CN112702401A - Multi-task cooperative allocation method and device for power Internet of things - Google Patents

Multi-task cooperative allocation method and device for power Internet of things Download PDF

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CN112702401A
CN112702401A CN202011480971.2A CN202011480971A CN112702401A CN 112702401 A CN112702401 A CN 112702401A CN 202011480971 A CN202011480971 A CN 202011480971A CN 112702401 A CN112702401 A CN 112702401A
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邵苏杰
邱雪松
赵东艳
原义栋
李奇
李长柏
汪千军
朱钰
董之微
卢岩
李桐
张庆航
吴磊
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Beijing University of Posts and Telecommunications
Electric Power Research Institute of State Grid Liaoning Electric Power Co Ltd
Beijing Smartchip Microelectronics Technology Co Ltd
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Electric Power Research Institute of State Grid Liaoning Electric Power Co Ltd
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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

Multi-task cooperative allocation method and device for power Internet of things
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:
Figure BDA0002837555840000021
Figure BDA0002837555840000031
Figure BDA0002837555840000032
Figure BDA0002837555840000033
Figure BDA0002837555840000034
Figure BDA0002837555840000035
Figure BDA0002837555840000036
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,
Figure BDA0002837555840000037
is a constraint on the computational resources of the edge nodes,
Figure BDA0002837555840000038
a storage resource element that is an edge node;
Figure BDA0002837555840000039
in order to be a time delay constraint,
Figure BDA00028375558400000310
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;
Figure BDA00028375558400000311
the subtask average completion time delay of all terminal nodes of the power internet of things is realized,
Figure BDA00028375558400000312
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 as
Figure BDA0002837555840000061
Comprising 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 satisfiedijThe proportion of the data quantity of the calculation result to the input data quantity is shown, and each subtask has no time sequence dependency relationship and can be independently completed. Is provided with
Figure BDA0002837555840000062
To be a task
Figure BDA0002837555840000063
Is required to be at
Figure BDA0002837555840000064
And 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,
Figure BDA0002837555840000065
and
Figure BDA0002837555840000066
respectively 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, and the time delay sensitivity task with large calculated amountThe tasks usually need a plurality of edge nodes to cooperate, and the tasks with smaller 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
Figure BDA0002837555840000067
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:
Figure BDA0002837555840000091
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
Figure BDA0002837555840000092
Figure BDA0002837555840000093
Neutron task wijAfter the calculation is completed, the result convergence time delay of the calculation result sent to the convergence point is as follows:
Figure BDA0002837555840000094
computation result return
Figure BDA0002837555840000095
All the calculation results reach the convergent point and are transmitted to the access point uiThe backhaul delay is:
Figure BDA0002837555840000096
considering that the calculation results are combined depending on the time point
Figure BDA0002837555840000097
The subtask of the latest arrival at the rendezvous point, and hence the subtask of UEi
Figure BDA0002837555840000098
The collaboration completion delay is as follows:
Figure BDA0002837555840000099
the available UEi requests from the sending task
Figure BDA00028375558400000910
Total time delay T to receipt of calculated resultiIs composed of
Figure BDA00028375558400000911
Wherein the content of the first and second substances,
Figure BDA00028375558400000912
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 smalleriTo UEiDownlink transmission delay, therefore, we get:
Figure BDA00028375558400000913
wherein the content of the first and second substances,
Figure BDA00028375558400000914
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,
Figure BDA00028375558400000915
representation UEi and ENuiSo UEi uploads the task input data in its entirety to ENuiThe network latency of (a) may be expressed as:
Figure BDA0002837555840000101
wherein the uplink data transmission rate v of the UEiiComprises the following steps:
Figure BDA0002837555840000102
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 of
Figure BDA0002837555840000103
Wherein p isiWhich indicates the transmission power of the ue i,
Figure BDA0002837555840000104
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:
Figure BDA0002837555840000105
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:
Figure BDA0002837555840000106
Figure BDA0002837555840000107
Figure BDA0002837555840000108
Figure BDA0002837555840000109
Figure BDA00028375558400001010
Figure BDA00028375558400001011
Figure BDA00028375558400001012
wherein X and Y are respectively a task allocation decision and a convergent point decision of the terminal node of the power internet of things(ii) a Wherein the content of the first and second substances,
Figure BDA0002837555840000111
is a constraint on the computational resources of the edge nodes,
Figure BDA0002837555840000112
a storage resource element that is an edge node;
Figure BDA0002837555840000113
in order to be a time delay constraint,
Figure BDA0002837555840000114
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;
Figure BDA0002837555840000115
the subtask average completion time delay of all terminal nodes of the power internet of things is realized,
Figure BDA0002837555840000116
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:
Figure BDA0002837555840000117
thus xijk∈(0，1}，
Figure BDA0002837555840000118
Considering that the subtask must be calculated by one node in the cooperative node set, the following constraint conditions need to be satisfied:
Figure BDA0002837555840000119
Figure BDA00028375558400001110
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.
Figure BDA00028375558400001111
And
Figure BDA00028375558400001112
