A collaborative wireless charging method based on phase scheduling

Abstract

The present invention provides a phase-scheduling-based collaborative wireless charging method, which relates to the field of wireless power transmission technology, including: constructing a wireless rechargeable sensor network; constructing a charging model based on the wireless rechargeable sensor network; introducing the concept of charging benefit to represent the rationality of the distribution of energy received by sensors with energy demand; defining the phase-scheduling-based collaborative wireless charging problem based on the wireless rechargeable sensor network, the charging model and the charging benefit; solving the phase-scheduling-based collaborative wireless charging problem; wherein, a charger grouping algorithm is used to decompose the entire network into multiple sub-networks; based on the generated charger grouping, a phase vector search algorithm is used to enable the charger to concentrate energy on sensors with urgent energy demand. The present invention strategically adjusts the charger phase to achieve constructive interference at the location of each sensor, maximizes the received energy, and meets the dynamic energy demand of the network.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless power transmission, and in particular to a collaborative wireless charging method based on phase scheduling. Background Art

[0002] Wireless power transfer (WPT) technology eliminates the need for traditional wired connections, bringing a paradigm shift towards ubiquitous wireless charging. Leveraging this advancement, wireless rechargeable sensor networks (WRSNs) provide a contactless and convenient way to meet the energy needs of sensors, thus becoming a promising platform in multiple fields such as environmental monitoring, smart agriculture, and industrial automation.

[0003] However, realizing wireless rechargeable sensor networks requires solving the challenge of efficiently replenishing sensor energy. Typically, existing wireless rechargeable sensor networks meet the energy needs of sensors through two main approaches: mobile charger scheduling, which uses mobile chargers to sequentially visit and charge sensors; and fixed charger deployment, which involves placing several fixed chargers to provide continuous charging coverage for all sensors.

[0004] Mobile charger scheduling consumes a lot of energy during the movement, thus reducing the available battery capacity. In addition, the sequential charging process causes long queuing delays for sensors. In extreme cases, long periods of energy starvation can cause sensor failure, affecting their functionality and undermining the performance of the entire network. In contrast, fixed charger deployment provides a simpler and reliable solution. Strategically placed fixed chargers can provide continuous and readily available energy supply, eliminating waiting time for sensors and minimizing downtime due to battery exhaustion. This makes fixed chargers the preferred method.

[0005] However, the charging efficiency of these wireless rechargeable sensor networks is limited by distance-dependent energy attenuation. A common mitigation approach is to increase the density of chargers so that sensors can draw energy from multiple chargers simultaneously. While intuitively, this seems to be a solution by deploying more chargers in the network, the phenomenon of wave interference poses a challenge. Specifically, in areas where the coverage of multiple chargers overlap, electromagnetic waves interact with each other, resulting in constructive or destructive interference. The former occurs when the waves are synchronized and may amplify the received energy. The latter, which occurs when the waves are not synchronized, significantly weakens the received energy and even forms nulls with almost zero energy in certain areas. Due to the superposition of waves and the resulting unpredictable wave interference, it becomes unreliable to achieve optimal charging efficiency simply by increasing the density of chargers.

[0006] Figure 1The energy distribution formed by deploying 4 chargers emitting 915MHz frequency waves and 8 sensors in a 12m×8m WRSN area (s1~s8 are sensors, c1~c4 are chargers), where each charger uses the same charger phase. The interference of waves significantly affects the energy distribution of the actual wireless rechargeable sensor network using omnidirectional chargers with the same initial phase. Figure 1 The uneven energy distribution is shown in Figure 1, which is manifested by alternating high-energy (red) and low-energy (black) areas. It is worth noting that although most sensors benefit from the overlap of charger coverage, sensors , and They are in a null region caused by destructive interference, and although they are close to the high-energy region, the energy they receive is almost zero.

[0007] To address the problem of wave interference, chargers that negatively affect the power reception of sensor nodes can be deactivated by strategically turning them on and off. However, this solution not only results in a reduction in overall energy availability, but can also create coverage gaps in certain areas. Another alternative approach is to exploit the potential of constructive interference by precisely relocating each sensor to a nearby high-energy area. However, this approach is extremely expensive due to the high deployment costs, making it unsuitable for large-scale networks or networks with dynamically changing energy demands. Summary of the invention

[0008] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0009] The present invention provides a phase scheduling-based collaborative wireless charging method, comprising:

[0010] Constructing a wireless rechargeable sensor network, the wireless rechargeable sensor network comprising N Charger and M A rechargeable sensor;

[0011] constructing a charging model based on the wireless rechargeable sensor network, the charging model including at least phase, power attenuation and wave interference of the charger;

[0012] The concept of charging benefit is introduced to represent the rationality of energy distribution received by sensors with energy demand;

[0013] The phase-scheduling-based collaborative wireless charging problem is defined based on a wireless rechargeable sensor network, a charging model, and charging benefits. The phase-scheduling-based collaborative wireless charging problem includes: for a given set of chargers, a set of rechargeable sensors, and the battery capacity of each sensor, searching for a set of charger phase vectors to provide maximum power to sensors with urgent energy demands within a given time period, thereby maximizing the overall charging benefits of the network.

[0014] The phase-scheduling-based collaborative wireless charging problem is solved; wherein, a charger grouping algorithm is used to decompose the entire network into multiple independent sub-networks; based on the generated charger grouping, a phase vector search algorithm is used to enable the charger to concentrate energy on sensors with urgent energy needs.

