CN113766446A - Data scheduling and resource allocation method for intelligent power grid information acquisition based on 5G network - Google Patents

Data scheduling and resource allocation method for intelligent power grid information acquisition based on 5G network Download PDF

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CN113766446A
CN113766446A CN202011216112.2A CN202011216112A CN113766446A CN 113766446 A CN113766446 A CN 113766446A CN 202011216112 A CN202011216112 A CN 202011216112A CN 113766446 A CN113766446 A CN 113766446A
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information acquisition
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谢民
章昊
王同文
于洋
张代新
陈�峰
高博
叶远波
程晓平
王栋
邵庆祝
俞斌
张骏
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State Grid Anhui Electric Power Co Ltd
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State Grid Anhui Electric Power Co Ltd
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Abstract

The invention discloses a data scheduling and resource allocation method for intelligent power grid information acquisition based on a 5G network, which comprises the following steps: 1. different types of sensors in the smart grid sample data to be monitored; 2. coding the sampled data according to the information acquisition nodes, the sensor numbers and the sampling times; 3. dividing the type of the sampling data into normal sampling data and abnormal alarm data, and reasonably reserving a wireless resource block for the abnormal alarm data according to a probability constraint planning model under random burst faults; 4. and allocating an optimal modulation coding scheme and resource blocks for each sampling data according to the total resource size of the 5G network, and transmitting the data to the 5G wireless private network base station more reliably. The invention can support the cooperative acquisition, scheduling and resource allocation of the normal sampling data and the abnormal alarm data with optimized energy efficiency, and reduce the packet loss rate under the condition of excessive data quantity.

Description

Data scheduling and resource allocation method for intelligent power grid information acquisition based on 5G network
Technical Field
The invention relates to the field of 5G uplink communication data transmission, in particular to a data scheduling and resource allocation method for intelligent power grid information acquisition based on a 5G network, which is suitable for an environment with random sudden faults in a power distribution network and supports the cooperative acquisition, scheduling and resource allocation of normal sampling data and abnormal alarm data with optimized energy efficiency.
Background
The smart power grid is a novel power grid which is highly integrated, completely interconnected and has a bidirectional communication function, and is a fully-automatic power transmission network. With the access of various distributed energy devices, an important feature of smart grids is the large-scale deployment of advanced two-sided architectures and two-way communication facilities. The high-level two-side system is composed of an intelligent electric meter installed on a demand side, a two-way communication network and a metering data management system in an electric power company, multi-source data (state data of energy equipment, power distribution network operation data, various information of terminal users and the like) in the intelligent power grid can be collected and monitored in real time, the purposes of remote service connection/disconnection, demand response, time-sharing pricing and the like are achieved, and the high-level two-side system is widely used for power failure management, service recovery and electric power market transaction.
The fifth generation mobile communication technology (5G) is a latest generation cellular mobile communication technology with low latency, high reliability, high rate, and large capacity, and two new machine type communication services are expected to be supported recently in the R15, R16, and R17 standards of 3 GPP: large-scale machine type communications and ultra-reliable low latency communications. Under the application environment of the smart grid, the 5G communication technology can well bear services such as information acquisition, data monitoring and the like, and is very suitable for being applied to an advanced measurement system.
The royal june of vingchun university and the like propose a dynamic exhaustive D2D resource allocation algorithm introducing simulated annealing (Jilin university school newspaper (engineering edition), 2020, "D2D resource allocation algorithm based on system outage probability in 5G"). The algorithm uses a dynamic interval exhaustive search algorithm to preliminarily determine user transmitting power, a two-dimensional multiplex table containing user QoS information is formulated, the QoS of a cellular user and the QoS of a D2D user are combined to determine multiplexing combination, a power adjusting module is added to a power dimension, and a simulated annealing algorithm is introduced to the combination dimension to jointly reduce the interruption probability. Simulation results show that compared with the traditional algorithm, the dynamic exhaustive resource allocation algorithm introducing simulated annealing has the advantages that the average power value is reduced by 78.3% in the power allocation stage, the average connection probability is improved by 10.2% in the channel allocation stage, and the average calculation time is reduced by 10.1%. However, the algorithm does not consider the energy efficiency problem in the communication transmission process, and may cause waste of communication resources.
The cambium, the university of west ampere electronic technology proposes a scheme combining non-orthogonal random access and data transmission (the university of west ampere electronic technology university master academic paper, 2019, "a high-efficiency random access and resource allocation scheme suitable for M2M communication"), which utilizes the characteristic of M2M communication short packet transmission to allow an M2M user to schedule corresponding physical uplink shared channel resources after sending a preamble, and to attach a transmission packet directly in the process of transmitting a connection establishment request message to simplify the random access process. The proposed resource allocation scheme aims at maximizing the system average access throughput and the resource utilization rate, and reasonably allocates uplink resources between a physical random access channel and a physical uplink shared channel. Simulation results show that compared with an orthogonal random access scheme, the proposed scheme can improve the resource utilization rate by about 30%. However, this scheme does not consider the situation of random burst failure that may occur during the M2M transmission process, and cannot guarantee the transmission reliability when the failure occurs.
