Scheduling method and device for airport electric ferry vehicle in photovoltaic direct charging scene

Abstract

The invention relates to the technical field of ferry vehicle scheduling, and discloses a scheduling method and device for an airport electric ferry vehicle in a photovoltaic direct charging scene. A ferry task needing to be executed at a target moment in a target time period is determined based on flight state information; based on the vehicle state information, determining a first ferry vehicle executing a ferry task at each target moment and a second ferry vehicle to be charged; the first ferry vehicle is the electric ferry vehicle which has the minimum residual electric quantity and is in an idle state in at least one electric ferry vehicle of which the residual electric quantity is greater than the minimum electric quantity required for executing the ferry task; the second ferry vehicle is an electric ferry vehicle of which the residual electric quantity is smaller than or equal to a preset electric quantity threshold value and which is in an idle state except the first ferry vehicle; the first ferry vehicle is dispatched to execute a corresponding ferry task at the target moment, and the second ferry vehicle is dispatched to the charging pile for charging according to the sequence of the residual electric quantity from low to high; the utilization rate of the electric energy of the photovoltaic module can be improved on the premise of ensuring that the ferrying task is completed.

Description

Technical Field

[0001] This disclosure relates to the field of shuttle bus scheduling technology, and in particular to a scheduling method and device for airport electric shuttle buses in a photovoltaic direct charging scenario. Background Technology

[0002] With the promotion of green energy technologies, photovoltaic power generation systems are being used more and more widely in airports. Airports have a variety of site resources for installing photovoltaic modules, such as the roofs of terminals and ancillary buildings, the canopies of open parking lots, and open spaces within the airport area. The electricity generated by the photovoltaic modules can be used to directly charge electric shuttle buses (i.e., direct photovoltaic charging), which can reduce the dependence of electric shuttle buses on the traditional power grid and reduce the airport's carbon emissions.

[0003] In traditional airport electric shuttle bus scheduling methods, task scheduling and charging scheduling are relatively independent. Specifically, task scheduling is mainly based on flight schedules to ensure timely passenger pick-up and drop-off. Charging scheduling is often based on fixed time intervals, preset power thresholds, or utilizing off-peak electricity prices at night, with charging power primarily relying on the power grid. In this model, photovoltaic power generation is only connected to the airport's power distribution network as an auxiliary or supplementary power source, and its electricity is incorporated into the airport's overall power load, rather than being directly used to charge the electric shuttle buses.

[0004] Therefore, in the scenario of direct photovoltaic charging, how to schedule tasks and charging for shuttle buses is an urgent problem to be solved. Summary of the Invention

[0005] In view of this, this disclosure proposes a scheduling method and device for airport electric shuttle buses in a photovoltaic direct charging scenario. This method can ensure that electric shuttle buses with low battery power complete their tasks first and are charged first, thereby enabling them to obtain more power generation from the photovoltaic modules. While ensuring the completion of the shuttle task, this method avoids the problem of repeated charging of electric shuttle buses with high battery power and improves the utilization rate of the photovoltaic modules' electrical energy.

[0006] According to one aspect of this disclosure, a method for scheduling airport electric shuttle buses in a photovoltaic direct charging scenario is provided, the method comprising:

[0007] Based on the airport's flight status information within the target time period, determine the shuttle task that needs to be performed at the target time within the target time period;

[0008] For each target time, based on the vehicle status information of each electric shuttle bus within the airport, a first shuttle bus to perform the shuttle task at the target time and a second shuttle bus to be charged are determined. The first shuttle bus is at least one electric shuttle bus with the smallest remaining battery level and currently idle, among those with a remaining battery level greater than the minimum battery level required to perform the shuttle task. The second shuttle bus is any electric shuttle bus other than the first one with a remaining battery level less than or equal to a preset battery threshold and currently idle. The idle state refers to a state where the shuttle bus is not charging and is not performing a shuttle task.

[0009] When the target time is reached at the current time, the first shuttle bus is dispatched to perform the corresponding shuttle task; based on the available photovoltaic power generation and charging pile status information at the target time, the second shuttle bus is dispatched to the charging pile for charging in order of the remaining power of the second shuttle bus from low to high; the first shuttle bus that has completed the shuttle task and the second shuttle bus that has completed charging are in an idle state.

