A collaborative edge computing service range optimization method and device

Abstract

The present invention provides a method and device for optimizing the service range of collaborative edge computing. The method includes: determining a target optimization model that satisfies a preset first constraint condition based on a first channel model between an unmanned aerial vehicle and a user terminal, a second channel model between an unmanned aerial vehicle and a base station, an upload rate model between the user terminal and the unmanned aerial vehicle, and a transmission rate model of the unmanned aerial vehicle on the second channel; and determining a resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption satisfies a preset energy consumption constraint condition based on the target optimization model. The method of the present invention is adopted, by considering the collaboration between an unmanned aerial vehicle carrying an edge computing server and a ground base station, and forwarding the user-side tasks to the ground base station through the local edge computing and relaying of the unmanned aerial vehicle for optimized processing, thereby improving the utilization of resources and maximizing the number of user terminals served, that is, maximizing the service coverage of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and more particularly to a method and device for optimizing a collaborative edge computing service range. In addition, the present invention also relates to an electronic device and a processor-readable storage medium. Background Art

[0002] Edge computing technology is to place the edge server on the user side, so that the user side can directly unload the data to be calculated to the edge server for calculation and return the calculation results, thereby shortening the calculation delay. It is one of the key technologies of 5G (5th Generation Mobile Communication Technology). With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicles equipped with edge servers have gradually been applied to various industries such as cargo transportation, aerial photography, search and rescue, etc. In the prior art, in the process of performing its own tasks, unmanned aerial vehicles will use the resources of the edge server they carry to complete each calculation link in the task. However, in the prior art, the actual problem of unmanned aerial vehicle-assisted edge computing is not considered, that is, when the number of users is large, the amount of unloaded tasks is large, and the complexity of the tasks is high, the unmanned aerial vehicle may not be able to achieve full coverage of ground user-side services. Therefore, how to design a stable and efficient unmanned aerial vehicle and ground base station collaborative edge computing service range optimization solution to improve the coverage of ground user-side services has become a problem that needs to be solved urgently. Summary of the invention

[0003] To this end, the present invention provides a collaborative edge computing service range optimization method and device to solve the problems in the prior art that the unmanned aerial vehicle may not be able to achieve full coverage of ground user-end services when the number of unmanned aerial vehicle-assisted edge computing users is large, the amount of offloaded tasks is high, and the task complexity is high, the service coverage range is highly limited, and the resource utilization rate is low.

[0004] In a first aspect, the present invention provides a collaborative edge computing service range optimization method, comprising: determining a target optimization model that satisfies a preset first constraint condition based on a first channel model between an unmanned aerial vehicle and a user terminal, a second channel model between the unmanned aerial vehicle and a base station, an upload rate model between the user terminal and the unmanned aerial vehicle, and a transmission rate model of the unmanned aerial vehicle in the second channel;

[0005] Based on the target optimization model, a corresponding resource allocation strategy is determined when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset energy consumption constraint condition.

[0006] In one embodiment, based on the target optimization model, the corresponding resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset energy consumption constraint conditions is determined, specifically including: using the preset block processing rules to analyze the target optimization model, and determining the corresponding resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset conditions.

[0007] In one embodiment, the target optimization model is analyzed by using a preset block processing rule to determine a corresponding resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset conditions, specifically including:

[0008] Initializing the initial plane position coordinates of the unmanned aerial vehicle;

[0009] Converting the service coefficient corresponding to the unmanned aerial vehicle from an integer variable to a continuous variable;

[0010] Based on the continuous variable, determine the target optimization model to be analyzed that satisfies the preset second constraint condition and corresponds to the target optimization model, and analyze the target optimization model to be analyzed based on the initialized initial plane position coordinates to obtain a corresponding service coefficient;

[0011] Arrange the service coefficients in descending order, set the service coefficients with large values ​​to 1, and set the service coefficients with small values ​​to 0, and re-analyze the target optimization model to be analyzed to determine the initial resource allocation strategy corresponding to the situation when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset conditions;

[0012] Wherein, the service coefficient is set to 1 to indicate that the unmanned aerial vehicle serves the target user terminal; the service coefficient is set to 0 to indicate that the unmanned aerial vehicle does not serve the target user terminal;

[0013] Using a preset genetic algorithm model or equally dividing the flight area corresponding to the unmanned aerial vehicle, taking the coordinates of the center point of each equally divided area as the new plane position coordinates corresponding to the unmanned aerial vehicle, and updating the initial plane position coordinates;

[0014] If the optimization model of the target to be analyzed has not converged or the number of iterations has not reached the preset iteration threshold, the optimization model of the target to be analyzed is iteratively analyzed using the updated plane position coordinates; if the optimization model of the target to be analyzed converges or the number of iterations reaches the preset iteration threshold, the corresponding resource allocation strategy is determined when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset conditions.

