CN111970145B - Internet of things equipment management and control method based on semantic virtual and task migration - Google Patents
Internet of things equipment management and control method based on semantic virtual and task migration Download PDFInfo
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Abstract
The invention discloses an Internet of things equipment control method based on semantic virtualization and task migration, which comprises the following steps: extracting related information from the multi-source heterogeneous devices accessed in the Internet of things system, and carrying out unified semantic virtual processing on the related information so as to preset the same type of semantic description on the physical information of different multi-source heterogeneous devices; and executing a migration strategy and an optimal strategy selection algorithm on the tasks of the multi-source heterogeneous equipment, distributing local tasks and the multi-source heterogeneous equipment migration tasks, and realizing effective management and control on the multi-source heterogeneous equipment of the Internet of things. The method combines the powerful expression capability of the semantic technology and the resource integration capability of the resource virtualization technology, designs the optimal multi-source heterogeneous device selection algorithm, and when the game reaches balance, the Internet of things system obtains the maximum benefit function, so that various software and hardware resources are effectively utilized, the resource fragmentation problem and the task allocation complexity are solved, and the resource management complexity is reduced.
Description
Technical Field
The invention relates to the field of information processing, in particular to an Internet of things equipment management and control method based on semantic virtualization and task migration.
Background
Along with the rapid development of the internet of things technology, the global internet of things industry has a great growing trend, and the application of the internet of things technology is very wide. Under the great background of the rapid development of the internet of things industry, how to realize the more extensive interactive scheduling between the multi-source heterogeneous equipment and how to realize the effective management and control of the multi-source heterogeneous equipment becomes a research hotspot in the related field from the management of the internet of things system.
In the process of resource management, the existing internet of things system has different sources and various structural forms of the multi-source heterogeneous equipment. For example, in a smart city, the presence of a large number of multi-source heterogeneous devices such as wireless sensor network nodes, RFID tags, M2M and the like brings convenience to social life, and meanwhile, the multi-source heterogeneous devices in the smart city also show great polymorphism and heterogeneous characteristics. How to solve the isomerism problem of a plurality of multi-source isomerism devices, effectively manage the multi-source isomerism devices and solve the problem of resource fragmentation so as to realize everything interconnection in the smart city.
Disclosure of Invention
The invention aims to provide an Internet of things equipment management and control method based on semantic virtual and task migration, and aims to provide a unified semantic representation mode for expressing common characteristics of multi-source heterogeneous equipment and tasks of the Internet of things, reduce complexity of management of the multi-source heterogeneous equipment, eliminate semantic understanding barriers for task interaction among the heterogeneous equipment, and the semantic virtual representation of the multi-source heterogeneous equipment and task execution commands. And meanwhile, a task allocation algorithm is researched, the energy consumption loss of the task execution of massive multi-source heterogeneous devices of the Internet of things is reduced, the task execution of the multi-source heterogeneous devices is quickly scheduled, the minimum time delay is obtained, and the maximum benefit is obtained.
In order to achieve the above purpose, the method for controlling the internet of things equipment based on semantic virtual and task migration, which is adopted by the invention, comprises the following steps:
Extracting related information from the multi-source heterogeneous devices accessed in the Internet of things system, and carrying out unified semantic virtual processing on the related information so as to preset the same type of semantic description on the physical information of different multi-source heterogeneous devices;
executing a migration strategy and an optimal strategy selection algorithm on the tasks of the multi-source heterogeneous equipment, and distributing local tasks and the multi-source heterogeneous equipment migration tasks;
Performing sentence semanteme on the task of the multi-source heterogeneous device;
generating semantic information in an XML format for the task for semantic sentence, analyzing the semantic information through a gateway, generating a perception and execution task, issuing the perception and execution task to the multi-source heterogeneous device, returning a task execution result, and returning the task execution result.
The related information of the multi-source heterogeneous device comprises basic information, capability characteristics, state attributes, position deployment and control methods of the multi-source heterogeneous device.
When a migration strategy is executed on the tasks of the multi-source heterogeneous devices, a load balancing model is constructed by utilizing a game idea and is used for calculating the maximum benefit and the minimum time delay energy consumption function of each multi-source heterogeneous device in the Internet of things system.
When an optimal strategy selection algorithm is executed on the task of the multi-source heterogeneous device, the characteristics of semantic virtualization and the characteristics of an augmented Lagrangian multiplier method are combined, and the augmented Lagrangian multiplier method is adopted as the optimal strategy selection algorithm.
