IoT edge computing method assisted by low-altitude platform base station

Abstract

The present application proposes an IoT edge computing method assisted by a low-altitude platform base station, which belongs to the field of information transmission technology of the agricultural IoT. The IoT edge computing method includes the following steps: constructing a network model of the agricultural IoT; using the relevant network parameters of the network model to respectively construct a task calculation model, a task transmission model, and an energy consumption model, and using these three models to construct the problem of minimizing the total task processing delay; solving the problem of minimizing the total task processing delay, obtaining the priority processing results of all subtasks in the network model, and calculating and processing all subtasks in the network model according to the priority processing results. The present application can significantly optimize the task processing performance and response speed of the agricultural IoT.

Description

Technical Field

[0001] The present application relates to the field of agricultural Internet of Things information transmission technology, and in particular to an Internet of Things edge computing method assisted by a low-altitude platform base station. Background Art

[0002] In the agricultural Internet of Things, the use of low-altitude platform base stations can provide significant technical advantages for data collection. Especially in areas with relatively backward infrastructure, low-altitude platform base stations can not only greatly expand the coverage of the agricultural Internet of Things and speed up data collection, but also improve the connection reliability of the agricultural Internet of Things.

[0003] The edge computing server is combined with the low-altitude platform base station to realize the processing of computing-intensive tasks at a geographical location close to the data source. This combination can not only significantly reduce the latency required for data processing, but also reduce the bandwidth burden of the agricultural Internet of Things. Thanks to edge computing technology, the agricultural Internet of Things can process data near the data generation point, optimize the task offloading process, and further enhance the elasticity and stability of the agricultural Internet of Things by reducing the distance of data transmission. Therefore, the combination of low-altitude platform base stations and edge computing servers not only optimizes the agricultural operation process, but also becomes a key technology to promote the development of agriculture towards intelligence and automation, accelerates the implementation of Agriculture 4.0, and marks the transformation of agricultural production methods towards more efficient and sustainable directions, so that agricultural production methods can better adapt to changing market demands and environmental challenges. However, there are also many problems with this combination of edge computing servers and low-altitude platform base stations, as follows:

[0004] First, in response to the integration requirements of low-altitude platform base stations and edge computing servers, existing technologies often only consider the impact of a single edge computing server, while ignoring the potential collaboration between multiple edge computing servers, resulting in slow processing capabilities and response speeds in the agricultural Internet of Things.

[0005] Secondly, when multiple edge computing servers are mounted on different low-altitude platform base stations, the low-altitude platform base stations are mobile, which will cause the load distribution between multiple edge computing servers to be uneven when multiple edge computing servers cooperate with each other, resulting in a decrease in user service quality;

[0006] Finally, for the computing task migration scheme, the existing technology generally divides the task to be offloaded into two parts, which cannot be further subdivided into more subtasks. However, with the increase in task complexity and computing requirements, it is difficult to meet the wider application requirements by only dividing the task into two parts. It is necessary to divide the task more finely to better match the processing capacity and network conditions of the edge computing server. At present, although there are some studies on the problem of multiple subtasks in edge computing, these methods cannot fully consider the execution processing of each subtask, such as the urgency, importance, and corresponding processing order of subtask processing. This leads to a decrease in the processing efficiency of the agricultural Internet of Things. For important subtasks, delayed processing may occur because they are ranked after secondary subtasks, affecting the overall performance and response speed of the agricultural Internet of Things. This may increase the probability of errors in the agricultural Internet of Things, causing the agricultural Internet of Things to mistakenly start executing subsequent tasks that depend on the results of these key subtasks when the key subtasks are not completed.

[0007] Therefore, it is necessary to propose a solution to improve one or more problems existing in the above-mentioned related technical solutions.

[0008] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0009] The present application embodiment provides an Internet of Things edge computing method assisted by a low-altitude platform base station, and the Internet of Things edge computing method includes the following steps:

[0010] Construct a network model of agricultural Internet of Things assisted by a low-altitude platform base station, wherein the network model includes multiple low-altitude platform base stations and multiple agricultural equipment, each of which is respectively equipped with an edge computing server, and each of the agricultural equipment is provided with multiple subtasks with dependencies, and all the subtasks set on each of the agricultural equipment can be edge-computed on any edge computing server, or locally computed using the computing resources of the agricultural equipment itself;

[0011] Using relevant network parameters of the network model, respectively construct a task calculation model, a task transmission model and an energy consumption model;

[0012] Constructing a total task processing delay minimization problem using the task calculation model, the task transmission model and the energy consumption model;

[0013] The total task processing delay minimization problem is solved to obtain priority processing results of all the subtasks in the network model, and all the subtasks in the network model are calculated and processed according to the priority processing results.

