CN115001975B - Power distribution network protection optimization method and system for 5G slice access - Google Patents
Power distribution network protection optimization method and system for 5G slice access Download PDFInfo
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Abstract
The invention provides a power distribution network protection optimization method for 5G slice access. And obtaining a corresponding mathematical model by adopting a data transmission scheme with minimized time delay, and then carrying out model solving by adopting a Lagrangian multiplier method and a branch-and-bound method according to the scheme so as to uniformly distribute 5G network resources. By adopting the method, the time delay generated in the protection communication process of the power distribution network can be reduced. The external equipment can be regarded as part of interference, is integrated into a formula of the data transmission rate, can optimize jitter delay generated when the external movable equipment is accessed to the 5G network, and has certain robustness to the external interference. According to the power distribution network protection optimization method based on 5G slice access, the problems of malfunction and refusal of power distribution network protection actions caused by 5G low-delay and jitter are solved, further accurate power distribution network protection actions are achieved, and the whole power distribution network system operates more stably and safely.
Description
Technical Field
The invention relates to the technical field of power distribution network protection, in particular to a power distribution network protection optimization method and system for 5G slice access.
Background
In recent years, 5G technology has been rapidly developed, and the ultra-high reliability and low delay communication service (ultra-reliable low latency communications, URLLC) brought by the technology has the advantages of high bandwidth, low delay and the like. The 5G technology is applied to the differential protection of the power distribution network, and technical advantages are provided for fault positioning, isolation and recovery. However, the transmission delay and jitter of 5G communication still have adverse effects on the calculation of the differential protection criterion, so that the problems of protection malfunction, protection refusal and the like are generated, which is unfavorable for the operation of the whole power distribution network system. The main reason why the 5G delay jitter is large is that the network resource is unevenly distributed.
Aiming at the problems, the invention discloses a distributed differential protection method and a distributed differential protection system [ ZH ] of a power distribution network based on a 5G network, which are disclosed by application No. CN201911175988.4, and the distributed differential protection method and the distributed differential protection system of the power distribution network based on the 5G network comprise the following steps: when the digital protection devices are in signal transmission, communication is carried out by using a communication channel based on a 5G network, so that the data interaction of the two digital protection devices is realized; the 5G slice network is used as a communication channel, so that the safety of communication is ensured, and on the basis of the low-delay characteristic of the 5G communication, a data link is optimized through the slice network to minimize the number of link transmission hops, so that the delay of data transmission is further reduced; and (3) simultaneously carrying out mutation detection on the current at two ends of the power distribution network line by using the digital protection device, taking the respective mutation time as a synchronous time reference of the digital protection devices at two ends of the line, starting the digital protection device once the mutation starting criterion is met, sending a starting signal to an opposite end, calculating a current fault component value after the digital protection device is started, and sending current fault component data to the opposite end after the calculation is completed. The data link is optimized through the slicing network to minimize the number of link transmission hops so as to reduce the time delay of data transmission. But this approach is not robust to external disturbances.
Disclosure of Invention
The technical problem to be solved by the invention is to provide the power distribution network protection optimization method for 5G slice access, so that the delay jitter generated by the access of external mobile equipment is robust to a certain extent, and the protection action of the power distribution network is more accurate.
The invention solves the technical problems by the following technical means:
a power distribution network protection optimization method for 5G slice access comprises the following steps:
step 1, modeling a power distribution network system adopting a 5G communication network in consideration of communication delay:
i data sampling nodes are arranged in the distribution network, S n (i) Slice ID currently served for the ith sampling node; among the K accessible access points of the sampling node, the kth access point deploys a slice S in the slice set m (k) M e {1,2, …, M }, where M represents the slice set size; b (B) m (k) Is S m (k) A corresponding bandwidth; in order to maximize the resource utilization rate of the system, uniformly distributing resources to all data sampling nodes; therefore, the data transmission rate of the i-th sampling node is shown as formula (1);
in the formula (1), a m (k) Representing the current slice S m (k) The number of sampling nodes accessed; d, d ik Representing the distance between the sampling node i and the access point to be accessed, its propagation loss PL (d ik ) A representation; n (N) 0 Noise power per bandwidth; p (P) k Representing the transmitting power of an access point to be accessed; delta represents the total interference received by the AP to be accessed;
step 2, taking the minimum time delay as an optimization target, wherein an objective function is shown in a formula (2);
wherein DS is i The size of the data packet sampled by the ith sampling node;
the constraint condition is as shown in the formula (3) -formula (6):
S m (k)≥S n (i) (4)
r ik (m)≥R i ,i∈I,k∈K,m∈M (5)
in the formula (2), x ik (m) is the 0-1 variable to be solved, when x ik (m) =1, indicating that the ith data sampling node accesses the mth slice of the kth AP, otherwise, indicating that the data sampling node does not access; constraint (3) indicates that a data sampling node can only access one slice at a time; constraint (4) indicates that the QoS service level of a slice accessed by the data sampling node should not be smaller than that of the original service slice; constraint (5) indicates that the data transmission rate of the data sampling node after being accessed into the slice should be greater than or equal to the minimum rate requirement R i The method comprises the steps of carrying out a first treatment on the surface of the Constraint (6) indicates that the number of nodes to be accommodated by the slice to be accessed should be equal to or less than the number of remaining nodes u before access m (k)；
Step 3, solving the optimal solution of the objective function in the step 2 by using Lagrangian multiplier method,
step 4, obtaining a 0-1 solution variable x of the objective function by a branch-and-bound method based on the optimal solution in the step 3 0-1 And determining whether the user accesses the corresponding slice according to the solving variable.
