Charging control method, charging system, intelligent lock and storage medium

Abstract

The invention relates to the technical field of charging control, in particular to a charging control method, a charging system, an intelligent lock and a storage medium. The charging control method comprises the following steps: constructing a multi-dimensional state space of a battery charging system of the charging system; designing a charging path planning model based on an ant colony optimization algorithm, and regarding a charging process as a process of finding an optimal path in a multi-dimensional state space by ants; collecting working state information and environment parameters of the charging system in real time; and converting the optimal charging path into a specific charging control strategy, executing the charging control strategy in real time, and continuously monitoring the charging effect. According to the charging control method provided by the invention, the problem of adaptive optimization of the intelligent lock battery charging system is effectively solved by constructing the multi-dimensional state space and applying the ant colony optimization algorithm.

Description

Technical Field

[0001] The present invention relates to the field of charging control technology, and in particular to a charging control method, a charging system, a smart lock and a storage medium. Background Art

[0002] Charging management of batteries or charging systems has always been the direction of industry research. Traditional charging methods mostly use fixed charging strategies, which cannot be adjusted according to actual usage and environmental conditions, resulting in charging efficiency that cannot be changed according to demand, affecting battery life.

[0003] The usage environment of many power-consuming devices, such as smart locks, is complex and changeable. Factors such as temperature changes, fluctuations in usage frequency, and network connection status can affect the charging process. In addition, different users' usage habits can also lead to differences in energy demand. These factors make it difficult to design a universal and efficient charging strategy.

[0004] In the existing technology, the optimization charging strategy usually only considers a single or a few factors, which cannot fully capture the complexity of the smart lock charging system. Summary of the invention

[0005] The present invention provides a charging control method, a charging system, a smart lock and a storage medium to solve the technical problem of low charging efficiency of the existing charging method due to the influence of adaptive adjustment capability.

[0006] The technical solution of the present invention to solve the above technical problems is as follows: A charging control method is provided, which is applied to a charging system. The steps of the charging control method include: Constructing the multidimensional state space of the battery charging system for the charging system; A charging path planning model is designed based on the ant colony optimization algorithm, which regards the charging process as a process in which ants search for the optimal path in a multi-dimensional state space. Collect the working status information and environmental parameters of the charging system in real time; The collected information is fed into a pre-trained deep neural network to predict the usage patterns and energy requirements of the charging system. Based on the prediction results, the parameters in the ant colony optimization algorithm are dynamically adjusted, and the ant colony optimization algorithm is executed in the adjusted multi-dimensional state space to find the optimal charging path; Convert the optimal charging path into a specific charging control strategy, execute the charging control strategy in real time, and continuously monitor the charging effect.

[0007] In another aspect, a charging system is provided, the charging system comprising: A multi-dimensional state space building module is used to build a multi-dimensional state space of a smart lock battery charging system; The ant colony optimization path planning module is used to design a charging path planning model based on the ant colony optimization algorithm, which regards the charging process as a process in which ants search for the optimal path in a multi-dimensional state space; Real-time data acquisition module, used to collect the working status information and environmental parameters of the smart lock in real time; A deep neural network prediction module that receives the collected information and predicts the usage pattern and energy demand of the smart lock; A parameter dynamic adjustment module is used to dynamically adjust the parameters in the ant colony optimization algorithm based on the prediction results; An optimization algorithm execution module, used to execute an ant colony optimization algorithm in the adjusted multi-dimensional state space to find an optimal charging path; A charging strategy conversion module, used to convert the optimal charging path into a specific charging control strategy; A charging control execution module, used to execute charging control strategies in real time; Closed-loop feedback monitoring module, used to continuously monitor charging effect and provide feedback.

[0008] On the other hand, a smart lock is provided, comprising the above charging system, and the smart lock further comprises: Integrated micro-supercapacitors for peak load management and fast response; Flexible solar cells and piezoelectric materials for ambient energy harvesting; Graphene-based heat dissipation structure for improved thermal management efficiency; Programmable charging interface to support multiple charging protocols and bidirectional charging function.

[0009] On the other hand, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above charging control method are implemented.

[0010] The beneficial effects of the present invention are: The charging control method proposed in the present invention effectively solves the adaptive optimization problem of the smart lock battery charging system by constructing a multi-dimensional state space and applying the ant colony optimization algorithm. The method can capture and analyze multiple influencing factors in real time, including battery status, environmental conditions, and usage patterns, so as to dynamically adjust the charging strategy.

[0011] By abstracting the charging process into the problem of ants finding the optimal path in multi-dimensional space, it can not only handle high-dimensional state space, but also quickly converge to a high-quality solution with limited computing resources.

[0012] Combined with the prediction function of deep neural networks, the method of the present invention can accurately predict the usage mode and energy demand of smart locks, further improving the adaptive ability of charging strategies. By dynamically adjusting algorithm parameters and executing optimized charging strategies in real time, the present invention achieves continuous optimization of the charging process, effectively improves charging efficiency, prolongs battery life, and can adapt to various complex usage scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a flow chart of a charging control method in one embodiment of the present invention; Figure 2 Schematic diagram of ant colony optimization path planning in one embodiment of the present invention Figure 1 ; Figure 3 Schematic diagram of ant colony optimization path planning in one embodiment of the present invention Figure 2 ; Figure 4 A diagram showing the steps of charging path planning in one embodiment of the present invention; Figure 5 It is a structural diagram of a charging system in one embodiment of the present invention; In the accompanying drawings, the components represented by the reference numerals are listed as follows: 100. Multi-dimensional state space construction module; 200. Ant colony optimization path planning module; 300. Real-time data acquisition module; 400. Deep neural network prediction module; 500. Parameter dynamic adjustment module; 600. Optimization algorithm execution module; 700. Charging strategy conversion module; 800. Charging control execution module; 900. Closed-loop feedback monitoring module. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] The term "comprise" and any variation thereof in the specification and claims of the present application are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. In addition, the use of "and / or" in the specification and claims to indicate at least one of the connected objects, such as A and / or B, means that A alone, B alone, and A and B exist in three cases.

[0016] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0017] The present invention provides the following preferred embodiments: Embodiment 1 In order to solve the problem of adaptive optimization of the charging system in a complex and changing environment, this embodiment proposes various steps of a charging control method to achieve efficient and intelligent charging management.