[0015] In one embodiment, the collaborative edge computing service range optimization method further includes: based on the plane position coordinates of the user terminal, the plane position coordinates of the unmanned aerial vehicle, and the plane position coordinates of the corresponding base station, respectively determining a first channel model between the unmanned aerial vehicle and the user terminal and a second channel model between the unmanned aerial vehicle and the base station.

[0016] In one embodiment, the collaborative edge computing service range optimization method further includes: determining the upload rate model between the user terminal and the unmanned aerial vehicle based on the first channel bandwidth of the unmanned aerial vehicle, the transmission power of the user terminal, the noise power at the unmanned aerial vehicle and the first channel model.

[0017] In one embodiment, the collaborative edge computing service range optimization method further includes: determining the transmission rate model of the unmanned aerial vehicle on the second channel based on the second channel bandwidth of the unmanned aerial vehicle, the transmission power of the unmanned aerial vehicle when the unmanned aerial vehicle sends a user-end task to the base station through the second channel, and the second channel model.

[0018] In a second aspect, the present invention further provides a collaborative edge computing service range optimization device, comprising: a target optimization model determination unit, configured to determine a target optimization model that satisfies a preset first constraint condition based on a first channel model between an unmanned aerial vehicle and a user terminal, a second channel model between the unmanned aerial vehicle and a base station, an upload rate model between the user terminal and the unmanned aerial vehicle, and a transmission rate model of the unmanned aerial vehicle on the second channel;

[0019] A resource allocation strategy determination unit is used to determine, based on the target optimization model, a corresponding resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets a preset energy consumption constraint condition.

[0020] In one embodiment, the resource allocation strategy determination unit is used to: analyze the target optimization model using a preset block processing rule to determine the corresponding resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets preset conditions.

[0021] In one embodiment, the resource allocation strategy determination unit is specifically configured to:

[0022] Initializing the initial plane position coordinates of the unmanned aerial vehicle;

[0023] Converting the service coefficient corresponding to the unmanned aerial vehicle from an integer variable to a continuous variable;

[0024] Based on the continuous variable, determine the target optimization model to be analyzed that satisfies the preset second constraint condition and corresponds to the target optimization model, and analyze the target optimization model to be analyzed based on the initialized initial plane position coordinates to obtain a corresponding service coefficient;

[0025] Arrange the service coefficients in descending order, set the service coefficients with large values ​​to 1, and set the service coefficients with small values ​​to 0, and re-analyze the target optimization model to be analyzed to determine the initial resource allocation strategy corresponding to the situation when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset conditions;

[0026] Wherein, the service coefficient is set to 1 to indicate that the unmanned aerial vehicle serves the target user terminal; the service coefficient is set to 0 to indicate that the unmanned aerial vehicle does not serve the target user terminal;

[0027] Using a preset genetic algorithm model or equally dividing the flight area corresponding to the unmanned aerial vehicle, taking the coordinates of the center point of each equally divided area as the new plane position coordinates corresponding to the unmanned aerial vehicle, and updating the initial plane position coordinates;

[0028] If the optimization model of the target to be analyzed has not converged or the number of iterations has not reached the preset iteration threshold, the optimization model of the target to be analyzed is iteratively analyzed using the updated plane position coordinates; if the optimization model of the target to be analyzed converges or the number of iterations reaches the preset iteration threshold, the corresponding resource allocation strategy is determined when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset conditions.

[0029] In one embodiment, the collaborative edge computing service range optimization device further includes: a second channel model determination unit, which is used to determine the first channel model between the unmanned aerial vehicle and the user terminal and the second channel model between the unmanned aerial vehicle and the base station based on the plane position coordinates of the user terminal, the plane position coordinates of the unmanned aerial vehicle, and the plane position coordinates of the corresponding base station.

[0030] In one embodiment, the collaborative edge computing service range optimization method also includes: an upload rate model determination unit, used to determine the upload rate model between the user terminal and the unmanned aerial vehicle based on the first channel bandwidth of the unmanned aerial vehicle, the transmission power of the user terminal, the noise power at the unmanned aerial vehicle and the first channel model.

[0031] In one embodiment, the collaborative edge computing service range optimization device also includes: a sending rate model determination unit, which is used to determine the sending rate model of the unmanned aerial vehicle on the second channel based on the second channel bandwidth of the unmanned aerial vehicle, the sending power of the unmanned aerial vehicle when the unmanned aerial vehicle sends a user-end task to the base station through the second channel, and the second channel model.

[0032] In a third aspect, the present invention also provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the collaborative edge computing service scope optimization method as described in any one of the above items are implemented.

[0033] In a fourth aspect, the present invention also provides a processor-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the collaborative edge computing service scope optimization method as described in any one of the above items are implemented.

