WO2023134403A1

WO2023134403A1 - Internet of things resource allocation method and system, terminal and storage medium

Info

Publication number: WO2023134403A1
Application number: PCT/CN2022/140066
Authority: WO
Inventors: 吴嘉澍; 王洋; 金铭; 叶可江; 须成忠
Original assignee: 中国科学院深圳先进技术研究院
Priority date: 2022-01-11
Filing date: 2022-12-19
Publication date: 2023-07-20
Also published as: CN114390102B; CN114390102A

Abstract

The present application relates to an Internet of Things resource allocation method and system, a terminal and a storage medium. The method comprises: on the basis of a received resource allocation task request, initializing a moth vector set of a moth-flame optimization algorithm by means of using an object and density-aware algorithm, thus obtaining an initialized moth vector set; evaluating a health value of each moth in the initialized moth vector set, and selecting as new flame vectors the first k moths having the highest health value; on the basis of the new flame vectors, by means of using an asymptotic step flame matching algorithm and an exploratory moth-flame matching algorithm, calculating step flames corresponding to the moths and matching the moths with the corresponding step flames; when there is only one survival flame, generating a resource allocation scheme for the task request according to the survival flame; and on the basis of the Internet of Things resource abstract architecture of a digital object, executing the resource allocation scheme by means of using a resource scheduling and allocation algorithm. The present application may better approximate an optimum, and prevents the algorithm from getting stuck in local optimums.

Description

A method, system, terminal and storage medium for Internet of Things resource allocation

technical field

The present application belongs to the field of Internet of Things application technology, and in particular relates to a resource allocation method, system, terminal and storage medium of the Internet of Things.

Background technique

With the rapid development of IoT technology and the rapid popularization of IoT devices in people's production and life, IoT devices have made many application scenarios more intelligent. For example, in smart medical care, patient health monitoring is performed through IoT devices, and in smart logistics, parcels are automatically delivered through IoT devices.

In real life, IoT devices are often heterogeneous. For example, different IoT devices may have different availability times and usage costs. Similarly, scenarios driven by IoT devices are also heterogeneous, such as scenarios with sufficient resources and scenarios with scarce resources. In actual IoT application tasks, each application task may request one or more types of IoT devices to complete this task. Also, each application task typically specifies a demand period for each resource required by the task. With the proliferation of IoT tasks, how to efficiently allocate heterogeneous resources for these tasks with resource requirements, so as to maximize the benefits obtained by satisfying tasks and reduce the cost of satisfying tasks through more effective resource allocation. The cost has become an urgent problem to be solved.

The Internet of Things resource allocation scheme in the prior art mainly proceeds from two perspectives, specifically:

1. From the perspective of the resource allocation model of the Internet of Things; for example, Zhao et al. Network 33,30–35.] proposed two algorithms, EOERA and CHERA, to optimize the computing resources of IoT devices in the smart city scenario, thereby optimizing the average service response time. Sangaiah et al. in [Sangaiah, A.K., Hosseinabadi, A.A.R., Shareh, M.B., Bozorgi Rad, S.Y., Zolfagharian, A., Chilamkurti, N., 2020. Iot resource allocation and optimization based on heuristic algorithm. Sensors 20,53 9.] proposed in The whale optimization algorithm is used to optimize the allocation of computing resources of the Internet of Things, thereby minimizing the communication cost. Tsai et al proposed the SEIRA algorithm in [Tsai, C.W., 2018. Seira: An effective algorithm for iot resource allocation problem. Computer Communications 119, 156–166.], which also focuses on the optimization of computing and communication resources in the Internet of Things. Thereby optimizing the overall communication cost. However, the above-mentioned IoT resource allocation model has the following disadvantages:

First of all, the above-mentioned IoT resource allocation models are only aimed at the allocation of IoT resources in a single scenario. Secondly, the above-mentioned IoT resource allocation models are aimed at the allocation of computing, storage, and communication resources in IoT devices, but do not regard IoT device resources as a whole and allocate resources at the IoT device level. At the same time, these allocation models do not take into account that the task's request for resources has a time limit, and do not adopt a reserved resource allocation method, which may cause resource deadlocks. In addition, the above-mentioned IoT resource allocation models do not adopt any unified IoT device resource management framework. With the rapid development and evolution of IoT technology and IoT devices, the management of various IoT device resources will change. very difficult. Finally, the above-mentioned IoT resource allocation model only optimizes a certain cost, without considering the cost of satisfying the service and the benefits of satisfying the service at the same time.

2. The perspective of optimization methods; for example: Yang proposed the firefly algorithm in [Yang, X., 2010.Firefly algorithm in engineering optimization.], which simulates the flashing mode of fireflies and their mutual attraction behavior, so as to guide fireflies in How to move in the search space. Mirjalili et al proposed the whale optimization algorithm in [Mirjalili, S., Lewis, A., 2016. The whale optimization algorithm. Advances in engineering software 95, 51–67.]. The algorithm imitates the bubble net behavior of whales in the process of predation, so as to guide the whales to search for a better solution in the solution space. Mirjalili et al. proposed the moth-flame optimization algorithm in [Mirjalili, S., 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradise. Knowledge-based systems 89, 228–249.], which simulates the moth The flight navigation mechanism of the moth guides the optimal search in the solution space. Among the above-mentioned optimization methods, the moth-flame algorithm has the best performance. However, the moth-flame algorithm still has the following disadvantages:

First of all, the moth-flame algorithm uses a purely random moth initialization mechanism that does not use additional information at all, and does not take any additional model information into account, so its effect is not good. In addition, each moth only matches one flame, which is easy to cause a local optimal solution dilemma, so that the algorithm cannot effectively approach the optimal solution. Finally, the moth flame-fighting algorithm adopts a matching mechanism that uniformly matches the worst survival flame for all moths whose corresponding flames have been eliminated. The matching mechanism for matching the worst survival flame is unreasonable, and the worst survival flame is often far from the optimal solution. Using multiple moths to match the same flame will also cause the algorithm to fall into a local optimal solution to a large extent, thus damaging the optimization effect of the algorithm.

