A charging scheduling method for multi-antenna chargers under energy budget constraints

Abstract

This invention discloses a charging scheduling method for multi-antenna chargers under energy budget constraints, including defining a network model, a charging model, and a charging utility model; formalizing the charger scheduling problem of unified control of multiple antennas under energy budget constraints; obtaining a limited number of candidate directions for the multi-antenna chargers using a dominance cover set extraction method; transforming the charger scheduling problem of unified control of multiple antennas under energy budget constraints; and calling the unified control scheduling algorithm for antenna switching of multi-antenna chargers under energy budget constraints to calculate the direction and switching scheduling scheme of the multi-antenna chargers. This invention maximizes the overall charging utility for sensors in energy-constrained scenarios by adjusting the direction and switching state of the multi-antenna chargers in multiple time periods throughout the charging process, thereby improving the energy utilization efficiency of multi-antenna chargers when performing charging tasks.

Description

Technical Field

[0001] This invention relates to the field of wireless rechargeable sensor network technology, specifically to a charging scheduling method for a multi-antenna charger under energy budget constraints. Background Technology

[0002] Wireless sensor networks have been widely used in environmental monitoring, disaster early warning, and other fields. However, sensor nodes typically rely on their built-in tiny batteries for power, and the limited energy severely restricts the network's uptime. Wireless power transfer technology can remotely charge sensor nodes, thereby significantly extending network lifespan. Among these technologies, multi-antenna chargers, which combine the high energy efficiency of single-antenna directional chargers with the high coverage of omnidirectional chargers, have become a research hotspot in this field.

[0003] However, current research on charging scheduling optimization for multi-antenna chargers focuses on one-time scheduling of charger states such as on / off and orientation to maximize sensor receiving power or received charge. This coarse-grained scheduling may lead to inefficient situations where sensors are fully charged but the charger continues to operate. Furthermore, existing research largely ignores energy budget constraints, despite their significant practical importance in system operation. From a network operation perspective, charging energy consumption directly corresponds to economic costs; therefore, it is necessary to control charging expenses through energy budget constraints and encourage network optimization of resource allocation strategies. For example, higher budgets can be configured in high-priority task scenarios to ensure reliability, while in non-emergency scenarios (such as fire warning systems during rainy weather), resource investment can be reduced to avoid all sensors being continuously operational. From an infrastructure construction perspective, the power grid has limited capacity to support charging equipment, especially in remote areas with weak power supply.

[0004] Therefore, research on scheduling methods for multi-antenna chargers under energy budget constraints has important practical significance for the deployment and efficient operation of actual systems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a multi-antenna charger scheduling method under energy budget constraints, comprising:

[0006] Define the network model, the charging model, and the charging utility model;

[0007] The charger scheduling problem under formalized energy budget constraints and unified control of multiple antennas;

[0008] A limited number of candidate directions for multi-antenna chargers are obtained by using the dominant coverage set extraction method.

[0009] The problem of charger scheduling under unified control of multiple antennas under the aforementioned energy budget constraint is transformed;

[0010] The unified control and scheduling algorithm for antenna switching of a multi-antenna charger under energy budget constraints is invoked to calculate the direction and switching scheduling scheme of the multi-antenna charger.

[0011] As a preferred embodiment of the multi-antenna charger charging scheduling method under energy budget constraints described in this invention, the network model includes:

[0012] Distributed in a two-dimensional plane The rechargeable sensor set on it is ,in Number of rechargeable sensors; each rechargeable sensor Corresponding to a charging request ,in and Represent The power requirements and charging cutoff time, The charging cutoff time is based on the time slot length as the basic unit, and the length of a single time slot is defined as... Let the number of time slots during the charging process be... , will the Each time slot is recorded as , ;flat There are also distributed within Multi-antenna charger Each charger Equipped with One directional charging antenna, among which ,definition The The root antenna is ,in The location information of all rechargeable sensors and multi-antenna chargers, as well as the charging request information of the rechargeable sensors, are known.

