Network slice based resource allocation method

Abstract

The application discloses a resource allocation method based on network slices, comprising the following steps: (1) constructing bidding information for a slice; (2) constructing bidding information for a terminal user; (3) screening out a user meeting the slice bid and a slice meeting the user condition; (4) calculating the utility of the matching between the user and the slice according to the user's evaluation of the resource and the cost of the slice resource, and constructing a utility matrix; (5) modeling the system as a bipartite graph model, initializing the auxiliary variable top index value according to the utility matrix; (6) randomly sorting the terminal users, and finding an augmented path for the users according to the order; (7) obtaining a matching matrix and the total utility of the system after matching according to a certain matching strategy. Compared with the traditional resource allocation method, the application not only uses the bidding, bidding and other technologies in the auction theory, but also uses the bipartite graph model to achieve matching.

Description

TECHNICAL FIELD

[0001] The application relates to a network slice-based resource allocation method and belongs to the field of wireless communication. BACKGROUND

[0002] Network slicing logically isolates the network, making multiple isolated logical networks independent of each other, providing customizable services for differentiated services, and virtualizing private networks for each industry. This makes it possible to effectively meet the safety, reliability and flexibility requirements of various energy services. 3GPP R17 further promotes the research progress of network slicing technology and proposes "eight questions" about network slicing. Since network slicing can provide customized services, operators can also sell custom slices to different tenants at different prices. In order to provide better performance and economically efficient maintenance, network slicing involves complex technical challenges, and intelligent innovation is eagerly awaited to keep resource management consistent with the behavior of each user.

[0003] Smart grid helps utilities monitor and manage resources and is one of the important application scenarios of green IoT. Driven by growing energy and power demand, the main features of the smart grid are the production of renewable energy, backbone networks and distributed energy, and the combination of backbone networks with local distribution networks and microgrids. The protection of the distribution network is also more "powerful", such as more intelligent load management, safer protection systems and more flexible network functions. The production, consumption and transmission of energy in the smart grid are changing. The future of the network must be flexible enough to rely heavily on interconnected smart devices based on the concept of IoT. The development and application of the smart grid are highly dependent on connectivity. At present, the number of communication terminals of the smart grid is 1 million, and in the future it will reach tens of millions or even billions. The power consumption of these large devices is a pressing problem. At the same time, various differentiated energy services in the smart grid have very high requirements for the bandwidth, security, latency, cost and other indicators of the communication network. Therefore, the flexibility and adaptability of the communication platform are facing great challenges.

[0004] The power Internet of Things refers to applying wireless communication technology, sensing technology and the like to each link of a power grid system, realizing interconnection and intercommunication with an existing Internet of the power system by widely deploying intelligent devices with certain sensing, computing and executing capabilities. As the latest generation of mobile communication technology, 5G communication has communication advantages of high bandwidth, low latency and wide connection. With large-scale access of "new energy and new business", "power grid control" is expanded to the end, and "information data" grows explosively. Advanced communication services provided by 5G technology are urgently needed by the power Internet of Things system. However, in the power Internet of Things system, there are still many problems in resource allocation based on network slices. Due to the diversity of power grid businesses, different businesses have different QoS requirements, and the existing resource allocation method is rarely able to develop a resource allocation strategy for this problem. In the 5G light communication network, how to realize reasonable allocation of network resources of slices through an access selection mechanism based on network slices to meet the demand of light terminal communication is a hot issue to be further studied. SUMMARY

[0005] Technical problem: In view of the problems in the prior art that resource allocation is unreasonable, resources are wasted, user satisfaction is low, and no differentiated network slice resource allocation strategy is developed for QoS requirements of different businesses, the application provides a resource allocation method based on network slices. The method realizes reasonable allocation of system resources, improves system total utility and user satisfaction and avoids waste of resources by maximizing system total utility as an objective through resource allocation after matching between slices and users.

[0006] Technical scheme: The technical problem to be solved by the application is to provide a resource allocation method based on network slices, which maximizes system total utility as an optimization objective on the basis of ensuring resource allocation efficiency, and solves the optimization problem through two stages of utility calculation and bipartite graph matching to realize reasonable allocation of resources.

