Wireless charging efficiency optimization method and system based on intelligent algorithm

Abstract

The present application relates to the technical field of wireless charging, and more particularly to a wireless charging efficiency optimization method and system based on intelligent algorithm. The method maps the battery state and charging demand parameters of each terminal to be charged to participating subjects and constructs an utility function by obtaining the battery state and charging demand parameters of each terminal to be charged. The method calculates the collaborative matching degree according to the spatial distribution and demand characteristics to divide the charging alliance and construct the alliance utility function. The method obtains the marginal contribution value set by calculating the change of the alliance utility function after removing the participating subjects. The method allocates the charging efficiency quota based on the set and the total available power constraint of the system. The method calculates the power coordination coefficient in combination with the spatial distribution characteristics, and finally generates the output power value to control the wireless charging transmission module to output the charging power. The method significantly improves the overall efficiency and resource allocation balance of the wireless charging system.

Description

Technical Field

[0001] This invention relates to the field of wireless charging technology, and in particular to a method and system for optimizing wireless charging efficiency based on intelligent algorithms. Background Technology

[0002] In the field of wireless charging technology, the conventional approach typically employs a centralized power allocation strategy. This involves a central controller calculating and allocating transmission power based on parameters such as the battery level and receiver coil position of each terminal. This method relies on a fixed priority order or simple proportional allocation rules, such as allocating power from lowest to highest remaining battery level, or adjusting the output value based on the distance between the terminal and the transmitter. Furthermore, some systems introduce time-slicing mechanisms, using a polling method to charge different terminals sequentially to avoid power conflicts. These methods are generally based on static parameters or preset thresholds and lack adaptability to dynamic charging environments.

[0003] Conventional approaches have two significant drawbacks. First, centralized power allocation strategies ignore the mutual interference between terminals. When multiple terminals charge simultaneously, magnetic field coupling leads to a significant decrease in energy transfer efficiency, and simple proportional allocation cannot effectively suppress this cross-interference. Second, allocation methods based on fixed rules cannot reflect the actual differences in charging power requirements of each terminal. For example, a terminal with nearly full charge may still receive a higher power allocation, resulting in energy waste, while a terminal urgently needing charging may not receive sufficient power due to priority rules, leading to low overall charging efficiency. Furthermore, existing methods struggle to cope with real-time adjustments required when terminal locations change or new terminals are added, resulting in system lag and further impacting charging performance. Summary of the Invention

[0004] This invention provides a method and system for optimizing wireless charging efficiency based on intelligent algorithms, which can solve the problems in the prior art.

[0005] A first aspect of this invention provides a wireless charging efficiency optimization method based on intelligent algorithms, comprising:

[0006] Obtain the battery status parameters and charging demand parameters of each terminal to be charged within the wireless charging area, map each terminal to be charged as a participating entity, and construct a utility function reflecting the charging benefits for each participating entity.

[0007] The collaborative matching degree is calculated based on the spatial distribution characteristics and charging demand characteristics of each participating entity. Multiple participating entities whose collaborative matching degree meets the alliance conditions are divided into charging alliances, and an alliance utility function is constructed for each charging alliance.

[0008] For each participating entity within a charging alliance, the marginal contribution value of each participating entity is obtained by calculating the change in the alliance's utility function after removing the participating entity from the charging alliance, thus forming a set of marginal contribution values ​​for each participating entity.

[0009] Based on the set of marginal contribution values ​​and the total available power of the system, charging efficiency quotas are allocated to each participating entity. According to the charging efficiency quotas of each participating entity and the spatial distribution characteristics of each participating entity in the charging alliance, the power coordination coefficient among each participating entity in the charging alliance is calculated.

[0010] The output power value of each terminal to be charged is generated based on the power coordination coefficient, and the wireless charging transmitter module is controlled to output charging power to each terminal to be charged according to the output power value.

[0011] Obtain the battery status parameters and charging demand parameters of each terminal to be charged within the wireless charging area, map each terminal to be charged as a participating entity, and construct a utility function reflecting the charging benefits for each participating entity, including:

[0012] The remaining battery power and battery health status information of each terminal to be charged within the wireless charging area are obtained to form battery status parameters.

[0013] Based on the remaining battery capacity and battery health status information in the battery status parameters, the charging time requirement and charging power requirement of each terminal to be charged are calculated to form the charging demand parameters.

[0014] Based on battery status parameters and charging demand parameters, each terminal to be charged in the wireless charging area is mapped as a participating entity, a participating entity identifier is assigned to each participating entity, and the battery status parameters and charging demand parameters are bound to the corresponding participating entity identifier.

[0015] For each participating entity, a utility weight coefficient reflecting the battery charging degradation characteristics is calculated based on the battery health status information bound to the participating entity's identifier.

[0016] For each participating entity, the charging duration requirement, charging power requirement, and utility weight coefficient bound to the participating entity's identifier are used as input variables to construct a utility function reflecting the charging benefits for each participating entity.

[0017] Based on the spatial distribution characteristics and charging demand characteristics of each participating entity, a collaborative matching degree is calculated. Multiple participating entities whose collaborative matching degree satisfies the alliance conditions are divided into charging alliances. An alliance utility function is constructed for each charging alliance, including:

[0018] The spatial coordinates of each participant within the wireless charging area are obtained, and the spatial distance and relative azimuth between each participant are calculated to form spatial distribution characteristics. The charging time requirement and charging power requirement are extracted from the utility function of each participant to form charging demand characteristics.

[0019] Based on the spatial distance and relative azimuth in the spatial distribution characteristics, the electromagnetic field coupling interference coefficient between each participating entity is calculated, and the coupling interference matrix is ​​constructed.

[0020] Based on the spatial distribution characteristics and charging demand characteristics, the electromagnetic field coupling interference coefficient in the coupling interference matrix is ​​used as a negative factor, and the difference between charging time demand and the difference between charging power demand are used as positive factors to perform weighted fusion calculation of the cooperative matching degree.

[0021] Based on the collaborative matching degree, an alliance association graph is constructed among the participating entities. Connectivity component detection is performed on the alliance association graph, and multiple participating entities in each connected component are divided into charging alliances.

[0022] For each charging alliance, the charging revenue and charging cost terms are extracted from the utility functions of each participating entity within the alliance. Based on the charging revenue and charging cost terms, an alliance utility function is constructed for each charging alliance.

[0023] Based on the collaborative matching degree, an alliance association graph is constructed among the participating entities. Connectivity component detection is performed on the alliance association graph, and multiple participating entities in each connected component are divided into charging alliances, including:

[0024] Using each participating entity as a node in the alliance association graph, the collaboration matching degree between each participating entity is traversed. When the collaboration matching degree between any two participating entities exceeds the preset association threshold, an edge connection is established between the nodes corresponding to the two participating entities. The collaboration matching degree is assigned as the edge weight to the edge connection to construct a weighted alliance association graph.

[0025] Initialize all nodes to be unvisited. Select any unvisited node in the weighted association graph as the starting node. Perform a depth-first traversal on the starting node and recursively visit all nodes directly connected to the starting node and all nodes indirectly connected through intermediate nodes. Mark all nodes visited during the traversal as the same connected component. Repeat the traversal until all nodes have been visited, and obtain multiple connected components.

[0026] Extract the node identifiers of all nodes contained in each connected component, obtain the set of participating entities corresponding to each connected component based on the mapping relationship between node identifiers and participating entities, and divide the participating entities corresponding to the nodes contained in each connected component into the same charging alliance.

[0027] For each participating entity within a charging alliance, the change in the alliance's utility function is calculated after removing the participating entity from the alliance to obtain the marginal contribution value of each participating entity, forming a set of marginal contribution values ​​for each participating entity, including:

[0028] For each charging alliance, the utility function parameters of all participating entities within the charging alliance are substituted into the alliance utility function to calculate the initial alliance utility value;

[0029] Iterate through all participating entities within the charging alliance, remove each participating entity from the charging alliance, and then recalculate the alliance utility function based on the remaining participating entities to obtain the alliance utility value after removing the participating entities;

[0030] Calculate the difference between the initial coalition utility value and the coalition utility value after removing participating entities, and use the difference as the initial marginal contribution value of the participating entities;

[0031] Identify pairs of participating entities within the charging alliance that have complementary time periods or power levels, calculate the synergistic utility gain generated by the complementarity between the participating entities, allocate the synergistic utility gain according to the proportion of the initial marginal contribution value of each participating entity in the participating entity pair, and superimpose the allocated synergistic utility gain onto the initial marginal contribution value of the corresponding participating entity to obtain the marginal contribution value of the participating entity.

[0032] The marginal contribution values ​​of each participating entity within the charging alliance will be aggregated to form a set of marginal contribution values ​​for each participating entity.

[0033] Based on the set of marginal contribution values ​​and the total available power constraint of the system, charging efficiency quotas are allocated to each participating entity. According to the charging efficiency quotas of each participating entity and the spatial distribution characteristics of each participating entity within the charging alliance, the power coordination coefficients among the participating entities within the charging alliance are calculated, including:

[0034] The marginal contribution values ​​of each participating entity in the charging alliance are extracted from the marginal contribution value set. The marginal contribution values ​​are normalized to obtain the marginal contribution ratio of each participating entity. The total available power constraint value of the system is obtained. Based on the marginal contribution ratio, the total available power constraint value of the system is allocated to each participating entity to allocate charging efficiency quotas.

