Energy consumption and information age optimization method based on end-to-end collaboration in wireless charging edge computing network

Abstract

The present invention discloses an energy consumption and information age optimization method based on end-to-end collaboration in a wireless charging edge computing network. First, a wireless charging edge computing network architecture is constructed, and the end device sampling mode, information age model, channel model, energy collection model, consumption model and energy update formula are defined; the energy consumption and information age optimization problem is formalized as a Steinberg game problem; for the follower problem, the theoretical optimal information age and the energy allocation to achieve this optimal information age are derived, and an information transmission strategy that optimizes the information age is designed; for the leader problem, a charging power and time decision method is designed so that the hybrid access point consumes the least energy to collect the freshest information. The method is simple to operate and highly practical, reduces the energy consumed by the hybrid access point, and improves the information freshness of all nodes.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy consumption and information age optimization, and in particular to an energy consumption and information age optimization method based on end-to-end collaboration in a wireless charging edge computing network. Background Art

[0002] Wireless charging edge computing network can solve the problem of limited energy of end devices in edge computing network, but charging the end devices requires consuming the energy of hybrid access points. Even if the hybrid access points are connected to the power grid and can obtain stable energy, they still need to pay for the consumed energy. Therefore, reducing the energy consumption of hybrid access points or improving energy efficiency is an important research topic in wireless charging edge computing network. There is a "double near-far effect" in wireless charging edge computing network. The performance of devices far away from the hybrid access points is poor. The end-to-end collaboration technology can be used to alleviate this problem. In addition, the freshness of information in wireless charging edge computing network is a crucial performance. The freshness of information is measured by the age of information AoI. When the freshness of information is low (that is, the AoI is large), it will not be able to respond to emergencies in time. Therefore, it is very important for hybrid access points to consume less energy to obtain fresh information in wireless charging edge computing network.

[0003] In terms of reducing energy consumption or improving energy efficiency while considering the "double near-far effect", the literature (Y. He, X. Wu, Z. He and M. Guizani, "Energy Efficiency Maximization of Backscatter-Assisted Wireless-Powered MEC With User Cooperation," IEEE Transactions on Mobile Computing, vol. 23, no. 2, pp. 1878-1887, 2024.) maximizes the energy efficiency of end devices in end-to-end cooperative wireless charging computing networks; the literature (S. Zeng, X. Huang and D. Li,"Joint Communication and Computation Cooperation in Wireless-Powered Mobile-Edge Computing Networks With NOMA," IEEE Internet of Things Journal, vol. 10, no. 11, pp. 9849-9862, 2023.) proposed a collaborative computing offloading scheme based on continuous convex approximation and Lagrangian duality, in which users overcome the influence of the "double near-far effect" through joint communication and computing to achieve the minimum charging energy consumption of hybrid access points; the literature (M. Sun, X. Xu, Y. Huang, Q. Wu, X. Tao and P. Zhang, "Resource Management for Computation Offloading in D2D-Aided Wireless PoweredMobile-Edge Computing Networks," IEEE Internet of Things Journal, vol. 8, no.10, pp. 8005-8020, 2021.) studied the resource management problem in end-to-end collaborative assisted wireless charging edge computing networks, and used Lyapunov optimization theory to improve energy efficiency under the condition of stable queue length.

[0004] In terms of improving the information freshness in wireless charging mobile computing networks, the literature (Q. Chen, S. Guo, Z. Cai, J. Li, T. Shi and H. Gao, "Peak AoI Minimization at Wireless-Powered Network Edge: From the Perspective of Both Charging and Transmitting," IEEE / ACM Transactions on Networking, vol. 32, no. 1, pp. 806-821, 2024.) studied the joint scheduling problem of data transmission and energy harvesting to optimize the maximum peak AoI of the wireless charging edge computing network using directional chargers; in order to optimize the average peak AoI of the wireless charging edge computing network with directional charging, the literature (Q. Chen et al., "Peak AoI Minimization With Directional Charging for Data Collectionat Wireless-Powered Network Edge," IEEE Transactions on Services Computing, vol. 17, no. 5, pp. 2747-2761, 2024.) proposes an approximate algorithm to obtain the charging time in each direction, and proposes an age-based scheduling algorithm to obtain charging decisions and data transmission decisions; the literature (J. Liu, X. Zhao, P. Qin, S. Geng, Z. Chen and H. Zhou, "Learning-Based Multi-UAV Assisted DataAcquisition and Computation for Information Freshness in WPT Enabled Space-Air-Ground PIoT," IEEE Transactions on Network Science and Engineering, vol.11, no. 1, pp. 48-63, 2024.) proposes a multi-UAV-assisted wireless charging edge computing framework, and optimizes the number of UAV hovering positions, hovering positions, connection between UAVs and power devices, energy transmission and data collection time of power devices, UAV computing resources, flight speed and trajectory to minimize the average information age of the device.

[0005] However, few existing solutions consider the “double near-far effect” of wireless charging edge computing networks while improving the freshness of information. Summary of the invention

[0006] The purpose of the present invention is to address the problems existing in the above-mentioned prior art and provide an energy consumption and information age optimization method based on end-to-end collaboration in a wireless charging edge computing network. Under the constraint of energy causality, the energy consumed by the hybrid access point and the maximum peak AoI of each device are minimized by optimizing the energy allocation scheme, information transmission time slot selection, charging power and time configuration.

[0007] The technical solution to achieve the purpose of the present invention is: on the one hand, a method for optimizing energy consumption and information age based on end-to-end collaboration in a wireless charging edge computing network is provided, and the method comprises the following steps:

[0008] Step 1: Build a wireless charging edge computing network architecture including a hybrid access point and multiple end devices, define the end device sampling mode, information age model, channel gain model, energy collection model, energy consumption model of end-to-end and end-to-hybrid access point communication, and determine the energy update model;

[0009] Step 2, define the hybrid access point energy consumption optimization problem and each end device information age optimization problem as a Stanger-Kerberg game problem;

[0010] Step 3: For the follower problem, obtain the theoretical optimal information age and the energy allocation to achieve this optimal information age, and design an information transmission strategy that optimizes the information age;

[0011] Step 4: Aiming at the leader problem, a charging power and time decision method is designed so that the hybrid access point consumes the least energy and collects the freshest information.