The invention provides a power distribution network protection optimization method for 5G slice access. And obtaining a corresponding mathematical model by adopting a data transmission scheme with minimized time delay, and then carrying out model solving by adopting a Lagrangian multiplier method and a branch-and-bound method according to the scheme so as to uniformly distribute 5G network resources. By adopting the method, the time delay generated in the protection communication process of the power distribution network can be reduced. The external equipment can be regarded as part of interference, is integrated into a formula of the data transmission rate, can optimize jitter delay generated when the external movable equipment is accessed to the 5G network, and has certain robustness to the external interference. According to the power distribution network protection optimization method based on 5G slice access, the problems of malfunction and refusal of power distribution network protection actions caused by 5G low-delay and jitter are solved, further accurate power distribution network protection actions are achieved, and the whole power distribution network system operates more stably and safely.
Further, the specific steps of the step 3 are as follows: establishing a lagrangian function of the nonlinear programming problem according to equation (7):
in the formula (7), L (x) ik (m), lambda represents the value of x ik (m) Lagrangian function of Lagrangian multiplier λ, h 1 (x ik (m))，h 2 (x ik (m))，h 3 (x ik (m))，h 4 (x ik (m))，h 5 (x ik (m)) respectively represent 5 constraint functions lambda 1 、λ 2 、λ 3 、λ 4 、λ 5 A lagrangian multiplier representing a corresponding constraint function, and having:
h 2 (x ik (m))＝x ik (m)-1 (9)
h 3 (x ik (m))＝S n (i)-S m (k) (10)
h 4 (x ik (m))＝R i -r ik (m) (11)
establishing a Carlo-Coulomb-Take condition according to formulas (13) - (16) to thereby find an optimal solution x of the relaxed nonlinear programming problem by combining the correlation equations of the Carlo-Coulomb-Take condition relax ：
h 1 (x ik (m))＝0，h 2 (x ik (m))≤0，h 3 (x ik (m))≤0，h 4 (x ik (m))≤0，h 5 (x ik (m))≤0 (14)
λ 1 ，λ 2 ，λ 3 ，λ 4 ，λ 5 ≥0 (15)
Further, the specific steps of the step 4 are as follows:
step 4.1, using the formula (2) as the problem p-1; the input of the branch-and-bound method is: relaxation problem optimal solution x meeting Carlo-Coulomb-Tak condition relax Optimal objective function value Z of relaxation problem relax Any value epsilon from 0 to 1; initializing k=0, l= 0,U =z relax ；
Step 4.2, from the optimal solution x relax Any one of solutions x which does not meet the constraint of 0-1 is selected j The method comprises the following steps: x is x j ∈(0,1)；
Step 4.3, if 0 is less than or equal to x j <Epsilon is true, constraint x is satisfied j =0 is added to problem p-1, forming sub-problem i; otherwise, constraint x j =1 is added to the problem p-1, forming a sub-problem ii, epsilon representing any value from 0 to 1;
step 4.4, k++, continuing to find the relaxation problem solution of the sub-problem I or the sub-problem II, denoted as x k And the corresponding optimal objective function value is recorded as Z k ；
Step 4.5, finding out the maximum value U of the optimal objective function as a new upper bound, namely:
U＝max{Z k′ |k′＝1,2,…,k},x k′ ∈[0,1]；
step 4.6, finding out the maximum value L of the objective function from branches meeting the 0-1 condition as a new lower bound, namely:
L＝max{Z k′ |k′＝1,2,…,k},x k′ ∈{0,1}；
step 4.7, if Z k′ <L, cut off the corresponding branch, otherwise Z k′ More than L, and not meeting the condition of 0-1, returning to the step 4.3;
step 4.8, otherwise, means that the optimal objective function value for all branches is equal to the lower bound: z is Z k′ =l, will Z k′ Assignment Z 0-1 Will x k′ Assignment to x 0-1 And serves as the optimal solution to problem p-1, where x 0-1 Representing the optimal solution meeting the 0-1 constraint.