[0035] The charging time period and charging power demand of each participating entity are obtained. Participating entities with overlapping charging time periods are identified to form a time period conflict group. When the total power demand in the time period conflict group exceeds the total available power constraint value of the system, the initial charging efficiency quota of each participating entity in the time period conflict group is compressed and adjusted according to the marginal contribution ratio to obtain the charging efficiency quota of each participating entity.

[0036] The geographical coordinates of each participant are obtained to construct a spatial topology network. Participants with adjacent geographical locations are identified in the spatial topology network to form spatial neighbor clusters. The variance of the charging efficiency quota of each participant in the spatial neighbor cluster is calculated as the power distribution dispersion.

[0037] Extract the power distribution dispersion of spatially adjacent clusters to which any two participating entities belong, and then calculate the power coordination coefficient among the participating entities within the charging alliance by fusing the difference in charging efficiency quotas between the two participating entities with the power distribution dispersion.

[0038] Based on the power coordination coefficient, the output power value of each terminal to be charged is generated, and the wireless charging transmitter module is controlled to output charging power to each terminal to be charged according to the output power value, including:

[0039] Obtain the power coordination coefficient and charging efficiency quota of each participating entity. When the real-time load of the power grid exceeds the load warning value, normalize the power coordination coefficient by taking the reciprocal to obtain the reduction allocation coefficient. Allocate the total power to be reduced according to the reduction allocation coefficient and deduct it from the charging efficiency quota to obtain the reduced charging efficiency quota.

[0040] Extract the peak load periods from the historical load data of the power grid, calculate the time overlap between the historical charging periods and the peak load periods of each participating entity, and multiply it by the power coordination coefficient to obtain the load contribution.

[0041] Reserved amounts are extracted from the reduction quotas of participating entities whose contributions are above average, and then added to the reduction quotas of participating entities whose contributions are below average to obtain the initial power value.

[0042] Based on the transmission distance and transmission angle between each terminal to be charged and the wireless charging transmitter module, a power transmission loss function is constructed. The initial output power value is coupled with the power transmission loss function to calculate the power compensation increment, which is then superimposed on the initial output power value to obtain the compensated output power value.

[0043] The wireless charging transmitter module is controlled to output charging power to each terminal to be charged according to the compensated output power value.

[0044] A second aspect of this invention provides a wireless charging efficiency optimization system based on intelligent algorithms, comprising:

[0045] The mapping modeling unit is used to obtain the battery state parameters and charging demand parameters of each terminal to be charged in the wireless charging area, map each terminal to be charged as a participating entity, and construct a utility function reflecting the charging benefits for each participating entity.

[0046] The alliance division unit is used to calculate the coordination matching degree based on the spatial distribution characteristics and charging demand characteristics of each participating entity, divide multiple participating entities that meet the alliance conditions into charging alliances, and construct an alliance utility function for each charging alliance.

[0047] The contribution calculation unit is used to calculate the change in the utility function of each participating entity within each charging alliance by removing the participating entity from the charging alliance, thereby obtaining the marginal contribution value of the participating entity and forming a set of marginal contribution values ​​for each participating entity.

[0048] The quota allocation unit is used to allocate charging efficiency quotas to each participating entity based on the set of marginal contribution values ​​and the total available power constraint of the system. Based on the charging efficiency quotas of each participating entity and the spatial distribution characteristics of each participating entity within the charging alliance, the unit calculates the power coordination coefficient among the participating entities within the charging alliance.

[0049] The power control unit is used to generate the output power value of each terminal to be charged based on the power coordination coefficient, and to control the wireless charging transmitter module to output charging power to each terminal to be charged according to the output power value.

[0050] A third aspect of the present invention provides an electronic device, comprising:

[0051] processor;

[0052] Memory used to store processor-executable instructions;

[0053] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0054] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0055] By mapping the terminals to be charged to participating entities and constructing a utility function based on charging benefits, precise quantification of individual charging needs is achieved. This allows each terminal to obtain a reasonable charging priority assessment based on its own battery status and demand parameters, avoiding the blind allocation of charging resources under traditional single strategies. The charging alliance division mechanism based on cooperative matching degree can dynamically identify spatially close-knit terminal groups with complementary charging needs, effectively reducing electromagnetic interference and energy loss when multiple terminals charge simultaneously, and improving the overall energy transmission efficiency of the system. The calculation of the alliance utility function and marginal contribution value provides a fair and accurate basis for power allocation for each participating entity, tilting charging resources towards entities with efficient utilization, thereby achieving Pareto optimal quota allocation under the system's total power constraint. The introduction of a power coordination coefficient further dynamically adjusts the power output ratio among terminals based on the spatial distribution differences within the alliance, reducing energy coupling losses caused by positional offsets or obstructions. The output power value generated based on the power coordination coefficient enables precise control of the wireless charging transmitter module, allowing each terminal to receive charging power in real time that matches its own needs and spatial location. The overall solution is suitable for high-density wireless charging environments such as smart homes and electric vehicle parking lots, and has high adaptability and robustness. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating the wireless charging efficiency optimization method based on intelligent algorithms according to an embodiment of the present invention.

[0057] Figure 2 This is a flowchart illustrating the charging alliance division process in an embodiment of the present invention. Detailed Implementation

[0058] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0060] Figure 1 This is a flowchart illustrating the wireless charging efficiency optimization method based on intelligent algorithms according to an embodiment of the present invention.

[0061] Wireless charging efficiency optimization methods based on intelligent algorithms include:

[0062] Obtain the battery status parameters and charging demand parameters of each terminal to be charged within the wireless charging area, map each terminal to be charged as a participating entity, and construct a utility function reflecting the charging benefits for each participating entity.

[0063] The collaborative matching degree is calculated based on the spatial distribution characteristics and charging demand characteristics of each participating entity. Multiple participating entities whose collaborative matching degree meets the alliance conditions are divided into charging alliances, and an alliance utility function is constructed for each charging alliance.

[0064] For each participating entity within a charging alliance, the marginal contribution value of each participating entity is obtained by calculating the change in the alliance's utility function after removing the participating entity from the charging alliance, thus forming a set of marginal contribution values ​​for each participating entity.

[0065] Based on the set of marginal contribution values ​​and the total available power of the system, charging efficiency quotas are allocated to each participating entity. According to the charging efficiency quotas of each participating entity and the spatial distribution characteristics of each participating entity in the charging alliance, the power coordination coefficient among each participating entity in the charging alliance is calculated.

[0066] The output power value of each terminal to be charged is generated based on the power coordination coefficient, and the wireless charging transmitter module is controlled to output charging power to each terminal to be charged according to the output power value.

[0067] Obtain the battery status parameters and charging demand parameters of each terminal to be charged within the wireless charging area, map each terminal to be charged as a participating entity, and construct a utility function reflecting the charging benefits for each participating entity, including:

[0068] The remaining battery power and battery health status information of each terminal to be charged within the wireless charging area are obtained to form battery status parameters.

[0069] Based on the remaining battery capacity and battery health status information in the battery status parameters, the charging time requirement and charging power requirement of each terminal to be charged are calculated to form the charging demand parameters.

[0070] Based on battery status parameters and charging demand parameters, each terminal to be charged in the wireless charging area is mapped as a participating entity, a participating entity identifier is assigned to each participating entity, and the battery status parameters and charging demand parameters are bound to the corresponding participating entity identifier.

[0071] For each participating entity, a utility weight coefficient reflecting the battery charging degradation characteristics is calculated based on the battery health status information bound to the participating entity's identifier.

[0072] For each participating entity, the charging duration requirement, charging power requirement, and utility weight coefficient bound to the participating entity's identifier are used as input variables to construct a utility function reflecting the charging benefits for each participating entity.

[0073] During the operation of a wireless charging system, it is necessary to collect real-time operational status information of all terminals to be charged within the charging area. A data channel is established with each terminal via the communication unit built into the wireless charging transmitter module to obtain the remaining battery capacity data output by the battery management system. The remaining battery capacity is typically expressed as a percentage, ranging from 0% to 100%, reflecting the proportion of currently available battery energy to total capacity. Simultaneously, battery health status information is acquired, including multi-dimensional parameters such as battery cycle count, internal resistance growth rate, and capacity decay rate. The battery cycle count records the number of complete charge-discharge cycles the battery has undergone; the internal resistance growth rate reflects the increase in the battery's internal impedance relative to its factory condition; and the capacity decay rate characterizes the degree to which the actual usable capacity of the battery decreases relative to its nominal capacity. These data collectively constitute the battery status parameters, providing a basis for subsequent charging strategy formulation.

[0074] Based on the acquired battery state parameters, the actual charging demand of each terminal to be charged needs to be calculated. For the remaining battery capacity data, the nominal capacity of the terminal's battery is obtained by consulting the battery capacity specification table. The nominal capacity is multiplied by the capacity decay rate compensation coefficient to obtain the current actual capacity. Then, the required additional charge is calculated based on the remaining percentage. Dividing the charge gap by the expected charging power yields the theoretical charging time, but the impact of battery health status on the charging process needs to be considered. When the battery cycle count exceeds 500, the charging time requirement will increase accordingly, typically adjusted by increasing the charging time by 5% for every 100 cycles exceeding 500. The calculation of charging power requirement needs to comprehensively consider the internal resistance growth rate in the battery health status information. Batteries with a higher internal resistance growth rate will generate more heat when receiving high-power charging, therefore, the charging power requirement needs to be reduced to protect battery safety. Specifically, the standard charging power is multiplied by an internal resistance correction factor. When the internal resistance growth rate exceeds 20%, the correction factor is set to 0.8; when it exceeds 40%, it is set to 0.6. The charging time requirement and charging power requirement obtained through the above calculations together constitute the charging demand parameters.