[0014] In an exemplary embodiment of the present application, the set consisting of all the edge computing servers is defined as: , Indicates Edge computing servers, represents the number of edge computing servers, where the number of edge computing servers is equal to the number of low-altitude platform base stations;

[0015] The set of all the agricultural equipment is defined as: , Indicates agricultural equipment, Indicates the quantity of agricultural equipment;

[0016] No. The set of all the subtasks set on the agricultural equipment is defined as: ,gather The elements in A subtask set on an agricultural device, Indicates The number of all subtasks set on the agricultural equipment; The processing process of all the subtasks set on the agricultural equipment is defined as a directed acyclic graph , , Indicates Dependencies between all subtasks set on a farming device;

[0017] The relevant network parameters include: the number of edge computing servers , the number of agricultural equipment , No. The number of all subtasks set on the agricultural equipment and The agricultural equipment is provided with The size of the subtask .

[0018] In an exemplary embodiment of the present application, the task calculation model is used to calculate the task processing delay, and the expression of the task processing delay is:

[0019] (1)

[0020] in, Indicates The first set of agricultural equipment The task processing delay of each subtask is Indicates The first set of agricultural equipment The computing server selected by each subtask, The value range is Agricultural equipment and The union of edge computing servers, Indicates The first set of agricultural equipment offload subtasks to computing servers The actual initial time on , Indicates The first set of agricultural equipment offload subtasks to computing servers The theoretical initial time on Represents a computing server Prepare to process The first set of agricultural equipment The earliest time for each subtask, , Indicates The first set of agricultural equipment The set of direct predecessor subtasks of subtasks, Indicates The first set of agricultural equipment The task processing delay of each subtask is Indicates The first set of agricultural equipment The computing server selected by each subtask, Indicates The first set of agricultural equipment subtasks and The information exchange time of each subtask is , Indicates subtask and The amount of information exchanged per subtask, Represents a computing server and computing servers The channel capacity between Indicates The first set of agricultural equipment Subtasks in the computing server The transmission and processing delay on Indicates taking the minimum value, Indicates taking the maximum value.

[0021] In an exemplary embodiment of the present application, the task transmission model is used to calculate the path loss, and the expression of the path loss is:

[0022] (2),

[0023] in, Indicates The edge computing server and The path loss between agricultural equipment, Indicates The edge computing server and The probability of a line-of-sight component between agricultural equipment, , represents the first S-band constant, represents the second S-band constant, Indicates that the The height of the low-altitude platform base station of the edge computing server, Indicates that the The low-altitude platform base station of the edge computing server and the The projection distance of agricultural equipment on the horizontal plane, Indicates The edge computing server and The probability of non-line-of-sight components between agricultural equipment, , Indicates The edge computing server and The loss value of the line-of-sight component between agricultural equipment, , represents the additional path loss of the line-of-sight component, Indicates the carrier frequency, represents the speed of light, Indicates The edge computing server and The loss value of the non-line-of-sight component between agricultural equipment, , Indicates the additional path loss of the non-line-of-sight component.

[0024] In an exemplary embodiment of the present application, the energy consumption model is used to calculate the total energy consumption of task offloading in the network model, and the expression of the total energy consumption of task offloading is:

[0025] (3),

[0026] in, represents the total energy consumption of task offloading in the network model, represents the energy consumption required for task calculation, , Indicates the unit time The energy consumption of agricultural equipment for task calculation, Indicates The first set of agricultural equipment The local processing delay of each subtask, Indicates The first set of agricultural equipment The computing server selected by each subtask, represents a binary indicator variable, ,when hour, ,when hour, , represents the energy consumption required for task transmission, The expression is: ,

[0027] Indicates The transmission power of agricultural equipment Indicates The first set of agricultural equipment The size of the subtask, Indicates Agricultural equipment and computing servers The channel capacity between Indicates The first set of agricultural equipment The computing server selected by each subtask, Indicates The first set of agricultural equipment The set of direct predecessor subtasks of subtasks, Indicates The first set of agricultural equipment subtask and The information exchange time of each subtask is , that is, when ,or hour, The value is 1, otherwise the value is 0.

[0028] In an exemplary embodiment of the present application, the expression for minimizing the total task processing delay is:

[0029] (4),

[0030] st (4a),

[0031] (4b),

[0032] in, represents the problem of minimizing the total task processing delay, Indicates Agricultural equipment set The sum of the task processing delays of the subtasks, represents the set of all computing servers, , Indicates The first set of agricultural equipment The computing server selected by each subtask, represents the set of processing orders of all subtasks, , Indicates The first set of agricultural equipment The processing order number of each subtask, st represents the constraint, represents the total energy consumption of task offloading in the network model, represents the energy consumption threshold, Represents any value. Indicates The computing server selected for the first subtask set on the agricultural equipment, Indicates The computing server selected by the last subtask set on the agricultural equipment, constraint 4a is used to ensure that the total energy consumption of task offloading in the network model does not exceed the energy consumption threshold, and constraint 4b is used to limit the The first and last subtasks set on each agricultural device must use the computing resources of the agricultural device itself for local computing. Indicates taking the maximum value.

[0033] In an exemplary embodiment of the present application, the total task processing delay minimization problem is solved to obtain priority processing results of all the subtasks in the network model, and the steps of calculating and processing all the subtasks in the network model according to the priority processing results include:

[0034] Pre-sorting all the subtasks set on each of the agricultural equipment according to an initial order, and forming a subtask candidate set with the pre-sorting results of all the subtasks in the network model;

[0035] Prioritizing all the subtasks in the subtask candidate set to obtain the priority processing results of all the subtasks;

[0036] All the subtasks in the network model are calculated and processed according to the priority processing result.