[0013] As a preferred embodiment of the multi-antenna charger charging scheduling method under energy budget constraints described in this invention, the charging model includes:

[0014] Multi-antenna charger antenna The effective charging area is a fan-shaped area, which is defined by... Centered on, with direction as The central angle is , radius is The relative angles between the antennas of the same multi-antenna charger are fixed. express upper antenna and the first antenna The angle between them; during the charging process, the overall orientation of the multi-antenna charger is adjusted over time; The first antenna direction is defined as overall direction , In the The overall direction of each time slot is denoted as ;definition The direction scheduling scheme is All multi-antenna chargers in The direction scheduling scheme for each time slot is represented as follows: ;

[0015] when Direction is At that time, rechargeable sensor From multi-antenna charger The The charging power obtained at the root antenna is:

[0016] (1)

[0017] in, and It is by and Two constants determined by the magnetic field environment and hardware parameters. for and The distance between them and They are respectively The transmit power and its time slot The unit vector in the inward direction. ;

[0018] Let the binary variable Characterizing multi-antenna chargers In the time slot The switch state inside, if ,but It is in the energy emission state, otherwise it is in the off state; let express exist The switching scheduling scheme for each time slot, and the switching scheduling scheme for all chargers throughout the entire charging process, are represented as follows: ;

[0019] Given a multi-antenna charger directional scheduling scheme and switch scheduling scheme , In the time slot Total power received for:

[0020] (2)

[0021] Multi-antenna charger switch scheduling scheme The total energy consumed is expressed as:

[0022] (3)

[0023] The total available energy of all multi-antenna chargers during the charging process is limited by a preset energy budget. , Given a positive real number.

[0024] As a preferred embodiment of the multi-antenna charger charging scheduling method under energy budget constraints described in this invention, the charging utility model includes:

[0025] Define each rechargeable sensor The effective amount of electricity actually received before its own charging cutoff time. for:

[0026]

[0027] (4)

[0028] in, It describes a time slot. Does it exceed Charging cutoff time Indicator functions:

[0029] (5)

[0030] Multi-antenna charger directional scheduling scheme and switch scheduling scheme The charging utility of a multi-antenna charger for all rechargeable sensors is expressed as follows:

[0031]

[0032] (6)

[0033] in, To indicate The weights are known constants.

[0034] As a preferred embodiment of the multi-antenna charger scheduling method under energy budget constraints described in this invention, the charger scheduling problem under formalized energy budget constraints and unified multi-antenna control includes:

[0035] Establish constraints Ensure that the total power consumption of all multi-antenna chargers does not exceed the energy budget. ;

[0036] Establish constraints The specification stipulates that the direction of all multi-antenna chargers in each time slot can be... Takes continuous values ​​within a range;

[0037] Establish constraints Indicates the on / off status of the multi-antenna charger. It is a binary variable;

[0038] With the optimization objective of maximizing the charging utility for all rechargeable sensors, we obtain the charger scheduling problem P1, which involves unified control of multiple antennas under a unified formal energy budget constraint:

[0039] (7)

[0040] (7-1)

[0041] (7-2)

[0042] (7-3).

[0043] As a preferred embodiment of the multi-antenna charger charging scheduling method under energy budget constraints described in this invention, the dominant coverage set extraction method includes:

[0044] A1. Definition of a multi-antenna charger The set of covers in any given direction forms a family of sets. ,in express In the current direction, the antenna A collection of rechargeable sensors that can be covered;

[0045] A2. Select a multi-antenna charger that has not been calculated. Initialize its orientation to 0 degrees, and initialize the multi-antenna charger. Covering set for Initialize the multi-antenna charger An auxiliary set for Initialize the multi-antenna charger Candidate direction set It is an empty set;

[0046] A3. Will The direction is rotated counterclockwise. During the rotation, if there is... If a newly added rechargeable sensor is added within the coverage area of ​​an antenna, stop rotating and proceed to step A4; otherwise, if a rechargeable sensor is about to leave... If the coverage area of ​​a certain antenna is reached, then stop rotating and proceed to steps A5 to A6;

[0047] A4. Add the newly added rechargeable sensor to the coverage set. Corresponding set Proceed to step A7;

[0048] A5. If covering set Compared to auxiliary sets If any subset corresponding to an antenna contains the newly added rechargeable sensor, then... Record as a dominant cover set, The current direction is added to the candidate direction set. Simultaneously update For the current ,Right now ;

[0049] A6. Will Rotate the direction counterclockwise by a small angle to move the rechargeable sensor away. The antenna's coverage area, and the sensor from Delete from the subset corresponding to the antenna;

[0050] A7. Repeat steps A3 to A6 until the rotation angle is greater than 360 degrees. That is, it includes All possible candidate directions;

[0051] A8. Repeat steps A2 to A7 until all candidate directions for the multi-antenna chargers have been calculated.