[0034] The collaborative edge computing service range optimization method described in the present invention is used to address the practical problem of unmanned aerial vehicle-assisted edge computing, namely, the defect that the unmanned aerial vehicle may not be able to achieve full coverage of ground user-end services when the number of users is large, the amount of unloaded tasks, and the complexity of computing tasks are high. By considering the collaboration between the unmanned aerial vehicle carrying the edge computing server and the ground base station, the user-end tasks are forwarded to the ground base station for processing through the local edge computing and relay of the unmanned aerial vehicle, the complexity of task processing is reduced, the number of ground user ends served is maximized, that is, the coverage of the unmanned aerial vehicle service is maximized, and the utilization of resources is improved at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A flowchart of a collaborative edge computing service range optimization method provided by an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of an unmanned aerial vehicle edge computing network system provided by an embodiment of the present invention;

[0038] Figure 3 A schematic diagram of a flow chart of analyzing the target optimization model using a preset block processing rule provided in an embodiment of the present invention;

[0039] Figure 4 A schematic diagram of the relationship between the number of user terminals served by an unmanned aerial vehicle and the maximum computing capacity of the unmanned aerial vehicle provided in an embodiment of the present invention;

[0040] Figure 5 A schematic diagram of the relationship between the number of user terminals served by an unmanned aerial vehicle and the energy consumption weight provided in an embodiment of the present invention;

[0041] Figure 6 A schematic diagram of the relationship between energy consumption and energy consumption weights of an unmanned aerial vehicle provided in an embodiment of the present invention;

[0042] Figure 7 A schematic diagram of the relationship between the deployment position of the unmanned aerial vehicle and the energy consumption weight value of 1 provided in an embodiment of the present invention;

[0043] Figure 8 A schematic diagram of the relationship between the deployment position of the unmanned aerial vehicle and the energy consumption weight value of 3 provided in an embodiment of the present invention;

[0044] Fig. 9 A schematic diagram of the relationship between the deployment position of the unmanned aerial vehicle and the energy consumption weight of 5 provided in an embodiment of the present invention;

[0045] Fig.10 A schematic diagram of the relationship between the deployment position of the unmanned aerial vehicle and the energy consumption weight of 7 provided in an embodiment of the present invention;

[0046] Fig.11 A schematic diagram of the structure of a collaborative edge computing service range optimization device provided by an embodiment of the present invention;

[0047] Fig.12 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] The following is a detailed description of the embodiment of the collaborative edge computing service range optimization method described in the present invention. Figure 1 As shown, it is a flow chart of the collaborative edge computing service range optimization method provided by an embodiment of the present invention, and the implementation process includes the following steps:

[0050] Step 101: Based on a first channel model between an unmanned aerial vehicle and a user terminal, a second channel model between the unmanned aerial vehicle and a base station, an upload rate model between the user terminal and the unmanned aerial vehicle, and a sending rate model of the unmanned aerial vehicle in the second channel, determine a target optimization model that satisfies a preset first constraint condition.

[0051] like Figure 2 As shown, the unmanned aerial vehicle edge computing network system used in the present invention includes at least one unmanned aerial vehicle, at least one base station and K ground user terminals. The ground user terminals include user 1, user 2, user 3, etc. The coordinates of each user terminal are respectively represented as in Let w 0 represents the base station coordinates. The UAV coordinates are represented by q = (x q ,y q ,H), where H is the flight altitude of the UAV.

[0052] In the specific implementation process, before executing this step, it is necessary to determine the first channel model between the unmanned aerial vehicle and the user terminal and the second channel model between the unmanned aerial vehicle and the base station based on the plane position coordinates of the user terminal, the plane position coordinates of the unmanned aerial vehicle, and the plane position coordinates of the corresponding base station. The first channel model is a channel power gain algorithm model between the unmanned aerial vehicle and the user terminal, and the second channel model is a channel power gain algorithm model between the unmanned aerial vehicle and the base station.

[0053] Specifically, first, the channel power gain between the UAV and the kth ground user and the base station is modeled separately (assuming that the channel has reciprocity), and the first channel model h between the UAV and the user terminal is determined. k and a second channel model h between the unmanned aerial vehicle and the base station u They are:

[0054]

[0055]

[0056] Among them, β 0 is the path loss caused by each meter of transmission distance; w k is the user-side coordinate, in w 0 represents the base station coordinates; q = (x q ,y q ,H) are the coordinates of the UAV, where H is the flight altitude of the UAV.

[0057] In the specific implementation process, the tasks to be offloaded at the user end k can be described as a three-element array (l k ,c k ,t k ), where the array elements represent the amount of tasks to be offloaded, the computational complexity of the task (the computing power required to process a unit of task), and the maximum delay in completing the task. Without loss of generality, let Let μ k represents the service factor of the kth user, when μ k =0 means that the UAV does not serve the kth user, that is, the kth user does not offload tasks to the UAV; when μ k =1 means that the UAV serves the k-th user, that is, the k-th user offloads the task to the UAV.

