CN112367275A - Multi-service resource allocation method, system and equipment for power grid data acquisition system - Google Patents
Multi-service resource allocation method, system and equipment for power grid data acquisition system Download PDFInfo
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Abstract
The invention discloses a method, a system and equipment for distributing multi-service resources of a power grid data acquisition system, wherein nodes are divided into BE nodes and QoS nodes, utility functions and effective capacity models are constructed according to different service types and combined to obtain a utility function model based on effective capacity, and a plurality of QoS influence factors are used for representing, so that the requirement for distributing multi-service type resources can BE effectively met, and the system utility of resource distribution is improved; and according to the utility function model based on the effective capacity and the data transmission rate, an optimization model which takes the resource satisfaction threshold of the QoS node as a limiting condition and takes the effective capacity of the maximum BE node as a target function is constructed, and the optimal resource allocation mode and the sending power of each node are obtained by solving, so that the effective capacities of all nodes can BE effectively improved while the QoS requirements of the nodes are met.
Description
Technical Field
The invention relates to the field of wireless communication, in particular to a method, a system and equipment for distributing multi-service resources of a power grid data acquisition system.
Background
The power network is an indispensable infrastructure of modern society and energy support for national development. The supply of electric energy cannot be kept no matter in industrial manufacturing, social production or daily life of people. The traditional power network is a typical rigid system, electric energy is transmitted to a power utilization side on a power line in a unidirectional mode, and mechanisms such as power management and electric energy transmission lack flexibility, organization and expandability. With the rapid development of society, the requirements of various applications in the power network on the coverage area of power supply and monitoring, the quantity of electric energy and the quality of electric energy are exponentially increased, and the performance of the traditional power network cannot gradually follow the increasing power demand, so that the introduction of a reliable and efficient smart power grid becomes an urgent task for the construction of the modern power system. The smart grid is deployed with a complex monitoring and control system, however, the monitoring and control system and the power network generate massive communication data, which requires a fast, efficient, intelligent, reliable and data-safe communication network to be used as a perception nerve ending of the smart grid for data acquisition. Through the development of many years, the electric power communication network in China forms a communication network pattern which mainly uses optical fiber communication and supplements carrier waves and satellites, the communication technical level and the service guarantee capability are continuously improved, and the reliable transmission of various electric power data services is fundamentally ensured.
The data service in the power grid comprises 1) EMS (energy Management System) service: the energy management system provides data information for real-time monitoring and control of the running state of the power grid, and EMS service has higher requirement on time delay for realizing high-level application function services of the power grid, power grid data acquisition, dispatcher simulation operation and the like; 2) safety and stability control system: the main functions of the system are power grid operation mode judgment, remote action and fault identification, and the system structurally comprises a main station, a control substation and an execution substation; 3) pmu (power management unit) system: namely, a power System synchronized phasor measurement System (GPS) monitors and controls phasors by means of a Global Positioning System (GPS) synchronized clock technique. The safety and stability control system and the PMU system have certain tolerance to time delay, but need higher data accuracy, so have certain requirements on packet loss rate. 4) Line protection: namely, the relay protection service is a key service for ensuring the safety and stable operation of a power grid system, and the bandwidth requirement of the service is small, but the requirements on time delay and quality of service (QoS) are extremely high. These data services can BE classified into QoS services, which are sensitive to delay and have minimum QoS requirements, and Best Effort (BE) services, which are insensitive to delay and have no minimum bandwidth requirements, according to their communication requirements.
With the increasingly diversified data service types and the increasingly vigorous data volume of the power grid, the communication transmission bottleneck still exists in a complex scene, and the optimization of the data acquisition technology in the communication transmission network is particularly important. The key to optimizing data acquisition technology lies in the communication resource allocation strategy, which needs to perform qualitative measurement on the QoS requirements of different users. The indexes affecting the QoS of the user mainly include bandwidth, delay, and packet loss rate. The bandwidth refers to the amount of data that can be transmitted in a transmission channel in a unit time, however, the larger the bandwidth is, the better the bandwidth is, in practical application, resources need to be reasonably allocated, and redundant unnecessary bandwidth resources are allocated to users who really have needs; latency refers to the average time that it takes for a network packet to be sent out of the source to be received by the destination. In this process, there are a plurality of links that generate time delay, mainly: packet delay, queuing delay, propagation delay, etc. Different services have different maximum delay limit requirements, for example, conversational and streaming services have higher requirements on delay, while interactive and background services have relatively lower requirements. The packet loss rate refers to that data transmission in communication is in units of data packets, and when the data packets to be transmitted exceed the capacity of channel transmission, packet loss occurs, thereby affecting communication quality. The current tool for quantifying the QoS is a utility function in economics, the prior art provides a classical utility function, and the function can be applicable to different resource allocation strategies by adjusting parameters in the function, but the function only considers the influence of bandwidth resources on the utility and is not applicable to a scene of a power grid, multiple services and multiple QoS indexes.