[0012] On the other hand, a system for optimizing energy consumption and information age based on end-to-end collaboration in a wireless charging edge computing network is provided, the system comprising:

[0013] The first module is used to build a wireless charging edge computing network architecture including a hybrid access point and multiple end devices, define the end device sampling mode, information age model, channel gain model, energy collection model, energy consumption model of end-to-end and end-to-hybrid access point communication, and determine the energy update model;

[0014] The second module is used to define the hybrid access point energy consumption optimization problem and each end device information age optimization problem as a Stanger-Kelberg game problem;

[0015] The third module is used to obtain the theoretical optimal information age and the energy allocation to achieve this optimal information age for the follower problem, and to design an information transmission strategy that optimizes the information age;

[0016] The fourth module is used to design a charging power and time decision method for the leader problem, so that the hybrid access point consumes the least energy and collects the freshest information.

[0017] Compared with the prior art, the present invention has the following significant advantages:

[0018] (1) The present invention utilizes end-to-end collaboration technology. Devices far from the hybrid access point can use less energy to transmit information to the assisting device, which then forwards the information to the hybrid access point, thereby effectively improving the performance of devices far from the hybrid access point and alleviating the problem of the "double near-far effect" of the wireless charging edge computing network. By optimizing the maximum peak AoI of information collected by each device, the freshness of the information of each device is ensured, thereby achieving the purpose of timely handling of emergencies. By optimizing the energy consumption of the hybrid access point, the purpose of energy saving and emission reduction is achieved.

[0019] (2) The optimal energy allocation scheme for nearby devices is derived so that the performance of the two devices that assist each other can reach the optimal level. An information transmission strategy that is simple to operate and highly practical is proposed so that the AoI of each device can reach the optimal level.

[0020] (3) For hybrid access points, a simple charging power and time decision method is designed, which enables it to obtain fresh information with the least energy consumption and achieve the goal of green energy saving.

[0021] (4) The information transmission strategy proposed in the present invention is simple to operate, has a small amount of calculation, is highly practical, and can ensure the freshness of information on all end devices; the charging power and time decision method proposed in the present invention can quickly obtain a stable optimal solution, has low algorithm complexity, requires a short decision time, is highly practical, and can quickly achieve the purpose of obtaining fresh information with less energy consumption. In general, the present invention is simple to operate, highly practical, and can effectively reduce the energy consumed by hybrid access points and improve the freshness of information on all nodes.

[0022] The present invention is further described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of the energy consumption and information age optimization method based on end-to-end collaboration in the wireless charging edge computing network of the present invention.

[0024] Figure 2 This is a schematic diagram of the wireless charging edge computing network architecture.

[0025] Figure 3 The AoI and utility values ​​of the near device and the far device in the first case of step 3-2 in an embodiment are as follows: Schematic diagram of the changes.

[0026] Figure 4 The AoI and utility values ​​of the near device and the far device in the second case in step 3-2 of an embodiment are as follows: Schematic diagram of the changes.

[0027] Figure 5 The AoI and utility values ​​of the near device and the far device in the third case in step 3-2 of an embodiment are as follows: Schematic diagram of the changes.

[0028] Figure 6 A schematic diagram of follower (end device) utility values ​​corresponding to different amounts of remote device sampling information data in one embodiment.

[0029] Figure 7 A schematic diagram of follower (end device) utility values ​​corresponding to different amounts of near device sampling information data in an embodiment.

[0030] Figure 8 A schematic diagram of the utility values ​​of a leader (hybrid access point) corresponding to different numbers of end devices in an embodiment. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0032] It should be noted that if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0033] The energy consumption and information age optimization method based on end-to-end collaboration in the wireless charging edge computing network proposed in the present invention is to consider the energy causality of the end device and the limited network resources when performing wireless charging and information transmission, so as to achieve the optimization of charging energy consumption and end device AoI. To solve this problem, the present invention formalizes the problem into the form of Stan Kohlberg game, derives the optimal energy allocation scheme through theoretical derivation, designs a simple and effective information transmission strategy, and finally designs a heuristic charging power and time decision method to achieve the purpose of consuming the least energy to obtain the freshest information.

[0034] In one embodiment, in combination Figure 1 and Figure 2 , provides an energy consumption and information age optimization method based on end-to-end collaboration in a wireless charging edge computing network, the method comprising the following steps:

[0035] Step 1: Build a wireless charging edge computing network architecture including a hybrid access point and multiple end devices, define the end device sampling mode, information age model, channel gain model, energy collection model, energy consumption model of end-to-end and end-to-hybrid access point communication, and determine the energy update model;

[0036] Step 2, define the hybrid access point energy consumption optimization problem and each end device information age optimization problem as a Stanger-Kerberg game problem;

[0037] Step 3: For the follower problem, obtain the theoretical optimal information age and the energy allocation to achieve this optimal information age, and design an information transmission strategy that optimizes the information age;

[0038] Step 4: Aiming at the leader problem, a charging power and time decision method is designed so that the hybrid access point consumes the least energy and collects the freshest information.

[0039] Further, in one of the embodiments, step 1 constructs a wireless charging edge computing network architecture including a hybrid access point and multiple end devices, defines an end device sampling mode, an information age model, a channel model, an energy collection model, an energy consumption model for end-to-end and end-to-hybrid access point communications, and determines an energy update model, as follows:

[0040] Step 1-1: Build a wireless charging edge computing network architecture, which includes a hybrid access point HAP and Terminal equipment , the hybrid access point charges the end device, the i-th end device The distance to the hybrid access point is , , terminal equipment The amount of data collected is ; According to the distance to the hybrid access point and whether there is a paired end device, the end device is divided into three categories: single device SD, near device ND and far device FD. If the end device has no paired end device, it is considered to be a single device SD. If the end device has a paired end device, the device is further classified according to its distance to the hybrid access point. If the distance between the end device and the hybrid access point is less than the threshold , the end device is considered to be a near device ND, otherwise the end device is a far device FD;

[0041] Depend on Remote device With nearby equipment Pairing, remote device With nearby equipment The distance between , the present invention considers that the far device and the near device are paired one-to-one, that is, it is considered that a near device is paired with only one far device, and similarly, a far device is paired with only one near device, and this pairing relationship is known;

[0042] For the convenience of the following description, it is assumed that Each device is a separate device, Devices are near devices, and the last A device is a remote device, that is, For a separate device, here , For near devices, here , For remote devices, here ;

[0043] The present invention focuses on time slots, and the length of each time slot is ,In each time slot, wireless energy transmission is first performed, i.e., the hybrid access point charges the end device, and then information transmission is performed;

[0044] Step 1-2, define the end device sampling mode, including: single cycle sampling mode PTSSM and general cycle sampling mode GPSM;

[0045] Here, if the device adopts the single-cycle sampling mode, it means that the device samples the surrounding information in each time slot, and if the device adopts the general cycle sampling mode, it means that the device samples the surrounding information in each interval. The surrounding information is sampled once in the time slot, where is the sampling period of the device and satisfies .