[0037] In an exemplary embodiment of the present application, the expression for pre-sorting all the subtasks set on the agricultural equipment in the initial order is:

[0038] (5)

[0039] in, Indicates The first set of agricultural equipment The pre-sorting results of subtasks, Indicated in The set of subtasks to be processed after the subtask, Indicates The first set of agricultural equipment The pre-sorting results of subtasks, Indicates The subtask and the one after it The average information exchange time between subtasks is , Indicates subtask and The amount of information exchanged per subtask, Indicates Agricultural equipment and The channel capacity between edge computing servers, Indicates The edge computing server and The channel capacity between edge computing servers, Indicates processing The first set of agricultural equipment The average delay required for each subtask is , Indicates The first set of agricultural equipment The local processing delay of each subtask, Indicates The first set of agricultural equipment The subtask is The transmission and processing delays on the edge computing server.

[0040] In an exemplary embodiment of the present application, the expression of the priority processing result is:

[0041] (6),

[0042] in, Indicates The first set of agricultural equipment The priority of each subtask, Indicates The first weight coefficient of agricultural equipment, Indicates The second weight coefficient of agricultural equipment, and satisfy , Indicates the unit time The energy consumption of agricultural equipment for task calculation, Indicates The transmission power of agricultural equipment Indicates The first set of agricultural equipment The size of each subtask.

[0043] Beneficial effects:

[0044] The present application provides an IoT edge computing method assisted by a low-altitude platform base station, which has at least the following beneficial effects:

[0045] (1) This application constructs a network model of agricultural Internet of Things assisted by low-altitude platform base stations, installs an edge computing server on each low-altitude platform base station in the network model to provide task offloading services, and considers the collaboration mechanism between multiple edge computing servers. Through task coordination and resource sharing among multiple edge computing servers, the task processing performance and response speed of agricultural Internet of Things are significantly optimized;

[0046] (2) This application divides the tasks in each agricultural device into multiple subtasks, thereby making more effective use of computing resources and improving processing efficiency. It also considers the priority relationship and dependency relationship between all subtasks, thereby reducing the error probability of the agricultural Internet of Things.

[0047] (3) This application uses the task computing model, task output model and energy consumption model to construct the total task processing delay minimization problem, which can formalize the total task processing delay minimization problem into an optimization problem related to the edge computing server, the processing order of subtasks and energy consumption. By solving the total task processing delay minimization problem, the task processing efficiency and response speed of the agricultural Internet of Things can be significantly improved, making agricultural operations more precise and timely. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A schematic diagram showing the steps of a low-altitude platform base station-assisted IoT edge computing method in an exemplary embodiment of the present application is shown;

[0050] Figure 2 A schematic diagram showing a network model of an agricultural Internet of Things assisted by a low-altitude platform base station in an exemplary embodiment of the present application;

[0051] Figure 3 A schematic diagram showing the dependency relationship between all subtasks in a directed acyclic graph in an exemplary embodiment of the present application;

[0052] Figure 4 A schematic diagram showing a comparison of the total task processing delay using the calculation method proposed in the present application and other four calculation methods in a simulation experiment of an exemplary embodiment of the present application;

[0053] Figure 5 A schematic diagram showing the comparison of the impact of the number of edge computing servers on the total task processing delay in a simulation experiment of an exemplary embodiment of the present application;

[0054] Figure 6 A comparative schematic diagram showing the impact of different distribution forms of all agricultural equipment on the total task processing delay in a simulation experiment of an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0055] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0056] In addition, the accompanying drawings are only schematic illustrations of the present application and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0057] To this end, this example implementation proposes a low-altitude platform base station-assisted IoT edge computing method, such as Figure 1 As shown, the IoT edge computing method may include the following steps:

[0058] Step S101: Construct a network model of the agricultural Internet of Things assisted by a low-altitude platform base station. The network model includes multiple low-altitude platform base stations and multiple agricultural equipment. An edge computing server is installed on each low-altitude platform base station. Multiple subtasks with dependencies are set on each agricultural equipment, and all subtasks set on each agricultural equipment can be edge-computed on any edge computing server, or locally calculated using the agricultural equipment's own computing resources.

[0059] Step S102: using relevant network parameters of the network model, respectively construct a task calculation model, a task transmission model and an energy consumption model.

[0060] Step S103: construct the total task processing delay minimization problem using the task calculation model, task transmission model and energy consumption model.

[0061] Step S104: Solve the problem of minimizing the total task processing delay, obtain the priority processing results of all subtasks in the network model, and perform calculation processing on all subtasks in the network model according to the priority processing results.