[0015] The phase scheduling-based cooperative wireless charging method according to the above technical solution of the present invention may also have the following additional technical features:

[0016] In the above technical solution, the construction of a wireless rechargeable sensor network includes:

[0017] deploy Charger , the maximum charging radius of each charger is ;

[0018] deploy Rechargeable sensors , the battery capacity of each sensor is ;

[0019] charger To sensor The distance is expressed as ;

[0020] In the wireless rechargeable sensor network, as the wireless rechargeable sensor network starts to operate, the energy reserve in the rechargeable sensor battery will gradually be exhausted. falls below a predefined critical threshold The rechargeable sensor will broadcast a request message to the charger when , to indicate the urgency of remaining energy;

[0021] in, Represents the characteristic energy consumption rate of a rechargeable sensor; Indicates the charger number. ; Indicates the number of the rechargeable sensor ， ;

[0022] After receiving the request message, the charger sends the identifier of the chargeable sensor Add to request set middle.

[0023] In the above technical solution, in the wireless rechargeable sensor network, once any rechargeable sensor in the request set reaches a fully charged state, the identifier of the rechargeable sensor is removed from the request set; when the request set changes, the charger will be triggered to adjust its phase in a cooperative manner so as to concentrate the power of the charger on the rechargeable sensors in the request set.

[0024] In the above technical solution, the construction of a charging model based on the wireless rechargeable sensor network includes:

[0025] Charger The waves emitted It is expressed as:

[0026]

[0027] in, A represents the amplitude of the radio wave, represents the angular frequency of the radio wave, Indicates charger The initial phase of t Indicates time;

[0028] Rechargeable sensor From charger Received radio waves It is expressed as:

[0029]

[0030] in, represents the power reduction factor, Indicated at angular frequency The wavelength of radio waves below;

[0031] The method for calculating the power attenuation factor includes:

[0032]

[0033] in, Indicates the antenna gain of the charger; represents the antenna gain of the rechargeable sensor;

[0034] definition For rechargeable sensors The received superimposed radio waves are:

[0035]

[0036] in, Indicates that it can cover rechargeable sensors A collection of all chargers; Representing a collection Internal charger Serial number; Representing a collection Internal charger Serial number; is a constant that depends on the amplitude of the radio wave, the initial phase, and the distance between the charger and the sensor; Indicates charger The initial phase of Indicates charger The initial phase of Indicates charger and charger The relative angular frequency of

[0037] The calculation method of the relative angular frequency includes:

[0038]

[0039] in, Indicates charger With rechargeable sensor distance; Indicates charger With rechargeable sensor distance;

[0040] set up for cycle, the sensor The power of the superimposed radio waves It is expressed as:

[0041]

[0042] in, represents the initial phase vector of the charger;

[0043] Set the request The sum of the power received by each sensor in is expressed as:

[0044]

[0045] In the above technical solution, the introduction of the charging benefit concept to represent the rationality of the distribution of energy received by the sensor with energy demand includes:

[0046] Define sensors based on the maximum receive power limit of rechargeable sensors In time period The charging benefits obtained within 10 days include:

[0047]

[0048] in, Indicates a rechargeable sensor In time period Charging benefits obtained within Indicates sensor The remaining energy Indicates the maximum receiving power limit of the rechargeable sensor;

[0049] Wireless rechargeable sensor networks in time The total charging benefit obtained within 1 hour is expressed as:

[0050]

[0051] in, Represents a collection of rechargeable sensors.

[0052] In the above technical solution, the phase scheduling-based cooperative wireless charging problem is expressed as:

[0053]

[0054]

[0055] in, Represents the problem of cooperative wireless charging based on phase scheduling; Indicates the total charging benefit in a charging cycle; Represents a request set The total number of add and remove operations in; Indicates that at the specified phase vector The next two consecutive update request sets The time interval between the two is a charging cycle; q represents the number of the charging cycle; Indicates restrictions; Indicates the total charging cycle, i.e. the set network operating time.

[0056] Among them, the phase scheduling-based cooperative wireless charging problem is an NP-hard problem.

[0057] In the above technical solution, the charger grouping algorithm is used to decompose the entire network into multiple independent sub-networks, including:

[0058] Set the request The rechargeable sensors are defined as key energy demand sensors, and the chargers are grouped according to the location of the key energy demand sensors and their positional relationship with the chargers; including:

[0059] For each key energy demand sensor, a corresponding charger subset is established, wherein the charger subset includes all chargers with the key energy demand sensor as the center and with a charging radius of for all chargers within a circular area of ​​radius;

[0060] If there are identical chargers between any two charger subsets, the charger subsets are merged to form a new charger subset until there are no identical chargers between each charger subset and other charger subsets;

[0061] Each subset of chargers after merging is defined as an independent sub-network.

[0062] In the above technical solution, the types of the independent subnetworks include a single charger subnetwork, a dual charger subnetwork and a multi-charger subnetwork; the number of chargers in the single charger subnetwork is 1, the number of chargers in the dual charger subnetwork is 2, and the number of chargers in the multi-charger subnetwork is greater than 2; the types of the independent subnetworks in the grouping results of the charger grouping algorithm include at least one of a single charger subnetwork, a dual charger subnetwork and a multi-charger subnetwork;

[0063] The phase vector search algorithm is used to enable the charger to focus energy on sensors with urgent energy demands, including:

[0064] Find the best phase vector for each independent sub-network;

[0065] The optimal phase vector of each independent sub-network is combined to obtain the phases of all chargers in the network, and the phase vector that maximizes the charging benefit of the entire network is obtained.

