A management method, system, equipment and medium for airport charging piles

Abstract

A management method, system, device, and medium for airport charging piles relate to the field of energy management. In this method, in response to receiving a charging request from a target user, the flight information of the person being picked up, bound to the target user, is obtained, and the first moment when the charging gun is inserted into the charging port of the target vehicle is recorded; the current remaining power and battery capacity of the target vehicle are obtained, the charging amount required to reach a full charge state is calculated based on the current remaining power and battery capacity, and the charging time period is determined based on the first and second moments; based on the charging amount, the charging time period, and the charging prices for different time periods, an optimization algorithm is used to select the optimal charging power and charging time combination, and a charging plan is formulated; and the target vehicle is charged according to the charging plan. Implementing the technical solution provided by this application dynamically adjusts the charging plan, reduces charging costs, and improves energy efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of energy management, and in particular to a management method, system, equipment and medium for charging piles at an airport. Background Art

[0002] With the increasing popularity of new energy vehicles, their number in airport areas is constantly increasing, and the demand for charging stations is becoming increasingly urgent. In airport scenarios, a large number of new energy vehicles used for airport pick-up and drop-off services require charging stations near the airport to ensure timely completion of pick-up and drop-off missions. This not only affects the normal use of these vehicles but also has a significant impact on airport traffic order and operational efficiency. Efficient and rational charging station management plays a key role in improving the overall service level and resource utilization efficiency of airports.

[0003] Existing technologies often manage charging stations using a relatively simple and straightforward approach: charging vehicles at a fixed power level, regardless of charging time or electricity prices. As soon as a vehicle is connected to the charging station, charging begins at a constant power level until it is fully charged. However, this fixed-power charging method fails to account for electricity price differences at different times of day, leading to higher charging costs. Furthermore, it fails to consider the specific needs of vehicle pickups and drop-offs, making it prone to overcharging. Summary of the Invention

[0004] The present application provides a management method, system, equipment and medium for airport charging piles, which intelligently plans charging based on flight information, dynamically adjusts charging plans, reduces charging costs, and improves energy utilization efficiency.

[0005] In a first aspect of the present application, a method for managing airport charging piles is provided, which is applied to an airport charging pile management platform. The method comprises:

[0006] In response to receiving a charging request from a target user, obtaining flight information of the person being picked up by the target user, and recording the first time when the charging gun is inserted into the charging port of the target vehicle, the flight information including the flight number and the second estimated arrival time;

[0007] Obtaining a current remaining power and a battery capacity of the target vehicle, calculating a charge amount required to reach a fully charged state based on the current remaining power and the battery capacity, and determining a chargeable time period based on the first moment and the second moment;

[0008] According to the charging amount, the charging time period, and the charging price in different time periods, an optimization algorithm is used to select the optimal charging power and charging time combination to formulate a charging plan;

[0009] The target vehicle is charged according to the charging plan.

[0010] Optionally, the use of an optimization algorithm to select an optimal combination of charging power and charging time and formulate a charging plan includes:

[0011] Establish a four-dimensional constraint space of time, electricity, power, and electricity price, set the time discrete granularity, and establish a two-dimensional state space of accumulated electricity and remaining time;

[0012] Based on the four-dimensional constraint space, the time discrete granularity, and the two-dimensional state space, reversely calculate the minimum cost path for each state node starting from the second moment to generate a decision matrix, where the state node is a combination of a time interval and a power state;

[0013] According to the decision matrix, a power time series starting from the first moment is obtained.

[0014] Optionally, the generating a decision matrix by reversely calculating the minimum cost path of each state node starting from the second moment based on the four-dimensional constraint space, the time discrete granularity, and the two-dimensional state space includes:

[0015] For the current state node, select the target charging power in sequence, where the target charging power is any power level of the charging pile;

[0016] Calculating a second accumulated power corresponding to a next state node based on the first accumulated power of the current state node and the target charging power to obtain a plurality of next state nodes to be selected;

[0017] Determine a target cost increment from the current state node to a target next state node to be selected based on the target charging power, the electricity price of the current time interval, and the time discrete granularity, where the target next state node to be selected is any one of multiple next state nodes to be selected;

[0018] Adding the target cost increment to the minimum cost of the target next state node to be selected to obtain a candidate total cost from the current state node to the charging end time;

[0019] Determine the path corresponding to the candidate minimum total cost as the minimum cost path;

[0020] A decision matrix is ​​generated according to each state node in the minimum cost path and the corresponding optimal charging power.

[0021] Optionally, calculating the second accumulated power corresponding to the next state node based on the first accumulated power of the current state node and the target charging power to obtain multiple next state nodes to be selected includes:

[0022] Calculate the second accumulated power corresponding to the next time interval according to the following formula:

[0023] E ( k +1) = E ( k )+ P ( k )×η×Δ t

[0024] in, E ( k ) represents the first accumulated power of the node in the current state, E ( k +1) represents the second accumulated power of the next state node, P ( k ) represents the current charging power, η represents the charging efficiency, Δ t Indicates the time discrete granularity.

[0025] Optionally, obtaining, according to the decision matrix, a power time series starting from the first moment includes:

[0026] Starting from the starting state node, according to the state transition relationship recorded in the decision matrix, the state node of each time interval is searched in chronological order, the corresponding optimal charging power is read from the decision matrix, and a first power time series is formed based on the optimal charging power;

[0027] Using a moving average filtering algorithm, performing weighted averaging on adjacent power values ​​in the first power time series to obtain a second power time series;

[0028] When the deviation between the current remaining power of the target vehicle and the power predicted based on the current power time series exceeds a preset threshold, a replanning of the charging plan is triggered;

[0029] The triggering condition is re-planned according to the second power time series and the power deviation, and a charging instruction set is generated.

