A wireless charging scheduling system, method and device based on green energy

Abstract

The application provides a wireless charging scheduling system, method and equipment based on green energy, and relates to the field of wireless charging scheduling. The method comprises the following steps: adopting a clustering algorithm to classify a plurality of sensor nodes in a sensor network, deploying a green base station to perform wireless charging on the same type of sensor nodes, and determining the position of the green base station; generating a Voronoi diagram according to the position of the green base station, introducing a Friis formula to determine the received power of the sensor nodes from the green base station; obtaining the battery capacity of the sensor nodes, determining the charging time of the sensor nodes according to the received power and the battery capacity, and generating a charging time set; introducing a queuing theory model, determining the service intensity of any green base station according to the charging time set; and according to the service intensity, adjusting the load of the green base station, generating an adjustment scheme, and enabling the green base station to complete all charging requests. The application can avoid resource waste and realize load balancing of the charging base station.

Description

Technical Field

[0001] This invention relates to the field of wireless charging scheduling, and in particular to a wireless charging scheduling system, method and device based on green energy. Background Technology

[0002] With the development of science and technology, Wireless Rechargeable Sensor Networks (WRSNs) have become an important means of acquiring information and have been widely used in various scenarios. These scenarios all rely on the continuous and effective operation of the nodes in the network. Only when all sensor nodes in the WRSN meet the requirements of maintaining connectivity, coverage, and stability can the network complete tasks such as data collection and environmental monitoring. Because sensor nodes are generally small in size, their built-in battery capacity is extremely limited. This leads to the problem that the batteries on the nodes in the WRSN are difficult to recharge, and the cost of manual maintenance is too high, which greatly restricts its practical development due to energy issues.

[0003] With the maturity of wireless charging technology, the use of electromagnetic waves for energy replenishment has been widely studied. Wireless Power Transmission (WPT) refers to the method of setting up active wireless charging devices in a WRSN (Wireless Power Receiver Network) to provide wireless power supplementation (WPS) to any sensor node to maintain its lifespan. This method often requires the deployment of static charging stations or mobile charging base stations in the network (e.g., drones and unmanned charging vehicles with wireless charging capabilities). When a node in the WRSN has low remaining power, it sends a charging request to the base station in the WRSN. Upon receiving the charging request, the base station actively transmits electromagnetic waves (radio frequency energy) to wirelessly transfer power to the node, ensuring its normal operation. However, wireless charging technology still faces many challenges.

[0004] First, electromagnetic waves suffer significant path loss during propagation in space, while the number of nodes in a WRSN is enormous (compared to rechargeable base stations in a network). Using brown energy (i.e., traditional electricity) to power the entire wireless charging system would result in substantial resource waste, and the carbon emissions would exacerbate the greenhouse effect, further damaging the natural environment.

[0005] Secondly, most existing research focuses solely on maximizing charging efficiency, leading to the majority of nodes being assigned to the nearest base station. This results in an uneven load distribution among charging base stations: some base stations in densely populated areas experience excessively long service queues, while base stations in sparsely populated areas remain idle for extended periods. While this improves the charging efficiency of a small number of nodes (i.e., increases their receiving power) and reduces their charging time, it causes excessively long queuing times, potentially leading to nodes running out of power and dying, resulting in low charging efficiency. Furthermore, most studies do not consider the operator's profit during the charging process. Summary of the Invention

[0006] The purpose of this invention is to provide a wireless charging scheduling system, method, and device based on green energy to solve the problems of resource waste and uneven load on charging base stations caused by driving the entire wireless charging system with brown energy.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] A wireless charging scheduling system based on green energy includes: a green base station and sensor nodes;

[0009] The green base station includes a wind turbine, a photovoltaic generator, an energy storage battery, and a radio frequency energy transmitter. The energy storage battery is connected to the wind turbine, the photovoltaic generator, and the radio frequency energy transmitter. The green energy collected by the wind turbine and the photovoltaic generator is converted into electrical energy and stored in the energy storage battery. The energy storage battery processes the green energy, provides alternating current to the radio frequency energy transmitter, and forms an energy beam at the radio frequency energy transmitter to generate a radio frequency signal.