[0052] As a preferred embodiment of the multi-antenna charger scheduling method under energy budget constraints described in this invention, the transformation of the charger scheduling problem under unified control of multiple antennas under energy budget constraints includes:

[0053] Define a set During the charging process Each time slot For merging all candidate direction sets for multi-antenna chargers, by... and By performing Cartesian product operations, we can obtain a set of candidate scheduling strategies. , Each element in express In the time slot It is in the open state, and the direction is... This candidate scheduling strategy; the direction and switching scheduling scheme for multi-antenna chargers can be used. A subset express;

[0054] definition Represents arbitrary direction and switch scheduling scheme ( The corresponding energy consumption;

[0055] Establish constraints Each multi-antenna charger is limited to charging in only one direction at any given time slot. , ;

[0056] Establish constraints Ensure that the total power consumption of all multi-antenna chargers does not exceed the energy budget. ;

[0057] Problem P1 can be equivalently transformed into problem P2 of the following form:

[0058] (8)

[0059] (8-1)

[0060] (8-2).

[0061] As a preferred embodiment of the multi-antenna charger charging scheduling method under energy budget constraints described in this invention, the unified control and scheduling algorithm for antenna switching of the multi-antenna charger under energy budget constraints includes:

[0062] B1: Initialize the direction and switching scheduling scheme of the multi-antenna charger to an empty set, i.e. ;

[0063] B2: If the number of scheduling policies in the candidate scheduling policy set is less than or equal to ,Right now If so, proceed to step B3; otherwise, proceed to steps B4 to B10.

[0064] B3: If ,and The corresponding multi-antenna charger direction and switching scheduling schemes satisfy the energy budget constraint, that is Then let ;Execute step B11;

[0065] B4: From the set of candidate scheduling strategies Find two scheduling strategies that produce the maximum utility increment while satisfying constraints (8-1) and (8-2). and ,

[0066] That is: let ,

[0067] ;

[0068] B5: Scheduling strategy and The constructed multi-antenna charger direction and switch scheduling scheme serves as an initial solution. ,Right now ;

[0069] B6: Perform iterative optimization. and Perform Cartesian product operations to generate a set of commutative operations. ,Right now ;in This is a virtual scheduling policy, and it is agreed that for any... They all ,and ;

[0070] B7: From the set of swap operations Find the exchange operation with the highest marginal utility density. ,Right now ;

[0071] B8: If and and and Then according to the swap operation Revise ,Right now Otherwise, let ;in, This is a preset constant used for precision control;

[0072] B9: Repeat steps B7 to B8 until... Modified once, or It is an empty set;

[0073] B10: Repeat steps B6 through B9 until... No further changes will occur, i.e., during the execution of steps B6 to B9. It has not been modified;

[0074] B11: Return to Multi-Antenna Charger Direction and Switching Scheme .

[0075] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned multi-antenna charger charging scheduling method under energy budget constraints.

[0076] A computer-readable storage medium storing computer instructions that, when executed by a processor, implement the aforementioned energy budget-constrained multi-antenna charger charging scheduling method.

[0077] The beneficial effects of this invention are:

[0078] 1. The objective function of P2 in the charger scheduling problem of unified control of multiple antennas under the converted energy budget constraint in this scheme is... It is non-negative, monotonic, and submodular.

[0079] Obviously, the function It is nonnegative and monotonic, which needs to be proven. The submodularity, that is, for any , They all To prove this inequality, it is necessary to prove that for any sensor... They all

[0080] (9)

[0081] in This indicates that all fixed chargers are in the scheduling policy set. Down The actual amount of electricity received. Definition for In scheduling strategy The cumulative received electricity. The correctness of inequality (9) will be proven in three cases below.

[0082] Case 1: If ,because Obviously there is

[0083] (10)

[0084] Scenario 2: If ,at this time

[0085] (11)

[0086] Therefore, there is

[0087]

[0088] (12)

[0090] Scenario 3: If ,at this time

[0091]

[0092]

[0093] (13)

[0094]

[0095]

[0096] (14)

[0097] according to and , The size relationship between them can be further subdivided into the following three cases for discussion:

[0098] Case 3.1: If Then there is

[0099] (15)

[0100] Case 3.2: If Then there is

[0101]

[0102]

[0103]

[0104] (16)

[0105] Case 3.3: If Then there is

[0106] (17)

[0107] Therefore, the objective function It is non-negative, monotonic, and submodular.

[0108] 2. In this scheme, the charger scheduling problem P2 under the converted energy budget constraint of unified control of multiple antennas is a non-negative, monotonic submode set function maximization problem subject to a knapsack constraint and a matroid constraint.

[0109] Based on beneficial effect 1, the objective function of P2 in the charger scheduling problem P2 under the transformed energy budget constraint with unified control of multiple antennas is... Since it is nonnegative, monotonically singular, and submodular, it is only necessary to prove that the two constraints on P2 are matroid constraints and knapsack constraints.

[0110] Constraint (8-1) requires the scheduling scheme to be... ,in ,here Indicates charger In the time slot The set of all candidate scheduling strategies. This constraint guarantees that each charger can only choose at most one direction at any given time. By verifying the three axioms of existing matroids, it can be proved that (8-1) is a matroid constraint.