[0066] In the above technical solution, the step of finding the best phase vector for each independent sub-network includes:

[0067] The phase of the charger in the single charger sub-network is freely selected or remains unchanged;

[0068] The phase selection method of each charger in the dual charger sub-network includes:

[0069]

[0070] The two chargers in the dual charger sub-network are represented as and ;but, Indicates that the charger is in and charger A collection of critical energy demand sensors within the overlapping areas of the rechargeable range; Representing a collection The total energy received by the rechargeable sensor in; Indicates charger and charger The phase difference between Indicates charger and charger The relative angular frequency between Indicates charger To rechargeable sensor distance; Indicates charger To rechargeable sensor distance;

[0071] By solving The extreme value of can obtain the optimal phase difference corresponding to the optimal phase vector of the two chargers in the dual charger sub-network. , the phase of one charger is freely selected, and the phase of the other charger is determined according to the optimal phase difference;

[0072] The phase selection method of each charger in the multi-charger sub-network includes:

[0073] Set the reference phase vector , the total power received by the key energy demand sensors in the multi-charger sub-network at the reference phase vector is approximately:

[0074]

[0075] in, Represents the reference phase vector of the charger The corresponding phase; Represents the reference phase vector of the charger The corresponding phase; Represents an approximate value of the total power; find the The extreme value of the total power The total power The reference phase vector corresponding to the extreme value of is used as the vector of each charger in the multi-charger sub-network.

[0076] The above technical solution also includes:

[0077] According to the fluctuation of energy demand of rechargeable sensors over time, a phase scheduling algorithm is used to timely remove fully charged sensors from the request set. Removed;

[0078] The phase scheduling algorithm includes:

[0079] In each update request set When the charging time of each sensor in the request set is estimated, and the time for the next update of the request set is determined by identifying the minimum value of the charging time; wherein the method for estimating the charging time of each sensor in the request set includes:

[0080]

[0081] in, Indicates charging time; Indicated in the phase vector The power received.

[0082] In summary, due to the adoption of the above technical features, the beneficial effects of the present invention are:

[0083] The present invention can maximize the received energy and effectively meet the dynamic energy demand of the network by strategically adjusting the charger phase to achieve constructive interference at each sensor location as much as possible.

[0084] Specifically, the present invention is the first method for dynamically adjusting the fixed charger phase for on-demand charging, in which a charger grouping algorithm is used to reduce computational complexity, and a phase search algorithm and a phase scheduling algorithm are proposed to facilitate dynamic wireless charging of sensors with different energy requirements.

[0085] Additional aspects and advantages of the present invention will become apparent from the following description or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0087] Figure 1 It is a schematic diagram of energy distribution formed by using the same charger phase in a wireless charging sensor network;

[0088] Figure 2 It is a schematic diagram of the energy distribution formed by fine-tuning the charger phase in the wireless charging sensor network;

[0089] Figure 3 is a flow chart of a collaborative wireless charging method based on phase scheduling according to an embodiment of the present invention;

[0090] Figure 4 It is a schematic diagram of the principle of grouping using a charger grouping algorithm in a phase scheduling-based collaborative wireless charging method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0091] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0092] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0093] Refer to the following Figures 2 to 4 The collaborative wireless charging method based on phase scheduling provided according to some embodiments of the present invention is described.

[0094] Some embodiments of the present application provide a collaborative wireless charging method based on phase scheduling.

[0095] The research found that the dynamic energy demand of the sensor can be met by optimizing the charger phase. Figure 2 Shown with Figure 1 Under the corresponding working conditions, the phase of the charger is finely adjusted, so that each sensor can fully utilize the potential of constructive interference and receive the maximum energy. This leads to the problem of collaborative wireless charging based on phase scheduling (CHESS), that is, how to determine the optimal scheduling scheme of the charger phase given a certain number of fixed chargers and sensors so as to transmit energy to the sensor as needed. The goal is to maximize the total energy received by sensors with energy needs.

[0096] To achieve the above goals, that is, to solve the CHESS problem, there are three main challenges:

[0097] (1) Due to the inherent nonlinearity of power attenuation and wave interference, it is crucial to develop a practical charging model that incorporates wave interference, and this complexity makes achieving optimal charging benefits extremely challenging.

[0098] (2) The charger phase can be between 0 and 2 Taking any value in the arc range means that there are infinite candidate phases for each charger. When considering multiple chargers, the search space expands exponentially, significantly increasing the complexity of finding the optimal phase schedule.

[0099] (3) Radio waves from multiple chargers interact with each other, producing unpredictable and spatially correlated interference patterns at the sensor location. This interaction greatly hinders precise control of the received power.

[0100] Based on the above research, Figure 3 As shown, the first embodiment of the present invention proposes a collaborative wireless charging method based on phase scheduling, which mainly includes the following steps S1-S5.

[0101] S1. Constructing a wireless rechargeable sensor network (WRSN), wherein the wireless rechargeable sensor network includes N Charger and M A rechargeable sensor. The charger is preferably a fixed omnidirectional charger.

[0102] S2. Constructing a charging model based on the wireless rechargeable sensor network to accurately simulate the complex power distribution in the wireless rechargeable sensor network; the charging model at least includes the phase of the charger, power attenuation and wave interference.

[0103] S3. The concept of charging benefit is introduced to represent the rationality of the distribution of energy received by sensors with energy requirements.

[0104] S4. Define the phase-scheduling-based collaborative wireless charging problem based on wireless rechargeable sensor networks, charging models and charging benefits; wherein the phase-scheduling-based collaborative wireless charging problem includes: for a given set of chargers, a set of rechargeable sensors and the battery capacity of each sensor, search for a set of charger phase vectors to provide maximum power to sensors with urgent energy demands within a given time period, thereby maximizing the overall charging benefit of the network.