[0018] like Figure 1 As shown, the steps of the charging control method include: S101. Construct a multi-dimensional state space of a battery charging system.

[0019] S102. Design a charging path planning model based on the ant colony optimization algorithm, and regard the charging process as a process in which ants search for the optimal path in a multi-dimensional state space.

[0020] S103: Collect working status information and environmental parameters of the charging system in real time.

[0021] S104: input the collected information into a pre-trained deep neural network to predict the usage pattern and energy demand of the charging system.

[0022] S105: Based on the prediction results, dynamically adjust the parameters in the ant colony optimization algorithm, and execute the ant colony optimization algorithm in the adjusted multi-dimensional state space to find the optimal charging path.

[0023] S106: Convert the optimal charging path into a specific charging control strategy, execute the charging control strategy in real time, and continuously monitor the charging effect.

[0024] Specifically, this embodiment first constructs a multidimensional state space of the charging system. The multidimensional state space includes multiple dimensions such as battery power, charging current, charging voltage, ambient temperature, usage frequency, number of unlocking times, network connection status, data transmission frequency, electromagnetic interference intensity, and battery health status. By constructing such a comprehensive state space, the working state of the charging system can be described more accurately, providing a basis for subsequent optimization.

[0025] Furthermore, this embodiment designs a charging path planning model based on the ant colony optimization algorithm. The model abstracts the charging process as a process in which ants search for the optimal path in a multi-dimensional state space. The ant colony optimization algorithm is characterized by being able to quickly find an approximate optimal solution in a complex solution space, and is suitable for processing multi-variable and multi-objective optimization problems of charging systems. Figure 2 As shown in the figure, a 2D space is created, which contains randomly distributed "states", which can be understood as different charging parameter combinations, defining the starting point (start of charging) and the end point (end of charging), and using the ant colony optimization algorithm to find the optimal path between these states. Figure 2 (a) to (d) show the effects of 1, 5, 20, and 50 iterations, respectively. The dots represent different charging states, the dotted line connects the starting point (start of charging) and the end point (end of charging), the line segments represent the connection between states, and the transparency of the lines represents the pheromone intensity. As the number of iterations increases, some paths become obvious, indicating that these paths are better charging strategies. Figure 3 As shown, 3D space is used to represent the multidimensional state space, and the three dimensions of battery power, charging current and temperature are arbitrarily selected. The X-axis represents the battery power (%), the Y-axis represents the charging current (A), and the Z-axis represents the temperature (°C). The dots represent different charging states. The two larger dots represent the starting point (start of charging) and the end point (end of charging). The transparency of the dots represents the pheromone intensity. As the number of iterations increases, some dots become opaque, indicating that these dots are better charging strategies.

[0026] Furthermore, this embodiment adopts a real-time data acquisition mechanism. Through a high-precision sensor network, the system can obtain the working status information and environmental parameters of the charging system in real time. These data include but are not limited to the real-time battery power, the temperature of each key part, the number of unlocking times, the network connection status, the data transmission frequency, and the temperature, humidity and electromagnetic interference intensity of the surrounding environment.

[0027] Understandably, the collected information is fed into a pre-trained deep neural network. The neural network is trained with a large amount of historical data and can accurately predict the usage patterns and energy requirements of smart locks with charging systems in the future. This predictive capability allows the charging system to prepare in advance to avoid functional failures caused by insufficient power.

[0028] Furthermore, based on the prediction results, the system will dynamically adjust the parameters in the ant colony optimization algorithm. These parameters include pheromone volatility, global search and local search balance factors, etc. By adjusting these parameters, the ant colony algorithm can better adapt to the current charging needs and environmental conditions. In the adjusted multidimensional state space, the system executes the ant colony optimization algorithm to find the optimal charging path.

[0029] In this embodiment, the optimal charging path needs to balance multiple objectives, including minimizing charging time, maximizing battery life, ensuring sufficient power during the predicted peak usage period, controlling the temperature rise during the charging process, etc. The system will convert the found optimal path into specific charging control strategies, which include designing pulse charging waveforms, determining multi-stage charging current and voltage change curves, and formulating temperature control strategies.

[0030] Furthermore, this embodiment uses a closed-loop feedback mechanism to continuously monitor the charging effect. The system uses high-speed data acquisition equipment to monitor the slight changes in charging current, voltage and temperature in real time, analyzes the harmonic components of the charging current through Fourier transform to evaluate the charging efficiency, and uses the Kalman filter algorithm to estimate and predict the charging state in real time.

[0031] The benefit of this embodiment is that by constructing a multi-dimensional state space and applying the ant colony optimization algorithm, the adaptive optimization of the charging system is achieved. The method can capture and analyze multiple influencing factors in real time, dynamically adjust the charging strategy, effectively improve the charging efficiency, extend the battery life, and adapt to various complex usage scenarios. At the same time, through the prediction function of the deep neural network and the closed-loop feedback mechanism, the method realizes the continuous optimization of the charging process.

[0032] Embodiment 2 In order to solve the problem of multi-dimensional information processing and optimization of the smart lock charging system in a complex environment, this embodiment further refines the construction method of the multi-dimensional state space. By accurately defining and quantifying various key factors affecting the charging process, this embodiment aims to provide a comprehensive and highly adaptable charging control framework.

[0033] The multi-dimensional state space in this embodiment includes but is not limited to the following dimensions: battery power, charging current, charging voltage, ambient temperature, usage frequency, unlocking times, network connection status, data transmission frequency, electromagnetic interference intensity, and battery health status. Each dimension represents a factor that affects the charging process.

[0034] Furthermore, the battery power dimension is monitored in real time using a high-precision coulomb meter with an accuracy of up to 0.1%. The charging current and charging voltage dimensions are achieved through a high-speed sampling ADC with a sampling rate of up to 1MHz to capture transient changes. The ambient temperature dimension is measured using a PT100 platinum resistance temperature sensor with a temperature range of -50°C to 150°C and an accuracy of ±0.1°C.

[0035] It is important to understand that the usage frequency and unlocking times dimensions reflect the actual usage of the smart lock. These two dimensions are implemented through embedded counters and synchronized with the real-time clock module to provide timestamp information. The network connection status dimension is quantified by monitoring the signal strength and connection stability of the wireless module, using the received signal strength indication RSSI value as the main indicator.

