A Fine-Grained Power Allocation Method and System for Charging Utility Optimization in the Internet of Things

Abstract

The present invention relates to a fine-grained power allocation method and system for charging utility optimization in the Internet of Things, belonging to the technical field of wireless sensor networks. The method includes: constructing a wireless sensor network model; during the charging cycle, determining the effective energy received by the sensors requesting charging, and determining the sum of the utilities of the sensors requesting charging according to the effective energy; under the dual constraints of the maximum off-duty time limit of the sensors and the charging cost limit, transforming the sum of the utilities into a wireless sensor network utility maximization problem model; solving the wireless sensor network utility maximization problem model to determine the power of each time slice of the charging station, and obtaining a power allocation strategy scheme. By formulating an optimal charging service power allocation strategy for the charging station, balancing the relationship among charging time, cost, and utility as needed, the present method can provide a more flexible and efficient charging service, and improve the quality and efficiency of the charging service of the entire network.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless sensor networks, and particularly relates to a fine-grained power allocation method and system for optimizing charging utility in the Internet of Things. Background Art

[0002] Wireless sensor networks have been widely penetrated into multiple fields such as military, disaster warning, and smart cities. They rely on ubiquitous sensors to capture multi-dimensional data from surrounding objects to meet various application requirements. However, since sensors are usually powered by batteries with limited energy, extending their service life has always been the focus of research. Although some researchers have proposed using environmental energy harvesting technologies such as solar energy and wind energy, these methods are greatly affected by environmental factors and are difficult to provide a stable energy supply. To overcome this challenge, wireless energy transfer technology provides a continuous and reliable power supply for rechargeable sensors without the need to frequently replace batteries. The greatest advantage of this technology is that it does not require the charger to be in direct contact with the sensor, as long as the sensor is within the wireless energy transfer range of the charger. Compared with renewable energy harvesting, wireless energy transfer provides a more stable energy source for sensors. In the application of wireless sensor networks, this technology has significantly improved the monitoring performance in various scenarios such as animal information collection and data center monitoring. Generally speaking, wireless rechargeable sensor networks have the characteristics of sustainable operation, low maintenance cost, controllable energy, and small environmental impact, providing strong support for applications in multiple fields.

[0003] In the pay-per-use charging service model of wireless rechargeable sensor networks, users request charging services from the CSP and pay fees to meet their charging needs. For users, the sensor off-duty time, service fees, and charging utility are three core concerns. To construct a reasonable device charging payment calculation model, it is necessary to comprehensively consider these factors to ensure that the model is both fair and effective. On this basis, it is crucial to achieve coordinated charging scheduling of sensors, which can balance the relationship between charging time, fees, and utility on demand, thereby providing users with a better charging experience. The solution to this problem not only has theoretical value but also has important significance in practical applications, but there is currently no good solution. Summary of the Invention

[0004] The main purpose of the present invention is to overcome the deficiencies of the prior art and provide a fine-grained power allocation method and system for optimizing charging utility in the Internet of Things. By formulating the optimal charging service power allocation strategy for the charging station, the relationship between charging time, fees, and utility can be balanced on demand, providing a more flexible and efficient charging service, and improving the quality and efficiency of the charging service of the entire network.

[0005] According to one aspect of the present invention, the present invention provides a fine-grained power allocation method for charging utility optimization in the Internet of Things, and the method includes the following steps:

[0006] S1: Construct a wireless sensor network model, where the wireless sensor network model includes a charging station and rechargeable sensors;

[0007] S2: During the charging cycle, determine the effective energy received by the sensors requesting charging, and determine the sum of the utilities of the sensors requesting charging according to the effective energy;

[0008] S3: Under the dual constraints of the maximum off-duty time limit of the sensors and the charging cost limit, convert the sum of the utilities into a wireless sensor network utility maximization problem model;

[0009] S4: Solve the wireless sensor network utility maximization problem model to determine the power of each time slice of the charging station and obtain a power allocation strategy scheme.

[0010] Preferably, the determination of the effective energy received by the sensors requesting charging includes:

[0011] Sensor s i The effective energy received during the charging cycle T D is:

[0012]

[0013] where represents that the charging station turns on the i-th gear power at the k-th time slice, and d ik ∈{0, 1} represents whether sensor s i arrives at the start time of the k-th time slice, 0 means not arrived, 1 means arrived, τ represents the length of a single time slice, n T represents the number of time slices within the charging cycle T D B is the upper limit of the battery capacity of the sensor, and H leg is a reasonable strategy set.