Corresponding to the method, the invention also provides a power distribution network protection optimization system for 5G slice access, which comprises the following steps:
the modeling module is used for modeling a power distribution network system adopting a 5G communication network in consideration of communication delay:
i data sampling nodes are arranged in the distribution network, S n (i) Slice ID currently served for the ith sampling node; among the K accessible access points of the sampling node, the kth access point deploys a slice S in the slice set m (k) M e {1,2, …, M }, where M represents the slice set size; b (B) m (k) Is S m (k) A corresponding bandwidth; in order to maximize the resource utilization rate of the system, uniformly distributing resources to all data sampling nodes; therefore, the data transmission rate of the i-th sampling node is shown as formula (1);
in the formula (1), a m (k) Representing the current slice S m (k) The number of sampling nodes accessed; d, d ik Representing the distance between the sampling node i and the access point to be accessed, its propagation loss PL (d ik ) A representation; n (N) 0 Noise power per bandwidth; p (P) k Representing the transmitting power of an access point to be accessed; delta represents the total interference received by the AP to be accessed;
the objective function building module takes the minimum time delay as an optimization target, and the objective function is shown as a formula (2);
wherein DS is i The size of the data packet sampled by the ith sampling node;
the constraint condition is as shown in the formula (3) -formula (6):
S m (k)≥S n (i) (4)
r ik (m)≥R i ,i∈I,k∈K,m∈M (5)
in the formula (2), x ik (m) is the 0-1 variable to be solved, when x ik (m) =1, indicating that the ith data sampling node accesses the mth slice of the kth AP, otherwise, indicating that the data sampling node does not access; constraint (3) indicates that a data sampling node can only access one slice at a time; constraint (4) indicates that the QoS service level of a slice accessed by the data sampling node should not be smaller than that of the original service slice; constraint (5) indicates that the data transmission rate of the data sampling node after being accessed into the slice should be greater than or equal to the minimum rate requirement R i The method comprises the steps of carrying out a first treatment on the surface of the ConstraintCondition (6) indicates that the number of nodes to be accommodated by the slice to be accessed should be equal to or less than the number of remaining nodes u before access m (k)；
An objective function solving module for solving the objective function in the step 2 by using Lagrangian multiplier method,
judging module for obtaining 0-1 solving variable x of target function by branch-and-bound method 0-1 And determining whether the user accesses the corresponding slice according to the solving variable.
Further, the objective function solving module specifically comprises the following steps: establishing a lagrangian function of the nonlinear programming problem according to equation (7):
in the formula (7), L (x) ik (m), lambda represents the value of x ik (m) Lagrangian function of Lagrangian multiplier λ, h 1 (x ik (m))，h 2 (x ik (m))，h 3 (x ik (m))，h 4 (x ik (m))，h 5 (x ik (m)) respectively represent 5 constraint functions lambda 1 、λ 2 、λ 3 、λ 4 、λ 5 A lagrangian multiplier representing a corresponding constraint function, and having:
h 2 (x ik (m))＝x ik (m)-1 (9)
h 3 (x ik (m))＝S n (i)-S m (k) (10)
h 4 (x ik (m))＝R i -r ik (m) (11)
establishing a Carlo-Coulomb-Take condition according to formulas (13) - (16) to thereby find an optimal solution x of the relaxed nonlinear programming problem by combining the correlation equations of the Carlo-Coulomb-Take condition relax ：
h 1 (x ik (m))＝0，h 2 (x ik (m))≤0，h 3 (x ik (m))≤0，h 4 (x ik (m))≤0，h 5 (x ik (m))≤0 (14)
λ 1 ，λ 2 ，λ 3 ，λ 4 ，λ 5 ≥0 (15)
Further, the specific steps of the judging module are as follows:
step 4.1, using the formula (2) as the problem p-1; the input of the branch-and-bound method is: relaxation problem optimal solution x meeting Carlo-Coulomb-Tak condition relax Optimal objective function value Z of relaxation problem relax Any value epsilon from 0 to 1; initializing k=0, l= 0,U =z relax ；
Step 4.2, from the optimal solution x relax Any one of solutions x which does not meet the constraint of 0-1 is selected j The method comprises the following steps: x is x j ∈(0,1)；
Step 4.3, if 0 is less than or equal to x j <Epsilon is true, constraint x is satisfied j =0 is added to problem p-1, forming sub-problem i; otherwise, constraint x j =1 is added to the problem p-1, forming a sub-problem ii, epsilon representing any value from 0 to 1;
step 4.4, k++, continuing to find the relaxation problem solution of the sub-problem I or the sub-problem II, denoted as x k And the corresponding optimal objective function value is recorded as Z k ；
Step 4.5, finding out the maximum value U of the optimal objective function as a new upper bound, namely:
U＝max{Z k′ |k′＝1,2,…,k},x k′ ∈[0,1]；
step 4.6, finding out the maximum value L of the objective function from branches meeting the 0-1 condition as a new lower bound, namely:
L＝max{Z k′ |k′＝1,2,…,k},x k′ ∈{0,1}；
step 4.7, if Z k′ <L, cut off the corresponding branch, otherwise Z k′ More than L, and not meeting the condition of 0-1, returning to the step 4.3;
step 4.8, otherwise, means that the optimal objective function value for all branches is equal to the lower bound: z is Z k′ =l, will Z k′ Assignment Z 0-1 Will x k′ Assignment to x 0-1 And serves as the optimal solution to problem p-1, where x 0-1 Representing the optimal solution meeting the 0-1 constraint.
The present invention also provides a processing device comprising at least one processor, and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by a processor that invokes the program instructions to perform the method described above.
The present invention also provides a computer-readable storage medium storing computer instructions that cause a computer to perform the above-described method.