[0010] In one possible implementation, the step of scheduling the second shuttle bus to the charging station for charging based on the available photovoltaic power generation at the target time and the charging pile status information, in ascending order of the remaining battery power of the second shuttle bus, includes:

[0011] For each second shuttle bus sorted in order of battery power from low to high, the target charging station is determined from each charging station whose status information indicates that the output battery power does not exceed the output threshold, the output battery power does not exceed the available photovoltaic power generation of the charging station, and is the closest to the second shuttle bus.

[0012] The second shuttle bus will be dispatched to the target charging station for charging.

[0013] In one possible implementation, before dispatching the second shuttle bus to the charging station based on the available photovoltaic power generation at the target time and the charging pile status information, in ascending order of the remaining battery power of the second shuttle bus, the method further includes:

[0014] Based on a preset power generation prediction model, the predicted power generation of the photovoltaic modules in the airport at the target time and the confidence interval of the predicted power generation are determined.

[0015] Based on the power generation prediction results and the confidence interval, the available photovoltaic power generation is determined.

[0016] In one possible implementation, determining the first shuttle bus to perform the shuttle task at the target time based on the vehicle status information of each electric shuttle bus within the airport includes:

[0017] Based on the task distance of the shuttle mission and the energy consumption per unit distance of the electric shuttle vehicle, the minimum amount of electricity required to complete the shuttle mission is determined.

[0018] From at least one electric shuttle bus whose remaining battery level, as indicated by the vehicle status information, is greater than the minimum battery level, the first shuttle bus closest to the shuttle task is determined in ascending order of remaining battery level.

[0019] In one possible implementation, determining the minimum amount of electricity required to complete the ferry task based on the task distance and the energy consumption per unit distance of the electric shuttle vehicle includes:

[0020] From a pre-established energy consumption table, obtain the energy consumption per unit distance that matches the vehicle status information; wherein, the energy consumption table is used to indicate the energy consumption per unit distance corresponding to different vehicle status information;

[0021] The task energy consumption is obtained by multiplying the task distance by the energy consumption per unit distance.

[0022] The minimum power consumption is determined based on the quotient of the task energy consumption and the battery capacity of the electric shuttle vehicle, as well as a preset safety redundancy value.

[0023] In one possible implementation, the method further includes:

[0024] Every preset time interval, the target time period is updated. The updated target time period is located after the original target time period by a preset time interval. This triggers the execution of the steps to determine the shuttle task to be performed at the target time within the target time period based on the airport's flight status information within the target time period, and subsequent steps.

[0025] In one possible implementation, the method further includes:

[0026] In response to an update of flight status information for the target time period, the steps of determining the shuttle task to be performed at the target time within the target time period based on the airport's flight status information within the target time period, and subsequent steps, are triggered.

[0027] According to another aspect of this disclosure, a scheduling device for airport electric shuttle buses in a photovoltaic direct charging scenario is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0028] According to another aspect of this disclosure, a non-volatile computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described method.

[0029] According to another aspect of this disclosure, a computer program product is provided, including a computer program or a non-volatile computer-readable storage medium carrying the computer program, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0030] Based on the airport's flight status information within a target time period, the shuttle bus tasks to be performed at a target time within that time period are determined. For each target time, based on the vehicle status information of each electric shuttle bus within the airport, the first shuttle bus to perform the shuttle bus task at that target time and the second shuttle bus to be charged are determined. The first shuttle bus is the electric shuttle bus with the smallest remaining battery level and currently idle, among at least one electric shuttle bus with a remaining battery level greater than the minimum battery level required to perform the shuttle bus task. The second shuttle bus is any electric shuttle bus other than the first shuttle bus with a remaining battery level less than or equal to a preset battery level threshold. The system utilizes idle electric shuttle buses. Upon reaching the target time, the first shuttle bus is dispatched to perform the corresponding shuttle task. Based on the available photovoltaic power generation and charging pile status information at the target time, the second shuttle bus is dispatched to the charging pile in ascending order of remaining battery power. This ensures that electric shuttle buses with low battery power complete their tasks first and are thus prioritized for charging, allowing them to obtain more power from the photovoltaic modules. While ensuring the completion of the shuttle task, this avoids the problem of high-battery electric shuttle buses repeatedly charging, thereby improving the utilization rate of photovoltaic module power.