The li xu jie et al of the river-sea university invented a resource allocation method of a 5G communication system based on a predation search algorithm (publication No. 106714083a), the method comprising: firstly, initializing system parameters including a channel, mobile terminal related parameters and predation search algorithm control parameters; then coding a resource allocation scheme of the system, randomly selecting an initial point in possible resource allocation schemes, initializing a limit set, and searching an optimal solution of resource allocation based on a predation search algorithm with a channel capacity value as a maximum target; and finally, carrying out channel resource allocation according to the allocation scheme corresponding to the optimal solution. The invention can quickly and effectively optimize resource allocation and effectively improve network capacity, but does not consider the problems of energy efficiency and power constraint in the transmission process and can not ensure the transmission reliability when a fault occurs.
Disclosure of Invention
The invention aims to avoid the defects of the prior art and provides a data scheduling and resource allocation method for intelligent power grid information acquisition based on a 5G network, so that the cooperative acquisition, scheduling and resource allocation of normal sampling data and abnormal alarm data with optimized energy efficiency can be supported, and the packet loss rate is reduced under the condition of excessive data quantity.
The invention adopts the following technical scheme for solving the technical problems:
the invention discloses a data scheduling and resource allocation method for intelligent power grid information acquisition based on a 5G network, which is applied to an uplink network environment consisting of N information acquisition nodes provided with K-type sensors and 1 5G wireless private network base station and is characterized in that the data scheduling and resource allocation method comprises the following steps:
step one, in the uplink network environment, numbering N information acquisition nodes, and marking as {1,2, ·, N, ·, N }, wherein N represents the serial number of the nth information acquisition node, and N is more than or equal to 1 and less than or equal to N; numbering the sensors in each information acquisition node, and recording as {1,2, K, K }, wherein K represents the serial number of the kth sensor in the node, and K is more than or equal to 1 and less than or equal to K;
let the sampling period of the kth sensor be TkMaking the least common multiple of sampling periods of all the sensors be T, and making T be uplink transmission time; the kth sensor samples {1,2, ·, J } repeatedly during the upstream transmission time T, where J denotes the sequence number of the jth sample, and
Figure BDA0002760410510000021
j represents the upper limit of sampling times, and J is more than or equal to 1 and less than or equal to J; the data quantity of the jth sensor in the nth information acquisition node sampled at the jth time is DSn,k,j；
Step two, assuming that the modulation and coding scheme of the uplink network environment is {1,2, …, M, …, M }, wherein M is the mth modulation and coding scheme,1≤m≤M；DSn,k,j,m,trepresenting the amount of data DSn,k,jSelecting the mth modulation coding mode, and transmitting normal sampling data in the tth time slot;
step three, dividing the sampling data types of the information acquisition nodes into normal sampling data and abnormal alarm data, and establishing a probability constraint planning model under random burst faults by adopting a sample average approximation strategy so as to reserve wireless resource blocks for the abnormal alarm data;
step four, taking the maximum value of the energy efficiency of the 5G network uplink transmission process as a target function, and establishing a series of constraint conditions according to the 5G communication protocol, the power control and the limiting factors of the sudden failures, thereby forming a mixed integer linear fraction programming model with linear constraint;
step five, the normal sampling data DSn,k,j,m,tReduced to one-dimensional variable DSaPerforming Charnes-Cooper transformation and Glover linearization on the mixed integer linear fractional programming model so as to reconstruct the mixed integer linear programming model with linear constraint;
step six, for a mixed integer linear programming model with linear constraint, combining a Lagrange dual and a trust domain algorithm, and constructing an iterative algorithm for searching a global optimal distribution scheme;
and seventhly, solving the mixed integer linear programming model by using the iterative algorithm for searching the global optimal allocation scheme based on an lp-solution solver, thereby obtaining an optimal data scheduling and resource allocation scheme.
The data scheduling and resource allocation method of the invention is characterized in that the third step is carried out according to the following processes:
3.1, establishing a probability constraint planning model under the random burst fault by using the formula (1):
Figure BDA0002760410510000031
in the formula (1), xn,k,j,m,tRepresents decision variables and decides the normal sample data DSn,k,j,m,tWhether or not to be transmitted; rn,k,j,m,tRepresents the normal sample data DSn,k,j,m,tThe transmission rate of (c); r isERepresenting the number of random burst faults; DS (direct sequence)EA data volume representing abnormal alarm data; rEA transmission rate indicating abnormal alarm data; y is the number of subchannels; delta is confidence;
Figure BDA0002760410510000032
represents rounding to the right;
step 3.2, setting
Figure BDA0002760410510000033
The sample is the ith independent and equally distributed sample of the random burst fault frequency within the uplink transmission time T, wherein I is 1,2, … I, and I represents the total amount of the samples;
and 3.3, obtaining probability constraint by using a sample average approximation strategy shown in the formula (2) to the formula (5), and using the probability constraint as a constraint condition of the objective function:
Figure BDA0002760410510000034
Figure BDA0002760410510000041
Figure BDA0002760410510000042
Figure BDA0002760410510000043
in formula (2) -formula (5), G represents a penalty factor; z is a radical ofiRepresenting the ith exponential function in the uplink transmission time T;
step 3.4, reserving abnormal alarm data by using the formula (2) -formula (5)
Figure BDA0002760410510000044
A radio resource block.
