Wireless power supply communication network information age minimization resource allocation method and system

Abstract

The invention discloses a wireless power supply communication network information age minimization resource allocation method and system, and belongs to the technical field of Internet of Things system optimizing.The method comprises the steps that scheduling decision variables of sensors in a wireless power supply communication network scene are defined; analyzing the information age change in the process of transmitting data to the base station by the sensor, and updating the information age of the data based on the scheduling decision and the data transmission result; the transmitting power and the scheduling strategy of the sensor are jointly optimized, and an optimization model is established with the purpose of minimizing the average information age of the system; and dynamically selecting the current optimal sensor for scheduling by utilizing a reinforcement learning algorithm, determining the transmitting power based on the energy state of the sensor, and iteratively executing until a reward function in the reinforcement learning algorithm is converged to obtain an optimal average AoI. According to the invention, while energy saving is ensured, the transmission success rate of the data packet is improved, the freshness of system information is improved, the service life of the Internet of Things terminal is prolonged, and the dependence on frequent charging and battery replacement is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of Internet of Things system optimization, and particularly relates to a method and system for minimizing the age of information in a wireless power supply communication network for resource allocation. Background Art

[0002] With the rapid development of Internet of Things technology, some real-time application scenarios, such as autonomous driving and intelligent healthcare, have extremely high requirements for the timeliness of information. In these application scenarios, the timeliness and accuracy of information directly affect the performance and security of the system. For example, in autonomous driving, the vehicle's control center system must quickly process and analyze the road surface information collected by sensors and then make corresponding operations. If the received data is delayed or fails to reflect the latest state of the current environment, the control center may make incorrect judgments based on outdated information, thereby triggering potential safety hazards such as collisions or out-of-control situations. However, traditional network performance metrics, such as throughput and delay, mainly focus on the quantity and delay of data transmission, but they are difficult to effectively characterize the freshness of data packets. To solve this problem, the concept of Age of Information (AoI) is introduced, aiming to quantify the freshness of data packets in the network. The age of information refers to the time interval from the generation of information to its reception or use. As the importance of AoI in measuring the timeliness of Internet of Things systems is gradually widely recognized, minimizing AoI has become one of the key goals for improving system performance and reliability. Especially in application scenarios that require real-time data to support decision-making, the size of AoI directly affects the effectiveness of information and the response ability of the system. Therefore, how to ensure the timely update of information by precisely controlling AoI has become a hot issue in the field of Internet of Things research.

[0003] Since most real-time application scenarios involve a large number of sensor devices, the multi-sensor scheduling problem has a crucial impact on AoI. If a data packet is not scheduled for a long time, its AoI will continuously increase, resulting in a decline in the system's AoI performance. Therefore, it is particularly important to design an effective scheduling strategy for multi-sensor systems. In these systems, sensors are usually equipped with batteries of limited capacity and need to operate continuously for a long time. However, if sensors are deployed in a dangerous area, the cost of manually replacing the battery is very high. Therefore, wireless power transfer (WPT) technology can transmit radio frequency signals through a wireless power supply station to provide wireless charging for low-power sensors. Compared with traditional clean energies such as solar energy and wind energy, the energy provided by radio frequency signals is more stable and less affected by environmental factors. Therefore, a wireless power supply communication network that uses radio frequency signals to provide stable energy for low-power sensors has become an effective solution.

[0004] In an actual Internet of Things (IoT) system, minimizing the Age of Information (AoI) not only depends on an effective scheduling strategy but is also closely related to power control. The transmission power of sensor nodes not only determines whether data packets can be successfully transmitted but also directly affects the energy efficiency and stability of the system. Excessive transmission power can ensure the transmission quality of data, but it will lead to rapid energy consumption, thereby shortening the working time of nodes and affecting the long-term operation of the system. On the contrary, too low power may cause data transmission failures or communication delays, thus increasing the AoI. Therefore, reasonable power control can not only ensure the timely transmission of data packets but also effectively save energy, so as to maintain sufficient energy during subsequent transmission processes and avoid communication interruptions or a sharp increase in AoI caused by battery depletion. However, in existing IoT systems, only one aspect of the scheduling strategy or power control strategy is focused on, lacking a comprehensive discussion on the combination of the two. Therefore, it is difficult to minimize the system AoI, resulting in room for improvement in the overall performance and efficiency of the network. Summary of the Invention

[0005] The present invention provides a method and system for minimizing the Age of Information in a wireless power supply communication network, aiming to solve the problem that in current IoT systems, only one aspect of the scheduling strategy or power control strategy is focused on, lacking a comprehensive discussion on the combination of the two, and thus it is difficult to minimize the system AoI, resulting in room for improvement in the overall performance and efficiency of the network.

