used to characterize the possibility of the current access user using a certain virtual resource after it is allocated to the current access user, or the probability that the current access user actually uses the virtual resource, that is, the possibility that the current access user really uses a certain virtual resource after obtaining it before the current access time. The embodiments of this specification do not make any limitations in this regard. The above target attribute information refers to some basic information of the current access user, such as, but not limited to, age, occupation, etc. The above target transaction frequency refers to the frequency of transactions carried out by the current access user within a certain period of time, which can reflect the activity of the current access user and the degree of demand for a certain resource. The above historical virtual resource allocation and related parameter information refers to the relevant records of the current access user's past acquisition and use of virtual resources, and these data help to predict the possibility of the current access user using virtual resources in the future.

[0048] Optionally, after obtaining the target dual variable corresponding to the current access user, the target virtual resource corresponding to the target dual variable can be directly determined from at least one virtual resource that can be allocated to users preset according to the mapping relationship between the preset dual variable and the type of virtual resource, and the target virtual resource can be issued to the current access account that is accessing the target application on the terminal corresponding to the current access user, so as to timely motivate the current access user to use the target virtual resource to conduct online transactions in the target application.

[0049] Optionally, in order to avoid the problem that the same group of users are allocated the same virtual resources, resulting in inaccurate virtual resource allocation, after obtaining the target dual variable corresponding to the current access user, the target dual variable and the target related parameters of the current access user under at least one virtual resource can also be directly input into a pre-trained virtual resource allocation model, and the target virtual resource that should be allocated to the current access user is output, so that the virtual resource allocation model can perform more accurate and targeted virtual resource allocation at the individual level of a single user at the group level through the target related parameters of the current access user itself under at least one virtual resource.

[0050] Optionally, the implementation process of allocating target virtual resources to the currently accessing user based on the target dual variable and the target-related parameters of the currently accessing user under at least one virtual resource in S202 above may include, but is not limited to: first, searching for the second target value on the corresponding decision optimization function of the dual function based on the target dual variable, the target-related parameters of the currently accessing user under at least one virtual resource, the target virtual resource cost, and at least one type of virtual resource to obtain the type of target virtual resource; then, allocating the corresponding target virtual resource to the currently accessing user according to the type of target virtual resource. The type of target virtual resource is the type of virtual resource corresponding to the decision optimization function that takes the second target value among at least one type of virtual resource when the currently accessing user and the target dual variable are known. The second target value on the corresponding decision optimization function of the dual function corresponds to the first target value on the dual function, that is, the value corresponding to the decision optimization function when the dual function obtains the first target value. The second target value may include, but is not limited to, the maximum value or the minimum value or a preset target value of the decision optimization function, etc.

[0051] Exemplarily, when the first target value is the minimum value on the dual function, the second target value is the maximum value on the corresponding decision optimization function of the dual function, and the type of target virtual resource , where refers to the target-related parameters corresponding to the currently accessing user i when allocating virtual resource j. refers to the target dual variable corresponding to the currently accessing user. refers to the target virtual resource cost corresponding to the currently accessing user i when allocating virtual resource j, where j is greater than or equal to 1 and less than or equal to the total value of the preset virtual resource types.

[0052] In the embodiments of this specification, in the first aspect, based on the target attribute information of the current access user, the target dual variable corresponding to the current access user is obtained. By deeply mining the user attribute information and accurately positioning the user characteristics, a scientific basis is provided for subsequent virtual resource allocation, which helps to improve the pertinence and effectiveness of virtual resource allocation. In the second aspect, by introducing the dual variable and the optimization problem, not only the virtual resources allocated to the user are determined through the optimization problem decision, that is, the virtual resource allocation is optimized, the maximization of user-related parameters is achieved, the user experience and satisfaction are improved, the user stickiness is enhanced, and a foundation is laid for long-term development, but also the complex virtual resource allocation problem is transformed into a solvable mathematical function (dual function). By accurately solving and predicting the target dual variable that makes the dual function obtain the first target value, it is convenient to realize automated and intelligent decision-making, avoid the limitations of manual decision-making, and significantly improve the efficiency and accuracy of resource allocation. In the third aspect, through the method of time series prediction, the target dual variable required for future virtual resource allocation is predicted in advance according to the historical time series data in the first historical time period. The first historical time period includes multiple different second historical time periods, and the historical time series data includes a historical dual variable sequence, that is, the historical time series data records the situation of virtual resource allocation and the corresponding dual variable values in the past period (the first historical time period). By analyzing and mining these data, the relationship and law between virtual resource allocation and the dual variable can be found, the user needs and the virtual resource allocation trend can be accurately grasped, and a useful reference can be provided for future virtual resource allocation, that is, strong support can be provided for the current resource allocation, which helps to improve the predictability and flexibility of virtual resource allocation, avoid the problem of solution deviation caused by the method of assuming the same distribution of adjacent samples and using the dual solution asynchronously in the related technology, resulting in efficiency loss, and improve the accuracy rate and maximization effect of the operation research solution in the virtual resource allocation process. In the fourth aspect, accurate virtual resource allocation can be realized based on the target dual variable corresponding to the current access user and its target-related parameters, forming a "data - decision - feedback" closed loop. For example, if the target-related parameter corresponding to the current access user is low, its virtual resource allocation strategy can be dynamically adjusted (such as increasing the coupon denomination or issuing points for high-frequency usage scenarios, etc.) to stimulate the current access user to generate transaction behavior and improve the relevant parameters. At the same time, the essence of solving the dual problem is to optimize the virtual resource allocation from a global perspective so that the overall user-related parameters meet the preset conditions, such as but not limited to maximizing the sum of the overall user-related parameters. Therefore, combining the user individual-related parameters (target-related parameters) of the current access user under at least one virtual resource and the target dual variable corresponding to the current access user obtained by solving the dual function to perform virtual resource allocation for the current access user takes into account both the group efficiency and individual needs, can avoid the over-concentration or waste of virtual resources, and makes the global optimization and personalization corresponding to the virtual resource allocation reach a balance, improving the virtual resource utilization rate and the overall operation benefit.