In order to solve the problem of multi-service resource allocation, resource allocation strategies based on QoS (quality of service) perception are proposed in recent years. For example, the VT-MLWDF scheduling algorithm takes into account QoS information such as the length of a queue to be transmitted, the maximum packet loss rate tolerable by a user, and a delay bound on the basis of a proportional fairness algorithm, thereby improving the performance of the user with a high real-time requirement, but the method does not take into account the user waiting delay. In order to meet the strict requirement of some users on delay and maintain the packet loss rate at a relatively low level, a DP-VT-MLWDF scheduling algorithm is provided, and the longer the user waiting time is, the more preferentially scheduled the user is by multiplying a waiting delay factor on the basis of the VT-MLWDF scheduling algorithm. But the situation that when the data packet is up to the time delay, the priority is rapidly increased so as to prevent the packet loss due to overtime cannot be effectively solved.
In summary, the multi-service resource allocation method of the power grid data acquisition system in the prior art has the technical problem that the effective capacities of all users cannot be improved while the QoS requirements of the users are met.
Disclosure of Invention
The invention provides a method, a system and equipment for distributing multi-service resources of a power grid data acquisition system, which are used for solving the technical problem that the multi-service resource distribution method of the power grid data acquisition system in the prior art cannot meet the QoS (quality of service) requirements of users and simultaneously improve the effective capacity of all users.
The invention provides a multi-service resource allocation method of a power grid data acquisition system, which comprises the following steps:
dividing the nodes into BE nodes and QoS nodes according to whether the nodes have QoS requests or not;
constructing utility functions suitable for BE nodes and QoS nodes according to the service types of the power grid data acquisition system;
obtaining QoS influence factors, and constructing an effective capacity model according to the QoS influence factors;
combining the utility function with the effective capacity model to obtain a utility function model based on the effective capacity;
constructing a power grid data acquisition system comprising a base station and nodes of different service types, and calculating the data transmission rate between the nodes and the base station according to the power grid data acquisition system;
constructing an optimization model taking the sum of the utility functions of each node as a target function under the limit of total power and resource allocation according to the utility function model based on the effective capacity and the data transmission rate;
simplifying the optimization model into an optimization model which takes the resources of the QoS node to meet the threshold as the limiting condition and the effective capacity of the maximum BE node as the objective function;
and solving the simplified optimization model to obtain a resource allocation scheme of the power grid data acquisition system.
Preferably, the service types of the power grid data acquisition system include an energy management service, a safety and stability control service, a power system synchronized phasor measurement service, and a relay protection service.
Preferably, the QoS influencing factors include an arrival rate, a packet loss rate and a maximum delay bound.
Preferably, the specific process of constructing the effective capacity model is as follows:
defining a QoS index theta;
establishing a relational expression of the QoS index theta with the arrival rate, the packet loss rate and the maximum time delay limit;
and establishing an effective capacity model according to the relation among the QoS index theta, the arrival rate, the packet loss rate and the maximum time delay limit.
Preferably, a power grid data acquisition system including a base station and nodes of different service types is constructed, and a specific process of calculating a data transmission rate between a node and the base station according to the power grid data acquisition system is as follows:
constructing a power grid data acquisition system comprising 1 base station and K nodes with different service types, wherein M resource blocks are arranged in a resource pool of the power grid data acquisition system;
and constructing a resource block index matrix, and calculating the data transmission rate between different nodes and base stations on the resource block M according to the resource block index matrix.
Preferably, the simplified optimization model is solved, and an objective function of the simplified optimization model is split into two sub-optimization models.
Preferably, the two sub-optimization models are a first sub-optimization model and a second sub-optimization model respectively, wherein the first sub-optimization model is: the sending power of a given node is used for solving the distribution mode of the optimal resource block; the second sub-optimization model is: and (4) giving the allocation mode of the resource block and solving the sending power of the optimal node.
Preferably, the allocation mode of the optimal resource block obtained by solving the first sub-optimization model is used as the allocation mode of the resource block of the second sub-optimization model, the transmission power of the optimal node obtained by solving the second sub-optimization model is used as the transmission power of each node of the first sub-optimization model, and the iterative solution is repeated until the allocation mode of the optimal resource block and the transmission power of the optimal node are not changed, so that the resource allocation scheme of the power grid data acquisition system is obtained.