[0006] To achieve the above object, the present invention adopts the following technical solutions: The present invention provides a method for minimizing the Age of Information in a wireless power supply communication network resource allocation, including the following steps: S1. Define the scheduling decision variables of sensors in the wireless power supply communication network scenario, and determine the sensors to be scheduled in each time slot through the scheduling decision variables, ensuring that only one sensor is scheduled for data transmission in each time slot; Among them, the wireless power supply communication network scenario is composed of a wireless power supply station, a receiving base station, and multiple sensors. The sensors receive energy through the WPT technology and collect environmental data; S2. Analyze the change in the Age of Information during the process of sensors transmitting data to the base station, and update the Age of Information of the data based on the scheduling decision and data transmission results; S3. Jointly optimize the transmission power and scheduling strategy of sensors, aiming to minimize the average Age of Information of the system, and establish an optimization model including an energy harvesting model, data transmission rate, and transmission power constraints; S4. Use a reinforcement learning algorithm to dynamically select the current optimal sensor for scheduling and determine the transmission power based on the energy state of the sensor; S5. Iteratively execute S4 until the reward function in the reinforcement learning algorithm converges to obtain the optimal average AoI.

[0007] In some embodiments, in S1, in the wireless powered communication network scenario, the wireless power station uses WPT technology to transmit energy to the sensors, and the sensors receive the energy through a single antenna and transmit data packets to the receiving base station.

[0008] Further, in S1, the scheduling decision variable is a binary variable, which takes the value of 1 when the sensor is scheduled within a time slot and 0 when not scheduled. The scheduling decision variable satisfies the following formula: ; where is the sensor index, representing the sensor , is the number of sensors, is the number of time slots, is the time slot index, representing the th time slot, is the scheduling decision variable, used to indicate whether the sensor is scheduled. In some embodiments, in S2, the age of information is updated as follows: when the sensor is scheduled, the age of information of the data from the sensor is reset to the length of one time slot for transmitting the data; when the sensor is not scheduled, the age of information of the data from the sensor increases by the current time slot length.

[0009] In some embodiments, in S3, when establishing the optimization model, the path loss and small-scale fading models are considered, and the energy harvesting model adopts the following formula: ; where is the power saturation threshold of the energy harvesting receiver, is the energy conversion efficiency, is the length of one time slot.

[0010] Further, in S3, is the path loss between the transmitter and the receiver ; where is the path loss constant with a reference distance of 1 meter, is the distance between the transmitter and the receiver , is the path loss factor.

[0011] Further, in S3, the channel gain under the small-scale fading model is calculated as follows: ; where and are the deterministic LoS component and the random non-LoS NLoS component respectively, is the Rice factor, represents the power ratio between LoS and NLoS.

[0012] Further, in S3, the stored energy of the sensor is updated by the following formula: ; where is the energy consumed by the sensor for communication in the current time slot , is the size of the sensor battery capacity.

[0013] Further, the optimization model is specifically: ; where is the transmission power of the sensor in the th time slot, is the maximum transmission power of the sensor.

[0014] The present invention also provides a resource allocation system for minimizing the age of information in a communication network. The system includes a scenario module, a sensor scheduling module, a data transmission module, an optimization module, a solution module, and a convergence module, where: Scenario module: used to construct and form a wireless power supply communication network scenario; Sensor scheduling module: used to define the scheduling decision variables of the sensors in the wireless power supply communication network scenario, and determine the sensors to be scheduled in each time slot through the scheduling decision variables to ensure that only one sensor is scheduled for data transmission in each time slot; Data transmission module: used to analyze the change in the age of information during the process of the sensor transmitting data to the base station, and update the age of information of the data based on the scheduling decision and the data transmission result; Optimization module: jointly optimize the transmission power and scheduling strategy of the sensor, with the goal of minimizing the average AoI of the system, and establish an optimization model including an energy harvesting model, data transmission rate, and transmission power constraints; Solution module: use the reinforcement learning algorithm to dynamically select the currently optimal sensor for scheduling, and determine the transmission power according to the energy state of the sensor; Convergence module: iteratively execute the solution module until the reward function in the reinforcement learning algorithm converges to obtain the optimal average AoI.

