Height-adjustable cooperative charging cost optimization method and system for agricultural mobile sensors

Abstract

The application discloses a height-adjustable cooperative charging cost optimization method and system for agricultural mobile sensors, and the method comprises the following steps: defining a charging station set, a mobile chargeable sensor device set and a target position set; defining a parallel cooperative charging scheduling pricing model; formalizing a mobile chargeable sensor device parallel cooperative charging scheduling problem; calling a mobile chargeable sensor device parallel cooperative charging scheduling algorithm with cost optimization benefits to obtain a charging distribution scheme of the mobile chargeable sensor device. The application can greatly reduce the charging cost.

Description

Technical Field

[0001] This invention belongs to the field of wireless power transmission and smart agriculture technology, and more specifically, relates to a highly adjustable cooperative charging cost optimization method and system for agricultural mobile sensors. Background Technology

[0002] With the booming development of precision agriculture, a large number of mobile rechargeable sensor devices (MRSDs), such as farmland inspection robots, crop growth monitoring drones, and mobile environmental data acquisition nodes, are being widely deployed in farmland, orchards, and greenhouses. These devices can collect key data such as soil moisture and pest and disease images in real time. However, due to the vast and complex farmland environment, providing these devices with a continuous and stable energy supply has become a key bottleneck restricting the large-scale implementation of smart agriculture.

[0003] Traditional contact charging requires devices to be precisely aligned with the charging interface, which not only restricts the device's freedom of movement but also poses safety hazards such as mechanical wear and contact sparks. In contrast, wireless charging technology based on radio frequency or magnetic coupling resonance is gradually becoming the mainstream solution due to its advantages such as being contactless, capable of charging multiple devices simultaneously, and offering flexible deployment.

[0004] To further reduce the construction and operation costs of wireless charging networks, academia and industry have proposed the concept of cooperative charging. This model allows multiple mobile devices to share the same wireless charger, spreading the fixed costs of charging facilities through time or space reuse, thereby improving energy utilization and reducing individual costs.

[0005] However, existing cooperative wireless charging scheduling technologies mainly focus on path planning in a two-dimensional plane or scheduling optimization of fixed charging stations, which have some significant technical defects and unresolved problems.

[0006] Firstly, the rigidity of physical deployment leads to poor resource adaptability. Existing wireless charging stations are typically fixed at a specific height on the ground or wall. According to the Friesian transmission equation, the electromagnetic field propagation theory, received power is inversely proportional to the square of the transmission distance, while coverage is directly proportional to height. A charger at a fixed height means that the trade-off between power density and coverage capacity is locked. If the deployment height is low, energy is concentrated and charging speed is fast, but the coverage area is small, serving only a very small number of devices and struggling to handle large-scale concurrent charging demands. If the deployment height is high, the coverage area is large, accommodating multiple devices simultaneously, but the ground power density drops sharply, resulting in excessively long charging times and increased time-related operating costs.

[0007] Secondly, existing cooperative wireless charging scheduling technologies struggle to adapt to dynamically changing network loads. In practical applications, charging demand often exhibits tidal characteristics. Sometimes only a small number of devices urgently need charging, requiring high-power fast charging; other times, a large number of devices simultaneously request charging, necessitating high-concurrency slow charging. Charging networks with fixed physical parameters cannot dynamically adjust their operating modes based on real-time load, leading to resource waste under low load and insufficient service capacity under high load.

[0008] Furthermore, in agricultural settings, high-power electromagnetic radiation poses a threat to plants, making it impossible to deploy one or more high-power wireless charging base stations to provide comprehensive coverage of farmland. Therefore, it is necessary to plan specific charging stations to allow MRSDs to move to these stations for unified charging.