[0111] Constraint (8-2) requires that the total energy consumption of the selected scheduling strategy does not exceed the energy budget. This is essentially a knapsack constraint.

[0112] In summary, problem P2 is a submodular set function maximization problem constrained by matroids and knapsack.

[0113] 3. The approximation ratio of the unified control and scheduling algorithm for antenna switches of the multi-antenna charger under the energy budget constraint is: .

[0114] Based on beneficial effect 2 and existing theoretical results, beneficial effect 3 is valid.

[0115] 4. The time complexity of the unified control and scheduling algorithm for antenna switches of the multi-antenna charger under the energy budget constraint is: .

[0116] Step B8 introduces exchange execution constraints. This ensures that the replacement operation within B8 will execute at most [number missing]. Once, or simply recorded as Each replacement requires starting from a size of The optimal swap operation is selected from the set of candidate swap operations, therefore the overall complexity of the algorithm is O(n log n). Because of the set It is a set of time slots With the set of candidate directions for all chargers The Cartesian product, and The number of elements is , The number of elements is Therefore, the time complexity of the unified control and scheduling algorithm for antenna switching of a multi-antenna charger under energy budget constraints is . This polynomial-time algorithm guarantees accurate results while saving a significant amount of computation time.

[0117] In summary, this invention provides a charging scheduling method for a multi-antenna charger under energy budget constraints. By adjusting the direction and switching state of the multi-antenna charger in multiple time periods throughout the charging process, energy is rationally allocated in a limited energy scenario, thereby maximizing the overall charging efficiency for all sensors and improving the energy utilization efficiency of the multi-antenna charger during charging. The algorithm used has polynomial time complexity and approximation guarantee, which can ensure accuracy while saving computation time. Attached Figure Description

[0118] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of 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. Wherein:

[0119] Figure 1 A flowchart of a multi-antenna charger charging scheduling method under energy budget constraints is provided as an embodiment of the present invention;

[0120] Figure 2 A flowchart of the dominance coverage set extraction method in a multi-antenna charger charging scheduling method under energy budget constraints, provided in one embodiment of the present invention;

[0121] Figure 3 A flowchart of a unified control and scheduling algorithm for antenna switches of a multi-antenna charger under energy budget constraints is provided in an embodiment of the present invention.

[0122] Figure 4 The diagram shows a comparison of the effects of a multi-antenna charger charging scheduling method under energy budget constraints, provided in the second embodiment of the present invention. Detailed Implementation

[0123] Example 1

[0124] Reference Figure 1 The present invention provides a charging scheduling method for a multi-antenna charger under energy budget constraints, which specifically includes the following steps:

[0125] S1: Define the network model, charging model, and charging utility model;

[0126] Specifically, the network models include:

[0127] (1) Distributed in a two-dimensional plane The rechargeable sensor set on it is ,in Number of rechargeable sensors; each rechargeable sensor Corresponding to a charging request ,in and Represent The power requirements and charging cutoff time, The charging cutoff time is based on the time slot length as the basic unit, and the length of a single time slot is defined as... Let the number of time slots during the charging process be... , will the Each time slot is recorded as , ;

[0128] (2) Plane There are also distributed within Multi-antenna charger Each charger Equipped with One directional charging antenna, among which ,definition The The root antenna is ,in ;

[0129] (3) The location information of all rechargeable sensors and multi-antenna chargers, as well as the charging request information of rechargeable sensors, are known.

[0130] Specifically, the charging model includes:

[0131] (1) Multi-antenna charger antenna The effective charging area is a fan-shaped area, which is defined by... Centered on, with direction as The central angle is , radius is The relative angles between the antennas of the same multi-antenna charger are fixed. express upper antenna and the first antenna The angle between them; during the charging process, the overall orientation of the multi-antenna charger is adjusted over time; The first antenna direction is defined as overall direction , In the The overall direction of each time slot is denoted as ;definition The direction scheduling scheme is All multi-antenna chargers in The direction scheduling scheme for each time slot is represented as follows: ;

[0132] (2) When Direction is At that time, rechargeable sensor From multi-antenna charger The The charging power obtained at the root antenna is:

[0133] (1)

[0134] in, and It is by and Two constants determined by the magnetic field environment and hardware parameters. for and The distance between them and They are respectively The transmit power and its time slot The unit vector in the inward direction. ;

[0135] (3) Let the two variables Characterizing multi-antenna chargers In the time slot The switch state inside, if ,but It is in the energy emission state, otherwise it is in the off state; let express exist The switching scheduling scheme for each time slot, and the switching scheduling scheme for all chargers throughout the entire charging process, are represented as follows: ;

[0136] Given a multi-antenna charger directional scheduling scheme and switch scheduling scheme , In the time slot Total power received for:

[0137] (2)

[0138] (4) Multi-antenna charger switch scheduling scheme The total energy consumed is expressed as:

[0139] (3)

[0140] (5) The total available energy of all multi-antenna chargers during the charging process is limited by the preset energy budget. , Given a positive real number.

