Electric two-wheeled vehicle charging system and method based on dynamic distribution and adjustment and storage medium

Abstract

The invention discloses an electric two-wheeled vehicle charging system and method based on dynamic distribution and adjustment and a storage medium, and relates to the technical field of electric two-wheeled vehicle charging management.The system and the method achieve digital modeling and dynamic correlation analysis of an electric two-wheeled vehicle charging pile cluster by building a virtual cluster system, and the dynamic distribution and adjustment of the electric two-wheeled vehicle charging pile cluster is achieved. On the basis of multi-dimensional data integration of geographical distribution, capacity scale and power utilization attributes, a digital twinborn model capable of mapping a physical cluster state in real time is formed by combining a comprehensive calculation mechanism of space correlation and load correlation, and the time sequence characteristics of historical charging demands are deeply learned through an LSTM model, so that the time sequence characteristics of the historical charging demands are obtained. A smooth transition mechanism of a current strategy and a prediction strategy is constructed by combining quantitative analysis of a dynamic connection index and a pre-connection reasonable value, so that the current charging distribution adjustment strategy is ensured to meet the current requirement, and meanwhile, connection and switching of subsequent strategies are facilitated to the greatest extent.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric two-wheeler charging management, more specifically, it relates to an electric two-wheeler charging system, method and storage medium based on dynamic allocation and adjustment. BACKGROUND

[0002] With the popularity of electric two-wheelers in urban micro-mobility scenarios, the contradiction between their fragmented and high-frequency charging needs and grid load management and efficient use of charging resources has become increasingly prominent. Existing electric two-wheeler charging management systems mostly use static scheduling mode, lacking dynamic modeling and collaborative scheduling capabilities for charging pile clusters: on the one hand, traditional solutions only allocate charging based on the real-time state of a single cluster, without considering multi-dimensional features such as geographic distribution and electricity consumption attributes, resulting in low resource allocation efficiency between adjacent clusters. Community test data shows that the average idle rate of charging piles is as high as 35%; on the other hand, existing technologies mostly rely on rule engines or simple heuristic algorithms to generate scheduling strategies, which cannot effectively handle fluctuations in peak and valley electricity prices for residential electricity consumption.

[0003] In the field of demand prediction and strategy connection, existing solutions have significant technical bottlenecks: statistical analysis-based prediction models are difficult to capture the nonlinear fluctuations of electric two-wheeler charging demand (such as sudden demand changes during the peak period of food delivery), and the 1-hour prediction error rate of traditional ARIMA models in a certain commercial district test exceeds 30%; at the same time, strategy generation does not consider the smoothness of the connection between the previous and subsequent periods, and when the grid load or user demand changes, strategy switching often leads to sudden increases and decreases in charging pile power, with a power fluctuation amplitude of up to 40% in a certain case, severely affecting grid safety and equipment life. In addition, existing systems lack optimization of electric two-wheeler-specific features (such as the proportion of emergency needs with SOC <20% and the distribution of super-fast charging power), and cannot meet the needs of high-time-efficiency users such as riders, with an average waiting time of more than 15 minutes.

[0004] To address the above technical problems, the present application proposes an electric two-wheeler charging system, method and storage medium based on dynamic allocation and adjustment. SUMMARY

[0005] To address the deficiencies of existing technologies, the present application aims to provide an electric two-wheeler charging system, method and storage medium based on dynamic allocation and adjustment.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions: The electric two-wheeler charging system based on dynamic allocation and adjustment includes a virtual cluster establishment unit for determining all charging pile clusters contained in the charging area and building a virtual cluster system; a current feasible strategy analysis unit that determines a current feasible strategy based on the periodic virtual cluster system; The prediction feasible strategy analysis unit determines the predicted charging demand characteristics of each charging pile cluster, and further determines the prediction feasible strategy; The charging distribution adjustment determination unit generates a plurality of strategy connection combinations according to the current feasible strategy and the prediction feasible strategy, determines the dynamic connection index of each strategy connection combination, further determines the pre-connection reasonable value of each current feasible strategy, and selects the current determination strategy from the current feasible strategy, and uses the current determination strategy to distribute and adjust the charging of all electric two-wheeled vehicles in the charging area.

[0007] Further, the current feasible strategy is determined according to the periodic virtual cluster system, specifically as follows: the actual charging demand characteristics of each charging pile cluster are determined, the actual charging demand characteristics of each charging pile cluster are converted into input parameters and introduced into the virtual cluster system, and the virtual cluster system generates a plurality of charging distribution adjustment strategies, and each generated charging distribution adjustment strategy is marked as a current feasible strategy.