The invention has the advantages that:
the invention provides a power distribution network protection optimization method for 5G slice access. And obtaining a corresponding mathematical model by adopting a data transmission scheme with minimized time delay, and then carrying out model solving by adopting a Lagrangian multiplier method and a branch-and-bound method according to the scheme so as to uniformly distribute 5G network resources. By adopting the method, the time delay generated in the protection communication process of the power distribution network can be reduced. The external equipment can be regarded as part of interference, is integrated into a formula of the data transmission rate, can optimize jitter delay generated when the external movable equipment is accessed to the 5G network, and has certain robustness to the external interference. According to the power distribution network protection optimization method based on 5G slice access, the problems of malfunction and refusal of power distribution network protection actions caused by 5G low-delay and jitter are solved, further accurate power distribution network protection actions are achieved, and the whole power distribution network system operates more stably and safely.
Drawings
Fig. 1 is a flow chart of a power distribution network protection optimization method of 5G slice access in an embodiment of the invention;
fig. 2 is an application scenario of a power distribution network protection optimization method for 5G slice access in an embodiment of the present invention;
FIG. 3 is a comparison of the effects of example 1 without and with the optimization method of the present invention;
fig. 4 is a comparison of effects of example 25G transmission delay with or without user access in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described in the following in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment provides a power distribution network protection optimization method for 5G slice access, which comprises the following steps:
step 1, modeling a power distribution network system adopting a 5G communication network in consideration of communication delay:
a scenario of the present embodiment for differential protection of a distribution network using a 5G communication network is shown in fig. 1. The 5G power distribution network protection comprises 1 5G base station and a plurality of protection devices which are connected to the base station. Each protection device has current sampling capability, is arranged on two sides of a line, and samples differential protection current data. The 5G base station provides a 5G network, provides communication service for the protection devices, and simultaneously provides network service for mobile terminal devices accessed by other users. The comparison of line current data between the protection devices can be realized through a 5G communication mode, so that whether the line needs to perform differential protection action or not is judged.
I data acquisition nodes and DS are arranged in the distribution network i Data packet size sampled for the ith node, S n (i) Slice ID currently served for the ith sampling node; among the K accessible Access Points (APs) of the acquisition node, the kth AP deploys any slice S in the slice set m (k) M e {1,2, …, M }, where M represents the slice set size; b (B) m (k) Is S m (k) Corresponding bandwidths. In order to maximize the resource utilization rate of the system, all the data acquisition nodes are uniformly allocated with resources. Therefore, the data transmission rate of the i-th node is shown as formula (1).
In the formula (1), a m (k) Representing the current slice S m (k) The number of nodes accessed; d, d ik Representing the distance between node i and the AP to be accessed, its propagation loss uses PL (d ik ) A representation; n (N) 0 Noise power per bandwidth; p (P) k Representing the transmitting power of the AP to be accessed; delta represents the total interference received by the AP to be accessed.
And 2, in order to make the data acquisition node maximally utilize slice resources during data transmission and meet the requirements of time delay and throughput, taking the minimum time delay as an optimization target, wherein an objective function is shown in a formula (2).
The constraint condition is as shown in the formula (3) -formula (6):
S m (k)≥S n (i) (4)
r ik (m)≥R i ,i∈I,k∈K,m∈M (5)
in the formula (2), x ik (m) is the 0-1 variable to be solved, when x ik (m) =1, indicating that the ith data acquisition node accesses the mth slice of the kth AP, otherwise, indicating that the data acquisition node does not access; constraint (3) indicates that a data acquisition node can only access one slice at a time; constraint (4) indicates that the QoS service level of a slice accessed by the data acquisition node should not be smaller than that of the original service slice; constraint (5) indicates that the data transmission rate of the data acquisition node after being accessed into the slice is greater than or equal to the minimum rate requirement R i The method comprises the steps of carrying out a first treatment on the surface of the Constraint (6) indicates that the number of nodes to be accommodated by the slice to be accessed should be equal to or less than the number of remaining nodes u before access m (k)。
Step 3, solving the model by using a Lagrange multiplier method, and establishing a Lagrange function of the nonlinear programming problem according to a formula (7):
in the formula (7), L (x) ik (m), lambda represents the value of x ik (m) Lagrangian function of Lagrangian multiplier λ, h 1 (x ik (m))，h 2 (x ik (m))，h 3 (x ik (m))，h 4 (x ik (m))，h 5 (x ik (m)) respectively represent 5 constraint functions lambda 1 、λ 2 、λ 3 、λ 4 、λ 5 A lagrangian multiplier representing a corresponding constraint function, and having:
h 2 (x ik (m))＝x ik (m)-1 (9)
h 3 (x ik (m))＝S n (i)-S m (k) (10)
h 4 (x ik (m))＝R i -r ik (m) (11)
the KKT condition (Karush-Kuhn-Tucker Conditions, carrush-Coulomb-Tack condition) is established according to the formulas (13) - (16), whereby the optimal solution x of the relaxed nonlinear programming problem is found by combining the KKT condition-dependent equations relax ：
h 1 (x ik (m))＝0，h 2 (x ik (m))≤0，h 3 (x ik (m))≤0，h 4 (x ik (m))≤0，h 5 (x ik (m))≤0 (14)
λ 1 ，λ 2 ，λ 3 ，λ 4 ，λ 5 ≥0 (15)
Step 4, obtaining a 0-1 solution variable x of the objective function by using a branch-and-bound method 0-1 And determining whether the user accesses the corresponding slice according to the solving variable. The method comprises the following specific steps:
step 4.1, the problem p-1 is represented by the formula (2). The input of the branch-and-bound method is: relaxation problem optimal solution x meeting KKT condition relax Optimal objective function value Z of relaxation problem relax Any value epsilon of 0-1. Initializing k=0, l= 0,U =z relax 。
Step 4.2, from the optimal solution x relax Any one of which does not meet the 0-1 constraintSolution x j The method comprises the following steps: x is x j ∈(0,1)；
Step 4.3, if 0 is less than or equal to x j <Epsilon is true, constraint x is satisfied j =0 is added to problem p-1, forming sub-problem i; otherwise, constraint x j =1 is added to the problem p-1, forming a sub-problem ii, epsilon representing any value from 0 to 1;
step 4.4, k++, continuing to find the relaxation problem solution of the sub-problem I or the sub-problem II, denoted as x k And the corresponding optimal objective function value is recorded as Z k ；
Step 4.5, finding out the maximum value U of the optimal objective function as a new upper bound, namely:
U＝max{Z k′ |k′＝1,2,…,k},x k′ ∈[0,1]；
step 4.6, finding out the maximum value L of the objective function from branches meeting the 0-1 condition as a new lower bound, namely:
L＝max{Z k′ |k′＝1,2,…,k},x k′ ∈{0,1}；
step 4.7, if Z k′ <L, cut off the corresponding branch, otherwise Z k′ More than L, and not meeting the condition of 0-1, returning to the step 4.3;
step 4.8, otherwise, means that the optimal objective function value for all branches is equal to the lower bound: z is Z k′ =l, will Z k′ Assignment Z 0-1 Will x k′ Assignment to x 0-1 And serves as the optimal solution to problem p-1, where x 0-1 Representing the optimal solution meeting the 0-1 constraint.
The embodiment discloses a power distribution network protection optimization method for 5G slice access, which aims at the optimization problem of time delay and jitter generated when power distribution network protection acquisition current data is transmitted through 5G, firstly analyzes the use scene of 5G communication in the power distribution network, and establishes a mathematical model of 5G communication access time delay; and then according to the model, researching an optimized objective function with minimized differential signal time delay, solving 5G slice selection of maximally utilizing communication channel resources by adopting a Lagrangian multiplier method and a branch-and-bound method according to the optimized objective function, and realizing minimum differential signal transmission time delay, thereby improving the safety of a power grid system and ensuring the stable operation of the power grid system.
The above steps are described below in connection with specific examples.
Example 1: comparing the time delay curves generated by the optimization method with the time delay curves generated by the optimization method not adopted in the invention, the comparison of the two cases is shown in fig. 2, and the time delay generated by the 5G transmission can change randomly along with the change of time under the condition that the minimum time delay method is not adopted in the text, so that the judgment of the differential protection action is not facilitated. With the method used herein, the delay generated by 5G transmission will gradually decrease with time, and eventually tend to a constant value. From the above comparison, the superiority of the minimization optimization method presented herein may be embodied.
Example 2: under the optimization method, 100 mobile terminal devices are respectively connected in 1s and 7s, a 5G base station can allocate certain network resources for use, the process can slightly increase the time delay of differential protection transmission, and the generated time delay tends to be stable due to the optimization effect of an algorithm and is almost the same as the condition without the mobile terminal device connection, so that the robustness of the method is shown. The specific results are shown in FIG. 3.
The embodiment also provides a power distribution network protection optimization system for 5G slice access, which corresponds to the method and comprises the following steps:
the modeling module is used for modeling a power distribution network system adopting a 5G communication network in consideration of communication delay:
i data sampling nodes are arranged in the distribution network, S n (i) Slice ID currently served for the ith sampling node; among the K accessible access points of the sampling node, the kth access point deploys a slice S in the slice set m (k) M e {1,2, …, M }, where M represents the slice set size; b (B) m (k) Is S m (k) A corresponding bandwidth; in order to maximize the resource utilization rate of the system, uniformly distributing resources to all data sampling nodes; therefore, the data transmission rate of the i-th sampling node is shown as formula (1);
in the formula (1), a m (k) Representing the current slice S m (k) The number of sampling nodes accessed; d, d ik Representing the distance between the sampling node i and the access point to be accessed, its propagation loss PL (d ik ) A representation; n (N) 0 Noise power per bandwidth; p (P) k Representing the transmitting power of an access point to be accessed; delta represents the total interference received by the AP to be accessed;
the objective function building module takes the minimum time delay as an optimization target, and the objective function is shown as a formula (2);
wherein DS is i The size of the data packet sampled by the ith sampling node;
the constraint condition is as shown in the formula (3) -formula (6):
S m (k)≥S n (i) (4)
r ik (m)≥R i ,i∈I,k∈K,m∈M (5)
in the formula (2), x ik (m) is the 0-1 variable to be solved, when x ik (m) =1, indicating that the ith data sampling node accesses the mth slice of the kth AP, otherwise, indicating that the data sampling node does not access; constraint (3) indicates that a data sampling node can only access one slice at a time; constraint (4) indicates that the QoS service level of a slice accessed by the data sampling node should not be smaller than that of the original service slice; constraint (5) indicates that the data transmission rate of the data sampling node after being accessed into the slice should be greater than or equal to the lowest rateR is calculated i The method comprises the steps of carrying out a first treatment on the surface of the Constraint (6) indicates that the number of nodes to be accommodated by the slice to be accessed should be equal to or less than the number of remaining nodes u before access m (k)；
An objective function solving module for solving the objective function in the step 2 by using Lagrangian multiplier method,
judging module for obtaining 0-1 solving variable x of target function by branch-and-bound method 0-1 And determining whether the user accesses the corresponding slice according to the solving variable.
