Maximum power point determination method and device based on improved sparrow search algorithm

Abstract

The invention provides a maximum power point determination method and device based on an improved sparrow search algorithm. The method comprises the following steps: acquiring operation data of a photovoltaic power generation system; based on Logistic chaotic mapping and operation data, determining positions of elite individuals in the sparrow population and fitness corresponding to the positions; executing the following iterative operations, and outputting the target fitness corresponding to the current optimal individual when the number of iterations reaches the set number of iterations; determining the maximum power point of the photovoltaic power generation system according to the target fitness; the iterative operation comprises the steps of updating positions of a plurality of common individuals except elite individuals in the sparrow population based on a cosine convergence factor, and determining fitness corresponding to the plurality of common individuals according to the updated positions; determining a target elite individual in the plurality of common individuals; the elite individual with the smaller fitness and the target elite individual are determined as the current optimal individual; and determining the sparrow population of the next iteration. Therefore, the determination precision of the maximum power point is improved.

Description

Technical Field

[0001] This application relates to the field of photovoltaic power control technology, and in particular to a method and apparatus for determining the maximum power point based on an improved sparrow search algorithm. Background Technology

[0002] With the growth of global energy demand and the increasing emphasis on sustainable energy, photovoltaic (PV) power generation, as a green and renewable energy source, has been widely adopted. The output power of a PV power generation system is significantly affected by environmental factors such as sunlight intensity, temperature, and load variations. The power-voltage characteristic curve of a PV power generation system exhibits non-linear characteristics and possesses a unique maximum power point under dynamic conditions. In PV power generation systems, Maximum Power Point Tracking (MPPT) technology is used to monitor the dynamic changes in power-voltage and adjust the system operating point to remain near the maximum power point, thereby improving power generation efficiency.

[0003] In related technologies, traditional MPTT control methods are improved based on intelligent algorithms (such as particle swarm optimization, genetic algorithms, and ant colony optimization) to search for the maximum power point through global optimization. However, the convergence speed is slow and the computational complexity is high, resulting in poor maximum power point tracking accuracy. Summary of the Invention

[0004] This application provides a method and apparatus for determining the maximum power point based on an improved sparrow search algorithm, which solves the problems of slow convergence speed and high computational complexity in related technologies, resulting in poor maximum power point tracking accuracy.

[0005] Firstly, this application provides a method for determining the maximum power point based on an improved sparrow search algorithm, applied to a photovoltaic power generation system. The method for determining the maximum power point includes:

[0006] Obtain operational data from the photovoltaic power generation system;

[0007] Based on Logistic chaotic mapping and operational data, the position and corresponding fitness of elite individuals in the sparrow population are determined. Elite individuals are sparrows with the lowest fitness in the sparrow population. Position represents voltage and fitness represents power.

[0008] Perform the following iterative operations, and when the set number of iterations is reached, output the target fitness corresponding to the current best individual;

[0009] Determine the maximum power point of the photovoltaic power generation system based on the target fitness.

[0010] The iterative operations include:

[0011] Based on the cosine convergence factor, the positions of multiple ordinary individuals in the sparrow population (excluding elite individuals) are updated, and the fitness of multiple ordinary individuals is determined according to the updated positions.

[0012] Among multiple ordinary individuals, a target elite individual is identified. The target elite individual is the sparrow individual with the lowest fitness among the multiple ordinary individuals.

[0013] The individual with the lower fitness between the elite individual and the target elite individual is identified as the current optimal individual.

[0014] Determine the sparrow population for the next iteration.

[0015] In one possible implementation, based on Logistic chaotic mapping and runtime data, the location of elite individuals in a sparrow population and the corresponding fitness are determined, including:

[0016] Based on the Logistic chaotic mapping, the position of the sparrow population is initialized to obtain the initial position of each individual sparrow in the population;

[0017] The fitness of each sparrow individual is determined based on its initial position.

[0018] The sparrow individual with the lowest fitness in the sparrow population was identified as the elite individual;

[0019] Determine the location of elite individuals and the corresponding fitness.

[0020] In one possible implementation, the iteration operation further includes:

[0021] Based on the chaotic perturbation factor, the position of elite individuals is updated to obtain the updated position;

[0022] Determine the fitness of elite individuals based on their updated positions.

[0023] In one possible implementation, determining the sparrow population for the next iteration includes:

[0024] Obtain a random factor, which is used to characterize the probability of executing the reverse learning strategy;

[0025] If the random factor is less than or equal to the set reverse learning probability threshold, multiple ordinary individuals will be identified as the sparrow population for the next iteration.

[0026] In one possible implementation, the maximum power point determination method further includes:

[0027] If the random factor is greater than the set reverse learning probability threshold, the position of multiple ordinary individuals is optimized based on the Logistic reverse learning strategy to obtain the reverse population.

[0028] Adding multiple ordinary individuals to the reverse population yields a merged sparrow population;

[0029] Determine the fitness of each sparrow individual in the merged sparrow population, and sort the fitness values ​​from largest to smallest.

[0030] The sparrow individuals corresponding to the fitness of the last set number will form the sparrow population for the next iteration.

[0031] In one possible implementation, the positions of multiple ordinary individuals in the sparrow population, excluding elite individuals, are updated based on the cosine convergence factor, satisfying the following formula:

[0032]

[0033] Where t is the current iteration number, T is the set iteration number, and X best The position of the current optimal individual. ω represents the update position of a normal individual after t+1 iterations, ω is the cosine convergence factor, and rand is the random factor.

[0034] In one possible implementation, determining the maximum power point of the photovoltaic power generation system based on the target fitness includes:

[0035] The negative value of the target fitness is defined as the maximum power point.