[0046] Step 1-3, the freshness of information is characterized by the information age AoI, which is calculated as the difference between the current time and the latest information sampling time; as a single device SD and as a near device ND end device The AoI update formula at the hybrid access point is:

[0047] (1)

[0048] in, express Time slot at the end device Local AoI, For The transmission decision of the time slot is Indicates that the device is The time slot transmits the information to the hybrid access point; Respectively represent the t time slot and t+1 time slot as a single device SD and a close device ND Information on the AoI of the hybrid access point;

[0049] As the end device of the remote device FD The AoI update formula at the hybrid access point is:

[0050] (2)

[0051] in, Respectively expressed in Time slot remote device Towards auxiliary equipment Transmission decisions and assistance for nearby devices Transmitting remote devices to hybrid access points Information transmission decisions, Indicated in Time slot remote device Towards auxiliary equipment Transmit information and assist nearby devices exist The time slot transmits the received information to the hybrid access point;

[0052] The maximum peak AoI of each end device’s information on the hybrid access point is expressed as :

[0053] (3)

[0054] in, is the total number of time slots of interest; and the update formula is applicable to all devices, whether they are single devices, close devices or far devices;

[0055] Step 1-4, establish the channel gain model between the two communication entities, specifically: use Represents the distance between two communicating entities, then its channel gain Calculated as:

[0056] (4)

[0057] in, is a random variable that follows a circularly symmetric Gaussian distribution. is the reference distance, For The power consumption at is the decline index;

[0058] Steps 1-5: Establish an energy collection model for the end device, specifically:

[0059] In each time slot, the end device The energy collected for:

[0060] (5)

[0061] in, is the energy harvesting efficiency, and They represent the power and time of charging the end device by the hybrid access point, represents the channel gain between the end device and the hybrid access point. The channel gain converts the Replaced by the distance between the device and the hybrid processor Calculated;

[0062] Step 1-6, establishing the energy consumption model of end-to-end and end-to-hybrid access point communications, specifically:

[0063] The energy consumption of the end device is divided into three categories: the energy consumption of the end device directly transmitting its own information to the hybrid access point, the energy consumption of the remote device transmitting information to the near device, and the energy consumption of the near device forwarding the remote device information to the hybrid access point;

[0064] Among them, any end device Time to transmit own information directly to the hybrid access point for:

[0065] (6)

[0066] Terminal equipment The energy consumption of directly transmitting its own information to the hybrid access point is expressed as :

[0067] (7)

[0068] in, For end devices The power of transmitting information to the hybrid access point, For end devices The amount of data collected, is the transmission bandwidth, for noise;

[0069] Remote equipment Approaching equipment Time of transmission of information for:

[0070] (8)

[0071] Remote equipment Approaching equipment Energy consumption for transmitting information It is expressed as:

[0072] (9)

[0073] in, Indicates the end device as a remote device To the end device as a near device The transmission power of the transmitted information, ; Remote device With nearby equipment The channel gain between is obtained by Replace with remote device With nearby equipment The distance between Calculated;

[0074] Near equipment Forwards traffic from remote devices to the hybrid access point Time of information for:

[0075] (10)

[0076] Near equipment Forwards traffic from remote devices to the hybrid access point Energy consumption of information It is expressed as:

[0077] (11)

[0078] in, For nearby equipment The power of transmitting information to the hybrid access point, For nearby equipment The channel gain between the hybrid access point and the Replace with a nearby device Distance to hybrid processor Calculated;

[0079] Steps 1-7, establish the energy update model of the terminal device, specifically:

[0080] According to the device classification, the energy update formula of the end device is divided into three categories;

[0081] (1) Single device In time slot Energy stored at the start The update formula is:

[0082] (12)

[0083] in, For time slot The energy stored at the beginning, For Transmission decision for time slots;

[0084] (2) Near equipment In time slot Energy stored at the start The update formula is:

[0085] (13)

[0086] (3) Remote equipment In time slot Energy stored at the start The update formula is:

[0087] (14)

[0088] in, Indicated in Time slot remote device Towards auxiliary equipment transmission decision.

[0089] Further, in one of the embodiments, the hybrid access point energy consumption optimization problem and each terminal device information age optimization problem are defined as a Stanger-Kelberg game problem in step 2, which specifically includes:

[0090] Step 2-1, the energy consumption of the hybrid access point and the AoI of each terminal device on the hybrid access point are dimensionless, specifically:

[0091] The maximum energy consumption that the hybrid access point can achieve in each time slot is used to maximize its actual energy consumption. The energy consumption after maximization is:

[0092] (15)

[0093] in, Indicates the maximum power that the hybrid access point can use to charge the end device. is the time slot length, and They respectively represent the power and time for the hybrid access point to charge the end device;

[0094] Since the maximum peak AoI of the device information on the HAP is not less than its maximum peak AoI locally, the maximum peak AoI locally can be used to minimize the maximum peak AoI of the peer device information on the hybrid access point. The minimized maximum peak AoI is :

[0095] (16)

[0096] in, Indicates the sampling period of the terminal device. Display terminal equipment The maximum peak AoI of the information on the hybrid access point;

[0097] Step 2-2, defining the utility values ​​of the hybrid access point and the end device, and formalizing all utility value optimization problems into a Stan Kohlberg game containing a leader and multiple followers, where the hybrid access point serves as the leader and the end device serves as the follower;

[0098] The leader’s utility function is defined as the weighted sum of the dedimensionalized energy consumption and the dedimensionalized average of the maximum peak AoI of all devices:

[0099] (17)

[0100] in, Indicates the preference of the hybrid access point between information freshness and energy consumption, Display terminal equipment The importance of the information, represents the leader’s utility function;

[0101] The follower’s utility function is defined as the weighted sum of its own and its paired device’s maximum peak AoI:

[0102] (18)

[0103] in, represents the follower’s utility function, Display terminal equipment and The pairing relationship, express and Pairing, N represents the total number of end devices, Display terminal equipment The maximum peak AoI of the information on the hybrid access point;

[0104] The energy consumption and maximum peak AoI optimization problem is defined as:

[0105] (19)

[0106] (20)

[0107] (twenty one)

[0108] (twenty two)

[0109] (twenty three)

[0110] (twenty four)

[0111] (25)

[0112] (26)

[0113] in, For end devices In time slot The energy stored at the beginning, is the total number of time slots of interest, is the time slot length, A time slot mid-end device The time to transmit its own information directly to the hybrid access point, For a time slot in the remote equipment Approaching equipment The time to transmit your own information, For a time slot near the device Transmitting remote devices to hybrid access points The time of the information, represents the maximum charging power of the hybrid access point. Formula (19) is the leader problem in the Stan Kohlberg game, formula (20) is the follower problem in the Stan Kohlberg game, formulas (21)-(26) are the constraints of the game, formula (21) indicates that each end device must satisfy the energy causality constraint, that is, the consumed energy cannot exceed the stored energy, formulas (22)-(25) indicate that the sum of the charging time and the transmission time in a time slot cannot exceed the total length of the time slot, and formula (26) indicates that the power charged by the hybrid access point for the end device cannot exceed its maximum power limit.

