CN112702401B - Multi-task cooperative allocation method and device for power Internet of things - Google Patents
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Abstract
The invention provides a multitask collaborative distribution method and a multitask collaborative distribution device for an electric power Internet of things, wherein the method comprises the following steps: determining the average completion time delay of the subtasks of all terminal nodes of the power internet of things according to the subtasks and the edge computing nodes of the terminal nodes of the power internet of things; constructing a multi-task cooperative distribution model according to the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things; and solving the multi-task cooperative allocation model through a task allocation algorithm of a biogeography optimization algorithm to obtain an optimal task allocation scheme. Through average completion time delay of subtasks of all terminal nodes of the power internet of things, a multi-task cooperative distribution model is constructed, the problems of task distribution and convergence routing of minimized average completion time delay of tasks are solved, an optimal task distribution scheme is solved through a task distribution algorithm based on a biogeography optimization algorithm, the solution search efficiency can be effectively improved, local optimization is avoided, and multi-task cooperative distribution of the power internet of things is better achieved.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a multitask cooperative distribution method and device for an electric power internet of things.
Background
Along with the expansion of the power internet of things, many new services are emerging and are accessed into the power internet of things, if the remote monitoring service realizes real-time transmission of power scene video and audio data, intelligent AR glasses sense operation and maintenance sites and guide operation and maintenance operation, and an unmanned aerial vehicle is used for carrying out all-around inspection on a remote power transmission line. These applications typically require a large amount of computation and are delay demanding. In the edge computing mode, the tasks can be computed only at the network edge nodes, so that the task transmission delay and the computing load pressure of the cloud center are reduced. However, the resources such as computation and storage of network edge nodes are very limited, and now, the requirement of meeting the business by using an edge cooperative computing mode is considered more and more.
In the prior art, a heuristic iterative algorithm framework using a simulated annealing idea is used for carrying out joint optimization on a task unloading strategy and a resource allocation scheme, but the joint problem has a large solution space, the searching efficiency is low by using the simulated annealing algorithm, and the local optimal solution is easy to be trapped, so that the allocation is unreasonable.
Therefore, how to better realize the multi-task cooperative allocation of the power internet of things has become an urgent problem to be solved in the industry.
Disclosure of Invention
The invention provides a multitask cooperative distribution method and device for an electric power Internet of things, which are used for solving the problem that the multitask cooperative distribution of the electric power Internet of things cannot be well realized in the prior art.
The invention provides a multitask collaborative distribution method for an electric power Internet of things, which comprises the following steps:
determining the average completion time delay of the subtasks of all terminal nodes of the power internet of things according to the subtasks and the edge computing nodes of the terminal nodes of the power internet of things;
constructing a multi-task cooperative distribution model according to the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things;
and solving the multi-task cooperative allocation model through a task allocation algorithm of a biogeography optimization algorithm to obtain an optimal task allocation scheme.
According to the multitask collaborative allocation method for the power internet of things, provided by the invention, after the step of obtaining the optimal task allocation scheme, the method further comprises the following steps:
and solving the multi-task cooperative allocation model through an ant colony algorithm based on the optimal task allocation scheme to obtain a target sink node.
According to the multitask collaborative allocation method of the power internet of things, the step of determining the average completion time delay of the subtasks of all terminal nodes of the power internet of things according to the subtasks and the edge computing nodes of the terminal nodes of the power internet of things specifically comprises the following steps:
determining a return time delay, a sending time delay of each subtask, a calculating time delay of each subtask and a result convergence time delay of each subtask according to the subtask and the edge calculating node of the power internet of things terminal node;
determining the subtask cooperation completion time delay of the terminal node of the power internet of things according to the return time delay, the sending time delay of each subtask, the calculation time delay of each subtask and the result convergence time delay of each subtask;
and determining the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things according to the subtask cooperation completion time delay of the terminal nodes of the power Internet of things and the subtask uploading time delay of the terminal nodes of the power Internet of things.
According to the multitask collaborative allocation method of the power Internet of things, provided by the invention, the multitask collaborative allocation model specifically comprises the following steps:
wherein X and Y are each an electric powerA task allocation decision and a convergent point decision of the networking terminal node; wherein the content of the first and second substances,is a constraint on the computational resources of the edge nodes,a storage resource element that is an edge node;in order to be a time delay constraint,is a converged routing constraint; x ═ XijkX in (b) }ijkA decision is allocated to the subtask of the terminal node of the power internet of things;the subtask average completion time delay of all terminal nodes of the power internet of things is realized,the computing task initiated for the terminal node of the power internet of things comprises a plurality of subtasks.