[0031] Meanwhile, since idle electric shuttle vehicles whose power has not reached the preset power threshold enter the charging queue for charging, each electric shuttle vehicle can make full use of the photovoltaic module's power as much as possible while ensuring the completion of the shuttle task, thereby further improving the utilization rate of the photovoltaic module's power.

[0032] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0033] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0034] Figure 1 A flowchart illustrating a scheduling method for airport electric shuttle buses in a photovoltaic direct charging scenario according to an embodiment of the present disclosure;

[0035] Figure 2 A block diagram of a scheduling device for an airport electric shuttle bus in a photovoltaic direct charging scenario according to an embodiment of the present disclosure is shown.

[0036] Figure 3A block diagram of a dispatching device for an airport electric shuttle bus in a photovoltaic direct charging scenario according to another embodiment of the present disclosure is shown. Detailed Implementation

[0037] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0038] As used herein, the terms “comprising,” “including,” “having,” or variations thereof are open-ended and include one or more of the stated features, integrals, elements, steps, components, or functions, but do not exclude the presence or addition of one or more other features, integrals, elements, steps, components, functions, or groups thereof.

[0039] When an element is referred to as “connected,” “coupled,” “responding,” or a variation thereof relative to another element, it may be directly connected, coupled, or responding to another element, or there may be an intermediate element present.

[0040] Although the terms first, second, third, etc., may be used herein to describe various elements / operations, these elements / operations should not be limited by these terms. These terms are only used to distinguish one element / operation from another. Therefore, without departing from the teachings of the inventive concept, a first element / operation in some embodiments may be referred to as a second element / operation in other embodiments.

[0041] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0042] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0043] A photovoltaic (PV) power generation system refers to a system that utilizes the photovoltaic effect to directly convert solar energy into electrical energy through photovoltaic cells. Therefore, PV power generation systems only generate electricity during the day, and their output fluctuates significantly due to weather and seasonal factors. Electric shuttle buses at airports need to transport passengers during the day; therefore, in a direct PV charging scenario, scheduling these electric shuttle buses would present at least the following challenges:

[0044] 1. There is an overlap between the power supply period and the power consumption period: that is, the peak period of photovoltaic module power generation and the peak period of electric shuttle bus operation partially overlap.

[0045] 2. The shuttle bus's shuttle task conflicts with its charging task: That is, the electric shuttle bus cannot charge at the same time while performing the shuttle task. If the scheduling is not done properly, it may lead to insufficient power or waste of photovoltaic power.

[0046] 3. The scheduling of shuttle and charging tasks is complex: the arrival and departure times of flights are unevenly distributed, and the intervals of shuttle tasks are unstable, requiring dynamic adjustment of shuttle vehicle scheduling strategies.

[0047] Based on the above difficulties, this application proposes a scheduling method for airport electric shuttle buses in a photovoltaic direct charging scenario. This scheduling method can combine flight operation patterns and photovoltaic power generation characteristics to schedule electric shuttle buses, optimize the execution of shuttle bus tasks and charging sequence, improve the utilization rate of photovoltaic energy, and ensure the reliability and task completion rate of electric shuttle buses during operation.

[0048] The following is a detailed description of the scheduling method for airport electric shuttle buses in the photovoltaic direct charging scenario proposed in this application. In this embodiment, the method is used in an electronic device as an example. The electronic device can communicate with the electric shuttle bus. The electronic device can be a user terminal or a server. The user terminal includes, but is not limited to, computers, laptops, mobile phones, etc. This embodiment does not limit the type of electronic device.

[0049] Figure 1 A flowchart illustrating a scheduling method for airport electric shuttle buses in a photovoltaic direct-charging scenario according to an embodiment of this disclosure is shown. Figure 1 As shown, the method includes:

[0050] Step 101: Based on the airport's flight status information within the target time period, determine the shuttle service task that needs to be performed at the target time within the target time period.