[0062] The present application embodiment proposes a low-altitude platform base station-assisted IoT edge computing method, which has at least the following beneficial effects:

[0063] (1) This application constructs a network model of agricultural Internet of Things assisted by low-altitude platform base stations, installs an edge computing server on each low-altitude platform base station in the network model to provide task offloading services, and considers the collaboration mechanism between multiple edge computing servers. Through task coordination and resource sharing among multiple edge computing servers, the task processing performance and response speed of agricultural Internet of Things are significantly optimized;

[0064] (2) This application divides the tasks in each agricultural device into multiple subtasks, thereby making more effective use of computing resources and improving processing efficiency. It also considers the priority relationship and dependency relationship between all subtasks, thereby reducing the error probability of the agricultural Internet of Things.

[0065] (3) This application uses the task computing model, task output model and energy consumption model to construct the total task processing delay minimization problem, which can formalize the total task processing delay minimization problem into an optimization problem related to the edge computing server, the processing order of subtasks and energy consumption. By solving the total task processing delay minimization problem, the task processing efficiency and response speed of the agricultural Internet of Things can be significantly improved, making agricultural operations more precise and timely.

[0066] Below, a low-altitude platform base station-assisted IoT edge computing method proposed in this example embodiment will be described in more detail.

[0067] In step S101 of this embodiment, Figure 2 As shown, a network model of agricultural Internet of Things assisted by low-altitude platform base stations is constructed.

[0068] Furthermore, the network model contains There are low-altitude platform base stations. Since each low-altitude platform base station is equipped with an edge computing server with limited computing power, the set of all edge computing servers can be defined as: , Indicates Edge computing servers, Represents the number of edge computing servers, that is, the number of edge computing servers is equal to the number of low-altitude platform base stations.

[0069] Furthermore, the network model includes multiple agricultural equipment. In this embodiment, the total task of each agricultural equipment is divided into multiple subtasks, so that each agricultural equipment includes multiple subtasks with dependencies. The set of all agricultural equipment can be defined as: , Indicates agricultural equipment, Represents the number of agricultural equipment, and ensures that all subtasks set on each agricultural equipment can be edge-computed on any edge computing server, or locally computed using the computing resources of the agricultural equipment itself. The set of all subtasks set on an agricultural device is defined as: ,gather The elements in A subtask set on an agricultural device, Indicates The number of all subtasks set on the agricultural equipment can also be expressed as The last subtask is set on agricultural equipment.

[0070] Furthermore, if Figure 3 As shown, in this embodiment, the first The processing process of all subtasks set on an agricultural device is defined as a directed acyclic graph , , Indicates The dependencies between all subtasks set on each agricultural device.

[0071] In step S101 of this embodiment, first, by setting up an edge computing server at each low-altitude platform base station and considering the task coordination and resource sharing between multiple edge computing servers, the task processing performance and response speed of the agricultural Internet of Things can be significantly optimized. In this embodiment, the use of a low-altitude platform base station equipped with an edge computing server to provide an unloading service can improve the task processing speed. By deploying edge computing servers on low-altitude platform base stations so that they can cover all farmland areas, this setting does not require data to be transmitted over long distances to the cloud, but can be analyzed and processed close to the source of data generation, thereby having faster network response time and real-time decision support, reducing network latency, and also helping to save bandwidth and improve the availability of the agricultural Internet of Things, especially in some remote agricultural areas where network connections are unstable.

[0072] Secondly, by dividing the tasks in each agricultural equipment into multiple subtasks, computing resources can be effectively utilized, processing efficiency can be improved, and the task processing performance of the agricultural Internet of Things can be effectively improved.

[0073] In step S102 of this embodiment, the task calculation model, the task transmission model and the energy consumption model are respectively constructed using relevant network parameters of the network model.

[0074] Furthermore, the relevant network parameters of the network model include the number of edge computing servers , the number of agricultural equipment , No. The number of all subtasks set on agricultural equipment and The first set of agricultural equipment The size of the subtask .

[0075] Furthermore, in this embodiment, a non-preemptive task scheduling mechanism is considered, that is, as long as one of the subtasks has not completed the calculation, the entire execution process will not be interrupted. The task calculation model in this embodiment is used to calculate the task processing delay, where the task processing delay refers to the total delay from the processing of the first subtask to the completion of the processing of the last subtask. Therefore, the expression of the task processing delay can be expressed as:

[0076] (1)

[0077] in, Indicates The first set of agricultural equipment The task processing delay of each subtask is Indicates The first set of agricultural equipment The computing server selected by each subtask, The value range is any Agricultural equipment and The union of edge computing servers, Indicates The first set of agricultural equipment offload subtasks to computing servers The actual initial time on , Indicates The first set of agricultural equipment offload subtasks to computing servers The theoretical initial time on Represents a computing server Prepare to process The first set of agricultural equipment The earliest time for each subtask, , Indicates The first set of agricultural equipment The set of direct predecessor subtasks of subtasks, Indicates The first set of agricultural equipment The task processing delay of each subtask is Indicates The first set of agricultural equipment The computing server selected by each subtask, Indicates The first set of agricultural equipment subtask and The information exchange time of each subtask is , Indicates subtask and The amount of information exchanged per subtask, Represents a computing server and computing servers The channel capacity between Indicates The first set of agricultural equipment Subtasks in the computing server The transmission and processing delay on Indicates taking the minimum value, Indicates taking the maximum value.