[0036] In one possible implementation, the position of the elite individual is updated based on the chaotic perturbation factor to obtain the updated position, which satisfies the following formula:

[0037]

[0038] in, X represents the updated position of the elite individual after iteration t+1. best Let X be the current optimal individual's position, ξ be the chaos perturbation factor, ω be the cosine convergence factor, and X be... max This represents the upper bound of the search variable's value, X. min This is the lower bound of the search variable's value.

[0039] Secondly, this application provides a maximum power point determination device based on an improved sparrow search algorithm, applied to a photovoltaic power generation system. The maximum power point determination device includes:

[0040] The acquisition module is used to acquire the operating data of the photovoltaic power generation system;

[0041] The first determination module is used to determine the position and fitness of the elite individuals in the sparrow population based on the Logistic chaotic mapping and running data. The elite individuals are the sparrow individuals with the lowest fitness in the sparrow population. The position represents voltage and the fitness represents power.

[0042] The processing module is used to perform the following iterative operations and output the target fitness corresponding to the current best individual when the number of iterations reaches the set number of iterations;

[0043] The second determination module is used to determine the maximum power point of the photovoltaic power generation system based on the target fitness.

[0044] The iterative operations include:

[0045] Based on the cosine convergence factor, the positions of multiple ordinary individuals in the sparrow population (excluding elite individuals) are updated, and the fitness of multiple ordinary individuals is determined according to the updated positions.

[0046] Among multiple ordinary individuals, a target elite individual is identified. The target elite individual is the sparrow individual with the lowest fitness among the multiple ordinary individuals.

[0047] The individual with the lower fitness between the elite individual and the target elite individual is identified as the current optimal individual.

[0048] Determine the sparrow population for the next iteration.

[0049] Thirdly, this application provides an electronic device, including: a memory and a processor;

[0050] The memory stores computer-executed instructions;

[0051] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0052] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible embodiments of the first aspect.

[0053] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0054] This application provides a method and apparatus for determining the maximum power point based on an improved sparrow search algorithm. The method acquires operational data of a photovoltaic power generation system; based on Logistic chaotic mapping and operational data, it determines the position and corresponding fitness of elite individuals in a sparrow population, where the elite individual is the sparrow with the lowest fitness in the population. Position represents voltage, and fitness represents power. The method performs the following iterative operations, and when the set number of iterations is reached, it outputs the target fitness corresponding to the current optimal individual; based on the target fitness, it determines the maximum power point of the photovoltaic power generation system. The iterative operations include: updating the positions of multiple ordinary individuals (excluding the elite individual) in the sparrow population based on a cosine convergence factor, and determining the fitness corresponding to the multiple ordinary individuals based on the updated positions; identifying a target elite individual among the multiple ordinary individuals, where the target elite individual is the sparrow with the lowest fitness among the multiple ordinary individuals; determining the individual with the lower fitness between the elite individual and the target elite individual as the current optimal individual; and determining the sparrow population for the next iteration. This application utilizes Logistic chaotic mapping to generate highly random and diverse initial positions for sparrow individuals, enhancing the uncertainty of elite individual position updates and thus increasing the diversity of the global search while reducing the possibility of getting trapped in local optima. A cosine convergence factor provides a dynamically adjusted mechanism, enabling large-scale exploration in the initial stage and fine-tuning the search in later stages through dynamic step size adjustment, enhancing convergence and ensuring greater stability and efficiency in finding the optimal solution. This effectively addresses the problem of the maximum power point getting trapped in local optima and slow convergence speed, reduces computational complexity, and improves the accuracy of maximum power point tracking, better adapting to dynamic environmental changes. Furthermore, the accurate determination of the maximum power point ensures that the photovoltaic power generation system always maintains maximum output power, improving the power generation efficiency of the photovoltaic system. Attached Figure Description

[0055] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0056] Figure 1 A schematic diagram illustrating a scenario for the maximum power point determination method based on the improved sparrow search algorithm provided in this application embodiment;

[0057] Figure 2 A flowchart illustrating the maximum power point determination method based on the improved sparrow search algorithm provided in this application embodiment. Figure 1 ;

[0058] Figure 3 A flowchart illustrating the maximum power point determination method based on the improved sparrow search algorithm provided in this application embodiment. Figure 2 ;

[0059] Figure 4 A comparison of the convergence curves of the global optimum values ​​of the improved sparrow search algorithm and the unimproved sparrow search algorithm provided in the embodiments of this application;

[0060] Figure 5 A convergence curve of the global optimum of the fitness function provided in the embodiments of this application;

[0061] Figure 6 Convergence curve of a Simulink model based on an improved sparrow search algorithm provided in this application embodiment;

[0062] Figure 7 The convergence curve of the Simulink model not based on the improved sparrow search algorithm provided in the embodiments of this application;

[0063] Figure 8 A schematic diagram of the maximum power point determination device based on the improved sparrow search algorithm provided in this application embodiment;

[0064] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0065] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0066] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0067] With the development of intelligent control theory and computing technology, many scholars have introduced advanced control methods into MPPT control. Currently, the main approach is to combine advanced intelligent algorithms with advanced control methods to improve control accuracy and system stability.

[0068] With the development of intelligent optimization algorithms, MPPT control methods based on intelligent algorithms have gradually gained attention, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO). These methods can effectively address the shortcomings of traditional MPPT algorithms in dynamic environments by finding the maximum power point through global optimization. However, these methods have high computational complexity and may encounter problems such as long computation time and slow convergence speed in practical applications, and they also have limitations in the accuracy of searching for and determining the maximum power point.