The method mainly comprises the steps of task basic information semanteme, constraint condition semanteme, relevance semanteme and execution return semanteme description in the step of performing sentence semanteme on the tasks of the multi-source heterogeneous equipment.
After performing a sentence semanteme step on the tasks of the multi-source heterogeneous devices, converting the multi-source heterogeneous devices into virtual multi-source heterogeneous devices according to a unified representation mode to form a virtual resource pool corresponding to the Internet of things system, wherein the operation of the virtual resource pool corresponds to task allocation and scheduling among the multi-source heterogeneous devices.
When the multi-source heterogeneous equipment is distributed locally, a load balancing model is built, and when the game of the Internet of things system reaches balance, the multi-source heterogeneous equipment nodes all acquire the most satisfactory task in the current state.
According to the method for managing and controlling the equipment of the Internet of things based on semantic virtualization and task migration, the tasks of the multi-source heterogeneous equipment are reasonably distributed, so that the multi-source heterogeneous equipment of the Internet of things is effectively managed and controlled. The method combines the powerful expression capability of the semantic technology and the resource integration capability of the resource virtualization technology, designs the optimal multi-source heterogeneous device selection algorithm, and when the game reaches balance, the Internet of things system obtains the maximum benefit function, so that various software and hardware resources are effectively utilized, the resource fragmentation problem and the task allocation complexity are solved, and the resource management complexity is reduced.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of steps of a method for managing and controlling an internet of things device based on semantic virtualization and task migration.
Fig. 2 is a semantic description model of the multi-source heterogeneous device of the internet of things.
Fig. 3 is a semantic description model of tasks of the multi-source heterogeneous device according to the internet of things.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Referring to fig. 1 to 3, an internet of things device management and control method based on semantic virtualization and task migration is characterized by comprising the following steps:
Extracting related information from the multi-source heterogeneous devices accessed in the Internet of things system, and carrying out unified semantic virtual processing on the related information so as to preset the same type of semantic description on the physical information of different multi-source heterogeneous devices; executing a migration strategy and an optimal strategy selection algorithm on the tasks of the multi-source heterogeneous equipment, and distributing local tasks and the multi-source heterogeneous equipment migration tasks; performing sentence semanteme on the task of the multi-source heterogeneous device; generating semantic information in an XML format for the task for semantic sentence, analyzing the semantic information through a gateway, generating a perception and execution task, issuing the perception and execution task to the multi-source heterogeneous device, returning a task execution result, and returning the task execution result.
And abstracting physical resources in the Internet of things and forming a unified virtualized resource pool based on the unified information model of the multiple heterogeneous devices. And then introducing the idea of game into a task migration strategy, when the game reaches relative equilibrium, each node of the multi-source heterogeneous equipment of the Internet of things obtains a task migration strategy which is most satisfactory in the current state, and reasonably distributes tasks to each multi-source heterogeneous equipment in the Internet of things system.
In this embodiment, the invention provides a method for managing and controlling devices of internet of things based on semantic virtualization and task migration, which mainly comprises the following steps: the internet of things system of the multi-source heterogeneous equipment is accessed, unified virtualization processing is carried out on the related information of different multi-source heterogeneous equipment, and the physical information of different multi-source heterogeneous equipment is ensured to have the same type of semantic description; combining the multi-source heterogeneous device migration strategy and an optimal strategy selection algorithm, correctly distributing a local task and the multi-source heterogeneous device migration task, and executing tasks with low time delay and low energy consumption of the multi-source heterogeneous device while obtaining maximum benefit by a system; after reasonably distributing the multi-source heterogeneous equipment to execute tasks, semantically executing sentences of the tasks, including task related information, constraint conditions, relevance and execution return value semantically, and facilitating management control of the multi-source heterogeneous equipment; and generating XML semantic information after the task semantezation, analyzing the XML semantic information by a gateway, generating a perception and execution task, issuing the perception and execution task to the multi-source heterogeneous equipment to realize management control on the multi-source heterogeneous equipment, and returning a task execution result.
Through the steps, the resource structure of the Internet of things is expressed as two layers: the first layer is a physical device layer, and the layer contains all sensing devices, executing devices and the like in the current Internet of things system, and is a representation of executing tasks of all entities of the Internet of things system. The second layer is a virtual resource layer, and by abstracting physical devices into virtual resources, the second layer is an abstract representation of the physical device layer, and the scheduling in the virtual resource pool is essentially scheduling corresponding devices of the physical device layer.