[0007] The application adopts the following technical scheme to solve the above problems: The application designs a resource allocation method based on network slices, which is used to realize resource allocation between slices and terminal users in a power grid system, and includes the following steps:

[0008] A resource allocation method based on network slices includes the following steps:

[0009] Bidding information is constructed for the slices, and bidding information is constructed for the users. The bidding information includes the number of bandwidth resources and computing resources of the slices and their asking prices, and the bidding information includes the demand of the users for resources and the bid of the users for the resources owned by the slices;

[0010] Slices that meet user criteria are selected based on the user's resource demand in the bidding information and the amount of resources the slice possesses in the tender information; users that meet slice criteria are selected based on the asking price in the tender information and the bid price in the bidding information.

[0011] After the screening is completed, users and slices that still meet the conditions of both parties are calculated to obtain the utility of both parties if a match is achieved, and a utility matrix is ​​constructed based on this.

[0012] The system is modeled as a bipartite graph matching model, and auxiliary variables are initialized according to the utility matrix. The auxiliary variables are the top labels of users and slices.

[0013] The end users are randomly sorted, and slices are searched for users according to the order. If the sum of the top index values ​​of the user and the slice is equal to the corresponding value in the utility matrix, the search is completed; if no slice can be found for the user, the top index value is modified.

[0014] The matching matrix is ​​obtained based on the user and slice matching strategy described above.

[0015] Preferably, the maximum value of the mutual utility achieved by each user and all slices in the utility matrix is ​​used as the initial value of the top label of the auxiliary variable.

[0016] Preferably, the optimization objective of the bipartite graph matching model is defined as follows:

[0017] (1)

[0018] On behalf of users slices The bid price, using Representative slice Cost price Represents the number of end-users of electricity. Represents the number of slices;

[0019] The constraints are:

[0020] (2)

[0021] and They represent slices respectively. The amount of bandwidth and computing resources possessed. and Indicates user The amount of bandwidth and computing resources required. and Representing actual latency and user latency respectively. Demand delay, and respectively represent the actual bit error rate and the user bit error rate required by users, I1, I2 and I3 respectively represent the set of patrol class users, control class users and collection class users, the bidding information of the slice.

[0022] Preferably, the matching strategy of the user and the slice is made according to the following formula:

[0023] (3)

[0024] wherein the current terminal user is the user , the slice at the end of the augmented path is the slice , the user that has been matched with the slice is the user , the second choice slice of the user is the slice , the second choice slice of the user is the slice , represents the utility of the matching of the user and the slice .

[0025] Preferably, if the augmented path of the user cannot be found, the top index value is modified, and the specific method is to subtract a constant from all the users on the augmented path being augmented and add a constant to all the slices, that is:

[0026] (4)

[0027] The calculation method of the augmented path is as follows:

[0028] (5)

[0029] wherein, is the edge weight of the user and the slice , that is, the utility of the matching of the user and the slice ; is the initial top index value of the user ; is the initial top index value of all the slices; is the augmented path found by the user .

[0030] The application further provides a resource allocation device based on network slices, comprising:

[0031] The construction module is used to build bidding information for slices and bid information for users. The bidding information includes the quantity of bandwidth and computing resources of the slice and the asking price for them. The bid information includes the user's demand for resources and the bid for the resources owned by the slice.

[0032] The filtering module is used to filter slices and users that meet certain criteria.

[0033] The construction module is also used to calculate the utility that can be obtained for users and slices that still meet the conditions after the screening is completed, if a match is achieved, and to construct a utility matrix based on this utility matrix; the system is modeled as a bipartite graph model, and auxiliary variables are initialized according to the utility matrix, wherein the auxiliary variables are the top label values ​​of users and slices;

[0034] The execution module is used to randomly sort the end users, find slices for the users according to the order, and obtain the matching matrix according to a certain matching strategy.

[0035] Preferably, the construction module is further configured to use the maximum bilateral utility achieved by each user and all slices in the utility matrix as the initial value of the top label of the auxiliary variable.