indicating that the amount of computing and storage resources of EN k should be sufficient at task allocation decision X, and therefore there are constraints,
Figure BDA00028375558400001113
Figure BDA00028375558400001114
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 task
Figure BDA0002837555840000121
The converged routing decision of Y ═ YikThe value specification is as follows:
Figure BDA0002837555840000122
there are therefore the following constraints:
Figure BDA0002837555840000123
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 with
Figure BDA0002837555840000124
To be a task
Figure BDA0002837555840000125
Is required to be at
Figure BDA0002837555840000126
The task is completed within a time frame, so there are the following constraints:
Figure BDA0002837555840000127
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 set
Figure BDA0002837555840000128
And 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
Figure BDA0002837555840000129
(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
Figure BDA00028375558400001210
(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, namely
Figure BDA0002837555840000131
And then solving the optimal task allocation. Problem P1 thus translates into P2:
Figure BDA0002837555840000132
s.t C2～C8
Figure BDA0002837555840000133
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
Figure BDA0002837555840000134
Figure BDA0002837555840000135
Wherein
Figure BDA0002837555840000136
Representing tasks
Figure BDA0002837555840000137
A set of cooperating nodes is provided that is,
Figure BDA0002837555840000138
representing the assignment of tasks in a set of cooperative nodes, where aij∈[1,qi]Represents a subtask wijBy
Figure BDA0002837555840000139
The calculation is completed. The symbols are unified for convenience
X＝(x1,x2,...,xD)
Wherein
Figure BDA0002837555840000141
Assuming a total of L habitats, each habitat represents a feasible solution in the problem solution space, where the first habitat is represented as
Figure BDA0002837555840000142
The BBO algorithm exchanges information with other habitats by using migration operation, and then searches a 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)
Figure BDA0002837555840000143
is provided with
Figure BDA0002837555840000144
Is a habitat suitability function, wherein
Figure BDA0002837555840000145
Indicating the decision at task distribution decision X, rendezvous Point
Figure BDA0002837555840000146
The following average task completion delays.
Degree of adaptabilityHigher habitats contain more populations and lower fitness habitats correspond to fewer 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):
Figure BDA0002837555840000147
wherein
Figure BDA0002837555840000148
Attribute representing the l-th habitat
Figure BDA0002837555840000149
Replacement with attributes of the l' th habitat
Figure BDA00028375558400001410
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:
Figure BDA0002837555840000151
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).
Figure BDA0002837555840000152
Mutation into interval [ ubi,lbi]A random value of, wherein lbi，ubiRespectively representing variables
Figure BDA0002837555840000153
Lower and upper limits of the value.
Figure BDA0002837555840000154
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, calculate
Figure BDA0002837555840000155
Transition probability p corresponding to each edge nodeik,
Figure BDA0002837555840000156
Based on roulette as a task
Figure BDA0002837555840000157
One 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
Figure BDA0002837555840000158
s.t C2～C8
Figure BDA0002837555840000161
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 tasks
Figure BDA0002837555840000162
The results of the calculations of (c) are aggregated at node ENk.
Figure BDA0002837555840000163
After the sink node selection, will
Figure BDA0002837555840000164
Adding into ant taboo list. Each ant creates a legal path through state transition rules. In the process of creating the path, each ant passes through the updating ruleThe pheromone is updated for the point walked. 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 task
Figure BDA0002837555840000165
Concentration 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
Figure BDA0002837555840000166
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:
Figure BDA0002837555840000167
where p represents the pheromone volatilization coefficient, with larger p the faster the pheromone volatilizes. D represents the number of ants.
Figure BDA0002837555840000168
Is an ant d on<i,k>The released pheromones on this path are calculated as follows:
Figure BDA0002837555840000171
wherein Y isdIs a convergent point decision constructed by the ant d,
Figure BDA0002837555840000172
represents YdThe following tasks are performed with average completion delay. Thus when
Figure BDA0002837555840000173
The 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.
Figure BDA0002837555840000174
Indicates ant d selection<i,k>Probability of this path, i.e. ENk as
Figure BDA0002837555840000175
The sink node of (1).
Figure BDA0002837555840000176
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 task
Figure BDA0002837555840000177
The expected value of (1) is obtained by calculating convergence delay and return delay in cooperative calculation, etaikThe calculation formula is as follows:
Figure BDA0002837555840000178
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 invention sets the simulation environment to be 1km x 1kmThe area comprises 20 ENs and 50 UEs, and the positions of the ENs and the UEs are randomly generated in the area. The CPU frequency (GHz) and the storage space (GB) of each edge node follow normal distribution, respectively
Figure BDA0002837555840000179
Setting 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 distributed
Figure BDA00028375558400001710
The quantity of the subtasks of each UE is subjected to uniform distribution U (1,5), the calculation of the subtasks, the quantity of the required storage virtual resource units and the calculation time delay are subjected to Poisson distribution, and the average values are lambda respectively1＝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
Figure BDA0002837555840000181
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
Figure BDA0002837555840000191
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 (10)