[0014] Preferably, the determination of the sum of the utilities of the sensors requesting charging according to the effective energy includes:

[0015] The sum of the utilities of the sensors requesting charging during the charging cycle is:

[0016]

[0017] where S′ is the set of sensors requesting charging, is the travel energy of sensor S i to the charging station, and β i represents sensor s iThe weight coefficient of the importance of the monitored location.

[0018] Preferably, under the dual constraints of the maximum off-duty time limit and charging cost limit of the sensor, transforming the sum of the utilities into a model of maximizing the utility of the wireless sensor network includes:

[0019] Under the dual constraints of the maximum off-duty time limit and charging cost limit of the sensor, the problem of maximizing the utility of the wireless sensor network is formalized as follows:

[0020]

[0021] Wherein, represents the charging service price to be paid for selecting the i-th power level in the k-th time slice, and Q(H leg ) is the total charging service cost to be paid by the charging station during the charging cycle, and T B is the maximum off-duty time tolerated by the wireless sensor network, is the travel time of the sensor S i to the charging station, is the effective charging time of the sensor S i .

[0022] Preferably, solving the model of the problem of maximizing the utility of the wireless sensor network to determine the power of each time slice of the charging station includes:

[0023] Initialize the time slice, perform a Cartesian product operation on the time slice and the power level to obtain a set of policy solutions H, and initialize two empty policy sets H 1 and H 2 ;

[0024] In the set of policy solutions H, first calculate the policy that allocates only one time slice to the sensor set to maximize its charging utility, and incorporate this policy into the set H 1 ;

[0025] In the set of policy solutions H, each time select a time slice policy that maximizes the marginal charging utility of the sensor set S'. If the sum of the cost of this policy and the previous cost is less than the given upper limit of the cost constraint, add this policy to the policy set H 2 , and at the same time delete this policy from the set of policy solutions H and update the current total cost;

[0026] Repeat the above steps, and update H 2 in each round of selection until the set of policy solutions H is empty or the cost exceeds the upper limit of the cost constraint.

[0027] According to another aspect of the present invention, the present invention also provides a fine-grained power allocation system for optimizing charging utility in the Internet of Things, and the system includes:

[0028] A building module for building a wireless sensor network model, the wireless sensor network model including a charging station and rechargeable sensors;

[0029] A determining module for determining, during a charging cycle, the effective energy received by a sensor requesting charging, and determining the sum of the utilities of the sensors requesting charging according to the effective energy;

[0030] A conversion module for converting the sum of the utilities into a wireless sensor network utility maximization problem model under the dual constraints of the maximum off-duty time limit of the sensors and the charging cost limit;

[0031] A solving module for solving the wireless sensor network utility maximization problem model, determining the power of each time slice of the charging station, and obtaining a power allocation strategy scheme.

[0032] Preferably, the determining module determining the effective energy received by the sensors requesting charging includes:

[0033] Sensor s i The effective energy received during the charging cycle T D is:

[0034]

[0035] Wherein, represents that the charging station turns on the i-th gear power at the k-th time slice, d ik ∈{0, 1} represents whether sensor s i arrives at the start time of the k-th time slice, τ represents the length of a single time slice, n T represents the number of time slices within the charging cycle T D B is the upper limit of the battery capacity of the sensor, and H leg is a set of reasonable strategies.

[0036] Preferably, the determining module determining the sum of the utilities of the sensors requesting charging according to the effective energy includes:

[0037] The sum of the utilities of the sensors requesting charging during the charging cycle is:

[0038]

[0039] Wherein, S′ is the set of sensors requesting charging, is the travel energy of sensor S i to the charging station, and β i represents the weight coefficient of the importance of the monitoring position where sensor s i is located.