[0008] Further, the prediction feasible strategy is determined according to the prediction charging demand characteristics of each charging pile cluster, specifically as follows: the prediction charging demand characteristics of each charging pile cluster are converted into input parameters and introduced into the virtual cluster system, and the virtual cluster system generates a plurality of charging distribution adjustment strategies, and each generated charging distribution adjustment strategy is marked as a prediction feasible strategy.

[0009] Further, the determination method of the prediction charging demand characteristics of the charging pile cluster is as follows: a plurality of actual charging demand characteristics of a charging pile cluster are obtained in succession, and the plurality of actual charging demand characteristics are integrated into an actual charging demand time sequence characteristic set in a time sequence set manner, the actual charging demand time sequence characteristic set is introduced into a cluster charging demand prediction model corresponding to the charging pile cluster, and the cluster charging demand prediction model outputs the prediction charging demand characteristics of the charging pile cluster.

[0010] Further, a plurality of strategy connection combinations are generated according to the current feasible strategy and the prediction feasible strategy, specifically as follows: each current feasible strategy is matched with each prediction feasible strategy one by one, and the matched current feasible strategy and prediction feasible strategy are marked as a strategy connection combination.

[0011] Further, the current determination strategy is marked as the current feasible strategy with the maximum pre-connection reasonable value.

[0012] Further, the determination method of the dynamic connection index of the strategy connection combination is as follows: the characteristic set of the current feasible strategy in the strategy connection combination is determined , and the characteristic set of the prediction feasible strategy in the strategy connection combination is determined , i represents the i-th feature in the current feasible strategy or the predicted feasible strategy, the similarity between the two feature sets is calculated by using the cosine similarity formula, and the calculated similarity is the dynamic connection index of the strategy connection combination.

[0013] Further, the determination method of the reasonable value of the front connection of the current feasible strategy: select a current feasible strategy, mark all strategy connection combinations containing the current feasible strategy as possible connection combinations, calculate the average dynamic connection index by summing the dynamic connection indexes of all possible connection combinations, match all possible connection combinations two by two, calculate the dynamic connection difference index by calculating the absolute difference of the dynamic connection indexes of the two matched possible connection combinations, calculate the average dynamic connection difference index by summing all dynamic connection difference indexes, and calculate the reasonable value of the front connection of the current feasible strategy by ratio calculation of the average dynamic connection index and the average dynamic connection difference index.

[0014] Further, the dynamic allocation and adjustment method for electric two-wheeled vehicle charging, the steps are as follows: Step one: build a virtual cluster system; Step two: periodically determine the current feasible strategy and the predicted feasible strategy by the virtual cluster system; Step three: select a current determination strategy from the current feasible strategy, and use the current determination strategy to allocate and adjust the charging of all electric two-wheeled vehicle charging piles in the charging area.

[0015] Compared with the prior art, the present application has the following beneficial effects: The system and method of the present application realize digital modeling and dynamic correlation analysis of the electric two-wheeled vehicle charging pile cluster by building a virtual cluster system, rely on multi-dimensional data integration of geographic distribution, capacity scale and power consumption attributes, combine spatial correlation and load correlation comprehensive computer mechanism, form a digital twin model that can map the physical cluster state in real time, through deep learning of the historical charging demand time series characteristics by the LSTM model, combined with the quantitative analysis of the dynamic connection index and the reasonable value of the front connection, a smooth transition mechanism of the current strategy and the predicted strategy is constructed, which ensures that the current charging allocation and adjustment strategy not only meets the current demand, but also maximizes the convenience of the connection and switching of subsequent strategies, through the closed-loop control of "real-time demand analysis-future trend prediction-strategy connection evaluation", providing core technical support for the intelligent upgrading of urban micro-mobile charging network. BRIEF DESCRIPTION OF DRAWINGS

[0016] Fig. 1 The operation flow chart of the dynamic allocation and adjustment electric two-wheeled vehicle charging system; Fig. 2 The determination flow chart of the reasonable value of the front connection of the current feasible strategy. DETAILED DESCRIPTION

[0017] Embodiment One: Reference Figs. 1-2 , based on dynamic allocation and adjustment of electric two-wheeled vehicle charging system, including virtual cluster establishment unit, current feasible strategy analysis unit, prediction feasible strategy analysis unit, charging allocation and adjustment determination unit.