[0114] Preferably, in some embodiments, in step 2-1, the end device sampling period The value is equal to the maximum peak AoI of the local device information.

[0115] Furthermore, in one embodiment, step 3 obtains the theoretical optimal information age and the energy allocation to achieve the optimal information age for the follower problem, and designs an information transmission strategy to optimize the information age, specifically including:

[0116] Step 3-1, calculate the theoretical optimal maximum peak AoI of a single device in single-cycle sampling mode;

[0117] The theoretical optimal maximum peak AoI for an individual device is the same as the number of time slots required for the individual device to collect enough energy to transmit its information. According to equations (6)-(7), the energy required for an individual device to transmit information to a hybrid access point is for: ;

[0118] According to the derivative analysis, for is a monotonically decreasing function, so when Get the maximum value hour, The theoretical optimal maximum peak AoI of a single device can be calculated as :

[0119] (27)

[0120] in, and Respectively represent the optimal charging power and time, is the time slot length, is the transmission bandwidth, for noise;

[0121] Step 3-2, calculating the theoretical optimal maximum peak AoI of the far device and the near device in the single-cycle sampling mode, and calculating the energy allocation of the near device when the theoretical optimal maximum peak AoI is obtained;

[0122] Step 3-3, design the information transmission strategy TS under the general periodic sampling mode, and calculate the optimal maximum peak AoI obtained by the three devices, namely, the single device SD, the near device ND and the far device FD, under this strategy.

[0123] Preferably, in some embodiments, the step 3-2 calculates the theoretical optimal maximum peak AoI of the far device and the near device in the single-cycle sampling mode, and calculates the energy allocation of the near device when the theoretical optimal maximum peak AoI is obtained, specifically:

[0124] When considering device collaboration, remote devices and nearby equipment The utility function can be expressed as:

[0125] (28)

[0126] In order to obtain the segmentation points of the piecewise function, we will and All are processed into real numbers and calculated ,get , which is the segmentation point of the utility function. By analyzing the utility function, it is easy to find that when When , the utility function is The second half of the utility function is not necessarily a monotonic function. Expand the range of the second half of the independent variable to , using the derivation technique, we can obtain The minimum value is obtained when Comparing the sizes, we can draw the following conclusions:

[0127] (1) When When It can minimize the utility function value of the equipment;

[0128] (2) When ,and In order to minimize the utility function value of the equipment, ;

[0129] (3) Otherwise, It can minimize the utility function value.

[0130] Compare the utility function value of the device when considering device collaboration with the utility function value of the device when not considering device collaboration, and choose the strategy that minimizes the utility function value. Devices can be divided into the following four situations:

[0131] (1) and ;

[0132] (2) and ;

[0133] (3) , and ;

[0134] (4) Other circumstances;

[0135] in Respectively represent end devices Distance to hybrid access point, end device The distance between the hybrid access point and the end device With terminal equipment The distance between and Respectively represent end devices , terminal equipment The importance of the information, , Terminal Device , terminal equipment The amount of data collected; and Respectively represent the optimal charging power and time; Indicates the transmission bandwidth;

[0136] In the first three cases, the remote device and nearby equipment Collaboration can minimize the utility function value, and their theoretical optimal peaks are , :

[0137] (29)

[0138] (30)

[0139] in, For end devices The energy allocation decision of is, in the first two cases,

[0140] (31)

[0141] In the third case:

[0142] (32)

[0143] In the fourth case, the end device acts as a remote device and the end device as a near device Without collaboration, the theoretical optimal maximum peak AoI of the two is obtained by the calculation method in step 3-1.

[0144] Preferably, in some embodiments, the information transmission strategy in the general periodic sampling mode in step 3-3 is specifically:

[0145] For end devices that do not require cooperation from other end devices, if the stored energy is sufficient for information transmission in the information sampling time slot, the information will be transmitted immediately; for end devices that require cooperation from other end devices, if the stored energy is sufficient to transmit the information to the cooperative end device in the information sampling time slot and the cooperative end device has sufficient energy to forward the information in the next time slot, the information will be transmitted immediately;

[0146] Under this information transmission strategy, the optimal maximum peak AoI of the end device is as follows:

[0147] For end devices that do not require cooperation from other end devices, the optimal maximum peak AoI is ; For end devices that require cooperation from other end devices, the optimal maximum peak AoI is ;in, represents the theoretical optimal maximum peak AoI in the single-cycle sampling mode obtained in step 3-1 and step 3-2, is the sampling period.

[0148] Further, in one of the embodiments, in step 4, a charging power and time decision method is designed for the leader problem so that the hybrid access point consumes the least energy to collect the latest information, specifically including:

[0149] Step 4-1: Classify the three types of end devices in more detail according to device type, device sampling mode, and energy allocation strategy. Use the conclusions obtained in step 3 to calculate the optimal maximum peak AoI of each type of device and bring it into the utility value function of the hybrid access point.

[0150] Step 4-2, design a charging power and time decision method based on simulated annealing to minimize the utility function of the leader, i.e., the hybrid access point, so that the hybrid access point consumes the least energy to collect the freshest information.