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;
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.
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 multitask cooperative allocation model is specifically as follows:
P1:
Figure FDA0002837555830000011
Figure FDA0002837555830000021
Figure FDA0002837555830000022
Figure FDA0002837555830000023
Figure FDA0002837555830000024
Figure FDA0002837555830000025
Figure FDA0002837555830000026
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,
Figure FDA0002837555830000027
is a constraint on the computational resources of the edge nodes,
Figure FDA0002837555830000028
a storage resource element that is an edge node;
Figure FDA0002837555830000029
in order to be a time delay constraint,
Figure FDA00028375558300000210
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;
Figure FDA00028375558300000211
the subtask average completion time delay of all terminal nodes of the power internet of things is realized,
Figure FDA00028375558300000212
the computing task initiated for the terminal node of the power internet of things comprises a plurality of subtasks.
5. 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.
6. 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.
7. 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;
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.
8. The multi-task cooperative distribution device of the power internet of things as claimed in claim 7, 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.
9. 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 6.
10. 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 6.
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113191654A (en) * 2021-05-12 2021-07-30 余绍祥 Distributed resource scheduling system and method based on multi-objective optimization
CN113590339A (en) * 2021-08-16 2021-11-02 重庆大学 Internet of things terminal cooperation method for batch computing tasks
CN113676357A (en) * 2021-08-30 2021-11-19 华北电力大学 Decision method for edge data processing in power internet of things and application thereof
CN114285880A (en) * 2021-12-27 2022-04-05 电子科技大学长三角研究院(衢州) Internet of things lightweight equipment intelligentization method based on enhanced twinning
CN114615294A (en) * 2022-02-25 2022-06-10 广州鲁邦通物联网科技股份有限公司 Electric power internet of things gateway edge calculation method
CN115409294A (en) * 2022-11-01 2022-11-29 江西江投电力技术与试验研究有限公司 Robust optimization method for power distribution network scheduling and charging cooperation
CN116132354A (en) * 2023-02-23 2023-05-16 中国科学院软件研究所 Unmanned aerial vehicle cluster networking transmission path optimization method and system