[0141] Specifically, the charging utility model includes:

[0142] (1) Define each rechargeable sensor The effective amount of electricity actually received before its own charging cutoff time. for:

[0143]

[0144] (4)

[0145] in, It describes a time slot. Does it exceed Charging cutoff time Indicator functions:

[0146] (5)

[0147] (2) Scheduling scheme for multi-antenna chargers and switch scheduling scheme The charging utility of a multi-antenna charger for all rechargeable sensors is expressed as follows:

[0148]

[0149] (6)

[0150] in, To indicate The weights are known constants.

[0151] S2: Charger scheduling problem under formal energy budget constraints with unified control of multiple antennas;

[0152] Furthermore, the charger scheduling problem under formalized energy budget constraints and unified control of multiple antennas includes:

[0153] (1) Establish constraints Ensure that the total power consumption of all multi-antenna chargers does not exceed the energy budget. ;

[0154] (2) Establish constraints The specification stipulates that the direction of all multi-antenna chargers in each time slot can be... Takes continuous values ​​within a range;

[0155] (3) Establish constraints Indicates the on / off status of the multi-antenna charger. It is a binary variable;

[0156] (4) Taking maximizing the charging utility for all rechargeable sensors as the optimization objective, we obtain the charger scheduling problem P1 under a unified formal energy budget constraint for unified control of multiple antennas:

[0157] (7)

[0158] (7-1)

[0159] (7-2)

[0160] (7-3).

[0161] S3: Use the dominant coverage set extraction method to obtain a limited number of candidate directions for multi-antenna chargers;

[0162] Furthermore, refer to Figure 2 Methods for extracting dominant cover sets include:

[0163] A1. Definition of a multi-antenna charger The set of covers in any given direction forms a family of sets. ,in express In the current direction, the antenna A collection of rechargeable sensors that can be covered;

[0164] A2. Select a multi-antenna charger that has not been calculated. Initialize its orientation to 0 degrees, and initialize the multi-antenna charger. Covering set for Initialize the multi-antenna charger An auxiliary set for Initialize the multi-antenna charger Candidate direction set It is an empty set;

[0165] A3. Will The direction is rotated counterclockwise. During the rotation, if there is... If a newly added rechargeable sensor is added within the coverage area of ​​an antenna, stop rotating and proceed to step A4; otherwise, if a rechargeable sensor is about to leave... If the coverage area of ​​a certain antenna is reached, then stop rotating and proceed to steps A5 to A6;

[0166] A4. Add the newly added rechargeable sensor to the coverage set. Corresponding set Proceed to step A7;

[0167] A5. If covering set Compared to auxiliary sets If any subset corresponding to an antenna contains the newly added rechargeable sensor, then... Record as a dominant cover set, The current direction is added to the candidate direction set. Simultaneously update For the current ,Right now ;

[0168] A6. Will Rotate the direction counterclockwise by a small angle to move the rechargeable sensor away. The antenna's coverage area, and the sensor from Delete from the subset corresponding to the antenna;

[0169] A7. Repeat steps A3 to A6 until the rotation angle is greater than 360 degrees. That is, it includes All possible candidate directions;

[0170] A8. Repeat steps A2 to A7 until all candidate directions for the multi-antenna chargers have been calculated.

[0171] S4: Charger scheduling problem under unified control of multiple antennas under energy conversion budget constraints;

[0172] Furthermore, the charger scheduling problem under unified control of multiple antennas under the constraint of energy conversion budget includes:

[0173] (1) Define a set During the charging process Each time slot For merging all candidate direction sets for multi-antenna chargers, by... and By performing Cartesian product operations, we can obtain a set of candidate scheduling strategies. , Each element in express In the time slot It is in the open state, and the direction is... This candidate scheduling strategy; the direction and switching scheduling scheme for multi-antenna chargers can be used. A subset express;

[0174] (2) Definition Represents arbitrary direction and switch scheduling scheme ( The corresponding energy consumption;

[0175] (3) Establish constraints Each multi-antenna charger is limited to charging in only one direction at any given time slot. , ;

[0176] (4) Establish constraints Ensure that the total power consumption of all multi-antenna chargers does not exceed the energy budget. ;

[0177] (5) Problem P1 can be equivalently transformed into problem P2 in the following form:

[0178] (8)

[0179] (8-1)

[0180] (8-2).