[0069] To address the aforementioned technical problems, this application provides a method for determining the maximum power point based on an improved sparrow search algorithm. Building upon the traditional MPPT control method, it incorporates the ideas of the sparrow search algorithm, utilizing a Logistic chaotic mapping to initialize the sparrow population. This mapping generates highly random and diverse initial positions for individual sparrows, enhancing the uncertainty of elite individual position updates and thus increasing the diversity of the global search, reducing the possibility of getting trapped in local optima. Furthermore, a cosine convergence factor is used to provide a dynamically adjusted mechanism, enabling large-scale exploration in the initial stage and refined searching through dynamic step size adjustment in the later stage. This enhances the algorithm's convergence, ensuring greater stability and efficiency in finding the optimal solution. This effectively solves the problem of the maximum power point getting trapped in local optima and slow convergence speed, reduces computational complexity, and improves the maximum power point tracking accuracy, better adapting to dynamic environmental changes.

[0070] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with 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. The embodiments of this application will now be described with reference to the accompanying drawings.

[0071] Figure 1 A schematic diagram of a scenario for determining the maximum power point based on an improved sparrow search algorithm provided in this application embodiment, as shown below. Figure 1As shown, this application can be specifically applied to photovoltaic power generation systems. The application scenario includes a photovoltaic power generation system composed of multiple photovoltaic power generation devices (e.g., photovoltaic cells, batteries, supercapacitors). This system deploys monitoring equipment or a monitoring platform to monitor the operation of each photovoltaic power generation device. The monitoring equipment (or platform) can communicate with the electronic equipment, which can run a maximum power point determination method based on an improved sparrow search algorithm. For example, the monitoring equipment collects operating data from each power generation device in the photovoltaic power generation system. The operating data includes at least voltage and current. The monitoring platform sends the operating data to the electronic equipment. After receiving the operating data, the electronic equipment executes the maximum power point determination method based on the improved sparrow search algorithm, thereby outputting the maximum power point of the photovoltaic power generation system. The electronic equipment sends the maximum power point to the power generation control unit in the photovoltaic power generation system, or the power generation control unit obtains the maximum power point from the electronic equipment. The power generation control unit can adjust the power generation of each photovoltaic power generation device according to the maximum power point to ensure that the photovoltaic power generation system always maintains maximum power output, thereby improving the power generation efficiency of the photovoltaic power generation system.

[0072] It should be noted that, Figure 1 This is merely an example and does not limit the structure of the photovoltaic power generation system. Furthermore, the number of electronic devices is not limited to one; multiple electronic devices can work together to complete the maximum power point determination method based on the improved sparrow search algorithm proposed in this application. In addition, electronic devices can be high-performance, high-computing-power intelligent terminals, customized edge devices, edge servers, embedded industrial control computers, servers, server clusters, virtual resources, etc., and this application does not limit them.

[0073] Figure 2 A flowchart illustrating the maximum power point determination method based on the improved sparrow search algorithm provided in this application embodiment. Figure 1 The maximum power point determination method based on the improved sparrow search algorithm provided in this application is applied to a photovoltaic power generation system. For example... Figure 2 As shown, the method for determining the maximum power point includes:

[0074] S201. Obtain the operating data of the photovoltaic power generation system.

[0075] For example, electronic devices acquire operational data of the photovoltaic power generation system from monitoring equipment or monitoring platform in real time or periodically, and the operational data includes at least current and voltage.

[0076] S202. Based on Logistic chaotic mapping and running data, determine the position and corresponding fitness of elite individuals in the sparrow population. Elite individuals are sparrow individuals with the lowest fitness in the sparrow population. Position represents voltage and fitness represents power.

[0077] In this system, individual sparrows can be considered as candidate solutions in the maximum power point determination method based on the improved sparrow search algorithm. The process of selecting the optimal solution from candidate solutions can be viewed as selecting elite individuals from a large number of sparrows, with the reference voltage serving as one sparrow. Fitness is negatively correlated with power; the lower the fitness, the higher the power. The position of a sparrow can be considered as the voltage, and fitness can characterize the power corresponding to that voltage. Individual sparrows represent the reference voltage (or target voltage) that is continuously evaluated and optimized by the algorithm. The real-time voltage in the operational data is the actual state value reached by the system in response to this reference voltage and is used to calculate the true power.

[0078] The core idea of ​​the sparrow search algorithm is to simulate the interaction between the discoverer (elite individual) and the follower (ordinary individual) during the sparrow's foraging process, as well as the sparrow's reaction mechanism when facing danger, to perform global search and local exploitation. However, because this algorithm is highly dependent on the initial solution, different initial solutions may lead to different search results. Furthermore, due to its local search nature, it is prone to getting trapped in local optima and failing to find the global optimum.

[0079] Before searching for the global optimal solution, this application embodiment first uses Logistic chaotic mapping to initialize the sparrow population and determine the elite individuals in the sparrow population (i.e., the initial solution).

[0080] For example, in some embodiments, the location of elite individuals in a sparrow population and the corresponding fitness of those locations are determined based on Logistic chaotic mapping and runtime data, including:

[0081] S2021. Based on Logistic chaotic mapping, the position of the sparrow population is initialized to obtain the initial position of each individual sparrow in the sparrow population.

[0082] For example, a Simulink simulation environment is pre-deployed in an electronic device, an MPPT control model is built in the Simulink simulation environment, and the sparrow population size and maximum number of iterations are set in the simulation environment.

[0083] Using Logistic chaotic mapping, the position of each sparrow in the sparrow population is initialized. Taking one sparrow as an example:

[0084] 1.1 Randomly generate the position of a d-dimensional sparrow.