[0058] At the same time, based on the first channel bandwidth of the unmanned aerial vehicle, the transmission power of the user terminal, the noise power at the unmanned aerial vehicle and the first channel model, an upload rate model between the user terminal and the unmanned aerial vehicle is determined.

[0059] Specifically, the ground user terminal and the unmanned aerial vehicle upload computing task data through frequency division multiple access (FDMA). The unmanned aerial vehicle has a total of N sub-channels (i.e., the first channel), and the corresponding upload rate model is:

[0060]

[0061] Among them, p k Send power to users; B is the noise power at the UAV; w Hz is the bandwidth of each subchannel (i.e. the first channel); h k It is a first channel model between the unmanned aerial vehicle and the user terminal.

[0062] In addition, it is also necessary to determine the sending rate model of the unmanned aerial vehicle in the second channel based on the second channel bandwidth of the unmanned aerial vehicle, the sending power of the unmanned aerial vehicle when the unmanned aerial vehicle sends a user-end task to the base station through the second channel, and the second channel model.

[0063] Specifically, the unmanned aerial vehicle is equipped with an edge server, but due to the limited carrying capacity of the unmanned aerial vehicle, the computing power of the server it carries is relatively weak. The unmanned aerial vehicle can communicate with the ground base station. Assuming that there is no effective link between the base station and the ground user terminal due to occlusion, the unmanned aerial vehicle can offload part of the computing task data sent by the user terminal to the ground base station for processing through the second channel. The base station is equipped with a server with strong computing power to assist in computing task processing. The FDMA method is still used between the unmanned aerial vehicle and the base station for task offloading. Without loss of generality, it is assumed that it has N sub-channels (i.e., the second channel), and the bandwidth of each sub-channel is B. u Hz. The transmission rate of the UAV in subchannel k is:

[0064]

[0065] Among them, p u [k] represents the UAV transmission power when sending a task to user k (using the kth subchannel); h u A second channel model between the UAV and the base station; B is the noise power at the UAV; u is the bandwidth of each sub-channel (ie, the second channel).

[0066] If the ground user receives the service, the time it takes to unload to the UAV is:

[0067]

[0068] It should be noted that since the calculation results are often small and the ground base station has strong computing power, the calculation result return time and the base station calculation time can be ignored in the actual implementation process.

[0069] After determining the first channel model between the unmanned aerial vehicle and the user terminal, the second channel model between the unmanned aerial vehicle and the base station, the upload rate model between the user terminal and the unmanned aerial vehicle, and the sending rate model of the unmanned aerial vehicle in the second channel, in this step, the target optimization model can be further determined based on the first channel model, the second channel model, the upload rate model and the sending rate model.

[0070] Specifically, a target optimization model P corresponding to the optimization problem of maximizing the coverage of the unmanned aerial vehicle is established according to the first channel model, the second channel model, the user upload rate model to the unmanned aerial vehicle, and the unmanned aerial vehicle data transmission rate model. 1 .

[0071] The target optimization model expression is as follows:

[0072]

[0073]

[0074]

[0075]

[0076] C 4 :x min ≤q x ≤x max ,y min ≤q y ≤y max

[0077] Where p = {p u [1],p u [1],…,p u [K]}; f = [f 1 ,f 2 ,…,f K ];μ=[μ 1 ,μ 2 ,…,μ K ]; q = [q x ,q y ]; is the energy consumption of the unmanned aerial vehicle; η is the energy consumption factor per unit computing power; f max With p max are the maximum computing power and transmission power of the UAV respectively; f k is the computing power allocated by the UAV to user k; ω≥0 is the energy consumption weight. The larger the ω is, the more attention the UAV pays to computing energy consumption while maximizing the coverage; θ≥0 is the magnitude normalization factor, which is used to unify the magnitude of the two quantities, the computing energy consumption of the UAV and the number of users served; The total number of users served by UAVs; Calculate energy consumption for unmanned aerial vehicles, Offload energy consumption for UAV missions; Constraint C 1 It means that the service user-side offloading task can be completed by the UAV through local calculation and task offloading to the base station within the required time, where represents the user-side task volume (or business volume) calculated by the UAV, (T-τ k )R u [k] represents the user-side task volume (or business volume) unloaded by the UAV to the base station; constraint C 2 is the service coefficient μ k The constraint, where μ k represents the service factor of the kth user, Indicates that each user can choose to serve or not serve, and the maximum number of service users is less than N; constraint C3 is the causal constraint of the local computing power and transmission power of the UAV, where Indicates that the maximum computing power allocated to the UAV cannot exceed its maximum value f max , Indicates that the UAV transmission power cannot exceed its maximum allowable transmission power p max ; Constraint C 4 Deploy position constraints for the UAV, x min ,x max ,y min ,y max Flying boundaries for unmanned aerial vehicles.