For a task selection strategy of the multi-source heterogeneous device in the virtual resource pool, the scheme refers to a game idea to construct a model of load balancing of the multi-source heterogeneous device in the whole Internet of things system. When the game reaches equilibrium, the optimal allocation proportion of task scheduling and local task allocation of other multi-source heterogeneous devices of the Internet of things is realized, so that the multi-source heterogeneous devices can obtain the maximum benefit, and meanwhile, the minimum task delay and the minimum energy consumption loss are obtained. And meanwhile, an optimal strategy selection algorithm step based on an augmented Lagrangian multiplier method is designed.
The method and the device combine the semantic virtual technology of the multi-source heterogeneous device and the task migration strategy of the multi-source heterogeneous device, solve the problems of difficult scheduling and resource fragmentation of the multi-source heterogeneous device, and can effectively schedule the execution related tasks of various multi-source heterogeneous devices. And meanwhile, the idea of game is introduced, the optimal strategy selection algorithm of the multi-source heterogeneous equipment is designed, the task allocation difficulty is solved, and the migration strategy of the task is realized, so that the maximum benefit function of the system is obtained. And when the effective management and control of the multi-source heterogeneous equipment are realized, the system obtains the lowest time delay and the minimum energy consumption, the migration distribution of tasks is efficiently executed, the management capacity of the multi-source heterogeneous equipment is optimized, and the performance of the whole Internet of things system is improved.
Further, when a migration strategy is executed on the tasks of the multi-source heterogeneous devices, a load balancing model is constructed by utilizing a game idea and is used for calculating the maximum benefit and the minimum time delay energy consumption function of each multi-source heterogeneous device in the internet of things system.
In this embodiment, in the internet of things system, no matter when a task is generated after a context change in the system is captured by the multi-source heterogeneous device in the system, the generated task needs to be responded by the multi-source heterogeneous device, or the multi-source heterogeneous device in the system needs to be manually mobilized to execute the corresponding task, the distributed task needs to be accurately calculated, so as to achieve the maximum benefit of executing the task by the multi-source heterogeneous device in the system. The invention considers the influence of the time delay function and the energy consumption function of the multi-source heterogeneous device in the system on the task allocation of the multi-source heterogeneous device, including the fact that the multi-source heterogeneous device executes local tasks or executes other scheduled migration tasks, and the time delay and the energy consumption are required to be considered so as to realize the maximum benefit of all the multi-source heterogeneous devices in the system.
The heterogeneous devices in the Internet of things system are independent of each other, and if the heterogeneous devices can obtain minimum time delay and have the lowest energy consumption when executing local tasks or gateway migration tasks, the resource scheduling effect of the system is very important, and a large amount of resources can be saved. And selecting a proper task migration strategy, wherein each multi-source heterogeneous device of the Internet of things finds an optimal task allocation strategy, namely, the optimal allocation proportion of the tasks migrated to the multi-source heterogeneous device and the local processing tasks. According to the situation, the method and the system combine the advantages of semantic virtual scheduling of the multi-source heterogeneous equipment, introduce the idea of game into a resource allocation strategy, and construct a task allocation model of the multi-source heterogeneous equipment in the Internet of things system.
The task allocation policy of the multi-source heterogeneous device d i of the internet of things may be represented by a probability P iF of migrating to the task of the multi-source heterogeneous device and a probability P id of processing the task locally of the multi-source heterogeneous device. Where P iF+Pid = 1, how to correctly allocate P id and P iF, and selecting an appropriate task migration policy, so that the multi-source heterogeneous device and even the system obtain the lowest latency and the smallest energy consumption loss of task processing.