[0105] S5. Solve the phase-scheduling-based collaborative wireless charging problem; wherein, a charger grouping algorithm is used to decompose the entire network into multiple independent sub-networks; based on the generated charger grouping, a phase vector search algorithm is used to enable the charger to concentrate energy on sensors with urgent energy demands.

[0106] In some embodiments, the step S1 of constructing a wireless rechargeable sensor network includes:

[0107] deploy Charger , the maximum charging radius of each charger is ; The set of fixed omnidirectional chargers is ;

[0108] deploy Rechargeable sensors , the battery capacity of each sensor is ; The set of rechargeable sensors is ;

[0109] charger To sensor The distance is expressed as ;

[0110] The positions of the sensors and the chargers are predetermined. In the wireless rechargeable sensor network, as the wireless rechargeable sensor network starts to operate, the energy reserve in the rechargeable sensor battery will gradually be exhausted. falls below a predefined critical threshold The rechargeable sensor will broadcast a request message to the charger when , to indicate the urgency of remaining energy;

[0111] in, Represents the characteristic energy consumption rate of a rechargeable sensor; Indicates the charger number. ; Indicates the number of the rechargeable sensor ， ;

[0112] After receiving the request message, the charger sends the identifier of the chargeable sensor Add to request set middle.

[0113] In the above technical solution, in the wireless rechargeable sensor network, once any rechargeable sensor in the request set reaches a fully charged state, the identifier of the rechargeable sensor is removed from the request set; when the request set changes, the charger will be triggered to adjust its phase in a cooperative manner so as to concentrate the power of the charger on the rechargeable sensors in the request set.

[0114] In some embodiments, step S2 constructs a charging model based on the wireless rechargeable sensor network, including:

[0115] Charger The waves emitted It is expressed as:

[0116]

[0117] in, A represents the amplitude of the radio wave, represents the angular frequency of the radio wave, Indicates charger The initial phase of t Indicates time;

[0118] Rechargeable sensor From charger Received radio waves It is expressed as:

[0119]

[0120] in, represents the power reduction factor, Indicated at angular frequency The wavelength of radio waves below;

[0121] The method for calculating the power attenuation factor includes:

[0122]

[0123] in, Indicates the antenna gain of the charger; represents the antenna gain of the rechargeable sensor;

[0124] definition For rechargeable sensors The received superimposed radio waves are:

[0125]

[0126] in, Indicates that it can cover rechargeable sensors A collection of all chargers; Representing a collection Internal charger Serial number; Representing a collection Internal charger Serial number; is a constant that depends on the amplitude of the radio wave, the initial phase, and the distance between the charger and the sensor; Indicates charger The initial phase of Indicates charger The initial phase of Indicates charger and charger The relative angular frequency of

[0127] The calculation method of the relative angular frequency includes:

[0128]

[0129] in, Indicates charger With rechargeable sensor distance; Indicates charger With rechargeable sensor distance;

[0130] set up for cycle, the sensor The power of the superimposed radio waves It is expressed as:

[0131]

[0132] in, represents the initial phase vector of the charger;

[0133] Set the request The sum of the power received by each sensor in is expressed as:

[0134]

[0135] Although the total power emitted by a fixed omnidirectional charger is constant, the location and signal attenuation of the rechargeable sensors can cause uneven power distribution. Some sensors may receive strong signals, while others may face the problem of weak received signals. However, sensors with high readings may not need to be charged immediately, while sensors with low readings may be more critical. This mismatch between received power and actual energy demand hinders the optimal performance of the network. Therefore, optimizing the network simply by maximizing the total power received by all sensors is not an ideal option. Charging yield is a metric involving the energy received by sensors with energy demand. In practical applications, allocating energy to sensors without energy demand is inefficient in terms of network performance. On the contrary, transferring energy to sensors with demand can significantly improve charging efficiency. Based on these insights, the present disclosure uses charging yield to prioritize charging operations for sensors with energy demand, ensuring that the network's limited charging resources are efficiently utilized. Due to hardware limitations, rechargeable sensors usually have an upper limit on the maximum received power they can handle.

[0136] Therefore, in some embodiments, step S3 introduces the concept of charging benefit to represent the rationality of the distribution of energy received by the sensor with energy demand, including:

[0137] Define sensors based on the maximum receive power limit of rechargeable sensors In time period The charging benefits obtained within 10 days include:

[0138]

[0139] in, Indicates a rechargeable sensor In time period Charging benefits obtained within Indicates sensor The remaining energy Indicates the maximum receiving power limit of the rechargeable sensor;

[0140] Wireless rechargeable sensor networks in time The total charging benefit obtained within 1 hour is expressed as:

[0141]

[0142] in, Represents a collection of rechargeable sensors.

[0143] In some embodiments, step S4 expresses the phase scheduling-based cooperative wireless charging problem as:

[0144]

[0145]

[0146] in, Represents the problem of cooperative wireless charging based on phase scheduling; Indicates the total charging benefit in a charging cycle; Represents a request set The total number of add and remove operations in; Indicates that at the specified phase vector The next two consecutive update request sets The time interval between is a charging cycle; q represents the charging cycle number; Indicates restrictions; Indicates the total charging cycle, i.e. the set network operating time.

[0147] Among them, the phase scheduling-based collaborative wireless charging problem is an NP-hard problem (NP-hard problem refers to a type of problem that cannot be solved by an effective algorithm in polynomial time).

