Resource allocation method for wireless energy supply edge computing network

Abstract

The invention relates to the technical field of wireless energy supply edge computing and task scheduling, in particular to a wireless energy supply edge computing network-oriented resource allocation method, which comprises the following steps of: firstly, comprehensively considering a heterogeneous access point, a mobile terminal, a time-varying channel and a heterogeneous task, and establishing a corresponding system model, an equipment and service model and a task model computing model; then, comprehensively considering various factors influencing the calculation performance, and designing a selection algorithm based on a mixed delay task; finally, the network model characteristics of the wireless energy supply edge computing network are considered, and the unloading decision and system time resource allocation problems of the mobile terminal are solved through a coordinate descent algorithm based on one-dimensional time variable binary search. According to the method, heterogeneous access point selection, unloading decision and time resource allocation schemes can be effectively made under the time-varying channel, so that the overall calculation efficiency of the system is improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless power supply edge computing and task scheduling technology, and in particular to a resource allocation method for a wireless power supply edge computing network. Background Art

[0002] Wireless powered mobile edge computing integrates mobile edge computing and wireless power transmission technologies, aiming to solve the problem of insufficient computing power and continuous energy supply of mobile terminals. However, when there are multiple mobile terminals in a wireless powered mobile edge computing network, not only the mobile terminal offloading decision problem must be considered, but also the distribution of power supply and task processing time of heterogeneous access points for mobile terminals must be weighed, which brings great challenges to resource allocation and offloading decisions.

[0003] Traditional resource allocation methods and decision-making methods only consider single heterogeneous access point scenarios. The computing network of a single heterogeneous access point can only provide limited power supply and computing services, which limits the improvement of mobile terminal experience quality and cannot overcome the dual near-far effect between multiple mobile terminals.

[0004] Therefore, it is extremely important to design an efficient resource allocation and offloading decision method that matches the scenario where there are multiple heterogeneous access points in the wireless power edge computing network. In the scenario of multiple heterogeneous access points, different heterogeneous access points have different transmission power and processing capabilities, the tasks of the mobile terminal may have heterogeneous delay requirements in different time slots, and the wireless channel between the mobile terminal and the heterogeneous access point will change over time. Therefore, how to comprehensively consider these factors and design a reasonable and efficient resource allocation and offloading decision scheme as much as possible to improve the computing performance of the entire system is an important challenge currently faced by the research institute. Summary of the invention

[0005] The purpose of the present invention is to provide a resource allocation method for a wireless power supply edge computing network, aiming to solve the problem of unreasonable resource allocation and unloading decision-making schemes in scenarios with multiple heterogeneous access points and multiple mobile terminals, and to improve the computing performance of the wireless power supply edge computing network.

[0006] To achieve the above object, the present invention provides a resource allocation method for a wireless power supply edge computing network, comprising the following steps:

[0007] Step 1: In the scenario of multiple heterogeneous access points and multiple mobile terminals, build the system model, device and service model, task model and calculation model to obtain the optimization target;

[0008] Step 2: Solve the heterogeneous access point selection problem based on the selection algorithm of the mixed delay task;

[0009] Step 3: A coordinate descent algorithm based on one-dimensional time variable binary search solves the mobile terminal offloading decision and time resource allocation problem in the subnetwork generated after heterogeneous access point selection;

[0010] Step 4: Use the binary search algorithm of one-dimensional time variable to obtain the time resource allocation strategy;

[0011] Step 5: Top-level problem solving, generating the entire network time resource allocation scheme and offloading decision.

[0012] Optionally, the execution process of step 1 includes the following steps:

[0013] Step 1.1: Consider a network with multiple heterogeneous access points and multiple mobile terminals and establish a system model;

[0014] Step 1.2: Establish equipment and service models according to the division of system time frames;

[0015] Step 1.3: Consider the heterogeneous delay requirements that different time slots may have and establish a task model;

[0016] Step 1.4: Establish a calculation model based on different uninstallation decisions of mobile terminals;

[0017] Step 1.5: Based on the established model, obtain the optimization target of the overall network.