The problem is solved by utilizing the game idea, each of the multi-source heterogeneous devices of the Internet of things is set as a participant to participate in the migration and execution of tasks, and the policy set of the participant is shown as a formula (1)
Si＝{piF,pid|piF+pid＝1,piF≥0,pid≥0} (1)
In the above model, the energy consumption of different heterogeneous devices is different, the time delay generated when executing the task is also different, the time delay energy consumption function of different heterogeneous devices is represented by C i, and different heterogeneous devices have different time delay energy consumption functions, as shown in formula (2). Meanwhile, U i is used for representing the profit function of the participants in the model, the profit function is opposite to the time delay energy consumption function, the bigger the time delay energy consumption function is, the smaller the profit function is, and the relation is shown as the formula (3)
Ui＝-Ci (3)
Wherein,For the average task sending power of the multi-source heterogeneous device and the average transmission delay of the multi-source heterogeneous device task migration of the internet of things, θ iT,θiE represents the delay and the weight of energy consumption in the task migration, θ iE+θiT =1,/>The average time delay and the energy consumption requirement of the multi-source heterogeneous equipment on the task are respectively constant and are different according to different task execution. Mu F,λF is the task processing rate and task arrival rate of the gateway, respectively, and mu i,λi is the task processing rate and task arrival rate of the multi-source heterogeneous device.
The policy function set of all other participants than the d i participant in the build model is shown as formula (4)
s-i＝{s1,…,si-1,si+1,…,sn} (4)
The time-lapse energy consumption of participant d i in the system can be represented by formula (5)
In the case that the policy set of the other participants is certain, the optimal policy s i * is represented by formula (6)
I.e. for any S i, policyThe time delay energy loss of the task allocation is minimum, and meanwhile, the multi-source heterogeneous device d i has the maximum benefit.
And the set D represents all the multi-source heterogeneous devices participating in the internet of things of the game, as shown in a formula (7). The set of policies that each participant can assign constitutes a joint policy space for the multi-source heterogeneous device in the model, as shown in equation (8)
D＝(D1,…,Dn) (7)
S＝(S1,…,Sn) (8)
The collection of the benefit functions of all participants in the system is of formula (9)
U＝(U1,…,Un) (9)
D, S, U in the model is represented in the form of triples (10)
G:(D,S,U) (10)
And selecting correct strategy migration in the system model to obtain the maximum benefit function and simultaneously minimize the time delay energy consumption function. Of course, the policy selection of each multi-source heterogeneous device needs to be changed continuously according to the policy selections of other multi-source heterogeneous devices, and when the policy selections of all the multi-source heterogeneous devices reach a relative equilibrium point, the task migration policy of the multi-source heterogeneous device is considered to be obtained.
Further, when an optimal strategy selection algorithm is executed on the task of the multi-source heterogeneous device, the characteristics of semantic virtualization and the characteristics of an augmented Lagrangian multiplier method are combined, and the augmented Lagrangian multiplier method is adopted as the optimal strategy selection algorithm.
In this embodiment, when the policy selection of the related multi-source heterogeneous device has been determined, the participant's own optimal policy problem is the solution optimization problem, i.e. the task allocation policy of the multi-source heterogeneous device when the delay energy consumption function C i is minimum, as shown in formula (11)
The constraint condition C1 is the sum of a local task and a migration task, C2 represents that the arrival rate of the task is a positive value, and the constraint conditions C3, C4 and C5 maintain the stability of the system, and the task processing rate is larger than the arrival rate of the task.
When the queuing system is in a steady state, obtaining a matrix of the matrix C i(si,s-i as shown in formula (12)
Wherein the method comprises the steps of
The main diagonal elements of H are positive values and H is positive, i.e., C i(si,s-i) is positive. The optimization problem is thus a convex optimization problem.
The convex optimization problem is solved, the method is solved by adopting an augmentation Lagrangian multiplier method, and the definition of the augmentation Lagrangian function is shown as a formula (13):
In the iteration process, the K-th iteration multiplier W (k) and v (k) are corrected, and the K+1-th iteration multipliers W (k+1) and v (k+1) are obtained by using the formulas (14) and (15).
Through the steps, the optimal strategy selection algorithm of the multi-source heterogeneous equipment of the Internet of things is summarized, and the algorithm is shown in the following table.
Table 1 optimal policy selection algorithm for multi-source heterogeneous devices in Internet of things
Further, in the step of performing sentence semanteme on the task of the multi-source heterogeneous device, the method mainly comprises the steps of semanteme of task basic information, semanteme of constraint conditions, semanteme of relevance and semanteme description of execution return.
In this embodiment, the heterogeneous multi-source devices in the internet of things system have certain capability characteristics, influence on external environmental stimulus, or the requirement that the heterogeneous multi-source devices are required to complete tasks in the aspect of a gateway, and the heterogeneous multi-source devices execute different control operations to complete different tasks.