[0075] After calculating the charging demand parameters, each terminal to be charged within the wireless charging area is mapped as a participating entity. This mapping process is essentially an abstraction of physical devices into decision-making entities. A unique participating entity identifier is assigned to each terminal to be charged. This identifier is in string format and is generated by combining a system timestamp, a device MAC address hash value, and a random sequence number, ensuring the global uniqueness of the identifier. The previously acquired battery status parameters and charging demand parameters are associated with the corresponding participating entity identifier through a data binding operation. The data binding process establishes a mapping relationship table in the system database, which records fields such as participating entity identifier, remaining battery capacity, battery health status information, charging duration requirement, and charging power requirement. This mapping mechanism allows subsequent charging strategy optimization to be based on participating entities without frequent access to underlying device information.

[0076] Before constructing a utility function for each participating entity, a utility weighting coefficient reflecting the battery's charging degradation characteristics needs to be calculated. Battery cycle count and capacity degradation rate are extracted from the battery health status information bound to the participating entity's identifier. During charging, electrochemical reactions occur, leading to the gradual depletion of active materials; the degree of this depletion is closely related to the battery's historical usage. The calculation of the utility weighting coefficient needs to quantify the impact of this degradation characteristic on charging benefits. When the battery has a low cycle count and a low capacity degradation rate, it can efficiently accept charging power and convert it into effective electrical energy; in this case, the utility weighting coefficient should be set to a higher value. As the cycle count increases and the capacity degradation rate rises, the energy conversion efficiency during charging decreases, and some energy is lost as heat; the corresponding utility weighting coefficient needs to be reduced. Specifically, the cycle count is normalized to the range of 0 to 1. The normalization method is to divide the actual cycle count by the cycle count corresponding to the battery's design life, typically 1000 cycles. The capacity degradation rate is directly used as a percentage. The utility weighting coefficient is obtained by subtracting 0.4 times the normalized value of the number of cycles from 1 and then subtracting 0.6 times the capacity decay rate. This coefficient reflects the charging efficiency potential of the battery in its current state.

[0077] After obtaining the utility weighting coefficients, a utility function reflecting the charging benefits is constructed for each participant. The utility function is a core concept in game theory, used to quantify the benefits a participant gains from a specific strategy. In the wireless charging scenario, the participant's benefit is the combined effect of the effective charging amount and charging time obtained at a given charging power. The input variables of the utility function include the charging time requirement, the charging power requirement, and the utility weighting coefficients. The charging time requirement reflects the time the participant expects to complete charging, the charging power requirement represents the charging power level that the participant can safely accept, and the utility weighting coefficients correct for the impact of battery health on charging benefits. The utility function is constructed in a non-linear form because the relationship between charging benefits and charging power is not a simple linear one. In the initial stage of charging, increasing the charging power can significantly improve the charging speed, but when the power approaches or exceeds the battery's capacity limit, the marginal benefits decrease or even have a negative effect. The function is designed as the product of the charging power requirement and the utility weighting coefficient, multiplied by the logarithmic function of the charging time requirement. The introduction of the logarithmic function reflects the diminishing marginal utility characteristics over time. When charging time is short, the increase in utility per unit time is large, but as charging time increases, the increase in utility per unit time gradually decreases. Through this function design, the utility function can accurately reflect the differences in charging benefits under different battery states and charging demand conditions, providing a quantitative assessment basis for subsequent power allocation decisions.

[0078] In practical applications, the construction of the utility function also needs to consider environmental factors in the charging area. When the temperature in the charging area is high, the thermal management pressure on the battery increases, necessitating the introduction of a temperature correction term into the utility function. This temperature correction term is calculated based on the deviation of the ambient temperature from the optimal charging temperature; the greater the deviation, the smaller the correction coefficient. Furthermore, for terminals supporting fast charging protocols, the utility function can include a protocol compatibility bonus term to increase their priority in power allocation. For urgent charging needs with extremely low battery levels, an urgency coefficient can be set in the utility function to prioritize the charging needs of such terminals. By comprehensively considering multiple dimensions such as battery status, charging demand, environmental conditions, and urgency, the constructed utility function can fully reflect the charging benefit characteristics of each participating entity, laying a solid foundation for optimizing the overall efficiency of the wireless charging system.

[0079] Based on the spatial distribution characteristics and charging demand characteristics of each participating entity, a collaborative matching degree is calculated. Multiple participating entities whose collaborative matching degree satisfies the alliance conditions are divided into charging alliances. An alliance utility function is constructed for each charging alliance, including:

[0080] Obtain the spatial coordinates of each participating entity within the wireless charging area, calculate the spatial distance and relative azimuth between each participating entity, and construct the spatial distribution characteristics.

[0081] The charging time requirement and charging power requirement are extracted from the utility functions of each participating entity to form the charging demand characteristics;

[0082] Based on the spatial distance and relative azimuth in the spatial distribution characteristics, the electromagnetic field coupling interference coefficient between each participating entity is calculated, and the coupling interference matrix is ​​constructed.

[0083] Based on the spatial distribution characteristics and charging demand characteristics, the electromagnetic field coupling interference coefficient in the coupling interference matrix is ​​used as a negative factor, and the difference between charging time demand and the difference between charging power demand are used as positive factors to perform weighted fusion calculation of the cooperative matching degree.

[0084] Based on the collaborative matching degree, an alliance association graph is constructed among the participating entities. Connectivity component detection is performed on the alliance association graph, and multiple participating entities in each connected component are divided into charging alliances.

[0085] For each charging alliance, the charging revenue and charging cost terms are extracted from the utility functions of each participating entity within the alliance. Based on the charging revenue and charging cost terms, an alliance utility function is constructed for each charging alliance.

[0086] When acquiring the specific location information of each participating entity within the wireless charging area, the physical coordinates of the terminal to be charged are captured in real time through Bluetooth positioning base stations, UWB positioning base stations, or WiFi positioning modules deployed in the charging area. A two-dimensional Cartesian coordinate system is established within the wireless charging area, with the geometric center of the charging transmitter module set as the origin. The collected spatial position coordinates of the participating entities are represented as planar coordinate pairs. For the first... The first participating entity and the first There are 1 participating entity, whose coordinates are denoted as follows: and The Euclidean distance between the two is calculated using the following formula: The relative azimuth angle is calculated with the center of the charging transmitter module as the reference point, involving the main body. Relative to the participating entities The azimuth angle is expressed as This azimuth angle reflects the relative spatial positions of the two participating entities, providing a geometric basis for subsequent assessment of the electromagnetic field interaction effects. The spatial distribution feature matrix contains both the distance matrix and the azimuth angle matrix, forming a complete feature set describing the spatial relationships of all participating entities.

[0087] The extraction of charging demand characteristics is directly related to the key parameters in the utility functions constructed by each participating entity. The utility function includes a time constraint parameter reflecting the user's expected charging duration and a power expectation parameter reflecting the charging rate demand. Specifically, the extraction process involves... Identify charging time requirements in the utility function and charging power requirements These parameters are calculated by comprehensively considering information such as the remaining battery power, target power, and acceptable charging time window reported by the terminal to be charged. For both participating entities... and Calculate the difference in charging time requirements Difference between charging power requirements A small demand difference indicates that the two participants have similar charging behaviors and are suitable to be organized into the same charging alliance for coordinated scheduling, while participants with significantly different demands are more suitable to be separated into different alliances to avoid scheduling conflicts.

[0088] The electromagnetic field coupling interference coefficient is calculated based on the mutual inductance coupling model in wireless power transfer theory. In near-field magnetic coupling wireless charging scenarios, adjacent receiving coils will experience cross-coupling effects, causing mutual interference in the received power of each participating entity. For a spatial distance of... The relative azimuth angle is The participating entities The calculation of electromagnetic field coupling interference coefficient involves system parameters such as coil radius, operating frequency, and dielectric permeability. Coupling interference coefficient The calculation formula is: ,in Let be the coupling constant. The radius of the receiving coil, This represents the perpendicular distance between the transmitting and receiving coils. This coefficient exhibits a rapid attenuation characteristic with increasing distance, and the cosine term of the azimuth angle reflects the influence of the coil direction on the coupling strength. The coupling interference coefficients between all participating entities are organized into a symmetric matrix, with diagonal elements set to zero and off-diagonal elements being calculated values. The values ​​constitute the complete coupling interference matrix. .

[0089] The calculation of the coordination matching degree comprehensively considers the negative effects of electromagnetic interference and the positive effects of consistent charging demand. A large electromagnetic field coupling interference coefficient means that if two participants charge simultaneously, there will be a significant loss in power transmission efficiency, thus acting as a negative factor that inhibits the formation of alliances; while participants with similar charging demand characteristics are more likely to coordinate and cooperate, acting as a positive factor that promotes the establishment of alliances. (Participating Entities) and The degree of synergistic matching between The calculation formula is ,in , These are the positive factor weighting coefficients. The three factors are negative factor weights, and they satisfy the normalization constraint. The difference in charging time and power demand are converted using their reciprocals, so that the smaller the difference, the greater the positive contribution. The weighting coefficients are adjusted based on the different emphases on charging speed, energy efficiency, and interference tolerance in actual application scenarios, increasing them in scenarios prioritizing high efficiency. The value is chosen to enhance interference avoidance and increase in scenarios that pursue service balance. and The values ​​are chosen to facilitate collaboration among terminals with similar needs.