[0018] Optionally, the execution process of step 2 includes the following steps:

[0019] Step 2.1: Calculate the correlation coefficients of local computing and offload computing of all mobile terminals;

[0020] Step 2.2: The correlation coefficient is normalized to the maximum and minimum values;

[0021] Step 2.3: Introduce the system delay-sensitive task ratio γ and calculate the relevant value;

[0022] Step 2.4: Obtain the heterogeneous access point selection result, and divide the overall network into multiple sub-networks.

[0023] Optionally, during the execution of step 3, first, all mobile terminals in the subnet randomly generate an initial offloading decision, and randomly select between two offloading decisions: local calculation / offloading to the selected heterogeneous access point for calculation; then, after calculating the calculation rate value after each mobile terminal flips the offloading mode, the calculation rate increment is selected, that is, the mobile terminal flipping strategy with the largest difference between the calculation rate after flipping and the calculation rate obtained by the initial decision is selected as the current strategy; finally, the above process is iterated until the calculation rate increment no longer increases after all mobile terminals flip their decisions, and the final offloading decision of the subnet is obtained.

[0024] Optionally, a binary search convex optimization algorithm is combined to obtain a computing rate increment, and the binary search algorithm is called to respectively calculate the computing rate values ​​of all mobile terminals in each sub-network after the offloading decision is flipped.

[0025] Optionally, the execution process of step 4 is specifically to first determine the interval to be optimized, that is, the time range to be optimized, then calculate the midpoint value of the interval, and finally update the interval according to the strategy.

[0026] Optionally, the execution process of step 5 is specifically as follows: after obtaining the heterogeneous access point selection strategy through the selection algorithm of the hybrid delay task, the coordinate descent algorithm of the one-dimensional time variable binary search and the binary search algorithm are used to obtain the unloading decision and time resource allocation scheme of different sub-networks, for each sub-network, the coordinate descent algorithm of the one-dimensional time variable binary search is called successively to obtain the heterogeneous access point selection, unloading decision and resource allocation scheme of the entire network.

[0027] The present invention provides a resource allocation method for a wireless power supply edge computing network. First, heterogeneous access points, mobile terminals, time-varying channels and heterogeneous tasks are comprehensively considered, and corresponding system models, equipment and service models, and task model calculation models are established; then, a variety of factors affecting computing performance are comprehensively considered, and a selection algorithm based on hybrid delay tasks is designed; finally, considering the network model characteristics of the wireless power supply edge computing network, a coordinate descent algorithm based on one-dimensional time variable binary search is used to solve the unloading decision of the mobile terminal and the system time resource allocation problem. Compared with the traditional single heterogeneous access point research, the method proposed in the present invention can meet the efficient unloading and power supply requirements of the mobile terminal in the multi-heterogeneous access point scenario, effectively overcome the dual near-far effect generated by the single heterogeneous access point, and improve the task computing efficiency of the system; compared with the ordinary multi-heterogeneous access point research, the delay-sensitive heterogeneous characteristics between different tasks are considered, the deadline violation of the task is significantly reduced, and the heterogeneous tasks with multiple delay requirements can be handled, and the system task computing rate is improved. In addition, it further adapts to the distribution of various devices and channel changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only 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.

[0029] Figure 1 It is a system principle flow chart of a resource allocation method for a wireless power supply edge computing network of the present invention.

[0030] Figure 2It is a schematic diagram of the BMDT algorithm flow of the present invention.

[0031] Figure 3 It is a schematic diagram of the CD algorithm flow of the present invention.

[0032] Figure 4 It is a schematic diagram showing the effective calculation rate of CD and its comparison algorithm in a specific embodiment of the present invention.

[0033] Figure 5 Schematic diagram of the impact of different delay-sensitive task proportions of the present invention. DETAILED DESCRIPTION

[0034] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0035] The following is a description of the English abbreviations used in the present invention:

[0036] BMDT: A hybrid delayed task-based selection algorithm;

[0037] CD: Coordinate descent algorithm based on one-dimensional time-variable binary search.

[0038] The present invention provides a resource allocation method for a wireless power supply edge computing network, comprising the following steps:

[0039] Step 1: In the scenario of multiple heterogeneous access points and multiple mobile terminals, build the system model, device and service model, task model and calculation model to obtain the optimization target;

[0040] Step 2: Solve the heterogeneous access point selection problem based on the selection algorithm of the mixed delay task;

[0041] Step 3: A coordinate descent algorithm based on one-dimensional time variable binary search solves the mobile terminal offloading decision and time resource allocation problem in the subnetwork generated after heterogeneous access point selection;

[0042] Step 4: Use the binary search algorithm of one-dimensional time variable to obtain the time resource allocation strategy;

[0043] Step 5: Top-level problem solving, generating the entire network time resource allocation scheme and offloading decision.