A multi-service resource allocation system of a power grid data acquisition system comprises a utility function construction module, an effective capacity model construction module, a combination module, a data transmission rate calculation module, an optimization model construction module, an optimization model simplification module and a resource allocation scheme solving module;
the utility function construction module is specifically used for constructing utility functions suitable for BE nodes and QoS nodes according to the service type of the power grid data acquisition system;
the effective capacity model building module is specifically used for obtaining QoS influence factors and building an effective capacity model according to the QoS influence factors;
the combining module is specifically used for combining the utility function with the effective capacity model to obtain a utility function model based on the effective capacity;
the data transmission rate calculation module is specifically used for constructing a power grid data acquisition system comprising a base station and nodes of different service types, and calculating the data transmission rate between the nodes and the base station according to the power grid data acquisition system;
the optimization model building module is specifically used for building an optimization model which takes the sum of the utility functions of each node as a target function under the limit of total power and resource allocation according to the utility function model based on the effective capacity and the data transmission rate;
the optimization model simplification module is specifically used for simplifying the optimization model, and the optimization model is simplified into the optimization model which takes the resource of the QoS node satisfying the threshold as the limiting condition and the effective capacity of the BE node as the target function;
and the resource allocation scheme solving module is specifically used for solving the simplified optimization model to obtain a resource allocation scheme of the power grid data acquisition system.
A multi-service resource allocation device of a power grid data acquisition system comprises a processor and a memory;
the memory is used for storing a computer program and transmitting the computer program to the processor;
the processor is used for executing the multi-service resource allocation method of the power grid data acquisition system according to the instructions in the computer program.
According to the technical scheme, the embodiment of the invention has the following advantages:
according to the embodiment of the invention, the nodes are divided into BE nodes and QoS nodes, the utility function and the effective capacity model are constructed according to different service types and combined to obtain the utility function model based on the effective capacity, and a plurality of QoS influence factors are used for representation, so that the requirement of multi-service type resource allocation can BE effectively met, and the system utility of resource allocation is improved; and according to the utility function model based on the effective capacity and the data transmission rate, an optimization model which takes the resource satisfaction threshold of the QoS node as a limiting condition and takes the effective capacity of the maximum BE node as a target function is constructed, and the optimal resource allocation mode and the sending power of each node are obtained by solving, so that the effective capacities of all nodes can BE effectively improved while the QoS requirements of the nodes are met.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a method flowchart of a method, a system, and a device for allocating multiple service resources of a power grid data acquisition system according to an embodiment of the present invention.
Fig. 2 is a total utility comparison diagram of a QGRA algorithm and a Max U algorithm system of a method, a system, and a device for allocating multi-service resources of a power grid data acquisition system according to an embodiment of the present invention.
Fig. 3 is a QoS utility and BE utility comparison diagram of a QGRA algorithm and a Max U algorithm of a method, a system, and a device for allocating multi-service resources of a power grid data acquisition system according to an embodiment of the present invention.
Fig. 4 is a graph comparing throughput of a QGRA algorithm and a Max U algorithm of a method, a system, and a device for allocating multiple service resources of a power grid data acquisition system according to an embodiment of the present invention.
Fig. 5 is a system framework diagram of a method, a system, and a device for allocating multiple service resources of a power grid data acquisition system according to an embodiment of the present invention.
Fig. 6 is an apparatus framework diagram of a method, a system, and an apparatus for allocating multiple service resources of a power grid data acquisition system according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a method, a system and equipment for distributing multi-service resources of a power grid data acquisition system, which are used for solving the technical problem that the multi-service resource distribution method of the power grid data acquisition system in the prior art cannot meet the QoS (quality of service) requirements of users and simultaneously improve the effective capacity of all users.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart of a method, a system and a device for allocating multiple service resources of a power grid data acquisition system according to an embodiment of the present invention.