[0085] 1.2. Iterate the generated sparrow individuals according to formula (1) to generate chaotic sequences of other individuals:

[0086] X n+1 =r·X n ·(1-X n (1)

[0087] Among them, X n+1 Let X be the (n+1)th value in the chaotic sequence. n Let r be the nth value in the chaotic sequence, and r be the chaos adjustment parameter. For example, the value range is [3.5, 4]. When r = 4, the system enters a completely chaotic state.

[0088] 1.3 After iterating based on the set sparrow population size, map it to the search space according to formula (2). The specific formula is as follows:

[0089] X o =X n +(X max -X min )*X n (2)

[0090] X o Let X be the initial individual position projected into the search space after the Logistic chaotic mapping. max X is the upper limit of the search variable's possible values. min This is the lower bound of the values ​​that the search variable can take.

[0091] In addition, a nonlinear random factor can be generated using the Logistic chaotic mapping to perturb the global optimum, thereby enhancing the ability to escape local optima (for example, a 20% probability can be set to randomly trigger the perturbation).

[0092] S2022. Determine the fitness of each sparrow individual based on its initial position.

[0093] For example, the MPPT control model in the simulation environment can first calculate the power corresponding to the position based on the position, and then further calculate the fitness corresponding to the power based on the fitness function, that is, the fitness corresponding to the position. The fitness function can be designed so that the fitness is equal to the negative number of the power. The larger the power, the smaller the fitness value. The fitness function takes the maximum power point as the tracking target and can use the negative value of the photovoltaic output power.

[0094] S2023. The sparrow individual with the lowest fitness in the sparrow population is identified as the elite individual.

[0095] The fitness of all sparrow individuals in the sparrow population is ranked, and the sparrow individual with the lowest fitness is identified as the elite individual. This elite individual can be regarded as the current best individual.

[0096] S2024. Determine the location of elite individuals and the fitness corresponding to that location.

[0097] The position of the sparrow with the lowest fitness is determined as the position of the elite individual, and the fitness of the sparrow with the lowest fitness is determined as the fitness corresponding to the position of the elite individual.

[0098] The initial position and fitness of the initial elite individuals are obtained through the initialization operation and used as data for the first iteration. Then, step S203 is executed to further search for new elite individuals in the sparrow population composed of ordinary individuals other than the elite individuals, thereby continuously iterating and updating the global optimal solution (the current optimal individual).

[0099] S203. Perform the following iterative operation, and when the number of iterations reaches the set number of iterations, output the target fitness corresponding to the current best individual.

[0100] The iterative operations include:

[0101] S2031. Based on the cosine convergence factor, update the positions of multiple ordinary individuals in the sparrow population (excluding elite individuals), and determine the fitness of the multiple ordinary individuals based on the updated positions.

[0102] The cosine convergence factor is used to adjust the update step size of ordinary individuals. A sparrow population includes elite individuals and ordinary individuals. For example, in some embodiments, the positions of multiple ordinary individuals (excluding elite individuals) in the sparrow population are updated based on the cosine convergence factor, satisfying the following formula:

[0103]

[0104] In the formula, t is the current iteration number, T is the set iteration number (maximum iteration number), and X... best The position of the current optimal individual. ω represents the update position of a normal individual after t+1 iterations, ω is the cosine convergence factor, and rand is a random factor, usually a random number between [0,1].

[0105] It is understandable that the cosine convergence factor is dynamically changing. For ordinary individuals, the position is updated based on the current best individual (global optimal solution) and the dynamically changing convergence factor, and the update step size gradually decreases as the iteration progresses, thereby achieving gradual convergence and effectively avoiding excessive updates. After the position is updated, the MPPT control model will recalculate the fitness of the ordinary individual based on the updated position, similar to the implementation principle of step S2022, and will not be elaborated further.

[0106] S2032. Identify the target elite individual among multiple ordinary individuals. The target elite individual is the sparrow individual with the lowest fitness among multiple ordinary individuals.

[0107] The fitness of multiple ordinary individuals is ranked, and the sparrow individual with the lowest fitness is identified as the elite individual among the multiple ordinary individuals (i.e., the target elite individual).

[0108] S2033. The individual with the lower fitness among the elite individuals and the target elite individuals is identified as the current optimal individual.

[0109] It's understandable that in each iteration, an elite individual is generated. The elite individual generated in the current iteration needs to be compared with the elite individual determined in the previous iteration. The better elite individual is then updated as the new current best individual. In other words, the individual with the lower fitness is updated as the new current best individual.

[0110] For example, after initialization based on Logistic chaotic mapping, an elite individual is determined. For the first iteration, the fitness of this elite individual is compared with that of the first target elite individual generated in the first iteration, and the individual with the smaller fitness is determined as the current optimal individual. For the second iteration, the fitness of the current optimal individual determined in the first iteration is compared with that of the second target elite individual generated in the second iteration, and the individual with the smaller fitness is re-determined as the current optimal individual, and so on.

[0111] The fitness of the current best individual is the target fitness.

[0112] S2034. Determine the sparrow population for the next iteration.

[0113] S204. Determine the maximum power point of the photovoltaic power generation system based on the target fitness.

[0114] Fitness represents power, and there is a mapping relationship between fitness and power. For example, in some embodiments, the maximum power point of a photovoltaic power generation system is determined based on the target fitness, including: determining the negative of the target fitness as the maximum power point.