[0044] The specific implementation principle block diagram is as follows Figure 1As shown, the resource allocation method for the wireless power supply edge computing network is divided into three stages, which correspond to the above steps 1, 2, 3 to 5 respectively, and are further described below in combination with the implementation steps:

[0045] In the first stage, in the scenarios of multiple heterogeneous access points and multiple mobile terminals, the corresponding system model, device and service model, and task model calculation model are established, and finally the optimization target is obtained.

[0046] The specific steps are as follows:

[0047] 1. Consider a network with multiple heterogeneous access points and multiple mobile terminals and establish a system model.

[0048] Specifically, the wireless power supply edge computing network model considered consists of multiple heterogeneous access points and mobile terminals, where the heterogeneous access points have a long-term stable wired power supply, which can broadcast radio frequency to the mobile terminal to supply energy, and can also receive and process computing tasks offloaded by the mobile terminal. The mobile terminal may be within the coverage of one or more heterogeneous access points, and the mobile terminal should select a suitable heterogeneous access point to provide services (energy supply service and task offloading service) according to its own needs.

[0049] In order to avoid mutual interference, the communication and energy collection circuits of each mobile terminal operate in a time-division multiplexing mode, and the entire system uses TDMA for multiple access to wireless channels. TDMA (Time Division Multiple Access) is a communication technology used for multiple users to share limited spectrum resources. In a TDMA system, time is divided into continuous time slots, and each user communicates in a different time slot to achieve parallel data transmission. The system time is divided into continuous time frames of equal length T, which is set less than the channel coherence time. In each time frame T, energy must be supplied to all mobile terminals served by heterogeneous access points before tasks are offloaded. When offloading tasks, a binary offloading strategy is considered, so that tasks are either calculated locally on the mobile terminal or offloaded to a designated heterogeneous access point for execution.

[0050] 2. Establish equipment and service models based on the division of system time frames.

[0051] Specifically, consider a wireless power supply edge computing network composed of multiple heterogeneous access points and multiple mobile terminals. Different heterogeneous access points have heterogeneous transmission power and computing capabilities. The subchannel between the heterogeneous access points and the mobile terminals is a block fading channel, that is, the wireless channel gain is constant in each time frame, but it will change between different time frames. In the same time slot, each mobile terminal can only select a specific heterogeneous access point to bind its service relationship (energy supply service and task offloading service), while the heterogeneous access point can serve multiple mobile terminals at the same time.

[0052] 3. Consider the heterogeneous delay requirements that different time slots may have and establish a task model.

[0053] Specifically, the task list that mobile terminal i needs to perform in the time slot is: Tasks are represented by triples in represents the number of clock cycles required to calculate task k of mobile terminal i, represents the input data size of task k of mobile terminal i (bits, i.e., the size of data that needs to be uploaded when unloading), represents the delay tolerance time of task k of mobile terminal i, and max represents the maximum number of tasks that mobile terminal i can execute in this time slot. In each time slot T, the system can obtain the proportion of delay-sensitive tasks of the entire wireless power supply edge computing network as γ∈(0,1). The tasks to be executed by each mobile terminal arrive randomly in different time slots, and the proportion of delay-sensitive tasks in the system in different time slots is also different.

[0054] 4. Establish a calculation model based on different uninstall decisions of mobile terminals.

[0055] Task local calculation: During local calculation, after obtaining energy supply, the mobile terminal locally executes its own computing tasks. Since the energy collection circuit and computing unit in the mobile terminal are separate, the mobile terminal can collect energy and calculate its tasks at the same time in local computing mode. It is assumed that the energy consumed by each mobile terminal during calculation cannot be greater than the energy obtained during the energy supply time, the execution of the task must be completed within the delay requirement time, and the mobile terminal randomly reaches tasks with different delay requirements in different time slots. When the task is calculated locally, the effective task calculation rate (bit / s) of mobile terminal i is:

[0056]

[0057] Where a represents the power supply time of the heterogeneous access point to the mobile terminal within the time frame, The number of tasks to meet the latency requirement.