[0090] The alliance graph is constructed by using participating entities as vertices and collaborative matching degree as the weights of the edges. A collaborative matching degree threshold is set. As a criterion for determining the conditions of the alliance, when At the peak and vertex Establish an undirected edge between them, and assign a weight to the edge. The choice of threshold directly affects the granularity of alliance partitioning. A threshold that is too high leads to an increased number of alliances but smaller scale, while a threshold that is too low leads to excessively large alliances and increased internal coordination complexity. After constructing the graph, a depth-first search or breadth-first search algorithm is used to detect connected components in the alliance association graph. The connected component detection process starts from any unvisited vertex and traverses all reachable vertices through edge connections, marking all vertices in the same connected component as the same charging alliance. Isolated vertices may exist in the graph, corresponding to terminals whose coordination matching degree does not meet the alliance conditions of all other participating entities. These terminals form a single-element alliance for independent charging scheduling. After the connected component detection is completed, the alliance set is output. ,in The total number of alliances, each alliance containing a set of indices of several participating entities.

[0091] Constructing a coalition utility function requires integrating the individual utilities of all participating entities within the coalition. (From a charging coalition) Each participating entity utility function The process involves extracting charging revenue and charging cost items. Charging revenue is typically represented as the product of the amount of electricity received and the value per unit of electricity, reflecting the effectiveness of the charging service. Charging cost items include charging time costs, power consumption costs, and efficiency loss costs due to electromagnetic interference. Participating entities The utility function can be expressed as ,in For charging revenue, The cost is for charging. The coalition utility function is expressed as a weighted sum of the utilities of all members within the coalition, as follows: Weight The weighting is determined based on the contribution or priority of the participating entities within the alliance, and can initially be set to equal weight. ,in For the alliance The number of participating entities. The coalition utility function further considers the cooperative gain within the coalition; when coalition members reduce mutual interference through power coordination, they generate additional benefits. This cooperative gain term can be expressed as the product of the interference reduction and the unit interference cost. The complete coalition utility function is as follows: ,in This is the alliance synergy gain term, calculated as the difference in total interference cost before and after the alliance's formation. The synergy gain term incentivizes participating entities to maintain alliance stability and prevent alliance disintegration due to individual pursuit of self-interest.

[0092] In practical deployments, the update frequency of spatial location coordinates is determined based on the terminal's movement speed within the charging area. For fixed-location charging scenarios such as wireless charging piles in parking lots, location information is collected only once upon initial terminal access. For mobile charging scenarios such as AGV robot charging areas, location information needs to be continuously updated at a rate of seconds to adapt to changes in terminal location. The extraction of charging demand parameters relies on data from the battery management system reported by the terminal, including current state of charge, health status, and charging curve characteristics. This data is transmitted to the charging controller in real time via wireless communication protocols. The calculation of the electromagnetic field coupling interference coefficient can employ a combination of offline calibration and online correction. By pre-establishing a lookup table for coupling coefficients under different distance and azimuth combinations, interpolation is performed based on the measured location during online operation, reducing the real-time computational burden. The cooperative matching degree threshold can be dynamically adjusted according to the charging system's load status. When the system load is light, the threshold is lowered to form more cooperative alliances and improve overall efficiency. When the system load is close to full load, the threshold is raised to reduce the alliance size and lower scheduling complexity.

[0093] The complexity of connectivity component detection algorithms in alliance graphs is related to the number of participating entities and the alliance size. Using a disjoint-set data structure can optimize the time complexity of connectivity determination to near constant levels, making it suitable for rapid alliance partitioning in large-scale charging scenarios. The weight coefficients in the alliance utility function can be adaptively optimized through reinforcement learning. Based on the system performance under different weight configurations in historical charging data, the weight allocation strategy can be adjusted to maximize long-term cumulative utility. Quantifying the cooperative gain term involves accurately evaluating the interference reduction effect. This can be achieved by sampling the actual received power of each participating entity before and after alliance formation, and calculating the power transmission efficiency improvement ratio as a metric for cooperative gain.

[0094] like Figure 2 As shown, Figure 2 This is a flowchart illustrating the charging alliance division process in an embodiment of the present invention.

[0095] Based on the collaborative matching degree, an alliance association graph is constructed among the participating entities. Connectivity component detection is performed on the alliance association graph, and multiple participating entities in each connected component are divided into charging alliances, including:

[0096] Using each participating entity as a node in the alliance association graph, the collaboration matching degree between each participating entity is traversed. When the collaboration matching degree between any two participating entities exceeds the preset association threshold, an edge connection is established between the nodes corresponding to the two participating entities. The collaboration matching degree is assigned as the edge weight to the edge connection to construct a weighted alliance association graph.

[0097] Initialize all nodes to be unvisited. Select any unvisited node in the weighted association graph as the starting node. Perform a depth-first traversal on the starting node and recursively visit all nodes directly connected to the starting node and all nodes indirectly connected through intermediate nodes. Mark all nodes visited during the traversal as the same connected component. Repeat the traversal until all nodes have been visited, and obtain multiple connected components.

[0098] Extract the node identifiers of all nodes contained in each connected component, obtain the set of participating entities corresponding to each connected component based on the mapping relationship between node identifiers and participating entities, and divide the participating entities corresponding to the nodes contained in each connected component into the same charging alliance.

[0099] When dividing the charging alliance, the first step is to logically abstract each participating entity into a connectable node structure. Specifically, each participating entity is assigned a unique node identifier, which corresponds one-to-one with the entity's device identification code. The nodes corresponding to all participating entities are then used to form a vertex set of the alliance's association graph, the size of which is equal to the number of terminals to be charged.

[0100] When constructing the edge connections in a federation graph, it is necessary to traverse all possible pairings of participating entities. For graphs containing... Theoretically, there exists a charging area for each participating entity. There are several pairing possibilities. During the traversal, the calculated coordination matching degree value between each pair of participating entities is extracted sequentially. The coordination matching degree reflects the degree of coordination between the two participating entities in multiple dimensions such as spatial location, charging demand, and battery status, and its value is usually normalized to between 0 and 1.

[0101] For any pair of participating entities, when their coordination matching degree exceeds a preset association threshold, it is determined that these two participating entities possess the basic conditions for alliance cooperation. At this point, an undirected edge is established between the nodes corresponding to these two participating entities in the alliance association graph. The coordination matching degree value of the pair of participating entities is used as the edge weight and assigned to the newly established edge connection. The introduction of edge weights allows the alliance association graph to quantify the differences in coordination strength between different pairings of participating entities. The setting of the association threshold needs to comprehensively consider the spatial density of the charging area, the power supply capacity of the system, and the impact of the alliance size on charging efficiency. Setting the threshold too low will lead to an excessively large alliance size, increasing the complexity of power coordination within the alliance; setting the threshold too high may prevent some participating entities from joining any alliance, reducing overall charging efficiency.

[0102] After traversing all pairings, a weighted undirected graph structure is obtained, consisting of a set of nodes and a set of edges. Nodes in the graph correspond to various participating entities, the existence of edges indicates alliances or cooperative relationships between the corresponding participating entities, and the edge weights quantify the strength of these cooperative relationships. This graph structure can intuitively reflect the topological characteristics of the connections between participating entities within the charging area.

[0103] After constructing the weighted association graph, it is necessary to identify the connected component structure within the graph. A connected component is a maximal connected subset of nodes in the graph. Any two nodes within the same connected component are connected by at least one path, while nodes in different connected components are not connected by any path. The detection of connected components is implemented using a depth-first traversal strategy.

[0104] During the initialization phase, an access flag attribute is set for each node in the graph, with all nodes initially set to an unvisited state. An empty list of connected components is created to store the detection results. A node with an unvisited access flag is arbitrarily selected from the node set of the weighted association graph and used as the starting node for the current traversal. A temporary node set is created to record the nodes contained in the current connected component.

[0105] The depth-first traversal begins from the starting node. The visit flag of the starting node is updated to "visited," and the node is added to the temporary node set. All neighboring nodes of the starting node are found; neighboring nodes are those connected to the starting node by an edge. For each neighboring node, its visit flag is checked. If the neighboring node is unvisited, it is used as the new starting node, and the depth-first traversal is recursively performed. During the recursion, the traversal extends along the edge connections towards unvisited nodes until a node with no unvisited neighboring nodes is reached.

[0106] Recursive traversal ensures that all first-order adjacent nodes directly connected to the initial node are visited, as well as all higher-order adjacent nodes indirectly connected via one or more intermediate nodes. When a recursion returns, it backtracks to the previous node and checks if that node has any other unvisited adjacent nodes. This backtracking mechanism guarantees the completeness of the traversal, ensuring that no reachable nodes are missed.

[0107] When the depth-first traversal starting from the starting node is completely completed, all nodes in the temporary node set constitute a connected component. These nodes are marked as belonging to the same connected component, and a unique identifier is assigned to that component. The temporary node set and its identifiers are stored in the connected component list.