[0010] The sensor node includes a radio frequency energy receiver, a supercapacitor, and a sensing module connected in sequence. The radio frequency energy receiver processes the radio frequency signal, generates radio frequency energy, stores it in the supercapacitor, and discharges the supercapacitor to the sensing module to drive the sensing module to work.

[0011] A wireless charging scheduling method based on green energy includes:

[0012] Clustering algorithms are used to classify multiple sensor nodes within a sensor network, and a green base station is deployed to wirelessly charge sensor nodes of the same type, thereby determining the location of the green base station.

[0013] A Voronoi diagram is generated based on the location of the green base stations;

[0014] Based on the Volonoi diagram, the Friesian transmission formula is introduced to determine the received power of the sensor node from the green base station;

[0015] Obtain the battery capacity of the sensor node, determine the charging time from the green base station to the sensor node based on the received power and the battery capacity, and generate a set of charging times;

[0016] Based on the set of charging times, the average charging time is calculated, and a queuing theory model is introduced to determine the service strength of any of the green base stations.

[0017] Based on the service intensity, the load of the green base station and the sensor node are allocated to generate an allocation scheme, so that the green base station can serve the charging requests of the sensor node.

[0018] Optionally, the load of the green base station and the sensor nodes are allocated according to the service intensity to generate an allocation scheme, which further includes:

[0019] Obtain the total charging service duration promised by the operator to the user and the total charging service duration actually performed by the user;

[0020] The operator's profit is determined based on the promised total charging service duration and the actual total charging service duration for the user.

[0021] Based on the distance from the sensor node to the green base station, the remaining power of the sensor node, and the queue length, the load allocation scheme that maximizes the operator's profit is selected to allocate the load of the green base station, thereby generating the final association scheme between the green base station and the sensor node.

[0022] Optionally, based on the distance from the sensor node to the green base station, the remaining power of the sensor node, and the queue length, a scheduling scheme that maximizes the operator's profit is selected to allocate the load of the green base station, generating the final association scheme between the green base station and the sensor node. This process further includes:

[0023] The non-interrupted charging priority of the sensor node is determined based on the charging time.

[0024] According to the non-interrupted charging priority, the charging action of the green base station corresponding to the area where the sensor node is located is controlled based on the remaining power of the sensor node.

[0025] Optionally, a clustering algorithm is used to classify multiple sensor nodes within the sensor network, and a green base station is deployed to wirelessly charge sensor nodes of the same type. The location of the green base station is determined, specifically including:

[0026] Clustering algorithms are used to classify multiple sensor nodes in the sensor network. Based on the power consumption of the sensors, the weight of each sensor node in the same category and the association scheme between green base stations and sensor nodes are determined.

[0027] The location of the green base station is determined based on the weights and the association scheme; the location of the green base station is at the weighted center of each type of sensor node.

[0028] Optionally, the received power is:

[0029]

[0030] Among them, P nk_r Let G be the received power, η be the rectification efficiency in the linear energy receiving model, and G be the power received. t For the gain of the transmitting antenna, G r For the gain of the receiving antenna, L p For polarization loss, λ is the wavelength of the radio frequency wave, ||k,n||² is the Euclidean distance from the green base station k to the sensor node n, β is the compensation parameter of the Fries free-space transmission equation in the short-range case, and P t This refers to the transmission power of green base stations.

[0031] Optionally, the charging time is:

[0032]

[0033] Among them, t kn For charging time, C n Let C be the battery capacity of sensor node n. n_t1 To set the power threshold for the sensor node, R k,n This is the association scheme between the green base station k and the sensor node n.

[0034] Optionally, the service strength is:

[0035] ρ k =λ k E(T k )

[0036] Where, ρ k For service intensity, λ k Let E(T) be the Poisson distribution parameter. k (T) represents the set of charging times. k The mean.

[0037] An electronic device includes a memory and a processor, the memory storing a computer program, and the processor running the computer program to cause the electronic device to perform the wireless charging scheduling method based on green energy as described above.