In order to ensure the integrity description of the access to the multi-source heterogeneous equipment, realize the description of the basic information of the multi-source heterogeneous equipment and the description of the capabilities of each aspect, and facilitate the allocation and the scheduling of tasks of the multi-source heterogeneous equipment, the invention provides a semantic virtual description framework of the multi-source heterogeneous equipment of the Internet of things, and semantic descriptions are carried out from five main aspects of the basic information, the capability characteristics, the state attribute, the position deployment and the control method of the multi-source heterogeneous equipment to form a unified multi-source heterogeneous equipment representation model. Mainly comprises the following parts of contents, and specific semanticalization parameters are shown in figure 2.
The basic information of the multi-source heterogeneous device is semantically changed. The multi-source heterogeneous device basic information semanteme includes semantic description of the multi-source heterogeneous device basic information such as name, model, technical parameters and the like of the multi-source heterogeneous device, and belongs to inherent attribute information of the multi-source heterogeneous device. Wherein the relevant parameters can be divided into physical parameters and technical parameters.
The multi-source heterogeneous device capability feature semantics. The description of the capabilities of the multi-source heterogeneous device is an important basis for resource allocation. In the internet of things system, on the whole, on one hand, the multi-source heterogeneous equipment needs to execute local tasks, on the other hand, a system gateway needs to schedule the multi-source heterogeneous equipment to execute corresponding tasks, and in addition, information interaction tasks can be generated among the multi-source heterogeneous equipment in the system. Thus, the multi-source heterogeneous device capability feature may be characterized in terms of perception capability, execution capability, communication capability, and information processing capability. By describing the four aspects, corresponding tasks can be quickly executed, and other multi-source heterogeneous devices with the same type of functions can be compatible to the maximum extent.
The multi-source heterogeneous device state attribute is semantically processed. The task execution of the multi-source heterogeneous device is limited by the current basic state of the multi-source heterogeneous device, and meanwhile, the state of the multi-source heterogeneous device is also a specific evaluation condition of the multi-source heterogeneous device under the current environment condition and the current task execution, and is a key constraint condition for resource allocation. Semantic description of the state attribute of the multi-source heterogeneous device is carried out on three aspects of physical state, perceived state and working state.
The multi-source heterogeneous device location deployment semantics. In the internet of things system, it is very important to accurately grasp the address of each multi-source heterogeneous device, and in view of the influence of the difference of the positions of the multi-source heterogeneous devices on task allocation of the multi-source heterogeneous devices, the relation is related to a task allocation migration policy, so that semantic virtualization of the position deployment of the multi-source heterogeneous devices is also required, and the description can be performed from an absolute position and a relative position.
The multi-source heterogeneous device controls semantics. Whether the local task of the multi-source heterogeneous device in the internet of things system is executed or the task is migrated to other multi-source heterogeneous devices based on a task migration strategy, operation control is required to be carried out on the multi-source heterogeneous device. The multi-source heterogeneous device needs to respond to the task and execute corresponding operation, so that effective semantic modeling is needed to be carried out on the control operation method of the multi-source heterogeneous device. The description is made from three aspects of the multi-source heterogeneous device control interface, the state acquisition interface, and the communication control interface.
Further, after performing a sentence semanteme step on the tasks of the multi-source heterogeneous devices, converting the multi-source heterogeneous devices into virtual multi-source heterogeneous devices according to a unified representation mode, forming a virtual resource pool corresponding to the internet of things system, and performing operation on the virtual resource pool, wherein the operation corresponds to task allocation and scheduling among the multi-source heterogeneous devices.
In this embodiment, based on the semanticalization of the multi-source heterogeneous device information and the task migration of the multi-source heterogeneous device, the multi-source heterogeneous device has allocated tasks to be executed, including tasks to be executed locally and tasks to be migrated to the multi-source heterogeneous device, but the multi-source heterogeneous device information has completed semanticalization virtualization, and a general structure is used to represent the basic information of the multi-source heterogeneous device, so that a user cannot use a conventional instruction to schedule. In order to conveniently realize effective management and control of the multi-source heterogeneous device, the task of the multi-source heterogeneous device is semantically described, and the method mainly comprises task related information semantication, constraint condition semantication, relevance semantication and execution return description. The specific semantical parameters are shown in fig. 3. Task related information semantication. The issued execution task is distributed, and relevant detailed information of the task is included, such as a task name, a unique identification of the task, response attributes and the like. And (5) semantically limiting conditions. And specific constraint condition descriptions of the task, such as a time delay constraint, an energy consumption constraint, an environment constraint, a state constraint and the like, are included, and feasibility constraint conditions of physical multi-source heterogeneous device information for executing the task are included. And (5) semantically relating. Task execution of the internet of things system is often not operation of a single multi-source heterogeneous device, and often needs to be mutually scheduled and executed by a plurality of multi-source heterogeneous devices, so that semantic description can be performed on sequence, correlation dependence and the like. Return value semanticalization is performed. The task execution often has expected benefits before the task execution, and the task execution needs to return execution benefits after the task execution for evaluating task execution results, representing system benefits after the task execution, and judging the task execution efficiency from the system level of the Internet of things.