[0058] Task offloading to the selected heterogeneous access point calculation: Due to the limitation of time-division multiplexing circuit, the mobile terminal in offloading mode can only offload its task to the heterogeneous access point after collecting energy. Since the computing power of heterogeneous access points is generally several orders of magnitude or more higher than that of mobile terminals, the processing delay caused by the heterogeneous access point integrated with the edge computing server can be ignored compared with the processing delay caused by offloading and transmission. In addition, the size of the calculation result is much smaller than the size of the original task, so the duration of the download phase can also be ignored, and each time slot is only occupied by wireless power transmission (i.e., energy supply) and task offloading. Although from the overall perspective of the system, the execution time of the task is ignored, for delay-sensitive tasks, slight time differences may also be fatal. Therefore, from the perspective of the task, by considering the execution delay of the task on the heterogeneous access point, when determining whether the task execution completion time is offloaded within the delay requirement time, the effective computing rate (ECR) (bit / s) of mobile terminal i is:

[0059] During unloading, the ECR (bit / s) of mobile terminal i is:

[0060]

[0061] Where a represents the power supply time of the heterogeneous access point to the mobile terminal within the time frame, τ i represents the uninstallation time of the i-th mobile terminal, The number of tasks to meet the latency requirement.

[0062] 5. Based on the established model, the optimization target is obtained.

[0063] Specifically, according to the established model, the sum effective computation rate (SECR) of a single sub-network is expressed as:

[0064]

[0065] where x = {x i |i∈N j}, τ={τ i |i∈N j}, uninstall indicator variable x i represents the task offloading decision of mobile terminal i in time slot T, x i =0 means local calculation, x i =1 means task offloading.

[0066] For the entire wireless power supply edge computing network with multiple heterogeneous access points and multiple mobile terminals, the optimization goal is to maximize the total effective computing rate of the system:

[0067]

[0068] That is, when the channel condition h is known, the heterogeneous access point selection, offloading decision x and time resource allocation scheme (τ, a) are made to maximize the effective task calculation rate of the system.

[0069] In the second stage, the hybrid delay task based selection algorithm (BMDT) solves the heterogeneous access point selection problem.

[0070] 1. Calculate the correlation coefficient between local computing and offload computing of all mobile terminals.

[0071] Specifically, first according to the formula and The local computation and offload computation correlation coefficients of all mobile terminals are calculated respectively.

[0072] 2. The correlation coefficient is normalized to its maximum and minimum values.

[0073] Specifically, in order to make all correlation values ​​comparable, the sum of the locally calculated correlation values ​​is offloaded to the heterogeneous access point to perform maximum and minimum value (max-min) normalization processing for the offloaded correlation calculation.

[0074] 3. Introduce the proportion of system delay-sensitive tasks γ and calculate the relevant values.

[0075] Specifically, after the data is normalized, the system delay-sensitive task ratio γ and the common task ratio 1-γ are introduced, and then according to the formula and Calculate the correlation values ​​between all mobile terminals and heterogeneous access points.

[0076] 4. Obtain the heterogeneous access point selection results, and divide the overall network into multiple sub-networks.

[0077] Specifically, for each mobile terminal, select the relevant value among all heterogeneous access points The largest heterogeneous access point is sufficient, and this operation is iterated to obtain the mobile terminal selection result of the entire system. Correspondingly, after the heterogeneous access point selection is performed, the network is divided into multiple sub-networks consisting of a single heterogeneous access point and the mobile terminals it serves.

[0078] The corresponding BMDT algorithm flow chart is shown in the figure.

[0079] In the third stage, a coordinate descent (CD) algorithm based on one-dimensional time variable binary search is designed to solve the sub-network mobile terminal offloading decision and time resource allocation problems. The CD algorithm flow chart is shown in the figure. Figure 3 shown.

[0080] The specific process is as follows:

[0081] 1. Initialize uninstall decision.

[0082] Specifically, the CD algorithm needs to calculate the system's computing rate in advance based on the initial decision. Each subnetwork has N mobile terminals, and N auxiliary binary variables x = [x 1 ,...,x N ]', where x i =0(or x i =1) indicates that mobile terminal i selects local computing tasks (or offloads tasks to its selected heterogeneous access point for execution) and randomly generates the initial offloading decision x 0 , where all x i Randomly choose between the two uninstall decisions 0 / 1.