[0038] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the wireless charging scheduling method based on green energy as described above.

[0039] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention provides a wireless charging scheduling system, method and device based on green energy. It uses green base stations to drive the wireless charging system, avoiding resource waste. At the same time, it classifies multiple sensor nodes in the sensor network to determine the location of green base stations, and finally generates a scheduling scheme to allocate sensor nodes under the responsibility of overloaded green base stations to more suitable green base stations, so that more green base stations can serve charging requests, shorten the queuing time of nodes, balance the load (sensor nodes) of charging base stations (green base stations), and improve charging efficiency. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 The diagram shows the structure of the wireless charging scheduling system based on green energy provided by this invention.

[0042] Figure 2 A scenario diagram of the wireless charging system based on a green base station provided by the present invention;

[0043] Figure 3 The flowchart illustrates the wireless charging scheduling method based on green energy provided by this invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] The purpose of this invention is to provide a wireless charging scheduling system, method, and device based on green energy, which can avoid resource waste and achieve load balancing of charging base stations.

[0046] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] like Figure 1 As shown, this invention provides a wireless charging scheduling system based on green energy, comprising: a green base station and sensor nodes; the green base station includes a wind turbine, a photovoltaic generator, an energy storage battery, and a radio frequency (RF) energy transmitter; the energy storage battery is connected to the wind turbine, the photovoltaic generator, and the RF energy transmitter respectively; the green energy collected by the wind turbine and the photovoltaic generator is converted into electrical energy and stored in the energy storage battery; the energy storage battery processes the green energy, provides alternating current to the RF energy transmitter, and forms an energy beam at the RF energy transmitter to generate an RF signal; the sensor node includes an RF energy receiver, a supercapacitor, and a sensing module connected in sequence; the RF energy receiver processes the RF signal to generate RF energy, stores it in the supercapacitor, and discharges the supercapacitor to the sensing module to drive the sensing module to work.

[0048] like Figure 1 As shown, the green base station is equipped with a wind turbine and a photovoltaic generator, both of which store the collected green energy in batteries to power the entire system. The energy storage batteries boost and invert the collected green energy to provide stable AC power to the radio frequency (RF) energy transmitter. In the RF energy transmitter, the output signal undergoes phase adjustment at the elements of the transmitting antenna array, forming a highly directional energy beam. The green base station uses beamforming technology to wirelessly charge the sensor nodes.

[0049] A sensor node generally consists of a radio frequency (RF) energy receiver, a supercapacitor, and a sensing module. The RF energy receiver uses a high-gain antenna to receive RF signals transmitted from the base station. After boosting and rectifying the RF signals, the RF energy is stored in the supercapacitor.

[0050] When the energy stored in the supercapacitor reaches the upper threshold, the supercapacitor discharges, driving the sensor load. When the remaining energy in the supercapacitor falls below the lower threshold, the supercapacitor stops supplying energy, and the sensor node switches to energy storage mode. The sensing module utilizes the energy periodically provided by the supercapacitor to perform three functions: information sensing, data processing, and wireless data transmission.

[0051] Wireless charging system scenarios based on green base stations, such as... Figure 2 As shown, the distribution of Matern hard core point process (MHCPP) has been widely used in sensor network modeling.

[0052] This invention assumes that the locations of the sensor nodes follow an MHCPP distribution, where set S represents these sensor nodes, n is the index, and v n Let S represent the power consumption of sensor node n, where n = 1, 2, ..., |S|.

[0053] Figure 3 The flowchart of the wireless charging scheduling method based on green energy provided by the present invention is as follows: Figure 3 As shown, the present invention also provides a wireless charging scheduling method based on green energy, comprising:

[0054] Step 301: Use a clustering algorithm to classify multiple sensor nodes in the sensor network, deploy a green base station to wirelessly charge sensor nodes of the same type, and determine the location of the green base station.