The objective function solving module comprises the following specific steps: establishing a lagrangian function of the nonlinear programming problem according to equation (7):
in the formula (7), L (x) ik (m), lambda represents the value of x ik (m) Lagrangian function of Lagrangian multiplier λ, h 1 (x ik (m))，h 2 (x ik (m))，h 3 (x ik (m))，h 4 (x ik (m))，h 5 (x ik (m)) respectively represent 5 constraint functions lambda 1 、λ 2 、λ 3 、λ 4 、λ 5 A lagrangian multiplier representing a corresponding constraint function, and having:
h 2 (x ik (m))＝x ik (m)-1 (9)
h 3 (x ik (m))＝S n (i)-S m (k) (10)
h 4 (x ik (m))＝R i -r ik (m) (11)
establishing a Carlo-Coulomb-Take condition according to formulas (13) - (16) to thereby find an optimal solution x of the relaxed nonlinear programming problem by combining the correlation equations of the Carlo-Coulomb-Take condition relax ：
h 1 (x ik (m))＝0，h 2 (x ik (m))≤0，h 3 (x ik (m))≤0，h 4 (x ik (m))≤0，h 5 (x ik (m))≤0 (14)
λ 1 ，λ 2 ，λ 3 ，λ 4 ，λ 5 ≥0 (15)
Further, the specific steps of the judging module are as follows:
step 4.1, using the formula (2) as the problem p-1; the input of the branch-and-bound method is: relaxation problem optimal solution x meeting Carlo-Coulomb-Tak condition relax Optimal objective function value Z of relaxation problem relax Any value epsilon from 0 to 1; initializing k=0, l= 0,U =z relax ；
Step 4.2, from the optimal solution x relax Any one of solutions x which does not meet the constraint of 0-1 is selected j The method comprises the following steps: x is x j ∈(0,1)；
Step 4.3, if 0 is less than or equal to x j <Epsilon is true, constraint x is satisfied j =0 is added to problem p-1, forming sub-problem i; otherwise, constraint x j =1 is added to the problem p-1, forming a sub-problem ii, epsilon representing any value from 0 to 1;
step 4.4, k++, continuing to find the relaxation problem solution of the sub-problem I or the sub-problem II, denoted as x k And the corresponding optimal objective function value is recorded as Z k ；
Step 4.5, finding out the maximum value U of the optimal objective function as a new upper bound, namely:
U＝max{Z k′ |k′＝1,2,…,k},x k′ ∈[0,1]；
step 4.6, finding out the maximum value L of the objective function from branches meeting the 0-1 condition as a new lower bound, namely:
L＝max{Z k′ |k′＝1,2,…,k},x k′ ∈{0,1}；
step 4.7, if Z k′ <L, cut off the corresponding branch, otherwise Z k′ More than L, and not meeting the condition of 0-1, returning to the step 4.3;
step 4.8, otherwise, means that the optimal objective function value for all branches is equal to the lower bound: z is Z k′ =l, will Z k′ Assignment Z 0-1 Will x k′ Assignment to x 0-1 And serves as the optimal solution to problem p-1, where x 0-1 Representing the optimal solution meeting the 0-1 constraint.
The present invention also provides a processing device comprising at least one processor, and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by a processor that invokes the program instructions to perform the method described above.
The present invention also provides a computer-readable storage medium storing computer instructions that cause a computer to perform the above-described method.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. The power distribution network protection optimization method for 5G slice access is characterized by comprising the following steps of:
step 1, modeling a power distribution network system adopting a 5G communication network in consideration of communication delay:
i data sampling nodes are arranged in the distribution network, S n (i) Slice ID currently served for the ith sampling node; among the K accessible access points of the sampling node, the kth access point deploys a slice S in the slice set m (k) M e {1,2, …, M }, where M represents the slice set size; b (B) m (k) Is S m (k) A corresponding bandwidth; in order to maximize the resource utilization rate of the system, uniformly distributing resources to all data sampling nodes; therefore, the data transmission rate of the i-th sampling node is shown as formula (1);
in the formula (1), a m (k) Representing the current slice S m (k) The number of sampling nodes accessed; d, d ik Representing the distance between the sampling node i and the access point to be accessed, its propagation loss PL (d ik ) A representation; n (N) 0 Noise power per bandwidth; p (P) k Representing the transmitting power of an access point to be accessed; delta represents the total interference received by the AP to be accessed;
step 2, taking the minimum time delay as an optimization target, wherein an objective function is shown in a formula (2);
wherein DS is i The size of the data packet sampled by the ith sampling node;
the constraint condition is as shown in the formula (3) -formula (6):
S m (k)≥S n (i) (4)
r ik (m)≥R i ,i∈I,k∈K,m∈M (5)
in the formula (2), x ik (m) is the 0-1 variable to be solved, when x ik (m) =1, indicating that the ith data sampling node accesses the mth slice of the kth AP, otherwise, indicating that the data sampling node does not access; constraint (3) indicates that a data sampling node can only access one slice at a time; constraint (4) indicates that the QoS service level of a slice accessed by the data sampling node should not be smaller than that of the original service slice; constraint (5) indicates that the data transmission rate of the data sampling node after being accessed into the slice should be greater than or equal to the minimum rate requirement R i The method comprises the steps of carrying out a first treatment on the surface of the Constraint (6) indicates that the number of nodes to be accommodated by the slice to be accessed should be equal to or less than the number of remaining nodes u before access m (k)；
Step 3, solving the optimal solution of the objective function in the step 2 by using Lagrangian multiplier method,
step 4, obtaining a 0-1 solution variable x of the objective function by a branch-and-bound method based on the optimal solution in the step 3 0-1 Determining whether a user accesses a corresponding slice according to the solving variable; the x is 0-1 Representing the optimal solution meeting the 0-1 constraint.