Example 1
As shown in fig. 1, a method for allocating multiple service resources of a power grid data acquisition system according to an embodiment of the present invention includes the following steps:
dividing the nodes into BE nodes and QoS nodes according to whether the nodes have QoS requests or not, and preparing for constructing a utility function subsequently; it should be further noted that QoS is quality of service (QoS), which means that a network can utilize various basic technologies to provide better service capability for a given network communication, and is a security mechanism of the network, which is a technology for solving the problems of network delay and congestion. BE is Best Effort (Best Effort), which is a standard internet service mode, and when congestion occurs at a network interface, regardless of users or applications, packets are immediately discarded until the traffic volume is reduced;
constructing utility functions suitable for BE nodes and QoS nodes according to the service types of the power grid data acquisition system; it needs to be further explained that, in order to better solve the problem of resource allocation of multiple nodes and multiple service types in the power grid, in this embodiment, a utility function concept in economics is introduced, and the utility function can construct a relationship between the demand of a node and resource supply to measure the change condition of the satisfaction of the node along with the change of resources, and performing reasonable quantization on the satisfaction of the node is the basis for performing resource allocation based on utility subsequently;
obtaining QoS influence factors, and constructing an effective capacity model according to the QoS influence factors; because only the variation of node satisfaction to rate is considered in the utility function, in the actual power grid service, different services have different requirements on QoS, for example, data services such as line protection mainly pursue low delay and have smaller requirements on bandwidth and the like. Therefore, in order to further consider Qos influencing factors such as time delay and packet loss rate in resource allocation, an effective capacity model is introduced in the embodiment;
combining the utility function with the effective capacity model to obtain a utility function model based on the effective capacity; therefore, a utility function considering QoS influence factors is established, and the utility function is characterized by using a plurality of QoS influence factors, so that the requirement of multi-service type resource allocation can be better met;
constructing a power grid data acquisition system comprising a base station and nodes of different service types, wherein in the system, an Orthogonal Frequency Division Multiple Access (OFDMA) mode is adopted, the nodes are divided into QoS nodes and BE nodes, and the data transmission rate between the nodes and the base station is calculated according to the power grid data acquisition system;
since the purpose of this embodiment is to allocate limited resources among multiple data service nodes to maximize the overall node satisfaction of the system, i.e., the utility function value. Therefore, an optimization model which takes the sum of the utility functions of each node as a target function under the limit of total power and resource allocation is constructed according to the utility function model based on the effective capacity and the data transmission rate;
according to the convex optimization theory, the objective function of the optimization model is verified to BE a non-convex function, and the solving complexity is very high, so that in order to solve the problem, the optimization model needs to BE simplified, the optimization model is simplified into the optimization model which takes the resources of the QoS node to meet the threshold as the limiting condition and the effective capacity of the maximized BE node as the objective function, and the solution is convenient;
and solving the simplified optimization model to obtain an optimal resource allocation mode and the transmission power of the nodes, and taking the optimal resource allocation mode and the transmission power of the nodes as a resource allocation scheme of the power grid data acquisition system.
Example 2
As shown in fig. 1, a method for allocating multiple service resources of a power grid data acquisition system according to an embodiment of the present invention includes the following steps:
dividing the nodes into BE nodes and QoS nodes according to whether the nodes have QoS requests or not, and preparing for constructing a utility function subsequently; it should be further noted that QoS is quality of service (QoS), which means that a network can utilize various basic technologies to provide better service capability for a given network communication, and is a security mechanism of the network, which is a technology for solving the problems of network delay and congestion. BE is Best Effort (Best Effort), which is a standard internet service mode, and when congestion occurs at a network interface, regardless of users or applications, packets are immediately discarded until the traffic volume is reduced;
constructing utility functions suitable for BE nodes and QoS nodes according to the service types of the power grid data acquisition system; it should be further explained that the service types of the grid data acquisition system include an energy management service, a safety and stability control service, a power system synchronized phasor measurement service, and a relay protection service. In order to better solve the problem of resource allocation of multiple nodes and multiple service types in a power grid, a utility function concept in economics is introduced in the embodiment, the utility function can construct a relationship between the demand of a node and resource supply to measure the change condition of the satisfaction degree of the node along with the change of resources, the reasonable quantification of the satisfaction degree of the node is the basis of resource allocation based on utility in the following process, and a specific expression formula of the utility function is as follows:
wherein A, B, C, D and d are determined by the traffic type; parameters A, B and D primarily affect the range of the utility function, and parameter C affects the shape of the curve by changing the slope; the parameter d is an inflection point of the utility function curve and represents the resources required by the node, when the allocated resources are less than the minimum required resources required by the node, the curve is a concave function, otherwise, the curve is a convex function, and r is the resources allocated to the node.
Obtaining QoS influence factors including an arrival rate, a packet loss rate and a maximum time delay limit, and constructing an effective capacity model according to the QoS influence factors; because only the variation of node satisfaction to rate is considered in the utility function, in the actual power grid service, different services have different requirements on QoS, for example, data services such as line protection mainly pursue low delay and have smaller requirements on bandwidth and the like. Therefore, in order to further consider QoS influencing factors such as time delay and packet loss rate in resource allocation, an effective capacity model is introduced in this embodiment, and a specific process of constructing the effective capacity model is as follows:
defining a QoS index theta, wherein theta represents the exponential decay rate of the timeout probability when the delay limit D (theta) tends to be infinite, and the definition of theta is expressed as formula (2):
theta can be continuously changed between 0 and ∞, the higher theta indicates that the delay requirement is more strict, the effective capacity is correspondingly deteriorated, the service rate of the system is smaller, and therefore a general delay constraint system model can be effectively described.