[0018] The virtual cluster establishment unit determines all charging pile clusters contained in the charging area, and builds a virtual cluster system. The building steps are: S1: determine the geographic distribution information (geographic distribution information is described by geographic coordinates and other information), capacity scale (capacity scale is the number of charging piles contained in each charging pile cluster) and electricity attribute (usually residential electricity, commercial electricity, etc. The characteristics of residential electricity are that the electricity price fluctuates with time, and the off-peak electricity price is cheaper. For example: a charging pile cluster accesses residential electricity, the peak period (7:00-22:00) electricity price 0.55 yuan / kWh, the valley period (22:00-7:00) electricity price 0.3 yuan / kWh, the characteristics of commercial electricity are that the electricity price is stable, for example: a charging pile cluster accesses commercial electricity, the whole day electricity price 0.8 yuan / kWh, no time period is distinguished); S2: design the virtual cluster system architecture, and abstract modeling for the charging pile cluster: charging pile cluster object definition: class PhysicalCluster: def __init__(self, cluster_id, coords, capacity, tariff_type, pile_types, service_scenario): self.id = cluster_id # cluster ID self.coordinates = coords # geographic coordinates self.pile_count = capacity # number of charging piles self.tariff_type = tariff_type # electricity price type self.pile_types = pile_types # pile type proportion (fast charging / slow charging) self.scenario = service_scenario # service scenario (community / commercial district, etc.) ​Correlation relationship construction: spatial correlation: construct adjacency matrix based on road network distance (threshold < 3 km); load correlation: calculate Pearson correlation coefficient (threshold > 0.7); comprehensive correlation degree: R = 0.6 x spatial correlation + 0.4 x load correlation (R > 0.6 to form a virtual cluster), build a digital twin model: C(t) = f(P(t), S(t), G(t), H), where: C(t) is the cluster state vector at time t, P(t) is the charging pile state matrix, S(t) is the vehicle demand vector, G(t) is the power grid state vector, and H is the historical feature matrix; allocation strategy engine: multi-objective optimization algorithm: NSGA-III (optimization objectives: load balancing, user waiting time, charging cost), NSGA-III algorithm characteristics: can generate a set of non-dominated solutions (usually 20-50), representing different trade-offs of optimization objectives; example: in a certain test, 32 Pareto optimal solutions were generated for 3 optimization objectives; reinforcement learning model: DDPG (dynamically adjust allocation strategy, reward function includes power grid synergy contribution), through the exploration-exploitation mechanism, different strategy variants can be generated, and the power grid synergy contribution in the reward function can guide the generation of diversified strategies, and a virtual cluster system is built.

[0019] The current feasible strategy analysis unit periodically determines the actual charging demand characteristics of each charging pile cluster (the time length of the corresponding period interval is adjusted according to the charging demand of the charging pile cluster, and each charging pile cluster corresponds to an actual charging demand characteristic. The actual charging demand characteristic contains the number of vehicles to be charged (data source: charging pile operator platform real-time state (idle / occupied), user APP charging request queue; collection method: through MQTT protocol from the charging pile controller, or from the user end APP request interface to pull; example data: a cluster currently shows 3 vehicles to be charged, data from charging pile real-time state reporting), emergency charging demand proportion (data source: vehicle battery SOC (State of Charge) data (externally collected); calculation logic: emergency demand is defined as the proportion of vehicles with SOC<20% (internal rule); data flow: vehicle-mounted BMS transmits SOC through CAN bus→charging pile interface collection→edge node calculation proportion), demand power distribution (data source: user-selected charging mode (fast charging / slow charging), vehicle-supported maximum charging power; collection method: user APP selection record (such as "quick charging" button click), vehicle parameter interface acquisition; typical data: 8 fast charging demands and 5 slow charging demands in a cluster), adjacent cluster overflow demand (data source: number of vehicles to be charged and charging pile utilization rate of adjacent clusters (externally collected); calculation logic: when the utilization rate of the adjacent cluster is >80%, overflow demand = the number of vehicles to be charged in the cluster x 20% (internal algorithm); data link: cloud obtains the state of adjacent clusters→calculates overflow based on spatial correlation→returns to the current cluster), etc.), converts the actual charging demand characteristics of each charging pile cluster into input parameters and imports them into the virtual cluster system, and the virtual cluster system generates multiple charging distribution adjustment strategies (each charging distribution adjustment strategy indirectly affects power distribution by controlling the charging pile power of the charging pile cluster, and each charging distribution adjustment strategy is a Pareto optimal solution). Each generated charging distribution adjustment strategy is labeled as a current feasible strategy.