According to the multitask cooperative allocation method of the power internet of things, the multitask cooperative allocation model is solved through a task allocation algorithm of a biophysical optimization algorithm to obtain an optimal task allocation scheme, and the method specifically comprises the following steps:
calculating the inhabitation suitability index HIS of each population;
performing migration operation on the population based on a preset migration rate function, performing mutation operation on the population based on a preset mutation probability function, if a preset termination condition is met, obtaining an optimal task allocation scheme, otherwise, repeating the iteration process of calculating the HIS value of each population until the termination condition is met;
wherein the population is a candidate task allocation plan.
According to the multitask cooperative allocation method of the power internet of things, provided by the invention, based on the optimal task allocation scheme, the multitask cooperative allocation model is solved through an ant colony algorithm to obtain a target sink node, and the method specifically comprises the following steps:
initializing initial parameters of the ant colony algorithm;
according to the initial parameters, performing circular search in a convergent point selection matrix through the ant colony algorithm until preset cycle times are completed to obtain a target convergent node;
the convergent point selection matrix is constructed according to a calculation task initiated by an edge node and a terminal node of the power internet of things;
wherein the initial parameters of the ant colony algorithm comprise: the initial pheromone concentration of the convergent point selection matrix, the initial node of the path to be searched, the termination node of the path to be searched, the total ant number, the total cycle number and the pheromone volatilization coefficient of the ant colony algorithm.
The invention also provides a multitask collaborative distribution device for the power Internet of things, which comprises the following components:
the computing module is used for determining the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things according to the subtasks and the edge computing nodes of the terminal nodes of the power Internet of things;
the construction module is used for constructing a multi-task cooperative distribution model according to the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things;
and the distribution module is used for solving the multi-task cooperative distribution model through a task distribution algorithm of a biophysical optimization algorithm to obtain an optimal task distribution scheme.
The allocation module is further configured to:
and solving the multi-task cooperative allocation model through an ant colony algorithm based on the optimal task allocation scheme to obtain a target sink node.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the processor executes the program, the processor realizes the steps of any one of the electric power internet of things multitask cooperative allocation methods.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the power internet of things multitask cooperative allocation method as described in any of the above.
According to the multitask collaborative allocation method and device for the power internet of things, the multitask collaborative allocation model is constructed through the average completion time delay of the subtasks of all terminal nodes of the power internet of things, the problems of task allocation and convergence routing of minimized average completion time delay of tasks are solved, edge node resources can be effectively utilized, the continuously increased business processing requirements are met, the time delay is reduced through optimizing a collaborative data route, the optimal task allocation scheme is solved through a task allocation algorithm based on a biophysical optimization algorithm, the solution search efficiency can be effectively improved, local optimization is avoided, and therefore multitask collaborative allocation of the power internet of things is better achieved.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for 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 some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a multitask cooperative distribution method of an electric power internet of things provided by the invention;
FIG. 2 is a diagram of a mobility model provided by the present invention;
fig. 3 is a schematic diagram of a process of ant algorithm for simulating ant to search for an optimal rendezvous point decision according to the present invention;
FIG. 4 is a graph of the convergence effect of the task allocation algorithm based on the biogeographic algorithm under different habitat numbers provided by the invention;
FIG. 5 is a graph of the convergence effect of the task allocation algorithm based on the biogeographic algorithm under different mutation probabilities according to the present invention;
FIG. 6 is a diagram illustrating the convergence effect of the algorithm under different ant numbers according to the present invention;
FIG. 7 is a diagram illustrating the average delay of the algorithm according to the present invention for different UE numbers;
fig. 8 is a schematic view of a multitask cooperative distribution device of the power internet of things provided by the invention;
fig. 9 is a schematic physical structure diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. 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.
Fig. 1 is a schematic flow diagram of a multitask cooperative allocation method for an electric power internet of things, as shown in fig. 1, including:
step S1, determining the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things according to the subtasks and the edge computing nodes of the terminal nodes of the power Internet of things;
specifically, the calculation task initiated by each power internet of things terminal node UEi described in the embodiment of the present invention is expressed asComprising a plurality of subtasks, using wij=(cij,eij,dij,tij,λij) And (4) showing. c. CijRepresenting computing resource requirements, eijRepresenting storage resource requirements, dijRepresenting the amount of input data, tijDenotes the calculation delay, λ, with the resource condition satisfiedijIndicating the proportional magnitude of the calculated data amount to the input data amount, eachThere is no time sequence dependency relationship between the subtasks, and the subtasks can be completed independently. Is provided withTo be a taskIs required to be atAnd completing the task within a time range.
The edge computing node described in the present invention refers to a network edge device with computing and storage capabilities,andrespectively representing a power internet of things terminal node UE set and an edge computing node EN set.