In order to better describe the requirements of different services in the power grid on the arrival rate, the packet loss rate and the maximum time delay limit, a relational expression of the QoS index theta with the arrival rate, the packet loss rate and the maximum time delay limit is constructed;
wherein D ismax,kRepresenting the delay bound, ε, of node kkIndicates the packet loss rate requirement, λ, of node kkRepresenting the arrival rate of node k.
Establishing an effective capacity model according to the relation among the QoS index theta, the arrival rate, the packet loss rate and the maximum time delay limit, wherein the expression of the effective capacity model is as follows:
where R denotes a data transmission rate of the physical layer channel service.
Combining the utility function with the effective capacity model to obtain a utility function model based on the effective capacity; therefore, a utility function considering QoS influence factors is established, and the utility function is characterized by using a plurality of QoS influence factors, namely, the independent variables in the utility function U (R) not only are the data transmission rate R, but also comprise a QoS index theta representing the constraints of time delay limit and the like, and the formula is shown as the formula (5):
wherein, Uk(Rk,θk) And expressing the QoS index theta of the node K under the constraints of time delay limit and data transmission rate.
Constructing a power grid data acquisition system comprising 1 base station and K nodes with different service types, wherein the power grid data acquisition system adopts an Orthogonal Frequency Division Multiple Access (OFDMA) mode, the nodes are divided into QoS nodes and BE nodes, and the number of the two nodes is K respectively1，K2The resource pool of the power grid data acquisition system is provided with M resource blocks; each resource block can only be used by one node in one scheduling period, and the power limit of a base station is P;
building a resource block index matrix, wherein the built index matrix is of a K × M type due to the fact that the resource block index matrix comprises K nodes and M resource blocks;
calculating the data transmission rate between different nodes and base stations on the resource block M according to the resource block index matrix, wherein the specific process is as follows:
as known from the Shannon formula, the data transmission rate between the node k and the base station on the resource block m is shown as follows:
where β ∈ {0,1}K×MFor resource block index matrix, betak,m1 denotes that the mth resource block is allocated to the kth node, β k,m0 is then inverseIt is also provided. Pk,mRepresents the transmission power, h, from the base station to node k on resource block mk,mRepresenting the channel gain, N0Representing the noise power spectral density and B representing the bandwidth of each resource block RB.
Therefore, the total data transmission rate obtained by node k from the base station is:
since the purpose of this embodiment is to allocate limited resources among multiple data service nodes to maximize the overall node satisfaction of the system, i.e., the utility function value. Therefore, according to the utility function model based on the effective capacity and the data transmission rate, an optimization model which takes the sum of the utility functions of each node as a target function under the limitation of total power and resource allocation is constructed, and the expression of the optimization model is as follows:
wherein
For the power allocation matrix, constraint 1) indicates that the power of the base station is limited to P, and constraint 2) indicates the resource block index matrix.
According to the convex optimization theory, the objective function of the optimization model is verified to BE a non-convex function, and the solving complexity is very high, so that in order to solve the problem, the optimization model needs to BE simplified, the optimization model is simplified into the optimization model which takes the resource of the QoS node satisfying the threshold as the limiting condition and the effective capacity of the BE node as the objective function to BE maximized, and the solution is convenient. The simplified optimization model has the following expression:
wherein
Representing the effective bandwidth of QoS node k, for a given QoS index θ, only if the effective capacity is greater than or equal to the equivalent bandwidth, i.e. the
It can be guaranteed that the delay requirement of the QoS node is met.
Solving the simplified optimization model to obtain an optimal resource allocation mode and transmission power of the nodes, and using the optimal resource allocation mode and the transmission power as a resource allocation scheme of the power grid data acquisition system; in this embodiment, a QoS Guaranteed Resource Allocation algorithm (QGRA) based on effective capacity is used to solve the simplified optimization model, and the specific process is as follows
Splitting the target function of the simplified optimization model into two sub-optimization models, wherein the two sub-optimization models are respectively a first sub-optimization model and a second sub-optimization model, and the first sub-optimization model is as follows: the sending power of a given node is used for solving the distribution mode of the optimal resource block; the second sub-optimization model is: the allocation mode of the resource blocks is given, and the sending power of the optimal node is solved;
for the first sub-optimization model, namely the fixed power distribution matrix, the resource block index matrix is optimized, and the expression formula (9) of the optimization model is converted into:
to make the first sub-optimization model solvable, the parameter β is first solvedk,mRelaxation being a continuous variable between 0 and 1, i.e. betak,m∈[0,1]Therefore, the optimization model can be converted into a convex function problem to be solved, and then the optimal resource block index matrix is solved by using a CVX optimization tool pack.