Contents of the invention

The present application provides an Internet of Things resource allocation method, system, terminal and storage medium, aiming to solve one of the above-mentioned technical problems in the prior art at least to a certain extent.

In order to solve the above problems, the application provides the following technical solutions:

A method for allocating Internet of Things resources, comprising:

Based on the received resource allocation task request, the target and density-aware algorithm is used to initialize the moth vector set of the moth-flaming algorithm, and the initialized moth vector set is obtained;

Evaluate the health value of each moth in the initialized moth vector set, and select the top k moths with the highest health value as new flame vectors;

Based on the new flame vector, using a progressive class flame matching algorithm and an exploratory moth flame matching algorithm to calculate the class flame corresponding to the moth, and matching the moth with the corresponding class flame;

When there is only one surviving flame, generating a resource allocation plan for the task request according to the surviving flame;

The resource abstraction architecture of the Internet of Things based on digital objects uses a resource scheduling and allocation algorithm to execute the resource allocation plan, and performs resource scheduling and allocation for the task request.

The technical solution adopted in the embodiment of the present application also includes: the initialization of the moth vector set of the moth-flaming algorithm by using the target and density-sensing algorithm is specifically:

For the received task request, calculate the income, cost and optimization goal of each task in different resource scenarios; the resource scenario includes a resource-sufficient scenario and a resource-poor scenario;

Judging whether the initialized moth vectors in the initialized moth vector set have reached the set multiple of the set number, if not reached the set multiple of the set number, assign each task request according to the size of the optimization target, and repeat the judgment ; If the set multiple of the set number is reached, the target and density-aware algorithm is used to find out the two initialization moth vectors with the smallest distance from the set of initialization moth vectors, and the mean vector of the two initialization moth vectors Add in the initialization moth vector collection as new moth vector, and delete described two initialization moth vectors from described initialization moth vector collection; Wherein, the length of each initialization moth vector is to be allocated The number of task requests for resources;

Determine whether the initialized moth vectors in the initialized moth vector set reach the set number, if not, continue to execute the target and density sensing algorithm; if reach the set number, output the initialized moth vector set.

The technical solution adopted in the embodiment of the present application also includes: based on the new flame vector, using the progressive class flame matching algorithm and the exploratory moth flame matching algorithm to calculate the class flame corresponding to the moth includes:

Judging whether the flame vector corresponding to the moth is eliminated, if the flame vector corresponding to the moth is not eliminated, adopt the progressive class flame matching algorithm, calculate the class flame according to the progressive coefficient, and match the moth with the corresponding class flame;

If the flame vector corresponding to the moth has been eliminated, the exploratory moth flame matching algorithm is used to calculate the class flame according to the progressive coefficient, and the moth is matched with the corresponding class flame.

The technical solution adopted in the embodiment of the present application also includes: the stepwise flame matching algorithm is used to calculate the class flame according to the progressive coefficient, and the moth is matched with the corresponding class flame, specifically:

Select the three flames with the highest health values from the k new flame vectors;

update asymptotic coefficient;

Combining the gradual coefficient and the three flames with the highest health value to calculate the class flame, and matching the moth with the corresponding class flame; the matching method is:

If the current number of surviving flames is greater than or equal to 3, flame matching is performed based on the flame corresponding to the moth and the three flames with the highest health values:

(1-0.3*ω)×F _i +0.15×ω×F ₁ +0.1×ω×F ₂ +0.05×ω×F ₃

Among them, w is a progressive coefficient that increases linearly from 0 to 1 as the iteration progresses, F _i is the flame corresponding to the moth, F ₁ , F ₂ , and F ₃ are the three flames with the highest health values, and F ₁ , F ₂ , and F ₃ are given weights of 0.15, 0.1, and 0.05 respectively, and the flame with the highest health value has the highest weight;

When only two flames exist among the three flames with the highest health value, the matching method is:

(1-0.25*ω)×F _i +0.15×ω×F ₁ +0.1×ω×F ₂

Update the moth position based on the flame matching result.

The technical solution adopted in the embodiment of the present application also includes: using the exploratory moth flame matching algorithm, calculating the class flame according to the progressive coefficient, and matching the moth with the corresponding class flame, specifically:

Randomly select the surviving flame F _I ;

update asymptotic coefficient;

Combining the gradual coefficient and the selected surviving flames to calculate the class flames, and matching the moths with the corresponding class flames; the matching method is:

If the current number of surviving flames is ≥ 3, moth-flame matching is performed based on the randomly selected surviving flames and the three flames with the highest health values:

When only two flames exist among the three flames with the highest health value, the matching formula is:

The moth position is updated according to the flame matching result.

