5G network slice access method for power service terminal

Abstract

The invention provides a 5G network slice access method for a power service terminal, which belongs to the technical field of 5G network communication, constructs a static decision analysis model, and comprises the following steps: constructing a network slice QoS parameter matrix; obtaining an index weight of the network slice QoS parameter matrix; the random consistency ratio of the parameter matrix is obtained, and the matrix is judged and adjusted to meet the consistency requirement; grey correlation degree analysis is carried out; obtaining a comprehensive correlation coefficient; determining an optimal network slice according to the comprehensive correlation coefficient of the network slices; constructing a network slice dynamic selection game model: constructing a user priority preference sorting list, and constructing a network priority preference sorting list; and performing multiple rounds of matching games according to the priority level, and dynamically binding the user with the network slice. The method has the advantages that the analytic hierarchy process and grey correlation analysis are fused, user preferences and network slice QoS parameter quantification are combined, subjectivity of a traditional method depending on expert experience is reduced, and scientificity and objectivity of network slice selection are improved.

Description

Technical Field

[0001] The present invention belongs to the field of 5G network communication technology, and specifically relates to a 5G network slicing access method for a power service terminal. Background Art

[0002] With the development of 5G networks, users' differentiated demands for network services are becoming increasingly prominent. Traditional network deployment methods use independent infrastructure to meet different needs, resulting in high equipment costs and low energy efficiency. Network slicing technology divides the physical network into multiple logical networks to carry services with different quality of service (QoS) requirements;

[0003] Currently, network slicing access is mostly achieved through the analytic hierarchy process (AHP), but AHP relies on expert experience and the decision results are easily affected by subjective factors. The existing solutions do not take into account both user satisfaction and network operating costs. Summary of the invention

[0004] The technical problem to be solved by the present invention is the problem of high subjectivity, poor dynamic adaptability and imbalance of interests between users and networks in the prior art. In view of the shortcomings of the prior art, a 5G network slice access method for a power business terminal is provided, which can realize network slice selection with low cost, high service quality and user satisfaction. In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] Constructing a static decision analysis model, including: constructing a network slice QoS parameter matrix; obtaining the indicator weights of the network slice QoS parameter matrix; obtaining the random consistency ratio of the parameter matrix, judging and adjusting the matrix to meet the consistency requirements according to the random consistency ratio; obtaining a reference sequence and performing grey correlation analysis on the reference sequence and the comparison sequence; combining the indicator weights with the correlation coefficient obtained by the grey correlation analysis method to obtain a comprehensive correlation coefficient; determining the optimal network slice according to the comprehensive correlation coefficient of the network slice;

[0006] Construct a network slice dynamic selection game model, including: constructing a user priority preference ranking list, and constructing a network priority preference ranking list based on the comprehensive correlation coefficient; based on the preference ranking list, multiple rounds of matching games are carried out according to the priority level to dynamically bind users to network slices.

[0007] Furthermore, the method of constructing a network slice QoS parameter matrix includes:

[0008] The user determines the QoS parameters of the evaluation network according to his / her preference for selecting network slices. Assume that there are m network slices in the network environment and n QoS parameters selected by the user. The 1-9 scaling method commonly used in the hierarchical analysis method is used to construct the multi-objective parameter matrix A.

[0009]

[0010] where a ij Represents the value of the j-th QoS parameter of the ith network slice.

[0011] Furthermore, the method for obtaining the indicator weight of the network slice QoS parameter matrix includes:

[0012] Normalize the column vectors of the multi-objective parameter matrix A to obtain the matrix B, b ij is the element in matrix B;

[0013]

[0014] Sum the rows of matrix B to get the column vector is a column vector Elements in

[0015]

[0016] Column vector Normalized to get the feature vector W, w i is the element in the feature vector W, i.e., the indicator weight of QoS;

[0017]

[0018] W = [w1, w2, ..., w n ] T .