And considering that the resource of a single edge node in the network is limited, the edge node task request is completed in an edge node cooperation mode. Due to different service characteristics, the initiated tasks have certain differences in the requirements of computing resources, storage resources and time delay, a time delay sensitive task with a large computation amount generally needs cooperation of a plurality of edge nodes, and a task with a small task amount can be completed by a small amount or a single node. According to the general service specification, the task of the UEi is assumed to be represented by qiThe EN nodes complete the cooperation, and the set of cooperation points is
Meanwhile, the final objective of the multitask collaborative allocation of the power internet of things is to ensure that the time delay for completing tasks issued by the terminal nodes is minimized, so that the average completion time delay of the subtasks of all the terminal nodes of the power internet of things is determined according to the subtasks and the edge computing nodes of the terminal nodes of the power internet of things, a model is conveniently established according to the average completion time delay of the subtasks in the subsequent steps, and therefore the task collaborative allocation scheme with the time delay minimized as the objective is realized.
Step S2, constructing a multi-task cooperative distribution model according to the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things;
specifically, the invention finds that in the whole cooperative distribution process, the starting point of the sending route is fixed, and the end point is determined by task distribution decision. After the calculation is completed, the starting point of the route is a calculation node, the end point is an access point, and the factor influencing the route selection is a convergent point decision which influences both the forward convergent route and the backward return route.
Therefore, the task allocation decision and the rendezvous point decision both obviously influence the final time delay, and after a multi-task cooperative allocation model is constructed, the problems of task allocation and rendezvous route selection with minimized task average completion time delay are solved, edge node resources can be effectively utilized, the continuously increased service processing requirement is met, and the time delay is reduced by optimizing data routing in cooperation.
And step S3, solving the multi-task collaborative allocation model through a task allocation algorithm of the biophysical optimization algorithm to obtain an optimal task allocation scheme.
More specifically, in the present invention, because a task allocation decision and a rendezvous point decision need to be considered at the same time, and it is difficult to solve the task allocation decision and the rendezvous point decision at the same time to obtain a result, it is considered that the closer the nodes participating in cooperative computation and rendezvous are to the access point, the shorter the transmission path is, and the smaller the average task completion delay is. Therefore, it is assumed that the sink node of each task is at the access point, and in this case, an optimal task allocation scheme is requested, so that a better solution can be obtained.
According to the method, the multitask cooperative distribution model is constructed through the average completion time delay of the subtasks of all terminal nodes of the power internet of things, the problems of task distribution and convergence routing of minimized average completion time delay of the tasks are solved, edge node resources can be effectively utilized, the continuously increased business processing requirements are met, the time delay is reduced through optimizing data routing in cooperation, the optimal task distribution scheme is solved through a task distribution algorithm based on a biophysical optimization algorithm, the solution search efficiency can be effectively improved, the local optimization is avoided, and therefore the multitask cooperative distribution of the power internet of things is better achieved.
Optionally, after the step of obtaining the optimal task allocation scheme, the method further includes:
and solving the multi-task cooperative allocation model through an ant colony algorithm based on the optimal task allocation scheme to obtain a target sink node.
Specifically, in the above embodiment, when the optimal task allocation scheme is selected, the method is performed under the condition that the sink node of each task is assumed to be at the access point, but actually, the method still does not solve the target sink node, but determines that the target sink node still plays an important role in reducing the delay.
According to the invention, the multi-task cooperative allocation model is solved through the ant colony algorithm to obtain the target sink node, so that the task delay can be further reduced according to the target sink node, and the effectiveness of task cooperative allocation is ensured.
Optionally, the step of determining the average completion time delay of the subtasks of all the terminal nodes of the power internet of things according to the subtasks and the edge computing nodes of the terminal nodes of the power internet of things specifically includes:
determining a return time delay, a sending time delay of each subtask, a calculating time delay of each subtask and a result convergence time delay of each subtask according to the subtask and the edge calculating node of the power internet of things terminal node;
determining the subtask cooperation completion time delay of the terminal node of the power internet of things according to the return time delay, the sending time delay of each subtask, the calculation time delay of each subtask and the result convergence time delay of each subtask;
and determining the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things according to the subtask cooperation completion time delay of the terminal nodes of the power Internet of things and the subtask uploading time delay of the terminal nodes of the power Internet of things.