[0151] Preferably, in some embodiments, step 4-1 further classifies the three types of end devices according to device type, device sampling mode, and energy allocation strategy, and uses the conclusion obtained in step 3 to calculate the optimal maximum peak AoI of each type of device, and brings it into the utility value function of the hybrid access point, specifically including:

[0152] (1) The sets of single devices, non-cooperative near devices, and non-cooperative far devices in single-cycle sampling mode are respectively denoted as , the optimal maximum peak AoI can be calculated as:

[0153] (33)

[0154] in, The end device is a single device or a non-cooperative near device or a non-cooperative far device. Theoretical optimal maximum peak AoI, and They represent the charging power and time to be decided respectively, is the time slot length, is the transmission bandwidth, For noise, For end devices The amount of data collected, For end devices Channel gain with hybrid access points;

[0155] (2) Collaborative Nearby Devices in Single-Cycle Sampling Mode , remote devices for collaboration , whose sets are recorded as , the optimal maximum peak AoI can be calculated as:

[0156] (34)

[0157] (35)

[0158] in, , The end devices are remote devices for collaboration. , as a near device for collaboration Theoretical optimal maximum peak AoI, For end devices Energy allocation decisions;

[0159] (3) The sets of single devices, non-cooperative near devices, and non-cooperative far devices in the general periodic sampling mode are respectively denoted as , the optimal maximum peak AoI can be calculated as:

[0160] (36)

[0161] in, Indicates the sampling period of the terminal device;

[0162] (4) Nearby devices collaborating in general periodic sampling mode , remote devices for collaboration , whose sets are recorded as , the optimal maximum peak AoI can be calculated as:

[0163] (37)

[0164] (38)

[0165] The utility value function of the hybrid access point, that is, the utility function of the leader, is rewritten as:

[0166] (39)

[0167] in, and They respectively represent the charging power and time to be decided.

[0168] Preferably, in some embodiments, the charging power and time decision method based on simulated annealing described in step 4-2 specifically includes:

[0169] Step 4-2-1, Initialize the temperature of the algorithm , randomly generate the initial optimal solution including charging power and time ,in and Respectively represent the charging power and time in the optimal solution, and use formula (39) in step 4-1 to calculate its utility value ;

[0170] Step 4-2-2, update solution, the charging power and time are updated as follows:

[0171] (40)

[0172] (41)

[0173] in, For two in A random number in the range, and Respectively represent the charging power and time in the current optimal solution, and denote the charging power and time in the newly generated solution, respectively. represents the maximum charging power of the hybrid access point; the new solution is calculated using formula (39) in step 4-1 The utility value ;

[0174] Step 4-2-3, update the optimal solution with a certain probability, the probability is :

[0175] (42)

[0176] Step 4-2-4, repeat steps 4-2-2 to 4-2-3 Second, obtain the optimal solution at a fixed temperature;

[0177] Step 4-2-5, update temperature:

[0178] (43)

[0179] in, is the cooling rate;

[0180] Step 4-2-6, repeat steps 4-2-2 to 4-2-5 until the system temperature reaches the threshold, and obtain the final optimal solution .

[0181] In one embodiment, a system for optimizing energy consumption and information age based on end-to-end collaboration in a wireless charging edge computing network is provided, the system comprising:

[0182] The first module is used to build a wireless charging edge computing network architecture including a hybrid access point and multiple end devices, define the end device sampling mode, information age model, channel gain model, energy collection model, energy consumption model of end-to-end and end-to-hybrid access point communication, and determine the energy update model;

[0183] The second module is used to define the hybrid access point energy consumption optimization problem and each end device information age optimization problem as a Stanger-Kelberg game problem;

[0184] The third module is used to obtain the theoretical optimal information age and the energy allocation to achieve this optimal information age for the follower problem, and to design an information transmission strategy that optimizes the information age;

[0185] The fourth module is used to design a charging power and time decision method for the leader problem, so that the hybrid access point consumes the least energy and collects the freshest information.

[0186] For the specific definition of the energy consumption and information age optimization system based on end-to-end collaboration in the wireless charging edge computing network, please refer to the definition of the energy consumption and information age optimization method based on end-to-end collaboration in the wireless charging edge computing network mentioned above, which will not be repeated here. Each module in the above-mentioned energy consumption and information age optimization system based on end-to-end collaboration in the wireless charging edge computing network can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0187] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following is achieved:

[0188] Step 1: Build a wireless charging edge computing network architecture including a hybrid access point and multiple end devices, define the end device sampling mode, information age model, channel gain model, energy collection model, energy consumption model of end-to-end and end-to-hybrid access point communication, and determine the energy update model;

[0189] Step 2, define the hybrid access point energy consumption optimization problem and each end device information age optimization problem as a Stanger-Kerberg game problem;

[0190] Step 3: For the follower problem, obtain the theoretical optimal information age and the energy allocation to achieve this optimal information age, and design an information transmission strategy that optimizes the information age;

[0191] Step 4: Aiming at the leader problem, a charging power and time decision method is designed so that the hybrid access point consumes the least energy and collects the freshest information.

[0192] For the specific limitations of each step, please refer to the above limitations on the energy consumption and information age optimization method based on end-to-end collaboration in the wireless charging edge computing network, which will not be repeated here.

[0193] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements:

[0194] Step 1: Build a wireless charging edge computing network architecture including a hybrid access point and multiple end devices, define the end device sampling mode, information age model, channel gain model, energy collection model, energy consumption model of end-to-end and end-to-hybrid access point communication, and determine the energy update model;

[0195] Step 2, define the hybrid access point energy consumption optimization problem and each end device information age optimization problem as a Stanger-Kerberg game problem;

[0196] Step 3: For the follower problem, obtain the theoretical optimal information age and the energy allocation to achieve this optimal information age, and design an information transmission strategy that optimizes the information age;

[0197] Step 4: Aiming at the leader problem, a charging power and time decision method is designed so that the hybrid access point consumes the least energy and collects the freshest information.

[0198] For the specific limitations of each step, please refer to the above limitations on the energy consumption and information age optimization method based on end-to-end collaboration in the wireless charging edge computing network, which will not be repeated here.

[0199] As a specific example, in one of the embodiments, the present invention is further verified and explained.