[0053] Next, please refer to Figure 3 , which is a schematic diagram of the construction process of a dual function provided by an exemplary embodiment of the present application. As Figure 3 shown, the construction process of the dual function may but is not limited to include the following steps: S301. Construct an optimization problem based on relevant parameters of the user under at least one virtual resource and a decision variable indicating whether to allocate a virtual resource to the user.

[0054] Specifically, the constraint conditions of the above optimization problem include that each user is allocated only one virtual resource, and the total cost of the virtual resources allocated to at least one user does not exceed a preset total cost. The above optimization problem refers to the process of finding a virtual resource allocation strategy that can make the user's relevant parameters meet the preset conditions (such as but not limited to the sum of the user's relevant parameters reaching the maximum or minimum, etc.) under the above constraint conditions. The above decision variable is ultimately used to determine which virtual resource among at least one virtual resource to allocate to the user. The above preset total cost refers to the upper limit of the total cost of virtual resource allocation preset before the start of the optimization problem.

[0055] Exemplarily, if it is desired to maximize the sum of the user's relevant parameters, then the above first target value is the minimum value on the dual function, the above second target value is the maximum value on the decision optimization function corresponding to the dual function, and the above optimization problem can be characterized as: , and its constraint conditions include: (i = 1, 2,..., C, j = 1, 2,..., R, C refers to the total number of users, and R refers to the total number of virtual resources), that is, the decision variable takes a value of 0 or 1, and when the decision variable takes a value of 0, it indicates that virtual resource j is not allocated to user i, and when the decision variable takes a value of 1, it indicates that virtual resource j is allocated to user i; (i = 1, 2,..., C), that is, each user i is allocated only one virtual resource j; , that is, the total cost of the virtual resources allocated to the user does not exceed the preset total cost B.

[0056] In the embodiments of the present specification, on the one hand, the user behavior can be quantified through relevant parameters, so that the virtual resource allocation is closely related to the actual needs of the user, improving the scientificity of the virtual resource allocation decision; on the other hand, the waste of virtual resources can be avoided through the constraint of the total cost of virtual resources, ensuring the economy of virtual resource allocation.

[0057] S302. Based on the constraint conditions of the optimization problem, transform the optimization problem into a dual problem to obtain a dual function.

[0058] Specifically, the original optimization problem is transformed into a dual problem. By swapping the dual variables and constraints, and converting the function and coefficients, the solution complexity is simplified to obtain a dual function. The above dual function can be but is not limited to being composed of dual variables, a preset total cost, relevant parameters of the user under at least one virtual resource, and the virtual resource cost.

[0059] Exemplarily, when the above first objective value is the minimum value on the dual function and the above second objective value is the maximum value on the corresponding decision optimization function of the dual function, the above optimization problem can be first transformed into a standard linear programming format based on the constraint conditions of the optimization problem and solved in the dual space using Lagrange multipliers to obtain: , where is a dual variable; assuming , then the dual function corresponding to the transformed dual problem can be obtained as: .

[0060] In the embodiments of this specification, on the one hand, by transforming the optimization problem with multiple constraints and multiple variables into a dual problem with a single constraint and a single variable, it is not only convenient to use efficient algorithms such as the simplex method and the interior point method to solve, improving the solution efficiency, but also can solve the problem that the original optimization problem is computationally difficult due to the existence of high-dimensional constraints, providing a low-dimensional and simple alternative solution path to ensure the feasibility of virtual resource allocation and enhance the robustness; on the other hand, the shadow price of virtual resource allocation is reflected through the dual variables, which is convenient for analyzing the impact of changes in virtual resource costs on virtual resource allocation strategies and enhancing decision-making flexibility.

[0061] Next, please refer to Figure 4 , which is a schematic diagram of the implementation process of operational research solution in a virtual resource allocation scenario provided by an exemplary embodiment of this application. As Figure 4 shown, the implementation process of the operational research solution in this virtual resource allocation scenario can be but is not limited to including the following steps: S401, obtain historical time-series data within the first historical time period.

[0062] Specifically, the above first historical time period includes multiple different second historical time periods, and the multiple second historical time periods do not overlap and are continuous. The above historical time-series data includes a historical dual variable sequence, and the historical dual variable sequence includes multiple historical dual variables related to virtual resource allocation within the second historical time periods within the first historical time period arranged in chronological order.

[0063] Optionally, the above historical time series data may also include, but is not limited to, at least one of the following data: the historical virtual resource cost sequence, the historical time feature sequence, and the historical traffic sequence within the first historical time period. The above historical virtual resource cost sequence may include, but is not limited to, the historical virtual resource costs that can be allocated to at least one historical access user within multiple second historical time periods arranged in chronological order (i.e., the total virtual resource cost that can be divided among all historical access users within the second historical time period). The above historical time feature sequence refers to a sequence composed of a series of time feature values extracted from each second historical time period within the first historical time period. These time features are usually used to predict future trends or behaviors, such as, but not limited to, timestamps, periodic features (such as hours, days, weeks, months, etc.), and special time attribute features (such as weekends, holidays, Double Eleven, etc.). The above historical traffic sequence can reflect the consumption speed of historical virtual resource costs within each second historical time period, such as, but not limited to, the historical access traffic corresponding to each of the multiple second historical time periods.