[0019] Furthermore, the method of obtaining the random consistency ratio of the parameter matrix and judging and adjusting the matrix to meet the consistency requirements according to the random consistency ratio includes:

[0020] Calculate the maximum eigenvalue λ of the judgment matrix A max , calculate λ max The formula is as follows:

[0021]

[0022] Calculate the deviation consistency index CI of the judgment matrix:

[0023] CI=(λ max -n) / (n-1)

[0024] To ensure the reliability of the parameter matrix, a consistency check is performed on the matrix, and the random consistency ratio CR of the parameter matrix is ​​calculated as follows:

[0025] CR=CI / RI

[0026] Where n is the order of the target parameter matrix and RI is the random consistency index.

[0027] When CR≤0.1, the matrix meets the consistency requirements; when CR>0.1, the matrix does not meet the consistency requirements, and the matrix is ​​adjusted and the consistency test is performed again.

[0028] Furthermore, the method of obtaining a reference sequence and performing grey correlation analysis on the reference sequence and the comparison sequence includes:

[0029] The reference sequence is represented by: X0 = (X0(1), X0(2), ···, X0(n)). Grey correlation analysis is performed on the reference sequence and the comparison sequence. The grey correlation coefficient is expressed as follows:

[0030]

[0031] Where ξ is the resolution coefficient, v i (k) is the relationship coefficient of the kth QoS parameter in the i-th network slice, x i (k) represents the value of the kth QoS parameter in the i-th network slice.

[0032] Furthermore, the method for determining the optimal network slice according to the comprehensive correlation coefficient of the network slice includes:

[0033] The grey correlation coefficient v is calculated by the CR formula by comparing the comparison sequence of the network slice with the reference sequence. i (k), forming the matrix C, and obtaining the evaluation values ​​of the evaluation indicators of different schemes;

[0034] Combining the index weights obtained by the hierarchical analysis method with the correlation coefficient obtained by the grey correlation analysis method, the comprehensive correlation coefficient V is obtained:

[0035] V=WC

[0036] Where W is the weight set of QoS parameters, C is the grey correlation coefficient matrix, and V is the comprehensive correlation coefficient.

[0037] Furthermore, the method of constructing a user priority preference ranking list and constructing a network priority preference ranking list based on a comprehensive correlation coefficient includes:

[0038] According to the comprehensive correlation coefficient, the network slices are ranked by preference to generate the user's priority ranking U(N) for the network slices;

[0039] According to the parameters required for network slicing, users are ranked by preference to generate a priority ranking N(U) of network slicing for users. The required parameters include the required latency, required data rate, required packet loss rate, required communication cost and required bandwidth when users use a certain service.

[0040] Furthermore, the method of dynamically binding users to network slices by performing multiple rounds of matching games according to the priority based on the preference ranking list includes:

[0041] First round of matching: The user selects the highest priority network based on U(N), and each network selects users based on the current matching window size to establish preliminary matching pairs;

[0042] Dynamic window adjustment: Update the network-side matching window for unmatched users, delete matched users and add new candidate users;

[0043] Iterative matching: multiple rounds of matching are performed according to the updated priority sorting, and the window is dynamically adjusted after each match until the termination condition is met;

[0044] Degraded Matching: Enables next-level priority network selection for consecutively unmatched users.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. By integrating the analytic hierarchy process (AHP) and grey correlation analysis, user preferences are quantitatively combined with network slice QoS parameters, reducing the subjectivity of traditional methods that rely on expert experience and improving the scientificity and objectivity of network slice selection.

[0047] 2. Based on a multi-round dynamic game matching mechanism, combined with the priority sorting of both users and the network, dynamic allocation of resources on demand is achieved to avoid resource redundancy or shortage caused by static allocation, thereby improving network resource utilization. In scenarios where network resources are tight, dynamic window adjustment and secondary priority downgrade matching are used to avoid service interruptions, ensure basic user communication needs, and significantly improve system fault tolerance and continuity.

[0048] 3. Accurately match the optimal network slice through the comprehensive correlation coefficient, and combine it with the downgrade matching strategy to ensure that high-priority services (such as power emergency communications and real-time monitoring) have priority access to the optimal slice, thereby improving the service compliance rate of delay-sensitive services; taking into account user QoS requirements and network operating costs (such as communication fees and bandwidth occupancy), while meeting business performance, reduce redundant resource allocation through dynamic game and reduce network operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0050] Figure 1 : Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0051] In order to better understand the present invention, the content of the present invention is further clearly described below in conjunction with the embodiments and the accompanying drawings, but the protection content of the present invention is not limited to the following embodiments. In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details.