The technical solution adopted in the embodiment of the present application also includes: the abstract architecture of the Internet of Things resources based on digital objects, and the resource allocation model adopts the resource scheduling and allocation algorithm to execute the resource allocation scheme, which is specifically:

The resource allocation model transmits the task request to the digital object of the IoT device, the digital object includes an API module and a communication module, and the API module is used to judge whether the IoT device can meet the task request, and if it can be satisfied, the Satisfied task request information is sent back to the resource allocation model; if the IoT device cannot satisfy the task request, it communicates with other similar IoT devices through the communication module, and sends the task request to other similar IoT devices.

The technical solution adopted in the embodiment of the present application also includes: the resource allocation model adopts a resource scheduling and allocation algorithm to implement a resource allocation plan, specifically:

Obtain the moth vector and the flame vector; the lengths of the moth vector and the flame vector are the quantity requested by the task;

Set a constant ub so that all the values in the moth vector and flame vector are between 0-ub, if the values in the moth vector and flame vector are between [0, ub/2] , assign the task request to a scenario with sufficient resources; if the values in the moth vector and flame vector are between (ub/2,ub], assign the task request to a scenario with scarce resources; The order of each value in the above moth vector and flame vector determines the order in which the corresponding tasks are assigned;

For the assigned task request, judge whether all the required resources of the task request in the assigned resource scenario can be satisfied on demand, and if it can be satisfied on demand, the task request in the assigned resource scenario is allocated on demand and on time For the requested resources, update the resource availability in the assigned resource scenario, and calculate the benefits, costs and optimization goals of meeting the task in the assigned resource scenario; otherwise, if it cannot be satisfied on demand, make the task request Lost.

Another technical solution adopted by the embodiment of the present application is: an Internet of Things resource allocation system, including:

Initialization module: used to initialize the moth vector set of the moth-flaming algorithm by using the target and density-aware algorithm based on the received resource allocation task request, and obtain the initialized moth vector set;

Health value evaluation module: used to evaluate the health value of each moth in the initialized moth vector set, and select the top k moths with the highest health value as new flame vectors;

Moth-flame matching module: used to calculate the class flame corresponding to the moth by using the progressive class flame matching algorithm and the exploratory moth flame matching algorithm based on the new flame vector, and match the moth with the corresponding class flame, And when there is only one surviving flame, generate a resource allocation plan for the task request according to the surviving flame;

Resource allocation module: used for the resource abstraction architecture of the Internet of Things based on digital objects, adopting resource scheduling and allocation algorithms to implement the resource allocation plan, and performing resource scheduling and allocation for the task requests.

Another technical solution adopted by the embodiment of the present application is: a terminal, the terminal includes a processor and a memory coupled to the processor, wherein,

The memory stores program instructions for realizing the resource allocation method of the Internet of Things;

The processor is configured to execute the program instructions stored in the memory to control resource allocation of the Internet of Things.

Another technical solution adopted by the embodiment of the present application is: a storage medium storing program instructions executable by a processor, and the program instructions are used to execute the method for allocating resources of the Internet of Things.

Compared with the prior art, the beneficial effect of the embodiment of the present application is that: the Internet of Things resource allocation method, system, terminal, and storage medium of the embodiment of the present application introduce an abstract framework of Internet of Things resources based on digital objects in the resource allocation model, Using the concept of digital objects, heterogeneous IoT devices form a digital object architecture, which facilitates the management of IoT device resources and the coordination between resources in resource allocation. When performing resource allocation, using the reserved resource allocation method, the resource allocation model simultaneously optimizes the benefits generated by satisfying the tasks and the costs caused by satisfying the tasks. The optimization goal considers both benefits and costs, which is more practical . And the moth-flame optimizer with multiple improvements is used for optimization, so as to meet the profit and cost of the task as the common optimization goal, and optimize the allocation of the proposed resource allocation model, so as to obtain a better resource allocation plan for IoT devices . Compared with the prior art, the present application has at least the following beneficial effects:

1. On the basis of the moth-flame algorithm, the target and density-sensing algorithm are introduced for initialization, which can eliminate the relatively dense moth vectors in the initialization moth vector set, thereby enhancing the performance of the swarm-based algorithm, so that the initialization process can optimize the target Under the guidance of , it becomes more refined and solves the damage to the effect of the optimization algorithm caused by random initialization.

2. By introducing the progressive flame matching algorithm and the exploratory moth flame matching algorithm, the exploration and excavation can be better balanced in the training process, thereby better approaching the optimal solution, avoiding a single matching mechanism, and making the algorithm It is less likely to fall into a local optimal solution.

3. The abstract framework of Internet of Things resources based on digital objects uses the concept of digital objects to manage and dispatch a variety of Internet of Things devices in a unified manner, covering up the diversity of various Internet of Things devices, thus making the Internet of Things devices The management of resources and the coordination between resources in resource allocation become more convenient, which solves the resource management difficulties caused by the heterogeneity of IoT devices.

4. By adopting the reserved IoT device resource allocation method in the specific IoT multi-scenario mode, resources can be allocated reasonably in various IoT scenarios with resource heterogeneity. When allocating, the time requirements of the tasks on the requested resources are taken into consideration, which is more practical and can avoid resource deadlocks in resource allocation.

5. While optimizing the cost of satisfying the task, it also optimizes the income generated by satisfying the task, making the optimization goal more comprehensive and practical.