[0030] Optionally, the method further includes:

[0031] By interacting with the airport's flight information management system in real time, the system can obtain the automatic dependent surveillance broadcast signal data of the passenger's flight;

[0032] parsing the automatic dependent surveillance broadcast signal data to extract key information of the flight, the key information including position, speed, and altitude;

[0033] Calculate and update the estimated arrival time of the flight based on the pre-set flight model, airport operating rules and the key information;

[0034] When the difference between the estimated arrival time and the second time exceeds a preset threshold, a charging plan is regenerated.

[0035] Optionally, the method further includes:

[0036] In response to receiving the target vehicle reaching a fully charged state indication sent by the charging pile, determining whether the target vehicle needs to participate in peak shaving of the power grid according to the time difference between the current moment and the second moment and the peak shaving demand signal of the power grid;

[0037] If the target vehicle is required to participate in the peak load regulation of the power grid, a discharge plan is determined according to the discharge benefits in different time periods, the charging prices in different time periods and the time difference.

[0038] In a second aspect of the present application, a management system for airport charging piles is provided, including a collection module, a power module, a calculation module, and an execution module, wherein:

[0039] a collection module configured to, in response to receiving a charging request from a target user, obtain flight information of a person to be picked up bound to the target user, and record the first moment when the charging gun is inserted into the charging port of the target vehicle, the flight information including the flight number and the second estimated arrival time;

[0040] a power module configured to obtain a current remaining power and a battery capacity of the target vehicle, calculate a charge amount required to reach a fully charged state based on the current remaining power and the battery capacity, and determine a chargeable time period based on the first moment and the second moment;

[0041] a calculation module configured to select an optimal charging power and charging time combination using an optimization algorithm based on the charging amount, the charging time period, and the charging prices in different time periods, and formulate a charging plan;

[0042] An execution module is configured to charge the target vehicle according to the charging plan.

[0043] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0044] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed.

[0045] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0046] 1. By obtaining the flight information of the person being picked up, including flight number and estimated arrival time, and combining it with the time the charging plug is plugged in, the available charging time period can be accurately determined. This closely links the charging plan with the flight time, ensuring that the vehicle is fully charged before the pick-up person arrives, avoiding delays due to low vehicle power, and improving user convenience and satisfaction with charging stations.

[0047] 2. Taking into account the differences in the current remaining power and battery capacity of different users' vehicles, it can calculate the required charging amount according to the specific situation and formulate personalized charging plans for users to meet the charging needs of different users in different scenarios;

[0048] 3. Using an optimization algorithm, the optimal combination of charging power and charging time is selected by comprehensively considering the charging capacity, available charging time periods, and charging prices in different time periods. This helps avoid excessive charging concentration at charging piles during peak hours, achieves a reasonable allocation of charging resources over time, improves the efficiency of charging piles, and reduces the number of idle charging piles or waiting in queues.

[0049] 4. Charging prices vary across time periods. An optimization algorithm selects charging times with lower electricity prices and rationally allocates charging power, minimizing user charging costs while still meeting charging needs. Because charging plans are tailored to the vehicle's actual needs and available charging timeframes, overcharging or undercharging is avoided, reducing battery loss and energy waste caused by improper charging. This, in turn, helps lower user charging and maintenance costs in the long term.

[0050] 5. Decisions are made based on multiple data points, including flight information, vehicle power levels, and charging prices, making charging plans more scientific and reasonable. This data-driven decision-making approach reduces human interference and improves system reliability and stability. When abnormal situations such as flight schedule changes or vehicle power levels occur, the management platform can quickly re-plan the charging plan based on the new data, ensuring that the charging process is not affected and that the system operates normally. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of a method for managing airport charging piles disclosed in an embodiment of the present application;

[0052] Figure 2 This is a module diagram of a management system for airport charging piles disclosed in an embodiment of the present application;

[0053] Figure 3This is a structural diagram of an electronic device disclosed in an embodiment of the present application.

[0054] Explanation of the accompanying symbols: 201, acquisition module; 202, power module; 203, calculation module; 204, execution module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0056] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0057] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0058] This embodiment discloses a method for managing charging piles at an airport. Figure 1 This is a flow chart of a method for managing airport charging piles disclosed in an embodiment of the present application, which is applied to an airport charging pile management platform. Figure 1 As shown, the method includes the following steps:

[0059] S101. In response to receiving a charging request from a target user, obtaining flight information of a person to be picked up bound to the target user, and recording the first time when a charging gun is inserted into a charging port of a target vehicle, the flight information including the flight number and the second estimated arrival time;

[0060] S102, obtaining a current remaining power and a battery capacity of the target vehicle, calculating a charge amount required to reach a fully charged state based on the current remaining power and the battery capacity, and determining a chargeable time period based on the first moment and the second moment;

[0061] S103, using an optimization algorithm to select an optimal combination of charging power and charging time based on the charging amount, the charging time period, and the charging prices in different time periods, and formulate a charging plan;

[0062] S104: Charging the target vehicle according to the charging plan.

[0063] When the system receives a charging request from a target user, a series of subsequent actions are triggered. First, the system obtains the flight information of the person being picked up, linked to the target user, and records the exact moment the charging plug was plugged into the target vehicle's charging port, defining this as the first moment. This flight information contains two key elements: the flight number and the second estimated time of arrival. The flight number uniquely identifies the flight, facilitating subsequent query and management; the second estimated time of arrival serves as a crucial time reference for calculating the available charging window. The system also obtains two key pieces of data: the target vehicle's current remaining charge and battery capacity. The current remaining charge reflects the vehicle's current charge level, while the battery capacity indicates the maximum charge the vehicle's battery can store. Based on these values, the system performs a simple mathematical calculation (battery capacity minus the current remaining charge) to determine the amount of charge required to fully charge the vehicle. Based on the first moment the charging plug was plugged into the vehicle's charging port and the second estimated time of arrival of the target user's flight, the system determines a charging window. This window defines the timeframe during which charging operations can occur, constrained by the time the user's charging request is triggered and the flight's arrival time. When developing a charging plan, the system considers multiple factors, including the previously calculated charge level required to reach full charge, the designated available charging timeframe, and the charging prices for different timeframes. Charging prices may vary depending on factors such as power availability and peak and off-peak electricity consumption. To achieve optimal charging results, the system applies an optimization algorithm. This algorithm selects the optimal combination of charging power and time within the available charging timeframe based on these factors. This approach minimizes charging costs while meeting vehicle charging needs, or allows for the development of a charging plan based on other optimization objectives, such as minimizing impact on the power grid. The system then performs actual charging operations on the target vehicle according to the planned charging plan. During charging, the system controls the charging process according to the planned charging power and timeframe, ensuring that the vehicle completes charging within the available charging timeframe and achieving optimal charging results.