[0078] In this embodiment of the present invention, the first constraint condition includes the constraint C 1 , Constraint C 2 , Constraint C 3 And the constraint C 4 .

[0079] Step 102: Based on the target optimization model, determine the corresponding resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset energy consumption constraint condition.

[0080] In this step, the target optimization model can be analyzed according to the preset block processing rules to determine the resource allocation strategy corresponding to the situation when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset conditions. That is, first initialize the initial plane position coordinates of the unmanned aerial vehicle, and convert the service coefficient corresponding to the unmanned aerial vehicle from an integer variable to a continuous variable. Based on the continuous variable, determine the target optimization model to be analyzed that meets the preset second constraint condition corresponding to the target optimization model, and analyze the target optimization model to be analyzed based on the initialized initial plane position coordinates to obtain the corresponding service coefficient. Arrange the service coefficients in order from large to small, set the service coefficient with a large value to 1, and set the service coefficient with a small value to 0, and re-analyze the target optimization model to be analyzed to determine the initial resource allocation strategy corresponding to the situation when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset conditions. Wherein, the service coefficient is set to 1 to indicate that the unmanned aerial vehicle serves the target user end; the service coefficient is set to 0 to indicate that the unmanned aerial vehicle does not serve the target user end. Furthermore, the flight area corresponding to the UAV is divided equally by using a preset genetic algorithm model, and the center point coordinates of each equally divided area are used as the new plane position coordinates corresponding to the UAV to update the initial plane position coordinates. If the optimization model of the target to be analyzed has not converged or the number of iterations has not reached the preset iteration threshold, the plane position coordinates obtained after the update are used to iteratively analyze the optimization model of the target to be analyzed; if the optimization model of the target to be analyzed has converged or the number of iterations has reached the preset iteration threshold, the resource allocation strategy corresponding to the situation where the service coverage of the UAV is maximized and the energy consumption meets the preset conditions is determined. Among them, the second constraint condition includes the constraint C 1 , Constraint C 3 And the constraint C 5 .

[0081] Specifically, Figure 3 As shown, the analysis process of the target optimization model can be determined as a non-convex problem, and a block solution method can be used. Step 1: Initialize the initial plane position coordinate q of the unmanned aerial vehicle; Step 2: Convert the integer variable Relax to a continuous variable Step 3: Solve the relaxed convex optimization problem based on the continuous variables to determine the target optimization model P to be analyzed that satisfies the preset second constraint condition corresponding to the target optimization model 2 , the expression corresponding to the target optimization model to be analyzed is as follows:

[0082]

[0083] sC 1 ,C 3,

[0084]

[0085] Step 4: Analyze the target optimization model to be analyzed based on the initialized initial plane position coordinates to obtain the corresponding service coefficient μ, arrange the μ solved in the third step in order of size, and replace the service coefficients with large values ​​one by one. Set to 1, and the service coefficient smaller than this value is set to 0, and bring it into the target optimization model P to be analyzed 2 (That is, question P 2 ) is re-solved to obtain the maximum feasible solution of the objective function (i.e., the target optimization model to be analyzed); the maximum feasible solution is the initial resource allocation strategy corresponding to the situation when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset conditions; the fifth step: using the genetic algorithm model or evenly dividing the flight area of ​​the unmanned aerial vehicle into blocks, taking the center point coordinates of each area as the new plane position coordinates of the unmanned aerial vehicle, and updating the initial plane position coordinates q; the sixth step: if the algorithm of the target optimization model to be analyzed has not converged or the number of iterations has not reached the maximum value, then return to the second step; otherwise, the algorithm ends, at this time, determine the optimal resource allocation strategy corresponding to the situation when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset conditions. It should be noted that the same identification parameters contained in all the above-mentioned formulas represent the same physical meanings, which will not be repeated here one by one.

[0086] In an actual implementation process, the target optimization model proposed by the present invention is simulated. Without loss of generality, the CPU operating frequency of the unmanned aerial vehicle is used as the computing power measurement index. The basic simulation parameters can be set as: min =0,x max =30,y min =0,y max =30, f max With p max 0.1GHz and 1W respectively. N=10,K=10,B u 200kHz, B w 50kHz, p k 10mW,l k is 100000 bits, c k is 1000, θ is 100. Figure 4 It can be seen that as the amount of user computing tasks increases, the number of user terminals served by the UAV (i.e., service coverage) decreases. As the computing power of the UAV increases, its coverage increases. Figure 5 and Figure 6 It can be seen that as the energy consumption weighted coefficient increases, the number of users served by the UAV and the computing energy consumption of the UAV both decrease. Figure 7-10It can be seen that with the increase of energy consumption weight, the deployment location of UAVs will tend more and more towards the base station location in order to save computing energy, and the number of service users will decrease.