[0020] The predicted feasible strategy analysis unit synchronously determines the predicted charging demand characteristics of each charging pile cluster, converts the predicted charging demand characteristics of each charging pile cluster into input parameters and imports them into the virtual cluster system, and the virtual cluster system generates multiple charging distribution adjustment strategies. Each generated charging distribution adjustment strategy is labeled as a predicted feasible strategy. The determination manner of the predicted charging demand feature of the charging pile cluster: obtaining a charging pile cluster in the previous continuous determination of a plurality of actual charging demand features (the form of each actual charging demand feature is {the number of vehicles to be charged, the proportion of emergency charging demand, the demand power distribution, and the overflow demand of adjacent clusters}), and integrating the plurality of actual charging demand features into an actual charging demand time series feature set in a time series set manner. The actual charging demand time series feature set is input into the cluster charging demand prediction model corresponding to the charging pile cluster. The cluster charging demand prediction model outputs the predicted charging demand feature of the charging pile cluster; The format of the actual charging demand time series feature set: JSON array format: { / * 2025-06-15T08:00:00 number of vehicles to be charged, proportion of emergency charging demand, demand power distribution, and overflow demand of adjacent clusters * / }, { / * 2025-06-15T08:15:00 number of vehicles to be charged, proportion of emergency charging demand, demand power distribution, and overflow demand of adjacent clusters * / }, { / * 2025-06-15T08:30:00 number of vehicles to be charged, proportion of emergency charging demand, demand power distribution, and overflow demand of adjacent clusters * / }, ...].

[0021] Each charging pile cluster corresponds to a cluster charging demand prediction model, all cluster charging demand prediction models are built based on LSTM model, each cluster charging demand prediction model exists on the difference of training data, the training process is similar, this embodiment takes charging pile cluster A as an example, the building method of cluster charging demand prediction model is disclosed: a plurality of actual charging demand time sequence feature sets of charging pile cluster A are collected, an LSTM model is built (the [time step, feature number] is specified, a Dropout layer is added to prevent overfitting, and the output layer is designed to be consistent with the input feature number), the actual charging demand time sequence feature set is used as basic data, the LSTM model is trained, and each actual charging demand time sequence feature set is assigned a predicted charging demand feature (the assignment process includes normalizing each feature in the actual charging demand time sequence feature set, for example, the original value range of the feature name: the number of vehicles to be charged is 8-12, the normalized range is 0-1, the original value range of the feature name: the proportion of emergency charging demand is 0.2-0.3, the normalized range is 0-1, the original value range of the feature name: adjacent overflow demand is 1-3, the normalized range is 0-1, the normalization process is not expanded, such as using Min-Max standardization method), the predicted charging demand feature is the actual charging demand feature of the next period of charging pile cluster A, then a plurality of actual charging demand time sequence feature sets are divided into training set, validation set and test set according to a certain proportion, the specific division proportion is determined as 70%:15%:15%, the training set is used to repeatedly train the LSTM model, the model training key parameters are as follows: model.compile( optimizer='adam', loss='mse', # Mean Squared Error loss function is suitable for regression task metrics=['mae'] # Mean Absolute Error for intuitive evaluation) # Training configuration history = model.fit( X_train, y_train, epochs=100, batch_size=32, validation_split=0.15, callbacks=[ EarlyStopping(patience=10, restore_best_weights=True), ReduceLROnPlateau(factor=0.5, patience=5) The model uses a validation set to verify its performance during the training phase. Based on the validation results, the model parameters are adjusted in a timely manner. Hyperparameter tuning, overfitting prevention, and training monitoring are employed. The final model is evaluated using a test set that was not used in the training process to ensure that the results do not depend on data snooping during the training process. Finally, the cluster charging demand prediction model for charging pile cluster A is completed.

[0022] The charging allocation and adjustment determination unit matches each currently feasible strategy with each predicted feasible strategy, marks the matched currently feasible strategies and predicted feasible strategies as a strategy connection combination, further determines the dynamic connection index of each strategy connection combination, further determines the reasonable value of the preceding connection for each currently feasible strategy, marks the currently feasible strategy with the largest reasonable value of the preceding connection as the currently determined strategy, and uses the currently determined strategy to allocate and adjust the charging of all electric two-wheeled vehicles in the charging pile cluster within the charging area.