Further, when the local distribution is carried out on the multi-source heterogeneous equipment, a load balancing model is built, and when the game of the Internet of things system reaches balance, the multi-source heterogeneous equipment nodes all obtain the most satisfactory task in the current state.
In this embodiment, after the multi-source heterogeneous device, which is actually the physical multi-source heterogeneous device, is connected to the internet of things system in a virtual manner, the virtual multi-source heterogeneous devices in all the systems together form a virtual resource pool. And distributing tasks at a virtual layer, determining local tasks to be executed by the multi-source heterogeneous equipment and task operations migrated to the multi-source heterogeneous equipment, wherein the tasks are executed by the multi-source heterogeneous equipment in the multi-source heterogeneous equipment layer, namely the multi-source heterogeneous equipment is required to be correspondingly physically executed, and responding to the tasks. The multi-source heterogeneous device is distributed to a plurality of Internet of things participating in task execution, so that the operation of the virtual layer task is converted into the operation of the multi-source heterogeneous device in actual physics.
And for the operations of task allocation scheduling, task migration and the like of the virtual layer, corresponding semantic information of XMLSchame models is automatically generated at the terminal to form an XML file, and unified semantic packaging is carried out on the heterogeneous multi-source heterogeneous device information and related task information.
After the encapsulation is executed, the information is issued to a gateway, the gateway analyzes an XML file, obtains semantic information and corresponding task information of related multi-source heterogeneous equipment, obtains related information of perception and execution, and distributes the related information to the corresponding multi-source heterogeneous equipment, so that management and control of the corresponding multi-source heterogeneous equipment are realized.
The above disclosure is only a preferred embodiment of the present invention, and it should be understood that the scope of the invention is not limited thereto, and those skilled in the art will appreciate that all or part of the procedures described above can be performed according to the equivalent changes of the claims, and still fall within the scope of the present invention.
Claims (5)
1. The method for managing and controlling the Internet of things equipment based on semantic virtualization and task migration is characterized by comprising the following steps of:
Extracting related information from multi-source heterogeneous devices accessed in an Internet of things system, and carrying out unified semantic virtual processing on the related information to preset the same type of semantic description on physical information of different multi-source heterogeneous devices;
executing a migration strategy and an optimal strategy selection algorithm on the tasks of the multi-source heterogeneous equipment, and distributing local tasks and the multi-source heterogeneous equipment migration tasks;
Performing sentence semanteme on the task of the multi-source heterogeneous device;
Generating semantic information in an XML format for a task for performing sentence semantezation, analyzing the semantic information through a gateway, generating a perception and execution task, issuing the perception and execution task to the multi-source heterogeneous device, returning a task execution result, and returning a task execution result;
executing a migration policy and an optimal policy selection algorithm on the tasks of the multi-source heterogeneous device, distributing local tasks and the multi-source heterogeneous device migration tasks, including:
The task allocation policy of the multi-source heterogeneous device d i of the internet of things can be represented by the probability P iF of migrating to the task of the multi-source heterogeneous device and the probability P id of locally processing the task of the multi-source heterogeneous device; wherein p iF+Pid = 1;
By utilizing the idea of game, each of the heterogeneous devices of the Internet of things is set as a participant, the migration and execution of tasks are participated, and the policy set of the participant is shown as a formula (1)
Si＝{piF,pid|piF+pid＝1,piF≥0,pid≥0} (1)
C i represents time delay energy consumption functions of different multi-source heterogeneous devices in a policy set of participants, wherein the different multi-source heterogeneous devices have different time delay energy consumption functions, as shown in a formula (2); meanwhile, U i is used for representing the profit function of the participant in the strategic concentration of the participant, the profit function is opposite to the time delay energy consumption function, the bigger the time delay energy consumption function is, the smaller the profit function is, and the relation is shown as the formula (3)
Ui＝-Ci (3)