[0036] Preferably, the optimization objective of the bipartite graph matching model is defined as follows:

[0037] (6)

[0038] On behalf of users slices The bid price, using Representative slice Cost price Represents the number of end-users of electricity. Represents the number of slices;

[0039] The constraints are:

[0040] (7)

[0041] and They represent slices respectively. The amount of bandwidth and computing resources possessed. and Indicates user The amount of bandwidth and computing resources required. and Representing actual latency and user latency respectively. Demand delay, and Representing the actual bit error rate and the user, respectively. The demand bit error rate, I1, I2, and I3 represent the sets of patrol class users, control class users, and collection class users, respectively, The bidding information for the slice.

[0042] The application further provides an electronic device, including a memory, one or more processors coupled with the memory, and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more application programs are configured to execute the network slice-based resource allocation method.

[0043] The application further provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to execute the network slice-based resource allocation method.

[0044] Compared with the prior art, the network slice-based resource allocation method has the following technical effects:

[0045] The network slice-based resource allocation method maximizes the total utility of the system based on the auction and bipartite graph matching theory, and realizes reasonable resource allocation between the network slice and the user by constructing a utility matrix and iteratively comparing and matching to solve the optimization problem. Compared with the traditional network slice resource allocation method, the total utility of the system is considered when designing the optimization target, which can benefit both the operator and the user. The bipartite graph matching theory is used to find the matching between the slice and the user to maximize the system utility. Compared with the traditional auction method, the utility of both the user and the slice can be effectively improved, and resource waste can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 FIG. 1 is a schematic diagram of a power internet of things system to which the network slice resource allocation method is applicable.

[0047] Figure 2 FIG. 2 is a system block diagram of the network slice-based resource allocation method. DETAILED DESCRIPTION

[0048] The specific embodiments of the application will be further described in detail below in combination with the drawings and the power internet of things scenario.

[0049] As Figure 1As shown, in the power Internet of Things system, there are multiple network slices and three types of power terminal users: inspection type, control type and collection type, and the QoS requirements of the users of the collection type business are consistent with the lightweight terminal users. Among them, the service provider (SP) owns the physical resources and manages the resource allocation (resources include communication resources, computing resources) to the users. Different types of power businesses have different QoS requirements, therefore, different network slices need to be allocated to different terminal users, and the slice is a combination of different amounts of the above resources. For example, the bandwidth resource requirement of the inspection type business in the power grid is particularly high, the control type business needs low delay and high reliability, and the collection type business needs to control the cost and can realize the massive access of terminal devices. The users request slice resources from the SP according to their different QoS requirements. According to the overall request received from all users according to the current state, the SP decides the slice allocated to the current user according to the current optimal allocation strategy. In this scenario, it is assumed that each slice can only serve one user at the same time, and correspondingly, each user can also only access one slice at the same time. Therefore, when the user accesses the slice, it means that the slice can provide the user with the resources he wants.

[0050] The present application improves the unreasonable resource allocation and low user satisfaction of the traditional resource allocation scheme based on the auction method in the current power Internet of Things system, and proposes a resource allocation scheme based on the bipartite graph matching theory to maximize the system utility. The method implements resource allocation under the premise of guaranteeing the QoS requirements of the users, and the method mainly includes two parts: the construction of the utility matrix of the bidding and tender of the users and the slices, and the matching resource allocation of the slices and the users. The resource allocation is modeled as a bipartite graph matching problem, so as to solve the problem and achieve the goal.

[0051] The present application designs a resource allocation method of network slices in a power Internet of Things system, which is used for realizing the reasonable allocation of communication resources and computing resources of the slices and the users in the power grid system. Figure 2 As shown, in actual application, the method comprises the following steps:

[0052] (1) The service provider sets the price according to the resource status of the slice, and the resource status and the price jointly constitute the bidding information, and waits for the tender of the user; the user sets the price according to the resource demand and the resource demand, and constitutes the tender information, and tenders to the slice.

[0053] (2) The slice and the user both filter the opposite party according to their own requirements.

[0054] (3) After the screening is completed, the slice and the user that still meet the requirements of both parties will calculate the utility sum after matching, and construct the utility matrix based on the utility sum.

[0055] (4) The maximum utility value reached by each user and all slices in the utility matrix is taken as the initial value of the auxiliary variable top index.

[0056] (5) The user is matched with the slice maximizing the user's utility according to the order of the randomly arranged users.