[0023] The method for determining the dynamic integration index of strategy integration combination: determine the feature set of currently feasible strategies in the strategy integration combination. (For example, a1 corresponds to the total power limit of charging pile cluster A of 120kW, a2 corresponds to the total power limit of charging pile cluster B of 80kW, a3 corresponds to the fast charging power of charging pile cluster A of 80kW, and a4 corresponds to the slow charging power of charging pile cluster A of 40kW), determine the feature set of predicted feasible strategies in the strategy connection combination. (For example, b1 corresponds to the total power limit of charging pile cluster A of 80W, b2 corresponds to the total power limit of charging pile cluster B of 160kW, b3 corresponds to the fast charging power of charging pile cluster A of 32kW, and b4 corresponds to the slow charging power of charging pile cluster A of 48kW), i represents the i-th feature in the current feasible strategy or the predicted feasible strategy. The similarity between the two feature sets is calculated using the cosine similarity formula. The calculated similarity is the dynamic connection index of the strategy connection combination (e.g., only a1 to a4 and b1 to b4 are calculated). * =120×80+80×160+80×32+40×48=9600+12800+2560+1920=26880; || ||= ≈169.7; || ||= ≈187.96, then the cosine similarity calculates the dynamic connection index of the strategy connection combination to be 0.84.

[0024] The determination method of the reasonable value of the preceding connection of the current feasible strategy is as follows: a current feasible strategy is selected, all strategy connection combinations containing the current feasible strategy are marked as possible connection combinations, the dynamic connection indicators of all possible connection combinations are summed and averaged to calculate the average dynamic connection indicator, all possible connection combinations are matched two by two, the dynamic connection indicators of the two matched possible connection combinations are calculated by absolute difference to calculate the dynamic connection difference indicator, all dynamic connection difference indicators are summed and averaged to calculate the average dynamic connection difference indicator, the average dynamic connection indicator is compared with the average dynamic connection difference indicator to calculate the reasonable value of the preceding connection of the current feasible strategy.

[0025] The above system realizes digital modeling and dynamic correlation analysis of the charging pile cluster of the electric two-wheeled vehicle by building a virtual cluster system, relies on multi-dimensional data integration of geographical distribution, capacity scale and power consumption attributes, combines a comprehensive computer mechanism of spatial correlation and load correlation, forms a digital twin model that can map the physical cluster state in real time, learns the historical charging demand time series characteristics through an LSTM model, combines quantitative analysis of dynamic connection indicators and preceding connection reasonable values, builds a smooth transition mechanism of the current strategy and the predicted strategy, and ensures that the current charging allocation and adjustment strategy not only meets the current demand, but also maximizes the convenience of the connection and switching of subsequent strategies.

[0026] Embodiment two: an electric two-wheeled vehicle charging method based on dynamic allocation and adjustment, the steps are as follows: Step one: build a virtual cluster system; Step two: periodically determine the current feasible strategy and the predicted feasible strategy by the virtual cluster system; Step three: select a current determination strategy from the current feasible strategy, and use the current determination strategy to allocate and adjust the charging of all electric two-wheeled vehicle charging pile clusters in the charging area.

[0027] The above method provides core technical support for the intelligent upgrading of urban micro-mobility charging networks through the closed-loop control of "real-time demand analysis-future trend prediction-strategy connection evaluation".

[0028] The above formulas are dimensionless values calculated, the formulas are obtained by software simulation of a large amount of data to obtain a formula of the latest real situation, and the preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0029] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0030] It should be understood that the size of the sequence number of each process described above in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0031] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized in electronic hardware, or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0032] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0033] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiments is only a logical function division, and there can be another division manner for actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or the among different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0034] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts of the technical solutions that make contributions to the prior art, or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and various other media that can store program codes.

[0035] The above describes only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A charging system for electric two-wheeled vehicles based on dynamic allocation and regulation, characterized by, The virtual cluster establishment unit is configured to determine all charging pile clusters contained in the charging area and build a virtual cluster system. The current feasible strategy analysis unit is configured to determine a current feasible strategy based on the periodic virtual cluster system. The predicted feasible strategy analysis unit is configured to determine predicted charging demand characteristics of each charging pile cluster and further determine a predicted feasible strategy. The charging distribution adjustment determination unit is configured to generate a plurality of strategy connection combinations based on the current feasible strategy and the predicted feasible strategy, determine a dynamic connection index of each strategy connection combination, further determine a front connection reasonable value of each current feasible strategy, and select a current determination strategy from the current feasible strategy, and use the current determination strategy to distribute and adjust charging of all electric two-wheeled vehicles in the charging area.