[0108] After detecting a connected component, select another node from the node set that is marked as unvisited as the new starting node, and repeat the depth-first traversal process. Each traversal will identify a new connected component until all nodes in the node set are marked as visited. At this point, the connected component list contains all connected components of the weighted federation graph.

[0109] For the number of nodes The time complexity of a depth-first traversal of a union graph is O(n log n). ,in Let be the number of edges in the graph. This traversal strategy can complete the detection of connected components in linear time, which has high computational efficiency.

[0110] After obtaining all connected components, it is necessary to map the connected components back to the actual set of participating entities. For each connected component, iterate through it and extract the node identifiers of all nodes contained within that component. Based on the pre-established mapping relationship between node identifiers and participating entities, look up the participating entity device corresponding to each node. Finally, aggregate the participating entities corresponding to all nodes in the same connected component to form the set of participating entities corresponding to that connected component.

[0111] The set of participating entities corresponding to each connected component is defined as a charging alliance. Participating entities within the same charging alliance have strong correlations under the cooperative matching degree evaluation, and they directly or indirectly satisfy the alliance cooperation conditions. Participating entities between different charging alliances do not satisfy the alliance conditions and will be processed independently during power allocation.

[0112] Each charging consortium is assigned a unique consortium identifier, and a mapping table is established between these identifiers and the sets of participating entities. This mapping table records the composition structure of all consortia within the charging area, providing fundamental data support for subsequent consortium utility calculations and power allocation. The connected component detection method can adapt to changes in the number of participating entities within the charging area. When a new terminal enters or leaves the charging area, the consortium re-partitioning can be quickly completed simply by recalculating the cooperative matching degree and updating the consortium association graph.

[0113] In practical applications, the size distribution of charging alliances exhibits diverse characteristics. A partially connected component may contain only a single node, indicating that the participant's coordination with any other participant does not exceed the association threshold; such participants will independently form a single-element alliance. A partially connected component may contain multiple nodes, forming multi-element alliances of varying sizes. The differences in alliance size reflect the heterogeneity of the spatial distribution of participants and charging demand within the charging area, which requires differentiated strategies during the power allocation phase.

[0114] For each participating entity within a charging alliance, the change in the alliance's utility function is calculated after removing the participating entity from the alliance to obtain the marginal contribution value of each participating entity, forming a set of marginal contribution values ​​for each participating entity, including:

[0115] For each charging alliance, the utility function parameters of all participating entities within the charging alliance are substituted into the alliance utility function to calculate the initial alliance utility value;

[0116] Iterate through all participating entities within the charging alliance, remove each participating entity from the charging alliance, and then recalculate the alliance utility function based on the remaining participating entities to obtain the alliance utility value after removing the participating entities;

[0117] Calculate the difference between the initial coalition utility value and the coalition utility value after removing participating entities, and use the difference as the initial marginal contribution value of the participating entities;

[0118] Identify pairs of participating entities within the charging alliance that have complementary time periods or power levels, calculate the synergistic utility gain generated by the complementarity between the participating entities, allocate the synergistic utility gain according to the proportion of the initial marginal contribution value of each participating entity in the participating entity pair, and superimpose the allocated synergistic utility gain onto the initial marginal contribution value of the corresponding participating entity to obtain the marginal contribution value of the participating entity.

[0119] The marginal contribution values ​​of each participating entity within the charging alliance will be aggregated to form a set of marginal contribution values ​​for each participating entity.

[0120] After obtaining the division results of each charging alliance, it is necessary to quantify the actual contribution of each participating entity to its alliance in order to ensure fair and efficient allocation of charging resources. The marginal contribution value is calculated using the Shapley value concept, which assesses its value by simulating the scenario of a participating entity leaving the alliance.

[0121] For each established charging alliance, the first step is to establish a baseline utility state. Utility function parameters for all participating entities within the alliance are collected. These parameters include each entity's remaining battery capacity, desired charging power, charging urgency, and location coordinates. These parameters are then fully substituted into the alliance utility function to calculate the initial alliance utility value assuming all members participate. The alliance utility function comprehensively considers overall charging efficiency, load balancing, and spatial resource utilization within the alliance, reflecting the overall operational quality of the alliance.

[0122] After determining the initial alliance utility value, an elimination-by-elimination method is used to evaluate the contribution of each participant. The process iterates through all participants in the charging alliance, selecting one target participant at a time for virtual removal. This removal operation does not actually remove the terminal from the system; rather, it assumes the participant does not exist during the calculation and reconstructs the alliance state based on the remaining participants. At this point, the alliance topology needs to be updated, the spatial distance matrix between the remaining participants needs to be recalculated, and the power allocation scheme within the alliance needs to be adjusted. Based on the updated alliance state, the utility function parameters of the remaining participants are substituted into the alliance utility function to calculate the alliance utility value after removing the participant.

[0123] By comparing the changes in alliance utility before and after removal, the value of the target participant can be intuitively reflected. The difference between the initial alliance utility and the alliance utility after removing the participant is calculated. This difference characterizes the direct impact of the target participant on alliance utility and serves as the participant's initial marginal contribution. A positive difference indicates that the participant's presence enhances the overall alliance utility; a larger difference indicates a more significant contribution. For certain special participants, their removal may fundamentally change the alliance topology, such as dividing a previously connected charging region into isolated sub-regions. In this case, the decrease in alliance utility will be more pronounced.

[0124] The initial marginal contribution value only reflects the independent value of the participating entities and does not yet consider the synergistic effects among alliance members. In actual wireless charging scenarios, different participating entities may exhibit time-based or power-based complementarity, which can generate additional synergistic utility gains. Time-based complementarity refers to the staggered charging demand periods of two participating entities. For example, participating entity A needs to charge quickly in the morning to cope with high-intensity use during the day, while participating entity B can charge slowly at night. Both can share charging resources without conflict. Power complementarity refers to the gradient difference in charging power demand between two participating entities. Terminals with high power demand can obtain additional power allocation during the charging gaps of terminals with low power demand, thereby improving the overall system throughput.

[0125] Identifying complementary charging partners within a charging alliance requires establishing a multi-dimensional matching mechanism. For time-based complementarity, the charging time windows of each participant are analyzed, and the overlap rate between the time windows of any two participants is calculated. Participant pairs with an overlap rate below a set threshold are marked as having time-based complementarity potential. For power complementarity, the expected charging power of each participant is compared with their actual tolerable power range, identifying participants with significantly different power demands and spatially adjacent locations. For participant pairs that simultaneously meet both time-based and power complementarity conditions, their synergistic effect is more prominent and they are prioritized for inclusion in the synergistic utility gain calculation.

[0126] Calculating the synergistic utility gain resulting from complementarity between participating entities requires quantifying the system performance improvement brought about by the complementarity relationship. Taking time-based complementarity as an example, assuming participating entity A and participating entity B form a complementary pair, without considering complementarity, they would need to independently occupy charging resources, potentially leading to resource overload during some periods and resource idleness during others. When coordination is achieved using complementary characteristics, a smooth allocation of resources over time can be realized, reducing peak load pressure and improving the utilization rate of charging infrastructure. The synergistic utility gain is reflected in a reduction in the overall charging completion time of the alliance, an improvement in energy transmission efficiency, or a reduction in system load variance. By establishing a synergistic utility gain evaluation model and inputting the characteristic parameters of the complementary participating entity pair, the additional utility improvement brought to the alliance by this complementarity relationship can be calculated.

[0127] The allocation of synergistic utility gains must adhere to the principle of fairness, ensuring that each party in a complementary relationship receives benefits commensurate with its contribution. A strategy based on the proportion of initial marginal contributions is adopted. First, the sum of the initial marginal contributions of each participant in the pair is calculated. Then, the proportion of each participant's initial marginal contribution in the total is calculated. The calculated synergistic utility gain is then distributed among the participants in the pair according to this proportion. For example, participant A and participant B form a complementary pair, generating a synergistic utility gain of... The initial marginal contribution value of participant A is The initial marginal contribution value of participant B is Then the collaborative utility gain allocated to participant A is The collaborative utility gain allocated to participant B is .

[0128] The allocated synergy gain is added to the initial marginal contribution value of the corresponding participating entity to obtain the final marginal contribution value considering the synergy effect. For participating entity A, its final marginal contribution value is... If a participant forms complementary pairs with multiple other participants simultaneously, the synergistic utility gain from all complementary relationships needs to be accumulated. This approach ensures that the independent contributions of each participant are reflected while also incentivizing collaborative behavior among alliance members, thereby improving the overall efficiency of the system.

[0129] After calculating the marginal contribution value for a single participating entity, the calculation results for all participating entities within the charging alliance need to be summarized and organized. A marginal contribution value set data structure is established, indexed by the participating entity identifier, storing the final marginal contribution value for each participating entity. The set also includes auxiliary information, such as the initial marginal contribution value, the number of complementary pairs involved, and the total allocated synergistic utility gain, facilitating subsequent result traceability and optimization. The marginal contribution value set will serve as a key input to the charging efficiency quota allocation algorithm, directly affecting the charging resource share obtained by each participating entity.