[0115] This embodiment, based on the traditional MPPT control method, incorporates the idea of ​​a sparrow search algorithm. It utilizes a Logistic chaotic mapping to initialize the sparrow population, generating highly random and diverse initial positions for individual sparrows. This enhances the uncertainty of elite individual position updates, thereby increasing the diversity of the global search and reducing the possibility of getting trapped in local optima. A nonlinear random factor generated by the Logistic chaotic mapping perturbs the global optimal solution, enhancing the ability to escape local optima (e.g., a 20% probability of random triggering). Furthermore, a cosine convergence factor provides a dynamic adjustment mechanism, enabling large-scale exploration in the initial stage and fine-tuning the search by dynamically adjusting the step size in the later stage. This enhances the algorithm's convergence, ensuring greater stability and efficiency in finding the optimal solution. This effectively solves the problem of the maximum power point getting trapped in local optima and slow convergence speed, while reducing computational complexity and improving maximum power point tracking accuracy, better adapting to dynamic environmental changes. In addition, the accurate determination of the maximum power point ensures that the photovoltaic power generation system always outputs maximum power, improving the power generation efficiency of the photovoltaic power generation system.

[0116] To enhance the exploration capabilities of elite individuals and prevent them from getting trapped in local optima, a chaotic perturbation mechanism can be applied to further update the position of elite individuals during each iteration. That is, during iteration, the position of elite individuals can be updated using a chaotic perturbation mechanism. In some embodiments, the iteration operation further includes: updating the position of elite individuals based on a chaotic perturbation factor to obtain the updated position; and determining the fitness of the elite individual based on the updated position.

[0117] Among them, the chaotic perturbation factor is a perturbation factor based on random numbers, which is used to control the intensity of the perturbation, and can increase the diversity of sparrow individual positions and enhance the global search capability.

[0118] For example, in some embodiments, the position of elite individuals is updated based on a chaotic perturbation factor to obtain an updated position that satisfies the following formula:

[0119]

[0120] in, X represents the updated position of the elite individual after iteration t+1. best Let X be the current optimal individual's position, ξ be the chaos perturbation factor, ω be the cosine convergence factor, and X be... max This represents the upper bound of the search variable's value, X. min This is the lower bound of the search variable's value.

[0121] For example, after initialization based on the Logistic chaotic mapping, an elite individual is determined. For the first iteration, the position of the elite individual is updated using a chaotic perturbation mechanism, and the positions of multiple ordinary individuals in the sparrow population (excluding the elite individual) are updated using a cosine convergence factor. A first target elite individual is generated from among the multiple ordinary individuals. The fitness of this elite individual is compared with that of the first target elite individual, and the individual with the lower fitness is determined as the current optimal individual. For the second iteration, the position of the current optimal individual is updated using a chaotic perturbation mechanism, and the positions of multiple ordinary individuals (excluding the first target elite individual) are updated using a cosine convergence factor, generating a second target elite individual. The fitness of this second target elite individual is compared with that of the current optimal individual determined in the first iteration, and the individual with the lower fitness is determined as the new current optimal individual, and so on.

[0122] In this embodiment, the position of elite individuals is updated through a chaotic perturbation mechanism, which increases the diversity of individual positions and enhances the global search capability. This prevents the search process from getting stuck in local optima and ensures a large search range in the early stages of the search. Subsequently, the solution is finely adjusted by reducing the perturbation amplitude, thereby enhancing the global search capability.

[0123] In some embodiments, determining the sparrow population for the next iteration includes: obtaining a random factor, which is used to characterize the probability of executing a reverse learning strategy; if the random factor is less than or equal to a set reverse learning probability threshold, then determining multiple ordinary individuals as the sparrow population for the next iteration.

[0124] For example, when the algorithm is executed, it uses two different logics based on the size of the random factor to determine the sparrow population for the next iteration. The range of the random factor is [0,1], and the back learning probability threshold can be set to 0.5, 0.6, 0.7, ... 0.9, etc.

[0125] When the random factor is less than or equal to the set back learning probability threshold, the Logistic back learning strategy is not activated. Instead, the sparrow population consisting of ordinary individuals (excluding elite individuals) is used as the sparrow population for the next iteration. For example, if the initial sparrow population size is 100, an elite individual is generated during initialization, and the remaining 99 individuals are used as the sparrow population for the first iteration. After the first iteration, another target elite individual is generated, and the remaining 98 individuals are used as the sparrow population for the second iteration, and so on, until the preset number of iterations is reached.

[0126] In some embodiments, the method for determining the maximum power point further includes: if the random factor is greater than a set back learning probability threshold, optimizing the positions of multiple ordinary individuals based on a Logistic back learning strategy to obtain a back population; adding multiple ordinary individuals to the back population to obtain a merged sparrow population; determining the fitness of each sparrow individual in the merged sparrow population and sorting the multiple fitness values ​​from largest to smallest; and forming the sparrow population for the next iteration with the last set number of fitness values.

[0127] Among them, the Logistic backward learning strategy is a heuristic method that expands the search range by generating backward solutions to the current solution, thus avoiding getting trapped in local optima. The idea of ​​the Logistic backward learning strategy is applied when determining the sparrow population for the next iteration to reduce the computational overhead during the iteration process.

[0128] The current population and the reverse population are merged, that is, multiple ordinary individuals are added to the reverse population to obtain a merged sparrow population. The fitness of each sparrow in the merged sparrow population is calculated according to the position of each sparrow. The fitness of all sparrows in the merged sparrow population is sorted, and a set number of better sparrows are selected to form the sparrow population for the next iteration. The set number is, for example, 1 / 2 of the size of the merged sparrow population.

[0129] Furthermore, the fitness of the current best individual is compared with the minimum fitness in the merged sparrow population, and the one with the smaller fitness is determined as the new current best individual.

[0130] For example, the optimization model for the Logistic backward learning strategy can be:

[0131] X i ′=X min +X max -X i (6)

[0132] In the formula, X i X represents the position of a sparrow individual in the current population (composed of multiple ordinary individuals). i ′ represents the position of a sparrow individual in the reverse population.