[0181] S5: Use the unified control and scheduling algorithm for multi-antenna charger antenna switching under energy budget constraints to calculate the direction and switching scheduling scheme of the multi-antenna charger;

[0182] Furthermore, refer to Figure 3 The unified control and scheduling algorithm for antenna switching of multi-antenna chargers under energy budget constraints includes:

[0183] B1: Initialize the direction and switching scheduling scheme of the multi-antenna charger to an empty set, i.e. ;

[0184] B2: If the number of scheduling policies in the candidate scheduling policy set is less than or equal to ,Right now If so, proceed to step B3; otherwise, proceed to steps B4 to B10.

[0185] B3: If ,and The corresponding multi-antenna charger direction and switching scheduling schemes satisfy the energy budget constraint, that is Then let ;Execute step B11;

[0186] B4: From the set of candidate scheduling strategies Find two scheduling strategies that produce the maximum utility increment while satisfying constraints (8-1) and (8-2). and ,

[0187] That is: let ,

[0188] ;

[0189] B5: Scheduling strategy and The constructed multi-antenna charger direction and switch scheduling scheme serves as an initial solution. ,Right now ;

[0190] B6: Perform iterative optimization. and Perform Cartesian product operations to generate a set of commutative operations. ,Right now ;in This is a virtual scheduling policy, and it is agreed that for any... They all ,and ;

[0191] B7: From the set of swap operations Find the exchange operation with the highest marginal utility density. ,Right now ;

[0192] B8: If and and and Then according to the swap operation Revise ,Right now Otherwise, let ;in, This is a preset constant used for precision control;

[0193] B9: Repeat steps B7 to B8 until... Modified once, or It is an empty set;

[0194] B10: Repeat steps B6 through B9 until... No further changes will occur, i.e., during the execution of steps B6 to B9. It has not been modified;

[0195] B11: Return to Multi-Antenna Charger Direction and Switching Scheme .

[0196] Example 2

[0197] The energy budget-constrained unified control and scheduling algorithm for antenna switching in a multi-antenna charger, as described in this invention, is named BMS-UA. (Refer to...) Figure 4 This is another embodiment of the present invention. In order to verify and explain the technical effects used in this method, this embodiment involves three schemes for comparative testing with the method of the present invention.

[0198] (1) Random Direction and Switching State Scheduling Algorithm ROSS: A completely random generation scheduling scheme. In each time slot, a direction is randomly selected from its candidate direction set for each multi-antenna charger to construct a direction scheduling scheme; at the same time, under the premise of satisfying the overall energy budget, random multi-antenna chargers are turned on in random time slots to construct a switching scheduling scheme.

[0199] (2) Random Direction and Maximum Marginal Utility Switching State Scheduling Algorithm ROMMSS: Contains two execution phases. In the first phase, for each multi-antenna charger... From candidate direction sets in each time slot In the first stage, a direction is randomly selected to generate a direction scheduling scheme. In the second stage, for this direction scheduling scheme, an iterative method is used to generate a multi-antenna charger switching scheduling scheme. In each iteration, a multi-antenna charger activation strategy that satisfies the energy budget constraint and has the maximum marginal utility density is selected from the remaining candidate strategies. The multi-antenna charger activation strategy express In the time slot It remains in the active state until the utility increment reaches 0 or the energy budget is exhausted.

[0200] (3) GCUSS, a direction and switch-state scheduling algorithm based on greedy request coverage and utility density optimization: The first stage of the random direction and maximum marginal utility switch-state scheduling algorithm is modified, replacing the random direction selection with a greedy coverage strategy, that is, selecting the direction that can cover the most charging requests for each multi-antenna charger in each time slot. The second stage is consistent with the random direction and maximum marginal utility switch-state scheduling algorithm.

[0201] Parameter settings: Distribute 20 rechargeable sensors evenly in... Within a square area, five randomly placed multi-antenna chargers are deployed simultaneously, each with 3-6 antennas. , , , , , , , , In the experiments, the values ​​of some key parameters were changed to explore their impact on the algorithm. All simulations were run on a computer with an Intel(R) Xeon(R) CPU i7-10750H and 8GB of memory, and the results of each experiment were the average of 100 simulations.