[0057] (6) If a matching conflict occurs, the top index value is modified to avoid the conflict according to the utility maximization principle, and step (5) is returned. If there is no conflict, it continues to be executed.

[0058] (7) The matching matrix is constructed according to the matching result, and the total utility of the system is calculated, and the process ends.

[0059] In the above step (1), the bidding and tender information is constructed according to the following formula:

[0060] (1)

[0061] (2)

[0062] The bidding information of the user is The tender information of the slice is And The following formula is obtained:

[0063] (3)

[0064] (4)

[0065] (5)

[0066] (6)

[0067] Different values of represent different user types, represent the bid price of the user, and represent the bid price of the user, and represent the bid price of the slice, and represent the bandwidth and computing resources owned by the slice , respectively, represent the actual delay, represent the delay required by the user , and represent the actual bit error rate, represent the bit error rate required by the user Required bit error rate.

[0068] In step (3) above, the utility is calculated according to the following formula:

[0069] (7)

[0070] (8)

[0071] (9)

[0072] (10)

[0073] (11)

[0074] Indicates user The utility is defined as the user's bid. Subtract the user's payment price , Indicates user With slices A match has been reached. Slice The utility of the slice is defined as the price charged for the slice. Subtract the cost of slicing It is worth mentioning that, because there are no auction intermediaries, and They are equal in numerical value. Indicates user With slices The mutual utility It represents the total utility of the system.

[0075] In steps (5) and (6) above, there will be an iterative comparison process to achieve the matching of slices and users. The objective function is defined as follows:

[0076] (12)

[0077] This is an estimate of the resources a user possesses on a slice, numerically equal to the bid price. The cost of providing resources for slices and operating on those resources is optimized to improve the overall system utility.

[0078] After determining the optimization objective, the constraints also need to be determined, as given by the following formula:

[0079] (13)

[0080] and They represent slices respectively. The amount of bandwidth and computing resources possessed. and Indicates user The amount of bandwidth and computing resources required. and Representing actual latency and user latency respectively. Demand delay, and Representing the actual bit error rate and the user, respectively. The required bit error rate.

[0081] During the matching phase, the utility matrix is ​​used. The value of the row represents the user. The utility of matching all slices, using the first The column values ​​represent slices. The utility of matching with all users, thus, the first The maximum value in the row is represented as This means representing the user The maximum utility that both parties can obtain when a match is achieved will be As a user initial top label value The initial top-level values ​​of all slices Set to 0. For fairness, users in the system are randomly sorted, and then augmenting paths are searched for users in order. If an augmenting path is found, the matching decision between users and slices is made according to the following formula:

[0082] (14)

[0083] Among them, the current terminal user is user. The slice at the end of the augmentation path is a slice. Already with slices The matched user is the user ,user The secondary slice is a slice ,user The secondary slice is a slice The above matching decision ensures that each user maximizes the current system utility when searching for slices, and maximizes the total system utility when all users participate in the matching. Represents the matching matrix, if , then Assigning a value of 1, the total system utility If similar situation happens after that, such as there is still conflict on the augmented path of the user, the same processing is still carried out, and a matching scheme maximizing the current system utility can be obtained.

[0084] If the augmented path of the user cannot be found, the value of the feasible top index is modified, and the specific method is to subtract a constant from all users on the augmented path being augmented and add a constant to all slices , that is:

[0085] (15)

[0086] The calculation method is as follows:

[0087] (16)

[0088] wherein, denotes the edge weight of the user and the slice , that is, the utility of both parties after the user and the slice are matched. After completing these works, the complete matching matrix and the final system total utility , are obtained, and the augmented path of the user i is found.

[0089] The technical scheme designs a resource allocation method based on network slices in a power Internet of Things system. For the power Internet of Things scene, based on network slice technology and bipartite graph matching theory, the system total utility is maximized by calculating the utility of both parties first, and then comparing the system total utility after matching to realize the maximization of the system total utility. Compared with the traditional auction algorithm, the resource allocation between slices and users is realized by using the bipartite graph matching theory, which can effectively avoid resource waste and improve user satisfaction under the consideration of utility, and can also ensure the utility of operators and users in the power grid system.