[0064] The implementation process of obtaining the historical time series data within the first historical time period may include, but is not limited to: first, obtaining the historical data within the first historical time period. The above historical data may include, but is not limited to, multiple historical dual variables related to virtual resource allocation within the second historical time period and other relevant historical data. The above other relevant historical data may include, but is not limited to, at least one of the following: the historical virtual resource costs corresponding to each of the multiple second historical time periods, historical time information, and historical traffic information. Then, preprocess the above historical data to obtain the historical time series data. The above preprocessing may include, but is not limited to, converting various data in the above historical data into corresponding time series data in chronological order and normalizing the above historical data to map the value ranges of corresponding data features to the same value range, so as to obtain the historical time series data that can be read and parsed by the time series prediction model used for prediction.

[0065] Optionally, the implementation process of obtaining the historical time series data within the first historical time period may include, but is not limited to: regularly obtaining the historical time series data of historical access users belonging to the same constraint dimension within the first historical time period at a preset time interval. The above constraint dimension may include, but is not limited to, user groups and / or user access scenarios. The above historical time series data is updated as the first historical time period is updated.

[0066] For example, but not limited to, after receiving the historical access operations of historical access users within the first historical time period, the user data of the historical access users can be collected, and groups can be dynamically divided according to user attributes (such as, but not limited to, region, consumption preference, etc.) or transaction habits (such as high consumption, low consumption, etc.). Then, the historical time-series data of historical access users belonging to the same constraint dimension is statistically stored according to the user groups and / or user access scenarios to which the historical access users belong within the first historical time period. When the end time of the first historical time period is reached, a solver call is triggered. The solver takes the historical time-series data of historical access users belonging to the same constraint dimension as input, constructs an optimization problem with constraints, with the goal of making the group-related parameters reach preset conditions (such as, but not limited to, maximizing the sum of group-related parameters), and through dual transformation, transforms the original optimization problem into a dual problem, and efficiently solves the dual function by means of time-series prediction to obtain a sequence of future dual variables that can reflect the marginal value of virtual resources in the next time period (i.e., the future time period) corresponding to the first historical time period.

[0067] Next, please continue to refer to Figure 4 , such as Figure 4 shown. After obtaining the historical time-series data within the first historical time period in S401 above, the implementation process of the operational research solution in this virtual resource allocation scenario may also, but not limited to, include: S402, predicting a sequence of future dual variables based on the historical time-series data.

[0068] Specifically, the above sequence of future dual variables includes predicted dual variables that make the dual function corresponding to the corresponding optimization problem obtain the first target value within multiple different future time periods, and the time interval corresponding to the above future time period is equal to the time interval corresponding to the second historical time period.

[0069] Optionally, the historical time-series data can be input into a time-series prediction model to output a sequence of future dual variables. The above time-series prediction model can be trained, but not limited to, based on the sample time-series data within the second time period of the sequence of dual variables within multiple known first time periods; the time interval corresponding to the above first time period is equal to the time interval corresponding to the second time period, and the first time period is the time period after the second time period. The above time-series prediction model can be, but not limited to, a Transformer variant structure specifically for multi-variable time-series prediction, which can achieve reversible transformation through transposition operations in the feature and time dimensions.

[0070] Optionally, the above prediction of the future dual variable sequence based on historical time series data may also include, but is not limited to: predicting the future dual variable sequence of the historical access user under the corresponding historical constraint dimension based on the historical time series data of the historical access users belonging to the same constraint dimension, so as to split the multi-constraint optimization problem of the overall application user into a single-constraint dual problem for the same constraint dimension, reduce the complexity of the operation research solution in the virtual resource allocation process, and improve the efficiency.

[0071] S403. Store the future dual variable sequence in the database.

[0072] Specifically, after obtaining the future dual variable sequence in the future time period through time series prediction, the future dual variable sequence can be directly stored in the database for efficient invocation when allocating virtual resources for the access users in the future time period.

[0073] Optionally, after obtaining the future dual variable sequence of the same historical constraint dimension in the future time period through time series prediction, the future dual variable sequence can be associated and stored with the historical constraint dimension identifier of the corresponding historical access user in the database or other data storage systems, so as to be able to specifically invoke the dual variables when allocating virtual resources for the attribute information of the access users in the future time period, and improve the pertinence and accuracy of virtual resource allocation.

[0074] The above historical constraint dimension identifier may include, but is not limited to, a group identifier and / or an access scenario identifier. The above group identifier is a unique identifier used to distinguish different groups, and may include, but is not limited to, a user identity identifier, a user group, a membership level, a user location, etc. Through the group identifier, it is possible to identify and distinguish which specific group each user belongs to. The above historical constraint dimension identifier can be obtained by encoding the group attributes of the user, or by encoding the group attributes and access scenarios of the user together. It can be a string composed of numbers and / or symbols, or a text or image that can directly represent the group identity. The embodiments of this specification do not limit this.

[0075] In some possible embodiments, after receiving the online access operation sent by the terminal, the server may also query the target attribute information of the current access user in the database based on the user identification of the current access user carried in the online access operation, and determine the target group to which the current access user belongs based on the target attribute information according to the preset correspondence between the attribute information and the group or by using a pre-trained group identification model. Then, based on the target group identification corresponding to the target group and / or the target access scenario identification of the current access user carried in the online access operation, query the target future dual variable sequence of the current access user under the target historical constraint dimension obtained by time series prediction in the database before the current access time. Finally, determine the target dual variable corresponding to the current access user based on the current access time of the current access user and the target future dual variable sequence.