Specifically, the task sending time delay of the subtask described in the present invention
Subtask wijThe network latency for transmission from the access point to the compute node is:
as described above, under the condition that the computing resource requirement and the storage resource requirement are satisfied, the computing time delay of each subtask is
Neutron task wijAfter the calculation is completed, the result convergence time delay of the calculation result sent to the convergence point is as follows:
computation result return
All the calculation results reach the convergent point and are transmitted to the access point uiThe backhaul delay is:
considering that the calculation results are combined depending on the time pointThe subtask of the latest arrival at the rendezvous point, and hence the subtask of UEiThe collaboration completion delay is as follows:
the available UEi requests from the sending taskTotal time delay T to receipt of calculated resultiIs composed of
Wherein the content of the first and second substances,the calculation result is from the terminal node ENu of the power internet of thingsiThe delay returned to the edge node UEi ignores the calculation result at ENu because the downlink bandwidth of UEi is much higher than the uplink bandwidth and the calculation result data volume is smalleriThe downlink transmission delay to the UEi, thus, results in:
wherein the content of the first and second substances,the method refers to that the power Internet of things terminal node UEi uploads all task input data to the edge node ENuiThe network latency of (1).
UEi to ENuiThe network delay includes transmission delay and propagation delay caused by port rate, c represents propagation delay of wired or wireless channel,representation UEi and ENuiSo UEi uploads the task input data in its entirety to ENuiThe network latency of (a) may be expressed as:
wherein the uplink data transmission rate v of the UEiiComprises the following steps:
wherein the bandwidth resource of ENk is BkHz, in the present invention, the UE associated with the EN is defaulted to allocate the bandwidth resources of the EN equally. UEi and ENuiHas a signal-to-noise ratio ofWherein p isiWhich indicates the transmission power of the ue i,representation of UEi and ENuiChannel gain of σ2Representing an additive white gaussian noise power.
Therefore, the average completion time delay of the subtasks of all the terminal nodes of the power internet of things is as follows:
according to the method, the multitask cooperative distribution target of the power Internet of things is determined by analyzing the average completion time delay of all the terminal nodes of the power Internet of things, and the method is the basis of subsequent cooperative distribution.
Optionally, the multitask collaborative allocation model specifically includes:
the method comprises the following steps that X and Y are task allocation decisions and convergent point decisions of a power internet of things terminal node respectively; wherein the content of the first and second substances,is a constraint on the computational resources of the edge nodes,a storage resource element that is an edge node;in order to be a time delay constraint,is a converged routing constraint; x ═ XijkX in (b) }ijkAs an electric power objectA subtask allocation decision of the networking terminal node;the subtask average completion time delay of all terminal nodes of the power internet of things is realized,the computing task initiated for the terminal node of the power internet of things comprises a plurality of subtasks.
In the invention, because the edge nodes have resource heterogeneity, the realization of EN resource allocation is supported by adopting a container and a virtualization technology, and the resource amount required by the subtasks is expressed by the number of virtual resource units. It is assumed that the task requests of all UEs are issued simultaneously.
X={xijkThe represents a subtask allocation decision, and the values are specified as follows:
Considering that the subtask must be calculated by one node in the cooperative node set, the following constraint conditions need to be satisfied:
the resources of edge node k are represented by a doublet of (C)k,Ek),CkFor virtual computing of the number of resource units, EkIs the number of virtual storage resource units.Andindicating that the amount of computing and storage resources of EN k should be sufficient at task allocation decision X, and therefore there are constraints,
meanwhile, the starting point of the sending route is fixed, and the end point is determined by task allocation decision. After the calculation is completed, the starting point of the route is a calculation node, the end point is an access point, and the factor influencing the route selection is an aggregation point decision which influences both the forward aggregation route and the backward return route. Therefore set taskThe converged routing decision of Y ═ YikThe value specification is as follows:
there are therefore the following constraints:
on the other hand, due to the fact that no time sequence dependency relationship exists among the subtasks of the terminal nodes of the power internet of things, the subtasks can be completed independently. Is provided withTo be a taskIs required to be atThe task is completed within a time frame, so there are the following constraints:
according to the method, the objective function for minimizing the average completion time delay of the subtasks of all terminal nodes of the power Internet of things is established, and meanwhile, various constraints are established, so that the sink node selection in task allocation and cooperative calculation is effectively guaranteed to be optimized under the condition that the service resource requirements and the time delay requirements are met, and the average completion time delay of the tasks is minimized.
Optionally, the step of solving the multi-task collaborative allocation model through the task allocation algorithm of the biophysical optimization algorithm to obtain an optimal task allocation scheme specifically includes:
(1) initializing algorithm parameters, namely habitat quantity L and habitat population maximum capacity smaxMaximum migration rate I, maximum migration rate E, maximum mutation rate mmaxIteration number Ω, task setAnd a fitness vector for each habitat, and setting the current iteration number n to 1.
(2) For each habitat XlTo produce [0,1 ]]Random number in the range, if the random number is less than the corresponding migration rate of the habitat, selecting a habitat X from all the habitats by taking the migration rate as probabilityl′Performing a migration operation
(3) For each habitat XlTo produce [0,1 ]]Random number within the range, and if the random number is less than the mutation probability of the habitat, performing mutation operation
(4) And n is equal to n +1, if n is less than omega, returning to the step (2), otherwise, outputting a task allocation scheme corresponding to the optimal habitat, and ending the algorithm operation.