2. The power distribution network protection optimization method for 5G slice access according to claim 1, wherein the specific steps of step 3 are as follows: establishing a lagrangian function of the nonlinear programming problem according to equation (7):
in the formula (7), L (x) ik (m), lambda represents the value of x ik (m) Lagrangian function of Lagrangian multiplier λ, h 1 (x ik (m))，h 2 (x ik (m))，h 3 (x ik (m))，h 4 (x ik (m))，h 5 (x ik (m)) respectively represent 5 constraint functions lambda 1 、λ 2 、λ 3 、λ 4 、λ 5 A lagrangian multiplier representing a corresponding constraint function, and having:
h 2 (x ik (m))＝x ik (m)-1 (9)
h 3 (x ik (m))＝S n (i)-S m (k) (10)
h 4 (x ik (m))＝R i -r ik (m) (11)
establishing a Carlo-Coulomb-Take condition according to formulas (13) - (16) to thereby find an optimal solution x of the relaxed nonlinear programming problem by combining the correlation equations of the Carlo-Coulomb-Take condition relax ：
h 1 (x ik (m))＝0，h 2 (x ik (m))≤0，h 3 (x ik (m))≤0，h 4 (x ik (m))≤0，h 5 (x ik (m))≤0 (14)
λ 1 ，λ 2 ，λ 3 ，λ 4 ，λ 5 ≥0 (15)
3. The power distribution network protection optimization method for 5G slice access according to claim 2, wherein the specific steps of step 4 are as follows:
step 4.1, using the formula (2) as the problem p-1; the input of the branch-and-bound method is: relaxation problem optimal solution x meeting Carlo-Coulomb-Tak condition relax Optimal objective function value Z of relaxation problem relax Any value epsilon from 0 to 1; initializing k=0, l= 0,U =z relax ；
Step 4.2, from the optimal solution x relax Any one of solutions x which does not meet the constraint of 0-1 is selected j The method comprises the following steps: x is x j ∈(0,1)；
Step 4.3, if 0 is less than or equal to x j <Epsilon is true, constraint x is satisfied j =0 is added to problem p-1, forming sub-problem i; otherwise, constraint x j =1 is added to the problem p-1, forming a sub-problem ii, epsilon representing any value from 0 to 1;
step 4.4, k++, continuing to find the relaxation problem solution of the sub-problem I or the sub-problem II, denoted as x k And the corresponding optimal objective function value is recorded as Z k ；
Step 4.5, finding out the maximum value U of the optimal objective function as a new upper bound, namely:
U＝max{Z k′ |k′＝1,2,…,k},x k′ ∈[0,1]；
step 4.6, finding out the maximum value L of the objective function from branches meeting the 0-1 condition as a new lower bound, namely:
L＝max{Z k′ |k′＝1,2,…,k},x k′ ∈{0,1}；
step 4.7, if Z k′ <L, cut off the corresponding branch, otherwise Z k′ >L, and does not meet the 0-1 condition, returning to the step 4.3;
step 4.8, otherwise, means that the optimal objective function value for all branches is equal to the lower bound: z is Z k′ =l, will Z k′ Assignment Z 0-1 Will x k′ Assignment to x 0-1 And serves as the optimal solution to problem p-1, where x 0-1 Representing the optimal solution meeting the 0-1 constraint.