Wherein,T i is the average task transmission power of the multi-source heterogeneous device and the average transmission delay of the multi-source heterogeneous device task migration in the internet of things, θ iT,θiE represents the delay and the weight of energy consumption in the task migration, and θ iE+θiT =1,/>Representing the average time delay and energy consumption requirements of the multi-source heterogeneous equipment on the task, wherein the average time delay and the energy consumption requirements are respectively constant and different according to different task execution; mu F,λF is the task processing rate and the task arrival rate of the gateway respectively, and mu i,λi is the task processing rate and the task arrival rate of the multi-source heterogeneous device;
constructing a policy function set of all other participants except d i participants in the policy set of the participant as shown in formula (4)
s-i＝{s1,…,si-1,si+1,…,sn} (4)
The time-lapse energy consumption of participant d i in the system can be represented by formula (5)
In the case that the policy set of the other participants is certain, the optimal policy s i * is represented by formula (6)
I.e. for any S i, policyThe time delay energy loss of the task is minimum, and meanwhile, the maximum benefit of the multi-source heterogeneous device d i is achieved;
The set D represents all the multi-source heterogeneous devices participating in the Internet of things of the game, as shown in a formula (7); the policy set that each participant can allocate constitutes the federated policy space of the multi-source heterogeneous device in the participant's policy set, as shown in equation (8)
D＝(D1,…,Dn) (7)
S＝(S1,…,Sn) (8)
The collection of the benefit functions of all participants in the system is of formula (9)
U＝(U1,…,Un) (9)
The policy set D, S, U of the participants is represented by a triplet (10)
G:(D,S,U) (10)
When the strategy selection of all the multi-source heterogeneous devices reaches a relative balance point, the optimal task migration strategy of the multi-source heterogeneous devices is considered to be obtained;
When the policy selection of the related multi-source heterogeneous device is determined, the self-optimal policy problem of the participant is the solution optimization problem, namely the task allocation policy of the multi-source heterogeneous device when the time delay energy consumption function C i is minimum, as shown in the formula (11)
The constraint condition C1 is the sum of a local task and a migration task, C2 represents that the arrival rate of the task is a positive value, and the constraint conditions C3, C4 and C5 maintain stable systems, and the task processing rate is greater than the arrival rate of the task;
When the queuing system is in a steady state, obtaining a matrix of the matrix C i(si,s-i as shown in formula (12)
Wherein,
The main diagonal elements of H are positive values, H is positive, i.e., the matrix of C i(si,s-i) is positive;
Solving by adopting an augmentation Lagrangian multiplier method, and defining an augmentation Lagrangian function as shown in a formula (13):
In the iteration process, the K-th iteration multiplier w (k) and v (k) are corrected, and the K+1-th iteration multipliers w (k+1) and v (k+1) are obtained by utilizing the correction of formulas (14) and (15);
2. the method for controlling the Internet of things equipment based on semantic virtualization and task migration according to claim 1, wherein,
The related information of the multi-source heterogeneous device comprises basic information, capability characteristics, state attributes, position deployment and control methods of the multi-source heterogeneous device.
3. The method for controlling the Internet of things equipment based on semantic virtualization and task migration according to claim 2, wherein,
And in the step of carrying out sentence semanteme on the task of the multi-source heterogeneous device, the method mainly comprises the steps of semanteme of basic information of the task, semanteme of constraint conditions, semanteme of relevance and return semanteme description of execution.
4. The method for controlling the Internet of things equipment based on semantic virtualization and task migration according to claim 3,
After the sentence semanteme step is carried out on the tasks of the multi-source heterogeneous devices, the multi-source heterogeneous devices are converted into virtual multi-source heterogeneous devices according to a unified representation mode, a virtual resource pool corresponding to the Internet of things system is formed, and the operation of the virtual resource pool corresponds to task allocation and scheduling among the multi-source heterogeneous devices.
5. The method for controlling the Internet of things equipment based on semantic virtualization and task migration according to claim 4,
When the multi-source heterogeneous equipment is distributed locally, a load balancing model is built, and when the game of the Internet of things system reaches balance, the multi-source heterogeneous equipment nodes all acquire the most satisfactory task in the current state.
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