[0064] Optionally, the use of an optimization algorithm to select an optimal combination of charging power and charging time and formulate a charging plan includes:

[0065] Establish a four-dimensional constraint space of time, electricity, power, and electricity price, set the time discrete granularity, and establish a two-dimensional state space of accumulated electricity and remaining time;

[0066] Based on the four-dimensional constraint space, the time discrete granularity, and the two-dimensional state space, reversely calculate the minimum cost path for each state node starting from the second moment to generate a decision matrix, where the state node is a combination of a time interval and a power state;

[0067] According to the decision matrix, a power time series starting from the first moment is obtained.

[0068] The time dimension covers the entire charging period, from the moment the charging plug is plugged into the target vehicle's charging port to the estimated arrival time of the passenger's flight. It sets a clear timeframe for the charging plan, within which all charging-related operations and decisions must be made. The charge dimension primarily addresses the vehicle's current remaining charge and the amount of charge required to reach full capacity. Capacity is a core element of the charging plan, determining how much energy the vehicle needs to replenish to meet usage needs. The power dimension affects charging speed and efficiency. Higher charging powers can charge more energy in a shorter time, but may also affect battery life and be subject to limitations of the charging equipment and power grid. The electricity price dimension explains that electricity prices may vary during different time periods due to the peak and valley characteristics of electricity supply. Prices may be higher during peak hours and lower during off-peak hours. Considering electricity prices can reduce charging costs while still meeting charging needs. The continuous charging period can be discretized into specific time intervals. For example, each hour can be divided into smaller time intervals (e.g., 15-minute intervals). The choice of time discretization granularity affects computational accuracy and complexity. Finer granularity improves computational accuracy but increases computational complexity; coarser granularity reduces computational complexity but may sacrifice some accuracy. The cumulative charge dimension represents the total amount of charge already charged to the vehicle during the charging process. As charging progresses, the cumulative charge gradually increases until it reaches full charge. The remaining time dimension represents the time remaining from the current time to the second scheduled arrival time of the flight. As charging progresses, the remaining time gradually decreases. A two-dimensional state space combines the cumulative charge and remaining time to describe different states of the charging process. A state node is a combination of a time interval and a charge state. For example, on a discretized timeline, each time point can be considered a time interval, and the cumulative charge corresponding to each time point constitutes the charge state. These state nodes form the basic unit in the charging planning decision-making process. Starting from the second scheduled arrival time of the flight, the minimum cost path to each state node is calculated backwards. The cost here can take into account factors such as charging costs and battery loss. The goal of the reverse calculation is to find the optimal charging strategy from the end moment to the start moment (the first moment), minimizing the cost of the entire charging process. During the reverse calculation process, for each state node, the minimum cost to reach that node and the corresponding charging power selection are recorded. This information is organized into a decision matrix. Each element in the decision matrix corresponds to a state node and contains the minimum cost to reach that node and the corresponding charging power selection. Starting from the first moment the charging gun is inserted into the charging port of the target vehicle, the charging power that should be selected at each time point is gradually derived based on the information in the decision matrix.Arranging the charging power corresponding to each time point in chronological order creates a power time series. This series describes how the charging power changes over time throughout the charging process and serves as a concrete implementation plan for the charging plan. Charging vehicles according to this power time series can minimize costs while meeting vehicle charging needs.

[0069] The two-dimensional state space includes the charge state dimension, enabling the algorithm to adapt charging planning to different initial charge states. Whether the vehicle's remaining charge is 20% or 50%, the algorithm can identify appropriate charging paths within the four-dimensional constraint space based on the current charge and the target charge (full charge), generating a feasible charging plan. Charging prices vary across time periods. The price dimension within the four-dimensional constraint space, along with the reverse calculation of the minimum cost path, enables the algorithm to select charging times with lower prices, thereby reducing overall charging costs. By setting a discrete time granularity, time is divided into smaller intervals, enabling more precise control of the charging process. Furthermore, reverse calculation of the minimum cost path for each state node avoids unnecessary charging time, ensuring that the vehicle reaches full charge in the shortest possible time. The four-dimensional constraint space comprehensively considers time, charge, power, and price, enabling it to handle a variety of complex charging scenarios. For example, if power grid fluctuations limit charging power during a certain time period, the algorithm can adjust the charging plan based on the four-dimensional constraint space, selecting a suitable charging power and time combination to ensure minimal impact on the charging process. Because the algorithm operates within a four-dimensional constraint space and state space, it is versatile and adaptable to the charging needs of different vehicle types (with varying battery capacities and charging characteristics) and different scenarios (with varying flight arrival times and electricity prices). Whether it's a small electric car or a large electric bus, or a domestic or international flight, the algorithm can generate a reasonable charging plan.

[0070] Optionally, the generating a decision matrix by reversely calculating the minimum cost path of each state node starting from the second moment based on the four-dimensional constraint space, the time discrete granularity, and the two-dimensional state space includes:

[0071] For the current state node, select the target charging power in sequence, where the target charging power is any power level of the charging pile;

[0072] Calculating a second accumulated power corresponding to a next state node based on the first accumulated power of the current state node and the target charging power to obtain a plurality of next state nodes to be selected;

[0073] Determine a target cost increment from the current state node to a target next state node to be selected based on the target charging power, the electricity price of the current time interval, and the time discrete granularity, where the target next state node to be selected is any one of multiple next state nodes to be selected;

[0074] Adding the target cost increment to the minimum cost of the target next state node to be selected to obtain a candidate total cost from the current state node to the charging end time;

[0075] Determine the path corresponding to the candidate minimum total cost as the minimum cost path;

[0076] A decision matrix is ​​generated according to each state node in the minimum cost path and the corresponding optimal charging power.