[0083] 2. Combine the binary search convex optimization algorithm to obtain the calculation rate increment.

[0084] Specifically, after the initial unloading decision is obtained, for all mobile terminals in each sub-network, the binary search algorithm is called to calculate the calculation rate value after the flipping of the unloading decision (local calculation flips to unloading calculation, and unloading calculation flips to local calculation). After calculating the calculation rate value after each mobile terminal flips the unloading mode, the calculation rate increment is selected, that is, the mobile terminal flipping strategy with the largest difference between the calculation rate after flipping and the calculation rate obtained by the initial decision is selected as the current strategy. The above process is iterated until the calculation rate increment no longer increases after the flipping decision of all mobile terminals, and the final unloading decision of the sub-network is obtained.

[0085] The main process of the binary search algorithm is: first determine the interval to be optimized, that is, the time range to be optimized. Then calculate the midpoint value of the interval, and finally update the interval according to the strategy.

[0086] 3. Time resource allocation scheme and offloading decision generation.

[0087] Specifically, after the heterogeneous access point selection strategy is obtained through the BMDT algorithm and the CD algorithm obtains the offloading decision and time resource allocation scheme of different sub-networks, the CD algorithm is called one by one for each sub-network to obtain the heterogeneous access point selection, offloading decision and resource allocation scheme of the entire network.

[0088] The present invention is different from the existing methods in that it studies the problem of maximizing the effective computing rate of tasks in a wireless powered edge computing network with multiple mobile terminals and multiple heterogeneous access points, and proposes an efficient joint scheduling algorithm. The present invention can effectively make heterogeneous access point selection, offloading decisions and time resource allocation plans under time-varying channels, thereby improving the overall computing efficiency of the system. By jointly optimizing the time-varying channel gain, the heterogeneous delay requirements of the system tasks, and the different transmission powers and computing capabilities of the heterogeneous access points, the BMDT selection strategy is used to make heterogeneous access point selection, and the CD algorithm combined with the time binary search algorithm suitable for the wireless powered edge computing network is used to solve the offloading decision and time resource allocation problems. The three-stage iterative optimization maximizes the effective computing rate of the system.

[0089] Furthermore, the present invention further demonstrates the beneficial effects of the present invention by comparing specific embodiments:

[0090] As shown in Table 1 below, under different system scales, the system weighted computing rate (WCR) and the weighted effective computing rate (WECR) considering task delay requirements are significantly improved, and the improvement is more obvious as the system scale increases. Among them, HAP represents heterogeneous access point and MD represents mobile terminal.

[0091] Table 1 Comparison of the computational rates of BMDT and its comparison algorithms at different device scales

[0092]

[0093]

[0094] Figure 4 and Figure 5 They respectively represent the computational performance comparison of the CD algorithm combined with BMDT and other comparative algorithms under different system scales and the effective completion of tasks of the present invention under different proportions of delay-sensitive tasks.

[0095] Specifically, Figure 4 The performance of each offloading algorithm under various system scales and distributions is shown. Each scale is performed under multiple random distributions, and the calculation results are the average of each distribution within 2000 time slots. Figure 4 It can be seen that compared with various baseline solutions, the BMDT_CD method proposed in the present invention can achieve the best computational performance.

[0096] Figure 5 The effect of the BMDT_CD method on the effective completion ratio of system tasks under different delay-sensitive task ratios is shown. The horizontal axis in the figure represents the different delay-sensitive task ratios from 20% to 80%, and the vertical axis represents the effective completion ratio of tasks under this ratio, that is, how many tasks are completed within the delay time. Figure 5 It can be seen that the BMDT_CD algorithm proposed in the present invention can work well in scenarios with different system scales and proportions of delay-sensitive tasks, ensuring the effective completion of system tasks, and this guarantee becomes more stable as the system scale increases. For example, at 8HAP and 30MD, the system's effective task completion ratio remains above 90%.