[0052] Example 1: See Figure 1 -, a method for accessing a 5G network slice of a power service terminal in this embodiment includes:

[0053] Building a static decision analysis model:

[0054] Constructing a network slice QoS parameter matrix: 5G power networks pay more attention to network quality and user experience. Users will choose the best network slice to access based on these two indicators. There are many factors that affect network quality and user experience, such as network bandwidth, network throughput, transmission power, transmission delay, network security, etc. These factors will affect the user's choice of the best network slice. First, the user determines the QoS parameters for evaluating the network based on his or her preference for selecting a network slice. Assume that there are m network slices in the network environment and there are n QoS parameters selected by the user. The present invention adopts the 1-9 scaling method commonly used in the hierarchical analysis method to construct the multi-objective parameter matrix A as follows.

[0055]

[0056] where a ij Represents the value of the j-th QoS parameter of the ith network slice.

[0057] Get the indicator weight of the network slice QoS parameter matrix: Normalize the column vector of the target parameter matrix A to get the matrix B, b ij are the elements in the matrix B.

[0058]

[0059] Sum the rows of matrix B to get the column vector is a column vector The elements in .

[0060]

[0061] Column vector Normalized to get the feature vector W, w i is the element in the feature vector W, i.e., the weight of QoS.

[0062]

[0063] but

[0064] W = [w1, w2, ..., w n ] T

[0065] Get the random consistency ratio of the parameter matrix: Calculate the maximum eigenvalue λ of the judgment matrix A max , calculate λ max The formula is as follows:

[0066]

[0067] Calculate the deviation consistency index CI of the judgment matrix:

[0068] CI=(λ max -n) / (n-1)

[0069] To ensure the reliability of the parameter matrix, the matrix must be checked for consistency and the random consistency ratio CR of the parameter matrix is ​​calculated as follows:

[0070] CR=CI / RI

[0071] Where n is the order of the target parameter matrix and RI is the random consistency index.

[0072] When CR≤0.1, the matrix is ​​considered to meet the consistency requirements. Otherwise, the matrix must be adjusted and the consistency check must be performed again until the matrix meets the requirements. The RI values ​​are shown in the table below, where n is the matrix order.

[0073] Random consistency index RI value

[0074] n 1 2 3 4 5 6 7 8 9 RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45

[0075] Grey relational analysis:

[0076] First, the reference sequence and comparison sequence are determined. Then the sequence values ​​obtained by the optimal values ​​of the target QoS parameters of each network slice are

[0077] As the reference sequence. It is expressed as: X0 = (X0(1), X0(2), ···, X0(n)). Then the reference sequence and the comparison sequence are subjected to grey relational analysis. The grey relational coefficient is expressed as follows:

[0078]

[0079] Where ξ is the resolution coefficient, v i (k) is the relationship coefficient of the kth QoS parameter in the i-th network slice. i (k) represents the value of the kth QoS parameter (QoS indicator factor value) in the i-th network slice.

[0080] The grey correlation coefficient v is calculated by the CR formula for the comparison sequence and the reference sequence of each network slice. i (k), forming the matrix C, that is, obtaining the evaluation values ​​of the evaluation indicators of different schemes.

[0081] Combining the index weights obtained by the hierarchical analysis method with the correlation coefficient obtained by the grey correlation analysis method, the comprehensive correlation coefficient V is obtained:

[0082] V=WC

[0083] Where W is the weight set of QoS parameters, C is the grey correlation coefficient matrix, and V is the comprehensive correlation coefficient.

[0084] Finally, the optimal network slice is determined based on the comprehensive correlation coefficient of each network slice.

[0085] Network slice selection algorithm based on matching game:

[0086] The algorithm is based on the idea of ​​matching game theory and combined with the GAHP algorithm. It not only ensures the speed and accuracy of network slice selection, but also takes into account the satisfaction of both users and the network.

[0087] Matching games can be divided into one-to-one, many-to-one, and many-to-many matching relationships based on the matching relationship.