[0076] Next, please refer to Figure 5 , which is a schematic diagram of the implementation process of operation research solution in another virtual resource allocation scenario provided by an exemplary embodiment of the present application. As Figure 5 shown, the implementation process of operation research solution in this virtual resource allocation scenario may but is not limited to include the following steps: S501, Obtain a target time series prediction request, where the target time series prediction request carries time series prediction constraint conditions.

[0077] Specifically, the target time series prediction request may be generated regularly at a preset time interval or the target time series prediction request sent by the terminal may be received. The above time series prediction constraint conditions may but are not limited to include that the total cost of virtual resources allocated to users in multiple different future time periods does not exceed a preset total cost.

[0078] Optionally, the above time series prediction constraint conditions may also but are not limited to include that the total cost of virtual resources allocated to users under the target constraint dimension in multiple different future time periods does not exceed a preset total cost; the above target constraint dimension may but is not limited to include at least one of the following: target user group, target user access scenario.

[0079] S502, Obtain historical time series data in the first historical time period.

[0080] Specifically, the implementation process of S502 is similar to that of S401, and will not be elaborated here.

[0081] S503, In response to the target time series prediction request, predict the future dual variable sequence based on the time series prediction constraint conditions and the historical time series data.

[0082] Specifically, since the solution of the independent variable that makes the dual function corresponding to the optimization problem achieve the first objective value is related not only to historical time-series data but also to the total virtual resource cost in its corresponding time period, therefore, in response to the target time-series prediction request, based on the time-series prediction constraint conditions and historical time-series data, by analyzing the comprehensive impact of the prediction constraint conditions and historical time-series data on the solution, a more flexible and accurate prediction of the future dual variable sequence can be realized.

[0083] Optionally, when the time-series prediction constraint conditions include that the total virtual resource cost allocated to users under the target constraint dimension in multiple different future time periods does not exceed the preset total cost, in response to the above target time-series prediction request, based on the above time-series prediction constraint conditions and historical time-series data under the target constraint dimension, the future dual variable sequence under the target constraint dimension can be predicted, so as to achieve a more accurate and targeted time-series prediction for the constraint dimension of virtual resource allocation.

[0084] S504, store the future dual variable sequence in the database.

[0085] Specifically, the above S504 is the same as the above S403 and will not be elaborated here.

[0086] In some possible embodiments, the overall implementation process of a virtual resource allocation method provided by an exemplary embodiment of the present application may but is not limited to include: first, regularly obtain historical time series data within a first historical time period. The historical time series data includes a historical dual variable sequence within the first historical time period and at least one of the following data: a historical virtual resource cost sequence within the first historical time period, a historical time feature sequence, a historical traffic sequence, and a historical dual intermediate function sequence. Then, input the historical time series data into a pre-trained time series prediction model. The encoding layer of the time series prediction model performs linear encoding on each sequence in time series, and uses a multi-variable attention mechanism layer to perform attention correlation calculation on variables and then uses a first normalization layer for normalization processing to obtain the first normalization results of each sequence. Then, use a feed-forward network layer to perform non-linear transformation on it to enhance the model's expression ability, and then use a second normalization layer (i.e., time series normalization layer) to perform layer normalization in the time series dimension, that is, independently calculate the mean and variance of the features of each time step, perform normalization, eliminate the distribution differences between time steps, and enhance the model's ability to capture time series patterns. Finally, perform parametric transformation (such as weight matrix and bias term) through a projection layer to learn the non-linear relationship of the data, extract higher-order feature representations, and obtain and output a future dual variable sequence within a future time period. At the same time, associate and store the future dual variable sequence under the same historical constraint dimension with the corresponding historical constraint dimension identifier in a database. The above time series prediction model can independently process each channel time series, avoiding information confusion between different variables, and at the same time having the ability to capture global information within each channel time series and the ability to capture the correlation between variables.

[0087] When receiving an access operation of a current access user, the target dual variable corresponding to the current access user can be directly obtained based on the target attribute information of the current access user and the pre-stored future dual variable sequence, and based on the above target dual variable and the target related parameters of the current access user under at least one virtual resource, allocate target virtual resources for the current access user.

[0088] In some possible embodiments, when the current access user accesses a target application through a terminal, the terminal can obtain the target virtual resources allocated for the current access user according to the virtual resource allocation method in the above embodiment. For example, but not limited to, receiving the target virtual resources allocated for the current access user by the server corresponding to the target application through the above virtual resource allocation method; after the terminal obtains the target virtual resources allocated for the current access user, it can directly display the target virtual resources on the current access page corresponding to the target application, so that the target user can timely view and use the target virtual resources.

[0089] To better understand the virtual resource allocation method provided in the above embodiments of the present application, Figure 6The structural schematic diagram of a virtual resource allocation device provided by an embodiment of the present application is exemplarily shown. Specifically, as Figure 6 shown, the virtual resource allocation device 600 includes: A first acquisition module 610, configured to acquire a target dual variable corresponding to the current accessing user based on the target attribute information of the current accessing user; the target dual variable is an independent variable that enables the dual function corresponding to the optimization problem to obtain a first target value; the optimization problem is used to determine the virtual resources allocated to the user; the target dual variable is predicted based on the historical time series data within a first historical time period; the first historical time period includes a plurality of different second historical time periods, the historical time series data includes a historical dual variable sequence, and the historical dual variable sequence includes a plurality of historical dual variables related to virtual resource allocation within the second historical time period arranged in chronological order; A virtual resource allocation module 620, configured to allocate target virtual resources to the current accessing user based on the target dual variable and the target related parameters of the current accessing user under at least one virtual resource.