[0130] During the generation of the marginal contribution value set, it is necessary to handle potential anomalies. For participants with a negative initial marginal contribution value, it indicates that they are reducing the overall utility of the alliance. This situation typically occurs at terminals with extremely unfavorable spatial locations or where charging needs severely conflict with the overall demand pattern of the alliance. For such participants, consideration can be given to removing them from the current alliance and attempting to reallocate them to another alliance, or setting a minimum guaranteed value for them in the marginal contribution value set to ensure that even participants with lower contributions can obtain basic charging services, reflecting the fairness and inclusivity of the system.

[0131] The aggregated set of marginal contribution values ​​is not only used for subsequent resource allocation but also serves as a basis for dynamic adjustments to the alliance structure. Regularly analyzing the changing trends of the marginal contribution values ​​of each participating entity identifies members whose contributions to the alliance are continuously declining or whose fit with the current alliance is gradually decreasing, triggering an alliance reorganization mechanism. Through the migration of participating entities and the adjustment of alliance boundaries, the long-term operational efficiency and stability of the system are maintained.

[0132] Based on the set of marginal contribution values ​​and the total available power constraint of the system, charging efficiency quotas are allocated to each participating entity. According to the charging efficiency quotas of each participating entity and the spatial distribution characteristics of each participating entity within the charging alliance, the power coordination coefficients among the participating entities within the charging alliance are calculated, including:

[0133] The marginal contribution values ​​of each participating entity in the charging alliance are extracted from the marginal contribution value set. The marginal contribution values ​​are normalized to obtain the marginal contribution ratio of each participating entity. The total available power constraint value of the system is obtained. Based on the marginal contribution ratio, the total available power constraint value of the system is allocated to each participating entity to allocate charging efficiency quotas.

[0134] The charging time period and charging power demand of each participating entity are obtained. Participating entities with overlapping charging time periods are identified to form a time period conflict group. When the total power demand in the time period conflict group exceeds the total available power constraint value of the system, the initial charging efficiency quota of each participating entity in the time period conflict group is compressed and adjusted according to the marginal contribution ratio to obtain the charging efficiency quota of each participating entity.

[0135] The geographical coordinates of each participant are obtained to construct a spatial topology network. Participants with adjacent geographical locations are identified in the spatial topology network to form spatial neighbor clusters. The variance of the charging efficiency quota of each participant in the spatial neighbor cluster is calculated as the power distribution dispersion.

[0136] Extract the power distribution dispersion of spatially adjacent clusters to which any two participating entities belong, and then calculate the power coordination coefficient among the participating entities within the charging alliance by fusing the difference in charging efficiency quotas between the two participating entities with the power distribution dispersion.

[0137] After obtaining the set of marginal contribution values ​​for each participating entity, it is necessary to transform these marginal contributions into an actual charging power allocation scheme. The marginal contribution values ​​of each participating entity within the charging alliance are extracted from this set. These marginal contribution values ​​reflect the degree to which each participating entity contributes to the overall improvement of the alliance's charging efficiency. Due to differences in the size and characteristics of different charging alliances, directly using marginal contribution values ​​for power allocation will lead to uneven allocation results. Therefore, the marginal contribution values ​​are normalized. Specifically, for the i-th participating entity within the charging alliance, its marginal contribution percentage is... The calculation is the marginal contribution value of the participating entity divided by the sum of the marginal contribution values ​​of all participating entities within the alliance. Normalization ensures that the sum of the marginal contribution percentages of all participating entities is 1, providing a standardized basis for subsequent power allocation.

[0138] The total available power constraint of the system is obtained, which is determined by the hardware capabilities, power supply capacity, and safety operation requirements of the wireless charging system. The total available power constraint is allocated based on the marginal contribution ratio. The initial charging efficiency quota allocated to the i-th participating entity is calculated as the product of the marginal contribution ratio and the total available power constraint. This allocation method ensures that participating entities that contribute more to the alliance receive more charging power resources, reflecting the principle of fairness in allocation based on contribution. The initial charging efficiency quota is allocated only based on marginal contribution and does not yet consider time-related conflict constraints.

[0139] The system acquires the charging time periods and charging power requirements of each participating entity. Charging time periods are recorded as timestamps, showing the start and end times of each entity's charging. Charging power requirements reflect the actual power level required by each entity to meet its charging target. Entities with overlapping charging time periods are grouped into time period conflict groups. The method for determining whether charging time periods overlap is as follows: if the charging time periods of two participating entities intersect (i.e., one entity's charging start time is earlier than the other's charging end time, and the first entity's charging end time is later than the other's charging start time), then their charging time periods are considered to overlap. By traversing the charging time periods of all participating entities, all entities with overlapping time periods are grouped into the same time period conflict group.

[0140] When the total power demand within a conflict group exceeds the system's total available power constraint, the charging efficiency quotas of each participant within the conflict group need to be adjusted to meet the system power constraint. The total power demand of all participants within the conflict group is calculated. If it exceeds the system's total available power constraint, quota compression is required. The initial charging efficiency quotas of each participant within the conflict group are compressed based on their marginal contribution percentage. The compression ratio is calculated as the ratio of the system's total available power constraint to the total power demand. For the i-th participant within the conflict group, its compressed charging efficiency quota is the product of its initial charging efficiency quota and the compression ratio. The compression adjustment process maintains the power allocation ratio among participants, ensuring that participants with larger marginal contributions retain their relative advantage after compression. After compression adjustment, the charging efficiency quotas for each participant are obtained, reflecting both the differences in marginal contributions and meeting the system's total power constraint.

[0141] Obtain the geographic coordinates of each participating entity. The geographic coordinates are represented in a two-dimensional Cartesian coordinate system. The location coordinates of the i-th participating entity are denoted as follows: Construct a spatial topology network, treating each participant as a node in the network, and calculate the Euclidean distance between any two participants. This is the square root of the sum of the squares of the differences in the location coordinates of two participating entities. In the spatial topology network, geographically adjacent participating entities are identified and grouped into spatial proximity clusters. The identification method is as follows: a spatial proximity threshold is set; if the Euclidean distance between two participating entities is less than this threshold, they are considered geographically adjacent. A clustering algorithm is then used to group all geographically adjacent participating entities into the same spatial proximity cluster. Participating entities within the same cluster form a densely distributed region in physical space.

[0142] The variance of the charging efficiency quota of each participant within a spatially neighboring cluster is calculated as the power distribution dispersion. For a spatially neighboring cluster containing m participants, the average charging efficiency quota of all participants within the cluster is first calculated. Then, the squared deviation of the charging efficiency quota of each participating entity from the average value is calculated. The sum of all squared deviations is then divided by the number of participating entities, m, to obtain the power distribution dispersion. Power distribution dispersion reflects the degree of balance in power allocation within a spatially adjacent region. A large dispersion indicates significant differences in power allocation within the region, which may lead to uneven distribution of local charging resources.

[0143] Extract the power distribution dispersion of the spatial neighbor clusters to which any two participating entities belong. For the i-th and j-th participating entities, obtain the power distribution dispersion of their respective spatial neighbor clusters. and The power coordination coefficient among the participating entities within the charging alliance is obtained by combining the difference in charging efficiency quotas between the two participating entities with the power distribution dispersion. The calculation of the power coordination coefficient comprehensively considers the difference in charging efficiency quotas. Mean of power distribution dispersion The charging efficiency quota difference reflects the direct difference in power allocation between the two participants, while the power distribution dispersion reflects the power allocation environment of their respective spatial regions. The power coordination coefficient is obtained by normalizing the charging efficiency quota difference by dividing it by the mean of the power distribution dispersion, resulting in a value between 0 and 1. A larger power coordination coefficient indicates a significant difference in power allocation between the two participants and a relatively balanced power allocation in their respective regions, requiring priority consideration for power coordination in subsequent power regulation; a smaller power coordination coefficient indicates that the power allocation of the two participants is relatively close or that the power allocation in their respective regions has already shown significant dispersion, making power coordination less necessary.

[0144] Through the above processing, not only are charging efficiency quotas determined for each participating entity, but a power coordination relationship is also established among the participating entities. The charging efficiency quotas provide a basic power allocation scheme for each terminal to be charged, while the power coordination coefficient provides a quantitative basis for dynamic power adjustment within the charging alliance. During actual charging, the power allocation between adjacent participating entities is fine-tuned based on the power coordination coefficient to ensure that the charging needs of each participating entity are met while maintaining the power allocation balance of the entire charging alliance, ultimately achieving overall efficiency optimization of the wireless charging system.

[0145] Based on the power coordination coefficient, the output power value of each terminal to be charged is generated, and the wireless charging transmitter module is controlled to output charging power to each terminal to be charged according to the output power value, including:

[0146] Obtain the power coordination coefficient and charging efficiency quota of each participating entity. When the real-time load of the power grid exceeds the load warning value, normalize the power coordination coefficient by taking the reciprocal to obtain the reduction allocation coefficient. Allocate the total power to be reduced according to the reduction allocation coefficient and deduct it from the charging efficiency quota to obtain the reduced charging efficiency quota.

[0147] Extract the peak load periods from the historical load data of the power grid, calculate the time overlap between the historical charging periods and the peak load periods of each participating entity, and multiply it by the power coordination coefficient to obtain the load contribution.

[0148] Reserved amounts are extracted from the reduction quotas of participating entities whose contributions are above average, and then added to the reduction quotas of participating entities whose contributions are below average to obtain the initial power value.

[0149] Based on the transmission distance and transmission angle between each terminal to be charged and the wireless charging transmitter module, a power transmission loss function is constructed. The initial output power value is coupled with the power transmission loss function to calculate the power compensation increment, which is then superimposed on the initial output power value to obtain the compensated output power value.