[0009] Existing scheduling schemes are mostly based on simple heuristic rules, lack mathematical modeling of the total system cost, and often lack theoretical analysis of the problem's complexity and proof of the performance boundaries of approximate algorithms in algorithm design, resulting in insufficient credibility and robustness of the schemes. Summary of the Invention

[0010] To address the challenges of balancing coverage and power density in existing wireless charging networks deployed at fixed heights, and the resulting high operating costs due to dynamic charging loads, this invention provides a height-adjustable cooperative charging cost optimization method for agricultural mobile sensors. This invention introduces the vertical height of the charging station as a new optimization dimension, combines it with a parallel cooperative charging pricing model, and utilizes a greedy strategy based on average marginal cost to minimize the total system operating cost.

[0011] To address at least one of the aforementioned technical problems, according to one aspect of the present invention, a highly adjustable cooperative charging cost optimization method for agricultural mobile sensors is provided, comprising the following steps:

[0012] Define the set of charging stations, the set of mobile rechargeable sensing devices, and the set of target locations;

[0013] Define a parallel cooperative charging scheduling pricing model;

[0014] Formalized problem of parallel cooperative charging scheduling for mobile rechargeable sensing devices;

[0015] A cost-effective parallel cooperative charging scheduling algorithm for mobile rechargeable sensing devices is invoked to obtain a charging allocation scheme for the mobile rechargeable sensing devices.

[0016] Furthermore, the set of charging stations, the set of mobile rechargeable sensing devices, and the set of target locations include:

[0017] Charging station collection Each charging station It has height adjustment capability and offers selectable height sequences. And they are arranged in ascending order;

[0018] For any work at a specific height , charging stations The number of charging stations formed within the effective coverage area on the ground is The corresponding charging position received power sequence is When the height is At that time, the number of charging positions is at its maximum;

[0019] For mobile rechargeable sensing device collections In each collection of mobile rechargeable sensing devices, With target location Task energy requirements Remaining battery power and unit mobile energy consumption ,also, It can also indicate the current location;

[0020] The distance the mobile rechargeable sensing device travels from its current location to the charging station is... The distance traveled from the charging station to the target location is .

[0021] Furthermore, the parallel cooperative charging scheduling pricing model includes calculating the charging station Need to provide mobile rechargeable sensing devices Actual energy replenishment during charging :

[0022]

[0023] set up To be allocated to charging stations A charging group is a sequence of mobile rechargeable sensing devices for charging. The order determines the allocation of charging positions for mobile rechargeable sensing devices; specifically, it will be based on... The arrangement order of China Mobile's rechargeable sensing devices prioritizes allocating each device to a charging position with the highest charging reception power; when the working height of the charger in the charging station is... At that time, the charging group Total charging time The longest charging time among the mobile rechargeable sensing devices in the group determines the charging time:

[0024]

[0025] in To be assigned The received power of the charging position; define the charging group. Operating cost function :

[0026]

[0027] in, Basic service fee, For time-varying rates, Based on the basic service time threshold, and satisfying .

[0028] Furthermore, the formalized highly tunable cooperative charging scheduling problem includes:

[0029] The objective function is to minimize the total cost of the entire network.

[0030]

[0031] The following constraints must be met:

[0032] Full coverage constraint: Ensure that all mobile rechargeable sensing devices are assigned and assigned to only one charging spot at one charging station;

[0033] Single height constraint: Each charging station Only one option Work at one of the heights;

[0034] Capacity constraints: allocated to charging stations The number of mobile rechargeable sensing devices must not exceed the height currently selected. Total number of charging positions ;

[0035] Physical feasibility constraints:

[0036]

[0037]

[0038]

[0039] If the feasibility constraints are not met, the corresponding mobile rechargeable sensing device will be removed before scheduling.

[0040] Furthermore, the step of invoking a cost-effective parallel cooperative charging scheduling algorithm for mobile rechargeable sensing devices to obtain a charging allocation scheme for the mobile rechargeable sensing devices includes the following steps:

[0041] Step 1: Input parameters: Collection of charging stations Mobile rechargeable sensing device collection High-level set and the corresponding power and capacity parameter;

[0042] Step 2: Initialization: Charging groups at all charging stations ,high The collection of mobile rechargeable sensing devices is not covered. ;

[0043] Step 3: If If the first step is to proceed to step 4, proceed to step 6; otherwise, proceed to step 7.