[0036] It is understandable that the data transmission frequency dimension reflects the interaction intensity between the smart lock and the external system. This dimension is quantified by counting the number of data packets transmitted per unit time and smoothed using a sliding window algorithm to eliminate the impact of short-term fluctuations.

[0037] Furthermore, the electromagnetic interference intensity dimension is measured using a broadband EMI receiver with a frequency range of 9kHz to 1GHz. The introduction of this dimension helps to evaluate the electromagnetic compatibility of the charging environment, thereby optimizing the charging strategy to reduce potential interference effects.

[0038] Furthermore, the battery health status dimension is a comprehensive indicator calculated through multiple parameters. These parameters include battery internal resistance, capacity decay rate, charge and discharge efficiency, etc. This embodiment uses electrochemical impedance spectroscopy (EIS) technology to regularly measure the battery internal resistance, and combines historical charge and discharge data to use a machine learning algorithm to predict the remaining life of the battery.

[0039] It should be noted that each dimension does not exist independently, but rather has a complex interaction relationship. For example, ambient temperature will affect the charging efficiency and health status of the battery; the frequency of use and the number of unlocking times will affect the rate at which the battery power is consumed; and the network connection status and data transmission frequency will affect the power consumption characteristics of the smart lock.

[0040] Furthermore, this embodiment introduces a data fusion algorithm to comprehensively process these multi-dimensional information. Specifically, a Kalman filter is used to suppress noise and estimate the state of each dimensional data, and then the principal component analysis PCA method is used to reduce the data dimension and extract the most representative features. Finally, the processed data is analyzed and predicted through a deep neural network model to provide a basis for the formulation of the charging control strategy.

[0041] Furthermore, this embodiment may also introduce an adaptive sampling mechanism. For dimensions that change faster (such as charging current and charging voltage), a higher sampling frequency is used; and for dimensions that change slower (such as battery health status), a lower sampling frequency is used to balance system resource consumption and data accuracy.

[0042] The benefit of this embodiment is that by constructing such a multi-dimensional state space, the charging control system can fully perceive and analyze the working state of the smart lock and the environment it is in. This multi-dimensional information acquisition and processing method provides an accurate data basis for subsequent charging path planning and optimization, and helps to formulate a more accurate and efficient charging strategy.

[0043] Embodiment 3 In order to solve the problem of multi-objective optimization in charging path planning, this embodiment further refines the charging path planning model based on the ant colony optimization algorithm, and realizes a more flexible and efficient charging control method by introducing a multi-dimensional evaluation function and a dynamic weight adjustment mechanism.

[0044] like Figure 4 As shown, the steps of designing a charging path planning model based on the ant colony optimization algorithm include: S301. Define a path evaluation function F(path), where the evaluation function F(path) includes the following evaluation parameters: charging time T, change in battery health status ΔH, charging energy efficiency E, temperature change ΔTemp, and weight coefficients w1, w2, w3, and w4.

[0045] S302 . For each possible charging path, calculate the charging time T, evaluate the battery health status change ΔH, calculate the charging energy efficiency E, and measure the temperature change ΔTemp.

[0046] S303, through the formula Calculate the evaluation score.

[0047] S304. Dynamically adjust the weight coefficient according to different charging scenarios and priorities.

[0048] S305: Select the path with the highest evaluation score as the optimal charging path.

[0049] In this embodiment, the charging path planning model is to define a comprehensive path evaluation function F(path). This evaluation function comprehensively considers multiple key parameters in the charging process, including charging time T, change in battery health status ΔH, charging energy efficiency E, and temperature change ΔTemp. These parameters correspond to considerations of efficiency, safety, energy utilization, and thermal management in the charging process.

[0050] Furthermore, in order to adapt the evaluation function to different charging scenarios and priority requirements, weight coefficients w1, w2, w3 and w4 are introduced. These weight coefficients can be dynamically adjusted according to specific application requirements, thereby achieving flexible configuration of charging strategies. For example, in a fast charging scenario, the weight w1 corresponding to the charging time T can be increased; while in a scenario that focuses on battery life, the weight w2 corresponding to the battery health status change ΔH can be increased.

[0051] Furthermore, in practical applications, for each possible charging path, the system will perform the following steps: First, calculate the time T required to complete charging, which can be achieved by simulating the charging process or a predictive model based on historical data. Secondly, evaluate the change in battery health status ΔH, which usually involves the battery's cycle life model and degradation mechanism analysis. Then, calculate the charging energy efficiency E, which needs to take into account the energy loss and conversion efficiency during the charging process. Finally, measure or predict the temperature change ΔTemp during the charging process, which is very important for preventing battery overheating and optimizing the thermal management system.

[0052] It is important to understand that the evaluation score is calculated using a comprehensive weighted approach to convert parameters of different dimensions to the same scale. This normalization process ensures the comparability between different parameters, so that the evaluation score can truly reflect the overall performance of the charging path.

[0053] It is understandable that the dynamic adjustment of the weight coefficient can be achieved through preset scenario modes, or it can be automatically optimized by machine learning algorithms based on historical charging data and user feedback. For example, the system can identify whether it is currently in the peak or valley period of the power grid, and adjust the weight of energy efficiency E accordingly; or dynamically adjust the weight of the battery health status change ΔH according to the age of the battery.

[0054] Furthermore, after completing the evaluation of all possible paths, the system will select the path with the highest evaluation score as the optimal charging path. This process can be achieved through the ant colony optimization algorithm, where each "ant" represents a possible charging path, and the pheromone is updated based on the evaluation score of the path. Through multiple iterations, the algorithm can gradually converge to the optimal or nearly optimal charging path.

[0055] The benefit of this embodiment is that by introducing a multidimensional evaluation function and a dynamic weight adjustment mechanism, the charging control system can adapt to different charging needs and environmental conditions more flexibly. This not only improves the overall efficiency of the charging process, but also takes into account the balance of multiple aspects such as battery life, energy utilization and safety. At the same time, the method has strong scalability, and can introduce new evaluation parameters or adjust the calculation method of existing parameters as needed to adapt to new battery technologies or charging scenarios that may appear in the future. For example, through the charging control method described in this embodiment, the charging process can be made intelligent and personalized, and a better charging method can be provided for electric vehicles. At the same time, it can also provide a more efficient charging resource allocation strategy for power grid operators and charging station managers. It can also be extended to the optimized management of the entire charging station or charging network.