[0090] In a possible implementation manner, the virtual resource allocation device 600 further includes: A second acquisition module, configured to acquire the historical time series data within the first historical time period; A time series prediction module, configured to predict a future dual variable sequence based on the historical time series data; the future dual variable sequence includes a plurality of predicted dual variables that enable the dual function corresponding to the corresponding optimization problem to obtain a first target value within a plurality of different future time periods; the time interval corresponding to the future time period is equal to the time interval corresponding to the second historical time period; A storage module, configured to store the future dual variable sequence in a database.

[0091] In a possible implementation manner, the historical time series data further includes at least one of the following data: a historical virtual resource cost sequence, a historical time feature sequence, and a historical traffic sequence within the first historical time period; the second acquisition module is specifically configured to: acquire the historical data within the first historical time period; the historical data includes a plurality of historical dual variables related to virtual resource allocation within the second historical time period and other related historical data; the other related historical data includes at least one of the following: the historical virtual resource cost, historical time information, and historical traffic information corresponding to each of the plurality of second historical time periods; preprocess the historical data to obtain the historical time series data.

[0092] In a possible implementation manner, the virtual resource allocation device 600 further includes: A third acquisition module, configured to acquire a target time series prediction request; the target time series prediction request carries time series prediction constraint conditions, and the time series prediction constraint conditions include that the total virtual resource costs allocated to users in multiple different future time periods do not exceed a preset total cost. The time series prediction module is specifically configured to: in response to the target time series prediction request, predict a future dual variable sequence based on the time series prediction constraint conditions and the historical time series data.

[0093] In a possible implementation manner, the time series prediction constraint conditions include that the total virtual resource costs allocated to users under a target constraint dimension in multiple different future time periods do not exceed a preset total cost; the target constraint dimension includes at least one of the following: a target user group, a target user access scenario. The time series prediction module is specifically configured to: In response to the target time series prediction request, predict a future dual variable sequence under the target constraint dimension based on the time series prediction constraint conditions and the historical time series data under the target constraint dimension.

[0094] In a possible implementation manner, the time series prediction module is specifically configured to: Input the historical time series data into a time series prediction model to output a future dual variable sequence; the time series prediction model is trained based on sample time series data in a second time period with dual variable sequences in multiple known first time periods; the time interval corresponding to the first time period is equal to the time interval corresponding to the second time period, and the first time period is a time period after the second time period.

[0095] In a possible implementation manner, the second acquisition module is specifically configured to: Regularly acquire historical time series data of historical access users belonging to the same constraint dimension in a first historical time period at a preset time interval; the constraint dimension includes a user group and / or a user access scenario; the historical time series data is updated as the first historical time period is updated. The time series prediction module is specifically configured to: predict a future dual variable sequence under the historical constraint dimension corresponding to the historical access users based on the historical time series data of the historical access users belonging to the same constraint dimension. The storage module is specifically configured to: associatively store the future dual variable sequence and the historical constraint dimension identifier corresponding to the historical access users in a database.

[0096] In a possible implementation manner, the above-mentioned first acquisition module 610 is specifically configured to: determine the target group to which the current access user belongs based on the target attribute information of the current access user; query in the above-mentioned database based on the target group identifier corresponding to the above-mentioned target group and / or the target access scenario identifier corresponding to the above-mentioned current access user to obtain the target future dual variable sequence corresponding to the above-mentioned current access user; determine the target dual variable corresponding to the above-mentioned current access user based on the current access time of the above-mentioned current access user and the above-mentioned target future dual variable sequence.

[0097] In a possible implementation manner, the above-mentioned virtual resource allocation module 620 is specifically configured to: Search for the second target value on the decision optimization function corresponding to the above-mentioned dual function based on the above-mentioned target dual variable, the target relevant parameters of the above-mentioned current access user under at least one virtual resource, the target virtual resource cost, and at least one type of virtual resource to obtain the target type of virtual resource; the above-mentioned target type of virtual resource is the type of virtual resource corresponding to the second target value of the above-mentioned decision optimization function among the above-mentioned at least one type of virtual resource when the above-mentioned current access user and the above-mentioned target dual variable are known; allocate the corresponding target virtual resource to the above-mentioned current access user according to the above-mentioned target type of virtual resource.

[0098] In a possible implementation manner, the above-mentioned virtual resource allocation device 600 further includes: The relevant parameter calculation module is configured to use machine learning technology to calculate the target relevant parameters of the above-mentioned current access user under at least one virtual resource based on the target user characteristics of the above-mentioned current access user; the above-mentioned target user characteristics include at least one of the following: target attribute information, target transaction frequency, historical virtual resource allocation and relevant parameter information.

[0099] In a possible implementation manner, the above-mentioned virtual resource allocation device 600 further includes: The problem construction module is configured to construct the above-mentioned optimization problem based on the relevant parameters of the user under at least one virtual resource and the decision variable indicating whether to allocate a virtual resource to the above-mentioned user; the constraint conditions of the above-mentioned optimization problem include that each user is allocated only one virtual resource, and the total cost of the virtual resources allocated to at least one user does not exceed the preset total cost. The problem transformation module is configured to transform the above-mentioned optimization problem into a dual problem based on the constraint conditions of the above-mentioned optimization problem to obtain the above-mentioned dual function.

[0100] The division of each module in the above virtual resource allocation device is only for illustrative purposes. In other embodiments, the virtual resource allocation device may be divided into different modules as needed to complete all or part of the functions of the above virtual resource allocation device. In the embodiments of the present application, the implementation of each module in the virtual resource allocation device may be in the form of a computer program. The computer program can run on a terminal or a server. The program module constituted by the computer program can be stored in the memory of the terminal or the server. When the computer program is executed by a processor, all or part of the steps of the virtual resource allocation method described in the embodiments of the present application are realized.