[0133] In this embodiment, the sparrow population for the next iteration is determined by a Logistic back learning strategy, which enhances population diversity, avoids the algorithm getting stuck in local optima, and reduces computational overhead during the iteration process.

[0134] Figure 3 A flowchart illustrating the maximum power point determination method based on the improved sparrow search algorithm provided in this application embodiment. Figure 2 .like Figure 3 As shown, the method for determining the maximum power point includes:

[0135] 1. Obtain the operating data of the photovoltaic power generation system.

[0136] 2. Based on the Logistic chaotic mapping, the position of the sparrow population is initialized to obtain the initial position of each individual sparrow in the sparrow population.

[0137] 3. Determine the fitness of each sparrow individual based on its initial position.

[0138] 4. The sparrow individual with the lowest fitness in the sparrow population is identified as the elite individual.

[0139] 5. Apply the chaotic perturbation mechanism to update the position of elite individuals. Based on the cosine convergence factor, update the positions of multiple ordinary individuals in the sparrow population other than elite individuals, and determine the fitness of multiple ordinary individuals according to the updated positions.

[0140] The chaotic perturbation mechanism exists throughout the iteration process, but it performs random perturbations with a certain preset probability, such as a 20% random perturbation on elite individuals.

[0141] The position of the elite individual is updated using formula (5), and the positions of multiple ordinary individuals in the sparrow population other than the elite individual are updated using formulas (3) and (4). After the position is updated, the MPPT control model will recalculate the fitness of the ordinary individuals based on the updated position. The implementation principle is similar to that of step S2022, and will not be repeated here.

[0142] 6. Identify the target elite individual among multiple ordinary individuals. The target elite individual is the sparrow individual with the lowest fitness among multiple ordinary individuals.

[0143] The fitness of multiple ordinary individuals is ranked, and the sparrow individual with the lowest fitness is identified as the elite individual among the multiple ordinary individuals (i.e., the target elite individual).

[0144] 7. The individual with the lower fitness among the elite individuals and the target elite individuals is identified as the current optimal individual.

[0145] 8. Determine whether the random factor is greater than the set reverse learning probability threshold.

[0146] If the random factor is less than or equal to the set reverse learning probability threshold, proceed to step 9;

[0147] If the random factor is greater than the set reverse learning probability threshold, proceed to step 10.

[0148] 9. Select multiple ordinary individuals as the sparrow population for the next iteration.

[0149] The sparrow population consisting of ordinary individuals (excluding elite individuals) is directly used as the sparrow population for the next iteration, and then step 15 is executed.

[0150] 10. Based on the Logistic backward learning strategy, the positions of multiple ordinary individuals are optimized to obtain a backward population.

[0151] 11. Add multiple ordinary individuals to the reverse population to obtain the merged sparrow population.

[0152] 12. Determine the fitness of each sparrow individual in the merged sparrow population, and sort the fitness values ​​from largest to smallest.

[0153] 13. The sparrow individuals corresponding to the fitness of the last set number will form the sparrow population for the next iteration.

[0154] 14. Compare the fitness of the current best individual with the minimum fitness in the merged sparrow population, and determine the smaller one as the new current best individual.

[0155] 15. Determine whether the set number of iterations has been reached.

[0156] If the set number of iterations has not been reached, return to step 5.

[0157] If the set number of iterations is reached, the target fitness corresponding to the current best individual is output.

[0158] 16. The negative value of the target fitness is determined as the maximum power point.

[0159] Furthermore, for example, the electronic device can synchronize the determined maximum power point to the power generation control unit in the photovoltaic power generation system. Based on the maximum power point, the power generation power of each photovoltaic power generation device can be adjusted to ensure that the photovoltaic power generation system always maintains maximum output power and improves the power generation efficiency of the photovoltaic power generation system.

[0160] Next, Figure 4 A comparison of the convergence curves of the global optimum values ​​of the improved sparrow search algorithm and the unimproved sparrow search algorithm provided in the embodiments of this application. Figure 4 (a) shows the peak-valley distribution of the improved Sparrow Search Algorithm (ISSA) function. Figure 4 In diagram (a), the horizontal axis (x1, x2) represents the optimization variable (which can be considered as individual sparrows), and the vertical axis F3 (x1, x2) represents the multimodal function (i.e., the Rastrigin function). Figure 4 As can be seen from (a), ISSA's algorithm has strong optimization capabilities and a wide search range. Figure 4 In Figure (b), the convergence curve of the optimization algorithm's iterative process is shown, with the horizontal axis representing the number of iterations and the vertical axis representing the optimal objective value. Figure 4As can be seen from (b), the ISSA algorithm converges faster than the unmodified Sparrow Search (SSA) algorithm. The final convergent target value of ISSA is smaller and tends to be stable, and its optimization accuracy is higher than that of SSA. The curve of SSA is more volatile, indicating that the optimal value obtained during the search process is unstable, while the curve of ISSA is relatively smooth, indicating that ISSA is more stable during the search process.

[0161] Figure 5 This is a convergence curve of the global optimum of the fitness function provided in the embodiments of this application. The horizontal axis represents the number of iterations, and the vertical axis represents the fitness value. Figure 5 It can be seen that the improved Sparrow Search Algorithm (ISSA) can obtain a lower fitness value in the MPPT control process, which shows that ISSA has better optimization ability.

[0162] Figure 6 The convergence curve of the Simulink model based on the improved sparrow search algorithm provided in the embodiments of this application is shown. Figure 7 The convergence curve of the Simulink model not based on the improved sparrow search algorithm is provided for the embodiments of this application.