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110365753A (en) * 2019-06-27 2019-10-22 北京邮电大学 Internet of Things service low time delay load allocation method and device based on edge calculations
CN111010295A (en) * 2019-11-28 2020-04-14 国网甘肃省电力公司电力科学研究院 SDN-MEC-based power distribution and utilization communication network task migration method
CN111445111A (en) * 2020-03-09 2020-07-24 国网江苏省电力有限公司南京供电分公司 Electric power Internet of things task allocation method based on edge cooperation
CN111475274A (en) * 2020-04-20 2020-07-31 北京邮电大学 Cloud collaborative multi-task scheduling method and device
CN111585916A (en) * 2019-12-26 2020-08-25 国网辽宁省电力有限公司电力科学研究院 LTE electric power wireless private network task unloading and resource allocation method based on cloud edge cooperation

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110365753A (en) * 2019-06-27 2019-10-22 北京邮电大学 Internet of Things service low time delay load allocation method and device based on edge calculations
CN111010295A (en) * 2019-11-28 2020-04-14 国网甘肃省电力公司电力科学研究院 SDN-MEC-based power distribution and utilization communication network task migration method
CN111585916A (en) * 2019-12-26 2020-08-25 国网辽宁省电力有限公司电力科学研究院 LTE electric power wireless private network task unloading and resource allocation method based on cloud edge cooperation
CN111445111A (en) * 2020-03-09 2020-07-24 国网江苏省电力有限公司南京供电分公司 Electric power Internet of things task allocation method based on edge cooperation
CN111475274A (en) * 2020-04-20 2020-07-31 北京邮电大学 Cloud collaborative multi-task scheduling method and device

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113191654A (en) * 2021-05-12 2021-07-30 余绍祥 Distributed resource scheduling system and method based on multi-objective optimization
CN113590339A (en) * 2021-08-16 2021-11-02 重庆大学 Internet of things terminal cooperation method for batch computing tasks
CN113590339B (en) * 2021-08-16 2023-06-23 重庆大学 Internet of things terminal collaboration method for batch computing tasks
CN113676357A (en) * 2021-08-30 2021-11-19 华北电力大学 Decision method for edge data processing in power internet of things and application thereof
CN113676357B (en) * 2021-08-30 2022-07-29 国网内蒙古东部电力有限公司 Decision method for edge data processing in power internet of things and application thereof
CN114285880A (en) * 2021-12-27 2022-04-05 电子科技大学长三角研究院(衢州) Internet of things lightweight equipment intelligentization method based on enhanced twinning
CN114285880B (en) * 2021-12-27 2023-06-23 电子科技大学长三角研究院(衢州) Intelligent method for lightweight equipment of Internet of things based on enhanced twinning
CN114615294A (en) * 2022-02-25 2022-06-10 广州鲁邦通物联网科技股份有限公司 Electric power internet of things gateway edge calculation method
CN115409294A (en) * 2022-11-01 2022-11-29 江西江投电力技术与试验研究有限公司 Robust optimization method for power distribution network scheduling and charging cooperation
CN116132354A (en) * 2023-02-23 2023-05-16 中国科学院软件研究所 Unmanned aerial vehicle cluster networking transmission path optimization method and system
CN116132354B (en) * 2023-02-23 2024-03-22 中国科学院软件研究所 Unmanned aerial vehicle cluster networking transmission path optimization method and system

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