[0097] In summary, the present invention uses this method in a wireless functional edge computing environment with multiple mobile terminals and multiple heterogeneous access points to quickly and efficiently make heterogeneous access point selections, offload decisions, and generate time resource allocation plans, and improve the system's task computing rate. Compared with traditional single heterogeneous access point research, the method proposed in the present invention can meet the efficient offloading and power supply requirements of mobile terminals in a multi-heterogeneous access point scenario, effectively overcome the dual near-far effect generated by a single heterogeneous access point, and improve the system's task computing efficiency; compared with ordinary multi-heterogeneous access point research, it takes into account the delay-sensitive heterogeneous characteristics between different tasks, significantly reduces the deadline violation of tasks, can respond to heterogeneous tasks with multiple delay requirements, improves the system task computing rate, and further adapts to various device distribution situations and channel changes.

[0098] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A resource allocation method for a wireless power supply edge computing network, characterized in that: The following steps are involved: Step 1: In the scenario of multiple heterogeneous access points and multiple mobile terminals, build the system model, device and service model, task model and calculation model to obtain the optimization target; Step 2: Solve the heterogeneous access point selection problem based on the selection algorithm of the mixed delay task; Step 3: A coordinate descent algorithm based on one-dimensional time variable binary search solves the mobile terminal offloading decision and time resource allocation problem in the subnetwork generated after heterogeneous access point selection; Step 4: Use the binary search algorithm of one-dimensional time variable to obtain the time resource allocation strategy; Step 5: Top-level problem solving, generating the entire network time resource allocation scheme and offloading decision.

2. The resource allocation method for a wireless power supply edge computing network according to claim 1, characterized in that: The execution process of step 1 includes the following steps: Step 1.1: Consider a network with multiple heterogeneous access points and multiple mobile terminals and establish a system model; Step 1.2: Establish equipment and service models according to the division of system time frames; Step 1.3: Consider the heterogeneous delay requirements that different time slots may have and establish a task model; Step 1.4: Establish a calculation model based on different uninstallation decisions of mobile terminals; Step 1.5: Based on the established model, obtain the optimization target of the overall network.

3. The resource allocation method for a wireless power supply edge computing network according to claim 2, characterized in that: The execution process of step 2 includes the following steps: Step 2.1: Calculate the correlation coefficients of local computing and offload computing of all mobile terminals; Step 2.2: The correlation coefficient is normalized to the maximum and minimum values; Step 2.3: Introduce the proportion of system delay-sensitive tasks γ , calculate the correlation value; Step 2.4: Obtain the heterogeneous access point selection result, and divide the overall network into multiple sub-networks.

4. The resource allocation method for a wireless power supply edge computing network according to claim 3, characterized in that: During the execution of step 3, first, all mobile terminals in the subnet randomly generate an initial offloading decision, and randomly select between two offloading decisions: local calculation / offloading to the selected heterogeneous access point calculation; then, after calculating the calculation rate value after each mobile terminal flips the offloading mode, the calculation rate increment is selected, that is, the mobile terminal flipping strategy with the largest difference between the calculation rate after flipping and the calculation rate obtained by the initial decision is selected as the current strategy; finally, the above process is iterated until the calculation rate increment no longer increases after all mobile terminals flip their decisions, and the final offloading decision of the subnet is obtained.

5. The resource allocation method for a wireless power supply edge computing network according to claim 4, characterized in that: The calculation rate increment is obtained by combining the binary search convex optimization algorithm, and the binary search algorithm is called to calculate the calculation rate values ​​of all mobile terminals in each subnetwork after the flipping offloading decision.

6. The resource allocation method for a wireless power supply edge computing network according to claim 5, characterized in that: The execution process of step 4 is to first determine the interval to be optimized, that is, the time range to be optimized, then calculate the midpoint value of the interval, and finally update the interval according to the strategy.

7. The resource allocation method for a wireless power supply edge computing network according to claim 6, characterized in that: The execution process of step 5 is specifically as follows: after obtaining the heterogeneous access point selection strategy through the selection algorithm of the hybrid delay task, the coordinate descent algorithm of the one-dimensional time variable binary search and the binary search algorithm are used to obtain the offloading decision and time resource allocation scheme of different sub-networks, for each sub-network, the coordinate descent algorithm of the one-dimensional time variable binary search is called successively to obtain the heterogeneous access point selection, offloading decision and resource allocation scheme of the entire network.

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