[0051] The target time within the target time period refers to the time period during which the photovoltaic modules can generate electricity, which has not yet occurred (or has not yet arrived). In other words, the target time has not yet been reached when the shuttle task is determined. The target time can be any time within the target time period. For example, if the time unit of the target time period is 5 minutes (i.e., the time interval between adjacent times is 5 minutes), and the target time period is 7:00-8:00, then the target time is the time every 5 minutes starting from 7:00; or, it can be a specified time within the target time period. For example, the target time is a specified time every 15 minutes starting from 7:15 within the target time period. This embodiment does not limit the way the target time is set.

[0052] Flight status information includes the flight plans of each aircraft taking off and landing at the airport at various times during the target time period. The flight plan includes, but is not limited to, at least one of the following: gate position, planned arrival time, planned departure time, number of passengers, priority of flight mission, delay information, flight addition information, etc. This embodiment does not limit the content included in the flight plan.

[0053] In one example, the electronic device determines the shuttle task for each target time by: identifying the target flight whose planned arrival and departure times in the flight status information are the same as the target time, or whose time difference with the target time is less than a preset time difference; using the gate of the target flight as the start or end point of the shuttle task; and using the passenger pick-up and drop-off area (such as a passenger pick-up and drop-off center, departure hall, etc.) corresponding to the target flight as the end or start point of the shuttle task; determining the number of electric shuttle vehicles to perform the shuttle task based on the number of passengers on the target flight; determining the shuttle time period based on the planned arrival or departure time of the target flight, wherein for a target flight scheduled to depart, the start and end times of the shuttle time period are both before the planned departure time; and for a target flight scheduled to arrive, the start time of the shuttle time period is before the planned arrival time, and the end time of the shuttle time period is after the planned arrival time; and determining the priority of the shuttle task based on the priority of the target flight in the flight status information, wherein the higher the priority of the target flight, the higher the priority of the shuttle task.

[0054] The shuttle time period refers to the time from when the electric shuttle vehicle starts running from its location to the task start point, waits at the task start point, runs from the task start point to the task end point, waits at the task end point, and continues until the passenger drop-off is completed. Optionally, the shuttle time period can be determined based on the historical duration of each electric shuttle vehicle performing the shuttle task. For example, it can be determined based on the maximum historical duration or the average historical duration. This embodiment does not limit the method of determining the shuttle time period.

[0055] In another example, the electronic device inputs flight status information for a target time period into a pre-trained task generation model to obtain a shuttle task for each target moment within the target time period. This shuttle task includes: the task start point, task end point, the number of electric shuttle vehicles required, the shuttle time period, and the priority of the shuttle task. The task generation model is built based on a machine learning model and trained using flight status information samples and the corresponding shuttle task labels for each flight status information sample. This embodiment does not limit the implementation method of the task generation model.

[0056] In other embodiments, the electronic device may also determine the ferry task to be performed at the target time in other ways. This embodiment does not limit the method of determining the ferry task.

[0057] Step 102: For each target time, based on the vehicle status information of each electric shuttle bus in the airport, determine the first shuttle bus to perform the shuttle task at the target time and the second shuttle bus to be charged; wherein, the first shuttle bus is at least one electric shuttle bus with the smallest remaining power and in an idle state among at least one electric shuttle bus with a remaining power greater than the minimum power required to perform the shuttle task; the second shuttle bus is an electric shuttle bus other than the first shuttle bus with a remaining power less than or equal to a preset power threshold and in an idle state; the idle state means that it is not charging and is not performing a shuttle task.

[0058] Vehicle status information is used to indicate the status of the electric shuttle bus at a target time. This status includes: whether it is performing a shuttle task, whether it is charging, the remaining battery power of the electric shuttle bus, and the vehicle's location. Optionally, the status indicated by the vehicle status information may also include the rated battery capacity of the electric shuttle bus, maximum charging power, etc. This embodiment does not limit the content indicated by the vehicle status information.