For the second sub-optimization model, namely the fixed resource block index matrix, the fixed power allocation matrix is optimized, and the expression formula (9) of the optimization model is converted into:
since this problem is a convex optimization problem, equation (11) is transformed into the lagrange dual function of the unconstrained problem, as shown in equation (12):
wherein u ═ u1,u2,...,uK]And λ are lagrange multipliers of constraint 1) and constraint 2) in equation (11), respectively, and K ∈ K2，uk＝1，
Then according to the K.K.T condition, obtaining:
can be further solved to obtain
For optimal power allocation.
When the KKT condition is satisfied, the Lagrangian dual function is expressed as an expression relating only to u and λ:
the lagrange dual problem is:
the problem can be solved by adopting the gradient descent algorithm, and iteratively updating the values of u and λ at the same time, gradually approaching to the minimum value of L (u, λ), and obtaining an optimal solution, wherein the descent directions of the sub-gradients of u and λ are respectively shown as formula (17a) and formula (17 b):
the iterative formulas for u and λ are:
wherein alpha ist＝a/t，ηtB/t represents the step length of the t-th iteration, and a and b are preset step length parameters and are adjusted according to actual parameter values.
And carrying out iterative solution on the first sub-optimization model and the second sub-optimization model, taking the distribution mode of the optimal resource block obtained by the solution of the first sub-optimization model as the distribution mode of the resource block of the second sub-optimization model, taking the sending power of the optimal node obtained by the solution of the second sub-optimization model as the sending power of each node of the first sub-optimization model, and repeating the iterative solution until the distribution mode of the optimal resource block and the sending power of the optimal node are not changed any more, so as to obtain the resource distribution scheme of the power grid data acquisition system.
And (3) simulation results:
in the embodiment, the coverage area of the base station is considered to be 500m, and the base station is located at the central position. Assuming equal numbers of QoS nodes and BE nodes, other simulation parameters are shown in table 1. In order to compare simulation results, the present embodiment studies a maximum utility algorithm (Max U), i.e., aims to maximize system utility, without distinguishing between QoS traffic and BE traffic types.
TABLE 1 simulation parameters
Fig. 2 compares the overall system utility of the two algorithms at different node numbers. As can be seen from fig. 2, both algorithms rise as the number of nodes increases, but the QGRA algorithm proposed in the present embodiment has higher system utility. This is because when the QoS index θ is small and the bandwidth resources available by the system are rich, the QoS node can bring higher system utility, and has higher priority than the BE node. And the QGRA algorithm preferentially allocates resources for the QoS nodes, so that the resource requirements of the QoS nodes are guaranteed, and higher system utility can be obtained compared with the Max U algorithm which does not distinguish the priorities of the two types of nodes.
Fig. 3 compares utility values of the QoS node and the BE node at different node numbers for the two types of algorithms. As can be seen from fig. 3, the utility value of the QoS node in both types of algorithms increases with the increase of the number of nodes, but the utility value of the QoS node in the QGRA algorithm is much higher than that of the Max U algorithm, which is because the QGRA algorithm guarantees the priority of the QoS node, more bandwidth resources are allocated to the QoS node, and the QoS node contributes more utility values accordingly. However, as the number of nodes increases, fig. 3 shows that the utility value of the BE node decreases even lower. This is because when the number of nodes is greater than a certain value, and the bandwidth resource of the system is not sufficiently allocated to all the nodes, part of the BE nodes are sacrificed to improve the utility of the QoS node, and thus the utility of the BE node is reduced. As can BE seen from fig. 2, even if the utility of part of the BE nodes is sacrificed, the total utility value of the system can BE maximized.
Fig. 4 compares the throughput of the system for two classes of algorithms at different node numbers. It can be seen from fig. 4 that the throughput of both algorithms increases with the increase of the number of nodes, but the QGRA algorithm still has better performance than the Max U algorithm, i.e. the QGRA algorithm guarantees the priority of QoS node resources, and at the same time, guarantees the higher throughput of the system. This is because in the utility function, the system rate is one of the important parameters, and is in a direct proportional relationship with the system utility, and the algorithm guarantees the QoS node resources while at the same time means guaranteeing the transmission rate of the QoS node, thereby also maximizing the throughput of the system.