[0055] In practical applications, step 301 specifically includes: classifying multiple sensor nodes in the sensor network using a clustering algorithm; determining the weight of each sensor node in the same category and the association scheme between the green base station and the sensor node according to the power consumption of the sensor; determining the location of the green base station according to the weight and the association scheme; and the location of the green base station is at the weighted center of each category of sensor nodes.

[0056] In practical applications, the K-means clustering algorithm is used to classify |S| sensors into K classes. Each sensor class is assigned a green base station responsible for wirelessly charging the sensor nodes in that class. The set K represents all green base stations, and k is the index of these green base stations. Considering that sensor nodes with higher power consumption require more charging, the weight of sensor node n is represented as:

[0057]

[0058] Wherein, weight w n This is equivalent to the normalized value of the power consumption of sensor node n. The relationship between green base station k and sensor node n is represented by R. k,n When sensor node n is assigned to green base station k, R k,n =1; otherwise, R k,n =0. Therefore, the location problem for green base station k can be expressed as:

[0059]

[0060] In the formula, the L2 norm ||k,n||2 represents the Euclidean distance from the green base station k to the sensor node n. The above formula represents a class of generalized weighted Fermat point problems. The actual goal is to obtain the weighted centers of a set of sensor nodes, which can be solved using the L-BFGS algorithm in the quasi-Newton iteration method. This invention uses the L-BFGS algorithm to solve for the weighted centers of each type of sensor node and deploys the green base stations at these locations.

[0061] Step 302: Generate a Voronoi diagram based on the location of the green base station.

[0062] In practical applications, such as Figure 2 As shown, a green base station is deployed at the weighting center of each type of sensor node. The green base station wirelessly charges the sensor nodes it is responsible for using beamforming technology. Based on the location of the green base station, a Voronoi diagram can be generated, which is widely used in the modeling and analysis of communication networks.

[0063] Step 303: Based on the Voronoi diagram, the Fries formula is introduced to determine the received power of the sensor node from the green base station.

[0064] In practical applications, after defining the association between green base stations and sensor nodes, this invention introduces the M / G / 1 queuing theory model to analyze the wireless charging workload of each green base station. In "M / G / 1", the first M indicates that the input follows a Poisson distribution, the second M / G indicates that the output follows a negative exponential distribution, and the number of parallel service stations is 1, indicating that there is only one service organization.

[0065] According to Friis's formula, the received power P from the green base station k at sensor node n is... nk_r for:

[0066]

[0067] In the formula, η is the rectification efficiency in the linear energy receiving model, and G t and G r These are the gains of the transmitting and receiving antennas, L and L, respectively. p For polarization loss, λ is the wavelength of the radio frequency wave, ||k,n||² is the Euclidean distance from the green base station k to the sensor node n, β is the compensation parameter of the Friis free-space transmission equation in the short-range case, and P t This is the transmission power of the green base station.

[0068] Step 304: Obtain the battery capacity of the sensor node, determine the charging time of the green base station to the sensor node based on the received power and the battery capacity, and generate a charging time set.

[0069] In practical applications, assume the battery capacity of sensor node n is C. n Set the power threshold to C n_t1 If 20% of the total battery capacity of the sensor node is set as the power threshold, then the charging time t for the green base station k to charge the sensor node n is... kn It can be represented as:

[0070]

[0071] Step 305: Calculate the average charging time based on the set of charging times, introduce a queuing theory model, and determine the service strength of any of the green base stations.

[0072] In practical applications, when R k,n When t = 1, kn Set T k Then set T k The mean E(T) k ) and variance D(T k It can be calculated that, for a green base station k, the arrival of charging requests within its service area follows a parameter λ. k The Poisson distribution can be used to analyze the charging process of green base station k using the M / G / 1 queuing theory model, where the service intensity ρ of green base station k is... k It can be represented as:

[0073] ρ k =λ k E(T k )

[0074] In ρ k When <1, the green base station ρ k Only then will it be able to serve all charging requests within its assigned area.

[0075] Step 306: Allocate the load of the green base station and the sensor node according to the service intensity to generate an allocation plan so that the green base station can serve the charging requests of the sensor node.