Specifically, under the condition of meeting the service resource requirement, the closer the nodes participating in cooperative computation and aggregation are to the access point, the shorter the transmission path is, and the smaller the average task completion delay is.
Therefore, when calculating the optimal task allocation scheme, the invention firstly assumes that the sink node of each task is at the access point, namelyAnd then solving the optimal task allocation. Problem P1 thus translates into P2:
s.t C2~C8
the invention provides a task allocation algorithm based on a Biogeography-based Optimization (BBO) to solve the problem. The basic idea of the BBO algorithm stems from the theory of biophysics, where biological species live in multiple habitats, each indicated by a Habitats Suitability Index (HSI), and HIS-related factors are rainfall, topographical features, land area, etc., which are referred to as suitability index variables. Higher HIS habitats are diverse in species, however habitats tend to have a large number of species migrating to adjacent habitats due to the tendency of the living space to saturate, with a small number of species migrating in. Lower HIS habitat species are fewer in number, with more species migrating and fewer species migrating. However, when HSI of a habitat is kept low at all times, species on that habitat tend to die out or seek additional habitats, i.e. mutations. The habitats are subjected to migration and mutation operation, so that the exchange and sharing of information among species are enhanced, and the diversity of regional species is improved.
Because the solution space dimensionality of the task allocation decision X is higher, in order to solve the problem conveniently, the invention recodes X and converts X into X
WhereinRepresenting tasksA set of cooperating nodes is provided that is,representing the assignment of tasks in a set of cooperative nodes, where aij∈[1,qi]Represents a subtask wijByThe calculation is completed. The symbols are unified for convenience
X=(x1,x2,...,xD)
Assuming a total of L habitats, each habitat represents a feasible solution in the problem solution space, where the first habitat is represented as
BBO algorithm utilization migration operationAnd exchanging information with other habitats, and further searching the solution space. Each habitat has its own mobility and migration rate which are functions of the number s of species in the habitat, FIG. 2 is a mobility model diagram provided by the present invention, as shown in FIG. 2, is a mathematical model of the relationship between the number of species and the mobility rate, where I and E represent the maximum mobility and the maximum migration rate of the habitat, smaxIs the maximum population. The migration rate and migration rate as a function of the number of species is therefore:
λ(s)=I(1-s/smax)
is provided withIs a habitat suitability function, whereinIndicating the decision at task distribution decision X, rendezvous PointThe following average task completion delays.
The habitat with higher fitness contains more populations, and the habitat with lower fitness corresponds to less populations. Establishing a mapping function of the population quantity and the habitats, and sequencing the goodness of all the habitats by the mapping function so as to obtain the population quantity s of the ith habitatl=smax-Index(Xl),Index(Xl) As a habitat XlThe ordered subscripts. The migration operation is shown in equation (11):
Mutation operations are mutations that mimic the habitat ecological environment, altering the number of habitat species. The mutation probability of the habitat is inversely proportional to the species number probability, and the mutation probability when the species number is s is m(s), namely:
wherein m ismaxFor maximum mutation rate, p(s) is the probability that the number of habitat species is s, pmaxIs the maximum value of p(s).
The mutation operation is shown in the publication (13).Mutation into interval [ ubi,lbi]A random value of, wherein lbi,ubiRespectively representing variablesLower and upper limits of the value.
And carrying out migration operation on the population based on a preset migration rate function, carrying out mutation operation on the population based on a preset mutation probability function, obtaining an optimal task allocation scheme if a preset termination condition is met, otherwise, repeating the process until an iteration process of the termination condition is met.
In the invention, under the condition of assuming that the sink node of each task is at the access point, the multi-task cooperative allocation model is solved through a biophysical optimization algorithm to obtain an optimal task allocation scheme, so that the reasonability and reliability of task allocation can be effectively ensured.
Optionally, based on the optimal task allocation scheme, the step of solving the multi-task collaborative allocation model through an ant colony algorithm to obtain a target sink node specifically includes:
(1) initializing initial parameters of the ant colony algorithm, wherein the initial parameters comprise a heuristic factor alpha, an pheromone factor beta, a volatility coefficient p, the ant colony number D and the maximum iteration number omega. And setting the initial iteration number n to be 1.
(2) For each ant, calculateTransition probability p corresponding to each edge nodeik,Based on roulette as a taskOne of the edge nodes is selected as its sink node.
(3) And updating the pheromone, wherein n is equal to n +1, if n is less than omega, returning to (2), and otherwise, entering into (4).
(4) And outputting a convergent point decision corresponding to the optimal ant, and ending the algorithm.