The fourth step is carried out according to the following processes:
step 4.1, establishing an objective function by using the formula (6):
Figure BDA0002760410510000045
equation (6) represents the maximum value of energy efficiency EE in the uplink transmission process of the 5G network, and is defined as the ratio of the data volume of all transmissions to the power consumption on all radio resource blocks; pn,k,j,m,tIndicating the transmission of said normal sample data DSn,k,j,m,tTransmitting power of the information acquisition node in the process;
and 4.2, constructing the rest constraint conditions of the objective function by using the formulas (7) to (11):
Figure BDA0002760410510000046
Figure BDA0002760410510000047
Figure BDA0002760410510000048
Figure BDA0002760410510000049
xn,k,j,m,t∈{0,1} (11)
the expression (7) indicates that the number of resource blocks allocated to each time slot cannot exceed Y;
equation (8) represents a power control model based on the uplink channel of the 5G network, and alpha represents a path loss compensation factor; PL (d)n) Representing the downlink path loss of the nth information-collecting node, dnIndicating the distance from the nth information collecting node to the base stationSeparating; IoT represents thermal interference; SINRmRepresenting the signal-to-noise ratio requirement of the mth modulation code selection;
formula (9) represents that the power consumed on the resource block cannot exceed the maximum transmission power of each information acquisition node;
equation (10) indicates that each packet can only have one modulation coding choice and be transmitted in a single time slot;
equation (11) indicates that the decision variable of the objective function can only take 0 or 1.
The fifth step is carried out according to the following processes:
step 5.1, simplifying the subscript parameters of the objective function and the constraint condition, and comprising the following steps: order to
Figure BDA0002760410510000051
Figure BDA0002760410510000052
Let A denote a length l1+l2+l3+l4+l5Binary number of (2), wherein first l1The bits representing the subscript n, l1+1 bit to l1+l2Bits representing the subscript k, l1+l2+1 bit to l1+l2+l3Bits representing subscript j, l1+l2+l3+1 bit to l1+l2+l3+l4The bits representing the subscript m, l1+l2+l3+l4+1 bit to l1+l2+l3+l4+l5The bits represent the subscript t; thereby obtaining the target function and the constraint condition after simplifying the parameters;
step 5.2, constructing an auxiliary variable u by using the formula (12);
Figure BDA0002760410510000053
in the formula (12), u is more than 0; a is a simplified subscript parameter, xaRepresenting the decision variable after simplifying the subscript, wherein a is more than or equal to 1 and less than or equal to A;
converting the target function after the parameters are simplified into a linear expression by using an auxiliary variable u, and multiplying both sides of an equation of all constraint conditions after the parameters are simplified by the auxiliary variable u, thereby completing Charnes-Cooper transformation and obtaining a mixed integer nonlinear programming model;
step 5.3, introducing two auxiliary decision variables wa＝xaX u and wi＝ziXu, thereby reconstructing a mixed integer linear programming model with linear constraints using equation (13) to complete Glover linearization of the mixed integer non-linear programming model;
Figure BDA0002760410510000054
constructing constraints of the mixed integer linear programming model by using the formula (14) to the formula (23):
Figure BDA0002760410510000055
Figure BDA0002760410510000061
Figure BDA0002760410510000062
Figure BDA0002760410510000063
Figure BDA0002760410510000064
Figure BDA0002760410510000065
Figure BDA0002760410510000066
Figure BDA0002760410510000067
wa∈{0,u} (22)
wi∈{0,u} (23)。
the sixth step is carried out according to the following processes:
step 6.1, constructing an unexpected constraint by using the formula (24):
Figure BDA0002760410510000068
in the formula (24), the reaction mixture is,
Figure BDA0002760410510000069
representing the decision value at the ith sample;
the objective function of equation (13) is used by equation (25)
Figure BDA00027604105100000610
Expressed as an objective function containing unintended constraints
Figure BDA00027604105100000611
Figure BDA00027604105100000612
Step 6.2, introducing a Lagrange multiplier lambda, and restating the mixed integer linear programming model into a two-layer optimization model, wherein the two-layer optimization model comprises the following steps: an inner layer optimization model and an outer layer optimization model;
order to
Figure BDA00027604105100000613
Wherein λ is1Lagrange multiplier, λ, of expression (18)2The Lagrangian multiplier of expression (24),
Figure BDA00027604105100000614
lagrange multipliers of the unexpected constraint under the ith sample in expression (24); the inner layer optimization model is built using equation (26):
Figure BDA0002760410510000071
based on the lagrange multiplier λ, an outer layer model is established using equation (27):
Figure BDA0002760410510000072
step 6.3, determining the search direction d of the iterative algorithm for searching the global optimal allocation scheme by using the formula (28):
Figure BDA0002760410510000073
step 6.4, constructing an iterative algorithm for searching the optimal Lagrange multiplier:
step 6.4.1, define the Lagrangian multiplier λ of the initial equation (18)1Is λ1(0)And initializing λ1(0)0; lagrange multiplier defining an initial equation (24)
Figure BDA0002760410510000074
Is composed of
Figure BDA0002760410510000075
And initialize
Figure BDA0002760410510000076
1,2, ·, I; defining the current iteration step number as h, and initializing h as 1; let thetaLIs the step size upper threshold, and θLIs more than 1; let thetaUIs a step size lower threshold, and0＜θUless than 1; let s(h)For the step size of the h-th iteration and initialize s(h)1 is ═ 1; making the upper iteration limit to H;
step 6.4.2, solving the iteration multiplier lambda corresponding to the h step(h)To obtain the optimal solution w of the h-th iterationa (h)And an optimum value VLR(λ(h))；
Step 6.4.3, if VLR(λ(h))-VLR(λ(h-1))＞θLThen let s(h)＝θL×s(h-1)(ii) a If VLR(λ(h))-VLR(λ(h-1))＜θUThen let s(h)＝θU×s(h-1)(ii) a Otherwise, let s(h)＝s(h-1)；
Step 6.4.4, calculating the search direction d of the h iteration through the formula (28)(h)And make λ(h+1)＝λh+s(h)×d(h)；
Step 6.4.5, if lambda1(h+1)If < 0, let λ1(h+1)0; if for any ith1A sample and the ith2A sample, present in the ith1A sample and the ith2The decision value is the same for each sample, i.e.