[0040] Preferably, under the dual constraints of the maximum off-duty time limit of the sensor and the charging cost limit, the conversion module converts the above utility into a wireless sensor network utility maximization problem model, which includes:

[0041] Under the dual constraints of the maximum off-duty time limit of the sensor and the charging cost limit, the wireless sensor network utility maximization problem is formalized as follows:

[0042]

[0043] where, represents the charging service price to be paid for selecting the i-th gear power in the k-th time slice, and Q(H leg ) is the total charging service cost to be paid by the charging station during the charging period. T B is the maximum off-duty time tolerated by the wireless sensor network, is the travel time for the sensor S i to go to the charging station, is the effective charging time of the sensor S i .

[0044] Preferably, the solving module solves the wireless sensor network utility maximization problem model to determine the power of each time slice of the charging station, including:

[0045] Initialize the time slice, perform a Cartesian product operation on the time slice and the power gear to obtain a set of policy solutions H, and initialize two empty policy sets H 1 and H 2 ;

[0046] In the set of policy solutions H, first calculate the policy that assigns only one time slice to the sensor set to maximize its charging utility, and incorporate this policy into the set H 1 ;

[0047] In the set of policy solutions H, each time select a time slice policy that maximizes the marginal charging utility of the sensor set S'. If the sum of the cost of this policy and the previous cost is less than the given cost constraint upper limit, add this policy to the policy set H 2 , and at the same time delete this policy from the set of policy solutions H and update the current total cost;

[0048] Repeat the above steps, and update H 2 in each round of selection until the set of policy solutions H is empty or the cost exceeds the cost constraint upper limit.

[0049] Beneficial effects: By formulating the optimal charging service power allocation strategy for the charging station, balancing the relationship among charging time, cost, and utility as needed, the present invention can provide more flexible and efficient charging services. The present invention breaks the traditional charging payment framework for devices and innovatively proposes a more reasonable charging payment calculation model. At the same time, a fine-grained charging service model based on dynamic power allocation is designed for the charging station, thereby effectively improving the quality and efficiency of the charging services of the entire network. The present invention has broad application prospects in wireless rechargeable sensor networks, especially in providing pay-per-use charging services for terminal devices, and has important practical application value and commercial potential.

[0050] The features and advantages of the present invention will become clear by referring to the following drawings and the detailed description of the specific embodiments of the present invention. Description of the Drawings

[0051] Figure 1 is a flowchart of a fine-grained power allocation method for charging utility optimization in the Internet of Things;

[0052] Figure 2 is a schematic diagram of a network model;

[0053] Figure 3 is a schematic diagram of a fine-grained power allocation system for charging utility optimization in the Internet of Things. Specific Embodiments

[0054] The following combines the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0055] Embodiment 1

[0056] Refer to Figure 1 and Figure 2 This embodiment provides a fine-grained power allocation method for charging utility optimization in the Internet of Things. The method includes the following steps:

[0057] S1: Construct a wireless sensor network model, where the wireless sensor network model includes a charging station and rechargeable sensors;

[0058] S2: During the charging cycle, determine the effective energy received by the sensors requesting charging, and determine the sum of the utilities of the sensors requesting charging according to the effective energy;

[0059] S3: Under the dual constraints of the maximum off-station time limit of the sensor and the charging cost limit, convert the sum of the utilities into a wireless sensor network utility maximization problem model;

[0060] S4: Solve the wireless sensor network utility maximization problem model to determine the power of each time slice of the charging station and obtain a power allocation strategy plan.

[0061] This method can provide more flexible and efficient charging services by formulating an optimal charging service power allocation strategy for the charging station, balancing the relationship between charging time, cost, and utility as needed, and improving the quality and efficiency of the charging services of the entire network.

[0062] Preferably, the determining of the effective energy received by the sensor requesting charging includes:

[0063] Sensor s i The effective energy received during the charging cycle T D is:

[0064]

[0065] where represents that the charging station turns on the i-th gear power in the k-th time slice, d ik ∈ {0, 1} represents whether sensor s i arrives at the start time of the k-th time slice, τ represents the length of a single time slice, n T represents the number of time slices within the charging cycle T D , B is the upper limit of the battery capacity of the sensor, and H leg is a set of reasonable strategies.

[0066] Specifically, assume that a wireless rechargeable network G = (C, S) is deployed in a two-dimensional plane region, S = {s 1 , s 2 ,..., s N} represents the sensor set, and C represents the charging station located at the center of the two-dimensional plane.