[0200] This embodiment verifies the correctness of the theoretical maximum peak AoI of different types of devices proposed in step 3-2 of the present invention, and compares the performance of the algorithms proposed in steps 3 and 4 with the existing benchmark algorithms to verify the effectiveness of the algorithm proposed in the present invention. The specific conditions include:

[0201] (1) Construct five sets of wireless charging edge computing network architectures. The number of hybrid access points in the five sets of network architectures is 1. The number of end devices is 30, 60, 90, 120, and 150 respectively. The ratio of single devices, near devices, and far devices is 1:1:1. Set the distance threshold to distinguish the types of devices. , that is, set the distance between the near device and the hybrid access point to The distance between the remote device and the hybrid access point is between , while the distance between a single device and a hybrid access point is between;

[0202] (2) Set the amount of data sampled by the end device to be evenly distributed In the bit range, the importance of information is in the range Inside;

[0203] (3) Observation of the present invention time slots, each time slot is 1 second long;

[0204] (4) The maximum charging power of the hybrid access point is set to 8 W, and the energy harvesting frequency of the end device is set to 0.7;

[0205] (3) Set at the reference distance Power consumption at , Decline Index ;

[0206] The benchmark algorithms compared with the transmission strategy proposed in step 3-3 of the present invention include:

[0207] TE: If the device has enough energy, the information is transmitted immediately;

[0208] JADE (Q. Chen, Z. Cai, L. Cheng, F. Wang and H. Gao, “Joint Near-Optimal Age-based Data Transmission and Energy Replenishment Scheduling atWireless-Powered Network Edge,” IEEE INFOCOM 2022, London, United Kingdom, 2022, pp. 770-779): When the device collects enough energy for information transmission, if the local AoI of the device is less than half of the collection period (the information is considered fresh), it will be transmitted immediately; otherwise, the information is considered old and new information will be sampled before transmission;

[0209] ACS (Q. Chen, S. Guo, W. Xu, Z. Cai, L. Cheng and H. Gao, “AoIMinimization Charging at Wireless-Powered Network Edge,” IEEE ICDCS 2022, Bologna, Italy, 2022, pp. 713-723): Calculate the total number of times that information can be transmitted, divide the total number of time slots by the total number of transmissions to get the number of time slots between each two transmissions, and evenly transmit in the total number of time slots;

[0210] noncoll: No collaboration between devices is performed.

[0211] The benchmark algorithms compared with the charging power and time decision method proposed in step 4 of the present invention include:

[0212] HC: Charging power and time decision method based on hill climbing algorithm;

[0213] DGC (J. Shi, P. Cong, L. Zhao, X. Wang, S. Wan and M. Guizani, “ATwo-Stage Strategy for UAV-Enabled Wireless Power Transfer in UnknownEnvironments,” IEEE Transactions on Mobile Computing, vol. 23, no. 2, pp.1785-1802, 2024): Charging power and time decision method based on dynamic genetic algorithm;

[0214] MORL (F. Song, M. Deng, H. Xing, Y. Liu, F. Ye and Z. Xiao, “Energy-Efficient Trajectory Optimization With Wireless Charging in UAV-Assisted MEC Based on Multi-Objective Reinforcement Learning,” IEEE Transactions on Mobile Computing, vol. 23, no. 12, pp. 10867-10884, 2024): Charging power and time decision method based on multi-objective reinforcement learning.

[0215] First, the correctness of the theoretical maximum peak AoI of different types of devices proposed in step 3-2 of the present invention is verified. Figure 3 , Figure 4 , Figure 5 The AoIs of three types of near devices, the AoIs of far devices, and their utility function values ​​in the single-cycle sampling mode are respectively illustrated. It can be seen from the three figures that the AoI and the utility function value before and after the real number conversion have the same change trend, so it is feasible to perform real number analysis on the AoI and the utility function value; in the three figures, the red hexagon represents the optimal utility value calculated by step 3-2 of the present invention. It can be observed that the optimal value of the utility function value after real number conversion obtained by the traversal algorithm in the three figures is the same as the optimal value calculated by the Benfa invention, that is, the correctness of the theoretical maximum peak AoI of different types of devices proposed in step 3-2 is verified.

[0216] Then, the performance of the algorithms proposed in step 3 and step 4 is compared with the existing benchmark algorithms to verify the effectiveness of the algorithm proposed in the present invention. Figure 6 It shows that when the amount of information sampled by the remote device gradually increases, the utility function value of the device obtained by the algorithm proposed in this invention and other benchmark algorithms. Figure 6 It can be observed that when the amount of information data gradually increases, the utility function value of the device gradually increases, and the algorithm proposed in the present invention can always reach the optimal utility value. Figure 7 The utility function value of the device obtained by the algorithm proposed in the present invention and other benchmark algorithms is shown when the amount of information sampled by the near device gradually increases. It can be observed that when the amount of information data gradually increases, the utility function value of the device gradually increases with a small amplitude, while the algorithm proposed in the present invention can always reach the optimal utility value. Figure 8 The change in the utility value of the leader (hybrid access point) when the total number of end devices changes is shown. It can be found that the utility function value has no obvious relationship with the total number of devices, and the algorithm proposed in the present invention can always achieve the optimal utility value. Therefore, it can be concluded that the algorithm proposed in the present invention is applicable to wireless charging edge computing networks of any scale.

[0217] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing energy consumption and information age based on end-to-end collaboration in a wireless charging edge computing network, characterized in that: The method comprises: Step 1: Build a wireless charging edge computing network architecture including a hybrid access point and multiple end devices, define the end device sampling mode, information age model, channel gain model, energy collection model, energy consumption model of end-to-end and end-to-hybrid access point communication, and determine the energy update model; Step 2: define the hybrid access point energy consumption optimization problem and each terminal device information age optimization problem as a Stanger-Kelberg game problem; specifically, it includes: Step 2-1, the energy consumption of the hybrid access point and the AoI of each terminal device on the hybrid access point are dimensionless, specifically: The maximum energy consumption that the hybrid access point can achieve in each time slot is used to maximize its actual energy consumption. The energy consumption after maximization is: (1) in, Indicates the maximum power that the hybrid access point can use to charge the end device. is the time slot length, and They respectively represent the power and time for the hybrid access point to charge the end device; The maximum peak AoI of the local peer device information is used to minimize the maximum peak AoI on the hybrid access point. The minimized maximum peak AoI is: : (2) in, Indicates the sampling period of the terminal device. Display terminal equipment The maximum peak AoI of the information on the hybrid access point; Step 2-2, defining the utility values ​​of the hybrid access point and the end device, and formalizing all utility value optimization problems into a Stan Kohlberg game containing a leader and multiple followers, where the hybrid access point serves as the leader and the end device serves as the follower; The leader’s utility function is defined as the weighted sum of the dedimensionalized energy consumption and the dedimensionalized average of the maximum peak AoI of all devices: (3) in, represents the preference of the hybrid access point between information freshness and energy consumption, Display terminal equipment The importance of the information, represents the leader’s utility function; The follower’s utility function is defined as the weighted sum of its own and its paired device’s maximum peak AoI: (4) in, represents the follower’s utility function, Display terminal equipment and The pairing relationship, express and Pairing, N represents the total number of end devices, Display terminal equipment The maximum peak AoI of the information on the hybrid access point; The energy consumption and maximum peak AoI optimization problem is defined as: (5) （6） （7） (8) (9) (10) (11) (12) in, For end devices In time slot The energy stored at the beginning, is the total number of time slots of interest, is the time slot length, A time slot mid-end device The time to transmit its own information directly to the hybrid access point, For a time slot in the remote equipment Approaching equipment The time to transmit your own information, For a time slot near the device Transmitting remote devices to hybrid access points The time of the information, Indicates the maximum charging power of the hybrid access point; Step 3: For the follower problem, obtain the theoretical optimal information age and the energy allocation to achieve this optimal information age, and design an information transmission strategy that optimizes the information age; Step 4: Aiming at the leader problem, design a charging power and time decision method so that the hybrid access point consumes the least energy and collects the latest information; specifically, it includes: Step 4-1: Classify the three types of end devices in more detail according to device type, device sampling mode, and energy allocation strategy. Use the conclusions obtained in step 3 to calculate the optimal maximum peak AoI of each type of device and bring it into the utility value function of the hybrid access point. Step 4-2, design a charging power and time decision method based on simulated annealing to minimize the utility function of the leader, i.e., the hybrid access point, so that the hybrid access point consumes the least energy to collect the freshest information.