[0015] Compared with the prior art, the method and system for minimizing the age of information in a wireless power supply communication network according to the present invention have the following beneficial effects: The method for minimizing the age of information in a wireless power supply communication network according to the present invention can minimize the average AoI of the overall system by jointly optimizing time and resource allocation. This optimization method not only improves the freshness of the overall system information, but also extends the working time of the sensor and enhances the performance of the overall system. In an environment with limited resources, this method shows significant advantages, capable of improving the freshness of the data packets received by the base station and enhancing data validity. The present invention incorporates a non-linear energy harvesting model and data transmission rate into the optimization objective, further reducing AoI from a system perspective. Current optimization methods usually consider the transmission power or scheduling strategy of the sensor separately, while the present invention simultaneously optimizes sensor scheduling and transmission power to achieve better information freshness and system stability. The present invention uses WPT technology through a wireless power supply station to provide power support for low-power sensors, and on this basis, optimizes sensor scheduling and transmission power to maximize the reduction of the average AoI of the system. In addition, the method of the present invention can improve the transmission success rate of data packets while ensuring energy conservation, thereby enhancing the freshness of the overall system information. The comprehensive optimization strategy of the present invention can not only extend the service life of IoT terminals, but also reduce the dependence on frequent charging and battery replacement, with strong practicability and sustainability. Description of the Drawings

[0016] The accompanying drawings in the specification are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0017] Figure 1 It is a schematic diagram of the architecture of the wireless power supply communication network scenario in the method for minimizing the age of information in a wireless power supply communication network according to the present invention. Figure 2 It is a schematic diagram of the operation process of the system for minimizing the age of information in a wireless power supply communication network according to the present invention. Figure 3 It is a schematic diagram of the framework of the method for minimizing the age of information in a wireless power supply communication network according to the present invention. Detailed Embodiments

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Generally, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0019] Accordingly, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0020] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0021] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper", "lower", "horizontal", "inner", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the invention product is usually placed during use, it is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation of the present invention. In addition, terms such as "first", "second", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0022] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but it can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but it can be slightly inclined.

[0023] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and limited, if terms such as "set", "installed", "connected", "connected" are understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0024] As Figure 1 , Figure 2 and Figure 3 shown, the method for minimizing the age of information of a wireless power supply communication network in the present invention includes the following steps: S1. Define the scheduling decision variables of sensors in the wireless power supply communication network scenario, and determine the sensors scheduled in each time slot through the scheduling decision variables to ensure that only one sensor is scheduled for data transmission in each time slot; Among them, the wireless power supply communication network scenario is composed of a wireless power supply station, a receiving base station, and multiple sensors. The sensors receive energy through WPT technology and collect environmental data; S2. Analyze the change in the age of information during the process of the sensor transmitting data to the base station, and update the age of information of the data based on the scheduling decision and data transmission result; S3. Jointly optimize the transmission power and scheduling strategy of the sensor, aiming to minimize the average AoI of the system, and establish an optimization model including an energy harvesting model, data transmission rate, and transmission power constraints; S4. Use the reinforcement learning algorithm to dynamically select the current optimal sensor for scheduling, and determine the transmission power based on the energy state of the sensor; S5. Iteratively execute S4 until the reward function in the reinforcement learning algorithm converges to obtain the optimal average AoI.

[0025] The method for minimizing the age of information and resource allocation in the wireless power supply communication network of the present invention applies WPT technology in the wireless power supply communication network to provide energy supply for low-power sensors. At the same time, it comprehensively considers factors such as the non-linear energy harvesting model, data transmission rate, and transmission power of the sensors during the optimization process. Finally, from the system level, it aims to maximize the reduction of the AoI of the system and improve the overall performance and efficiency of the network.