[0044] Step 4: For each charging station Find the charging group that minimizes the average marginal cost. and corresponding height ;

[0045] Step 5: Find the charging station with the lowest average marginal cost among all charging stations, i.e. ;

[0046] Step 6: Update system status and set up charging station The height is Update its charging pack to The newly covered MRSD from Remove from the list and return to step 3;

[0047] Step 7: Output the height settings of all charging stations and the grouping results of mobile rechargeable sensing devices.

[0048] Furthermore, step 4 involves finding the optimal expansion scheme that minimizes the average marginal cost, specifically including:

[0049] Step 4-1: For a given charging station Each candidate height Execute steps 4-2 to 4-6. If all heights have been traversed, execute step 4-7.

[0050] Step 4-2: Cover the uncovered set Mobile rechargeable sensing devices in the middle are replenished with energy as needed. Sort the data from smallest to largest and calculate the number of remaining available charging points at the current height. ,initialization , , ;

[0051] Step 4-3: If If the above steps are not executed, proceed to steps 4-4 to 4-6; otherwise, proceed to step 4-1.

[0052] Step 4-4: From Take out The smallest mobile rechargeable sensing device, namely ;

[0053] Steps 4-5: From Delete from middle ,Right now ;

[0054] Steps 4-6: Let , ,Will join in At the very beginning, return to step 4-3;

[0055] Steps 4-7: Find and return the case where the average marginal cost is minimized, i.e.

[0056] .

[0057] According to another aspect of the present invention, a highly tunable cooperative charging cost optimization system for agricultural mobile sensors is provided for implementing the above-described method, comprising:

[0058] At least one height-adjustable wireless charging station is suspended by a height adjustment mechanism configured to vertically displace between one or more heights of the charging station.

[0059] Multiple mobile rechargeable sensing devices;

[0060] A central processing unit is communicatively coupled to the height adjustment mechanism.

[0061] According to another aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the highly adjustable cooperative charging cost optimization method for agricultural mobile sensors of the present invention.

[0062] According to another aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the highly adjustable cooperative charging cost optimization method for agricultural mobile sensors of the present invention.

[0063] Compared with existing technologies, the beneficial effects of the above-described method of the present invention are as follows:

[0064] 1. This invention can significantly reduce charging costs;

[0065] Agricultural monitoring involves a large number of MRSDs of different uses and specifications, resulting in significant task imbalance. When a large number of MRSDs simultaneously request recharging, this invention rapidly expands the charging area by increasing the charger height, leveraging the direct proportionality between effective coverage area and the square of the height. This allows for parallel charging of more devices, avoiding the queuing and congestion caused by traditional fixed-height charging stations. When only a few high-energy-consuming tasks require rapid charging, the system lowers the height, utilizing the inverse proportionality between received power and the square of the distance to achieve ultra-fast charging. This dynamic trade-off mechanism resolves the contradiction between fixed physical deployment and fluctuating energy demands in agricultural environments.

[0066] Simulation results show that, compared with the baseline algorithm based on a fixed height, the height-adjustable scheduling scheme proposed in this invention can reduce the total operating cost of the system from 4.63% (GBC) to 67.64% (IBC) in a scenario with 24 MRSDs and 8 charging stations. The cost optimization effect is particularly significant in complex scenarios with high or unevenly distributed MRSDs. This cost advantage becomes increasingly pronounced with the increase in the number of devices, demonstrating the excellent adaptability of this invention in handling dynamic loads.

[0067] 2. This invention possesses The approximation is improved, which significantly reduces computational complexity while ensuring the quality of the solution;

[0068] The highly adjustable cooperative charging scheduling problem involves multiple complex models, including charging station activation selection, height-level decision-making, and device-to-charging-position assignment. Its mathematical essence has been proven to be a variant of the generalized facility location problem with capacity constraints, and it is a typical NP-hard problem. Traditional exhaustive search or mixed-integer linear programming methods experience exponential growth in computation time as the network scales, failing to meet the real-time scheduling requirements of IoT scenarios.