2. The method for optimizing energy consumption and information age based on end-to-end collaboration in a wireless charging edge computing network according to claim 1, characterized in that: Step 1 constructs a wireless charging edge computing network architecture including a hybrid access point and multiple end devices, defines the end device sampling mode, information age model, channel model, energy collection model, energy consumption model of end-to-end and end-to-hybrid access point communication, and determines the energy update model, as follows: Step 1-1: Build a wireless charging edge computing network architecture, which includes a hybrid access point HAP and Terminal equipment , the hybrid access point charges the end device, the i-th end device The distance to the hybrid access point is , , terminal equipment The amount of data collected is ; According to the distance to the hybrid access point and whether there is a paired device, the end device is divided into three categories: single device SD, near device ND and far device FD. If the end device has no paired device, it is considered to be a single device SD. If the end device has a paired device, the device is further classified according to its distance to the hybrid access point. If the distance between the end device and the hybrid access point is less than the threshold , the end device is considered to be a near device ND, otherwise the end device is a far device FD; Depend on Remote device With nearby equipment Pairing, remote device With nearby equipment The distance between ; Before Each device is a separate device, Devices are near devices, and the last A device is a remote device, that is, For a separate device, here , For near devices, here , For remote devices, here ; Step 1-2, define the end device sampling mode, including: single cycle sampling mode PTSSM and general cycle sampling mode GPSM; Step 1-3, the freshness of information is characterized by the information age AoI, which is calculated as the difference between the current time and the latest information sampling time; as a single device SD and as a near device ND end device The AoI update formula at the hybrid access point is: （13） in, express Time slot at the end device Local AoI, For The transmission decision of the time slot is Indicates that the device is The time slot transmits the information to the hybrid access point; Respectively represent the t time slot and t+1 time slot as a single device SD and a close device ND Information on the AoI of the hybrid access point; As the end device of the remote device FD The AoI update formula at the hybrid access point is: （14） in, Respectively expressed in Time slot remote device Towards auxiliary equipment Transmission decisions and assistance for nearby devices Transmitting remote devices to hybrid access points Information transmission decisions, Indicated in Time slot remote device Towards auxiliary equipment Transmit information and assist nearby devices exist The time slot transmits the received information to the hybrid access point; The maximum peak AoI of each end device’s information on the hybrid access point is expressed as : （15） in, is the total number of time slots of interest; Step 1-4, establish the channel gain model between the two communication entities, specifically: use Represents the distance between two communicating entities, then its channel gain Calculated as: （16） in, is a random variable that follows a circularly symmetric Gaussian distribution. is the reference distance, For The power consumption at is the decline index; Steps 1-5: Establish an energy collection model for the end device, specifically: In each time slot, the end device The energy collected for: (17) in, is the energy harvesting efficiency, and They represent the power and time of charging the end device by the hybrid access point, represents the channel gain between the end device and the hybrid access point. The channel gain converts the Replaced by the distance between the device and the hybrid processor Calculated; Step 1-6, establishing the energy consumption model of end-to-end and end-to-hybrid access point communications, specifically: The energy consumption of the end device is divided into three categories: the energy consumption of the end device directly transmitting its own information to the hybrid access point, the energy consumption of the remote device transmitting information to the near device, and the energy consumption of the near device forwarding the remote device information to the hybrid access point; Among them, any end device Time to transmit own information directly to the hybrid access point for: (18) Terminal equipment The energy consumption of directly transmitting its own information to the hybrid access point is expressed as : (19) in, For end devices The power of transmitting information to the hybrid access point, For end devices The amount of data collected, is the transmission bandwidth, for noise; Remote equipment Approaching equipment Time of transmission of information for: (20) Remote equipment Approaching equipment Energy consumption for transmitting information It is expressed as: (21) in, Indicates the end device as a remote device To the end device as a near device The transmission power of the transmitted information, ; Remote device With nearby equipment The channel gain between is obtained by Replace with remote device With nearby equipment The distance between Calculated; Near equipment Forwards traffic from remote devices to the hybrid access point Time of information for: (22) Near equipment Forwards traffic from remote devices to the hybrid access point Energy consumption of information It is expressed as: (23) in, For nearby equipment The power of transmitting information to the hybrid access point, For nearby equipment The channel gain between the hybrid access point and the Replace with a nearby device Distance to hybrid processor Calculated; Steps 1-7, establish the energy update model of the terminal device, specifically: According to the device classification, the energy update formula of the end device is divided into three categories; (1) Single device In time slot Energy stored at the start The update formula is: (24) in, For time slot The energy stored at the beginning, For Transmission decision for time slots; (2) Near equipment In time slot Energy stored at the start The update formula is: (25) (3) Remote equipment In time slot Energy stored at the start The update formula is: (26) in, Indicated in Time slot remote device Towards auxiliary equipment transmission decision.

3. The method for optimizing energy consumption and information age based on end-to-end collaboration in a wireless charging edge computing network according to claim 1, characterized in that: Step 2-1 Mid-range device sampling period The value is equal to the maximum peak AoI of the local device information.