[0067] Assume that the moving speed of sensor s i is μ, the energy consumed by sensor s i per unit time of movement is ρ, and the Euclidean distance from sensor S i to the charging station C is d(s i ). Then the travel time of sensor S i to the charging station is The travel energy of sensor S i to the charging station is:

[0068] In the periodic charging schedule, the user base station periodically sends charging instructions to the devices in the network. After receiving the instructions, the devices immediately report their current remaining battery levels to the base station. Based on the collected battery level information and the system parameters of the device locations, the user calculates the cooperative charging schedule strategy in the base station and sends it to the CSP to request charging services. After receiving the request, the CSP immediately broadcasts the charging schedule strategy to the charging stations, thereby specifying the charging power and the corresponding service time of the charging stations. Assume that the charger can flexibly adjust its transmission power, and each charger can work at d + 1 different power levels. The power of the charger is p i ∈ {0, p 1 ,p 2 ,…,p d}, where p 1 <p 2 <…<p d ,0 indicates that the charger is in the off state. Since the higher the power level setting of the charger, the greater the operating cost of the charging service provider, the charging service prices per unit time corresponding to different power levels are different, which are M = {0, m 1 ,m 1 ,…,m d}.

[0069] The actual monitoring positions of each sensor are different, so the distances from the charging stations are also different. Define the arrival time of the first sensor arriving at the charging station as the start time T D of this charging cycle T s . Define the departure time of the last sensor leaving the charging station as the end time T D of this charging cycle T E . To meet the requirements of some specific monitoring tasks, we define the time when the sensor leaves its monitoring position as the off-duty time, and the maximum off-duty time tolerated by the wireless sensor network is T B .

[0070] Within a charging cycle T D , the effective charging time of the sensor can be determined. Due to the symmetry of the travel time, the time for the sensor s i to leave the monitoring position and go to the charging station is equal to the time for the sensor to leave the charging station and return to the monitoring position, that is

[0071] The on-time duration of the charger is calculated in time slices. For each time slice, the power adjustment strategy and the sensor scheduling strategy are carried out separately. Define the time slice size as τ, then the number of time slices n D within the charging cycle T T = T D / τ, t kDenote the \(k\)th time slice, where \(k\in N = \{1, 2, \ldots, n\}\), \(t\) T \(=\ [T\) k \(+(k - 1)\tau, T\) s \(+k\tau]\). Here, \(\tau\) represents the length of a single time slice, and \(n\) s represents the number of time slices within the charging period \(T\). T The set of power strategy schemes selected for each time slice is D where

[0072] denotes that the charging station turns on the \(i\)th gear power at the \(k\)th time slice. Any subset of \(H\) can be a potential scheduling strategy. Since only one power can be adjusted at the same time slice, for those strategy sets where the power gears of a single time slice do not overlap, they are defined as reasonable strategy sets, and vice versa as unreasonable strategy sets. For those repeated gears of a single time slice in the unreasonable strategy sets, the highest gear is taken by default. By this operation, the unreasonable strategy sets can be transformed into reasonable strategy sets: where represents the set of powers selected for the \(i\)th time slice, \(|H|\) represents the number of elements in the set of powers selected for the \(i\)th time slice, and \(\max(H)\) is defined as:

[0073]

[0074] Define \(D\) i as the set of decision variables for sensor \(s\) i at each time slice during the charging period \(T\), i where \(d\)

[0075]

[0076] \(\in \{0, 1\}\) is defined as whether sensor \(s\) i arrives at the start time of the \(k\)th time slice. \(0\) means not arrived, and \(1\) means arrived. i The energy received by sensor \(s\) D during the charging period \(T\) is: ik

[0077] i

[0078] i

[0079] D

[0080]

[0081] The energy received by the sensor set \(S'\) during the charging period \(T\) is: D

[0080]

[0081]

[0081] Since the upper limit of the battery capacity of the sensor is B, the sensor s i in the charging cycle T D the effective energy received is:

[0082]

[0083] The effective energy received by the sensor S′ in the charging cycle T D is:

[0084]

[0085] Preferably, determining the utility of the sensors requesting charging according to the effective energy includes:

[0086] The sum of the utilities of the sensors requesting charging in the charging cycle is:

[0087]

[0088] where S′ is the set of sensors requesting charging, is the travel energy of the sensor S i to the charging station, and β i represents the weight coefficient of the importance of the monitoring position where the sensor s i is located.