[0026] The method and system for minimizing the age of information and resource allocation in the wireless power supply communication network of the present invention will be further described in detail below through specific embodiments.

[0027] The method for minimizing the age of information and resource allocation in the wireless power supply communication network of the present invention is specifically as follows: S1: First Figure 1 shows the schematic diagram of the wireless power supply communication network scenario. This scenario consists of a wireless power supply station, a receiving base station, and a group of sensors. The sensor index is represented by , and the quantity is . These sensors are randomly deployed in the specified area, responsible for monitoring the surrounding environment and generating corresponding data. To reduce the difficulty of manually replacing the battery, this system uses WPT technology to transmit energy to low-power sensors. Each sensor is equipped with a single antenna, which is used to receive the energy transmitted by the radio frequency signal and transmit data packets to the base station, and is also equipped with a battery with a capacity of . Similar to other systems, this system uses time division multiple access technology, and the system operation is as shown in Figure 2 . Time is divided into time slots of the same length. The length of each time slot is , and there are a total of time slots. In addition, the system adopts a demand generation strategy. Within each time slot, the sensor generates a new status update data packet only when it is scheduled for data transmission to save energy. To avoid mutual interference, energy transmission and information transmission are carried out on orthogonal frequency bands, and only one sensor is allowed to be scheduled for data transmission within each time slot.

[0028] S2: For the scenario constructed in S1, by using the scheduling decision variable to express the scheduling decision of the sensor: at the beginning of the time slot, if the sensor is scheduled to transmit the collected environmental data to the receiving base station in the form of a data packet, the scheduling decision variable is 1; otherwise, the scheduling decision variable is 0, indicating that the sensor n is not scheduled. Therefore, the scheduling decision variable must satisfy: (1); where is the sensor index, indicating the sensor , is the number of sensors, is the number of time slots, is the time slot index, indicating the th time slot, is the scheduling decision variable, used to indicate whether the sensor is scheduled. S3: Based on the scenario constructed in S1, the sensor transmits data to the base station, and analyzes the change of AoI of the sensor during the data transmission process.

[0029] The wireless power station transmits energy to the sensor through WPT technology, and the sensor uses the collected energy to send the collected data packet to the base station. In this invention, when considering channel modeling, the path loss and small-scale fading model are considered. Among them, the path loss is mainly affected by the distance of the propagation path and the occlusion of the propagation environment, and is the key factor affecting the signal attenuation of the wireless communication system. For the simplicity of the formula, we use A to represent the transmitting end, such as the wireless power station and the sensor, and use B to represent the receiving end, such as the sensor and the receiving base station. Therefore, the path loss between the transmitting end A and the receiving end B is expressed as , where is the path loss constant when the reference distance is 1 meter, is the distance between the transmitting end A and the receiving end B , is the path loss factor.

[0030] The small-scale fading model is modeled considering the Rice fading model, where the signal from the transmitting end A to the receiving endB is defined as: (2); where and are the deterministic LoS component and the random non-line-of-sight (NLoS) component respectively, K is the Rice factor, which represents the power ratio between LoS and NLoS.

[0031] The wireless power station continuously charges the sensor with a fixed transmission power Adopting a non-linear energy harvesting method in the present invention, the energy collected by the sensor in a time slot is defined as: (3); where is the power saturation threshold of the energy harvesting receiver, is the energy conversion efficiency, is the length of a time slot.

[0032] Use to represent the amount of energy remaining in the sensor's current battery. The power for each scheduled sensor to transmit a data packet must satisfy: ; where represents the maximum transmission power of the sensor.

[0033] Therefore, the sensor is updated by the following formula: (4); where is the current time slot the energy consumed by the sensor for communication, is the size of the sensor battery capacity.

[0034] According to the Shannon formula, the amount of data that can be transmitted by the sensor in the time slot can be obtained by the following formula: (5); where W is the size of the channel bandwidth, is the power of additive white Gaussian noise.

[0035] S3: Based on S2, the data packet size can be expressed as , when the scheduling decision variable is set to 1, the sensor The AoI is reset to , otherwise it is incremented by one based on the AoI of the previous time slot , so according to the above formula, the sensor The corresponding AoI evolution formula is: (6); where is the packet size.