Figure BDA0002760410510000077
The optimal solution w for the h-th iterationa (h)The final optimal solution is obtained; if H is more than H, the optimal solution w of the H-th iteration is takena (h)Is an approximate solution; otherwise, after h +1 is given to h, the step is shifted to step 6.4.2.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention considers the problems of random burst faults and resource allocation in the information acquisition and transmission process of the intelligent power grid, comprises the selection of modulation coding modes, power control, wireless resource allocation and probability constraint under the random burst faults, and improves the energy efficiency and reliability of the information acquisition and transmission process of the intelligent power grid.
2. By adopting a sample average approximation strategy, the invention overcomes the dependence of the traditional algorithm on the distribution type, reasonably reserves wireless resource blocks for abnormal alarm data, and can support the cooperative acquisition, scheduling and resource allocation of normal sampling data and the abnormal alarm data.
3. The invention constructs an iterative algorithm for searching the global optimal distribution scheme by adopting Lagrange dual and trust domain algorithms, and the algorithm can optimize the energy efficiency of the system, reduce the packet loss rate under the condition of excessive data quantity and achieve reasonable resource distribution.
4. The resource allocation problem with maximized energy efficiency is converted into a mixed integer linear programming problem with linear constraint, and the existing lp _ solution solver can be directly used for solving, so that the operation time for solving the problem is greatly reduced.
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FIG. 1 is a system architecture diagram of a data scheduling and resource allocation method according to the present invention;
fig. 2 is a schematic diagram of resource allocation of the allocation method of the present invention.
Detailed Description
In this embodiment, as shown in fig. 1, a data scheduling and resource allocation method for smart grid information acquisition based on a 5G network is applied to an uplink network environment formed by N information acquisition nodes equipped with K types of sensors and 1 base station of a 5G wireless private network, where the sensors of different types are responsible for acquiring voltage, current, active/reactive power and control data of a power distribution network, and transmitting the acquired data to the base station of the 5G wireless private network through an uplink, and the base station transmits the processed data to a cloud monitoring system, and the data scheduling and resource allocation method is performed according to the following steps:
step one, in the uplink network environment, numbering N information acquisition nodes {1,2, ·, N, ·, N }, wherein N represents the serial number of the nth information acquisition node, and N is more than or equal to 1 and less than or equal to N; numbering the sensors in each information acquisition node by {1,2, ·, K }, wherein K represents the serial number of the kth sensor in the node, and K is more than or equal to 1 and less than or equal to K; let the kth sensorHas a sampling period of TkIn this embodiment, the serial number of the information collection node is 1 to 100, where each sensor is numbered {1,2, 3, 4}, T1＝100ms，T2＝200ms，T3＝300ms，T4400 ms; the minimum common multiple of the sampling periods of all the sensors is made to be T400 ms, and T is also uplink transmission time; the kth sensor repeatedly samples {1,2, ·, J } times within 400ms of the uplink transmission time T, where J denotes the sequence number of the jth sample, and
Figure BDA0002760410510000081
j represents the upper limit of sampling times, and J is more than or equal to 1 and less than or equal to J; within 400ms, the first sensor samples 4 times, the second sensor samples 3 times, the third sensor samples 2 times, and the fourth sensor samples 1 time; the data quantity of the jth sensor in the nth information acquisition node sampled at the jth time is DSn,k,j(ii) a In this embodiment, the data volume DSn,k,jThe size of (1) is [300,500 ]]Within bytes range;