[0089] Specifically, define the utility of the sensor s i in the charging cycle T D as U(S′, H leg ), and the sum of the utilities of the set S′ of sensors requesting charging in the charging cycle T D is: U(S′, H leg ). The larger the β value, the more important the position; the smaller the β value, the less important the position.

[0090] Preferably, under the dual constraints of the maximum off-duty time limit of the sensor and the charging cost limit, transforming the sum of utilities into a wireless sensor network utility maximization problem model includes:

[0091] Under the dual constraints of the maximum off-duty time limit of the sensor and the charging cost limit, formalize the wireless sensor network utility maximization problem as follows:

[0092]

[0093] where, represents the charging service price to be paid for selecting the i-th power level in the k-th time slice, and Q(H leg ) is the total charging service cost required for the charging station during the charging cycle, and T B is the maximum off-duty time tolerated by the wireless sensor network, For sensor S i The travel time to the charging station, For sensor S i The effective charging time.

[0094] Specifically, generally, the support capacity of the power transmission network for the deployed chargers is limited at any time, and the transmission power of the chargers is directly related to the economic cost. In order to reasonably control the operating cost of the wireless sensor network and reduce the economic budget of the entire wireless sensor network. In each charging cycle, a charging cost upper limit M B is set. Define Q as the total sum of the charging service fees that the charging station needs to pay within the charging cycle T D :

[0095]

[0096] Wherein, represents the charging service price required to select the i-th gear power in the k-th time slice.

[0097] Under the dual constraints of the maximum off-duty time limit of the sensor and the charging cost limit, the problem of maximizing the utility of the wireless sensor network is formalized.

[0098] Preferably, solving the model of the problem of maximizing the utility of the wireless sensor network to determine the power of each time slice of the charging station includes:

[0099] Initialize the time slice, and perform a Cartesian product operation on the time slice and the power gear to obtain a set of policy solutions H, and initialize two empty policy sets H 1 and H 2 ;

[0100] In the set of policy solutions H, first calculate the policy that assigns only one time slice to the sensor set to maximize the charging utility, and incorporate this policy into the set H 1 ;

[0101] In the set of policy solutions H, each time select a time slice policy that maximizes the marginal charging utility of the sensor set S′. If the sum of the cost of this policy and the previous cost is less than the given cost constraint upper limit, add this policy to the policy set H 2 and at the same time delete this policy from the set of policy solutions H and update the current total cost;

[0102] Repeat the above steps, and update H 2 in each round of selection until the set of policy solutions H is empty or the cost exceeds the cost constraint upper limit.

[0103] Specifically, initialize the time slices according to the previous steps, and perform the Cartesian product of the time slices and power levels to obtain a set of policy solutions. Furthermore, obtain sensor s i During the charging cycle T D The set of decision variables for each time slice Initialize two empty policy sets H 1 and H 2 .

[0104] In the set of policy solutions H, first calculate the policy that assigns only one time slice to the sensor set S′ to maximize its charging utility, that is:

[0105] In the set of policy solutions H, each time greedily select a time slice policy that maximizes the marginal charging utility of the sensor set S′, that is: If the sum of the cost of this policy and the previous costs is less than the given upper limit of the cost constraint, that is: Add this policy to the policy set H 2 At the same time, delete this policy from the set of policy solutions H and update the current total cost, that is:

[0106] Repeat the above steps, and update H for each round of selection 2 until the set of policy solutions H is empty or the cost exceeds the upper limit of the cost constraint.

[0107] In this embodiment, by formulating the optimal charging service power allocation strategy for the charging station and balancing the relationship between charging time, cost, and utility as needed, more flexible and efficient charging services can be provided. This embodiment breaks the traditional charging payment framework for devices and innovatively proposes a more reasonable charging payment calculation model. At the same time, a fine-grained charging service model based on dynamic power allocation is designed for the charging station, thus effectively improving the quality and efficiency of the entire network's charging services. This embodiment has broad application prospects in wireless rechargeable sensor networks, especially in providing pay-per-use charging services for terminal devices, with important practical application value and commercial potential.

[0108] To evaluate the reliability of this method (abbreviated as the DSTP algorithm) in different environments and scenarios and its performance in improving charging utility, simulation experiments were carried out for different environmental parameters.