[0076] In practical applications, the load of the green base station is allocated according to the service intensity to generate an allocation scheme. This process further includes: obtaining the total charging service duration promised by the operator to the user and the actual total charging service duration of the user; determining the operator's profit based on the promised total charging service duration and the actual total charging service duration of the user; and selecting the allocation scheme that maximizes the operator's profit based on the distance from the sensor node to the green base station, the remaining power of the sensor node, and the queue length to allocate the load of the green base station, thereby generating the final association scheme between the green base station and the sensor node.

[0077] In practical applications, considering that some green base stations may not be able to serve all charging requests after optimization and allocation, this invention innovatively optimizes the load of wireless charging between green base stations, thereby reducing the load on overloaded green base stations (i.e., ρ). k The sensor nodes responsible for a portion of the green base stations (k) with a load greater than 1 are allocated to suitable green base stations. Then, to minimize the average base station load and find the optimal solution to this minimization problem, this invention employs the simulated annealing algorithm (an optimization algorithm with a time-varying probability of sudden jumps that eventually converge to zero, effectively avoiding getting trapped in local minima and ultimately reaching the global optimum) to solve this problem. This algorithm finds a reasonable "base station-user" allocation scheme so that as many green base stations as possible can serve the charging requests (i.e., minimizing the number of green charging base stations with a load greater than 1 in the network). The simulated annealing algorithm may find multiple feasible allocation schemes. To select a suitable scheme from these, this invention further considers operator revenue: in providing wireless charging services, operators will charge corresponding service fees. Initially, the operator will sign a contract with the user, promising a total charging service duration T. th Among them, the total user service time T all The calculation method is as follows:

[0078] T all =T wait +T service

[0079] Wherein: T wait T refers to the time from when a node in the network sends a charging request to when the base station begins providing service. service This refers to the time it takes for a node to be served by a base station until its battery level reaches a high threshold. The profit earned by the operator from this period is calculated as follows:

[0080]

[0081] Wherein: F init The monthly fee for each node is the fee charged by the operator at the agreed time T. th Previously (i.e., T) all ≤T th The profit that can be obtained after completing the charging service; if the timeout (i.e., T) all >T th If the charging time exceeds the limit, a portion of the charging fee needs to be deducted; the longer the time exceeds the limit, the less profit the operator earns from the charging service. Therefore, when the simulated annealing algorithm yields multiple solutions, the system estimates the profit based on factors such as the distance between the node and the base station, the node's remaining power, and the queue length, selecting the solution with the largest profit to complete the association R between the base station and the user.k,n The allocation.

[0082] In practical applications, based on the distance from the sensor node to the green base station, the remaining power of the sensor node, and the queue length, a scheduling scheme that maximizes the operator's profit is selected to allocate the load of the green base station, generating the final association scheme between the green base station and the sensor node. This process further includes: determining the non-interrupted charging priority of the sensor node based on the charging time; and controlling the charging action of the green base station corresponding to the area where the sensor node is located according to the non-interrupted charging priority and the remaining power of the sensor node.

[0083] In practical applications, after finalizing the association scheme R between the green base station and the sensor node... k,n Subsequently, this invention further considers the charging priority of sensor nodes. When t kn When ≠0, set the non-interrupt priority of sensor node n to 1 / t. kn (That is, sensor nodes with shorter charging times have higher non-interrupted charging priority).

[0084] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the wireless charging scheduling method based on green energy as described above.

[0085] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the wireless charging scheduling method based on green energy as described above.

[0086] This invention designs a wireless power supply system based on WPT fixed base stations using green energy. Through a heuristic charging scheduling optimization algorithm that determines the number of base stations and their locations, adjusts the association between base stations and nodes, and optimizes the charging sequence, the WRSN achieves a shorter charging wait time or higher charging efficiency, while also having good charging benefits and / or higher charging efficiency.