[0078] Furthermore, in the air-to-ground channel, communication can be divided into a Line-of-Sight (LoS) component and a Non-Line-of-Sight (NLoS) component. Therefore, in this embodiment, the task transmission model is used to calculate the path loss, and the expression of the path loss is:

[0079] (2),

[0080] in, Indicates The edge computing server and The path loss between agricultural equipment, Indicates The edge computing server and The probability of a line-of-sight component between agricultural equipment, , represents the first S-band constant, represents the second S-band constant, Indicates that the The height of the low-altitude platform base station of the edge computing server, Indicates that the The low-altitude platform base station of the edge computing server and the The projection distance of agricultural equipment on the horizontal plane, Indicates The edge computing server and The probability of non-line-of-sight components between agricultural equipment, , Indicates The edge computing server and The loss value of the line-of-sight component between agricultural equipment, , represents the additional path loss of the line-of-sight component, Indicates the carrier frequency, represents the speed of light, Indicates The edge computing server and The loss value of the non-line-of-sight component between agricultural equipment, , Indicates the additional path loss of the non-line-of-sight component.

[0081] Furthermore, in the network model of the agricultural Internet of Things assisted by the low-altitude platform base station in this embodiment, the energy consumption model is used to calculate the total energy consumption of task unloading in the network model. The expression of the total energy consumption of task unloading is:

[0082] (3),

[0083] in, represents the total energy consumption of task offloading in the network model, represents the energy consumption required for task calculation, , Indicates the unit time The energy consumption of agricultural equipment for task calculation, Indicates The first set of agricultural equipment The local processing delay of each subtask, Indicates The first set of agricultural equipment The computing server selected by each subtask, represents a binary indicator variable, ,when hour, ,when hour, , represents the energy consumption required for task transmission, The expression is:

[0084] ,

[0085] Indicates The transmission power of agricultural equipment Indicates The first set of agricultural equipment The size of the subtask, Indicates Agricultural equipment and computing servers The channel capacity between Indicates The first set of agricultural equipment The computing server selected by each subtask, Indicates The first set of agricultural equipment The set of direct predecessor subtasks of subtasks, Indicates The first set of agricultural equipment subtask and The information exchange time of each subtask is , that is, when ,or hour, The value is 1, otherwise the value is 0.

[0086] In step S102 of this embodiment, in the agricultural Internet of Things assisted by the low-altitude platform base station, the advantages of considering the task calculation model, the task transmission model and the energy consumption model are:

[0087] First, by breaking down the tasks of each agricultural device into multiple smaller, easier-to-process subtasks, these subtasks can be processed in parallel on multiple computing nodes, shortening the task processing time and improving the task processing efficiency of the agricultural Internet of Things. At the same time, different subtasks may require different processing capabilities to store resources. Task decomposition allows the agricultural Internet of Things to allocate resources according to the specific needs of each subtask, thereby making more efficient use of edge computing resources. For applications that require fast responses, task segmentation and processing geographically close to the data source can significantly reduce data transmission time, thereby reducing the overall response delay of the agricultural Internet of Things.

[0088] Secondly, jointly considering the task computing model and the task transmission model can optimize the strategies of agricultural Internet of Things in computing resource allocation and data transmission, so that it can adapt to different channel conditions and computing requirements.

[0089] Finally, in the resource-constrained agricultural IoT environment, considering the energy consumption model can optimize the resource usage of agricultural equipment, extend the operating time of agricultural equipment, and reduce the number of frequent charging or battery replacement. By accurately calculating and predicting the energy consumption of each computing node, more effective energy management strategies can be implemented, such as energy harvesting, energy allocation, and sleep scheduling strategies, to ensure the priority execution of critical subtasks while limiting non-critical subtasks with higher energy consumption.

[0090] In step S103 of this embodiment, the selection of edge computing servers, the processing order of subtasks and energy consumption are jointly considered. In this embodiment, the task calculation model, task transmission model and energy consumption model obtained in step S102 are used to construct the total task delay minimization problem. The total task delay minimization problem can be modeled as:

[0091] (4),

[0092] st (4a),

[0093] (4b),

[0094] in, represents the problem of minimizing the total task processing delay, Indicates Agricultural equipment set The sum of the task processing delays of the subtasks, represents the set of all computing servers, , Indicates The first set of agricultural equipment The computing server selected by each subtask, represents the set of processing orders of all subtasks, , Indicates The first set of agricultural equipment The processing order number of each subtask, st represents the constraint, represents the total energy consumption of task offloading in the network model, represents the energy consumption threshold, Represents any value. Indicates The computing server selected for the first subtask set on the agricultural equipment, Indicates The computing server selected by the last subtask set on the agricultural equipment, constraint 4a is used to ensure that the total energy consumption of task offloading in the network model does not exceed the energy consumption threshold, and constraint 4b is used to limit the The first and last subtasks set on each agricultural device must use the computing resources of the agricultural device itself for local calculation. Indicates taking the maximum value.