[0150] The wireless charging transmitter module is controlled to output charging power to each terminal to be charged according to the compensated output power value.

[0151] After calculating the aforementioned power coordination coefficient, it needs to be converted into an actual output power value to drive the wireless charging transmitter module. This process first requires real-time monitoring of the power grid load status. Real-time load data is obtained through a power monitoring interface, reflecting the instantaneous power supply pressure on the grid. The real-time load is compared with a pre-set load warning value, typically set at 85% to 90% of the grid capacity, to determine if the grid is operating under high load. When the real-time load exceeds the load warning value, it indicates that the grid is under strain, requiring the activation of a power reduction mechanism to ensure safe grid operation.

[0152] The core of the power reduction mechanism lies in the fair and reasonable allocation of reduction tasks. This involves reciprocalizing the power coordination coefficients of each participating entity, i.e., the power coordination coefficient for each participating entity... Calculate its reciprocal The logic behind this operation is that participants with higher power coordination coefficients receive more power allocations under normal circumstances, but should bear more reduction responsibility during reduction scenarios. The reciprocals of the power coordination coefficients of all participants are summed to obtain a normalized base. Then, the reciprocal of each participant's power coordination coefficient is divided by this base to obtain the reduction allocation coefficient. The reduction allocation coefficient ranges from 0 to 1, and the sum of the reduction allocation coefficients of all participants equals 1, ensuring the complete allocation of reduction tasks.

[0153] The total power reduction to be determined is based on the difference between the maximum output power that the power grid can currently withstand and the actual power demand. This total amount is typically equal to the portion exceeding the load warning value multiplied by a safety margin factor, which is generally between 1.1 and 1.3, to allow for a safety margin. The total power reduction to be determined is then multiplied by the reduction allocation factor for each participating entity to obtain the power reduction amount that each participating entity should bear. The corresponding power reduction amount is then deducted from each participating entity's original charging efficiency quota to obtain the reduced charging efficiency quota. This reduced quota will serve as the benchmark value for subsequent power allocation, ensuring the system can still operate safely under high grid load conditions.

[0154] To achieve more refined power management, historical load characteristic analysis needs to be introduced. Recent grid load data is extracted from the historical database of the power dispatching system, typically covering the last 30 to 90 days, to capture the cyclical variation patterns of the load. Peak load identification is performed on the historical load data, using a sliding window method to detect local maxima in the load curve. Periods where the load exceeds 1.2 times the daily average load and lasts for more than 30 minutes are identified as peak load periods. These periods typically correspond to peak electricity consumption times, such as 9:00 AM to 11:00 AM, 2:00 PM to 5:00 PM, and 7:00 PM to 9:00 PM on weekdays.

[0155] For each participant, its historical charging records are extracted, and the start and end times of each charging activity are calculated. The participant's historical charging periods are compared with identified peak load periods to calculate the time overlap. The time overlap is calculated by dividing the intersection of the charging period and the peak period by the total duration of the charging period. For example, if a participant's charging duration is 2 hours, with 1 hour occurring during peak load, the time overlap for that charging period is 0.5. A weighted average of the time overlaps of all historical charging records for that participant is then calculated, with more recent charging records given higher weights, to obtain the participant's average time overlap.

[0156] Multiplying the average time overlap by the power coordination coefficient yields the load contribution index. This index comprehensively reflects the historical contribution of participating entities to the grid's peak load and their current power demand intensity. A high load contribution indicates that the participating entity has historically frequently charged during periods of high grid load, placing significant pressure on the grid. The arithmetic mean of the load contributions of all participating entities is calculated as the judgment benchmark. By comparing the load contribution of each participating entity with the average, groups of participating entities with contributions above the average and groups with contributions below the average are identified.

[0157] Based on differences in load contribution, a power redistribution strategy is implemented. A reserve is extracted from the reduced charging efficiency quota of participants whose contributions are above average. The extraction ratio is determined by the degree to which the contribution exceeds the average; the greater the exceedance, the higher the extraction ratio, typically ranging from 5% to 15% of the reduced quota. This extracted reserve is then allocated to participants whose contributions are below average according to the gap ratio. The allocation weight is proportional to the difference between the participant's contribution and the average, with larger gaps resulting in greater compensation. This compensation is then added to the reduced quota of participants whose contributions are below average to form the adjusted power allocation value, i.e., the initial output power value. This mechanism incentivizes and constrains historical charging behavior, guiding users to charge during off-peak hours and reducing peak load pressure on the power grid.

[0158] After obtaining the initial output power value, the actual transmission loss during wireless charging must also be considered. Wireless charging is based on the principles of electromagnetic induction or magnetic resonance, and power inevitably attenuates during spatial transmission. The degree of attenuation is mainly affected by the transmission distance and transmission angle. Using the spatial positioning device built into the wireless charging transmitter module, the three-dimensional coordinates of each terminal to be charged relative to the transmitting coil are obtained, and the straight-line distance between the terminal and the center of the transmitting coil is calculated. Simultaneously, based on the terminal's attitude sensor data, the angle between the normal vector of the receiving coil plane and the normal vector of the transmitting coil plane is calculated; this angle reflects the relative deflection of the two coil planes.

[0159] A power transmission loss function is constructed, comprehensively considering both distance attenuation and angle mismatch. Distance attenuation follows the inverse square law, meaning transmission efficiency is inversely proportional to the square of the distance. The loss due to angle mismatch is related to the cosine of the angle; efficiency is highest when the two coil planes are perfectly parallel (zero angle), and gradually decreases as the angle increases. The power transmission loss function is expressed as the ratio of initial power to actual received power; a ratio greater than 1 indicates the presence of loss. Substituting the initial output power value into the loss function, the expected received power for each terminal to be charged under the current spatial configuration is calculated.

[0160] To ensure that the actual power received by each terminal to be charged reaches the target initial output power value, power compensation is required at the transmitting end. The calculation method is as follows: By inversely solving the loss function, the amount of power the transmitting end needs to increase to achieve the target power at the receiving end is determined; this is the power compensation increment. This increment is equal to the product of the initial output power value and the loss factor in the loss function. The power compensation increment is then added to the initial output power value to obtain the compensated output power value. This compensated power value ensures that, considering transmission losses, the actual received power of each terminal to be charged meets the charging requirements.

[0161] After calculating the power value, a power command is sent to the wireless charging transmitter module via the control interface. Based on the received compensated output power value, the transmitter module adjusts the operating parameters of its internal power amplifier circuit, including the inverter's switching frequency, duty cycle, and power transistor on-state current, to generate an alternating magnetic field of appropriate intensity in the transmitting coil. The receiving coil of each terminal being charged generates an induced electromotive force in this magnetic field, which, after rectification and filtering, charges the battery. The transmitter module continuously monitors the charging status feedback information of each terminal, including parameters such as received power, charging current, and battery voltage, forming a closed-loop control system. It dynamically adjusts the output power to cope with status changes during the charging process, ensuring continuous optimization of charging efficiency.

[0162] A second aspect of this invention provides a wireless charging efficiency optimization system based on intelligent algorithms, comprising:

[0163] The mapping modeling unit is used to obtain the battery state parameters and charging demand parameters of each terminal to be charged in the wireless charging area, map each terminal to be charged as a participating entity, and construct a utility function reflecting the charging benefits for each participating entity.

[0164] The alliance division unit is used to calculate the coordination matching degree based on the spatial distribution characteristics and charging demand characteristics of each participating entity, divide multiple participating entities that meet the alliance conditions into charging alliances, and construct an alliance utility function for each charging alliance.

[0165] The contribution calculation unit is used to calculate the change in the utility function of each participating entity within each charging alliance by removing the participating entity from the charging alliance, thereby obtaining the marginal contribution value of the participating entity and forming a set of marginal contribution values ​​for each participating entity.

[0166] The quota allocation unit is used to allocate charging efficiency quotas to each participating entity based on the set of marginal contribution values ​​and the total available power constraint of the system. Based on the charging efficiency quotas of each participating entity and the spatial distribution characteristics of each participating entity within the charging alliance, the unit calculates the power coordination coefficient among the participating entities within the charging alliance.

[0167] The power control unit is used to generate the output power value of each terminal to be charged based on the power coordination coefficient, and to control the wireless charging transmitter module to output charging power to each terminal to be charged according to the output power value.