[0087] The collaborative edge computing service range optimization method described in the embodiment of the present invention is adopted to consider the resource scheduling problem modeling method of maximizing the service coverage of a single unmanned aerial vehicle for multiple ground user terminals in collaboration between the base station and the unmanned aerial vehicle while taking into account computing energy consumption. This is aimed at the practical problem of unmanned aerial vehicle-assisted edge computing, that is, the defect that the unmanned aerial vehicle may not be able to achieve full coverage of ground user-terminal services when the number of users is large, the amount of unloaded tasks is large, and the task complexity is high. By considering the collaboration between the unmanned aerial vehicle carrying the edge computing server and the ground base station, the user-end tasks are forwarded to the ground base station for processing through the local edge computing and relay of the unmanned aerial vehicle, which reduces the complexity of task processing and maximizes the number of ground user terminals served, that is, maximizes the service coverage of the unmanned aerial vehicle.

[0088] Corresponding to the above-mentioned method for optimizing the service range of collaborative edge computing located on the base station side, the present invention also provides a device for optimizing the service range of collaborative edge computing located on the base station side. Since the embodiment of the device is similar to the above-mentioned method embodiment, the description is relatively simple. For relevant parts, please refer to the description of the above-mentioned method embodiment. The embodiment of the device for optimizing the service range of collaborative edge computing described below is only illustrative. Please refer to Fig.11 As shown, it is a structural diagram of a collaborative edge computing service range optimization device provided by an embodiment of the present invention.

[0089] The collaborative edge computing service range optimization device described in the present invention includes the following parts:

[0090] The target optimization model determining unit 1101 is used to determine a target optimization model that satisfies a preset first constraint condition based on a first channel model between the UAV and the user terminal, a second channel model between the UAV and the base station, an upload rate model between the user terminal and the UAV, and a transmission rate model of the UAV on the second channel;

[0091] The resource allocation strategy determination unit 1102 is used to determine, based on the target optimization model, a corresponding resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets a preset energy consumption constraint condition.

[0092] The collaborative edge computing service range optimization device described in the embodiment of the present invention is used to address the practical problem of unmanned aerial vehicle-assisted edge computing, namely, the defect that the unmanned aerial vehicle may not be able to achieve full coverage of ground user-end services when the number of users is large, the amount of unloaded tasks is large, and the task complexity is high. By considering the collaboration between the unmanned aerial vehicle carrying the edge computing server and the ground base station, the user-end tasks are forwarded to the ground base station for processing through the local edge computing and relay of the unmanned aerial vehicle, the complexity of task processing is reduced, the number of ground user ends served is maximized, that is, the coverage of the unmanned aerial vehicle service is maximized, and the utilization of resources is improved at the same time.

[0093] Corresponding to the collaborative edge computing service range optimization method provided above, the present invention also provides an electronic device. Since the embodiment of the electronic device is similar to the above method embodiment, the description is relatively simple. For relevant parts, please refer to the description of the above method embodiment. The electronic device described below is only exemplary. Fig.12 As shown, it is a schematic diagram of the physical structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may include: a processor 1201, a memory 1202 and a communication bus 1203, wherein the processor 1201 and the memory 1202 complete mutual communication through the communication bus 1203, and communicate with the outside through the communication interface 1204. The processor 1201 can call the logic instructions in the memory 1202 to execute the collaborative edge computing service range optimization method. The method includes: based on the first channel model between the unmanned aerial vehicle and the user terminal, the second channel model between the unmanned aerial vehicle and the base station, the upload rate model between the user terminal and the unmanned aerial vehicle, and the transmission rate model of the unmanned aerial vehicle in the second channel, determine the target optimization model that meets the preset first constraint condition; based on the target optimization model, determine the corresponding resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset energy consumption constraint condition.

[0094] In addition, the logic instructions in the above-mentioned memory 1202 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a storage chip, a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk.

[0095] On the other hand, an embodiment of the present invention further provides a computer program product, the computer program product comprising a computer program stored on a processor-readable storage medium, the computer program comprising program instructions, and when the program instructions are executed by a computer, the computer can execute the collaborative edge computing service range optimization method provided by the above-mentioned method embodiments. The method comprises: based on a first channel model between an unmanned aerial vehicle and a user terminal, a second channel model between the unmanned aerial vehicle and a base station, an upload rate model between the user terminal and the unmanned aerial vehicle, and a transmission rate model of the unmanned aerial vehicle on the second channel, determining a target optimization model that satisfies a preset first constraint condition; based on the target optimization model, determining a corresponding resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption satisfies a preset energy consumption constraint condition.

[0096] On the other hand, an embodiment of the present invention further provides a processor-readable storage medium, on which a computer program is stored, and when the computer program is executed by the processor, it is implemented to execute the collaborative edge computing service range optimization method provided by the above embodiments. The method includes: based on the first channel model between the unmanned aerial vehicle and the user terminal, the second channel model between the unmanned aerial vehicle and the base station, the upload rate model between the user terminal and the unmanned aerial vehicle, and the transmission rate model of the unmanned aerial vehicle on the second channel, determining a target optimization model that meets the preset first constraint condition; based on the target optimization model, determining the corresponding resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset energy consumption constraint condition.