[0088] Assume that the network set is

[0089] Net={net1,net2,…,net m}

[0090] User set is

[0091] User={user1, user2,…,user n}

[0092] The matching game adopts a many-to-one matching game, that is, in the first matching game, all users play the matching game at the same time. In each round of matching, the participants who are ranked ahead of the network ranking U(N) and the user ranking N(U) will complete the matching first, and the unmatched users will complete the matching after multiple matches. The specific steps of the matching game are as follows.

[0093] 1. Determination and collection of parameters. The first part is the parameters required by users to sort network slices. This part of parameters can build the priority of network slices according to the comprehensive correlation coefficient mentioned above. The second part is the parameters required by network slices to sort users. According to the operation requirements of network operators, this part of parameters is determined as the required delay, required data rate, required packet loss rate, required communication fee and required bandwidth when users use a certain service.

[0094] 2. Determination of preference ranking: In this model, it is divided into the preference ranking of users for network slices and the preference ranking of network slices for users.

[0095] 3. First match. Assume that the matched user is not in the user set. Each user on the user side sorts U(N) according to his / her network priority and selects the network with the highest priority. The network side needs to set the size of the matching window, that is, how many users the network side considers in one match. If the network side user is within the matching window, the match is successful.

[0096] 4. Shifting the matching window. If network j is the network with the highest priority among user i, and user i is in the user priority window of network j, the matching is complete. If user i fails to match network j, that is, user i is not in the network window. After the first matching is completed, the matching window needs to be shifted. The window of network j deletes the users who successfully matched in the first matching of network j and the users who successfully matched in the first matching of other networks, and adds the users who did not participate in the matching in the priority sorting.

[0097] 5. Second match. Network j is still the highest priority network for user i, and the matching window of network j has shifted. If user i is in the shifted matching window, the match is complete. The second match can be performed multiple times.

[0098] 6. User matching fails. If user i fails to match in both the first and second matching, user i selects the network with the lower priority. Repeat the above process until all users are matched.

[0099] Beneficial effects:

[0100] 1. By integrating the analytic hierarchy process (AHP) and grey correlation analysis, user preferences are quantitatively combined with network slice QoS parameters, reducing the subjectivity of traditional methods that rely on expert experience and improving the scientificity and objectivity of network slice selection.

[0101] 2. Based on a multi-round dynamic game matching mechanism, combined with the priority sorting of both users and the network, dynamic allocation of resources on demand is achieved to avoid resource redundancy or shortage caused by static allocation, thereby improving network resource utilization. In scenarios where network resources are tight, dynamic window adjustment and secondary priority downgrade matching are used to avoid service interruptions, ensure basic user communication needs, and significantly improve system fault tolerance and continuity.

[0102] 3. Accurately match the optimal network slice through the comprehensive correlation coefficient, and combine it with the downgrade matching strategy to ensure that high-priority services (such as power emergency communications and real-time monitoring) have priority access to the optimal slice, thereby improving the service compliance rate of delay-sensitive services; taking into account user QoS requirements and network operating costs (such as communication fees and bandwidth occupancy), while meeting business performance, reduce redundant resource allocation through dynamic game and reduce network operating costs.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in the field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for accessing a 5G network slice of a power service terminal, characterized in that: The following steps are involved: Constructing a static decision analysis model, including: constructing a network slice QoS parameter matrix; obtaining the indicator weights of the network slice QoS parameter matrix; obtaining the random consistency ratio of the parameter matrix, judging and adjusting the matrix to meet the consistency requirements according to the random consistency ratio; obtaining a reference sequence and performing grey correlation analysis on the reference sequence and the comparison sequence; combining the indicator weights with the correlation coefficient obtained by the grey correlation analysis method to obtain a comprehensive correlation coefficient; determining the optimal network slice according to the comprehensive correlation coefficient of the network slice; Construct a network slice dynamic selection game model, including: constructing a user priority preference ranking list, and constructing a network priority preference ranking list based on the comprehensive correlation coefficient; based on the preference ranking list, multiple rounds of matching games are carried out according to the priority level to dynamically bind users to network slices.