[0077] For the currently processed state node, the algorithm sequentially selects any available power level at the charging station as the target charging power. Charging stations typically offer a variety of charging power levels, such as 7kW, 22kW, and 60kW. By sequentially selecting different target charging powers, the algorithm provides a basis for subsequent calculations of cost and energy consumption changes under different charging scenarios, ultimately finding the optimal charging power combination. Based on the first cumulative energy level at the current state node (i.e., the energy level at the current state) and the selected target charging power, the algorithm uses the relationship between energy, power, and time (energy = power × time) to calculate the second cumulative energy level corresponding to the next state node. Because time is discretized, the time interval corresponding to the next state node is known. By multiplying the target charging power by the time interval, the energy increase during that time interval is calculated, resulting in the second cumulative energy level. Since multiple target charging power options are available, multiple different second cumulative energy levels are generated, each corresponding to a candidate next state node. Based on the selected target charging power, the electricity price for the current time interval, and the time discretization granularity, the algorithm calculates the target cost increment from the current state node to the target candidate next state node. Cost delta = target charging power × time granularity × electricity price for the current time interval. For example, if the target charging power is 22 kW, the time granularity is 1 hour, and the electricity price for the current time interval is 0.5 yuan / kWh, the cost delta is 22 × 1 × 0.5 = 11 yuan. This quantifies the additional cost of charging at a specific target charging power within the current time interval, providing a basis for subsequent total cost calculations. The target cost delta is added to the minimum cost of the target next-state node to obtain the candidate total cost from the current state node to the charging end time. The minimum cost of the target next-state node was determined in the previous calculation and represents the minimum cost from that state node to the charging end time. By comprehensively considering the charging cost for the current time interval and the minimum cost to reach the charging end time, the total cost of multiple possible paths from the current state node is calculated, allowing the optimal path to be selected. All candidate total costs are compared, and the path with the minimum candidate total cost is determined as the minimum cost path. This means that among all possible charging power options and path combinations, this path can achieve a full charge at the flight's estimated arrival time at the lowest cost. By determining the minimum-cost path, we can find the most cost-effective charging strategy while still meeting charging requirements, providing a key basis for subsequent charging planning. A decision matrix is ​​generated based on each state node and the corresponding optimal charging power in the minimum-cost path. The decision matrix is ​​a two-dimensional data structure, where rows represent different state nodes and columns represent the corresponding optimal charging power.The decision matrix clearly shows the appropriate charging power for each time interval during the charging process, guiding the charging equipment to charge the vehicle according to the optimal strategy. The decision matrix is ​​the concrete manifestation of the charging plan, providing clear operational instructions for the charging system, ensuring that the charging process proceeds according to the optimal plan, achieving efficient and economical charging.

[0078] For each current state node, the system selects any power level from the charging station as the target charging power. The target cost increment from the current state node to the target next state node is accurately calculated by combining the first accumulated charge at the current state node, the target charging power, the electricity price for the current time interval, and the time discretization granularity. This refined calculation method fully accounts for the cost differences between different charging powers and different electricity price time periods, providing an accurate basis for subsequent search for the minimum cost path. By adding the target cost increment to the minimum cost of the target next state node, the candidate total cost from the current state node to the charging end time is calculated. The path with the minimum candidate total cost is then selected as the minimum cost path. This cost-minimization-based path selection strategy ensures that the vehicle always charges along the lowest-cost path throughout the charging process, effectively optimizing charging costs. The minimum cost path for each state node is calculated backwards from the second estimated arrival time of the flight. This backward calculation method quickly determines the optimal charging strategy within a given time constraint (full charging before flight arrival). Compared to forward calculation, reverse calculation is more directly guided by the target time (flight arrival time), quickly converging to the charging path that meets the time requirements and minimizes costs. This avoids unnecessary calculations and attempts, and improves the efficiency of charging planning. During the calculation process, the algorithm dynamically adjusts the charging power selection based on the electricity price and power level in different time intervals. For example, when the flight arrival time is far away and the electricity price is low, a higher power fast charging method may be selected to quickly increase the power level. However, when the flight arrival time is close or the electricity price is high, the power is appropriately reduced to ensure a full charge within the specified time while avoiding unnecessary costs. This reasonable power allocation method improves charging efficiency while meeting the charging time requirements.

[0079] Optionally, calculating the second accumulated power corresponding to the next state node based on the first accumulated power of the current state node and the target charging power to obtain multiple next state nodes to be selected includes:

[0080] Calculate the second accumulated power corresponding to the next time interval according to the following formula:

[0081] E ( k +1) = E ( k )+P ( k )×η×Δ t

[0082] in, E ( k ) represents the first accumulated power of the node in the current state, E ( k +1) represents the second accumulated power of the next state node, P ( k ) represents the current charging power, η represents the charging efficiency, Δ t Indicates the time discrete granularity.

[0083] In state analysis scenarios involving the charging process, there are multiple state nodes, each corresponding to a certain cumulative charge. The current state node is the state point of interest and has a corresponding cumulative charge (first cumulative charge); the target charging power is the set charging power value; the next state node is the next state point that may be reached after the current state node, and its corresponding cumulative charge (second cumulative charge) needs to be calculated. Using the known first cumulative charge of the current state node, the target charging power, and other relevant parameters, the second cumulative charge corresponding to the next state node is calculated, resulting in multiple candidate next state nodes. These candidate next state nodes can be used in subsequent analysis, decision-making, and other processes. For example, in electric vehicle charging planning, they can help determine the vehicle's charge state at various points in time under different charging strategies.