Example 3
As shown in fig. 5, a multi-service resource allocation system of a power grid data acquisition system includes a utility function construction module 201, an effective capacity model construction module 202, a combination module 203, a data transmission rate calculation module 204, an optimization model construction module 205, an optimization model simplification module 206, and a resource allocation scheme solving module 207;
the utility function constructing module 201 is specifically configured to construct a utility function suitable for BE nodes and QoS nodes according to the service type of the power grid data acquisition system;
the effective capacity model building module 202 is specifically configured to obtain QoS influencing factors, and build an effective capacity model according to the QoS influencing factors;
the combining module 203 is specifically configured to combine the utility function with the effective capacity model to obtain a utility function model based on the effective capacity;
the data transmission rate calculation module 204 is specifically configured to construct a power grid data acquisition system including a base station and nodes of different service types, and calculate a data transmission rate between the nodes and the base station according to the power grid data acquisition system;
the optimization model building module 205 is specifically configured to build, according to the utility function model based on the effective capacity and the data transmission rate, an optimization model that takes the sum of the utility functions of the maximized nodes as an objective function under the constraints of total power and resource allocation;
the optimization model simplification module 206 is specifically configured to simplify the optimization model, and simplify the optimization model into an optimization model that takes the resource of the QoS node satisfying the threshold as the constraint condition and maximizes the effective capacity of the BE node as the objective function;
the resource allocation scheme solving module 207 is specifically configured to solve the simplified optimization model to obtain a resource allocation scheme of the power grid data acquisition system.
As shown in fig. 6, a multi-service resource allocation device 30 of a grid data acquisition system includes a processor 300 and a memory 301;
the memory 301 is used for storing a computer program 302 and transmitting the computer program 302 to the processor;
the processor 300 is configured to execute the steps in the method for allocating multiple service resources of a grid data acquisition system according to the instructions in the computer program 302.
Illustratively, the computer program 302 may be partitioned into one or more modules/units that are stored in the memory 301 and executed by the processor 300 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 302 in the node device 30.
The node device 30 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The node device 30 may include, but is not limited to, a processor 300, a memory 301. Those skilled in the art will appreciate that fig. 6 is merely an example of the node device 30, and does not constitute a limitation of the node device 30, and may include more or less components than those shown, or combine certain components, or different components, for example, the node device 30 may also include input-output devices, network access devices, buses, etc.
The Processor 300 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf ProgrammaBle Gate Array (FPGA) or other ProgrammaBle logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 301 may be an internal storage unit of the node device 30, such as a hard disk or a memory of the node device 30. The memory 301 may also be an external storage device of the node device 30, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the node device 30. Further, the memory 301 may also include both an internal storage unit and an external storage device of the node device 30. The memory 301 is used for storing the computer program 302 and other programs and data required by the node device 30. The memory 301 may also be used to temporarily store data that has been output or is to be output.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing computer programs.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A multi-service resource allocation method for a power grid data acquisition system is characterized by comprising the following steps:
dividing the nodes into BE nodes and QoS nodes according to whether the nodes have QoS requests or not;
constructing utility functions suitable for BE nodes and QoS nodes according to the service types of the power grid data acquisition system;
obtaining QoS influence factors, and constructing an effective capacity model according to the QoS influence factors;
combining the utility function with the effective capacity model to obtain a utility function model based on the effective capacity;
constructing a power grid data acquisition system comprising a base station and nodes of different service types, and calculating the data transmission rate between the nodes and the base station according to the power grid data acquisition system;
constructing an optimization model taking the sum of the utility functions of each node as a target function under the limit of total power and resource allocation according to the utility function model based on the effective capacity and the data transmission rate;
simplifying the optimization model into an optimization model which takes the resources of the QoS node to meet the threshold as the limiting condition and the effective capacity of the maximum BE node as the objective function;
and solving the simplified optimization model to obtain a resource allocation scheme of the power grid data acquisition system.
2. The method as claimed in claim 1, wherein the service types of the grid data collection system include energy management service, safety and stability control service, power system synchronized phasor measurement service, and relay protection service.
3. The method according to claim 1, wherein the QoS influencing factors include an arrival rate, a packet loss rate, and a maximum delay bound.
4. The method for allocating the multiple service resources of the power grid data acquisition system according to claim 3, wherein the specific process for constructing the effective capacity model comprises the following steps:
defining a QoS index theta;
establishing a relational expression of the QoS index theta with the arrival rate, the packet loss rate and the maximum time delay limit;
and establishing an effective capacity model according to the relation among the QoS index theta, the arrival rate, the packet loss rate and the maximum time delay limit.
5. The method for allocating the multiple service resources of the power grid data acquisition system according to claim 1, wherein the power grid data acquisition system including the base station and the nodes of different service types is constructed, and the specific process of calculating the data transmission rate between the nodes and the base station according to the power grid data acquisition system comprises the following steps:
constructing a power grid data acquisition system comprising 1 base station and K nodes with different service types, wherein M resource blocks are arranged in a resource pool of the power grid data acquisition system;
and constructing a resource block index matrix, and calculating the data transmission rate between different nodes and base stations on the resource block M according to the resource block index matrix.