step two, assuming that the modulation coding mode of the network environment is {1,2, …, M, …, M }, wherein M is the mth modulation coding mode, M is greater than or equal to 1 and less than or equal to M, and M is greater than or equal to 16; DS (direct sequence)n,k,j,m,tRepresenting the amount of data DSn,k,jSelecting the m modulation coding mode, and transmitting normal sampling data in the t time slot;
step three, dividing the sampling data types of the information acquisition nodes into normal sampling data and abnormal alarm data, establishing a probability constraint planning model under random burst faults by adopting a sample average approximation strategy, and reasonably reserving wireless resource blocks for the abnormal alarm data, as shown in figure 2;
3.1, establishing a probability constraint planning model under the random burst fault by using the formula (1):
Figure BDA0002760410510000091
in the formula (1), xn,k,j,m,tRepresents decision variables and decides the normal sample data DSn,k,j,m,tWhether or not to be transmitted; rn,k,j,m,tThe transmission rate of the normal sampling data is represented, and the transmission rate can be obtained by looking up a table 1; r isERepresenting the random burst fault times, which can be detected by a sensor; DS (direct sequence)EIndicating abnormal alarm data size, DSE＝200bytes；REIndicating the transmission rate of abnormal alarm data, RE378; y is the number of subchannels, and is 273; δ is confidence δ being 0.99;
Figure BDA0002760410510000092
represents rounding to the right;
step 3.2, setting
Figure BDA0002760410510000093
The sample is the ith independent and equally distributed sample of the random burst fault frequency in the uplink transmission time T, wherein I is 1,2, … I, I represents the total amount of the samples, and I is 1000;
and 3.3, obtaining probability constraint by using a sample average approximation strategy shown in the formula (2) to the formula (5), and using the probability constraint as a constraint condition of the objective function:
Figure BDA0002760410510000094
Figure BDA0002760410510000095
Figure BDA0002760410510000096
Figure BDA0002760410510000097
in formula (2) -formula (5), G represents a penalty factor; z is a radical ofiRepresenting the ith exponential function in the uplink transmission time T;
step 3.4, reserving abnormal alarm data by using the formula (2) -formula (5)
Figure BDA0002760410510000101
A radio resource block.
Step four, taking the maximum value of the energy efficiency of the 5G network uplink transmission process as a target function, and establishing a series of constraint conditions according to the 5G communication protocol, the power control and the limiting factors of the sudden failures, thereby forming a mixed integer linear fraction programming model with linear constraint;
step 4.1, establishing an objective function by using the formula (6):
Figure BDA0002760410510000102
equation (6) represents the maximum value of energy efficiency EE in the uplink transmission process of the 5G network, and is defined as the ratio of the data volume of all transmissions to the power consumption on all radio resource blocks; pn,k,j,m,tIndicating the transmission of said normal sample data DSn,k,j,m,tTransmitting power of the information acquisition node in the process;
and 4.2, constructing the rest constraint conditions by using the formulas (7) to (11):
Figure BDA0002760410510000103
Figure BDA0002760410510000104
Figure BDA0002760410510000105
Figure BDA0002760410510000106
xn,k,j,m,t∈{0,1} (11)
the expression (7) indicates that the number of resource blocks allocated to each time slot cannot exceed Y;
equation (8) represents a power control model based on an uplink channel of a 5G network, where α represents a path loss compensation factor, and α is 0.9; PL (d)n) Representing the downlink path loss of the nth information-collecting node, dnThe distance between the nth information acquisition node and the base station is represented, and the distance can be obtained by looking up a table 2; IoT represents thermal interference, IoT ═ 0; SINRmThe signal-to-noise ratio requirement of the mth modulation code selection is represented, and the signal-to-noise ratio requirement can be obtained by looking up a table 1;
formula (9) represents that the power consumed on the resource block cannot exceed the maximum transmission power of each information acquisition node;
equation (10) indicates that each packet can only have one modulation coding choice and be transmitted in a single time slot;
equation (11) indicates that the decision variable of the objective function can only take 0 or 1.