[0101] Next, please refer to Figure 7 , Figure 7 which is a schematic structural diagram of an electronic device provided by an exemplary embodiment of the present application. As Figure 7 shown, the electronic device 700 may include: at least one processor 710, at least one network interface 720, a user interface 730, a memory 740, and at least one communication bus 750.

[0102] Among them, the communication bus 750 can be used to realize the connection and communication of the above-mentioned various components.

[0103] Among them, the user interface 730 may include a display screen (Display) and a camera (Camera). Optionally, the user interface may further include a standard wired interface and a wireless interface.

[0104] Among them, the network interface 720 may optionally include a Bluetooth module, a Near Field Communication (NFC) module, a Wireless Fidelity (Wi-Fi) module, etc.

[0105] Among them, the processor 710 may include one or more processing cores. The processor 710 connects various parts within the entire electronic device 700 through various interfaces and lines, and executes various functions of the routing electronic device 700 and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 740, and by calling the data stored in the memory 740. Optionally, the processor 710 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 710 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for the rendering and drawing of the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 710 and may be implemented separately by a single chip.

[0106] Among them, the memory 740 may include a random access memory (RAM) and may also include a read-only memory (ROM). Optionally, the memory 740 includes a non-transitory computer-readable medium. The memory 740 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 740 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as acquisition function, virtual resource allocation function, dual solution function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 740 may also be at least one storage device located far from the aforementioned processor 710. As Figure 7 shown, the memory 740, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and application programs.

[0107] In some possible embodiments, the electronic device 700 is the aforementioned Figure 6For the virtual resource allocation device 600 mentioned in the above embodiments, the processor 710 may be used to call the virtual resource allocation application program stored in the memory 740 and specifically perform the following operations: obtaining the target dual variable corresponding to the current access user based on the target attribute information of the current access user; the target dual variable is the independent variable that makes the dual function corresponding to the optimization problem obtain the first target value; the optimization problem is used to determine the virtual resources allocated to the user; the target dual variable is predicted based on the historical time series data within the first historical time period; the first historical time period includes a plurality of different second historical time periods, the historical time series data includes a historical dual variable sequence, and the historical dual variable sequence includes a plurality of historical dual variables related to virtual resource allocation within the second historical time period arranged in chronological order; allocating target virtual resources to the current access user based on the target dual variable and the target related parameters of the current access user under at least one virtual resource.

[0108] In some possible embodiments, the processor 710 is further configured to perform: obtaining the historical time series data within the first historical time period; predicting a future dual variable sequence based on the historical time series data; the future dual variable sequence includes predicted dual variables that make the dual function corresponding to the corresponding optimization problem obtain the first target value within a plurality of different future time periods; the time interval corresponding to the future time period is equal to the time interval corresponding to the second historical time period; storing the future dual variable sequence in the database.

[0109] In a possible implementation manner, the historical time series data further includes at least one of the following data: the historical virtual resource cost sequence, the historical time feature sequence, and the historical traffic sequence within the first historical time period; when the processor 710 executes the operation of obtaining the historical time series data within the first historical time period, it is specifically configured to perform: obtaining the historical data within the first historical time period; the historical data includes a plurality of historical dual variables related to virtual resource allocation within the second historical time period and other related historical data; the other related historical data includes at least one of the following: the historical virtual resource cost, the historical time information, and the historical traffic information corresponding to each of the plurality of second historical time periods; preprocessing the historical data to obtain the historical time series data.

[0110] In some possible embodiments, before the processor 710 executes the operation of predicting the future dual variable sequence based on the historical time series data, it is further configured to perform: obtaining a target time series prediction request; the target time series prediction request carries time series prediction constraint conditions, and the time series prediction constraint conditions include that the total virtual resource cost allocated to the user within a plurality of different future time periods does not exceed a preset total cost.

[0111] When the above-mentioned processor 710 executes the prediction of the future dual variable sequence based on the above-mentioned historical timing data, it is specifically used to execute: in response to the above-mentioned target timing prediction request, predict the future dual variable sequence based on the above-mentioned timing prediction constraint conditions and the above-mentioned historical timing data.

[0112] In some possible embodiments, the above-mentioned timing prediction constraint conditions include that the total virtual resource costs allocated to users under the target constraint dimension in multiple different future time periods do not exceed a preset total cost; the above-mentioned target constraint dimension includes at least one of the following: target user group, target user access scenario; when the above-mentioned processor 710 executes the prediction of the future dual variable sequence based on the above-mentioned historical timing data, it is specifically used to execute: in response to the above-mentioned target timing prediction request, predict the future dual variable sequence under the target constraint dimension based on the above-mentioned timing prediction constraint conditions and the above-mentioned historical timing data under the target constraint dimension.

[0113] In some possible embodiments, when the above-mentioned processor 710 executes the prediction of the future dual variable sequence based on the above-mentioned historical timing data, it is specifically used to execute: input the above-mentioned historical timing data into a timing prediction model, and output the future dual variable sequence; the above-mentioned timing prediction model is trained based on the sample timing data in the second time period of the dual variable sequence in multiple known first time periods; the time interval corresponding to the above-mentioned first time period is equal to the time interval corresponding to the above-mentioned second time period, and the above-mentioned first time period is the time period after the above-mentioned second time period.