[0090] The embodiment provides a resource allocation device based on network slices,

[0091] a construction module configured to construct bidding information for slices and bidding information for users, wherein the bidding information comprises the number of bandwidth resources and computing resources of the slices and the asking price thereof, and the bidding information comprises the demand of the users for resources and the bid of the users for the resources owned by the slices;

[0092] a screening module configured to screen the slices and the users meeting the conditions respectively;

[0093] The construction module is further configured to calculate the double-party utility that can be obtained by the user and the slice if a match is reached, and construct an utility matrix based on the double-party utility; model the system as a bipartite graph model, and take the maximum value of the double-party utility reached by each user and all slices in the utility matrix as an initial value of the auxiliary variable top index value;

[0094] The execution module is configured to randomly sort the end users, find a slice for a user according to the order, and obtain a matching matrix according to a certain matching strategy.

[0095] The embodiment further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the method for measuring the coaxiality between the rotor and the stator in real time when executing the program.

[0096] The embodiment provides a non-transitory computer readable storage medium, which stores computer instructions for causing a computer to execute the method for measuring the coaxiality between the rotor and the stator in real time.

[0097] It should be noted that the method of one or more embodiments of the present specification can be executed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scenario, and be completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only execute one or more steps in the method of one or more embodiments of the present specification, and the multiple devices can interact with each other to complete the method.

[0098] The above describes specific embodiments of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in an order different than the order in the embodiments and still achieve the desired result. In addition, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In some implementations, multitasking and parallel processing can be advantageous or necessary.

[0099] For the convenience of description, the above device is described as various modules respectively described in terms of functions. Of course, the functions of each module can be implemented in the same or multiple software and / or hardware when implementing one or more embodiments of the present specification.

[0100] The device of the above embodiment is used to implement the corresponding method in the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described here again.

[0101] An electronic device hardware structure can include a processor, a memory, an input / output interface, a communication interface, and a bus. The processor, the memory, the input / output interface, and the communication interface are communicatively connected with each other through the bus.

[0102] The processor can be implemented in a general-purpose CPU, a microprocessor, an application-specific integrated circuit, or one or more integrated circuits, and the like, and is configured to execute a related program to implement the technical solutions provided by the embodiments of the present disclosure.

[0103] The memory can be implemented in a ROM, a RAM, a static storage device, a dynamic storage device, or the like. The memory can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present disclosure are implemented by software or firmware, the related program codes are stored in the memory and are invoked and executed by the processor.

[0104] The input / output interface is configured to connect with input / output modules to implement information input and output. The input / output modules can be configured as components in the device or externally connected to the device to provide corresponding functions. The input devices can include a keyboard, a mouse, a touch screen, a microphone, various sensors, and the like, and the output devices can include a display, a speaker, a vibrator, an indicator, and the like.

[0105] The communication interface is configured to connect with a communication module to implement communication interaction between the device and other devices. The communication module can communicate through a wired manner (for example, a USB, a network cable, or the like) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, or the like).

[0106] The bus includes a path to transmit information between various components (for example, the processor, the memory, the input / output interface, and the communication interface) of the device.

[0107] It should be noted that, although the above device only shows the processor, the memory, the input / output interface, the communication interface, and the bus, in the specific implementation process, the device can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device can also only include the components necessary to implement the technical solutions provided by the embodiments of the present disclosure, and does not have to include all the components shown in the figure.

[0108] The computer readable media of the present embodiment includes permanent and non-permanent, removable and non-removable media can be realized by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device or any other non-transmission medium that can be used to store information accessible by a computing device.

[0109] The embodiments of the present application are described in detail above in combination with the embodiments, but the present application is not limited to the above-described embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present application.