[0069] Therefore, this invention shifts to an approximate algorithm for solving the problem, and proves that the proposed algorithm has... The approximation is as follows: Since the MRSD set is selected with priority given to those with smaller actual charging amounts, the cost is always minimized for the given charging station and altitude. Considering a given charging station, altitude, and MRSD set, since the MRSD with larger actual charging amounts is placed at the beginning of the charging sequence (i.e., assigned to a location with higher received power), the cost of this charging station is always minimized. Since all feasible MRSD sets have been traversed, the resulting average marginal cost is always minimized for the given charging station and altitude. Furthermore, because we have traversed every altitude for every charging station and selected the case with the minimum average marginal cost in each iteration, the result obtained is the case with the minimum average marginal cost in the current iteration.

[0070] Furthermore, based on the idea of ​​set covering proof, it can be seen that this algorithm has... The approximation is high. This conclusion is based on the mathematical derivation that maps this problem to a variant of the set covering problem, providing solid theoretical support for the robustness of the algorithm. Attached Figure Description

[0071] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments will be briefly described below. Obviously, the drawings described below only relate to some embodiments of the present invention and are not intended to limit the present invention.

[0072] Figure 1 This is a schematic diagram of the overall environment in a preferred embodiment of the present invention;

[0073] Figure 2 This is a conceptual diagram illustrating the trade-off between charger height, ground coverage, and power density in a preferred embodiment of the present invention.

[0074] Figure 3 This is a conceptual diagram illustrating the trade-off between charger height, ground coverage, and power density in a preferred embodiment of the present invention.

[0075] Figure 4 This is a flowchart of the subroutine for finding the optimal extension group in a preferred embodiment of the present invention;

[0076] Figure 5 This is a comparison chart of the average total cost and running time of the method of this invention and the optimal solution in a small-scale scenario;

[0077] Figure 6 This is a charging allocation diagram of the HCSA algorithm in a small-scale scenario according to a preferred embodiment of the present invention. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention.

[0079] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0080] Example 1:

[0081] like Figure 1-6 As shown, this invention provides a highly adjustable cooperative charging cost optimization method for agricultural mobile sensors, comprising the following steps:

[0082] The method mainly includes three core parts: defining the system model, constructing the pricing model, and formalizing the optimization problem.

[0083] 1. System Model

[0084] The construction of this method first defines a set of charging stations, a set of mobile rechargeable sensing devices (MRSDs), and a set of target locations.

[0085] Charging station collection Each charging station It has height adjustment capability and offers selectable height sequences. And they are arranged in ascending order. For any work at a specific height... , charging stations The number of charging stations it forms within its effective ground coverage area is The corresponding charging position received power sequence is When the height is At that time, the number of charging positions is at its maximum.

[0086] In radio frequency wireless charging scenarios, the height of the charger has diametrically opposed effects on the energy transmission efficiency and effective coverage area of ​​the ground receiver. This invention utilizes this physical trade-off as the core driving force for optimization. According to the Friesian transmission equation, the received power is inversely proportional to the square of the height. As the height decreases, the energy density increases significantly, enabling high-power fast charging and thus shortening the charging time.

[0087] On the other hand, the effective coverage of a charger is limited by its beamwidth. The coverage area is proportional to the square of the height. As the height increases, the available effective charging area on the ground increases quadratically, accommodating more MRSDs charging simultaneously.

[0088] For MRSD set Each MRSD With target location Task energy requirements Remaining battery power and unit mobile energy consumption ,also, It can also indicate the current location.

[0089] The system calculates the distance the MRSD travels from its current location to the charging station. The distance traveled from the charging station to the target location is .