[0114] In some possible embodiments, when the above-mentioned processor 710 executes the acquisition of the historical timing data in the first historical time period, it is specifically used to execute: regularly acquire the historical timing data of historical access users belonging to the same constraint dimension in the first historical time period at a preset time interval; the above-mentioned constraint dimension includes user group and / or user access scenario; the above-mentioned historical timing data is updated as the first historical time period is updated; When the above-mentioned processor 710 executes the prediction of the future dual variable sequence based on the above-mentioned historical timing data, it is specifically used to execute: predict the future dual variable sequence under the corresponding historical constraint dimension of the above-mentioned historical access users based on the historical timing data of the above-mentioned historical access users belonging to the same constraint dimension. When the above-mentioned processor 710 executes the storage of the above-mentioned future dual variable sequence into the database, it is specifically used to execute: associate and store the above-mentioned future dual variable sequence and the historical constraint dimension identifier corresponding to the above-mentioned historical access users into the database.

[0115] In some possible embodiments, when the above-mentioned processor 710 executes obtaining the target dual variable corresponding to the current access user based on the target attribute information of the current access user, it is specifically used to execute: determining the target group to which the current access user belongs based on the target attribute information of the current access user; querying in the above-mentioned database based on the target group identifier corresponding to the above-mentioned target group and / or the target access scenario identifier corresponding to the above-mentioned current access user to obtain the target future dual variable sequence corresponding to the above-mentioned current access user; determining the target dual variable corresponding to the above-mentioned current access user based on the current access time of the above-mentioned current access user and the above-mentioned target future dual variable sequence.

[0116] In some possible embodiments, when the above-mentioned processor 710 executes allocating target virtual resources for the above-mentioned current access user based on the above-mentioned target dual variable and the target relevant parameters of the above-mentioned current access user under at least one virtual resource, it is specifically used to execute: searching for a second target value on the corresponding decision optimization function of the above-mentioned dual function based on the above-mentioned target dual variable, the target relevant parameters of the above-mentioned current access user under at least one virtual resource, the target virtual resource cost, and at least one virtual resource type to obtain the target virtual resource type; the above-mentioned target virtual resource type is the virtual resource type corresponding to the second target value of the above-mentioned decision optimization function among the above-mentioned at least one virtual resource types when the above-mentioned current access user and the above-mentioned target dual variable are known; allocating the corresponding target virtual resources for the above-mentioned current access user according to the above-mentioned target virtual resource type.

[0117] In some possible embodiments, before the above-mentioned processor 710 executes allocating target virtual resources for the above-mentioned current access user based on the above-mentioned target dual variable and the target relevant parameters of the above-mentioned current access user under at least one virtual resource, it is further used to execute: Using machine learning technology, calculating the target relevant parameters of the above-mentioned current access user under at least one virtual resource based on the target user characteristics of the above-mentioned current access user; the above-mentioned target user characteristics include at least one of the following: target attribute information, target transaction frequency, historical virtual resource allocation and relevant parameter information.

[0118] In some possible embodiments, the above-mentioned processor 710 is further used to execute: constructing the above-mentioned optimization problem based on the relevant parameters of the user under at least one virtual resource and the decision variable indicating whether to allocate a virtual resource to the above-mentioned user; the constraint conditions of the above-mentioned optimization problem include that each user is allocated only one virtual resource, and the total cost of the virtual resources allocated to at least one user does not exceed a preset total cost; based on the constraint conditions of the above-mentioned optimization problem, transforming the optimization problem into a dual problem to obtain a dual function.

[0119] The embodiments of the present application also provide a computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are run on a computer or a processor, the computer or the processor is caused to execute one or more steps in the above embodiments. If each component module of the above electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in the above computer-readable storage medium.

[0120] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a Digital Versatile Disc (DVD)), or a semiconductor medium (such as a Solid State Disk (SSD), etc.).

[0121] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The foregoing storage medium includes various media that can store program codes, such as ROM, RAM, magnetic disks, or optical discs. Without conflict, the technical features in this embodiment and the implementation solutions can be combined arbitrarily.

[0122] The above-described embodiments are only descriptions of the preferred embodiments of the present application, and do not limit the scope of the present application. Without departing from the design spirit of the present application, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present application shall fall within the protection scope determined by the claims.

[0123] The above description has been made of specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order shown or sequential order to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A virtual resource allocation method, characterized in that, The method includes: Obtaining a target dual variable corresponding to the currently accessing user based on the target attribute information of the currently accessing user; the target dual variable is the independent variable that makes the dual function corresponding to the optimization problem obtain a first target value; the optimization problem is used to determine the virtual resources allocated to the user; the target dual variable is predicted based on the historical time series data within a first historical time period; the first historical time period includes a plurality of different second historical time periods, the historical time series data includes a historical dual variable sequence, and the historical dual variable sequence includes a plurality of historical dual variables related to virtual resource allocation within the second historical time period arranged in chronological order; Allocating target virtual resources to the currently accessing user based on the target dual variable and the target related parameters of the currently accessing user under at least one virtual resource.

2. The method according to claim 1, wherein The method further includes: Obtaining historical time series data within a first historical time period; Predicting a future dual variable sequence based on the historical time series data; the future dual variable sequence includes predicted dual variables that make the dual function corresponding to the corresponding optimization problem obtain a first target value within a plurality of different future time periods; the time interval corresponding to the future time period is equal to the time interval corresponding to the second historical time period; Storing the future dual variable sequence in a database.