4. A power distribution network protection optimization system for 5G slice access, comprising:
the modeling module is used for modeling a power distribution network system adopting a 5G communication network in consideration of communication delay:
i data sampling nodes are arranged in the distribution network, S n (i) Slice ID currently served for the ith sampling node; among the K accessible access points of the sampling node, the kth access point deploys a slice S in the slice set m (k) M e {1,2, …, M }, where M represents the slice set size; b (B) m (k) Is S m (k) A corresponding bandwidth; in order to maximize the resource utilization rate of the system, uniformly distributing resources to all data sampling nodes; therefore, the data transmission rate of the i-th sampling node is shown as formula (1);
in the formula (1), a m (k) Representing the current slice S m (k) The number of sampling nodes accessed; d, d ik Representing the distance between the sampling node i and the access point to be accessed, its propagation loss PL (d ik ) A representation; n (N) 0 Noise power per bandwidth; p (P) k Representing the transmitting power of an access point to be accessed; delta represents the total interference received by the AP to be accessed;
the objective function building module takes the minimum time delay as an optimization target, and the objective function is shown as a formula (2);
wherein DS is i The size of the data packet sampled by the ith sampling node;
the constraint condition is as shown in the formula (3) -formula (6):
S m (k)≥S n (i) (4)
r ik (m)≥R i ,i∈I,k∈K,m∈M (5)
in the formula (2), x ik (m) is the 0-1 variable to be solved, when x ik (m) =1, indicating that the ith data sampling node accesses the mth slice of the kth AP, otherwise, indicating that the data sampling node does not access; constraint (3) indicates that a data sampling node can only access one slice at a time; constraint (4) indicates that the QoS service level of a slice accessed by the data sampling node should not be smaller than that of the original service slice; constraint (5) indicates that the data transmission rate of the data sampling node after being accessed into the slice should be greater than or equal to the minimum rate requirement R i The method comprises the steps of carrying out a first treatment on the surface of the Constraint (6) indicates that the number of nodes to be accommodated by the slice to be accessed should be equal to or less than the number of remaining nodes u before access m (k)；
An objective function solving module for solving the objective function in the step 2 by using Lagrangian multiplier method,
the judging module is used for obtaining a 0-1 solution variable x of the objective function by using a branch-and-bound method based on the solution of the objective function 0-1 Determining whether a user accesses a corresponding slice according to the solving variable; x is x 0-1 Representing the optimal solution meeting the 0-1 constraint.
5. The power distribution network protection optimization system for 5G slice access of claim 4, wherein the objective function solving module specifically comprises the following steps: establishing a lagrangian function of the nonlinear programming problem according to equation (7):
in the formula (7), L (x) ik (m), lambda represents the value of x ik (m) Lagrangian function of Lagrangian multiplier λ, h 1 (x ik (m))，h 2 (x ik (m))，h 3 (x ik (m))，h 4 (x ik (m))，h 5 (x ik (m)) respectively represent 5 constraint functions lambda 1 、λ 2 、λ 3 、λ 4 、λ 5 A lagrangian multiplier representing a corresponding constraint function, and having:
h 2 (x ik (m))＝x ik (m)-1 (9)
h 3 (x ik (m))＝S n (i)-S m (k) (10)
h 4 (x ik (m))＝R i -r ik (m) (11)
establishing a Carlo-Coulomb-Take condition according to formulas (13) - (16) to thereby find an optimal solution x of the relaxed nonlinear programming problem by combining the correlation equations of the Carlo-Coulomb-Take condition relax ：
h 1 (x ik (m))＝0，h 2 (x ik (m))≤0，h 3 (x ik (m))≤0，h 4 (x ik (m))≤0，h 5 (x ik (m))≤0 (14)
λ 1 ，λ 2 ，λ 3 ，λ 4 ，λ 5 ≥0 (15)
6. The power distribution network protection optimization system for 5G slice access of claim 5, wherein the judging module specifically comprises the following steps:
step 4.1, using the formula (2) as the problem p-1; the input of the branch-and-bound method is: relaxation problem optimal solution x meeting Carlo-Coulomb-Tak condition relax Optimal objective function value Z of relaxation problem relax Any value epsilon from 0 to 1; initializing k=0, l= 0,U =z relax ；
Step 4.2, from the optimal solution x relax Any one of solutions x which does not meet the constraint of 0-1 is selected j The method comprises the following steps: x is x j ∈(0,1)；
Step 4.3, if 0 is less than or equal to x j <Epsilon is true, constraint x is satisfied j =0 is added to problem p-1, forming sub-problem i; otherwise, constraint x j =1 is added to the problem p-1, forming a sub-problem ii, epsilon representing any value from 0 to 1;
step 4.4, k++, continuing to find the relaxation problem solution of the sub-problem I or the sub-problem II, denoted as x k And the corresponding optimal objective function value is recorded as Z k ；
Step 4.5, finding out the maximum value U of the optimal objective function as a new upper bound, namely:
U＝max{Z k′ |k′＝1,2,…,k},x k′ ∈[0,1]；
step 4.6, finding out the maximum value L of the objective function from branches meeting the 0-1 condition as a new lower bound, namely:
L＝max{Z k′ |k′＝1,2,…,k},x k′ ∈{0,1}；
step 4.7, if Z k′ <L, cut off the corresponding branch, otherwise Z k′ >L, and does not meet the 0-1 condition, returning to the step 4.3;
step 4.8, otherwise, means that the optimal objective function value for all branches is equal to the lower bound: z is Z k′ =l, will Z k′ Assignment Z 0-1 Will x k′ Assignment to x 0-1 And serves as the optimal solution to problem p-1, where x 0-1 Representing the optimal solution meeting the 0-1 constraint.
7. A processing device comprising at least one processor, and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by a processor, the processor invoking the program instructions to perform the method of any of claims 1-3.
8. A computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 3.
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