Description of drawings

Fig. 1 is the flowchart of the Internet of Things resource allocation method of the embodiment of the present application;

Fig. 2 is a schematic diagram of the initialization process based on the target and density sensing algorithm in the embodiment of the present application;

Fig. 3 is a schematic diagram of the asymptotic class flame matching algorithm in the embodiment of the present application;

Fig. 4 is the schematic diagram of the exploratory moth flame matching algorithm of the embodiment of the present application;

FIG. 5 is a schematic diagram of a resource scheduling and allocation algorithm in an embodiment of the present application;

FIG. 6 is a schematic structural diagram of an Internet of Things resource allocation system according to an embodiment of the present application;

FIG. 7 is a schematic structural diagram of a terminal according to an embodiment of the present application;

FIG. 8 is a schematic structural diagram of a storage medium according to an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, not to limit the present application.

Please refer to FIG. 1 , which is a flowchart of a method for allocating resources of the Internet of Things according to an embodiment of the present application. The Internet of Things resource allocation method in the embodiment of the present application includes the following steps:

S10: Receive a task request requesting resource allocation through the resource allocation model, and based on the received task request, use the target and density sensing algorithm to initialize the moth vector set of the moth-flaming algorithm, and obtain the initialized moth vector set;

In this step, since the moth-to-flame algorithm is a population-based genetic algorithm, by using multiple searchers, it has a greater possibility of approaching the global optimal solution. Therefore, in the initial stage of the algorithm, a set of moth vectors needs to be initialized to generate a set number of moth vectors. The present invention initializes the moth vector set of the moth flame attack algorithm based on the target and density sensing algorithm. The algorithm uses the optimization target as prior knowledge to guide the initialization, and increases the alienation of the initialized moth vectors according to its density sensing characteristics. to improve the performance of the algorithm. Specifically, as shown in Figure 2, it is a schematic diagram of the initialization process based on the target and density-aware algorithm in the embodiment of the present application, and the initialization process includes the following steps:

S11: For the received task requests, calculate the benefits, costs and optimization goals of each task under different resource scenarios;

Among them, the embodiment of the present application includes two kinds of resource scenarios with resource heterogeneity, that is, the scenario R with sufficient resources and the scenario S with scarce resources, so as to reflect the uneven resource allocation that usually occurs in real application scenarios. Condition. For example, in a smart medical scenario, a large hospital is a scenario with sufficient resources, while a community clinic is a scenario with scarce resources; in a smart logistics scenario, a central warehouse is a scenario with sufficient resources, while a regional post office is a scenario with scarce resources.

Further, the calculation method of the income, cost and optimization goal of the task in different resource scenarios is as follows:

In the resource allocation model, a total of M different resources are considered, defined as:

in,

Represents the mth resource in scene X. Each resource has

resource copies, resource

The jth copy of is:

For each resource copy, it has its start time and end time, which are recorded as

and

The availability period of each resource copy is recorded as

Its initial representation is:

For example, if a mobile health monitor starts to be available at 9:00 am and ends at 6:00 pm, then its available period is from 9:00 am to 6:00 pm. If the mobile health monitor is requested to be occupied between 10:00 am and 11:00 am, the available time period of the device is updated as 9:00 am to 10:00 am, and 11:00 am to 6:00 pm.

Assuming that the resource allocation model considers N tasks, denoted as S _n , and each task can request at most K types of IoT devices, the

Denote as the set of resources requested by task S _n ,

record as task

The i-th resource request in

Indicates the number of resources requested by task S _n . So there are:

In task S _n , each resource request

will specify the start time and end use time, recorded as

and

For example, the resource request of a certain task is: to use an IoT device from 9:00 am to 9:30 am, that is, the request specifies a start time of 9:00 am and an end time of 9:30 am.

Each task will generate a benefit when it is satisfied in scenario X, denoted as

Therefore, under the distribution plan A, the total income generated is defined as:

Using each IoT device will generate a cost per unit time, which is denoted as

Define the usage period length len of each resource as:

Therefore, the total cost generated under the allocation scheme A is defined as:

in,

It is equal to 1 when the task S _n is allocated in the scene R with sufficient resources, otherwise it is equal to 0.

Due to the heterogeneity of IoT devices, that is, IoT devices have different available quantities and available time, therefore, different IoT devices have different usage costs. The usage cost may also include other costs such as energy usage overhead, making the definition of heterogeneous cost universal.

The optimization objective of the resource allocation model in the embodiment of the present application takes into account both the cost and the income generated by satisfying the task, so the final optimization objective is to minimize the cost while optimizing the income. The final optimization goal is:

Among them, w is the trade-off coefficient, which is a negative value,

Represents the optimized resource allocation scheme.

The embodiment of the present application guides the initialization process of initializing the moth vector set by using the income, the cost and the optimization objective, so that the initialization method is more reasonable, thereby effectively improving the efficiency of the algorithm.

S12: Determine whether the number of initialized moth vectors in the initialized moth vector set has reached the set multiple of the set number, if not, execute S13; otherwise, execute S14;

Among them, the length of each initialization moth vector is the number of tasks to be allocated resources. When generating the initialized moth vectors, generate 1.5 times more initialized moth vectors than the set number, and the specific multiple can be set according to the actual application.