[0036] S4: By using the AoI of each sensor obtained in S3, the transmit power of the sensor is optimized and the scheduling decision variables in S2 are used to schedule the sensor to transmit data to the base station within each time slot. The optimization objective is to minimize the average AoI of the system. The optimization model is specifically: .

[0037] S5: This optimization problem involves binary variables and non-linear constraints, belonging to a mixed integer non-linear programming problem. At the same time, this problem is also affected by the time-varying channel state, being neither convex nor concave, so it belongs to a non-deterministic polynomial-time hard (NP-hard) problem, which makes the solution very complex and computationally intensive. For this reason, the present invention uses a reinforcement learning algorithm to optimize the average AoI of the system through a reasonable scheduling strategy and power control.

[0038] S6: Through S5, using the reinforcement learning algorithm, the currently best sensor is dynamically selected for scheduling. Then, according to the energy state of the sensor, a suitable transmit power is further determined to ensure the successful transmission of the data packet to the base station.

[0039] S7: Repeat S6 until the reward function in the reinforcement learning algorithm converges, and finally obtain the optimal average AoI of the system, thereby improving the freshness of the data.

[0040] The present invention also provides a resource allocation system for minimizing the age of information in a communication network. The system includes a scenario module, a sensor scheduling module, a data transmission module, an optimization module, a solution module, and a convergence module, where: Scenario module: used to construct and form a wireless power supply communication network scenario; Sensor scheduling module: used to define the scheduling decision variables of the sensors in the wireless power supply communication network scenario, and determine the sensors to be scheduled within each time slot through the scheduling decision variables, ensuring that only one sensor is scheduled for data transmission in each time slot; Data transmission module: used to analyze the change of AoI during the process of the sensor transmitting data to the base station, and update the AoI of the sensor based on the scheduling decision and data transmission result; Optimization module: Jointly optimize the transmission power and scheduling strategy of the sensor, aiming to minimize the average AoI of the system, and establish an optimization model that includes an energy harvesting model, data transmission rate, and transmission power constraints. Solution module: Use the reinforcement learning algorithm to dynamically select the current optimal sensor for scheduling and determine the transmission power according to the energy state of the sensor. Convergence module: Iteratively execute the solution module until the reward function in the reinforcement learning algorithm converges to obtain the optimal average AoI.

[0041] The method and system for minimizing the age of information in a wireless powered communication network of the present invention aim to minimize the average AoI of the system. This method comprehensively considers the non-linear energy harvesting model of the sensor, data transmission rate, and transmission power of the sensor, and maximally reduces the average AoI of the overall system from the system level. Specifically, the present invention calculates the energy state of the sensor by analyzing the energy collected by the sensor. Energy transmission and data transmission are carried out on orthogonal frequencies, and the corresponding data volume is calculated. Combining the calculated transmission data volume, a corresponding AoI model is constructed. The reinforcement learning method is used to optimize sensor scheduling and transmission power control to solve the optimal system average AoI, improving the freshness of the overall system information and the stability of the system.

[0042] Finally, it should be noted that the above is only a preferred embodiment of the present invention and does not impose any form of limitation on the present invention; any person skilled in the art can smoothly implement the present invention according to the instructions and the above description. Equivalent changes made by making some modifications, decorations, and evolutions using the technical content disclosed above are all equivalent embodiments of the present invention; at the same time, any equivalent changes, modifications, and evolutions made to the above embodiments based on the essential technology of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for minimizing the age of information in a wireless power supply communication network, characterized in that It includes the following steps: S1. Define the scheduling decision variables of sensors in the wireless power supply communication network scenario, and determine the sensors scheduled in each time slot through the scheduling decision variables to ensure that only one sensor is scheduled for data transmission in each time slot; Among them, the wireless power supply communication network scenario is composed of a wireless power supply station, a receiving base station, and multiple sensors. The sensors receive energy through the WPT technology and collect environmental data; S2. Analyze the change of the age of information during the process of sensors transmitting data to the base station, and update the age of information of the data based on the scheduling decision and data transmission results; S3. Jointly optimize the transmission power and scheduling strategy of sensors. With the goal of minimizing the average AoI of the system, establish an optimization model including an energy harvesting model, data transmission rate, and transmission power constraints; S4. Use the reinforcement learning algorithm to dynamically select the current optimal sensor for scheduling, and determine the transmission power based on the energy state of the sensor; S5. Iteratively execute S4 until the reward function in the reinforcement learning algorithm converges to obtain the optimal average AoI.