[0109] In the simulation, the sensing area is a square area of 100m×100m. The initial monitoring positions of the mobile devices are randomly generated. Unless otherwise specified, assume the number of sensors N = 20, and the minimum power P of the charger min= 100, and the power increases by 100 for each higher gear. The speed of the sensor is evenly generated between 0.5 m / s and 1 m / s. The default cost budget M B = 800, and the upper limit of the battery capacity B of the sensor is 6000.

[0110] The present method will be compared with three algorithms below. 1. The average power maximum algorithm (AVG) distributes the power obtained by dividing the total cost by the number of time slices to each time slice of the charging station; 2. The greedy algorithm (GA) enables the charging station to charge with the maximum power as much as possible; 3. The random algorithm (RDM) allows the charging station to randomly select a charging scheme for itself.

[0111] Referring to the chargers and chargeable devices in reality, grading tests were conducted for the following three variable parameters: the number of chargeable devices in the wireless rechargeable network, the number of power grades, and the charging cost budget. It can be seen from the test results that the present method has a great advantage over the baseline algorithms, achieves the pre-set goals well, and demonstrates the feasibility and superiority of the present method.

[0112] When the number of sensors is very small, the performance of each algorithm differs little, and the DSTP algorithm maintains a slight lead. As the number of sensors increases, although the utility values of all algorithms increase steadily, the utility value of the DSTP algorithm has a gap with the utility values of other algorithms, and it is about 30% higher than that of the sub-optimal algorithm, as shown in Table 1.

[0113] Table 1 Influence of the number of chargeable devices on the charging utility

[0114]

[0115] The charging utility of the DSTP algorithm increases with the increase in the number of maximum power grades, and it leads significantly compared with the other three algorithms, being 25% higher than the sub-optimal algorithm. Among the four algorithms, only the utility value of the greedy algorithm shows a downward trend with the increase in the number of power grades, as shown in Table 2.

[0116] Table 2 Influence of the number of power grades on the charging utility

[0117]

[0118] When the budget is very limited, obviously the DSTP algorithm is superior to the other three algorithms, and the charging utility value of the DSTP algorithm is 35% higher than that of the sub-optimal algorithm. As the budget continues to increase, the utility values of all algorithms increase steadily, but the DSTP algorithm still maintains the highest level, being about 15% higher than the utility value of the sub-optimal algorithm. When the budget reaches the peak, that is, when the cost factor is ignored, the greedy algorithm and our algorithm reach almost the same level, as shown in Table 3.

[0119] Table 3 Influence of Charging Cost Budget on Charging Utility

[0120]

[0121] Embodiment 2

[0122] Figure 3 It is a schematic diagram of a fine-grained power allocation system for charging utility optimization in the Internet of Things. As Figure 3 shown, this embodiment provides a fine-grained power allocation system for charging utility optimization in the Internet of Things. The system includes:

[0123] A construction module 301, configured to construct a wireless sensor network model, where the wireless sensor network model includes a charging station and rechargeable sensors;

[0124] A determination module 302, configured to determine the effective energy received by the sensors requesting charging during a charging cycle, and determine the sum of the utilities of the sensors requesting charging according to the effective energy;

[0125] A conversion module 303, configured to convert the sum of the utilities into a wireless sensor network utility maximization problem model under the dual constraints of the maximum off-duty time limit of the sensors and the charging cost limit;

[0126] A solution module 304, configured to solve the wireless sensor network utility maximization problem model, determine the power of each time slice of the charging station, and obtain a power allocation strategy scheme.

[0127] Preferably, the determination module 302 determines the effective energy received by the sensors requesting charging, including:

[0128] Sensor s i The effective energy received during the charging cycle T D is:

[0129]

[0130] Wherein, represents that the charging station turns on the i-th gear power at the k-th time slice, d ik ∈ {0, 1} represents whether sensor s i arrives at the start time of the k-th time slice, τ represents the length of a single time slice, n T represents the number of time slices within the charging cycle T D B is the upper limit of the battery capacity of the sensor, and H leg is a set of reasonable strategies.