[0095] The advantage of constructing a method to minimize the total task processing delay in step S103 of this embodiment is that it can significantly improve data processing efficiency and response speed, thereby making agricultural operations more accurate and timely. Reducing the total task processing delay means that data can be quickly transmitted from the collection point to the processing unit, and tasks can be processed and fed back in real time or near real time. This is particularly important for performing time-sensitive agricultural tasks, such as real-time monitoring of crop conditions, automatic adjustment of irrigation systems, or timely handling of pest and disease problems. In addition, fast data processing capabilities also help to improve the overall level of automation. In this setting, every unit of time saved may directly affect the growth environment and yield of the effect.

[0096] In step S104 of this embodiment, based on step S103 of this embodiment, a total task processing delay minimization problem is constructed, and a heuristic task processing scheme based on offloading priority can be used to solve the total task processing delay minimization problem, which specifically includes the following sub-steps:

[0097] Sub-step S1041: pre-sort all subtasks set on each agricultural equipment according to the initial order, that is, pre-sort according to the initial numbering order of all subtasks, and form the pre-sorting results of all subtasks in the network model into a subtask candidate set.

[0098] Furthermore, since it is difficult to directly solve the problem of minimizing the total task processing delay of the model, in this embodiment, a heuristic algorithm including a directed acyclic graph is used to solve it. Specifically, in order to ensure the dependency between all subtasks, all subtasks contained in each agricultural equipment are pre-sorted in the initial order, and the expression is:

[0099] (5)

[0100] in, Indicates The first set of agricultural equipment The pre-sorting results of subtasks, Indicated in The set of subtasks to be processed after the subtask, Indicates The first set of agricultural equipment The pre-sorting results of subtasks, Indicates The subtask and the one after it The average information exchange time between subtasks is , Indicates subtask and The amount of information exchanged per subtask, Indicates Agricultural equipment and The channel capacity between edge computing servers, Indicates The edge computing server and The channel capacity between edge computing servers, Indicates processing The first set of agricultural equipment The average delay required for each subtask is , Indicates The first set of agricultural equipment The local processing delay of each subtask, Indicates The first set of agricultural equipment The subtask is The transmission and processing delays on the edge computing server.

[0101] And make , thus ensuring the dependencies between all subtasks. The dependencies between subtasks cannot be guaranteed.

[0102] Sub-step S1042: Prioritizing all subtasks in the subtask candidate set to obtain the priority processing results of all subtasks.

[0103] Furthermore, in order to optimize the processing of subtasks with high computational overhead and high energy consumption, while minimizing the total task processing delay and reducing the energy consumption of agricultural equipment, it is necessary to prioritize all subtasks in the subtask candidate set and obtain the priority processing result. The expression of the priority processing result is:

[0104] (6),

[0105] in, Indicates The first set of agricultural equipment The priority of each subtask, Indicates The first weight coefficient of agricultural equipment, Indicates The second weight coefficient of agricultural equipment, and satisfy , Indicates the unit time The energy consumption of agricultural equipment for task calculation, Indicates The transmission power of agricultural equipment Indicates The first set of agricultural equipment The size of each subtask.

[0106] Sub-step S1043: performing calculation processing on all sub-tasks in the network model according to the priority processing result.

[0107] In summary, the heuristic task processing algorithm based on offloading priority is summarized as shown in Table 1 below:

[0108] Table 1: Heuristic task processing algorithm based on offloading priority

[0109]

[0110] The advantages of constructing a heuristic task processing algorithm based on offloading priority in step S104 of this embodiment are:

[0111] First, the advantages of low-altitude platform base station assistance are comprehensively utilized, and the delay and energy consumption of all subtasks are fully considered. Subtasks with high computational overhead and high energy consumption are unloaded first, so as to minimize the execution delay of the total task and the energy consumption of agricultural equipment. At the same time, the algorithm first pre-sorts the subtasks, which can reduce the complexity of the algorithm.

[0112] Secondly, since there may be multiple local optima in the total task processing delay minimization problem constructed, traditional optimization methods often find it difficult to obtain the global optimum within a reasonable time frame. Therefore, the algorithm can effectively explore the solution space within an acceptable computing time to identify an approximate global optimum or a satisfactory local optimum.

[0113] Finally, the algorithm distributes data processing and analysis tasks to the edge of the network, thereby reducing the load on the central server and improving the responsiveness of the agricultural IoT system. This greatly improves the overall performance and efficiency of the agricultural IoT assisted by the low-altitude platform base station.

[0114] In order to verify the low-altitude platform base station-assisted IoT edge computing method proposed in this application, the following simulation experiment was carried out.

[0115] The network parameters used in this simulation experiment are: , , , dBm, m. , and KB. The number of CPU cycles required for each subtask is GHz, the CPU cycle number of each local device is 0.3GHz, and the CPU cycle number of each edge computing server installed on the low-altitude base station is 6GHz. In addition, the simulation results are The average value after .

[0116] When all the above network parameters are set the same, the low-altitude platform base station-assisted IoT edge computing method proposed in this application is compared with the other four comparison schemes.