[0163] Figure 6 and Figure 7 The graph represents the power generation of different photovoltaic power generation devices (batteries, photovoltaic cells, and supercapacitors). The horizontal axis represents time in seconds, and the vertical axis represents power in watts. Figure 6 and Figure 7 The comparison shows that the introduction of the improved sparrow search algorithm (ISSA) enhances the robustness of the MPPT control system, and the curve is smoother, indicating that the anti-interference capability of the MPPT control system has been improved.

[0164] In summary, this application has the following advantages:

[0165] I. Based on the traditional MPPT control method, this paper incorporates the idea of ​​a sparrow search algorithm and uses a Logistic chaotic mapping to initialize the sparrow population. This mapping generates highly random and diverse initial positions for individual sparrows, increasing the uncertainty of elite individual position updates and thus improving the diversity of the global search, reducing the possibility of getting trapped in local optima. Furthermore, a cosine convergence factor is used to provide a dynamically adjusted mechanism, enabling large-scale exploration in the initial stage and fine-tuning the search by dynamically adjusting the step size in the later stage. This enhances the algorithm's convergence, ensuring greater stability and efficiency in finding the optimal solution. This effectively solves the problem of the maximum power point getting trapped in local optima and slow convergence speed, while reducing computational complexity and improving the maximum power point tracking accuracy, better adapting to dynamic environmental changes. In addition, the accurate determination of the maximum power point ensures that the photovoltaic power generation system always outputs maximum power, improving the power generation efficiency of the photovoltaic system.

[0166] Second, the position of elite individuals is updated through a chaotic perturbation mechanism, which increases the diversity of individual positions and enhances the global search capability. This prevents the search process from getting stuck in local optima and ensures a large search range in the early stages of the search. Subsequently, the solution is finely adjusted by reducing the perturbation amplitude, thereby enhancing the global search capability.

[0167] Third, the sparrow population for the next iteration is determined by using the Logistic backward learning strategy, which enhances population diversity, avoids the algorithm getting stuck in local optima, and reduces the computational cost during the iteration process.

[0168] Figure 8 A schematic diagram of the maximum power point determination device based on the improved sparrow search algorithm provided in the embodiments of this application is shown below. Figure 8 As shown, the maximum power point determination device 80 based on the improved sparrow search algorithm provided in this embodiment is applied to a photovoltaic power generation system. The maximum power point determination device 80 based on the improved sparrow search algorithm includes: an acquisition module 81, a first determination module 82, a processing module 83, and a second determination module 84. Wherein:

[0169] Module 81 is used to acquire operating data of the photovoltaic power generation system;

[0170] The first determining module 82 is used to determine the position and fitness of the elite individuals in the sparrow population based on the Logistic chaotic mapping and running data. The elite individuals are the sparrow individuals with the lowest fitness in the sparrow population. The position represents voltage and the fitness represents power.

[0171] Processing module 83 is used to perform the following iterative operations and output the target fitness corresponding to the current best individual when the number of iterations reaches the set number of iterations;

[0172] The second determining module 84 is used to determine the maximum power point of the photovoltaic power generation system based on the target fitness.

[0173] The iterative operations include:

[0174] Based on the cosine convergence factor, the positions of multiple ordinary individuals in the sparrow population (excluding elite individuals) are updated, and the fitness of multiple ordinary individuals is determined according to the updated positions.

[0175] Among multiple ordinary individuals, a target elite individual is identified. The target elite individual is the sparrow individual with the lowest fitness among the multiple ordinary individuals.

[0176] The individual with the lower fitness between the elite individual and the target elite individual is identified as the current optimal individual.

[0177] Determine the sparrow population for the next iteration.

[0178] In one possible implementation, the first determining module 82 is specifically used for: initializing the sparrow population position based on the Logistic chaotic mapping to obtain the initial position corresponding to each sparrow individual in the sparrow population and the running data corresponding to each sparrow individual; determining the fitness of each sparrow individual according to the initial position corresponding to each sparrow individual; determining the sparrow individual with the lowest fitness in the sparrow population as the elite individual; and determining the position of the elite individual and the fitness corresponding to the position.

[0179] In one possible implementation, the processing module 83 is used to: update the position of the elite individual based on the chaotic perturbation factor to obtain the updated position; and determine the fitness of the elite individual based on the updated position.

[0180] In one possible implementation, the processing module 83 is specifically used to: obtain a random factor, which is used to characterize the probability of executing the reverse learning strategy; if the random factor is less than or equal to a set reverse learning probability threshold, determine multiple ordinary individuals as the sparrow population for the next iteration.

[0181] In one possible implementation, the processing module 83 is further configured to: if the random factor is greater than a set back learning probability threshold, optimize the positions of multiple ordinary individuals based on the Logistic back learning strategy to obtain a back population; add multiple ordinary individuals to the back population to obtain a merged sparrow population; determine the fitness of each sparrow individual in the merged sparrow population and sort the multiple fitness values ​​from largest to smallest; and form the sparrow population for the next iteration by selecting the sparrow individuals corresponding to the fitness values ​​of the last set number.

[0182] In one possible implementation, the processing module 83 is also used to implement the following formula:

[0183]

[0184] Where t is the current iteration number, T is the set iteration number, and X best The position of the current optimal individual. ω represents the update position of a normal individual after t+1 iterations, ω is the cosine convergence factor, and rand is the random factor.

[0185] In one possible implementation, the second determining module 84 is specifically used to: determine the negative number of the target fitness as the maximum power point.

[0186] In one possible implementation, the processing module 83 is also used to implement the following formula:

[0187]

[0188] in, X represents the updated position of the elite individual after iteration t+1. best Let X be the current optimal individual's position, ξ be the chaos perturbation factor, ω be the cosine convergence factor, and X be... max This represents the upper bound of the search variable's value, X. min This is the lower bound of the search variable's value.