[0097] The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drive (SSD)), etc.

[0098] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0099] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A collaborative edge computing service range optimization method, characterized in that: include: Determine a target optimization model that satisfies a preset first constraint condition based on a first channel model between the unmanned aerial vehicle and a user terminal, a second channel model between the unmanned aerial vehicle and a base station, an upload rate model between the user terminal and the unmanned aerial vehicle, and a transmission rate model of the unmanned aerial vehicle in the second channel; Based on the target optimization model, determining a corresponding resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset energy consumption constraint condition; The determining, based on the target optimization model, a corresponding resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset energy consumption constraint condition specifically includes: initializing the initial plane position coordinates of the unmanned aerial vehicle; Converting the service coefficient corresponding to the unmanned aerial vehicle from an integer variable to a continuous variable; Based on the continuous variable, determine the target optimization model to be analyzed that satisfies the preset second constraint condition and corresponds to the target optimization model, and analyze the target optimization model to be analyzed based on the initialized initial plane position coordinates to obtain a corresponding service coefficient; Arrange the service coefficients in descending order, set the service coefficients with large values ​​to 1, and set the service coefficients with small values ​​to 0, and re-analyze the target optimization model to be analyzed to determine the initial resource allocation strategy corresponding to the maximum coverage of the unmanned aerial vehicle service and the energy consumption meeting the preset conditions; Wherein, the service coefficient is set to 1 to indicate that the unmanned aerial vehicle serves the target user terminal; the service coefficient is set to 0 to indicate that the unmanned aerial vehicle does not serve the target user terminal; Using a preset genetic algorithm model or equally dividing the flight area corresponding to the unmanned aerial vehicle, taking the coordinates of the center point of each equally divided area as the new plane position coordinates corresponding to the unmanned aerial vehicle, and updating the initial plane position coordinates; If the target optimization model to be analyzed has not converged or the number of iterations has not reached a preset iteration threshold, the updated plane position coordinates are used to iteratively analyze the target optimization model to be analyzed; if the target optimization model to be analyzed has converged or the number of iterations has reached a preset iteration threshold, the corresponding resource allocation strategy is determined when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset conditions; The target optimization model to be analyzed corresponding to the optimization problem of maximizing the coverage of the unmanned aerial vehicle is established according to the first channel model, the second channel model, the user upload rate model to the unmanned aerial vehicle and the unmanned aerial vehicle data sending rate model. ; The target optimization model to be analyzed is expressed as follows: ； in, ,in, Indicates sending user The UAV sends power during the mission; ; ; ; Indicates the user end The amount of tasks to be unloaded; is the energy consumption factor per unit computing power; and They are the maximum computing power and transmission power of the UAV respectively; For UAV users Allocated computing power; is the energy consumption weight, The larger it is, the more attention the UAV pays to computing energy consumption while maximizing its coverage; is the magnitude normalization factor, which is used to unify the magnitudes of the two quantities, the computing energy consumption of the UAV and the number of service users; Calculate energy consumption for unmanned aerial vehicles, Offloading energy consumption for UAV missions; Constraints It means that the unloaded task of the service user end is completed by the unmanned aerial vehicle through local calculation and task offloading to the base station within the required time, where represents the user-side task volume calculated by the UAV, represents the amount of user-side tasks that the UAV offloads to the base station; constraint Service factor The constraints, where Indicates The service factor of each user, Indicates that each user chooses to serve or not serve, and the maximum number of service users is ; The total number of users served by UAVs; Indicates that unmanned aerial vehicles are not User service, that is, Individual users do not offload tasks to UAVs; Indicates that the unmanned aerial vehicle is User service, that is, Users offload tasks to UAVs; constraints is the causal constraint of the local computing power and transmission power of the UAV, where Indicates that the maximum computing power allocated to the UAV cannot exceed its maximum value , Indicates that the UAV transmission power cannot exceed its maximum allowed transmission power ;constraint Deploy position constraints for UAVs, is the flight boundary of the unmanned aerial vehicle; the first constraint condition includes the constraint ,constraint ,constraint And constraints .

2. The collaborative edge computing service range optimization method according to claim 1, characterized in that: Also includes: Based on the plane position coordinates of the user terminal, the plane position coordinates of the unmanned aerial vehicle and the plane position coordinates of the corresponding base station, a first channel model between the unmanned aerial vehicle and the user terminal and a second channel model between the unmanned aerial vehicle and the base station are determined respectively.

3. The collaborative edge computing service range optimization method according to claim 1, characterized in that: Also includes: An upload rate model between the user terminal and the unmanned aerial vehicle is determined based on the first channel bandwidth of the unmanned aerial vehicle, the transmission power of the user terminal, the noise power at the unmanned aerial vehicle, and the first channel model.