[0202] like Figure 4 As shown in (a), in terms of energy budget, BMS-UA offers an average improvement of 224%, 27.33%, and 13.65% over ROSS, ROMMSS, and GCUSS, respectively. Figure 4 As shown in Figure (b), in terms of the energy requirements of rechargeable sensors, BMS-UA improves by an average of 225.85%, 24.21%, and 10.93% compared to ROSS, ROMMSS, and GCUSS, respectively. Figure 4 As shown in (c), in terms of charging cutoff time, BMS-UA improves by an average of 178.69%, 26.2%, and 9.54% compared to ROSS, ROMMSS, and GCUSS, respectively. Figure 4 As shown in (d), in terms of the number of rechargeable sensors, BMS-UA is up by an average of 228.57%, 24.22%, and 9.03% compared to ROSS, ROMMSS, and GCUSS, respectively.

[0203] It should be noted that the above embodiments are not intended to limit the scope of protection of the present invention. Equivalent transformations or substitutions made based on the above technical solutions all fall within the scope of protection of the claims of the present invention.

Claims

1. A charging scheduling method for a multi-antenna charger under energy budget constraints, characterized in that, include: Define the network model, the charging model, and the charging utility model; The charger scheduling problem under formalized energy budget constraints and unified control of multiple antennas; A limited number of candidate directions for multi-antenna chargers are obtained by using the dominant coverage set extraction method. The problem of charger scheduling under unified control of multiple antennas under the aforementioned energy budget constraint is transformed; The unified control and scheduling algorithm for antenna switching of a multi-antenna charger under energy budget constraints is invoked to calculate the direction and switching scheduling scheme of the multi-antenna charger. The charger scheduling problem under formalized energy budget constraints and unified control of multiple antennas includes: Establish constraints Ensure that the total power consumption of all multi-antenna chargers does not exceed the energy budget. ; Establish constraints The specification stipulates that the direction of all multi-antenna chargers in each time slot can be... Takes values ​​continuously within a range; Establish constraints Indicates the on / off status of the multi-antenna charger. It is a binary variable; With the optimization objective of maximizing the charging utility for all rechargeable sensors, we obtain the charger scheduling problem P1, which involves unified control of multiple antennas under a unified formal energy budget constraint: (7) (7-1) (7-2) (7-3)； in, Multi-antenna charger switch scheduling scheme Total energy consumed express In the The overall direction of each time slot To indicate The known constant of the weights, Indicates each rechargeable sensor The effective amount of electricity actually received before its own charging deadline; The charger scheduling problem under unified control of multiple antennas under energy conversion budget constraints includes: Define a set During the charging process Each time slot For merging all candidate direction sets for multi-antenna chargers, by... and By performing Cartesian product operations, we can obtain a set of candidate scheduling strategies. , Each element in express In the time slot It is in the open state, and the direction is... This candidate scheduling strategy; the direction and switching scheduling scheme for multi-antenna chargers can be used. A subset express; definition Represents arbitrary direction and switch scheduling scheme ( The corresponding energy consumption; Establish constraints Each multi-antenna charger is limited to charging in only one direction at any given time slot. , ; Establish constraints Ensure that the total power consumption of all multi-antenna chargers does not exceed the energy budget. ; Problem P1 can be equivalently transformed into problem P2 of the following form: (8) (8-1) (8-2)。 2. The charging scheduling method for a multi-antenna charger under energy budget constraints as described in claim 1, characterized in that: The network model includes: Distributed in a two-dimensional plane The rechargeable sensor set on it is ,in Number of rechargeable sensors; each rechargeable sensor Corresponding to a charging request ,in and Represent The power demand and charging cut-off time, The charging cutoff time is based on the time slot length as the basic unit, and the length of a single time slot is defined as... Let the number of time slots during the charging process be... , will the Each time slot is recorded as , ;flat There are also distributed within Multi-antenna charger Each charger Equipped with One directional charging antenna, among which ,definition The The root antenna is ,in The location information of all rechargeable sensors and multi-antenna chargers, as well as the charging request information of the rechargeable sensors, are known.

3. The charging scheduling method for a multi-antenna charger under energy budget constraints as described in claim 2, characterized in that: The charging model includes: Multi-antenna charger antenna The effective charging area is a fan-shaped area, which is based on... Centered on, with direction as The central angle is , radius is The relative angles between the antennas of the same multi-antenna charger are fixed. express upper antenna and the first antenna The angle between them; during the charging process, the overall orientation of the multi-antenna charger is adjusted over time; The first antenna direction is defined as overall direction , In the The overall direction of each time slot is denoted as ;definition The direction scheduling scheme is All multi-antenna chargers in The direction scheduling scheme for each time slot is represented as follows: ; when Direction is At that time, rechargeable sensor From multi-antenna charger The The charging power obtained at the root antenna is: (1) in, and It is by and Two constants determined by the magnetic field environment and hardware parameters. for and The distance between them and They are respectively The transmit power and its time slot The unit vector in the inward direction. ; Let the binary variable Characterizing multi-antenna chargers In the time slot The switch state inside, if ,but It is in the energy emission state, otherwise it is in the off state; let express exist The switching scheduling scheme for each time slot, and the switching scheduling scheme for all chargers throughout the entire charging process, are represented as follows: ; Given a multi-antenna charger directional scheduling scheme and switch scheduling scheme , In the time slot Total power received for: (2) Multi-antenna charger switch scheduling scheme The total energy consumed is expressed as: (3) The total available energy of all multi-antenna chargers during the charging process is limited by a preset energy budget. , Given a positive real number.