[0148] The interference of the waves creates an uneven energy distribution throughout the network, and even a small phase adjustment of a single charger can significantly affect the energy distribution. This complexity arises from the nonlinear interference between the waves reaching each sensor, making it extremely difficult to find the optimal phase vector that ensures all energy-demanding sensors receive maximum energy. In essence, this is a challenging multivariable nonlinear optimization problem. The complexity of the problem rises dramatically as the size of the network increases. To address this growing complexity, a charger grouping algorithm is proposed to reduce the computational burden.

[0149] The key to the charger grouping algorithm is that each charger has a charging radius, and only a limited number of chargers will dominate the received energy at a certain rechargeable sensor location. In other words, only a few chargers will significantly affect the energy received by a specific rechargeable sensor. In addition, although the energy demand of the rechargeable sensor changes dynamically, the number of critical energy demand sensors is limited. Based on this, a charger grouping algorithm is proposed. In general, the algorithm groups chargers according to the location of the critical energy demand sensors and their positional relationship with the chargers to reduce the difficulty of finding the optimal phase vector and speed up the calculation.

[0150] In some embodiments, the charger grouping algorithm described in step S5 is used to decompose the entire network into multiple sub-networks, including:

[0151] Set the request The rechargeable sensors are defined as key energy demand sensors, and the chargers are grouped according to the location of the key energy demand sensors and their positional relationship with the chargers; including:

[0152] For each key energy demand sensor, a corresponding charger subset is established, wherein the charger subset includes all chargers with the key energy demand sensor as the center and with a charging radius of for all chargers within a circular area of ​​radius;

[0153] If there are identical chargers between any two charger subsets, the charger subsets are merged to form a new charger subset until there are no identical chargers between each charger subset and other charger subsets;

[0154] Each subset of chargers after merging is defined as an independent sub-network.

[0155] It is understood that charger grouping refers to a group of chargers whose emitting wave phase can significantly affect the energy received by the critical energy demand sensor located in their combined coverage area. If there is no confusion, in the case where no critical energy demand sensor is located in the coverage area of ​​a charger, the charger will remain as a separate group.

[0156] In one embodiment, the charger grouping algorithm proceeds from iteratively checking the request set Each rechargeable sensor in the , establish a dedicated charger subset, denoted as ( ). This subset includes all Centered by charging radius The chargers within the circular area with a radius of , that is, the chargers located in this area can have a significant impact on the energy received by the rechargeable sensor. Subsequently, a post-processing step is performed on all identified subsets of chargers. By strategically merging related subsets, the final set of charger groups is ensured to be independent. In other words, any modification to the phase of a charger within a specific group will only affect the energy received by the critical energy demand sensors within the area covered by the group. Since all chargers that can significantly affect the energy received by the high energy demand sensors have been assigned to the group, the impact of the remaining chargers can be ignored. The remaining chargers are placed in separate groups, each of which contains only one charger. Finally, the chargers in the entire network are divided into a limited number of groups to speed up the process of finding the optimal phase vector.

[0157] In a specific embodiment, Figure 4 As shown, For chargers, the charging range covered by each charger overlaps. Each charger may cover the critical energy demand sensor (indicated by red dots in the figure) and the non-critical energy demand sensor (indicated by gray dots in the figure). When the critical energy demand sensor is located in the overlapping area, it indicates that there are multiple chargers with overlapping charging ranges, which will have a significant impact on the critical energy demand sensor. Before grouping, it is a major challenge to achieve constructive interference at each sensor position. The results after charger grouping based on the above embodiment are as follows. Figure 4 As shown, the chargers are divided into five different groups, namely independent sub-networks. , , , and These independent sub-networks effectively divide the problem and significantly reduce its complexity.

[0158] After applying the charger grouping algorithm, the challenge of determining the optimal phase vector for the overall network turns into finding the optimal phase vector for each independent sub-network. That is, the grouping structure is used as a basis to solve the phase vector problem within each sub-network. It is worth noting that the sub-networks exhibit three different size categories: a single charger, two chargers, and more than two chargers.

[0159] Specifically, the types of the independent subnetworks include a single charger subnetwork, a dual charger subnetwork and a multi-charger subnetwork; the number of chargers in the single charger subnetwork is 1, the number of chargers in the dual charger subnetwork is 2, and the number of chargers in the multi-charger subnetwork is greater than 2; the types of the independent subnetworks in the grouping results of the charger grouping algorithm include at least one of a single charger subnetwork, a dual charger subnetwork and a multi-charger subnetwork.

[0160] Then, the phase vector search algorithm is used in step S5 to enable the charger to focus energy on the sensor with urgent energy demand, including:

[0161] Find the best phase vector for each independent sub-network;

[0162] The optimal phase vector of each independent sub-network is combined to obtain the phases of all chargers in the network, and the phase vector that maximizes the charging benefit of the entire network is obtained.

[0163] In some embodiments, finding the best phase vector for each independent sub-network includes:

[0164] The phases of the chargers in the single charger sub-network are freely selected or remain unchanged. Specifically, in the single charger sub-network, the critical energy demand sensors may only appear at the initial network activation because they are not charged at that time. However, as long as the chargers are deployed properly, these sensors will continue to receive enough energy to exceed their consumption, preventing them from becoming critical energy demand sensors. Since these sensors are covered by only one charger, the interference of the waves will hardly affect the energy they receive. Therefore, all sensors in these sub-networks can be ignored and assumed to have no energy demand. In addition, our main goal is to maximize the energy of the critical energy demand sensors. Although careful selection of the phase may increase the energy received by sensors in certain overlapping areas, this approach has little practical benefit to the ultimate goal and may even hinder the overall optimization process due to the significant increase in computational complexity. Therefore, regardless of whether there is a critical energy demand sensor in the single charger sub-network, the phase of this charger can be freely selected or remain unchanged.