[0056] Embodiment 4 In order to solve the problem of low optimization efficiency of traditional charging control methods in complex scenarios, this embodiment further optimizes the charging control method and introduces a charging strategy optimization step based on a quantum annealing algorithm. This method uses the parallelism of quantum computing and the quantum tunneling effect to effectively improve the ability to find the global optimal solution in a high-dimensional complex solution space.

[0057] In this embodiment, the charging control method further includes the step of optimizing the charging strategy based on the quantum annealing algorithm: Construct a qubit representation of the charging strategy.

[0058] Design a quantum Hamiltonian operator to map the charging optimization problem to the quantum system.

[0059] A quantum annealing process is performed to find the optimal charging strategy by adjusting the quantum tunneling effect.

[0060] Convert the optimal charging strategy obtained by quantum state collapse into an actual charging strategy.

[0061] Specifically, a quantum bit representation of the charging strategy is first constructed. This step involves encoding the various control parameters in the charging process into a sequence of quantum bits. For example, n quantum bits can be used to represent the discrete levels of charging power, m quantum bits to represent the division of charging time, and k quantum bits to represent the temperature control strategy. This quantum encoding method allows the system to perform parallel searches in the 2n+m+k-dimensional solution space, greatly expanding the explorable strategy space.

[0062] Furthermore, designing a quantum Hamiltonian is to map the charging optimization problem to a quantum system. The Hamiltonian H can be expressed as a linear combination of the problem Hamiltonian HP and the driving Hamiltonian HD: H(t) = A(t)HP+ B(t)HD. Among them, HP encodes the objective function and constraints of the charging optimization problem, such as minimizing charging time, maximizing battery life, etc.; HD is used to introduce quantum fluctuations, usually in the form of a transverse magnetic field. A(t) and B(t) are time-varying coefficients used to control the quantum annealing process.

[0063] It is important to understand that during quantum annealing, the system starts from an initial superposition state (usually the ground state of HD), gradually reduces B(t) and increases A(t), so that the system gradually evolves to the ground state of HP. This process can be simulated by solving the Schrödinger equation or using the Monte Carlo method. The quantum tunneling effect allows the system to cross energy barriers that are classically impossible during this process, thus having the opportunity to jump out of the local optimal solution and find the global optimal solution.

[0064] It is understandable that the parameters of the quantum annealing process have a significant impact on the optimization effect. The design of the annealing schedule requires a trade-off between annealing speed and solution quality. One feasible approach is to use an adaptive annealing schedule to dynamically adjust the rate of change of A(t) and B(t) according to the instantaneous state of the system. In addition, the introduction of Quantum Fluctuation Assisted Annealing (QFAA) technology can further improve the performance of the algorithm, especially when dealing with complex constraints.

[0065] Furthermore, after the quantum annealing process is completed, the obtained quantum state needs to be collapsed into a classical state, which is usually achieved through measurement. The measurement result corresponds to a specific charging strategy. However, due to the probabilistic nature of quantum measurement, a single measurement may not directly result in the optimal solution. Therefore, in actual operation, multiple measurements are usually performed, and the result with the lowest energy (i.e., the optimal objective function value) is selected as the final charging strategy.

[0066] Furthermore, converting the optimal charging strategy obtained by quantum state collapse into an actual charging strategy involves decoding the quantum bit sequence into specific charging parameters, such as charging power curve, charging time schedule, etc. In this process, the limitations and safety boundaries of the actual hardware need to be considered, and some post-processing may be required, such as smoothing the power curve and adjusting the charging time to match the requirements of the battery management system.

[0067] The benefit of this embodiment is that by introducing the quantum annealing algorithm, the charging control system can more effectively find the global optimal solution in a complex multi-dimensional solution space. Compared with traditional optimization methods, the quantum annealing algorithm has stronger parallelism and the ability to jump out of local optimality, and is particularly suitable for dealing with complex charging optimization problems with multiple constraints and objectives. Through the charging control method described in this embodiment, a more sophisticated and efficient charging strategy optimization can be achieved.

[0068] Embodiment 5 In order to solve the limitations of the traditional integer-order battery model in describing the dynamic characteristics of the battery, this embodiment further optimizes the charging control method and introduces a battery model construction step based on fractional calculus. This method can more accurately capture the nonlinearity and memory effect of the battery during the charging and discharging process, thereby providing more accurate battery state prediction and charging strategy optimization.

[0069] In this embodiment, the charging control method further includes a battery model construction step based on fractional calculus: The dynamic characteristics of the battery are described using fractional differential equations.

[0070] The Caputo fractional derivative is used to define the battery state equation.

[0071] The order and coefficients of the fractional-order battery model are determined through the parameter identification algorithm.

[0072] Predict the battery response under different charging strategies based on the fractional-order battery model.

[0073] The state equation of the fractional-order battery model is: , where D α and D β They represent α-order and β-order fractional derivatives respectively, V(t) is the battery voltage, I(t) is the charging current, a, b, c, d are model parameters, α and β are fractional orders, 0<α, β<1.

[0074] Specifically, fractional differential equations are first used to describe the dynamic characteristics of the battery. Fractional calculus has unique advantages in dealing with systems with long-range correlations and non-Markov processes. Battery systems happen to conform to such characteristics due to the complexity of their electrochemical reactions. By introducing fractional derivatives, the diffusion process inside the battery, charge transport, and the electrochemical reaction kinetics on the electrode surface can be better described.

[0075] Furthermore, this embodiment uses Caputo fractional derivatives to define the battery state equation. The fractional derivatives defined by Caputo have good physical meaning and mathematical properties, and are suitable for describing systems such as batteries that have initial value problems. The state equation comprehensively considers the battery's voltage response, current change rate, and the influence of historical states, and can more comprehensively describe the dynamic behavior of the battery.