S13: Allocate each task request according to the size of the scene optimization target, and re-execute S12;

S14: Use the target and density sensing algorithm to find out the two initialization moth vectors with the smallest distance (that is, the closest distance) from the initialization moth vector set, and use the mean vector of the two initialization moth vectors as a new moth vector. The moth vector is added to the set of initialized moth vectors, and the two initialized moth vectors are deleted from the set of initialized moth vectors;

S15: Determine whether the initialized moth vectors in the initialized moth vector set have reached the set number, if not, execute S14 again; if reach the set number, execute S16;

S16: Output the initialized moth vector set.

Based on the above, the embodiment of the present application continuously finds two initialization moth vectors with the smallest distance from the generated initialization moth vector set by using the target and density sensing algorithm, and adds the mean vector of the two to the initialization moth vector set , and delete the two from the set, so as to detect the initialized moth vector group with higher density and closer distance, and merge and remove them until the moth vector in the initialized moth vector set reaches the set value number, so that the moth vectors in the final initialization moth vector set are as far away from each other as possible, thereby enhancing the exploration ability of the algorithm.

S20: Evaluate the health value of each moth in the initialized moth vector set, and select the top k moths with the highest health values as new flame vectors;

S30: update the number of flame vectors, and judge whether there is only one surviving flame, if there are multiple surviving flames, execute S40; if there is only one surviving flame vector, execute S70;

S40: Determine whether the flame vector corresponding to the moth is eliminated, if the flame vector corresponding to the moth is not eliminated, execute S50; otherwise, execute S60;

S50: Use the progressive class flame matching algorithm to calculate the class flame corresponding to the moth according to the progressive coefficient, match the moth with the corresponding class flame, and re-execute S30;

In this step, as shown in Figure 3, it is a schematic diagram of the asymptotic class flame matching algorithm in the embodiment of the present application, which specifically includes the following steps:

S51: Select three flames with the highest health value from the k new flame vectors;

S52: update the gradual coefficient;

S53: Combining the gradual coefficient and the three flames with the highest health value to calculate the class flame, and matching the moth with the corresponding class flame;

Among them, in order to solve the problem of falling into a local optimal solution existing in the original moth-flaming algorithm, the embodiment of the present invention adopts an asymptotic hierarchical flame matching method, and the specific matching method is:

If the current number of surviving flames is greater than or equal to 3, the flame matching is performed according to the flame corresponding to the moth and the three flames with the highest health values. The matching formula is:

(1-0.3*ω)×F _i +0.15×ω×F ₁ +0.1×ω×F ₂ 0.05×ω×F ₃ (8)

Among them, w is the asymptotic coefficient, F _i is the flame corresponding to the moth, F ₁ , F ₂ , and F ₃ are the three flames with the highest health values, respectively, and F ₁ , F ₂ , and F ₃ are assigned 0.15, 0.1, and With a weight of 0.05, the flame with the highest health value has the highest weight (ie influence) to represent the class level that each flame has.

When only two flames exist among the three flames with the highest health value, the matching formula is as follows:

(1-0.25*ω)×F _i +0.15×ω×F ₁ +0.1×ω×F ₂ (9)

In the asymptotic class flame matching algorithm, the asymptotic coefficient w is a coefficient that increases linearly from 0 to 1 as the iteration progresses. The use of the asymptotic coefficient makes the influence of the top three flames with the health value on the moth matching Grow incrementally as iterations progress. The reason is as follows: at the initial stage of the iteration, the top three flames in terms of health value may not be very close to the global optimal solution. Therefore, using a small asymptotic coefficient w at this time can reduce the difficulty of matching the moths and flames for these three flames. , which encourages moths to explore the search space more. As the iteration continues, the top three flames with the health value will become closer to the optimal solution. At this time, the asymptotic coefficient that increases with the iteration will gradually strengthen the three flames for the moth flame matching process. Therefore, the algorithm is gradually transformed from the exploration stage to the excavation stage. Therefore, the use of the asymptotic coefficient w enables the algorithm to effectively balance exploration and excavation when using the asymptotic class flame matching algorithm, and better approach the optimal solution. After updating the asymptotic coefficients, the algorithm calculates which class flame each moth is matched to and matches the moth to it.

S54: Update the moth position according to the flame matching result.

S60: using an exploratory moth flame matching algorithm, calculating the class flame corresponding to the moth according to the progressive coefficient, and matching the moth with the corresponding class flame;

In this step, as shown in Figure 4, it is a schematic diagram of the exploratory moth flame matching algorithm of the embodiment of the present application, which specifically includes the following steps:

S61: Select three flames with the highest health values from the k new flame vectors;

S62: Randomly select surviving flames;

Among them, in order to prevent all moths that have lost their corresponding flames from uniformly matching the worst surviving moths, the embodiment of this application adopts an exploratory method to randomly select the surviving flames as the flames corresponding to the moths that have lost their flames, record for F _I .

S63: update the gradual coefficient;

S64: Combining the progressive coefficient and the selected surviving flames to calculate class flames, and matching the moths with the corresponding class flames;

Among them, in order to solve the problem of falling into the local optimal solution existing in the original moth-to-flame algorithm, the embodiment of the present invention adopts an exploratory hierarchical flame matching method, which is similar to the asymptotic hierarchical flame matching algorithm. The exploratory hierarchical flame matching algorithm uses randomly explored flames , with the effect of the asymptotic class flame, and being the corresponding matching flame of the moth. The specific matching method is:

If the current number of surviving flames is ≥ 3, moth-flame matching will be performed based on the randomly selected surviving flames and the three flames with the highest health values. The matching formula is:

S65: Update the position of the moth according to the flame matching result.