[0165] The dual charger sub-network includes at least one key energy demand sensor, and the energy received by the key energy demand sensor outside the overlapping charging range of the two chargers will not be affected. The phase selection method of each charger in the dual charger sub-network includes:

[0166]

[0167] The two chargers in the dual charger sub-network are represented as and ;but, Indicates that the charger is in and charger A collection of sensors for critical energy requirements within the overlapping areas of the rechargeable range, ; Representing a collection The total energy received by the rechargeable sensor in; Indicates charger and charger The phase difference between Indicates charger and charger The relative angular frequency between Indicates charger To rechargeable sensor distance; Indicates charger To rechargeable sensor distance;

[0168] By solving The extreme value of can obtain the optimal phase difference corresponding to the optimal phase vector of the two chargers in the dual charger sub-network. , the phase of one charger is freely selected, and the phase of the other charger is determined according to the optimal phase difference; thus, by optimizing the charger and charger The phase selection makes The total energy received by the sensors in is maximized.

[0169] In practical applications, when the collection When the scale is small, for example when it contains only one critical energy demand sensor, the charger and charger The phase alignment is to satisfy , you can directly obtain the maximum charging benefits.

[0170] In a multi-charger sub-network, the number of chargers is three or more. Determining the optimal phase vector for the chargers in these sub-networks is essentially a multivariate nonlinear optimization problem. Although the phase can be arbitrarily selected within [0,2π), the objective function exhibits periodic extreme values ​​due to the inherent trigonometric function characteristics. Specifically, in order to address the computational complexity of the charging model, an approximation is first constructed based on the Taylor series. The phase selection method for each charger in the multi-charger sub-network includes:

[0171] Set the reference phase vector , the total power received by the key energy demand sensors in the multi-charger sub-network at the reference phase vector is approximately:

[0172]

[0173] in, Represents the reference phase vector of the charger The corresponding phase; Represents the reference phase vector of the charger The corresponding phase; Represents an approximate value of the total power; find the The extreme value of the total power The total power The reference phase vector corresponding to the extreme value of is used as the vector of each charger in the multi-charger sub-network.

[0174] Specifically, using the total power approximation and the total power raw function The local similarity between them can be obtained by Found nearby To identify the extreme value of This requires solving the problem about of The first-order partial derivative equations are implemented as:

[0175]

[0176] In practical applications, the initial reference phase vector Found nearby The extreme values ​​of may not directly correspond to the original function Therefore, every time we find After finding the extreme value of , the approximation is iteratively updated by re-evaluating at the newly found extreme point. This process continues until convergence is reached, that is, no newly found extreme point can provide higher power.

[0177] Although the continuous approximation can be found However, it is not always guaranteed that the found extreme value corresponds to the original function The global optimal solution of To overcome the inherent limitations of local optimal solutions in the neighborhood of , a dynamic search strategy is adopted. This strategy uses random reselection of the reference phase vector , thereby exploring the domain of the objective function and identifying potentially better extreme values ​​that help maximize the charging benefits of the entire network.

[0178] Through the above method, the optimal phase vectors for the individual sub-networks of single charger, two chargers and multiple chargers have been determined to maximize the total power received by the energy-demanding rechargeable sensors in each sub-network. These optimal phase vectors are combined to derive the phase vector that maximizes the charging benefit of the entire network.

[0179] In a specific embodiment, the phase search algorithm includes: initializing the initial phase vector of all chargers in the network to . Secondly, after obtaining the request set in the network and grouped collections Afterwards, we targeted Each subgroup in Judge each one individually. When the number of elements in is 1, that is, , we group Charger in Randomly choose the initial phase of the radio wave it emits and update ;when When the number of elements in is 2, that is, , refer to the phase selection method of each charger in the dual charger sub-network provided in the above embodiment to determine the best and one of the chargers in the group Random Selection , while the other charger The phase of ,renew ;when When the number of elements in is greater than or equal to 3, that is, , by presetting the upper limit of the number of iterations and for The charger in the random selection phase vector (reference phase vector), at the same time, initialize the maximum charging power is 0. In each iteration, the following process is performed: Find the extreme value in the neighborhood of and will (Intermediate process quantity) is updated to .like Greater than , it means that the extreme value obtained in this round has improved the charging income of the network and is used and Record the maximum charging power and the corresponding phase vector during the iteration process. Less than , then randomly select a new phase , in use renew Then enter the next round of iteration until convergence or reaching the upper limit of iteration number. Finally, the phase of all chargers in the network is obtained. .

[0180] It is understandable that in the sub-network scenario of multiple chargers, the search space is continuous. It is computationally infeasible to enumerate all possible points in the continuous domain. In order to ensure the final convergence of the algorithm and improve computational efficiency, we predefine a maximum number of iterations. After this iterative process, the solution with the highest total power received by the sensor with urgent energy demand is considered to be the optimal phase vector of the sub-network.

[0181] Although the methods proposed in the above embodiments can effectively determine the optimal phase vector for a given network state, the energy demand of the sensor fluctuates over time. Therefore, using a static phase vector throughout the network life cycle cannot adapt to these dynamic conditions. To address this limitation, the present disclosure also includes:

[0182] According to the fluctuation of energy demand of rechargeable sensors over time, a phase scheduling algorithm is used to timely remove fully charged sensors from the request set. Removed.