TABLE 1 Transmission Rate and SNR Range under different modulation modes
Figure BDA0002760410510000111
Step five, the normal sampling data DSn,k,j,m,tReduced to one-dimensional variable DSaPerforming Charnes-Cooper transformation and Glover linearization on the mixed integer linear fractional programming model so as to reconstruct the mixed integer linear programming model with linear constraint;
step 5.1, simplifying the subscript parameters of the objective function and the constraint condition, including: order to
Figure BDA0002760410510000112
Let A denote a length l1+l2+l3+l4+l5Binary number of (2), wherein first l1The bits representing the subscript n, l1+1 bit to l1+l2Bits representing the subscript k, l1+l2+1 bit to l1+l2+l3Bits representing subscript j, l1+l2+l3+1 bit to l1+l2+l3+l4The bits representing the subscript m, l1+l2+l3+l4+1 bit to l1+l2+l3+l4+l5The bits represent the subscript t; thereby obtaining the target function and the constraint condition after simplifying the parameters;
step 5.2, constructing an auxiliary variable u by using the formula (12);
Figure BDA0002760410510000121
in the formula (12), u is more than 0; a is a simplified subscript parameter, xaRepresenting the decision variables after the subscript is simplified, a is more than or equal to 1 and less than or equal to A;
converting the target function after the parameters are simplified into a linear expression by using an auxiliary variable u, and multiplying both sides of an equation of all constraint conditions after the parameters are simplified by the auxiliary variable u, thereby completing Charnes-Cooper transformation and obtaining a mixed integer nonlinear programming model;
step 5.3, introducing two auxiliary decision variables wa＝xaX u and wi＝ziXu, thereby reconstructing a mixed integer linear programming model with linear constraints using equation (13) to complete Glover linearization of the mixed integer non-linear programming model;
Figure BDA0002760410510000122
constructing constraints of the mixed integer linear programming model by using the formula (14) to the formula (23):
Figure BDA0002760410510000123
Figure BDA0002760410510000124
Figure BDA0002760410510000125
Figure BDA0002760410510000126
Figure BDA0002760410510000127
Figure BDA0002760410510000128
Figure BDA0002760410510000129
Figure BDA00027604105100001210
wa∈{0,u} (22)
wi∈{0,u} (23)
step six, for a mixed integer linear programming model with linear constraint, combining a Lagrange dual and a trust domain algorithm, and constructing an iterative algorithm for searching a global optimal distribution scheme;
step 6.1, constructing an unexpected constraint by using the formula (24):
Figure BDA0002760410510000131
in the formula (24), the reaction mixture is,
Figure BDA0002760410510000132
representing the decision value at the ith sample;
the objective function of equation (13) is used by equation (25)
Figure BDA0002760410510000133
Expressed as an objective function containing unintended constraints
Figure BDA0002760410510000134
Figure BDA0002760410510000135
Step 6.2, introducing a Lagrange multiplier lambda, and restating the mixed integer linear programming model into a two-layer optimization model, wherein the two-layer optimization model comprises the following steps: inner layer optimization model and outer layer optimization model:
order to
Figure BDA0002760410510000136
Wherein λ is1Lagrange multiplier, λ, of expression (18)2The Lagrangian multiplier of expression (24),
Figure BDA0002760410510000137
lagrange multipliers of the unexpected constraint under the ith sample in expression (24); the inner layer optimization model is built using equation (26):
Figure BDA0002760410510000138
based on the lagrange multiplier λ, an outer layer model is established using equation (27):
Figure BDA0002760410510000139
step 6.3, determining the search direction d of the iterative algorithm for searching the global optimal allocation scheme by using the formula (28):
Figure BDA00027604105100001310
step 6.4, constructing an iterative algorithm for searching the optimal Lagrange multiplier:
step 6.4.1, define the Lagrangian multiplier λ of the initial equation (18)1Is λ1(0)And initializing λ1(0)0; lagrange multiplier defining an initial equation (24)
Figure BDA00027604105100001311
Is composed of
Figure BDA00027604105100001312
And initialize
Figure BDA00027604105100001313
1,2, ·, I; defining the current iteration step number as h, and initializing h as 1; let thetaLIs the step size upper threshold, and θLIs more than 1; let thetaUIs a step lower threshold value, and 0 < thetaULess than 1; let s(h)For the step size of the h-th iteration and initialize s(h)1 is ═ 1; making the upper iteration limit to H;
step 6.4.2, solving the iteration multiplier lambda corresponding to the h step(h)To obtain the optimal solution w of the h-th iterationa (h)And an optimum value VLR(λ(h))；
Step 6.4.3, if VLR(λ(h))-VLR(λ(h-1))＞θLThen let s(h)＝θL×s(h-1)(ii) a If VLR(λ(h))-VLR(λ(h-1))＜θUThen let s(h)＝θU×s(h-1)(ii) a Otherwise, let s(h)＝s(h-1)；
Step 6.4.4, calculating the search direction d of the h iteration through the formula (28)(h)And make λ(h+1)＝λh+s(h)×d(h)；
Step 6.4.5, if lambda1(h+1)If < 0, let λ1(h+1)0; if for any ith1A sample and the ith2A sample, present in the ith1A sample and the ith2The decision value is the same for each sample, i.e.
Figure BDA0002760410510000141
The optimal solution w for the h-th iterationa (h)The final optimal solution is obtained; if H is more than H, the optimal solution w of the H-th iteration is takena (h)Is an approximate solution; otherwise, after h +1 is given to h, the step is shifted to 6.4.2;
all parameters in the above steps are given in table 2.
TABLE 2 resource allocation system parameters for smart grid information collection based on 5G network
Figure BDA0002760410510000151
And seventhly, solving the mixed integer linear programming model by using the iterative algorithm for searching the global optimal allocation scheme based on an lp-solution solver, thereby obtaining an optimal data scheduling and resource allocation scheme.