[0090] 2. Parallel Cooperative Charging Pricing Model This invention constructs a cooperative charging pricing model based on height adjustment, used to calculate the pricing of charging stations. For MRSD The actual cost of providing the service:

[0091] Considering mobile energy consumption, the actual replenishment energy required by MRSD It includes not only the energy required for the task and the energy consumption to move to the target, but also the remaining power and the energy consumption to go to the charging station.

[0092] Energy consumption during the journey includes the energy consumed when moving from the current location to a charging station. Energy consumption, and from Move to target point The energy consumption. The mathematical expression is:

[0093]

[0094] in For MRSD The model calculates the energy consumption per unit distance traveled. It ensures that the MRSD has sufficient remaining power to meet mission requirements after charging and moving, avoiding ineffective scheduling where charging is wasted due to excessive travel distance.

[0095] set up To be allocated to charging stations MRSD charging sequence, The order is determined by the charging time of the MRSD, and the working height of the charging station is [not specified]. The charging pack Total charging time The charging time is determined by the MRSD with the longest charging time within the group, where the individual charging time is obtained by dividing the actual energy replenished by the power received by the allocated charging slot:

[0096]

[0097] in To be assigned The received power of the charging position; define the charging group. Operating cost function :

[0098]

[0099] in, Basic service fee, For time-varying rates, Based on the basic service time threshold, and satisfying As shown in Table 1.

[0100] Table 1 Definition of Total Cost of MRSD

[0101]

[0102] 3. Formal Optimization Problem

[0103] The objective function is to minimize the total cost of the entire network.

[0104]

[0105] The following constraints must be met:

[0106] (1) Full coverage constraint: Ensure that all MRSDs are allocated and only allocated to one charging spot at one charging station;

[0107] (2) Single height constraint: Each charging station Only one choice can be made at a time. Work at one of the heights;

[0108] (3) Capacity constraints: allocated to charging stations The number of MRSDs must not exceed their currently selected height. Total number of charging positions ;

[0109] (4) Physical feasibility constraints:

[0110]

[0111]

[0112]

[0113] If the above feasibility constraints are not met, the corresponding MRSD will be marked or removed before scheduling.

[0114] To address the aforementioned problems, this invention employs a greedy approximation algorithm based on average marginal cost (AMC). The strategy involves initializing all charging stations with empty initial charging groups, thus not covering the MRSD set. The entire set is considered. When there are still uncovered MRSDs, a loop is executed: for each charging station, all candidate heights are traversed to find the optimal expansion group and corresponding height that minimizes the average marginal cost. AMC is defined as the ratio of the marginal cost increment from adding devices to this group to the number of newly covered devices. A greedy approximation algorithm based on average marginal cost is used to obtain the charging station height settings and MRSD charging allocation scheme as follows:

[0115] 1. Input parameter: Collection of charging stations MRSD collection High-level set and the corresponding power and capacity parameter;

[0116] 2. Initialization: Charging groups at all charging stations ,high MRSD set not covered ;

[0117] 3. If If the first step is to proceed to step 4, proceed to step 6; otherwise, proceed to step 7.

[0118] 4. For each charging station Find the charging group that minimizes the average marginal cost. and corresponding height ;

[0119] 5. Find the charging station with the lowest average marginal cost among all charging stations, i.e. , recorded as and its corresponding minimum AMC value ;

[0120] 6. Update system status and set up charging stations. The height is Update its charging pack to The newly covered MRSD from Remove from the list and return to step 3;

[0121] 7. Output the height settings and MRSD grouping results for all charging stations.

[0122] When searching for the optimal expansion group, the strategy is to sort the uncovered MRSDs (Medium-Range Storage Devices) in ascending order of their required replenishment energy, prioritizing the absorption of devices with lower energy demands to fill the remaining charging spaces at the current height. This minimizes the time cost caused by the "weakest link" effect while meeting capacity constraints. The specific steps for finding the optimal expansion scheme that minimizes the average marginal cost are as follows:

[0123] 1. For a given charging station Each candidate height Execute steps 2 to 6. If all heights have been traversed, proceed to step 7.