[0183] In the disclosed charging scheme, a key parameter affecting the sensor behavior and the overall network performance is the energy threshold Setting the threshold too low (i.e. low remaining energy) will result in delayed response to charging requests, causing more sensors to go offline prematurely; setting the threshold too high will reduce charging efficiency. Sensors with sufficient energy can easily reach the threshold, diverting resources from sensors that need more urgent charging. Therefore, choosing the optimal Both responsiveness and efficiency need to be carefully considered. Appropriate energy thresholds can ensure that charging requests are made in a timely manner to prevent energy exhaustion, while minimizing redundant requests that put pressure on network resources. The specific value of may depend on many factors, such as energy consumption rate, charger power, and network deployment. It can be set according to specific circumstances.

[0184] During the operation of the network constructed in the present disclosure, the energy of the sensor will continue to be consumed. In order to minimize the communication overhead and allow the sensor to allocate more energy to task execution, the sensor only starts the communication protocol when its remaining energy drops below a predetermined threshold and broadcasts a charging request message to the nearby fixed chargers. After the charger receives charging requests from the sensors, it stores them in the request set. Based on The charger grouping algorithm and phase vector search algorithm are used to calculate the phase vector of the charger based on the sensor information in the charger.

[0185] Since the sensor will not send a notification when the battery is full, it is necessary to remove the fully charged sensor from the request set in a timely manner. To solve this problem, this embodiment proposes to update After that, it is estimated The charging time of each sensor in the

[0186] Specifically, the phase scheduling algorithm includes:

[0187] In each update request set When the charging time of each sensor in the request set is estimated, and the time for the next update of the request set is determined by identifying the minimum value of the charging time; wherein the method for estimating the charging time of each sensor in the request set includes:

[0188]

[0189] in, Indicates charging time; Indicated in the phase vector The power received.

[0190] In a specific embodiment, after combining the charger grouping algorithm and the phase vector search algorithm, the phase scheduling algorithm execution process is as follows:

[0191] First, get the request set , Energy Capacity , Energy Threshold , total charging cycle After obtaining the network location information, the current time t of the network is initialized to 0. Secondly, the grouping result is calculated according to the charger grouping algorithm The optimal phase vector calculated by the phase vector search algorithm is Applies to the entire network. For the request set Each sensor in , calculate the estimated charging time , while the smallest Recorded as It should be noted that the network operation time has not reached the total charging cycle. Before, the phase vector is updated whenever a new request is received or the minimum estimated charging time expires. When the minimum charging time expires, the request set One of the sensors is full. We have updated calculate And update it through the phase vector search algorithm . And when the network When a new request is received, the same calculation is performed and update For the request set Each sensor in , recalculate the estimated charging time , and update . In the update After that, the chargers in the network Provides the phase vector Time for wireless charging.

[0192] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0193] Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A collaborative wireless charging method based on phase scheduling, characterized in that: include: Constructing a wireless rechargeable sensor network, the wireless rechargeable sensor network comprising N Charger and M A rechargeable sensor; constructing a charging model based on the wireless rechargeable sensor network, the charging model including at least phase, power attenuation and wave interference of the charger; The concept of charging benefit is introduced to represent the rationality of energy distribution received by sensors with energy demand; A phase-scheduling-based collaborative wireless charging problem is defined based on a wireless rechargeable sensor network, a charging model, and charging benefits. The phase-scheduling-based collaborative wireless charging problem includes: for a given set of chargers, a set of rechargeable sensors, and the battery capacity of each sensor, searching for a set of charger phase vectors to provide maximum power to sensors with urgent energy demands within a given time period, thereby maximizing the overall charging benefits of the network. The phase-scheduling-based cooperative wireless charging problem is solved; wherein, a charger grouping algorithm is used to decompose the entire network into multiple independent sub-networks; based on the generated charger grouping, a phase vector search algorithm is used to enable the charger to concentrate energy on sensors with urgent energy needs; The types of the independent subnetworks include a single charger subnetwork, a dual charger subnetwork and a multi-charger subnetwork; the number of chargers in the single charger subnetwork is 1, the number of chargers in the dual charger subnetwork is 2, and the number of chargers in the multi-charger subnetwork is greater than 2; the types of the independent subnetworks in the grouping results of the charger grouping algorithm include at least one of a single charger subnetwork, a dual charger subnetwork and a multi-charger subnetwork; The phase vector search algorithm is used to enable the charger to focus energy on sensors with urgent energy demands, including: Find the best phase vector for each independent sub-network; The best phase vector of each independent sub-network is combined to obtain the phase of all chargers in the network, and the phase vector that maximizes the charging benefit of the entire network is obtained; The method of finding the optimal phase vector for each independent sub-network includes: The phase of the charger in the single charger sub-network is freely selected or remains unchanged; The phase selection method of each charger in the dual charger sub-network includes: The two chargers in the dual charger sub-network are represented as and ;but, Indicates that the charger is in and charger A collection of critical energy demand sensors within the overlapping areas of the rechargeable range; Representing a collection The total energy received by the rechargeable sensor in; represents the power reduction factor; Indicates charger and charger The phase difference between Indicates charger and charger The relative angular frequency between Indicates charger To rechargeable sensor distance; Indicates charger To rechargeable sensor distance; By solving The extreme value of can obtain the optimal phase difference corresponding to the optimal phase vector of the two chargers in the dual charger sub-network. , the phase of one charger is freely selected, and the phase of the other charger is determined according to the optimal phase difference; The phase selection method of each charger in the multi-charger sub-network includes: Set the reference phase vector , the total power received by the key energy demand sensors in the multi-charger sub-network at the reference phase vector is approximately: in, represents a request set; Indicates that it can cover rechargeable sensors A collection of all chargers; Representing a collection Internal charger Serial number; Representing a collection Internal charger Serial number; Indicates charger With rechargeable sensor distance; Indicates charger With rechargeable sensor distance; Indicates charger To sensor distance; Represents the reference phase vector of the charger The corresponding phase; Represents the reference phase vector of the charger The corresponding phase; Indicates charger and charger The relative angular frequency of Indicates charger The initial phase of Indicates charger The initial phase of Represents the approximate value of total power; Represents the initial phase vector of the charger; find the The extreme value of the total power The total power The reference phase vector corresponding to the extreme value of is used as the vector of each charger in the multi-charger sub-network.