[0076] It should be understood that the order and coefficients of the fractional-order battery model are determined in order to construct an accurate battery model. This embodiment achieves this goal through a parameter identification algorithm. Specifically, an intelligent optimization method such as a particle swarm optimization algorithm PSO or a genetic algorithm GA can be used to minimize the error between the model prediction value and the actual measurement data as the objective function, while optimizing the parameters α, β, a, b, c, and d. In order to improve the accuracy and efficiency of parameter identification, a multi-objective optimization strategy can be adopted, while considering the fitting errors in the time domain and frequency domain.

[0077] Understandably, the parameter identification process requires high-quality experimental data support. It usually involves systematic testing of the battery's response under different charging and discharging conditions, including constant current charging, pulse charging, dynamic load testing, etc. In order to capture the battery's behavior under different operating states and environmental conditions, the test should cover temperature, state of charge SOC, and charge and discharge rate. In addition, considering the change of battery performance with the number of cycles, the identification process of the model parameters should be carried out regularly to adapt to the aging characteristics of the battery.

[0078] Furthermore, based on the constructed fractional-order battery model, the response of the battery under different charging strategies can be predicted. This step involves the numerical solution of fractional-order differential equations. Commonly used methods include the improved Adams method, the prediction-correction method, etc. In practical applications, in order to improve computational efficiency, a fast convolution algorithm or an approximate method based on frequency domain analysis can be used to accelerate the calculation of fractional-order derivatives. In this way, the effects of different charging strategies on key parameters such as battery voltage, temperature, and state of charge can be simulated in real time, providing a theoretical basis for the optimization of charging strategies.

[0079] The benefit of this embodiment is that by introducing fractional-order calculus theory, the battery model can more accurately describe the nonlinear dynamic characteristics and memory effect of the battery. This high-precision battery model provides a more reliable basis for optimizing the charging strategy and helps to achieve more sophisticated charging control. Especially under fast charging and extreme temperature conditions, the advantages of the fractional-order battery model are more obvious, which can better predict the response of the battery, thereby formulating a safer and more efficient charging strategy.

[0080] The battery model construction method based on fractional calculus described in this embodiment can significantly improve the performance of the charging control system. This method is not only applicable to the modeling of single cells, but can also be extended to the design of battery packs and battery management systems BMS.

[0081] Embodiment 6 In order to solve the problems of low efficiency and battery performance degradation in the traditional constant current and constant voltage charging method during battery charging, this embodiment further optimizes the charging control method and introduces a charging pulse technology based on chaos theory. This method uses the irregularity and sensitive dependence of the chaotic system to generate a dynamically changing charging pulse sequence, thereby improving charging efficiency, reducing battery damage, and extending battery life.

[0082] In this embodiment, a Lorenz chaotic system is first constructed as the basis for generating a charging pulse sequence. The Lorenz system is a classic three-dimensional nonlinear dynamic system, and its mathematical model is defined as follows: dx / dt = σ(yx) dy / dt = x(ρ-z)-y dz / dt = xy-γz Among them, σ, ρ and γ are system parameters used to control the chaotic characteristics of the system. Typical parameter selections can be σ=10, ρ=28, γ=8 / 3, but these parameters can be fine-tuned according to actual charging needs. It should be noted that the analytical solution of the Lorenz system cannot be obtained directly, so in practical applications, numerical integration methods, such as the fourth-order Runge-Kutta method, are needed to solve the trajectory of the system.

[0083] Furthermore, the chaotic sequence is mapped to the amplitude and frequency of the charging pulse to achieve chaotic charging. This process can be accomplished by designing an appropriate mapping function. For example, the x variable can be mapped to the pulse amplitude, and the y variable can be mapped to the pulse frequency. The mapping function needs to consider the charging characteristics and safety limitations of the battery to ensure that the generated pulse sequence is within the safe operating range of the battery. The mapping method can be expressed as:

[0084]

[0085] Where, I(t) is the charging current amplitude, f(t) is the pulse frequency, I min and I max are the minimum and maximum charging currents allowed, respectively, min and f max are the minimum and maximum pulse frequencies allowed, respectively. x(t) and y(t) are the x and y variables of the Lorenz system. min 、x max,y min ,y max is the value range of the x and y variables.

[0086] It is important to understand that by adjusting the Lorenz system parameters, charging pulse trains with different characteristics can be generated. For example, increasing the value of ρ can cause the system to produce more complex chaotic behavior, thereby generating more diverse pulse trains. This parameter adjustment can be performed dynamically based on factors such as the battery's charging stage, temperature conditions, and state of charge. To achieve this, a parameter adaptation mechanism can be designed to adjust the values ​​of σ, ρ, and γ based on real-time monitored battery status information.

[0087] It is understandable that real-time evaluation of charging effects and dynamic selection of the optimal chaotic charging pulse mode are important links to ensure charging efficiency and safety. This can be achieved by establishing a multi-objective evaluation function that comprehensively considers factors such as charging speed, battery temperature change, and coulomb efficiency. The evaluation function can be expressed as:

[0088] Among them, SOC is the current state of charge, SOC target is the target state of charge, T is the battery temperature, T opt is the optimal operating temperature, η is the charging efficiency, and k1, k2, and k3 are weight coefficients. The system will continuously calculate the value of this evaluation function and dynamically adjust the Lorenz system parameters based on the results to optimize the charging pulse mode.

[0089] The benefit of this embodiment is that by introducing chaos theory into the charging pulse design, the charging control system can generate a more complex and dynamic charging strategy. This method can not only effectively reduce the polarization phenomenon inside the battery, but also promote the convection of the electrolyte through irregular charging pulses and improve the ion transmission efficiency. At the same time, the unpredictability of the chaotic pulse helps to prevent the formation of a fixed stress distribution inside the battery, thereby delaying the degradation of battery performance. Through the charging pulse method based on chaos theory described in this embodiment, a more intelligent and efficient battery charging process can be achieved.

[0090] Embodiment 7 In order to solve the shortcomings of traditional charging control methods in long-term optimization and dynamic adaptation, this embodiment further optimizes the charging control method and introduces a long-term charging strategy optimization step based on reinforcement learning. Through continuous interactive learning with the environment, the charging strategy is adaptively adjusted to adapt to different battery states, usage patterns and environmental conditions, thereby achieving a balance between charging efficiency and battery life during long-term use.