In the above, the algorithm will continue to repeat the above operations. During the iterative process, the parameter k of the number of flames will gradually decrease, and finally reduce to 1, that is, only one flame vector can survive, then the algorithm outputs the final surviving flame vector, and uses It guides the resource scheduling and allocation module to perform resource scheduling and allocation.

S70: A resource allocation scheme according to the final surviving flame generation task request;

S80: The resource abstraction architecture of the Internet of Things based on digital objects, the resource allocation model uses resource scheduling and allocation algorithms to implement resource allocation plans, and performs resource scheduling and allocation for received task requests;

In this step, by adopting the abstract structure of Internet of Things resources based on digital objects, the Internet of Things devices with different characteristics are digitized and unified, so as to facilitate communication and distribution with Internet of Things devices, as well as collaboration and cooperation between devices. The abstract architecture of IoT devices based on digital objects is as follows: abstract each IoT device into a digital object, thereby transforming the management and collaboration of IoT devices into the management and collaboration of digital objects with a unified architecture. The abstraction of objects masks the heterogeneity inherent in IoT devices. For IoT devices in different resource scenarios, when they communicate with the resource allocation algorithm as an IoT device, the resource allocation algorithm communicates with the abstract architecture of the digital object of the IoT device and will not be affected by the IoT device itself. have a differential impact.

Specifically, for each digital object, there are two modules, namely API module and collaboration module. Among them, the API module is responsible for reflecting the current status of each IoT device, including whether the device is available, and the information generated by using the device. The cost and whether the specified task request is acceptable, etc. The collaboration module is responsible for the cooperative communication between the IoT device and other similar IoT devices. When the resource allocation model receives a task request, it transmits the task request to the digital object of the IoT device. It is judged through the API module of the digital object whether the IoT device can meet the task request, and if it can be satisfied, the task request information is sent back to the resource allocation model through the digital object. Conversely, if the IoT device cannot satisfy the task request, the IoT device communicates with other IoT devices of the same type through the communication module of its digital object, and sends the task request to other IoT devices of the same type to try to satisfy the task Requests enable collaboration between similar IoT devices in the process of resource allocation.

Further, as shown in FIG. 5, it is a schematic diagram of the resource scheduling and allocation algorithm in the embodiment of the present application, which specifically includes the following steps:

S81: Acquiring moth vectors and flame vectors;

S82: Allocate each task sequentially according to the resource allocation scheme in the moth vector and the flame vector;

Among them, the resource allocation scheme specifically includes: setting a constant ub so that all values in the moth vector and the flame vector are between 0-ub, and the lengths of the moth vector and the flame vector are equal to the number of tasks. Each value in the moth vector and the flame vector is regarded as the allocation method of the task. If the value in the moth vector and the flame vector is between [0, ub/2], the task is assigned to In a scenario with sufficient resources; if the value in the moth vector and the flame vector is between (ub/2,ub], then assign the task to a resource-poor scenario. Each value in the moth vector and flame vector The order of determines the order in which the corresponding tasks are assigned. For example, if ub is 10 and the moth vector is [3,7,2,9], it means that the value corresponding to task 1 is 3, the value corresponding to task 2 is 7, and the value corresponding to task 3 The corresponding value is 2, and the value corresponding to task 4 is 9. Since the values of task 1 and task 3 are between [0,10/2], assign task 1 and task 3 to the scene with sufficient resources; and Since the values of task 2 and task 4 are between (10/2,10], assign task 2 and task 4 to the resource-poor scenario. After the scenario assignment, since the value corresponding to task 3 is smaller than task 1 , so task 3 will be executed before task 1. Because tasks have strict time requirements for resource requests, if task 3 is executed, task 1 cannot be executed within the time requirement because the requested resources are occupied by task 3, Then task 1 will be defeated. Similarly, task 2 will be executed before task 4.

S83: Determine whether the task is assigned to a scene with sufficient resources or a scene with insufficient resources, if the task is assigned to a scene with sufficient resources, execute S84; if the task is assigned to a scene with scarce resources, execute S87;

S84: Judging whether all required resources of the task can be met on demand in the resource-sufficient scenario, if it can be met on demand, execute S85; otherwise, execute S86;

S85: Allocate the requested resources for the task on demand and on time in the resource-sufficient scene, update the resource availability in the resource-sufficient scene, and calculate the benefit, cost and optimization goal of satisfying the task in the resource-sufficient scene;

S86: In order to avoid deadlock, make the task out of service;

S87: Judging whether all required resources of the task can be met on demand in resource-poor scenarios, and if so, go to S88; otherwise, go to S86;

S88: Allocate the requested resources for the task on demand and on time in the resource-poor scenario, update the resource availability in the resource-poor scenario, and calculate the benefit, cost and optimization goal of meeting the task in the resource-poor scenario.

Based on the above, the embodiment of the present application adopts the reserved allocation method. For tasks assigned in different scenarios, it is necessary to judge whether all resources requested by the task in this scenario can be allocated on demand and on time. In this scenario, the requested resources are allocated to the task on time as needed, otherwise, the task will be out of service, effectively avoiding resource deadlocks caused by a certain task receiving only part of the resource allocation. After allocating resources to a task, update the resource availability in the scenario, and calculate the benefits, costs and optimization goals that meet the task. Finally, the optimization goal under this resource allocation scheme is returned.