3. The method according to claim 2, characterized in that The historical time series data further includes at least one of the following data: a historical virtual resource cost sequence, a historical time feature sequence, and a historical traffic sequence within the first historical time period; The obtaining of the historical time series data within a first historical time period includes: Obtaining historical data within a first historical time period; the historical data includes a plurality of historical dual variables related to virtual resource allocation within the second historical time period and other related historical data; the other related historical data includes at least one of the following: the historical virtual resource cost, historical time information, and historical traffic information corresponding to each of the plurality of second historical time periods; Preprocessing the historical data to obtain historical time series data.

4. The method according to claim 2, wherein Before predicting the future dual variable sequence based on the historical time series data, the method further includes: Obtaining a target time series prediction request; the target time series prediction request carries time series prediction constraint conditions, and the time series prediction constraint conditions include that the total virtual resource cost allocated to users within a plurality of different future time periods does not exceed a preset total cost; The predicting of the future dual variable sequence based on the historical time series data includes: In response to the target time series prediction request, predicting the future dual variable sequence based on the time series prediction constraint conditions and the historical time series data.

5. The method according to claim 4, characterized in that, The time series prediction constraint conditions include that the total virtual resource cost allocated to users under a target constraint dimension within a plurality of different future time periods does not exceed a preset total cost; The target constraint dimension includes at least one of the following: a target user group and a target user access scenario; The predicting of the future dual variable sequence based on the historical time series data includes: In response to the target time series prediction request, predict the future dual variable sequence in the target constraint dimension based on the time series prediction constraint condition and the historical time series data in the target constraint dimension.

6. The method according to claim 2, wherein The predicting the future dual variable sequence based on the historical time series data includes: Input the historical time series data into a time series prediction model to output a future dual variable sequence; the time series prediction model is trained based on sample time series data in a second time period of dual variable sequences within multiple known first time periods; the time interval corresponding to the first time period is equal to the time interval corresponding to the second time period, and the first time period is a time period after the second time period.

7. The method according to claim 2, wherein The obtaining the historical time series data in the first historical time period includes: Regularly obtain, at a preset time interval, the historical time series data of historical visiting users belonging to the same constraint dimension in the first historical time period; the constraint dimension includes user groups and / or user access scenarios; the historical time series data is updated as the first historical time period is updated; The predicting the future dual variable sequence based on the historical time series data includes: Predict the future dual variable sequence in the corresponding historical constraint dimension of the historical visiting users based on the historical time series data of the historical visiting users belonging to the same constraint dimension; The storing the future dual variable sequence into a database includes: Associatively store the future dual variable sequence and the historical constraint dimension identifier corresponding to the historical visiting users into the database.

8. The method according to claim 7, characterized in that, The obtaining the target dual variable corresponding to the current visiting user based on the target attribute information of the current visiting user includes: Determine the target group to which the current visiting user belongs based on the target attribute information of the current visiting user; Query in the database based on the target group identifier corresponding to the target group and / or the target access scenario identifier corresponding to the current visiting user to obtain the target future dual variable sequence corresponding to the current visiting user; Determine the target dual variable corresponding to the current visiting user based on the current access time of the current visiting user and the target future dual variable sequence.

9. The method according to claim 1, wherein The allocating target virtual resources for the current visiting user based on the target dual variable and the target related parameters of the current visiting user under at least one virtual resource includes: Search for a second target value on the decision optimization function corresponding to the dual function based on the target dual variable, the target related parameters of the current visiting user under at least one virtual resource, the target virtual resource cost, and at least one virtual resource type to obtain a target virtual resource type; the target virtual resource type is the virtual resource type corresponding to the decision optimization function taking the second target value among the at least one virtual resource types when the current visiting user and the target dual variable are known; Allocate corresponding target virtual resources for the current visiting user according to the target virtual resource type.

10. The method according to claim 1, characterized in that Before allocating target virtual resources to the current access user based on the target dual variable and the target related parameters of the current access user under at least one virtual resource, the method further includes: Using machine learning techniques, calculating the target related parameters of the current access user under at least one virtual resource based on the target user characteristics of the current access user; the target user characteristics include at least one of the following: target attribute information, target transaction frequency, historical virtual resource allocation and related parameter information.

11. The method according to any one of claims 1 to 10, characterized in that, The method further includes: Constructing the optimization problem based on the related parameters of the user under at least one virtual resource and the decision variable indicating whether to allocate a virtual resource to the user; the constraint conditions of the optimization problem include that each user is allocated only one virtual resource, and the total cost of the virtual resources allocated to at least one user does not exceed the preset total cost; Based on the constraint conditions of the optimization problem, transforming the optimization problem into a dual problem to obtain the dual function.

12. A virtual resource allocation method, characterized in that The method includes: Obtaining the target virtual resources allocated to the current access user according to the virtual resource allocation method according to any one of claims 1-11; Displaying the target virtual resources.

13. A virtual resource allocation device, characterized in that, The virtual resource allocation device includes: An acquisition module, configured to acquire the target dual variable corresponding to the current access user based on the target attribute information of the current access user; the target dual variable is the independent variable that makes the dual function corresponding to the optimization problem obtain the first target value; the optimization problem is used to decide the virtual resources allocated to the user; the target dual variable is predicted based on the historical time series data in the first historical period; the first historical period includes a plurality of different second historical periods, the historical time series data includes a historical dual variable sequence, and the historical dual variable sequence includes a plurality of historical dual variables related to virtual resource allocation in the second historical period arranged in chronological order; A virtual resource allocation module, configured to allocate target virtual resources to the current access user based on the target dual variable and the target related parameters of the current access user under at least one virtual resource.

14. An electronic device, characterized in that, Including: A processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs the program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method steps according to any one of claims 1 to 12.

15. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions, and the instructions are adapted to be loaded and executed by the processor to execute the method steps according to any one of claims 1 to 12.

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