[0091] In this embodiment, the state space, action space and reward function of the charging process are first defined. The state space can include parameters such as the battery's state of charge SOC, state of health SOH, temperature, voltage, internal resistance, and external factors such as ambient temperature and charging time. The action space can be defined as different charging current levels or charging power curves. The design of the reward function needs to comprehensively consider multiple goals such as charging speed, battery life and energy efficiency, which can be expressed as:

[0092] Where ΔSOC / Δt represents the charging rate, ΔSOH represents the change in health status, η represents the charging efficiency, T is the battery temperature, and T opt is the optimal operating temperature, and m1, m2, m3, and m4 are weight coefficients. The reward function encourages fast charging while penalizing adverse effects on battery health and excessive temperature deviations.

[0093] Furthermore, this embodiment uses a deep Q network DQN algorithm to learn the optimal charging strategy. The DQN algorithm combines Q learning with a deep neural network and can process high-dimensional state spaces. The network structure can use a multi-layer perceptron MLP, where the input layer corresponds to the state vector and the output layer corresponds to the Q value of each action. The hidden layer can use a ReLU activation function to introduce nonlinearity.

[0094] It is understandable that balancing exploration and exploitation through the ε-greedy strategy is a key issue in reinforcement learning. In this embodiment, a ε value that decays over time can be used. A higher ε is initially set to encourage exploration, and the ε value is gradually reduced as learning proceeds to increase the utilization of the learned strategy. The decay of ε can follow the exponential decay law:

[0095] Among them, ε start and ε end are the initial and final ε values, d rate is the decay rate, and t is the number of training steps.

[0096] In this embodiment, the policy network is updated regularly to continuously optimize the charging control strategy. A fixed update frequency can be set, such as updating the network parameters once every N charging cycles. During the update process, stochastic gradient descent SGD or its variants such as Adam optimizer can be used to minimize the loss function. In order to improve the efficiency and generalization ability of learning, the Prioritized Experience Replay technology can be introduced to adjust the probability of a sample being selected according to the size of the TD error.

[0097] The benefit of this embodiment is that by introducing long-term charging strategy optimization based on reinforcement learning, the charging control system can adaptively learn and improve the charging strategy. It not only considers the effect of a single charge, but also focuses on the long-term performance and life of the battery. It can dynamically adjust the charging strategy according to the battery's usage mode, environmental conditions and aging status, and achieve multi-objective optimization of charging efficiency and battery life.

[0098] Embodiment 8 In order to solve the problems of low charging efficiency, short battery life and unintelligent energy management in traditional smart lock charging systems under complex usage scenarios, this embodiment further proposes an overall architecture of the charging system and introduces an intelligent charging control solution based on multi-dimensional state space and ant colony optimization algorithm. The system realizes all-round optimization and dynamic adjustment of the charging process through the collaborative work of multiple functional modules.

[0099] As shown in FIG5 , the charging system of this embodiment includes: The multi-dimensional state space construction module 100 is used to construct the multi-dimensional state space of the smart lock battery charging system.

[0100] The ant colony optimization path planning module 200 is used to design a charging path planning model based on the ant colony optimization algorithm, and regards the charging process as a process in which ants search for an optimal path in a multi-dimensional state space.

[0101] The real-time data acquisition module 300 is used to collect the working status information and environmental parameters of the smart lock in real time.

[0102] The deep neural network prediction module 400 is used to receive the collected information and predict the usage pattern and energy demand of the smart lock.

[0103] The parameter dynamic adjustment module 500 is used to dynamically adjust the parameters in the ant colony optimization algorithm based on the prediction results.

[0104] The optimization algorithm execution module 600 is used to execute the ant colony optimization algorithm in the adjusted multi-dimensional state space to find the optimal charging path.

[0105] The charging strategy conversion module 700 is used to convert the optimal charging path into a specific charging control strategy.

[0106] The charging control execution module 800 is used to execute the charging control strategy in real time.

[0107] The closed-loop feedback monitoring module 900 is used to continuously monitor the charging effect and provide feedback.

[0108] In this embodiment, the multi-dimensional state space construction module 100 first comprehensively models the state of the smart lock battery charging system. This state space may include internal parameters such as the battery's state of charge SOC, state of health SOH, temperature, voltage, internal resistance, and external factors such as ambient temperature, humidity, frequency of use, and time. Each factor can be regarded as a dimension in the state space, which together constitute a high-dimensional space. In order to effectively process such high-dimensional data, dimensionality reduction techniques such as principal component analysis PCA or autoencoders can be used to map the original high-dimensional space to a more compact low-dimensional representation to improve the efficiency of subsequent optimization algorithms.

[0109] Furthermore, the ant colony optimization path planning module 200 designs an adaptable charging path planning model based on the constructed multi-dimensional state space. In this model, the charging process is abstracted as the process of ants searching for the optimal path in the multi-dimensional state space. Each ant represents a possible charging strategy, and its path corresponds to the transition sequence from the initial state to the target charging state.

[0110] It should be understood that the real-time data acquisition module 300 plays the role of an information provider in the entire system. It collects the working status information and environmental parameters of the smart lock in real time through the embedded sensor network. This may include high-frequency sampling of battery voltage, current, and temperature, as well as information such as ambient temperature and humidity, door lock switch times, and user operation mode. In order to ensure the reliability and real-time nature of the data, a distributed data acquisition architecture can be used, combined with edge computing technology for preliminary data cleaning and compression.

[0111] It is understandable that after receiving the collected real-time data, the deep neural network prediction module 400 needs to predict the future usage pattern and energy demand of the smart lock. Here, a time series prediction model such as a long short-term memory network LSTM or a time convolution network TCN can be used. The input of the network can be the usage data and environmental parameters in the past period of time, and the output is the predicted usage frequency and energy consumption in the future period of time. The model can be trained using a sliding window method, and the model parameters are updated regularly with the latest data to adapt to the dynamic changes of user behavior and environment.

[0112] Furthermore, the parameter dynamic adjustment module 500 adjusts the key parameters in the ant colony optimization algorithm in real time based on the prediction results of the deep neural network. These parameters may include pheromone importance factors, heuristic factors, pheromone volatility coefficients, etc. The adjustment strategy can use a fuzzy logic controller to dynamically balance the global search ability and local optimization ability of the algorithm according to the predicted usage intensity and energy demand. For example, when it is predicted that the frequency of use in the future will be high, the value of the pheromone importance factor can be increased to enhance the use of historical high-quality paths.