[0084] By calculating the second accumulated energy at multiple candidate next-state nodes, the charging management system can evaluate energy variations under different charging strategies. This helps the system select the optimal charging strategy to meet charging needs while maximizing charging efficiency or minimizing charging costs. By accurately predicting energy variations, the charging management system can more precisely control charging power and time, avoiding overcharging or undercharging. This helps improve charging efficiency, reduce energy waste, and extend battery life. Because this calculation method considers the discrete time granularity (Δt), it can easily adapt to charging management needs at different time scales. When charging conditions or user needs change, the charging management system can dynamically adjust the charging strategy and recalculate the second accumulated energy at the next state node, ensuring flexibility and adaptability of the charging process. Through accurate energy prediction and optimized charging strategies, this technical solution helps enhance the reliability of the charging management system. It can reduce charging failures and safety hazards caused by inaccurate energy predictions or unreasonable charging strategies, thereby improving the stability and safety of the entire charging system.

[0085] Optionally, obtaining, according to the decision matrix, a power time series starting from the first moment includes:

[0086] Starting from the starting state node, according to the state transition relationship recorded in the decision matrix, the state node of each time interval is searched in chronological order, the corresponding optimal charging power is read from the decision matrix, and a first power time series is formed based on the optimal charging power;

[0087] Using a moving average filtering algorithm, performing weighted averaging on adjacent power values ​​in the first power time series to obtain a second power time series;

[0088] When the deviation between the current remaining power of the target vehicle and the power predicted based on the current power time series exceeds a preset threshold, a replanning of the charging plan is triggered;

[0089] The triggering condition is re-planned according to the second power time series and the power deviation, and a charging instruction set is generated.

[0090] Starting from the starting state node, the state nodes for each time interval are searched sequentially in chronological order based on the state transition relationships recorded in the decision matrix. The corresponding optimal charging power is read from the decision matrix, and the first power time series is formed based on these optimal charging powers. The decision matrix is ​​a data structure that contains different state nodes and their corresponding optimal charging powers. It reflects the charging power that should be selected under different charging states to achieve a certain optimization goal (such as minimizing charging time or maximizing charging efficiency). The state transition relationship describes the rules or conditions for transitioning from one state node to another. In charging management, this is typically related to factors such as the vehicle's state of charge, charging time, and charging power. The first power time series: This power series is formed by chronologically arranging the state transition relationships and optimal charging power in the decision matrix. This series represents the charging power that should be used in different time intervals to achieve the optimization goal. The moving average filter algorithm: A common signal processing method used to smooth data series and reduce the impact of random noise. It calculates new data points by taking a weighted average of adjacent data points. The second power time series: This power time series is processed by the moving average filter. Compared to the first power time series, the second power time series is smoother, reducing sudden power fluctuations caused by random fluctuations or noise, thereby improving the stability and reliability of charging management. Battery level deviation refers to the difference between the target vehicle's current remaining battery level and the predicted battery level based on the current power time series. This difference can be caused by various factors, such as battery aging, changes in charging efficiency, and charging equipment failure. A preset threshold determines whether the battery level deviation is significant enough to trigger a rescheduling of the charging plan. Rescheduling: When the battery level deviation exceeds the preset threshold, it indicates that the current charging plan may not meet charging demand or poses a safety hazard, requiring rescheduling. A charging instruction set controls the charging process, including charging power, charging time, and charging start and end times. These instructions are generated based on the second power time series and take into account the battery level deviation rescheduling trigger conditions to ensure the accuracy and reliability of the charging process. Incorporating battery level deviation rescheduling trigger conditions: When generating the charging instruction set, both the second power time series and the battery level deviation rescheduling trigger conditions are considered. This means that if the power deviation exceeds the preset threshold, the system will re-plan the charging plan and generate a new charging instruction set to adapt to the new charging requirements.

[0091] Using the state transition relationships recorded in the decision matrix, starting from the starting state node, the state nodes for each time interval are searched sequentially in chronological order, and the corresponding optimal charging power is retrieved, thereby forming a first power time series. This step ensures that charging power allocation is based on the optimal strategy, improving the accuracy and rationality of the charging plan. A moving average filtering algorithm is used to perform a weighted average of adjacent power values ​​in the first power time series to generate a second power time series. This step helps smooth power fluctuations and reduce the impact of sudden power changes on the battery and charging equipment, thereby improving the stability and safety of the charging process. A rescheduling of the charging plan is triggered when the deviation between the target vehicle's current remaining power and the power predicted based on the current power time series exceeds a preset threshold. This mechanism allows the charging system to dynamically adjust the charging plan based on actual conditions, ensuring that the charging process adapts to the vehicle's actual power demand and external environmental changes. Based on the second power time series and the power deviation rescheduling trigger conditions, a charging instruction set is generated. This step converts the optimized power time series and dynamic adjustment mechanism into specific charging instructions, providing clear operational guidance for the charging equipment and ensuring the automation and intelligence of the charging process. The overall technical solution improves charging efficiency, reduces charging time, and enhances the user experience by accurately generating power time series, smoothing power fluctuations, dynamically adjusting charging plans, and generating optimized charging instruction sets. It also helps extend battery life and reduce charging costs.

[0092] Optionally, the method further includes:

[0093] By interacting with the airport's flight information management system in real time, the system can obtain the automatic dependent surveillance broadcast signal data of the passenger's flight;

[0094] parsing the automatic dependent surveillance broadcast signal data to extract key information of the flight, the key information including position, speed, and altitude;

[0095] Calculate and update the estimated arrival time of the flight based on the pre-set flight model, airport operating rules and the key information;

[0096] When the difference between the estimated arrival time and the second time exceeds a preset threshold, a charging plan is regenerated.