4. The method for optimizing energy consumption and information age based on end-to-end collaboration in a wireless charging edge computing network according to claim 2, characterized in that: In step 3, for the follower problem, the theoretical optimal information age and the energy allocation to achieve the optimal information age are obtained, and an information transmission strategy is designed to optimize the information age, which specifically includes: Step 3-1: Calculate the theoretical optimal maximum peak AoI of a single device in single-cycle sampling mode: : (27) in, and Respectively represent the optimal charging power and time, is the time slot length, is the transmission bandwidth, for noise; Step 3-2, calculating the theoretical optimal maximum peak AoI of the far device and the near device in the single-cycle sampling mode, and calculating the energy allocation of the near device when the theoretical optimal maximum peak AoI is obtained; Step 3-3, design the information transmission strategy TS under the general periodic sampling mode, and calculate the optimal maximum peak AoI obtained by the three devices, namely, the single device SD, the near device ND and the far device FD, under this strategy.

5. The method for optimizing energy consumption and information age based on end-to-end collaboration in a wireless charging edge computing network according to claim 4, characterized in that: Step 3-2 calculates the theoretical optimal maximum peak AoI of the far device and the near device in the single-cycle sampling mode, and calculates the energy allocation of the near device when the theoretical optimal maximum peak AoI is obtained, specifically: To calculate the remote device in single cycle sampling mode and nearby equipment The theoretical optimal maximum peak AoI is divided into four cases: (1) and ; (2) and ; (3) , and ; (4) Other circumstances; in Respectively represent end devices Distance to hybrid access point, end device The distance between the hybrid access point and the end device With terminal equipment The distance between and Respectively represent end devices , terminal equipment The importance of the information, , Terminal Device , terminal equipment The amount of data collected; and Respectively represent the optimal charging power and time; Indicates the transmission bandwidth; In the first three cases, the end device acts as a remote device. and the end device as a near device The theoretical optimal peak values ​​are , : (28) (29) in, For end devices The energy allocation decision of is, in the first two cases, (30) In the third case: (31) In the fourth case, the end device acts as a remote device and the end device as a near device Without collaboration, the theoretical optimal maximum peak AoI of the two is obtained by the calculation method in step 3-1.

6. The method for optimizing energy consumption and information age based on end-to-end collaboration in a wireless charging edge computing network according to claim 5, characterized in that: The information transmission strategy TS in the general periodic sampling mode described in step 3-3 is specifically: For end devices that do not require cooperation from other end devices, if the stored energy is sufficient for information transmission in the information sampling time slot, the information will be transmitted immediately; for end devices that require cooperation from other end devices, if the stored energy is sufficient to transmit the information to the cooperative end device in the information sampling time slot and the cooperative end device has sufficient energy to forward the information in the next time slot, the information will be transmitted immediately; Under this information transmission strategy, the optimal maximum peak AoI of the end device is as follows: For end devices that do not require cooperation from other end devices, the optimal maximum peak AoI is ; For end devices that require cooperation from other end devices, the optimal maximum peak AoI is ;in, represents the theoretical optimal maximum peak AoI in the single-cycle sampling mode obtained in step 3-1 and step 3-2, is the sampling period.

7. The method for optimizing energy consumption and information age based on end-to-end collaboration in a wireless charging edge computing network according to claim 3 or 6, characterized in that: Step 4-1 classifies the three types of end devices in more detail according to device type, device sampling mode, and energy allocation strategy. The optimal maximum peak AoI of each type of device is calculated using the conclusions obtained in step 3 and is introduced into the utility value function of the hybrid access point, including: (1) The sets of single devices, non-cooperative near devices, and non-cooperative far devices in single-cycle sampling mode are respectively denoted as , the optimal maximum peak AoI can be calculated as: (32) in, The end device is a standalone device or a non-cooperative near device or a non-cooperative far device. Theoretical optimal maximum peak AoI, and They represent the charging power and time to be decided respectively, is the time slot length, is the transmission bandwidth, For noise, For end devices The amount of data collected, For end devices Channel gain with hybrid access points; (2) Collaborative Nearby Devices in Single-Cycle Sampling Mode , remote devices for collaboration , whose sets are recorded as , the optimal maximum peak AoI can be calculated as: (33) (34) in, , The end devices are remote devices for collaboration. , as a near device for collaboration Theoretical optimal maximum peak AoI, For end devices Energy allocation decisions; (3) The sets of single devices, non-cooperative near devices, and non-cooperative far devices in the general periodic sampling mode are respectively denoted as , the optimal maximum peak AoI can be calculated as: (35) in, Indicates the sampling period of the terminal device; (4) Nearby devices collaborating in general periodic sampling mode , remote devices for collaboration , whose sets are recorded as , the optimal maximum peak AoI can be calculated as: (36) (37) The utility value function of the hybrid access point, that is, the utility function of the leader, is rewritten as: (38) in, and They respectively represent the charging power and time to be decided.

8. The method for optimizing energy consumption and information age based on end-to-end collaboration in a wireless charging edge computing network according to claim 7, characterized in that: The charging power and time decision method based on simulated annealing described in step 4-2 specifically includes the following steps: Step 4-2-1, Initialize the temperature of the algorithm , randomly generate the initial optimal solution including charging power and time ,in and Respectively represent the charging power and time in the optimal solution, and use formula (38) in step 4-1 to calculate its utility value ; Step 4-2-2, update solution, the charging power and time are updated as follows: (39) (40) in, For two in A random number in the range, and Respectively represent the charging power and time in the current optimal solution, and denote the charging power and time in the newly generated solution, respectively. represents the maximum charging power of the hybrid access point; the new solution is calculated using formula (38) in step 4-1 The utility value ; Step 4-2-3, update the optimal solution with a certain probability, the probability is : (41) Step 4-2-4, repeat steps 4-2-2 to 4-2-3 Second, obtain the optimal solution at a fixed temperature; Step 4-2-5, update temperature: (42) in, is the cooling rate; Step 4-2-6, repeat steps 4-2-2 to 4-2-5 until the system temperature reaches the threshold, and obtain the final optimal solution .

9. An energy consumption and information age optimization system based on end-to-end collaboration in a wireless charging edge computing network based on the method according to any one of claims 1 to 8, characterized in that: The system comprises: The first module is used to build a wireless charging edge computing network architecture including a hybrid access point and multiple end devices, define the end device sampling mode, information age model, channel gain model, energy collection model, energy consumption model of end-to-end and end-to-hybrid access point communication, and determine the energy update model; The second module is used to define the hybrid access point energy consumption optimization problem and each end device information age optimization problem as a Stanger-Kelberg game problem; The third module is used to obtain the theoretical optimal information age and the energy allocation to achieve this optimal information age for the follower problem, and to design an information transmission strategy that optimizes the information age; The fourth module is used to design a charging power and time decision method for the leader problem, so that the hybrid access point consumes the least energy and collects the freshest information.

10. A computer 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 computer program, the method according to any one of claims 1 to 8 is implemented.

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