[0113] The optimization algorithm execution module 600 executes the ant colony optimization algorithm in the multi-dimensional state space under the adjusted parameter settings. In each iteration, the algorithm generates a set of candidate charging paths and updates the pheromone distribution according to the path quality. The evaluation of path quality can comprehensively consider multiple indicators such as charging speed, battery life impact, energy efficiency, etc. In order to improve the search efficiency, a local search strategy, such as 2-opt or 3-opt algorithm, can be introduced to locally optimize the better path found.

[0114] Furthermore, the charging strategy conversion module 700 converts the optimal path found by the ant colony algorithm into a specific charging control strategy. This process involves mapping the abstract state transition sequence to actual charging parameters, such as charging current, voltage curve, etc. The conversion process can be achieved with the help of a pre-established lookup table or by training an additional neural network. The generated charging strategy needs to take into account hardware limitations and safety boundaries to ensure the safety and reliability of the charging process.

[0115] Furthermore, the charging control execution module 800 is responsible for executing the converted charging strategy in real time. This requires a high-precision, fast-response power management system that can accurately control the charging current and voltage. A digitally controlled DC-DC converter can be used in combination with pulse width modulation PWM technology to achieve fine control of charging parameters. In addition, protection mechanisms such as overvoltage, overcurrent, and overtemperature protection need to be implemented to deal with abnormal situations.

[0116] Furthermore, the closed-loop feedback monitoring module 900 continuously monitors the actual effect of the charging process and feeds back the results to other modules of the system. This includes real-time tracking of changes in battery status, evaluating charging efficiency and the impact on battery life. Feedback information can be used to adjust the prediction model of the deep neural network, optimize the parameter settings of the ant colony algorithm, and even correct the way the multidimensional state space is constructed. Through this closed-loop feedback mechanism, the system can continuously learn and improve to adapt to changes in battery characteristics and the dynamics of the usage environment.

[0117] The benefit of this embodiment is that by introducing multi-dimensional state space and ant colony optimization algorithm, combined with deep learning prediction and real-time parameter adjustment, a highly intelligent and adaptive charging system is realized. It can dynamically optimize the charging strategy according to the actual use of the smart lock and environmental conditions, effectively extending the battery life while improving the charging efficiency.

[0118] Embodiment 9 In order to solve the limitations of traditional smart locks in energy management, environmental adaptability and functional scalability, this embodiment further optimizes the overall design of the smart lock and introduces a number of advanced technologies and functional modules. These improvements are aimed at improving the energy efficiency, self-sustainability and flexibility of smart locks, so that they can better adapt to complex and changing usage environments and user needs.

[0119] In this embodiment, the smart lock integrates a micro supercapacitor module to manage peak loads and achieve fast response. This supercapacitor can be made of graphene-based materials with high power density and fast charging and discharging characteristics. Its capacity can be designed in the range of 1-10 F, and the operating voltage is 2.7-3.0 V, which is sufficient to support the instantaneous high power requirements of the lock, such as the sudden operation of the motor drive and wireless communication modules. The supercapacitor is connected in parallel with the main battery and works in coordination through an intelligent power management circuit. In daily use, the supercapacitor is mainly responsible for handling short-term high power demands, while the main battery provides a continuous low power supply. This configuration can significantly reduce the instantaneous high current impact on the main battery and extend the battery life.

[0120] Furthermore, the smart lock integrates an ambient energy harvesting system, including flexible solar cells and piezoelectric materials. The flexible solar cells can be made of perovskite materials, which have an efficiency of more than 20%, and can be bent and fitted to the outer surface of the lock, using ambient light for continuous charging. Piezoelectric materials can be integrated into the mechanical moving parts of the lock, such as buttons or shafts, and the mechanical energy generated during user operation is converted into electrical energy. The outputs of these two energy harvesting methods are regulated and integrated through the energy management integrated circuit to provide auxiliary power for the system and reduce dependence on the main battery.

[0121] It needs to be understood that the introduction of graphene-based heat dissipation structure improves the thermal management efficiency of smart locks. This heat dissipation structure can be made of graphene composite materials with a thermal conductivity of up to 2000-3000 W / (m·K). The heat dissipation structure is designed as a multi-layer network and is embedded around key heat sources inside the lock, such as processors, power management units, and charging modules. Through efficient heat diffusion and heat dissipation, the operating temperature of the system can be controlled at a low level, usually not exceeding 10°C above the ambient temperature, which not only improves the reliability and life of electronic components, but also indirectly improves the charging and discharging efficiency of the battery.

[0122] It is understandable that the design of the programmable charging interface provides smart locks with higher compatibility and functional scalability. This interface adopts the USB Type-C standard and supports multiple charging protocols such as USB, PD, QC, etc. The control logic of the interface is managed by a programmable MCU (such as the STM32L4 series), and new charging standards can be supported through firmware updates. In addition, this interface also supports bidirectional charging, allowing the smart lock to be used as a mobile power supply to power other devices when necessary. The power management of the interface uses a dedicated power path management IC (such as TPS2113A), which can intelligently switch internal and external power supplies to ensure the safety and efficiency of the charging and discharging process.

[0123] The benefit of this embodiment is that by integrating advanced energy management and thermal management technologies, the energy efficiency and environmental adaptability of the smart lock are improved. The introduction of supercapacitors and environmental energy harvesting systems reduces the dependence on the main battery and extends the working time of the device. The graphene heat dissipation structure ensures the stable operation of the system in various environments. The programmable charging interface provides the device with higher compatibility and functional scalability.

[0124] Embodiment 10 In order to solve the portability and compatibility issues of the charging control method in practical applications, this embodiment further optimizes the software implementation of the charging control method, encapsulates it into a computer program that can be executed on multiple computing platforms, and stores it on a computer-readable storage medium. This implementation method allows the charging control method to be more flexibly deployed in various smart devices and systems, thereby improving the scope of application and practical value of the method.