Specifically, since the aggregation route affects data transmission delay in cooperative computing, based on the optimal task allocation scheme obtained in the above embodiment, the problem P1 is transformed into the following problem
s.t C2~C8
In order to obtain a global optimal rendezvous point decision, the invention provides a rendezvous point selection algorithm based on an ant colony algorithm. The ant colony algorithm is a colony optimization algorithm for searching the shortest path by simulating ant foraging. Ants can leave pheromones on the passing path in foraging and seeking ways, and other ants guide the later movement direction of the ants by sensing the existence and the strength of the substance. The ants search for a shortest path from their nest to the food source through this communication of pheromones.
FIG. 3 is a schematic diagram of the ant algorithm for simulating ant to search for the optimal rendezvous point decision process, as shown in FIG. 3, D ants are randomly placed on the first row of four nodes, and point yikRepresenting tasksThe results of the calculations of (c) are aggregated at node ENk.After the sink node selection, willAdding into ant taboo list. Each ant creates a legal path through state transition rules. In the process of creating the path, each ant updates the pheromone for the points which are walked through by the updating rule. All ants complete path creation indicating that one iteration process is over.
Specifically, the ant updates pheromones of the walking points through the updating rule specifically as follows:
let tikPresentation selection ENk as a taskConcentration of the selected pheromone of the sink node, the selection being represented by a binary group<i,k>。tikThe larger the likelihood that other ants will choose this decision. Setting all selected pheromone concentration initial values to
After all tasks select the sink node, the pheromones on all the selected paths need to be updated, including pheromone volatilization and new pheromone release, and the updating formula is as follows:
where p represents the pheromone volatilization coefficient, with larger p the faster the pheromone volatilizes. D represents the number of ants.Is an ant d on<i,k>The released pheromones on this path are calculated as follows:
wherein Y isdIs a convergent point decision constructed by the ant d,represents YdThe following tasks are performed with average completion delay. Thus whenThe smaller, the path<i,k>The greater the concentration of pheromone left.
Criterion of transition probability
Ants select paths with higher expected values and pheromones from the current paths each time.Indicates ant d selection<i,k>Probability of this path, i.e. ENk asThe sink node of (1).
alloweddThe ant d is represented to allow the selected task set, i.e. the tasks that have not matched the corresponding sink node. Alpha is an pheromone factor, and the bigger alpha is, the closer the ant is to partial solutions selected by other ants. EtaikRepresentation ENk as a taskThe expected value of (1) is obtained by calculating convergence delay and return delay in cooperative calculation, etaikThe calculation formula is as follows:
beta is an expected heuristic factor, the bigger beta is, the closer the ant is to a greedy selection solution according to an expected value, and the target sink node is finally obtained.
Under the condition of determining the optimal task allocation scheme, the optimal target sink node is determined through the ant colony algorithm, so that the reasonability and the effectiveness of the final allocation scheme are ensured.
Optionally, the simulation environment is set to be a region of 1km × 1km, the region includes 20 ENs, 50 UEs, and the positions of the ENs and the UEs are randomly generated in the region. The CPU frequency (GHz) and the storage space (GB) of each edge node follow normal distribution, respectivelySetting a virtual computing resource unit to be 0.1GHz and a virtual storage unit to be 0.5 GB. Data transmission rate v between two edge nodesk,k′(KB/s) is normally distributedThe quantity of the subtasks of each UE is subject to uniform distribution U (1,5), and the calculation and the storage of the subtasks need to be carried out on virtual resource unitsCalculating the quantity and calculating the time delay and obeying the Poisson distribution, wherein the mean values are respectively lambda1=8,λ2=10,λ 340. The same [ Task off flow for Mobile Edge Computing in Software Defined Ultra-Dense Network ] [ Energy-aware mobility management for Mobile Edge Computing in Ultra-Dense networks ] the channel gains are expressed as follows: h 127+30logd (din killometers).
The parameters of the task allocation algorithm based on the biophysical optimization algorithm are shown in the following table 1:
table 1: distribution algorithm parameters
Fig. 4 is a graph of convergence effect of the task allocation algorithm based on the biophysical algorithm under different habitat numbers, as shown in fig. 4, when the habitat numbers are 30, 40, and 50, the algorithm converges to 191, 172, and 139, and when the habitat number is 50, the algorithm converges at the earliest stage. Generally, the greater the number of habitats, the greater the probability of finding an optimal solution.
Fig. 5 is a graph of convergence effect of the task allocation algorithm based on the biophysical algorithm under different mutation probabilities provided by the present invention, and as shown in fig. 5, the final convergence results are 181, 133, and 154 when the number of habitats is 0.005, 0.02, and 0.035, respectively. The mutation probability is best at 0.02. The algorithm is prone to fall into local optima when the mutation rate is large or small.