[0117] The first comparison scheme is: using a single low-altitude platform base station for IoT edge computing;

[0118] The second comparison scheme is: using multiple mobile low-altitude platform base stations for IoT edge computing;

[0119] The third comparison scheme is: the case where the offloading task is divided into two subtasks;

[0120] The fourth comparison scheme is: dividing the offloading task into multiple subtasks without prioritizing them.

[0121] like Figure 4 As shown in Figure 1, Comparative Scheme 1 only uses a single low-altitude platform base station as the edge computing server and does not divide the task into multiple subtasks. Comparative Schemes 2, 3, and 4 all ignore the performance improvement brought by air-ground integrated communication. In addition, Comparative Scheme 4 shows the highest total task processing delay, which is mainly due to the limited computing power of the agricultural equipment itself. Figure 4 It can be seen that the algorithm proposed in this application can effectively enhance the task processing performance of the agricultural Internet of Things and improve the productivity and sustainability of agricultural practices.

[0122] like Figure 5 As shown in the figure, increasing the number of edge computing servers can further reduce the total task processing delay. The reason is that the agricultural Internet of Things assisted by low-altitude platform base stations can use more computing resources for task processing.

[0123] like Figure 6 As shown in the figure, different distribution forms of all agricultural equipment will also lead to different total task processing delays. Compared with uniform distribution, random distribution and strip distribution, cluster distribution shows lower total task processing delay. This is because edge computing servers can be effectively deployed in clusters, thereby enhancing the performance of offloading.

[0124] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0125] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0126] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and these modifications or substitutions should be included in the protection scope of the present application.

[0127] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary technical means in the art that are not disclosed in the present application.

Claims

1. A low-altitude platform base station-assisted IoT edge computing method, characterized in that: The IoT edge computing method includes the following steps: Construct a network model of agricultural Internet of Things assisted by a low-altitude platform base station, wherein the network model includes multiple low-altitude platform base stations and multiple agricultural equipment, each of which is respectively equipped with an edge computing server, and each of the agricultural equipment is provided with multiple subtasks with dependencies, and all the subtasks set on each of the agricultural equipment can be edge-computed on any edge computing server, or locally computed using the computing resources of the agricultural equipment itself; The set of all edge computing servers is defined as: l represents the lth edge computing server, L represents the number of edge computing servers, and the number of edge computing servers is equal to the number of low-altitude platform base stations; The set of all the agricultural equipment is defined as: d represents the dth agricultural equipment, and D represents the number of agricultural equipment; The set of all the subtasks set on the d-th agricultural equipment is defined as: Collection V d The elements in represent a subtask set on the d-th agricultural equipment, |V d | represents the number of all subtasks set on the d-th agricultural equipment; the processing process of all the subtasks set on the d-th agricultural equipment is defined as a directed acyclic graph G d , G d =(V d ,E d ), E d represents the dependencies between all subtasks set on the d-th agricultural equipment; The relevant network parameters include: the number of edge computing servers L, the number of agricultural equipment D, the number of all subtasks set on the dth agricultural equipment |V d | and the size S(d,v) of the vth subtask set on the dth agricultural equipment; Using the relevant network parameters of the network model, respectively constructing a task calculation model, a task transmission model and an energy consumption model; The task calculation model is used to calculate the task processing delay. The expression of the task processing delay is: Among them, θ(d,v) represents the task processing delay of the vth subtask set on the dth agricultural equipment, represents the computing server selected by the vth subtask set on the dth agricultural device, The value range of is the union of the d-th agricultural equipment and L edge computing servers. Indicates that the vth subtask set on the dth agricultural equipment is offloaded to the computing server The actual initial time on Indicates that the vth subtask set on the dth agricultural equipment is offloaded to the computing server The theoretical initial time on Represents a computing server The earliest time to prepare to process the vth subtask set on the dth agricultural equipment, Pre{v} represents the set of direct predecessor subtasks of the vth subtask set on the dth agricultural equipment, θ(d,v′) represents the task processing delay of the v′th subtask set on the dth agricultural equipment, represents the computing server selected by the v′th subtask set on the dth agricultural equipment, represents the information exchange time between the vth subtask and the v′th subtask set on the dth agricultural equipment, I(v,v′) represents the amount of information exchange between the vth subtask and the v′th subtask. Represents a computing server and computing servers The channel capacity between Indicates that the vth subtask set on the dth agricultural equipment is on the computing server The transmission and processing delay on the network, min means the minimum value, and max means the maximum value; Constructing a total task processing delay minimization problem using the task calculation model, the task transmission model and the energy consumption model; The total task processing delay minimization problem is solved to obtain priority processing results of all the subtasks in the network model, and all the subtasks in the network model are calculated and processed according to the priority processing results.