2. The 5G network slice access method for electric power service terminals according to claim 1, characterized in that: The method of constructing a network slice QoS parameter matrix includes: The user determines the QoS parameters of the evaluation network according to his / her preference for selecting network slices. Assume that there are m network slices in the network environment and n QoS parameters selected by the user. The 1-9 scaling method commonly used in the hierarchical analysis method is used to construct the multi-objective parameter matrix A. where a ij Represents the value of the j-th QoS parameter of the ith network slice.

3. The 5G network slice access method for electric power service terminals according to claim 2, characterized in that: The method for obtaining the indicator weight of the network slice QoS parameter matrix includes: Normalize the column vectors of the multi-objective parameter matrix A to obtain the matrix B, b ij is the element in matrix B; Sum the rows of matrix B to get the column vector is a column vector Elements in Column vector Normalized to get the feature vector W, w i is the element in the feature vector W, i.e., the indicator weight of QoS; W=[w1,w2,...,w n ] T 。 4. The method for accessing a 5G network slice of a power service terminal according to claim 3, characterized in that: The method of obtaining the random consistency ratio of the parameter matrix and judging and adjusting the matrix to meet the consistency requirements according to the random consistency ratio includes: Calculate the maximum eigenvalue λ of the judgment matrix A max , calculate λ max The formula is as follows: Calculate the deviation consistency index CI of the judgment matrix: CI=(λ max −n) / (n−1) To ensure the reliability of the parameter matrix, a consistency check is performed on the matrix, and the random consistency ratio CR of the parameter matrix is ​​calculated as follows: CR=CI / RI Where n is the order of the target parameter matrix and RI is the random consistency index. When CR≤0.1, the matrix meets the consistency requirements; when CR>0.1, the matrix does not meet the consistency requirements, and the matrix is ​​adjusted and the consistency test is performed again.

5. The method for accessing a 5G network slice of a power service terminal according to claim 1, characterized in that: The method of obtaining a reference sequence and performing grey relational analysis on the reference sequence and the comparison sequence includes: The reference sequence is represented by: X0 = (X0(1), X0(2), ···, X0(n)). Grey correlation analysis is performed on the reference sequence and the comparison sequence. The grey correlation coefficient is expressed as follows: Where ξ is the resolution coefficient, v i (k) is the relationship coefficient of the kth QoS parameter in the i-th network slice, x i (k) represents the value of the kth QoS parameter in the i-th network slice.

6. The method for accessing a 5G network slice of a power service terminal according to claim 5, characterized in that: The method for determining the optimal network slice based on the comprehensive correlation coefficient of the network slice includes: The grey correlation coefficient v is calculated by the CR formula by comparing the comparison sequence of the network slice with the reference sequence. i (k), forming the matrix C, and obtaining the evaluation values ​​of the evaluation indicators of different schemes; Combining the index weights obtained by the hierarchical analysis method with the correlation coefficient obtained by the grey correlation analysis method, the comprehensive correlation coefficient V is obtained: V=WC Where W is the weight set of QoS parameters, C is the grey correlation coefficient matrix, and V is the comprehensive correlation coefficient.

7. The method for accessing a 5G network slice of a power service terminal according to claim 1, characterized in that: The method of constructing a user priority preference ranking list and a network priority preference ranking list based on a comprehensive correlation coefficient includes: According to the comprehensive correlation coefficient, the network slices are ranked by preference to generate the user's priority ranking U(N) for the network slices; According to the parameters required for network slicing, users are ranked by preference to generate a priority ranking N(U) of network slicing for users. The required parameters include the required latency, required data rate, required packet loss rate, required communication cost and required bandwidth when users use a certain service.

8. The method for accessing a 5G network slice of a power service terminal according to claim 7, characterized in that: The method of dynamically binding users to network slices by performing multiple rounds of matching games based on the priority of the preference ranking list includes: First round of matching: The user selects the highest priority network based on U(N), and each network selects users based on the current matching window size to establish preliminary matching pairs; Dynamic window adjustment: Update the network-side matching window for unmatched users, delete matched users and add new candidate users; Iterative matching: multiple rounds of matching are performed according to the updated priority sorting, and the window is dynamically adjusted after each match until the termination condition is met; Degraded Matching: Enables next-level priority network selection for consecutively unmatched users.

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