And simulating the rendezvous point selection algorithm based on the ant colony algorithm by combining the optimal task allocation strategy calculated by the task allocation algorithm based on the biophysical algorithm. The simulation parameters are shown in table 2 below:
table 2: rendezvous point selection algorithm simulation parameters
Fig. 6 is a schematic diagram of the convergence effect of the algorithm under different ant numbers provided by the present invention, as shown in fig. 6, when the ant numbers are respectively 30, 40, and 50, the convergence is respectively 121, 110, and 92 ms. The larger the number of ants is, the higher the probability of searching the optimal solution is, and the trapping of the optimal solution in a local area is avoided. Fig. 7 is a schematic diagram of an average delay condition of the algorithm under different UE quantities, as shown in fig. 7, when the UE quantity is gradually increased, a plurality of nodes jointly complete a task initiated by the UE in a cooperative manner, and meanwhile, sink node selection in cooperative calculation is optimized, so that the problem of insufficient resource of a single node is solved, and data transmission delay in a cooperative process is reduced, so that the average delay of task completion is slowly increased.
Fig. 8 is a schematic view of a multitask cooperative distribution device of an electric power internet of things provided by the present invention, as shown in fig. 8, including: a calculation module 810, a construction module 820, and an assignment module 830; the computing module 810 is configured to determine, according to the subtasks and edge computing nodes of the terminal nodes of the power internet of things, average completion time delays of the subtasks of all the terminal nodes of the power internet of things; the building module 820 is used for building a multi-task cooperative distribution model according to the average completion time delay of the subtasks of all terminal nodes of the power internet of things; the distribution module 830 is configured to solve the multi-task collaborative distribution model through a task distribution algorithm of a biophysical optimization algorithm to obtain an optimal task distribution scheme.
The allocation module is further configured to:
and solving the multi-task cooperative allocation model through an ant colony algorithm based on the optimal task allocation scheme to obtain a target sink node.
According to the invention, through average completion time delay of subtasks of all terminal nodes of the power Internet of things, a multi-task cooperative distribution model is constructed, and the problems of task distribution and convergence routing of minimized average completion time delay of tasks are provided, so that edge node resources can be effectively utilized, the continuously increased business processing requirements are met, time delay is reduced through optimizing data routing in cooperation, an optimal task distribution scheme is solved through a task distribution algorithm based on a biophysical optimization algorithm, the search efficiency of the solution can be effectively improved, local optimization is avoided, and therefore, the multi-task cooperative distribution of the power Internet of things is better realized.
Fig. 9 is a schematic physical structure diagram of an electronic device provided in the present invention, and as shown in fig. 9, the electronic device may include: a processor (processor)910, a communication Interface (Communications Interface)920, a memory (memory)930, and a communication bus 940, wherein the processor 910, the communication Interface 920, and the memory 930 communicate with each other via the communication bus 940. Processor 910 may invoke logic instructions in memory 930 to perform a method of power internet of things multitask co-allocation comprising: determining the average completion time delay of the subtasks of all terminal nodes of the power internet of things according to the subtasks and the edge computing nodes of the terminal nodes of the power internet of things; constructing a multi-task cooperative distribution model according to the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things; and solving the multi-task cooperative allocation model through a task allocation algorithm of a biogeography optimization algorithm to obtain an optimal task allocation scheme.
Furthermore, the logic instructions in the memory 930 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. 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 program codes.
In another aspect, the present invention also provides a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer being capable of executing the power internet of things multitask cooperative allocation method provided by the above methods, the method including: determining the average completion time delay of the subtasks of all terminal nodes of the power internet of things according to the subtasks and the edge computing nodes of the terminal nodes of the power internet of things; constructing a multi-task cooperative distribution model according to the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things; and solving the multi-task cooperative allocation model through a task allocation algorithm of a biogeography optimization algorithm to obtain an optimal task allocation scheme.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to perform the power internet of things multitask cooperative allocation method provided by the foregoing embodiments, the method including: determining the average completion time delay of the subtasks of all terminal nodes of the power internet of things according to the subtasks and the edge computing nodes of the terminal nodes of the power internet of things; constructing a multi-task cooperative distribution model according to the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things; and solving the multi-task cooperative allocation model through a task allocation algorithm of a biogeography optimization algorithm to obtain an optimal task allocation scheme.