Claims

1. A method for resource allocation based on network slicing, characterized in that, The method comprises the following steps: constructing bidding information for the slices and constructing bidding information for the users, the bidding information including the bandwidth resources and the number of computing resources of the slices and the asking price thereof, and the bidding information including the demand of the users for the resources and the bid of the users for the resources owned by the slices; screening the slices meeting the conditions of the users according to the demand of the users for the resources in the bidding information and the number of resources owned by the slices in the bidding information; screening the users meeting the conditions of the slices according to the asking price in the bidding information and the bid in the bidding information; calculating the utility that the users and the slices meeting the conditions of both sides after the screening can obtain when a match is reached, and constructing an utility matrix according to the utility; modeling the system as a bipartite graph matching model, initializing auxiliary variables according to the utility matrix, the auxiliary variables being the upper bound values of the users and the slices, wherein the maximum utility that each user and all the slices can reach is taken as the initial upper bound value of the user, and the initial upper bound values of all the slices are set to 0; randomly sorting the end users, and finding a slice for a user according to the order, and if the sum of the upper bound values of the user and the slice is equal to the corresponding value in the utility matrix, the finding is completed, and if a slice cannot be found for the user, the upper bound value is modified; obtaining a matching matrix according to the matching strategy of the users and the slices. 2.The method of claim 1, wherein, The optimization objective of the bipartite graph matching model is defined as follows: (1) representing a user bid price for a slice , use cost price of a slice , use number of power end users number of slices The constraint condition is: (2) and respectively represent the slice owned bandwidth resource and the number of computing resources, and represent the user demand number of bandwidth resource and computing resource, and respectively represent the actual delay and the user required delay, and respectively represent the actual bit error rate and the user required bit error rate, I1, I2, I3 respectively represent the set of the inspection type user, the control type user and the collection type user, is the bidding information of the slice. 3.The method of claim 1, wherein, The matching strategy of the users and the slices is made according to the following formula: (3) wherein the current end user is user , the slice at the end of the augmented path is slice , the user that has already matched with slice is user , the secondary slice of user is slice , the secondary slice of user is slice , denotes the utility of both parties, user and slice , to reach a match.

4. The method of claim 3, wherein, If no augmented path for the user is found The topmark value is modified by subtracting a constant from all users on the augmented path being augmented and adding a constant to all slices, i.e.: (4) The calculation method is as follows: (5) in, Refers to users With slices The edge weight, i.e., the user With slices The utility of both parties after matching; For users The initial top-level value; The initial top-level values ​​for all slices; For users The augmentation path found. 5.A network slice based resource allocation apparatus, characterized in that, The method comprises the following steps: a constructing module, configured to construct bidding information for the slices and construct bidding information for the users, the bidding information including the bandwidth resources and the number of computing resources of the slices and the asking price thereof, and the bidding information including the demand of the users for the resources and the bid of the users for the resources owned by the slices; a screening module, configured to screen the slices and the users meeting the conditions respectively; the constructing module is further configured to calculate the utility that the users and the slices meeting the conditions of both sides after the screening can obtain when a match is reached, and construct an utility matrix according to the utility, and model the system as a bipartite graph matching model, initialize auxiliary variables according to the utility matrix, the auxiliary variables being the upper bound values of the users and the slices, wherein the maximum utility that each user and all the slices can reach is taken as the initial upper bound value of the user, and the initial upper bound values of all the slices are set to 0; an executing module, configured to randomly sort the end users, and find a slice for a user according to the order, and obtain a matching matrix according to the matching strategy, the matching strategy being that if the sum of the upper bound values of the user and the slice is equal to the corresponding value in the utility matrix, the finding is completed, and if a slice cannot be found for the user, the upper bound value is modified.

6. The network slice based resource allocation apparatus according to claim 5, wherein, The optimization objective of the bipartite graph matching model is defined as follows: (6) On behalf of users slices The bid price, using Representative slice Cost price Represents the number of end-users of electricity. Represents the number of slices; The constraint condition is: (7) and respectively represent the slice owned bandwidth resource and the number of computing resources, and represent the user demand number of bandwidth resource and computing resource, and respectively represent the actual delay and the user required delay, and respectively represent the actual bit error rate and the user required bit error rate, I1, I2, I3 respectively represent the set of patrol class users, control class users and collection class users, is the bidding information of the slice.

7. An electronic device, comprising: The method comprises the following steps: a memory; one or more processors coupled to the memory; one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more application programs are configured to execute the method according to any one of claims 1-5.

8. A non-transitory computer-readable storage medium, comprising: A non-transitory computer readable storage medium stores computer instructions for causing a computer to execute the method according to any one of claims 1-5.

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