4. The collaborative edge computing service range optimization method according to claim 1, characterized in that: Also includes: A transmission rate model of the unmanned aerial vehicle on the second channel is determined based on the second channel bandwidth of the unmanned aerial vehicle, the transmission power of the unmanned aerial vehicle when the unmanned aerial vehicle sends a user-end task to the base station through the second channel, and the second channel model.

5. A collaborative edge computing service range optimization device, characterized in that: include: a target optimization model determination unit, configured to determine a target optimization model that satisfies a preset first constraint condition based on a first channel model between the unmanned aerial vehicle and a user terminal, a second channel model between the unmanned aerial vehicle and a base station, an upload rate model between the user terminal and the unmanned aerial vehicle, and a transmission rate model of the unmanned aerial vehicle on the second channel; A resource allocation strategy determination unit is used to determine, based on the target optimization model, a corresponding resource allocation strategy when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets a preset energy consumption constraint condition; the resource allocation strategy determination unit is specifically used to: Initializing the initial plane position coordinates of the unmanned aerial vehicle; Converting the service coefficient corresponding to the unmanned aerial vehicle from an integer variable to a continuous variable; Based on the continuous variable, determine the target optimization model to be analyzed that satisfies the preset second constraint condition and corresponds to the target optimization model, and analyze the target optimization model to be analyzed based on the initialized initial plane position coordinates to obtain a corresponding service coefficient; Arrange the service coefficients in descending order, set the service coefficients with large values ​​to 1, and set the service coefficients with small values ​​to 0, and re-analyze the target optimization model to be analyzed to determine the initial resource allocation strategy corresponding to the maximum coverage of the unmanned aerial vehicle service and the energy consumption meeting the preset conditions; Wherein, the service coefficient is set to 1 to indicate that the unmanned aerial vehicle serves the target user terminal; the service coefficient is set to 0 to indicate that the unmanned aerial vehicle does not serve the target user terminal; Using a preset genetic algorithm model or equally dividing the flight area corresponding to the unmanned aerial vehicle, taking the coordinates of the center point of each equally divided area as the new plane position coordinates corresponding to the unmanned aerial vehicle, and updating the initial plane position coordinates; If the target optimization model to be analyzed has not converged or the number of iterations has not reached a preset iteration threshold, the updated plane position coordinates are used to iteratively analyze the target optimization model to be analyzed; if the target optimization model to be analyzed has converged or the number of iterations has reached a preset iteration threshold, the corresponding resource allocation strategy is determined when the service coverage of the unmanned aerial vehicle is maximized and the energy consumption meets the preset conditions; The target optimization model to be analyzed corresponding to the optimization problem of maximizing the coverage of the unmanned aerial vehicle is established according to the first channel model, the second channel model, the user upload rate model to the unmanned aerial vehicle and the unmanned aerial vehicle data sending rate model. ; The target optimization model to be analyzed is expressed as follows: ； in, ,in, Indicates sending user The UAV sends power during the mission; ; ; ; Indicates the user end The amount of tasks to be unloaded; is the energy consumption factor per unit computing power; and They are the maximum computing power and transmission power of the UAV respectively; For UAV users Allocated computing power; is the energy consumption weight, The larger it is, the more attention the UAV pays to computing energy consumption while maximizing its coverage; is the magnitude normalization factor, which is used to unify the magnitudes of the two quantities, the computing energy consumption of the UAV and the number of service users; Calculate energy consumption for unmanned aerial vehicles, Offloading energy consumption for UAV missions; Constraints It means that the unloaded task of the service user end is completed by the unmanned aerial vehicle through local calculation and task offloading to the base station within the required time, where represents the user-side task volume calculated by the UAV, represents the amount of user-side tasks that the UAV offloads to the base station; constraint Service factor The constraints, where Indicates The service factor of each user, Indicates that each user chooses to serve or not serve, and the maximum number of service users is ; The total number of users served by UAVs; Indicates that unmanned aerial vehicles are not User service, that is, Individual users do not offload tasks to UAVs; Indicates that the unmanned aerial vehicle is User service, that is, Users offload tasks to UAVs; constraints is the causal constraint of the local computing power and transmission power of the UAV, where Indicates that the maximum computing power allocated to the UAV cannot exceed its maximum value , Indicates that the UAV transmission power cannot exceed its maximum allowed transmission power ;constraint Deploy position constraints for UAVs, is the flight boundary of the unmanned aerial vehicle; the first constraint condition includes the constraint ,constraint ,constraint And constraints .

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the collaborative edge computing service range optimization method as described in any one of claims 1-4 are implemented.

7. A processor-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the collaborative edge computing service range optimization method as described in any one of claims 1 to 4 are implemented.

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