[0125] In this embodiment, the computer-readable storage medium may be a non-volatile storage device, such as a solid-state drive SSD, an embedded multimedia card eMMC, a NAND flash memory, etc. The storage capacity may be selected according to the complexity of the program and the data requirements. The storage medium adopts high-reliability industrial-grade specifications to ensure data integrity and long-term stability in harsh environments. In order to improve data security, an encrypted file system may be used to encrypt and protect the stored programs and data.

[0126] The benefit of this embodiment is that by implementing the charging control method as a computer program stored on a computer-readable storage medium, the portability and applicability of the method are greatly improved. This implementation method allows the charging control algorithm to be easily deployed in a variety of smart devices, from small IoT devices to large-scale energy management systems.

[0127] The computer program implementation described in this embodiment not only makes the charging control method easier to integrate and maintain, but also facilitates future functional expansion and algorithm optimization. This software-based approach lays the foundation for the continuous iteration and upgrade of the intelligent charging system and helps promote the application of charging technology in various fields.

[0128] The above embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the present invention should be included in the scope of protection of the present invention.

Claims

1. A charging control method, applied to a charging system, characterized in that: The steps of the charging control method include: Constructing a multidimensional state space of the charging system; A charging path planning model is designed based on the ant colony optimization algorithm, and the charging process is regarded as a process in which ants search for the optimal path in the multi-dimensional state space; Collecting working status information and environmental parameters of the charging system in real time; Inputting the collected information into a pre-trained deep neural network to predict usage patterns and energy requirements of the charging system; Based on the prediction results, dynamically adjusting the parameters in the ant colony optimization algorithm, and executing the ant colony optimization algorithm in the adjusted multi-dimensional state space to find the optimal charging path; The optimal charging path is converted into a specific charging control strategy, the charging control strategy is executed in real time, and the charging effect is continuously monitored.

2. The charging control method according to claim 1, characterized in that: The multidimensional state space includes at least one of battery power, charging current, charging voltage, ambient temperature, usage frequency, number of unlocking times, network connection status, data transmission frequency, electromagnetic interference intensity and battery health status.

3. The charging control method according to claim 1, characterized in that: The step of designing a charging path planning model based on an ant colony optimization algorithm includes: A path evaluation function F(path) is defined, wherein the evaluation function F(path) includes the following evaluation parameters: charging time T, change in battery health status ΔH, charging energy efficiency E, temperature change ΔTemp, and weight coefficients w1, w2, w3, and w4; For each possible charging path, the charging time T is calculated, the battery health state change ΔH is evaluated, the charging energy efficiency E is calculated, and the temperature change ΔTemp is measured; By formula Calculate the assessment score; Dynamically adjust the weight coefficient according to different charging scenarios and priorities; The path with the highest evaluation score is selected as the optimal charging path.

4. The charging control method according to claim 1, characterized in that: The charging control method further comprises the step of optimizing the charging strategy based on a quantum annealing algorithm: Construct a qubit representation of the charging strategy; Design quantum Hamiltonian operators to map charging optimization problems to quantum systems; Perform quantum annealing process to find the optimal charging strategy by adjusting the quantum tunneling effect; The optimal charging strategy obtained by quantum state collapse is converted into an actual charging strategy.

5. The charging control method according to claim 1 or 4, characterized in that: The charging control method further comprises the step of constructing a battery model based on fractional calculus: Use fractional differential equations to describe the dynamic characteristics of the battery; The battery state equation is defined using Caputo fractional derivatives; Determine the order and coefficients of the fractional-order battery model through a parameter identification algorithm; Predicting the response of the battery under different charging strategies based on the fractional-order battery model; The state equation of the fractional-order battery model is: , where D α and D β They represent α-order and β-order fractional derivatives respectively, V(t) is the battery voltage, I(t) is the charging current, a, b, c, d are model parameters, α and β are fractional orders, 0<α, β<1.

6. The charging control method according to claim 5, characterized in that: The charging control method further comprises the step of charging pulse based on chaos theory: Construct Lorenz chaotic system; Mapping the chaotic sequence to the amplitude and frequency of the charging pulse; By adjusting the parameters of the Lorenz system, charging pulse sequences with different characteristics are generated; Real-time evaluation of charging effect and dynamic selection of the optimal chaotic charging pulse mode; The Lorenz chaotic system is defined as follows: dx / dt=σ(yx) dy / dt=x(ρ-z)-y dz / dt=xy-γz Among them, σ, ρ and γ are system parameters used to change the chaotic characteristics.

7. The charging control method according to claim 1 or 6, characterized in that: The charging control method further includes the step of optimizing a long-term charging strategy based on reinforcement learning: Define the state space, action space and reward function of the charging process; Use the Deep Q Network (DQN) algorithm to learn the optimal charging strategy; Use experience replay and target network techniques to improve learning stability; Use ε-greedy strategy to balance exploration and exploitation; The strategy network is updated regularly to continuously optimize the charging control strategy.

8. A charging system, characterized in that: The charging system comprises: A multi-dimensional state space building module is used to build a multi-dimensional state space of a smart lock battery charging system; The ant colony optimization path planning module is used to design a charging path planning model based on the ant colony optimization algorithm, which regards the charging process as a process in which ants search for the optimal path in a multi-dimensional state space; Real-time data acquisition module, used to collect the working status information and environmental parameters of the smart lock in real time; A deep neural network prediction module that receives the collected information and predicts the usage pattern and energy demand of the smart lock; A parameter dynamic adjustment module is used to dynamically adjust the parameters in the ant colony optimization algorithm based on the prediction results; An optimization algorithm execution module, used to execute an ant colony optimization algorithm in the adjusted multi-dimensional state space to find an optimal charging path; A charging strategy conversion module, used to convert the optimal charging path into a specific charging control strategy; A charging control execution module, used to execute charging control strategies in real time; Closed-loop feedback monitoring module, used to continuously monitor charging effect and provide feedback.

9. A smart lock, comprising the charging system according to claim 8, characterized in that: The smart lock also includes: Integrated micro-supercapacitors for peak load management and fast response; Flexible solar cells and piezoelectric materials for ambient energy harvesting; Graphene-based heat dissipation structure for improved thermal management efficiency; Programmable charging interface to support multiple charging protocols and bidirectional charging function.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the charging control method according to any one of claims 1 to 7 are implemented.

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