[0124] 2. Cover the uncovered set The MRSD in the middle is replenished with energy as needed. Sort the data from smallest to largest and calculate the number of remaining available charging points at the current height. ,initialization , , ;

[0125] 3. If If the above steps are not executed, proceed to steps 4-4 to 4-6; otherwise, proceed to step 1.

[0126] 4. From Take out The smallest MRSD, i.e. ;

[0127] 5. From Delete from middle ,Right now ;

[0128] 6. Order , ,Will join in At the very beginning, return to step 3;

[0129] 7. Return the case where the average marginal cost is minimized, i.e.

[0130]

[0131] 4. The difficulty of optimizing highly adjustable cooperative charging

[0132] Based on the above model, this invention formalizes the scheduling problem of highly adjustable pendant cooperative charging into a joint optimization problem, namely the CMHPC (Cost Minimization for Height-adjustable Pendant Cooperativecharging) problem.

[0133] To demonstrate the necessity of designing efficient heuristic algorithms, this invention includes a theoretical analysis of the computational complexity of the CMHPC problem.

[0134] Theorem: The CMHPC problem is an NP-hard problem.

[0135] Proof logic: This invention is proved by reducing the classical capacity-constrained generalized facility location problem CGFL polynomial to a special case of CMHPC.

[0136] The CGFL issue requires a trade-off between facility startup costs and user connection costs.

[0137] Consider a special case of CMHPC: assuming all charging stations have a fixed height ( And ignore mobile energy consumption. The corresponding facility setup cost. However, the connection cost in CMHPC is not a fixed constant, but a non-linear function determined by the maximum charging time within the charging group.

[0138] Even this simplified special case has a more complex structure than CGFL. Since CGFL is known to be NP-hard, the CMHPC problem, which includes it as a special case, must also be NP-hard. This shows that it is impossible to find the global optimal solution to CMHPC in polynomial time, thus establishing the rationality of the approximation algorithm approach adopted in this invention.

[0139] Example 2:

[0140] like Figure 1 As shown, the system described in this invention was deployed in a smart agriculture experimental field, and its parallel cooperative charging scheduling method includes the following:

[0141] There are 4 charging stations in this test site, denoted as set. 10 MRSDs, denoted as set MRSD target location set Candidate height set for charging stations Evenly distributed in Within the designated area, the MRSD performs its mission in farmland, possessing its current coordinates, target location, and energy requirements for mission completion. Table 1, MRSD parameters, lists the coordinates, energy requirements, and target locations of 10 devices. The unit mobile energy consumption is uniformly set at 0.0050 J / m. Table 2, charging station parameters, shows the charging station... All models feature height adjustment capabilities. Different power levels can be set to selectable heights.

[0142] Table 2 MRSD parameters in small-scale scenarios

[0143]

[0144] Table 3 Charging station parameters in small-scale scenarios

[0145]

[0146] Table 4. Results of HCSA and Enumeration Optimal Solution Algorithm in Small-Scale Scenarios

[0147] algorithm Average cost Average time (s) Activate charging station Charging time (h) Cost comparison (%) HCSA 29.6389 4.5972e-04 1.00 0.53 0.00 % OPT_DFS 29.6389 6.49052e+01 1.00 0.53 0.00 %

[0148] This embodiment satisfies all the preprocessing constraints proposed in this invention.

[0149] In this embodiment S4, the specific process of the cost-effective parallel cooperative charging scheduling algorithm for mobile rechargeable sensing devices is as follows:

[0150] A1: Input parameter: Collection of charging stations MRSD set target location set Candidate height set and other parameters;

[0151] A2: Initialize the MRSD set for all charging groups and charging facilities, i.e., for all ,make ,high ,by For example;

[0152] A3: Initialize the uncovered MRSD set ;

[0153] A4: If If the above steps are not executed, proceed to steps A5 to A7; otherwise, proceed to step A8.