[0097] Through real-time data exchange with the airport's flight information management system (often referred to as the airport operations center), the system acquires Automatic Dependent Surveillance-Broadcast (ADS-B) signal data from the passenger's flight. ADS-B is an aviation surveillance technology that uses satellite navigation systems to determine the aircraft's position and automatically broadcasts this information from the aircraft to ground stations and other aircraft. This data is typically transmitted by an ADS-B transmitter on the aircraft, received by receiving equipment at the airport or relevant agencies, and then transmitted to the system underlying this method via a data interface. The acquired ADS-B signal data is parsed to extract key flight information. This information typically includes the aircraft's position (latitude and longitude), speed (ground speed or airspeed), and altitude (flight level). Using specific data parsing algorithms or tools, the binary data in the ADS-B signal is converted into readable text or numerical form. The flight's estimated time of arrival (ETA) is calculated based on a pre-defined flight model (such as the aircraft's cruising speed and climb / descent rate), combined with airport operating rules (such as runway usage and airspace restrictions), and the extracted key information. The ETA is updated in real time as the flight's flight status (such as changes in position, speed, and altitude) changes. When the difference between the calculated ETA and a pre-set second time (e.g., the originally planned charging completion time) exceeds a preset threshold, a re-charging plan is triggered. Based on the new ETA, the charging plan is re-planned, including parameters such as charging start time, charging power, and charging duration, to ensure that the vehicle is fully charged or reaches the required charge level upon flight arrival.

[0098] By interacting with the airport's flight information management system in real time, this method acquires the Automatic Dependent Surveillance (ADS) broadcast signal data of the passenger's flight. This real-time data acquisition ensures timely and accurate information, providing a reliable foundation for subsequent flight status analysis and charging plan adjustments. The ADS broadcast signal data is parsed to extract key flight information, such as position, speed, and altitude. This critical information is essential for assessing the flight's current status and predicting its future dynamics, facilitating more accurate calculation of the flight's ETA. Based on a pre-defined flight model, airport operating rules, and the extracted key information, the method calculates and updates the flight's ETA. This dynamic update mechanism ensures that the ETA is more accurate and responsive to actual conditions, improving the accuracy and adaptability of charging plans. When the difference between the ETA and a pre-set second time (which may be a key time point in the charging plan) exceeds a preset threshold, the method regenerates the charging plan. This flexible adjustment mechanism ensures that the charging plan can be dynamically optimized based on actual conditions, avoiding waste or insufficient charging resources due to flight delays or early arrivals. By acquiring real-time flight information, accurately calculating estimated arrival times, and flexibly adjusting charging plans, this approach can significantly improve the overall operational efficiency of airport pickup services. It also enhances the user experience, as passengers can more accurately predict the arrival time of their pickup vehicles and plan their trips accordingly.

[0099] Optionally, the method further includes:

[0100] In response to receiving the target vehicle reaching a fully charged state indication sent by the charging pile, determining whether the target vehicle needs to participate in peak shaving of the power grid according to the time difference between the current moment and the second moment and the peak shaving demand signal of the power grid;

[0101] If the target vehicle is required to participate in the peak load regulation of the power grid, a discharge plan is determined according to the discharge benefits in different time periods, the charging prices in different time periods and the time difference.

[0102] When the charging station detects that the target vehicle has reached a full charge, it sends a full charge status indicator to the system. This indicator is crucial for the system to determine whether the vehicle has completed charging and is eligible for subsequent peak load shaving operations. Upon receiving the full charge status indicator, the system calculates the time difference between the current moment and a predetermined second moment (which could be the user's desired vehicle usage time, a key time point in the charging plan, or another predetermined time). This time difference reflects the amount of idle time available after the vehicle completes charging and before the user needs to use the vehicle. The system obtains peak load shaving demand signals from the grid operator in real time or periodically. These signals typically reflect the grid load at different time periods and the grid's demand for adjustable loads (such as electric vehicles). Peak load shaving demand signals may include information such as the amount of load to be increased or decreased and the peak load shaving time period. Based on the time difference between the current moment and the second moment and the grid's peak load shaving demand signal, the system determines whether the target vehicle should participate in peak load shaving. If the time difference is sufficiently long and the grid has peak load shaving demand, then participating in peak load shaving becomes a viable option. If the system decides to allow the target vehicle to participate in grid peak shaving, it develops a specific discharge plan based on the discharge benefits, charging prices, and the calculated time difference. The system considers the benefits of discharging to the grid during different time periods. These benefits may be based on factors such as the grid's peak shaving incentives and price differences. The system also considers the price of recharging the vehicle in the future. This helps the system balance the benefits of discharging with future charging costs when developing the discharge plan. The time difference determines the length of time the vehicle can participate in peak shaving, which in turn influences the discharge plan. For example, if the time difference is long, the vehicle may participate in peak shaving across multiple time periods to maximize benefits. Once the discharge plan is developed, the system sends instructions to the vehicle to discharge to the grid during the specified time period. The vehicle executes the discharge operation according to the instructions, thereby participating in the grid's peak shaving process.

[0103] By responding to the target vehicle's fully charged status indicator sent by the charging station and combining the time difference between the current moment and a preset second moment with the grid's peak-shaving demand signal, this method intelligently determines whether the target vehicle is suitable for participating in grid peak-shaving. This mechanism helps balance grid loads, improve grid stability, and improve operational efficiency. In particular, during peak and off-peak periods, the vehicle's charging and discharging behavior can smooth the grid load curve. If the target vehicle is determined to be suitable for grid peak-shaving, the method formulates a discharge plan based on the discharge benefits and charging prices during different time periods, as well as the time difference between the current moment and the second moment. This discharge plan formulation, based on market signals and vehicle status, helps maximize the vehicle's discharge benefits while reducing charging costs, achieving efficient energy utilization and maximizing economic benefits. This method promotes bidirectional interaction between the vehicle and the grid. The vehicle is no longer simply an energy consumer; it can flexibly transform into an energy producer or storer based on grid demand and its own status. This interactivity contributes to a more intelligent, flexible, and sustainable energy system. For the target vehicle's users, this method allows them to gain additional economic benefits by participating in grid peak-shaving without affecting their normal vehicle use. At the same time, since the discharge plan takes time differences and grid demands into consideration, users can also arrange their car use plans more flexibly, improving convenience and satisfaction.