2. The dynamically allocated and regulated electric scooter charging system of claim 1, wherein, The current feasible strategy is determined based on the periodic virtual cluster system, and the specific process is as follows: the actual charging demand characteristics of each charging pile cluster are determined, the actual charging demand characteristics of each charging pile cluster are converted into input parameters and input into the virtual cluster system, the virtual cluster system generates a plurality of charging distribution adjustment strategies, and each generated charging distribution adjustment strategy is marked as a current feasible strategy.

3. The dynamically allocated and regulated electric scooter charging system of claim 1, wherein, The predicted feasible strategy is determined based on the predicted charging demand characteristics of each charging pile cluster, and the specific process is as follows: the predicted charging demand characteristics of each charging pile cluster are converted into input parameters and input into the virtual cluster system, the virtual cluster system generates a plurality of charging distribution adjustment strategies, and each generated charging distribution adjustment strategy is marked as a predicted feasible strategy.

4. The dynamically allocated and regulated electric scooter charging system of claim 3, wherein, The predicted charging demand characteristics of each charging pile cluster are determined as follows: a plurality of actual charging demand characteristics of a charging pile cluster are obtained in succession, the plurality of actual charging demand characteristics are integrated into an actual charging demand time sequence characteristic set in a time sequence set manner, the actual charging demand time sequence characteristic set is input into a cluster charging demand prediction model corresponding to the charging pile cluster, and the cluster charging demand prediction model outputs the predicted charging demand characteristics of the charging pile cluster.

5. The dynamically allocated and regulated electric scooter charging system of claim 1, wherein, The plurality of strategy connection combinations are generated based on the current feasible strategy and the predicted feasible strategy, and the specific process is as follows: each current feasible strategy is matched with each predicted feasible strategy one by one, and the matched current feasible strategy and predicted feasible strategy are marked as a strategy connection combination.

6. The dynamically allocated and regulated electric scooter charging system of claim 1, wherein, The current determination strategy is marked as the current feasible strategy with the maximum front connection reasonable value.

7. The dynamically allocated and regulated electric scooter charging system of claim 1, wherein, The determination manner of the dynamic connection index of the strategy connection combination: determining the feature set of the current feasible strategy in the strategy connection combination , determining the feature set of the predicted feasible strategy in the strategy connection combination , i represents the i-th feature in the current feasible strategy or the predicted feasible strategy, the similarity of the two feature sets is calculated by using the cosine similarity formula, and the calculated similarity is the dynamic connection index of the strategy connection combination.

8. The dynamically allocated and regulated electric scooter charging system of claim 1, wherein, The front connection reasonable value of the current feasible strategy is determined as follows: a current feasible strategy is selected, all strategy connection combinations containing the current feasible strategy are marked as possible connection combinations, the dynamic connection indexes of all possible connection combinations are summed and averaged to calculate an average dynamic connection index, all possible connection combinations are matched two by two, the dynamic connection indexes of the two matched possible connection combinations are calculated by absolute difference to calculate a dynamic connection difference index, all dynamic connection difference indexes are summed and averaged to calculate an average dynamic connection difference index, and the average dynamic connection index and the average dynamic connection difference index are calculated by ratio to calculate the front connection reasonable value of the current feasible strategy.

9. The method for charging electric two-wheeled vehicles based on dynamic allocation and adjustment, applied to the system for charging electric two-wheeled vehicles based on dynamic allocation and adjustment according to any one of claims 1-8, characterized in that, The steps are as follows: Step 1: build a virtual cluster system; Step two: the periodic virtual cluster system determines the current feasible strategy and the predicted feasible strategy; Step three: select the current determined strategy from the current feasible strategy, and use the current determined strategy to allocate and adjust the charging of the cluster of charging piles for all electric two-wheeled vehicles in the charging area.

10. Electric two-wheeler charging storage medium based on dynamic allocation and regulation, characterized in that, The application is applied to the storage of the dynamic allocation and adjustment based electric two-wheeled vehicle charging system as claimed in any one of claims 1-8.

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