2. According to the low-altitude platform base station-assisted Internet of Things edge computing method of claim 1, it is characterized in that: The task transmission model is used to calculate the path loss, and the expression of the path loss is: Where PL(l,d) represents the path loss between the lth edge computing server and the dth agricultural equipment. represents the probability of a line-of-sight component between the lth edge computing server and the dth agricultural equipment, ω1 represents the first S-band constant, ω2 represents the second S-band constant, h l represents the height of the low-altitude platform base station with the lth edge computing server, ψ l,d represents the projection distance between the low-altitude platform base station with the lth edge computing server and the dth agricultural equipment on the horizontal plane, represents the probability of a non-line-of-sight component between the lth edge computing server and the dth agricultural equipment, represents the loss value of the line-of-sight component between the lth edge computing server and the dth agricultural equipment, χ LoS represents the additional path loss due to the presence of line-of-sight component, f c represents the carrier frequency, c represents the speed of light, represents the loss value of the non-line-of-sight component between the lth edge computing server and the dth agricultural equipment, χ NLoS Indicates the additional path loss of the non-line-of-sight component.

3. The low-altitude platform base station-assisted IoT edge computing method according to claim 1 is characterized in that: The energy consumption model is used to calculate the total energy consumption of task offloading in the network model. The expression of the total energy consumption of task offloading is: AND tot =And trans +E com (3) Among them, E tot represents the total energy consumption of task offloading in the network model, E com represents the energy consumption required for task calculation, Φ d represents the energy consumption of the d-th agricultural equipment for task calculation per unit time, θ2(d,v,d) represents the local processing delay of the v-th subtask set on the d-th agricultural equipment, represents the computing server selected by the vth subtask set on the dth agricultural device, ζ {·} represents a binary indicator variable, when hour, when hour, E trans represents the energy consumption required for task transmission, P d represents the transmission power of the d-th agricultural device, S(d,v) represents the size of the v-th subtask set on the d-th agricultural device, Represents the dth agricultural equipment and computing server The channel capacity between represents the computing server selected by the v′th subtask set on the dth agricultural device, Pre{v} represents the set of direct predecessor subtasks of the vth subtask set on the dth agricultural device, represents the information exchange time between the vth subtask and the v′th subtask set on the dth agricultural equipment, When and or and hour, The value is 1, otherwise the value is 0.

4. The low-altitude platform base station-assisted IoT edge computing method according to claim 1, characterized in that: The expression of the total task processing delay minimization problem is: s.t.E tot ≤E th (4a) in, represents the total task processing delay minimization problem, θ(d,|V d |) represents the |V set on the dth agricultural equipment d The sum of the task processing delays of | subtasks, represents the set of all computing servers, represents the computing server selected by the vth subtask set on the dth agricultural device, represents the set of processing orders of all subtasks, represents the processing sequence number of the vth subtask set on the dth agricultural equipment, st represents the constraint, E tot represents the total energy consumption of task offloading in the network model, E th represents the energy consumption threshold, Represents any value. represents the computing server selected by the first subtask set on the d-th agricultural device, It represents the computing server selected for the last subtask set on the d-th agricultural device. Constraint 4a is used to ensure that the total energy consumption of task unloading in the network model does not exceed the energy consumption threshold. Constraint 4b is used to limit that the first subtask and the last subtask set on the d-th agricultural device must use the computing resources of the agricultural device itself for local calculation. Max represents the maximum value.

5. According to the low-altitude platform base station-assisted Internet of Things edge computing method of claim 4, it is characterized in that: The total task processing delay minimization problem is solved to obtain priority processing results of all the subtasks in the network model, and the steps of calculating and processing all the subtasks in the network model according to the priority processing results include: Pre-sorting all the subtasks set on each of the agricultural equipment according to an initial order, and forming a subtask candidate set with the pre-sorting results of all the subtasks in the network model; Prioritizing all the subtasks in the subtask candidate set to obtain the priority processing results of all the subtasks; All the subtasks in the network model are calculated and processed according to the priority processing result.

6. The low-altitude platform base station-assisted IoT edge computing method according to claim 5 is characterized in that: The expression for pre-sorting all the subtasks set on the agricultural equipment in the initial order is: Among them, Level(d,v) represents the pre-sorting result of the vth subtask set on the dth agricultural equipment, Seq{v} represents the set of subtasks to be processed after the vth subtask, and Level(d,v′) represents the pre-sorting result of the v′th subtask set on the dth agricultural equipment. represents the average information exchange time between the vth subtask and the v′th subtask after it, I(v,v′) represents the amount of information exchange between the vth subtask and the v′th subtask, G(d,l) represents the channel capacity between the dth agricultural equipment and the lth edge computing server, and G(l,l′) represents the channel capacity between the lth edge computing server and the l′th edge computing server. represents the average delay required to process the vth subtask set on the dth agricultural equipment, θ2(d,v,d) represents the local processing delay of the vth subtask set on the dth agricultural device, and θ2(d,v,l) represents the transmission and processing delay of the vth subtask set on the dth agricultural device on the lth edge computing server.

7. The low-altitude platform base station-assisted IoT edge computing method according to claim 6 is characterized in that: The expression of the priority processing result is: Among them, Θ(d,v) represents the priority of the vth subtask set on the dth agricultural equipment, represents the first weight coefficient of the d-th agricultural equipment, represents the second weight coefficient of the d-th agricultural equipment and satisfies Φ d represents the energy consumption of the d-th agricultural equipment for task calculation per unit time, P d represents the transmission power of the d-th agricultural device, and S(d,v) represents the size of the v-th subtask set on the d-th agricultural device.

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