[0131] Preferably, the determination module 302 determines the sum of the utilities of the sensors requesting charging according to the effective energy, including:

[0132] The utility sum of the requested charging sensors during the charging cycle is as follows:

[0133]

[0134] where S′ is the set of sensors requesting charging, is the sensor S i The travel energy to the charging station, β i represents the weight coefficient of the importance of the monitoring location where the sensor s is located. i

[0135] Preferably, under the dual constraints of the maximum off-duty time limit of the sensor and the charging cost limit, the conversion module 303 converts the utility sum into a model of the wireless sensor network utility maximization problem, including:

[0136] Under the dual constraints of the maximum off-duty time limit of the sensor and the charging cost limit, the wireless sensor network utility maximization problem is formalized as follows:

[0137]

[0138] where, represents the charging service price to be paid for selecting the i-th gear power in the k-th time slice, and Q(H leg ) is the total charging service cost required for the charging station during the charging cycle, T B is the maximum off-duty time tolerated by the wireless sensor network, is the sensor S i The travel time to the charging station, is the sensor S i The effective charging time.

[0139] Preferably, the solving module 304 solves the wireless sensor network utility maximization problem model to determine the power of each time slice of the charging station, including:

[0140] Initialize the time slice, perform a Cartesian product operation on the time slice and the power gear to obtain a set of policy solutions H, and initialize two empty policy sets H 1 and H 2 ;

[0141] In the set of policy solutions H, first calculate the policy that assigns only one time slice to the sensor set to maximize the charging utility, and incorporate this policy into the set H 1 ;

[0142] ​In the set H of policy solutions, each time a time-slot policy that maximizes the marginal charging utility of the sensor set S′ is selected. If the sum of the cost of this policy and the previous costs is less than the given upper limit of the cost constraint, add this policy to the policy set H 2 , and at the same time delete this policy from the set H of policy solutions, and update the current total cost;

[0143] Repeat the above steps, and update H for each round of selection 2 , until the set H of policy solutions is empty or the cost exceeds the upper limit of the cost constraint.

[0144] The specific implementation processes of the functions implemented by each module in this Embodiment 2 are the same as those in Embodiment 1, and will not be elaborated here.

[0145] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structural transformation made by using the content of the specification and drawings of the present invention under the concept of the present invention, or direct / indirect application in other related technical fields is included in the patent protection scope of the present invention.

Claims

1. A fine-grained power allocation method for optimizing charging utility in the Internet of Things, characterized in that: The method comprises the following steps: S1: constructing a wireless sensor network model, wherein the wireless sensor network model includes a charging station and a rechargeable sensor; S2: during a charging cycle, determining effective energy received by the sensor requesting charging, and determining the utility sum of the sensor requesting charging according to the effective energy; S3: Under the dual constraints of the maximum off-duty time limit of the sensor and the charging cost limit, the utility sum is transformed into a wireless sensor network utility maximization problem model; S4: solving the utility maximization problem model of the wireless sensor network, determining the power of each time slice of the charging station, and obtaining a power allocation strategy solution; Under the dual constraints of the maximum off-duty time limit of the sensor and the charging cost limit, the utility sum is converted into a wireless sensor network utility maximization problem model including: Under the dual constraints of the maximum off-duty time limit of the sensor and the charging cost limit, the utility maximization problem of the wireless sensor network is formalized as follows: in, S′ is the set of sensors requesting charging, H leg is a reasonable strategy set, For sensor S i During the charging cycle T D The effective energy received in For sensor S i Energy for trips to charging stations, β i Indicates sensor S i The weight coefficient of the importance of the monitoring location; represents the charging service price required to select the i-th power level in the k-th time slice, Q(H leg ) is the total charging service fee that the charging station needs to pay during the charging cycle, T B is the maximum absence time tolerated by the wireless sensor network, For sensor S i travel time to the charging station, For sensor S i Effective charging time; n T Indicates the charging cycle T D The number of time slices within; The step of solving the wireless sensor network utility maximization problem model to determine the power of each time slice of the charging station includes: Initialize the time slice, and perform Cartesian product operation on the time slice and the power level to obtain the strategy set H, and initialize two empty strategy sets H1 and H2; In the strategy solution set H, firstly, a strategy is calculated to allocate only one time slice to the sensor set to maximize its charging utility, and this strategy is incorporated into the set H1; In the strategy set H, a time slice strategy that maximizes the marginal charging utility of the sensor set S' is selected each time. If the sum of the cost of this strategy and the previous cost is less than the given cost constraint upper limit, this strategy is added to the strategy set H2, and at the same time, this strategy is deleted from the strategy set H, and the current cost sum is updated; Repeat the above steps and update H2 in each round of selection until the strategy set H is empty or the cost exceeds the upper limit of the cost constraint.