[0189] The maximum power point determination device based on the improved sparrow search algorithm provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0190] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 9 As shown, the electronic device 90 provided in this embodiment includes at least one processor 901 and a memory 902. Optionally, the electronic device 90 further includes a communication component 903. The processor 901, memory 902, and communication component 903 are connected via a bus 904.

[0191] In a specific implementation, at least one processor 901 executes computer execution instructions stored in memory 902, causing at least one processor 901 to perform the above-described method.

[0192] The specific implementation process of processor 901 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0193] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0194] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0195] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0196] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0197] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0198] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0199] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0200] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0201] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0202] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0203] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0204] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0205] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for determining the maximum power point based on an improved sparrow search algorithm, characterized in that, The maximum power point determination method, applied to photovoltaic power generation systems, includes: Obtain the operating data of the photovoltaic power generation system; Based on the Logistic chaotic mapping and the running data, the position of the elite individual in the sparrow population and the fitness corresponding to the position are determined. The elite individual is the sparrow individual with the lowest fitness in the sparrow population. The position represents voltage and the fitness represents power. Perform the following iterative operations, and when the set number of iterations is reached, output the target fitness corresponding to the current best individual; The maximum power point of the photovoltaic power generation system is determined based on the target fitness. The iterative operation includes: Based on the cosine convergence factor, the positions of multiple ordinary individuals in the sparrow population, excluding elite individuals, are updated, and the fitness of the multiple ordinary individuals is determined according to the updated positions. Among the plurality of ordinary individuals, a target elite individual is determined, wherein the target elite individual is the sparrow individual with the lowest fitness among the plurality of ordinary individuals; The individual with the lower fitness between the elite individual and the target elite individual is determined as the current optimal individual; Determine the sparrow population for the next iteration.

2. The method for determining the maximum power point according to claim 1, characterized in that, The determination of the position of elite individuals in the sparrow population and the corresponding fitness based on the Logistic chaotic mapping and the operational data includes: Based on the Logistic chaotic mapping, the position of the sparrow population is initialized to obtain the initial position corresponding to each individual sparrow in the sparrow population; The fitness of each sparrow individual is determined based on its initial position. The sparrow individual with the lowest fitness in the sparrow population was identified as the elite individual; Determine the location of the elite individual and the fitness corresponding to that location.

3. The method for determining the maximum power point according to claim 2, characterized in that, The iterative operation also includes: Based on the chaotic perturbation factor, the position of the elite individual is updated to obtain the updated position; Based on the updated position, the fitness of the elite individual is determined.

4. The method for determining the maximum power point according to any one of claims 1 to 3, characterized in that, The process of determining the sparrow population for the next iteration includes: Obtain a random factor, which is used to characterize the probability of executing the reverse learning strategy; If the random factor is less than or equal to the set reverse learning probability threshold, the multiple ordinary individuals are determined as the sparrow population for the next iteration.

5. The method for determining the maximum power point according to claim 4, characterized in that, Also includes: If the random factor is greater than the set reverse learning probability threshold, the positions of the multiple ordinary individuals are optimized based on the Logistic reverse learning strategy to obtain a reverse population. The multiple ordinary individuals are added to the reverse population to obtain the merged sparrow population; Determine the fitness of each sparrow individual in the merged sparrow population, and sort the fitness values ​​from largest to smallest. The sparrow individuals corresponding to the fitness of the last set number will form the sparrow population for the next iteration.

6. The method for determining the maximum power point according to claim 5, characterized in that, The positions of multiple ordinary individuals (excluding elite individuals) in the sparrow population are updated based on the cosine convergence factor, satisfying the following formula: Where t is the current iteration number, T is the set iteration number, and X best The position of the current optimal individual. This represents the update position of a regular individual after iteration t+1. Let ω be the update position of a normal individual after t iterations, ω be the cosine convergence factor, and rand be the random factor.

7. The method for determining the maximum power point according to any one of claims 1 to 3, characterized in that, Determining the maximum power point of the photovoltaic power generation system based on the target fitness includes: The negative value of the target fitness is determined as the maximum power point.

8. The method for determining the maximum power point according to claim 3, characterized in that, The position of the elite individual is updated based on the chaotic perturbation factor to obtain the updated position, which satisfies the following formula: in, X represents the updated position of the elite individual after iteration t+1. best Let X be the current optimal individual's position, ξ be the chaos perturbation factor, ω be the cosine convergence factor, and X be... max This represents the upper bound of the search variable's value, X. min This is the lower bound of the search variable's value.

9. A device for determining the maximum power point based on an improved sparrow search algorithm, characterized in that, The maximum power point determination device, applied to photovoltaic power generation systems, includes: The acquisition module is used to acquire the operating data of the photovoltaic power generation system; The first determining module is used to determine the position of an elite individual in the sparrow population and the fitness corresponding to the position based on the Logistic chaotic mapping and the running data. The elite individual is the sparrow individual with the lowest fitness in the sparrow population. The position represents voltage and the fitness represents power. The processing module is used to perform the following iterative operations and output the target fitness corresponding to the current best individual when the number of iterations reaches the set number of iterations; The second determining module is used to determine the maximum power point of the photovoltaic power generation system based on the target fitness. The iterative operation includes: Based on the cosine convergence factor, the positions of multiple ordinary individuals in the sparrow population, excluding elite individuals, are updated, and the fitness of the multiple ordinary individuals is determined according to the updated positions. Among the plurality of ordinary individuals, a target elite individual is determined, wherein the target elite individual is the sparrow individual with the lowest fitness among the plurality of ordinary individuals; The individual with the lower fitness between the elite individual and the target elite individual is determined as the current optimal individual; Determine the sparrow population for the next iteration.

10. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1 to 8.

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