[0168] A third aspect of the present invention provides an electronic device, comprising:

[0169] processor;

[0170] Memory used to store processor-executable instructions;

[0171] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0172] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0173] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0174] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wireless charging efficiency optimization method based on intelligent algorithms, characterized in that, include: Obtain the battery status parameters and charging demand parameters of each terminal to be charged within the wireless charging area, map each terminal to be charged as a participating entity, and construct a utility function reflecting the charging benefits for each participating entity. The collaborative matching degree is calculated based on the spatial distribution characteristics and charging demand characteristics of each participating entity. Multiple participating entities whose collaborative matching degree meets the alliance conditions are divided into charging alliances, and an alliance utility function is constructed for each charging alliance. For each participating entity within a charging alliance, the marginal contribution value of each participating entity is obtained by calculating the change in the alliance's utility function after removing the participating entity from the charging alliance, thus forming a set of marginal contribution values ​​for each participating entity. Based on the set of marginal contribution values ​​and the total available power of the system, charging efficiency quotas are allocated to each participating entity. According to the charging efficiency quotas of each participating entity and the spatial distribution characteristics of each participating entity in the charging alliance, the power coordination coefficient among each participating entity in the charging alliance is calculated. The output power value of each terminal to be charged is generated based on the power coordination coefficient, and the wireless charging transmitter module is controlled to output charging power to each terminal to be charged according to the output power value. Based on the spatial distribution characteristics and charging demand characteristics of each participating entity, a collaborative matching degree is calculated. Multiple participating entities whose collaborative matching degree satisfies the alliance conditions are divided into charging alliances. An alliance utility function is constructed for each charging alliance, including: The spatial coordinates of each participant within the wireless charging area are obtained, and the spatial distance and relative azimuth between each participant are calculated to form spatial distribution characteristics. The charging time requirement and charging power requirement are extracted from the utility function of each participant to form charging demand characteristics. Based on the spatial distance and relative azimuth in the spatial distribution characteristics, the electromagnetic field coupling interference coefficient between each participating entity is calculated, and the coupling interference matrix is ​​constructed. Based on the spatial distribution characteristics and charging demand characteristics, the electromagnetic field coupling interference coefficient in the coupling interference matrix is ​​used as a negative factor, and the difference between charging time demand and the difference between charging power demand are used as positive factors to perform weighted fusion calculation of the cooperative matching degree. Based on the collaborative matching degree, an alliance association graph is constructed among the participating entities. Connectivity component detection is performed on the alliance association graph, and multiple participating entities in each connected component are divided into charging alliances. For each charging alliance, extract the charging revenue and charging cost terms from the utility functions of each participating entity within the alliance, and construct an alliance utility function for each charging alliance based on the charging revenue and charging cost terms.

2. The method according to claim 1, characterized in that, Obtain the battery status parameters and charging demand parameters of each terminal to be charged within the wireless charging area, map each terminal to be charged as a participating entity, and construct a utility function reflecting the charging benefits for each participating entity, including: The remaining battery power and battery health status information of each terminal to be charged within the wireless charging area are obtained to form battery status parameters. Based on the remaining battery capacity and battery health status information in the battery status parameters, the charging time requirement and charging power requirement of each terminal to be charged are calculated to form the charging demand parameters. Based on battery status parameters and charging demand parameters, each terminal to be charged in the wireless charging area is mapped as a participating entity, a participating entity identifier is assigned to each participating entity, and the battery status parameters and charging demand parameters are bound to the corresponding participating entity identifier. For each participating entity, a utility weight coefficient reflecting the battery charging degradation characteristics is calculated based on the battery health status information bound to the participating entity's identifier. For each participating entity, the charging duration requirement, charging power requirement, and utility weight coefficient bound to the participating entity's identifier are used as input variables to construct a utility function reflecting the charging benefits for each participating entity.

3. The method according to claim 1, characterized in that, Based on the collaborative matching degree, an alliance association graph is constructed among the participating entities. Connectivity component detection is performed on the alliance association graph, and multiple participating entities in each connected component are divided into charging alliances, including: Using each participating entity as a node in the alliance association graph, the collaboration matching degree between each participating entity is traversed. When the collaboration matching degree between any two participating entities exceeds the preset association threshold, an edge connection is established between the nodes corresponding to the two participating entities. The collaboration matching degree is assigned as the edge weight to the edge connection to construct a weighted alliance association graph. Initialize all nodes to be marked as unvisited. Select any unvisited node in the weighted association graph as the starting node. Perform a depth-first traversal on the starting node and mark it. Recursively visit adjacent nodes directly connected to the starting node and all nodes indirectly connected through intermediate nodes. Mark all nodes visited during the traversal as the same connected component. Repeat the traversal until all nodes have been visited, and obtain multiple connected components. Extract the node identifiers of all nodes contained in each connected component, obtain the set of participating entities corresponding to each connected component based on the mapping relationship between node identifiers and participating entities, and divide the participating entities corresponding to the nodes contained in each connected component into the same charging alliance.

4. The method according to claim 1, characterized in that, For each participating entity within a charging alliance, the change in the alliance's utility function is calculated after removing the participating entity from the alliance to obtain the marginal contribution value of each participating entity, forming a set of marginal contribution values ​​for each participating entity, including: For each charging alliance, the utility function parameters of all participating entities within the charging alliance are substituted into the alliance utility function to calculate the initial alliance utility value; Iterate through all participating entities within the charging alliance, remove each participating entity from the charging alliance, and then recalculate the alliance utility function based on the remaining participating entities to obtain the alliance utility value after removing the participating entities. Calculate the difference between the initial coalition utility value and the coalition utility value after removing participating entities, and use the difference as the initial marginal contribution value of the participating entities; Identify pairs of participating entities within the charging alliance that have complementary time periods or power levels, calculate the synergistic utility gain generated by the complementarity between the participating entities, allocate the synergistic utility gain according to the proportion of the initial marginal contribution value of each participating entity in the participating entity pair, and superimpose the allocated synergistic utility gain onto the initial marginal contribution value of the corresponding participating entity to obtain the marginal contribution value of the participating entity. The marginal contribution values ​​of each participating entity within the charging alliance will be aggregated to form a set of marginal contribution values ​​for each participating entity.

5. The method according to claim 1, characterized in that, Based on the set of marginal contribution values ​​and the total available power constraint of the system, charging efficiency quotas are allocated to each participating entity. According to the charging efficiency quotas of each participating entity and the spatial distribution characteristics of each participating entity within the charging alliance, the power coordination coefficients among the participating entities within the charging alliance are calculated, including: The marginal contribution values ​​of each participating entity in the charging alliance are extracted from the marginal contribution value set. The marginal contribution values ​​are normalized to obtain the marginal contribution ratio of each participating entity. The total available power constraint value of the system is obtained. Based on the marginal contribution ratio, the total available power constraint value of the system is allocated to each participating entity to allocate charging efficiency quotas. The charging time period and charging power demand of each participating entity are obtained. Participating entities with overlapping charging time periods are identified to form a time period conflict group. When the total power demand in the time period conflict group exceeds the total available power constraint value of the system, the initial charging efficiency quota of each participating entity in the time period conflict group is compressed and adjusted according to the marginal contribution ratio to obtain the charging efficiency quota of each participating entity. The geographical coordinates of each participant are obtained to construct a spatial topology network. Participants with adjacent geographical locations are identified in the spatial topology network to form spatial neighbor clusters. The variance of the charging efficiency quota of each participant in the spatial neighbor cluster is calculated as the power distribution dispersion. Extract the power distribution dispersion of spatially adjacent clusters to which any two participating entities belong, and then calculate the power coordination coefficient among the participating entities within the charging alliance by fusing the difference in charging efficiency quotas between the two participating entities with the power distribution dispersion.

6. The method according to claim 1, characterized in that, Based on the power coordination coefficient, the output power value of each terminal to be charged is generated, and the wireless charging transmitter module is controlled to output charging power to each terminal to be charged according to the output power value, including: Obtain the power coordination coefficient and charging efficiency quota of each participating entity. When the real-time load of the power grid exceeds the load warning value, normalize the power coordination coefficient by taking the reciprocal to obtain the reduction allocation coefficient. Allocate the total power to be reduced according to the reduction allocation coefficient and deduct it from the charging efficiency quota to obtain the reduced charging efficiency quota. Extract the peak load periods from the historical load data of the power grid, calculate the time overlap between the historical charging periods and the peak load periods of each participating entity, and multiply it by the power coordination coefficient to obtain the load contribution. Reserved amounts are extracted from the reduction quotas of participating entities whose contributions are above average, and then added to the reduction quotas of participating entities whose contributions are below average to obtain the initial power value. Based on the transmission distance and transmission angle between each terminal to be charged and the wireless charging transmitter module, a power transmission loss function is constructed. The initial output power value is coupled with the power transmission loss function to calculate the power compensation increment, which is then superimposed on the initial output power value to obtain the compensated output power value. The wireless charging transmitter module is controlled to output charging power to each terminal to be charged according to the compensated output power value.

7. A wireless charging efficiency optimization system based on intelligent algorithms, used to implement the method as described in any one of claims 1-6, characterized in that, include: The mapping modeling unit is used to obtain the battery state parameters and charging demand parameters of each terminal to be charged in the wireless charging area, map each terminal to be charged as a participating entity, and construct a utility function reflecting the charging benefits for each participating entity. The alliance division unit is used to calculate the coordination matching degree based on the spatial distribution characteristics and charging demand characteristics of each participating entity, divide multiple participating entities that meet the alliance conditions into charging alliances, and construct an alliance utility function for each charging alliance. The contribution calculation unit is used to calculate the change in the utility function of each participating entity within each charging alliance by removing the participating entity from the charging alliance, thereby obtaining the marginal contribution value of the participating entity and forming a set of marginal contribution values ​​for each participating entity. The quota allocation unit is used to allocate charging efficiency quotas to each participating entity based on the set of marginal contribution values ​​and the total available power constraint of the system. Based on the charging efficiency quotas of each participating entity and the spatial distribution characteristics of each participating entity within the charging alliance, the unit calculates the power coordination coefficient among the participating entities within the charging alliance. The power control unit is used to generate the output power value of each terminal to be charged based on the power coordination coefficient, and to control the wireless charging transmitter module to output charging power to each terminal to be charged according to the output power value.

8. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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