2. The phase scheduling-based cooperative wireless charging method according to claim 1, characterized in that: The construction of a wireless rechargeable sensor network comprises: deploy Charger , the maximum charging radius of each charger is ; deploy Rechargeable sensors , the battery capacity of each sensor is ; charger To sensor The distance is expressed as ; In the wireless rechargeable sensor network, as the wireless rechargeable sensor network starts to operate, the energy reserve in the rechargeable sensor battery will gradually be exhausted. falls below a predefined critical threshold The rechargeable sensor will broadcast a request message to the charger when , to indicate the urgency of remaining energy; in, Represents the characteristic energy consumption rate of a rechargeable sensor; Indicates the charger number. ; Indicates the number of the rechargeable sensor ， ; After receiving the request message, the charger sends the identifier of the chargeable sensor Add to request set middle.

3. The phase scheduling-based cooperative wireless charging method according to claim 2, characterized in that: In the wireless rechargeable sensor network, once any rechargeable sensor in the request set reaches a fully charged state, the identifier of the rechargeable sensor is removed from the request set; when the request set changes, the charger will be triggered to adjust its phase in a cooperative manner so as to concentrate the power of the charger on the rechargeable sensors in the request set.

4. The phase scheduling-based cooperative wireless charging method according to claim 3, characterized in that: The step of constructing a charging model based on the wireless rechargeable sensor network includes: Charger The waves emitted It is expressed as: in, A represents the amplitude of the radio wave, represents the angular frequency of the radio wave, Indicates charger The initial phase of t Indicates time; Rechargeable sensor From charger Received radio waves It is expressed as: in, Indicated at angular frequency The wavelength of radio waves below; The method for calculating the power attenuation factor includes: in, Indicates the antenna gain of the charger; represents the antenna gain of the rechargeable sensor; definition For rechargeable sensors The received superimposed radio waves are: in, is a constant that depends on the amplitude of the radio wave, the initial phase, and the distance between the charger and the sensor; The calculation method of the relative angular frequency includes: set up for cycle, the sensor The power of the superimposed radio waves It is expressed as: Set the request The sum of the power received by each sensor in is expressed as:

5. The phase scheduling-based cooperative wireless charging method according to claim 4, characterized in that: The introduction of the charging benefit concept to represent the rationality of the distribution of energy received by the sensor with energy demand includes: Define sensors based on the maximum receive power limit of rechargeable sensors In time period The charging benefits obtained within 10 days include: in, Indicates a rechargeable sensor In time period Charging benefits obtained within Indicates sensor The remaining energy Indicates the maximum receiving power limit of the rechargeable sensor; Wireless rechargeable sensor networks in time The total charging benefit obtained within 1 hour is expressed as: in, Represents a collection of rechargeable sensors.

6. The phase scheduling-based cooperative wireless charging method according to claim 5, characterized in that: The phase scheduling-based cooperative wireless charging problem is expressed as: in, Represents the problem of cooperative wireless charging based on phase scheduling; Indicates the total charging benefit in a charging cycle; Represents a request set The total number of add and remove operations in; Indicates that at the specified phase vector The next two consecutive update request sets The time interval between charges is called a charging cycle. q Indicates the number of the charging cycle; Indicates restrictions; Indicates the total charging cycle, i.e. the set network operating time.

7. The phase scheduling-based cooperative wireless charging method according to claim 6, characterized in that: The charger grouping algorithm is used to decompose the entire network into multiple independent sub-networks, including: Set the request The rechargeable sensors are defined as key energy demand sensors, and the chargers are grouped according to the location of the key energy demand sensors and their positional relationship with the chargers; including: For each key energy demand sensor, a corresponding charger subset is established, wherein the charger subset includes all chargers with the key energy demand sensor as the center and with a charging radius of for all chargers within a circular area of ​​radius; If there are identical chargers between any two charger subsets, the charger subsets are merged to form a new charger subset until there are no identical chargers between each charger subset and other charger subsets; Each subset of chargers after merging is defined as an independent sub-network.

8. The phase scheduling-based cooperative wireless charging method according to claim 7, characterized in that: Also includes: According to the fluctuation of energy demand of rechargeable sensors over time, a phase scheduling algorithm is used to timely remove fully charged sensors from the request set. Removed; The phase scheduling algorithm includes: In each update request set When the charging time of each sensor in the request set is estimated, and the time for the next update of the request set is determined by identifying the minimum value of the charging time; wherein the method for estimating the charging time of each sensor in the request set includes: in, Indicates charging time; Indicates that at the specified phase vector The power received.

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