2. The method according to claim 1, characterized in that The determining of the effective energy received by the sensor requesting charging comprises: Sensors i During the charging cycle T D The effective energy received is: in, Indicates that the charging station turns on the i-th power level in the k-th time slice, d ik ∈{0,1} represents the sensor S i Whether it arrives at the start time of the kth time slice, 0 means not arrived, 1 means arrived, τ represents the length of a single time slice, n T Indicates the charging cycle T D B is the upper limit of the sensor’s battery capacity.

3. The method according to claim 2, characterized in that The utility of the sensor for determining the request for charging based on the available energy includes: The sum of the utilities of the requesting charging sensors during the charging cycle is:

4. A fine-grained power distribution system for optimizing charging utility in the Internet of Things, characterized in that: The system comprises: A construction module, used to construct a wireless sensor network model, wherein the wireless sensor network model includes a charging station and a rechargeable sensor; a determination module, configured to determine, during a charging cycle, effective energy received by the sensor requesting charging, and determine a utility sum of the sensor requesting charging according to the effective energy; A conversion module, used for converting the utility sum into a wireless sensor network utility maximization problem model under the dual constraints of the maximum off-duty time limit of the sensor and the charging cost limit; A solution module, used to solve the utility maximization problem model of the wireless sensor network, determine the power of each time slice of the charging station, and obtain a power allocation strategy solution; The conversion module converts the utility sum into a wireless sensor network utility maximization problem model under the dual constraints of the maximum off-duty time limit of the sensor and the charging cost limit, including: Under the dual constraints of the maximum off-duty time limit of the sensor and the charging cost limit, the utility maximization problem of the wireless sensor network is formalized as follows: in, S′ is the set of sensors requesting charging, H leg is a reasonable strategy set, For sensor S i During the charging cycle T D The effective energy received in For sensor S i Energy for trips to charging stations, β i Indicates sensor S i The weight coefficient of the importance of the monitoring location; represents the charging service price required to select the i-th power level in the k-th time slice, Q(H leg ) is the total charging service fee that the charging station needs to pay during the charging cycle, T B is the maximum absence time tolerated by the wireless sensor network, For sensor S i travel time to the charging station, For sensor S i Effective charging time; n T Indicates the charging cycle T D The number of time slices within; The solving module solves the wireless sensor network utility maximization problem model to determine the power of each time slice of the charging station, including: Initialize the time slice, and perform Cartesian product operation on the time slice and the power level to obtain the strategy set H, and initialize two empty strategy sets H1 and H2; In the strategy solution set H, firstly, a strategy is calculated to allocate only one time slice to the sensor set to maximize its charging utility, and this strategy is incorporated into the set H1; In the strategy set H, a time slice strategy that maximizes the marginal charging utility of the sensor set S' is selected each time. If the sum of the cost of this strategy and the previous cost is less than the given cost constraint upper limit, this strategy is added to the strategy set H2, and at the same time, this strategy is deleted from the strategy set H, and the current cost sum is updated; Repeat the above steps and update H2 in each round of selection until the strategy set H is empty or the cost exceeds the upper limit of the cost constraint.

5. The system according to claim 4, characterized in that The determining module determines the effective energy received by the sensor requesting charging, including: Sensors i During the charging cycle T D The effective energy received is: in, Indicates that the charging station turns on the i-th power level in the k-th time slice, d ik ∈{0,1} represents the sensor S i Whether it arrives at the start time of the kth time slice, 0 means not arrived, 1 means arrived, τ represents the length of a single time slice, n T Indicates the charging cycle T D The number of time slices in the sensor, B is the upper limit of the battery capacity of the sensor, H leg A reasonable strategy set.

6. The system according to claim 5, characterized in that The determining module determines the utility of the sensor requesting charging according to the effective energy and includes: The sum of the utilities of the requesting charging sensors during the charging cycle is:

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