6. The method according to claim 5, wherein the simplified optimization model is solved, and an objective function of the simplified optimization model is split into two sub-optimization models.
7. The method for allocating the multiple service resources of the power grid data acquisition system according to claim 6, wherein the two sub-optimization models are a first sub-optimization model and a second sub-optimization model, respectively, wherein the first sub-optimization model is: the sending power of a given node is used for solving the distribution mode of the optimal resource block; the second sub-optimization model is: and (4) giving the allocation mode of the resource block and solving the sending power of the optimal node.
8. The method according to claim 7, wherein the allocation manner of the optimal resource block obtained by solving the first sub-optimization model is used as the allocation manner of the resource block of the second sub-optimization model, the transmission power of the optimal node obtained by solving the second sub-optimization model is used as the transmission power of each node of the first sub-optimization model, and the iterative solution is repeated until the allocation manner of the optimal resource block and the transmission power of the optimal node do not change, so as to obtain the resource allocation scheme of the power grid data acquisition system.
9. A multi-service resource allocation system of a power grid data acquisition system is characterized by comprising a utility function construction module, an effective capacity model construction module, a combination module, a data transmission rate calculation module, an optimization model construction module, an optimization model simplification module and a resource allocation scheme solving module;
the utility function construction module is specifically used for constructing utility functions suitable for BE nodes and QoS nodes according to the service type of the power grid data acquisition system;
the effective capacity model building module is specifically used for obtaining QoS influence factors and building an effective capacity model according to the QoS influence factors;
the combining module is specifically used for combining the utility function with the effective capacity model to obtain a utility function model based on the effective capacity;
the data transmission rate calculation module is specifically used for constructing a power grid data acquisition system comprising a base station and nodes of different service types, and calculating the data transmission rate between the nodes and the base station according to the power grid data acquisition system;
the optimization model building module is specifically used for building an optimization model which takes the sum of the utility functions of each node as a target function under the limit of total power and resource allocation according to the utility function model based on the effective capacity and the data transmission rate;
the optimization model simplification module is specifically used for simplifying the optimization model, and the optimization model is simplified into the optimization model which takes the resource of the QoS node satisfying the threshold as the limiting condition and the effective capacity of the BE node as the target function;
and the resource allocation scheme solving module is specifically used for solving the simplified optimization model to obtain a resource allocation scheme of the power grid data acquisition system.
10. A multi-service resource allocation device of a power grid data acquisition system is characterized by comprising a processor and a memory;
the memory is used for storing a computer program and transmitting the computer program to the processor;
the processor is used for executing the multi-service resource allocation method of the power grid data acquisition system according to any one of claims 1 to 8 according to instructions in the computer program.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114629852A (en) * | 2022-03-14 | 2022-06-14 | 中国银行股份有限公司 | Bank business data transmission method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040136379A1 (en) * | 2001-03-13 | 2004-07-15 | Liao Raymond R | Method and apparatus for allocation of resources |
| CN105050191A (en) * | 2015-08-27 | 2015-11-11 | 山东大学 | Multi-user security cognition network resource allocation method based on time delay QoS (Quality of Service) and fairness |
| CN108832979A (en) * | 2018-06-11 | 2018-11-16 | 南京邮电大学 | A kind of channel owes MU-MIMO system multiple-objection optimization resource allocation algorithm when order |
| CN111475301A (en) * | 2020-04-09 | 2020-07-31 | 清华大学 | Satellite resource allocation method and device and electronic equipment |
-
2020
- 2020-10-30 CN CN202011195317.7A patent/CN112367275A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040136379A1 (en) * | 2001-03-13 | 2004-07-15 | Liao Raymond R | Method and apparatus for allocation of resources |
| CN105050191A (en) * | 2015-08-27 | 2015-11-11 | 山东大学 | Multi-user security cognition network resource allocation method based on time delay QoS (Quality of Service) and fairness |
| CN108832979A (en) * | 2018-06-11 | 2018-11-16 | 南京邮电大学 | A kind of channel owes MU-MIMO system multiple-objection optimization resource allocation algorithm when order |
| CN111475301A (en) * | 2020-04-09 | 2020-07-31 | 清华大学 | Satellite resource allocation method and device and electronic equipment |
Non-Patent Citations (2)
| Title |
|---|
| JIN YAQI等: "Multi⁃QoS Guaranteed Resource Allocation for Multi⁃Services Based on Opportunity Costs", 《ZTE COMMUNICATIONS》 * |
| 金亚琪: "多业务QoS保障的资源分配算法研究", 《中国优秀博硕士学位论文全文数据库(硕士) 信息科技辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114629852A (en) * | 2022-03-14 | 2022-06-14 | 中国银行股份有限公司 | Bank business data transmission method and device |
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