[0104] This embodiment also discloses a management system for charging piles at airports. Figure 2 This is a module diagram of a management system for airport charging piles disclosed in an embodiment of the present application, such as Figure 2 As shown, the system includes a collection module 201, a power module 202, a calculation module 203 and an execution module 204, wherein:

[0105] The acquisition module 201 is configured to, in response to receiving a charging request from a target user, obtain flight information of a person to be picked up by the target user, and record the first moment when the charging gun is inserted into the charging port of the target vehicle, wherein the flight information includes the flight number and the second estimated arrival time;

[0106] a power module 202 configured to obtain a current remaining power and a battery capacity of the target vehicle, calculate a charge amount required to reach a full charge state based on the current remaining power and the battery capacity, and determine a chargeable time period based on the first time and the second time;

[0107] The calculation module 203 is configured to select an optimal charging power and charging time combination based on the charging amount, the charging time period, and the charging price of different time periods using an optimization algorithm to formulate a charging plan;

[0108] The execution module 204 is configured to charge the target vehicle according to the charging plan.

[0109] Optionally, the calculation module 203 is configured to:

[0110] Establish a four-dimensional constraint space of time, electricity, power, and electricity price, set the time discrete granularity, and establish a two-dimensional state space of accumulated electricity and remaining time;

[0111] Based on the four-dimensional constraint space, the time discrete granularity, and the two-dimensional state space, reversely calculate the minimum cost path for each state node starting from the second moment to generate a decision matrix, where the state node is a combination of a time interval and a power state;

[0112] According to the decision matrix, a power time series starting from the first moment is obtained.

[0113] Optionally, the calculation module 203 is configured to:

[0114] For the current state node, select the target charging power in sequence, where the target charging power is any power level of the charging pile;

[0115] Calculating a second accumulated power corresponding to a next state node based on the first accumulated power of the current state node and the target charging power to obtain a plurality of next state nodes to be selected;

[0116] Determine a target cost increment from the current state node to a target next state node to be selected based on the target charging power, the electricity price of the current time interval, and the time discrete granularity, where the target next state node to be selected is any one of multiple next state nodes to be selected;

[0117] Adding the target cost increment to the minimum cost of the target next state node to be selected to obtain a candidate total cost from the current state node to the charging end time;

[0118] Determine the path corresponding to the candidate minimum total cost as the minimum cost path;

[0119] A decision matrix is ​​generated according to each state node in the minimum cost path and the corresponding optimal charging power.

[0120] Optionally, the calculation module 203 is configured to:

[0121] Calculate the second accumulated power corresponding to the next time interval according to the following formula:

[0122] E ( k +1) = E ( k )+P ( k )×η×Δ t

[0123] in, E ( k ) represents the first accumulated power of the node in the current state, E ( k +1) represents the second accumulated power of the next state node, P ( k ) represents the current charging power, η represents the charging efficiency, Δ t Indicates the time discrete granularity.

[0124] Optionally, the calculation module 203 is configured to:

[0125] Starting from the starting state node, according to the state transition relationship recorded in the decision matrix, the state node of each time interval is searched in chronological order, the corresponding optimal charging power is read from the decision matrix, and a first power time series is formed based on the optimal charging power;

[0126] Using a moving average filtering algorithm, performing weighted averaging on adjacent power values ​​in the first power time series to obtain a second power time series;

[0127] When the deviation between the current remaining power of the target vehicle and the power predicted based on the current power time series exceeds a preset threshold, a replanning of the charging plan is triggered;

[0128] The triggering condition is re-planned according to the second power time series and the power deviation, and a charging instruction set is generated.

[0129] Optionally, the system further includes an adjustment module configured to:

[0130] By interacting with the airport's flight information management system in real time, the system can obtain the automatic dependent surveillance broadcast signal data of the passenger's flight;

[0131] parsing the automatic dependent surveillance broadcast signal data to extract key information of the flight, the key information including position, speed, and altitude;

[0132] Calculate and update the estimated arrival time of the flight based on the pre-set flight model, airport operating rules and the key information;

[0133] When the difference between the estimated arrival time and the second time exceeds a preset threshold, a charging plan is regenerated.

[0134] Optionally, the system further includes a discharge module configured to:

[0135] In response to receiving the target vehicle reaching a fully charged state indication sent by the charging pile, determining whether the target vehicle needs to participate in peak shaving of the power grid according to the time difference between the current moment and the second moment and the peak shaving demand signal of the power grid;

[0136] If the target vehicle is required to participate in the peak load regulation of the power grid, a discharge plan is determined according to the discharge benefits in different time periods, the charging prices in different time periods and the time difference.

[0137] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0138] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0139] The communication bus 302 is used to implement the connection and communication between these components.

[0140] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0141] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0142] The processor 301 may include one or more processing cores. Using various interfaces and circuits, the processor 301 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, as well as accesses data stored in the memory 305, to perform various server functions and process data. Optionally, the processor 301 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 301 but implemented as a separate chip.

[0143] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a method for managing airport charging piles.

[0144] exist Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call an application program for a management method of an airport charging pile stored in the memory 305. When executed by one or more processors 301, the electronic device executes one or more methods in the above embodiments.

[0145] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0146] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0147] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0148] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0149] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0150] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 305 and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory 305 includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a magnetic disk, or an optical disk.