Based on the above, the Internet of Things resource allocation method of the embodiment of the present application introduces an abstract framework of Internet of Things resources based on digital objects in the resource allocation model, and uses the concept of digital objects to form a digital object architecture with heterogeneous Internet of Things devices, so that It facilitates the management of IoT device resources and the coordination between resources in resource allocation. When performing resource allocation, using the reserved resource allocation method, the resource allocation model simultaneously optimizes the benefits generated by satisfying the tasks and the costs caused by satisfying the tasks. The optimization goal considers both benefits and costs, which is more practical . And the moth-flame optimizer with multiple improvements is used for optimization, so as to meet the profit and cost of the task as the common optimization goal, and optimize the allocation of the proposed resource allocation model, so as to obtain a better resource allocation plan for IoT devices . Compared with the prior art, the present application has at least the following beneficial effects:

Please refer to FIG. 6 , which is a schematic structural diagram of an IoT resource allocation system according to an embodiment of the present application. The Internet of Things resource allocation system 40 of the embodiment of the present application includes:

Initialization module 41: used to initialize the moth vector set of the moth-flaming algorithm by using the target and density sensing algorithm based on the received resource allocation task request, to obtain the initialized moth vector set;

Health value evaluation module 42: used to evaluate the health value of each moth in the initial moth vector set, and select the top k moths with the highest health values as new flame vectors;

Moth-flame matching module 43: used to calculate the class flame corresponding to the moth based on the new flame vector, using the progressive class flame matching algorithm and the exploratory moth flame matching algorithm, matching the moth with the corresponding class flame, and When there is only one surviving flame, generate the resource allocation plan requested by the task according to the surviving flame;

Resource allocation module 44: used for the resource abstraction architecture of the Internet of Things based on digital objects, adopting resource scheduling and allocation algorithms to implement resource allocation schemes, and performing resource scheduling and allocation for task requests.

Please refer to FIG. 7 , which is a schematic diagram of a terminal structure according to an embodiment of the present application. The terminal 50 includes a processor 51 and a memory 52 coupled to the processor 51 .

The memory 52 stores program instructions for realizing the above-mentioned Internet of Things resource allocation method.

The processor 51 is used to execute the program instructions stored in the memory 52 to control resource allocation of the Internet of Things.

Wherein, the processor 51 may also be referred to as a CPU (Central Processing Unit, central processing unit). The processor 51 may be an integrated circuit chip with signal processing capabilities. The processor 51 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components . A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

Please refer to FIG. 8 , which is a schematic structural diagram of a storage medium according to an embodiment of the present application. The storage medium of the embodiment of the present application stores a program file 61 capable of realizing all the above-mentioned methods, wherein the program file 61 can be stored in the above-mentioned storage medium in the form of a software product, and includes several instructions to make a computer device (which can It is a personal computer, a server, or a network device, etc.) or a processor (processor) that executes all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disc, etc., which can store program codes. , or terminal devices such as computers, servers, mobile phones, and tablets.

The above description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined in this application may be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the present application will not be limited to the embodiments shown in the present application, but is to be accorded the widest scope consistent with the principles and novel features disclosed in the present application.

Claims

A resource allocation method for the Internet of Things, characterized in that, comprising:

Based on the received resource allocation task request, the target and density-aware algorithm is used to initialize the moth vector set of the moth-flaming algorithm, and the initialized moth vector set is obtained;

Evaluate the health value of each moth in the initialized moth vector set, and select the top k moths with the highest health value as new flame vectors;

Based on the new flame vector, using a progressive class flame matching algorithm and an exploratory moth flame matching algorithm to calculate the class flame corresponding to the moth, and matching the moth with the corresponding class flame;

When there is only one surviving flame, generating a resource allocation plan for the task request according to the surviving flame;

The resource abstraction architecture of the Internet of Things based on digital objects uses a resource scheduling and allocation algorithm to execute the resource allocation plan, and performs resource scheduling and allocation for the task request.
The resource allocation method of the Internet of Things according to claim 1, wherein the initialization of the moth vector set of the moth-flaming algorithm by using the target and density sensing algorithm is specifically:

For the received task request, calculate the income, cost and optimization goal of each task in different resource scenarios; the resource scenario includes a resource-sufficient scenario and a resource-poor scenario;

Judging whether the initialized moth vectors in the initialized moth vector set have reached the set multiple of the set number, if not reached the set multiple of the set number, assigning each task request according to the size of the optimization target and repeating the judgment; If the set multiple of the set number is reached, the target and density-aware algorithm is used to find two initialized moth vectors with the smallest distance from the set of initialized moth vectors, and the mean vector of the two initialized moth vectors is used as A new moth vector is added to the set of initialized moth vectors, and the two initialized moth vectors are deleted from the set of initialized moth vectors; wherein, the length of each initialized moth vector is the resource to be allocated The number of task requests;

Determine whether the initialized moth vectors in the initialized moth vector set reach the set number, if not, continue to execute the target and density sensing algorithm; if reach the set number, output the initialized moth vector set.
The resource allocation method for the Internet of Things according to claim 2, wherein the calculation of the class flame corresponding to moths using the progressive class flame matching algorithm and the exploratory moth flame matching algorithm based on the new flame vector includes:

Judging whether the flame vector corresponding to the moth is eliminated, if the flame vector corresponding to the moth is not eliminated, adopt the progressive class flame matching algorithm, calculate the class flame according to the progressive coefficient, and match the moth with the corresponding class flame;

If the flame vector corresponding to the moth has been eliminated, the exploratory moth flame matching algorithm is used to calculate the class flame according to the progressive coefficient, and the moth is matched with the corresponding class flame.
The resource allocation method of the Internet of Things according to claim 3, wherein the stepwise flame matching algorithm is used to calculate the stage flame according to the gradual coefficient, and matching the moth with the corresponding stage flame is specifically as follows:

Select the three flames with the highest health values from the k new flame vectors;

update asymptotic coefficient;

Combining the gradual coefficient and the three flames with the highest health value to calculate the class flame, and matching the moth with the corresponding class flame; the matching method is:

If the current number of surviving flames is greater than or equal to 3, flame matching is performed based on the flame corresponding to the moth and the three flames with the highest health values:

Among them, w is a progressive coefficient that increases linearly from 0 to 1 as the iteration progresses, F i is the flame corresponding to the moth, F 1 , F 2 , and F 3 are the three flames with the highest health values, and F 1 , F 2 , and F 3 are given weights of 0.15, 0.1, and 0.05 respectively, and the flame with the highest health value has the highest weight;

When only two flames exist among the three flames with the highest health value, the matching method is:

Update the moth position based on the flame matching results.
The resource allocation method of the Internet of Things according to claim 4, characterized in that, using the exploratory moth flame matching algorithm, calculating the class flame according to the gradual coefficient, and matching the moth with the corresponding class flame is specifically:

Select the three flames with the highest health values from the k new flame vectors;

Randomly select the surviving flame F I ;

update asymptotic coefficient;

Combining the gradual coefficient and the selected surviving flames to calculate the class flames, and matching the moths with the corresponding class flames; the matching method is:

If the current number of surviving flames is ≥ 3, moth-flame matching is performed based on the randomly selected surviving flames and the three flames with the highest health values:

When only two flames exist among the three flames with the highest health value, the matching formula is:

The moth position is updated according to the flame matching result.
According to the resource allocation method of the Internet of Things according to any one of claims 1 to 5, it is characterized in that, the resource abstraction framework of the Internet of Things based on digital objects, and the resource allocation model adopts resource scheduling and allocation algorithms to execute the resource allocation plan as follows:

The resource allocation model transmits the task request to the digital object of the IoT device, the digital object includes an API module and a communication module, and the API module is used to judge whether the IoT device can meet the task request, and if it can be satisfied, the Satisfied task request information is sent back to the resource allocation model; if the IoT device cannot satisfy the task request, it communicates with other similar IoT devices through the communication module, and sends the task request to other similar IoT devices.
The resource allocation method for the Internet of Things according to claim 6, wherein the resource allocation model uses a resource scheduling and allocation algorithm to execute a resource allocation plan specifically as follows:

Obtain the moth vector and the flame vector; the lengths of the moth vector and the flame vector are the quantity requested by the task;

Set a constant ub so that all the values in the moth vector and flame vector are between 0-ub, if the values in the moth vector and flame vector are between [0, ub/2] , assign the task request to a scenario with sufficient resources; if the values in the moth vector and flame vector are between (ub/2,ub], assign the task request to a scenario with scarce resources; The order of each value in the above moth vector and flame vector determines the order in which the corresponding tasks are assigned;

For the assigned task request, judge whether all the required resources of the task request in the assigned resource scenario can be satisfied on demand, and if it can be satisfied on demand, the task request in the assigned resource scenario is allocated on demand and on time For the requested resources, update the resource availability in the assigned resource scenario, and calculate the benefits, costs and optimization goals of meeting the task in the assigned resource scenario; otherwise, if it cannot be satisfied on demand, make the task request Lost.
An Internet of Things resource allocation system is characterized in that it comprises:

Initialization module: used to initialize the moth vector set of the moth-flaming algorithm by using the target and density-aware algorithm based on the received resource allocation task request, and obtain the initialized moth vector set;

Health value evaluation module: used to evaluate the health value of each moth in the initialized moth vector set, and select the top k moths with the highest health value as new flame vectors;

Moth-flame matching module: used to calculate the class flame corresponding to the moth by using the progressive class flame matching algorithm and the exploratory moth flame matching algorithm based on the new flame vector, and match the moth with the corresponding class flame, And when there is only one surviving flame, generate a resource allocation plan for the task request according to the surviving flame;

Resource allocation module: used for the resource abstraction architecture of the Internet of Things based on digital objects, adopting resource scheduling and allocation algorithms to implement the resource allocation plan, and performing resource scheduling and allocation for the task requests.
A terminal, characterized in that the terminal includes a processor and a memory coupled to the processor, wherein,

The memory stores program instructions for implementing the resource allocation method of the Internet of Things according to any one of claims 1-7;

The processor is configured to execute the program instructions stored in the memory to control resource allocation of the Internet of Things.
A storage medium, characterized in that it stores program instructions executable by a processor, and the program instructions are used to execute the method for allocating resources of the Internet of Things according to any one of claims 1 to 7.