4. The charging scheduling method for a multi-antenna charger under energy budget constraints as described in claim 3, characterized in that: The charging utility model includes: Define each rechargeable sensor The effective amount of electricity actually received before its own charging cutoff time. for: (4) in, It describes a time slot. Does it exceed Charging cutoff time Indicator functions: (5) Multi-antenna charger directional scheduling scheme and switch scheduling scheme The charging utility of a multi-antenna charger for all rechargeable sensors is expressed as follows: (6) in, To indicate The weights are known constants.

5. The charging scheduling method for a multi-antenna charger under energy budget constraints as described in claim 4, characterized in that: The method for extracting the dominance cover set includes: A1. Definition of a multi-antenna charger The set of covers in any given direction forms a family of sets. ,in express In the current direction, the antenna A collection of rechargeable sensors that can be covered; A2. Select a multi-antenna charger that has not been calculated. Initialize its orientation to 0 degrees, and initialize the multi-antenna charger. Covering set for Initialize the multi-antenna charger An auxiliary set for Initialize the multi-antenna charger Candidate direction set It is an empty set; A3. Will The direction is rotated counterclockwise. During the rotation, if there is... If a newly added rechargeable sensor is added within the coverage area of ​​an antenna, stop rotating and proceed to step A4; otherwise, if a rechargeable sensor is about to leave... If the coverage area of ​​a certain antenna is reached, then stop rotating and proceed to steps A5 to A6; A4. Add the newly added rechargeable sensors to the coverage set. Corresponding set Proceed to step A7; A5. If covering set Compared to auxiliary sets If any subset corresponding to an antenna contains the newly added rechargeable sensor, then... Record as a dominant cover set, The current direction is added to the candidate direction set. Simultaneously update For the current ,Right now ; A6. Will Rotate the direction counterclockwise by a small angle to move the rechargeable sensor away. The antenna's coverage area, and the sensor from Delete from the subset corresponding to the antenna; A7. Repeat steps A3 to A6 until the rotation angle is greater than 360 degrees. That is, it includes All possible candidate directions; A8. Repeat steps A2 to A7 until all candidate directions for the multi-antenna chargers have been calculated.

6. The charging scheduling method for a multi-antenna charger under energy budget constraints as described in claim 5, characterized in that: The unified control and scheduling algorithm for antenna switching of multi-antenna chargers under energy budget constraints includes: B1: Initialize the direction and switching scheduling scheme of the multi-antenna charger to an empty set, i.e. ; B2: If the number of scheduling policies in the candidate scheduling policy set is less than or equal to ,Right now If so, proceed to step B3; otherwise, proceed to steps B4 to B10. B3: If ,and The corresponding multi-antenna charger direction and switching scheduling schemes satisfy the energy budget constraint, that is Then let ;Execute step B11; B4: From the set of candidate scheduling strategies Find two scheduling strategies that produce the maximum utility increment while satisfying constraints (8-1) and (8-2). and , That is: Order , ； B5: Scheduling strategy and The constructed multi-antenna charger direction and switch scheduling scheme serves as an initial solution. ,Right now ; B6: Perform iterative optimization. and Perform Cartesian product operations to generate a set of commutative operations. ,Right now ;in This is a virtual scheduling policy, and it is agreed that for any... They all ,and ; B7: From the set of swap operations Find the exchange operation with the highest marginal utility density. ,Right now ; B8: If and and and Then according to the swap operation Revise ,Right now Otherwise, let ;in, This is a preset constant used for precision control; B9: Repeat steps B7 to B8 until... Modified once, or It is an empty set; B10: Repeat steps B6 through B9 until... No further changes will occur, i.e., during the execution of steps B6 to B9. It has not been modified; B11: Return to Multi-Antenna Charger Direction and Switching Scheme .

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a multi-antenna charger charging scheduling method under energy budget constraints as described in any one of claims 1 to 6.

8. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, the computer instructions implement a multi-antenna charger charging scheduling method under energy budget constraints as described in any one of claims 1-6.

Images