[0151] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variations, uses or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for managing charging piles at an airport, characterized in that: Applied to the airport charging pile management platform, the method includes: In response to receiving a charging request from a target user, obtaining flight information of the person being picked up by the target user, and recording the first time when the charging gun is inserted into the charging port of the target vehicle, the flight information including the flight number and the second estimated arrival time; Obtaining a current remaining power and a battery capacity of the target vehicle, calculating a charge amount required to reach a fully charged state based on the current remaining power and the battery capacity, and determining a chargeable time period based on the first moment and the second moment; According to the charging amount, the charging time period, and the charging price in different time periods, an optimization algorithm is used to select the optimal charging power and charging time combination to formulate a charging plan; charging the target vehicle according to the charging plan, The use of an optimization algorithm to select the optimal combination of charging power and charging time and formulate a charging plan includes: Establish a four-dimensional constraint space of time, electricity, power, and electricity price, set the time discrete granularity, and establish a two-dimensional state space of accumulated electricity and remaining time; Based on the four-dimensional constraint space, the time discrete granularity, and the two-dimensional state space, reversely calculate the minimum cost path for each state node starting from the second moment to generate a decision matrix, where the state node is a combination of a time interval and a power state; According to the decision matrix, a power time series starting from the first moment is obtained, The generating of the decision matrix by reversely calculating the minimum cost path of each state node starting from the second moment according to the four-dimensional constraint space, the time discrete granularity, and the two-dimensional state space includes: For the current state node, select the target charging power in sequence, where the target charging power is any power level of the charging pile; Calculating a second accumulated power corresponding to a next state node based on the first accumulated power of the current state node and the target charging power to obtain a plurality of next state nodes to be selected; Determine a target cost increment from the current state node to a target next state node to be selected based on the target charging power, the electricity price of the current time interval, and the time discrete granularity, where the target next state node to be selected is any one of multiple next state nodes to be selected; Adding the target cost increment to the minimum cost of the target next state node to be selected to obtain a candidate total cost from the current state node to the charging end time; Determine the path corresponding to the candidate minimum total cost as the minimum cost path; A decision matrix is ​​generated according to each state node in the minimum cost path and the corresponding optimal charging power.

2. The management method of airport charging piles according to claim 1, characterized in that: The calculating, based on the first accumulated power of the current state node and the target charging power, a second accumulated power corresponding to the next state node to obtain a plurality of next state nodes to be selected includes: Calculate the second accumulated power corresponding to the next time interval according to the following formula: E ( k +1)= E ( k )+ P ( k )×η×Δ t in, E ( k ) represents the first accumulated power of the node in the current state, E ( k +1) represents the second accumulated power of the next state node, P ( k ) represents the current charging power, η represents the charging efficiency, Δ t Indicates the time discrete granularity.

3. The management method of airport charging piles according to claim 1, characterized in that: Obtaining the power time series starting from the first moment according to the decision matrix includes: Starting from the starting state node, according to the state transition relationship recorded in the decision matrix, the state node of each time interval is searched in chronological order, the corresponding optimal charging power is read from the decision matrix, and a first power time series is formed based on the optimal charging power; Using a moving average filtering algorithm, performing weighted averaging on adjacent power values ​​in the first power time series to obtain a second power time series; When the deviation between the current remaining power of the target vehicle and the power predicted based on the current power time series exceeds a preset threshold, a replanning of the charging plan is triggered; The triggering condition is re-planned according to the second power time series and the power deviation, and a charging instruction set is generated.

4. The management method of airport charging piles according to claim 1, characterized in that: The method further comprises: By interacting with the airport's flight information management system in real time, the system can obtain the automatic dependent surveillance broadcast signal data of the passenger's flight; parsing the automatic dependent surveillance broadcast signal data to extract key information of the flight, the key information including position, speed, and altitude; Calculate and update the estimated arrival time of the flight based on the pre-set flight model, airport operating rules and the key information; When the difference between the estimated arrival time and the second time exceeds a preset threshold, a charging plan is regenerated.

5. The management method of airport charging piles according to claim 1, characterized in that: The method further comprises: In response to receiving the target vehicle reaching a fully charged state indication sent by the charging pile, determining whether the target vehicle needs to participate in peak shaving of the power grid according to the time difference between the current moment and the second moment and the peak shaving demand signal of the power grid; If the target vehicle is required to participate in the peak load regulation of the power grid, a discharge plan is determined according to the discharge benefits in different time periods, the charging prices in different time periods and the time difference.

6. A management system for charging piles at airports, characterized in that: It includes acquisition module, power module, calculation module and execution module, among which: a collection module configured to, in response to receiving a charging request from a target user, obtain flight information of a person to be picked up bound to the target user, and record the first moment when the charging gun is inserted into the charging port of the target vehicle, the flight information including the flight number and the second estimated arrival time; a power module configured to obtain a current remaining power and a battery capacity of the target vehicle, calculate a charge amount required to reach a fully charged state based on the current remaining power and the battery capacity, and determine a chargeable time period based on the first moment and the second moment; a calculation module configured to select an optimal charging power and charging time combination using an optimization algorithm based on the charging amount, the charging time period, and the charging prices in different time periods, and formulate a charging plan; an execution module configured to charge the target vehicle according to the charging plan, The use of an optimization algorithm to select the optimal combination of charging power and charging time and formulate a charging plan includes: Establish a four-dimensional constraint space of time, electricity, power, and electricity price, set the time discrete granularity, and establish a two-dimensional state space of accumulated electricity and remaining time; Based on the four-dimensional constraint space, the time discrete granularity, and the two-dimensional state space, reversely calculate the minimum cost path for each state node starting from the second moment to generate a decision matrix, where the state node is a combination of a time interval and a power state; According to the decision matrix, a power time series starting from the first moment is obtained, The generating of the decision matrix by reversely calculating the minimum cost path of each state node starting from the second moment according to the four-dimensional constraint space, the time discrete granularity, and the two-dimensional state space includes: For the current state node, select the target charging power in sequence, where the target charging power is any power level of the charging pile; Calculating a second accumulated power corresponding to a next state node based on the first accumulated power of the current state node and the target charging power to obtain a plurality of next state nodes to be selected; Determine a target cost increment from the current state node to a target next state node to be selected based on the target charging power, the electricity price of the current time interval, and the time discrete granularity, where the target next state node to be selected is any one of multiple next state nodes to be selected; Adding the target cost increment to the minimum cost of the target next state node to be selected to obtain a candidate total cost from the current state node to the charging end time; Determine the path corresponding to the candidate minimum total cost as the minimum cost path; A decision matrix is ​​generated according to each state node in the minimum cost path and the corresponding optimal charging power.

7. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 5 is executed.

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