[0154] A5: For each Find the charging group that minimizes the average marginal cost. and corresponding height ;by For example, go to A5.1;

[0155] A5.1: Initialization , , ;

[0156] A5.2: If If the condition is met, proceed to steps A5.3 through A5.7; otherwise, proceed to step A5.8.

[0157] A5.3: Take out middle The minimum MRSD is at this point. ;

[0158] A5.4: From Delete from middle ,Right now ;

[0159] A5.5: Order , ,Will join in At the very front, Return to step A5.2;

[0160] A6: Find the charging station with the lowest average marginal cost among all charging stations, update the system status, and configure the charging station. The height is Update its charging pack to The newly covered MRSD from Remove from the list and return to step A4;

[0161] Finally, the solution with the minimum average marginal cost is found and returned, with a total cost of 29.6389. The scheduling result is illustrated in the diagram below. Figure 6 As shown. Simulation results show that, in a small-scale scenario with 10 MRSDs and 4 charging stations, the highly adjustable scheduling scheme proposed in this invention accelerates the computation time by 99.999292% compared to the enumerated optimal solution algorithm, while ensuring complete consistency in results, thus guaranteeing the efficiency and accuracy of the algorithm.

[0162] Example 3:

[0163] The computer-readable storage medium of this embodiment stores a computer program that, when executed by a processor, implements the steps in the highly adjustable cooperative charging cost optimization method for agricultural mobile sensors of Embodiment 1.

[0164] The computer-readable storage medium in this embodiment can be an internal storage unit of the terminal, such as the terminal's hard disk or memory; the computer-readable storage medium in this embodiment can also be an external storage device of the terminal, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc. equipped on the terminal; furthermore, the computer-readable storage medium can include both the terminal's internal storage unit and external storage devices.

[0165] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0166] Example 4:

[0167] The computer device of this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the highly adjustable cooperative charging cost optimization method for agricultural mobile sensors of Embodiment 1.

[0168] In this embodiment, the processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The memory can include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0169] Those skilled in the art will understand that the content disclosed in the embodiments can be provided as a method, system, or computer program product. Therefore, this solution can take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this solution can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage) containing computer-usable program code.

[0170] This solution is described with reference to flowchart illustrations and / or block diagrams of methods and computer program products according to embodiments of this solution. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0171] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0172] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0173] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0174] The examples described herein are merely preferred embodiments of the invention and are not intended to limit the concept and scope of the invention. Any modifications and improvements made by those skilled in the art to the technical solutions of the invention without departing from the design concept of the invention should fall within the protection scope of the invention.

[0175] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the specific embodiments described above. The specific embodiments and descriptions in the specification are merely for further illustrating the principles of the invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the claims and their equivalents.

Claims

1. A highly adjustable cooperative charging cost optimization method for agricultural mobile sensors, characterized in that, Includes the following steps: Define the set of charging stations, the set of mobile rechargeable sensing devices, and the set of target locations; Define a parallel cooperative charging scheduling pricing model; Formalized problem of parallel cooperative charging scheduling for mobile rechargeable sensing devices; A cost-effective parallel cooperative charging scheduling algorithm for mobile rechargeable sensing devices is invoked to obtain a charging allocation scheme for the mobile rechargeable sensing devices.

2. The method as described in claim 1, characterized in that, The set of charging stations, the set of mobile rechargeable sensing devices, and the set of target locations include: Charging station collection Each charging station It has height adjustment capability and offers selectable height sequences. And they are arranged in ascending order; For any work at a specific height , charging stations The number of charging stations formed within the effective coverage area on the ground is The corresponding charging position received power sequence is When the height is At that time, the number of charging positions is at its maximum; For mobile rechargeable sensing device collections In each collection of mobile rechargeable sensing devices, With target location Task energy requirements Remaining battery power and unit mobile energy consumption ,also, It can also indicate the current location; The distance the mobile rechargeable sensing device travels from its current location to the charging station is... The distance traveled from the charging station to the target location is .