The above-described embodiments of the apparatus are merely illustrative, and 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; 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 (9)
1. A multitask cooperative allocation method for an electric power Internet of things is characterized by comprising the following steps:
determining the average completion time delay of the subtasks of all terminal nodes of the power internet of things according to the subtasks and the edge computing nodes of the terminal nodes of the power internet of things;
constructing a multi-task cooperative distribution model according to the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things;
solving the multi-task cooperative allocation model through a task allocation algorithm of a biophysical optimization algorithm to obtain an optimal task allocation scheme,
the multitask collaborative allocation model specifically comprises the following steps:
the method comprises the following steps that X and Y are task allocation decisions and convergent point decisions of a power internet of things terminal node respectively; wherein the content of the first and second substances,is a constraint on the computational resources of the edge nodes,a storage resource element that is an edge node;in order to be a time delay constraint,is a converged routing constraint; x ═ XijkX in (b) }ijkA decision is allocated to the subtask of the terminal node of the power internet of things;the subtask average completion time delay of all terminal nodes of the power internet of things is realized,the computing task initiated for the terminal node of the power internet of things comprises a plurality of subtasks.
2. The electric power internet of things multitask cooperative allocation method according to claim 1, wherein after the step of obtaining an optimal task allocation scheme, the method further comprises the following steps:
and solving the multi-task cooperative allocation model through an ant colony algorithm based on the optimal task allocation scheme to obtain a target sink node.
3. The electric power internet of things multitask cooperative distribution method according to claim 1, wherein the step of determining the average completion time delay of the subtasks of all the terminal nodes of the electric power internet of things according to the subtasks and the edge computing nodes of the terminal nodes of the electric power internet of things specifically comprises the following steps:
determining a return time delay, a sending time delay of each subtask, a calculating time delay of each subtask and a result convergence time delay of each subtask according to the subtask and the edge calculating node of the power internet of things terminal node;
determining the subtask cooperation completion time delay of the terminal node of the power internet of things according to the return time delay, the sending time delay of each subtask, the calculation time delay of each subtask and the result convergence time delay of each subtask;
and determining the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things according to the subtask cooperation completion time delay of the terminal nodes of the power Internet of things and the subtask uploading time delay of the terminal nodes of the power Internet of things.
4. The electric power internet of things multitask cooperative allocation method according to claim 1, wherein the step of solving the multitask cooperative allocation model through a task allocation algorithm of a biogeography optimization algorithm to obtain an optimal task allocation scheme specifically comprises the following steps:
calculating the inhabitation suitability index HIS of each population;
performing migration operation on the population based on a preset migration rate function, performing mutation operation on the population based on a preset mutation probability function, if a preset termination condition is met, obtaining an optimal task allocation scheme, otherwise, repeating the iteration process of calculating the HIS value of each population until the termination condition is met;
wherein the population is a candidate task allocation plan.
5. The electric power internet of things multitask cooperative allocation method according to claim 2, wherein the step of solving the multitask cooperative allocation model through an ant colony algorithm based on the optimal task allocation scheme to obtain a target sink node specifically comprises the following steps:
initializing initial parameters of the ant colony algorithm;
according to the initial parameters, performing circular search in a convergent point selection matrix through the ant colony algorithm until preset cycle times are completed to obtain a target convergent node;
the convergent point selection matrix is constructed according to a calculation task initiated by an edge node and a terminal node of the power internet of things;
wherein the initial parameters of the ant colony algorithm comprise: the initial pheromone concentration of the convergent point selection matrix, the initial node of the path to be searched, the termination node of the path to be searched, the total ant number, the total cycle number and the pheromone volatilization coefficient of the ant colony algorithm.
6. The utility model provides an electric power thing networking multitask is distribution device in coordination which characterized in that includes:
the computing module is used for determining the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things according to the subtasks and the edge computing nodes of the terminal nodes of the power Internet of things;
the construction module is used for constructing a multi-task cooperative distribution model according to the average completion time delay of the subtasks of all the terminal nodes of the power Internet of things;
the distribution module is used for solving the multi-task collaborative distribution model through a task distribution algorithm of a biophysical optimization algorithm to obtain an optimal task distribution scheme,
the multitask collaborative allocation model in the building module specifically comprises the following steps:
the method comprises the following steps that X and Y are task allocation decisions and convergent point decisions of a power internet of things terminal node respectively; wherein the content of the first and second substances,is a constraint on the computational resources of the edge nodes,a storage resource element that is an edge node;in order to be a time delay constraint,is a converged routing constraint; x ═ XijkX in (b) }ijkA decision is allocated to the subtask of the terminal node of the power internet of things;the subtask average completion time delay of all terminal nodes of the power internet of things is realized,the computing task initiated for the terminal node of the power internet of things comprises a plurality of subtasks.
7. The electric power internet of things multitask cooperative distribution device according to claim 6, wherein the distribution module is further configured to:
and solving the multi-task cooperative allocation model through an ant colony algorithm based on the optimal task allocation scheme to obtain a target sink node.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the power internet of things multitask co-allocation method according to any one of claims 1 to 5.
9. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of the power internet of things multitask cooperative distribution method according to any one of claims 1 to 5.
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