Claims (5)

1. A data scheduling and resource allocation method for intelligent power grid information acquisition based on a 5G network is applied to an uplink network environment formed by N information acquisition nodes provided with K-type sensors and 1 base station of the 5G wireless private network, and is characterized in that the data scheduling and resource allocation method comprises the following steps:
step one, in the uplink network environment, numbering N information acquisition nodes, and marking as {1,2, ·, N, ·, N }, wherein N represents the serial number of the nth information acquisition node, and N is more than or equal to 1 and less than or equal to N; numbering the sensors in each information acquisition node, and recording as {1,2, K, K }, wherein K represents the serial number of the kth sensor in the node, and K is more than or equal to 1 and less than or equal to K;
let the sampling period of the kth sensor be TkMaking the least common multiple of sampling periods of all the sensors be T, and making T be uplink transmission time; the kth sensor samples {1,2, ·, J } repeatedly during the upstream transmission time T, where J denotes the sequence number of the jth sample, and
Figure FDA0002760410500000011
j represents the upper limit of sampling times, and J is more than or equal to 1 and less than or equal to J; the data quantity of the jth sensor in the nth information acquisition node sampled at the jth time is DSn,k,j；
Step two, assuming that the modulation coding mode of the uplink network environment is {1,2, …, M, …, M }, wherein M is the mth modulation coding mode, and M is more than or equal to 1 and less than or equal to M; DS (direct sequence)n,k,j,m,tRepresenting the amount of data DSn,k,jSelecting the mth modulation coding mode, and transmitting normal sampling data in the tth time slot;
step three, dividing the sampling data types of the information acquisition nodes into normal sampling data and abnormal alarm data, and establishing a probability constraint planning model under random burst faults by adopting a sample average approximation strategy so as to reserve wireless resource blocks for the abnormal alarm data;
step four, taking the maximum value of the energy efficiency of the 5G network uplink transmission process as a target function, and establishing a series of constraint conditions according to the 5G communication protocol, the power control and the limiting factors of the sudden failures, thereby forming a mixed integer linear fraction programming model with linear constraint;
step five, the normal sampling data DSn,k,j,m,tReduced to one-dimensional variable DSaPerforming Charnes-Cooper transformation and Glover linearization on the mixed integer linear fractional programming model so as to reconstruct the mixed integer linear programming model with linear constraint;
step six, for a mixed integer linear programming model with linear constraint, combining a Lagrange dual and a trust domain algorithm, and constructing an iterative algorithm for searching a global optimal distribution scheme;
and seventhly, solving the mixed integer linear programming model by using the iterative algorithm for searching the global optimal allocation scheme based on an lp-solution solver, thereby obtaining an optimal data scheduling and resource allocation scheme.
2. The data scheduling and resource allocation method according to claim 1, wherein the third step is performed as follows:
3.1, establishing a probability constraint planning model under the random burst fault by using the formula (1):
Figure FDA0002760410500000021
in the formula (1), xn,k,j,m,tRepresents decision variables and decides the normal sample data DSn,k,j,m,tWhether or not to be transmitted; rn,k,j,m,tRepresents the normal sample data DSn,k,j,m,tThe transmission rate of (c); r isERepresenting the number of random burst faults; DS (direct sequence)EA data volume representing abnormal alarm data; rEA transmission rate indicating abnormal alarm data; y is the number of subchannels; delta is confidence;
Figure FDA0002760410500000022
represents rounding to the right;
step 3.2, setting
Figure FDA0002760410500000023
The sample is the ith independent and equally distributed sample of the random burst fault frequency within the uplink transmission time T, wherein I is 1,2, … I, and I represents the total amount of the samples;
and 3.3, obtaining probability constraint by using a sample average approximation strategy shown in the formula (2) to the formula (5), and using the probability constraint as a constraint condition of the objective function:
Figure FDA0002760410500000024
Figure FDA0002760410500000025
Figure FDA0002760410500000026
Figure FDA0002760410500000027
in formula (2) -formula (5), G represents a penalty factor; z is a radical ofiRepresenting the ith exponential function in the uplink transmission time T;
step 3.4, reserving abnormal alarm data by using the formula (2) -formula (5)
Figure FDA0002760410500000028
A radio resource block.
3. The data scheduling and resource allocation method according to claim 2, wherein said fourth step is performed as follows:
step 4.1, establishing an objective function by using the formula (6):
Figure FDA0002760410500000031
equation (6) represents the maximum value of energy efficiency EE in the uplink transmission process of the 5G network, and is defined as the ratio of the data volume of all transmissions to the power consumption on all radio resource blocks; pn,k,j,m,tIndicating the transmission of said normal sample data DSn,k,j,m,tTransmitting power of the information acquisition node in the process;
and 4.2, constructing the rest constraint conditions of the objective function by using the formulas (7) to (11):
Figure FDA0002760410500000032
Figure FDA0002760410500000033
Figure FDA0002760410500000034
Figure FDA0002760410500000035
xn,k,j,m,t∈{0,1} (11)
the expression (7) indicates that the number of resource blocks allocated to each time slot cannot exceed Y;
equation (8) represents a power control model based on the uplink channel of the 5G network, and alpha represents a path loss compensation factor; PL (d)n) Representing the downlink path loss of the nth information-collecting node, dnThe distance between the nth information acquisition node and the base station is represented; IoT represents thermal interference; SINRmRepresenting the signal-to-noise ratio requirement of the mth modulation code selection;
formula (9) represents that the power consumed on the resource block cannot exceed the maximum transmission power of each information acquisition node;
equation (10) indicates that each packet can only have one modulation coding choice and be transmitted in a single time slot;
equation (11) indicates that the decision variable of the objective function can only take 0 or 1.