3. The method as described in claim 2, characterized in that, The parallel cooperative charging scheduling pricing model includes calculating the charging station Need to provide mobile rechargeable sensing devices Actual energy replenishment during charging : ； set up To be allocated to charging stations A charging group is a sequence of mobile rechargeable sensing devices for charging. The order determines the allocation of charging positions for the mobile rechargeable sensing device v; specifically, it will be based on... The arrangement order of China Mobile's rechargeable sensing devices prioritizes allocating each device to a charging position with the highest charging reception power; when the working height of the charger in the charging station is... At that time, the charging group Total charging time The longest charging time among the mobile rechargeable sensing devices in the group determines the charging time: ； in To be assigned The received power of the charging position; define the charging group. Operating cost function : ； in, Basic service fee, For time-varying rates, Based on the basic service time threshold, and satisfying .

4. The method as described in claim 3, characterized in that, The formalized highly adjustable cooperative charging scheduling problem includes: The objective function is to minimize the total cost of the entire network. ； The following constraints must be met: Full coverage constraint: Ensure that all mobile rechargeable sensing devices are assigned and assigned to only one charging spot at one charging station; Single height constraint: Each charging station Only one option Work at one of the heights; Capacity constraints: allocated to charging stations The number of mobile rechargeable sensing devices must not exceed the height currently selected. Total number of charging positions ; Physical feasibility constraints: ； ； ； If the feasibility constraints are not met, the corresponding mobile rechargeable sensing device will be removed before scheduling.

5. The method as described in claim 4, characterized in that, The step of invoking a cost-effective parallel cooperative charging scheduling algorithm for mobile rechargeable sensing devices to obtain a charging allocation scheme for the mobile rechargeable sensing devices includes the following steps: Step 1: Input parameters: Collection of charging stations Mobile rechargeable sensing device collection High-level set and the corresponding power and capacity parameter; Step 2: Initialization: Charging groups at all charging stations ,high The collection of mobile rechargeable sensing devices is not covered. ; Step 3: If If the first step is to proceed to step 4, proceed to step 6; otherwise, proceed to step 7. Step 4: For each charging station Find the charging group that minimizes the average marginal cost. and corresponding height ; Step 5: Find the charging station with the lowest average marginal cost among all charging stations, i.e. ; Step 6: Update system status and set up charging station The height is Update its charging pack to The newly covered MRSD from Remove from the list and return to step 3; Step 7: Output the height settings of all charging stations and the grouping results of mobile rechargeable sensing devices.

6. The method as described in claim 5, characterized in that, Step 4 involves finding the optimal expansion scheme that minimizes the average marginal cost, specifically including: Step 4-1: For a given charging station Each candidate height Execute steps 4-2 to 4-6. If all heights have been traversed, execute step 4-7. Step 4-2: Cover the uncovered set Mobile rechargeable sensing devices in the middle are replenished with energy as needed. Sort the data from smallest to largest and calculate the number of remaining available charging points at the current height. ,initialization , , ; Step 4-3: If If the above steps are not executed, proceed to steps 4-4 to 4-6; otherwise, proceed to step 4-1. Step 4-4: From Take out The smallest mobile rechargeable sensing device, namely ; Steps 4-5: From Delete from middle ,Right now ; Steps 4-6: Let , ,Will join in At the very beginning, return to step 4-3; Steps 4-7: Find and return the case where the average marginal cost is minimized, i.e. 。 7. A highly adjustable cooperative charging cost optimization system for agricultural mobile sensors, characterized in that, To implement the method according to any one of claims 1 to 6, comprising: At least one height-adjustable wireless charging station is suspended by a height adjustment mechanism configured to vertically displace between one or more heights of the charging station. Multiple mobile rechargeable sensing devices; A central processing unit is communicatively coupled to the height adjustment mechanism.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the steps in the highly adjustable cooperative charging cost optimization method for agricultural mobile sensors as described in any one of claims 1 to 6.

9. A computer 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 the steps in the highly adjustable cooperative charging cost optimization method for agricultural mobile sensors as described in any one of claims 1 to 6.

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