[0087] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0088] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A wireless charging scheduling method based on green energy, characterized in that, Applied to a wireless charging scheduling system based on green energy, including: green base stations and sensor nodes; The green base station includes a wind turbine, a photovoltaic generator, an energy storage battery, and a radio frequency energy transmitter. The energy storage battery is connected to the wind turbine, the photovoltaic generator, and the radio frequency energy transmitter. The green energy collected by the wind turbine and the photovoltaic generator is converted into electrical energy and stored in the energy storage battery. The energy storage battery processes the green energy, provides alternating current to the radio frequency energy transmitter, and forms an energy beam at the radio frequency energy transmitter to generate a radio frequency signal. The sensor node includes a radio frequency energy receiver, a supercapacitor, and a sensing module connected in sequence; the radio frequency energy receiver processes the radio frequency signal, generates radio frequency energy, stores it in the supercapacitor, and discharges the supercapacitor to the sensing module to drive the sensing module to work. The wireless charging scheduling method includes: Clustering algorithms are used to classify multiple sensor nodes within a sensor network, and a green base station is deployed to wirelessly charge sensor nodes of the same type, thereby determining the location of the green base station. A Voronoi diagram is generated based on the location of the green base stations; Based on the Volonoui diagram, the Fries formula is introduced to determine the received power of the sensor node from the green base station; Obtain the battery capacity of the sensor node, determine the charging time from the green base station to the sensor node based on the received power and the battery capacity, and generate a set of charging times; Based on the set of charging times, the average charging time is calculated, and a queuing theory model is introduced to determine the service strength of any of the green base stations; the service strength is: in, For service intensity, The parameters are Poisson distribution parameters. For charging time set The mean; exist At that time, green base stations Only then will all charging requests within the area of ​​responsibility be served; The load of the green base station and the sensor node are allocated according to the service intensity to generate an allocation plan, so that the green base station can serve the charging requests of the sensor node. Obtain the total charging service duration promised by the operator to the user and the total charging service duration actually performed by the user; The operator's profit is determined based on the promised total charging service duration and the actual total charging service duration for the user. Based on the distance from the sensor node to the green base station, the remaining power of the sensor node, and the queue length, the load allocation scheme that maximizes the operator's profit is selected to allocate the load of the green base station, thereby generating the final association scheme between the green base station and the sensor node.

2. The wireless charging scheduling method based on green energy according to claim 1, characterized in that, Based on the distance from the sensor node to the green base station, the remaining power of the sensor node, and the queue length, a load allocation scheme that maximizes the operator's profit is selected to allocate the load of the green base station, generating the final association scheme between the green base station and the sensor node. This process further includes: The non-interrupted charging priority of the sensor node is determined based on the charging time. According to the non-interrupted charging priority, the charging action of the green base station corresponding to the area where the sensor node is located is controlled based on the remaining power of the sensor node.

3. The wireless charging scheduling method based on green energy according to claim 1, characterized in that, Clustering algorithms are used to classify multiple sensor nodes within the sensor network. A green base station is deployed to wirelessly charge sensor nodes of the same type. The location of the green base station is determined, specifically including: Clustering algorithms are used to classify multiple sensor nodes in the sensor network. Based on the power consumption of the sensors, the weight of each sensor node in the same category and the association scheme between green base stations and sensor nodes are determined. The location of the green base station is determined based on the weights and the association scheme; the location of the green base station is at the weighted center of each type of sensor node.

4. The wireless charging scheduling method based on green energy according to claim 1, characterized in that, The receiving power is: in, For received power, This refers to the rectification efficiency in the linear energy receiving model. For the gain of the transmitting antenna, For the gain of the receiving antenna, For polarization loss, It is the wavelength of radio frequency waves. Green base station To sensor node Euclidean distance, These are the compensation parameters for the Fries free-space transport equation in the short-range case. This refers to the transmission power of green base stations.

5. The wireless charging scheduling method based on green energy according to claim 4, characterized in that, The charging time is: in, For sensor nodes Charging time, For sensor nodes Battery capacity, To set a power threshold, For sensor nodes The receiving power, For the green base station With the sensor node The association scheme.

6. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to enable the electronic device to perform the wireless charging scheduling method based on green energy as described in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the wireless charging scheduling method based on green energy as described in any one of claims 1-5.

Images