4. The data scheduling and resource allocation method according to claim 3, wherein said step five is performed as follows:
step 5.1, simplifying the subscript parameters of the objective function and the constraint condition, and comprising the following steps: order to
Figure FDA0002760410500000036
Figure FDA0002760410500000037
Let A denote a length l1+l2+l3+l4+l5Binary number of (2), wherein first l1The bits representing the subscript n, l1+1 bit to l1+l2Bits representing the subscript k, l1+l2+1 bit to l1+l2+l3Bits representing subscript j, l1+l2+l3+1 bit to l1+l2+l3+l4The bits representing the subscript m, l1+l2+l3+l4+1 bit to l1+l2+l3+l4+l5The bits represent the subscript t; thereby obtaining the target function and the constraint condition after simplifying the parameters;
step 5.2, constructing an auxiliary variable u by using the formula (12);
Figure FDA0002760410500000041
in the formula (12), u is more than 0; a is a simplified subscript parameter, xaRepresenting the decision variable after simplifying the subscript, wherein a is more than or equal to 1 and less than or equal to A;
converting the target function after the parameters are simplified into a linear expression by using an auxiliary variable u, and multiplying both sides of an equation of all constraint conditions after the parameters are simplified by the auxiliary variable u, thereby completing Charnes-Cooper transformation and obtaining a mixed integer nonlinear programming model;
step 5.3, introducing two auxiliary decision variables wa＝xaX u and wi＝ziXu, thereby reconstructing a mixed integer linear programming model with linear constraints using equation (13) to complete Glover linearization of the mixed integer non-linear programming model;
Figure FDA0002760410500000042
constructing constraints of the mixed integer linear programming model by using the formula (14) to the formula (23):
Figure FDA0002760410500000043
Figure FDA0002760410500000044
Figure FDA0002760410500000045
Figure FDA0002760410500000046
Figure FDA0002760410500000047
Figure FDA0002760410500000048
Figure FDA0002760410500000049
Figure FDA00027604105000000410
wa∈{0,u} (22)
wi∈{0,u} (23)。
5. the data scheduling and resource allocation method according to claim 4, wherein the sixth step is performed as follows:
step 6.1, constructing an unexpected constraint by using the formula (24):
Figure FDA0002760410500000051
in the formula (24), the reaction mixture is,
Figure FDA0002760410500000052
representing the decision value at the ith sample;
the objective function of equation (13) is used by equation (25)
Figure FDA0002760410500000053
Expressed as an objective function containing unintended constraints
Figure FDA0002760410500000054
Figure FDA0002760410500000055
Step 6.2, introducing a Lagrange multiplier lambda, and restating the mixed integer linear programming model into a two-layer optimization model, wherein the two-layer optimization model comprises the following steps: an inner layer optimization model and an outer layer optimization model;
order to
Figure FDA0002760410500000056
Wherein λ is1Lagrange multiplier, λ, of expression (18)2The Lagrangian multiplier of expression (24),
Figure FDA00027604105000000510
lagrange multipliers of the unexpected constraint under the ith sample in expression (24); the inner layer optimization model is built using equation (26):
Figure FDA0002760410500000057
based on the lagrange multiplier λ, an outer layer model is established using equation (27):
Figure FDA0002760410500000058
step 6.3, determining the search direction d of the iterative algorithm for searching the global optimal allocation scheme by using the formula (28):
Figure FDA0002760410500000059
step 6.4, constructing an iterative algorithm for searching the optimal Lagrange multiplier:
step 6.4.1, define the Lagrangian multiplier λ of the initial equation (18)1Is λ1(0)And initializing λ1(0)0; lagrange multiplier defining an initial equation (24)
Figure FDA00027604105000000511
Is composed of
Figure FDA00027604105000000512
And initialize
Figure FDA00027604105000000513
1,2, ·, I; defining the current iteration step number as h, and initializing h as 1; let thetaLIs the step size upper threshold, and θLIs more than 1; let thetaUIs a step lower threshold value, and 0 < thetaULess than 1; let s(h)For the step size of the h-th iteration and initialize s(h)1 is ═ 1; making the upper iteration limit to H;
step 6.4.2, solving the iteration multiplier lambda corresponding to the h step(h)To obtain the optimal solution w of the h-th iterationa (h)And an optimum value VLR(λ(h))；
Step 6.4.3, if VLR(λ(h))-VLR(λ(h-1))＞θLThen let s(h)＝θL×s(h-1)(ii) a If VLR(λ(h))-VLR(λ(h-1))＜θUThen let s(h)＝θU×s(h-1)(ii) a Whether or notThen, let s(h)＝s(h-1)；
Step 6.4.4, calculating the search direction d of the h iteration through the formula (28)(h)And make λ(h+1)＝λh+s(h)×d(h)；
Step 6.4.5, if lambda1(h+1)If < 0, let λ1(h+1)0; if for any ith1A sample and the ith2A sample, present in the ith1A sample and the ith2The decision value is the same for each sample, i.e.
Figure FDA0002760410500000061
The optimal solution w for the h-th iterationa (h)The final optimal solution is obtained; if H is more than H, the optimal solution w of the H-th iteration is takena (h)Is an approximate solution; otherwise, after h +1 is given to h, the step is shifted to step 6.4.2.
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