2. The method for minimizing the age of information in a wireless power supply communication network for resource allocation according to claim 1, wherein In the S1, in the wireless power supply communication network scenario, the wireless power supply station transmits energy to the sensors through the wireless power transfer technology, and the sensors receive energy through a single antenna and transmit data packets to the receiving base station.

3. The method for minimizing the age of information of a wireless power supply communication network according to claim 2, wherein, In the S1, the scheduling decision variable is a binary variable, which takes the value of 1 when the sensor is scheduled in the time slot and 0 when it is not scheduled. The scheduling decision variables satisfy the following formula: ； Among them, is the sensor index, indicating the sensor , is the number of sensors, is the number of time slots, is the time slot index, indicating the th time slot, is the scheduling decision variable, used to indicate whether the sensor is scheduled.

4. The method for minimizing the age of information in a wireless power supply communication network resource allocation according to claim 1, characterized in that, In S2, the information age is updated as follows: When the sensor is scheduled, the information age of the data from the sensor is reset to the length of one time slot spent on transmitting the data; when the sensor is not scheduled, the information age of the data from the sensor increases by the current time slot length.

5. The method for minimizing the age of information of a wireless power supply communication network resource allocation according to claim 1, wherein In the S3, when establishing the optimization model, consider the path loss and small-scale fading model. The energy harvesting model adopts the following formula: ； wherein is the power saturation threshold of the energy harvesting receiver, is the energy conversion efficiency, is the length of a time slot.

6. The method for minimizing the age of information in a wireless power supply communication network according to claim 5, wherein In the step S3, is the path loss between the transmitting end and the receiving end; Among them, is the path loss constant with a reference distance of 1 meter, is the transmitting end and the receiving end the distance between, is the path loss factor.

7. The method for minimizing the age of information of a wireless power supply communication network according to claim 5, characterized in that, In the S3, the channel gain under the small-scale fading model is calculated as follows: ； wherein and are the deterministic LoS component and the random non-LoS NLoS component respectively, is the Rice factor, indicating the power ratio between LoS and NLoS.

8. The method for minimizing the age of information in a wireless power supply communication network according to claim 5, characterized in that, In the S3, the stored energy of the sensor is updated through the following formula: ； Among them, is the current time slot sensor is the energy consumed for communication, is the size of the sensor battery capacity.

9. The method for minimizing the age of information in a wireless power supply communication network according to claim 5, wherein The optimization model is specifically: ； Among them, is the sensor at the transmission power in the nth time slot, and is the maximum transmission power of the sensor.

10. The system on which the method for minimizing the age of information of a wireless power supply communication network resource allocation according to any one of claims 1-9 is based, characterized in that The system includes a scenario module, a sensor scheduling module, a data transmission module, an optimization module, a solution module, and a convergence module, where: Scenario module: used to construct a wireless power supply communication network scenario; Sensor scheduling module: used to define the scheduling decision variables of sensors in the wireless power supply communication network scenario, and determine the sensors scheduled in each time slot through the scheduling decision variables to ensure that only one sensor is scheduled for data transmission in each time slot; Data transmission module: used to analyze the change of the age of information during the process of sensors transmitting data to the base station, and update the age of information of the data based on the scheduling decision and data transmission results; Optimization module: jointly optimize the transmission power and scheduling strategy of sensors. With the goal of minimizing the average age of information of the system, establish an optimization model including an energy harvesting model, data transmission rate, and transmission power constraints; Solution module: use the reinforcement learning algorithm to dynamically select the current optimal sensor for scheduling, and determine the transmission power according to the energy state of the sensor; Convergence module: iteratively execute the solution module until the reward function in the reinforcement learning algorithm converges to obtain the optimal average age of information.

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