[0059] Optionally, since the target time is a time that has not yet occurred, the vehicle state information can be obtained by predicting (or estimating) the actual vehicle state at a time that has already occurred. For example, the vehicle state information at the initial time of the target time period can be predicted based on the driving parameters and / or charging parameters of the electric shuttle bus between the time that has already occurred and the initial time of the target time period. Starting from the vehicle state information at the initial time of the target time period, the vehicle state information at each target time within the target time period can be predicted sequentially. The prediction method for the vehicle state information can be based on a machine learning model, that is, inputting the actual vehicle state prediction at a time that has already occurred into a pre-trained state estimation model to obtain the vehicle state information at each target time; or it can be based on a state estimation model such as an Unscented Kalman Filter (UKF). This embodiment does not limit the method for determining the vehicle state information at the target time.

[0060] Based on the vehicle status information of each electric shuttle bus within the airport, the first shuttle bus to perform the shuttle mission at the target time is determined, including: determining the minimum amount of electricity required to complete the shuttle mission based on the mission distance and the energy consumption per unit distance of the electric shuttle bus; and determining the first shuttle bus closest to the shuttle mission from at least one electric shuttle bus whose remaining electricity indicated by the vehicle status information is greater than the minimum amount of electricity, in ascending order of remaining electricity.

[0061] The task distance refers to the length of the task path for each electric shuttle vehicle from the task start point to the task end point. Optionally, the task path is obtained by the electronic device through path planning based on an airport map. For example, the electronic device stores an airport map, which includes the location information required for path planning. The airport map includes, but is not limited to, the location information of aircraft stands, passenger pick-up and drop-off areas, charging piles, vehicle waiting areas, etc. The electronic device determines the feasible paths from the task start point to the task end point within the airport based on the airport map, and determines the shortest path and / or the path with the shortest travel time from each feasible path to obtain the task path. Optionally, when determining the task path, the electronic device can determine the congestion situation and driving speed limit of each feasible path based on its internally stored time-sharing speed limit table, congestion coefficient table, etc., and combine the congestion situation and driving speed limit to determine the task path. This embodiment does not limit the method of determining the task path.

[0062] Energy consumption per unit distance indicates the amount of electricity consumed by the electric shuttle bus per unit distance traveled. The unit distance can be 1 kilometer or other values; this embodiment does not limit the method of setting the unit distance. Optionally, the energy consumption per unit distance of the electric shuttle bus can be a fixed value, for example, set to the maximum value of the energy consumption per unit distance under different scenarios; or, the energy consumption per unit distance of the electric shuttle bus can be a dynamic value, and the value of the energy consumption per unit distance can be determined based on the vehicle status information of the electric shuttle bus.

[0063] Taking energy consumption per unit distance as a dynamic value as an example, based on the task distance of the shuttle mission and the energy consumption per unit distance of the electric shuttle vehicle, the minimum amount of electricity required to complete the shuttle mission is determined, including:

[0064] From the pre-established energy consumption table, obtain the energy consumption per unit distance that matches the vehicle status information; determine the product of the task distance and the energy consumption per unit distance to obtain the task energy consumption; based on the quotient of the task energy consumption and the battery capacity of the electric shuttle vehicle, as well as the preset safety redundancy value, determine the minimum power consumption.

[0065] The energy consumption meter indicates the energy consumption per unit distance corresponding to different vehicle status information. For example, vehicle status information includes at least one of the following: the load status of the electric shuttle bus (e.g., empty, fully loaded), operating speed, ambient temperature, air conditioning load, etc. Each type of vehicle status information corresponds to a unit distance energy consumption, resulting in the energy consumption meter. From the energy consumption meter, the unit distance energy consumption matching the vehicle status information at the target time can be read.

[0066] Task energy consumption can be expressed by the following formula:

[0067] ;

[0068] in, This represents the energy consumption of a ferry mission. This indicates the distance of the ferry mission. This indicates the energy consumption per unit distance for the ferry mission.

[0069] The minimum battery level can be expressed by the following formula:

[0070] ;

[0071] in, This indicates the minimum amount of electricity required to perform a ferry task. This indicates the energy consumption of the ferry mission. This indicates the battery capacity of each electric shuttle bus. This represents a preset safety redundancy value, which can cover the amount of electricity required for the electric shuttle to travel from its location to the starting point of the mission, as well as the amount of electricity required to transfer from the mission endpoint (such as to a charging station or waiting area). The safety redundancy value can be a value between 3% and 8%, or it can be other values. This embodiment does not limit the value of the safety redundancy value.

[0072] After determining the minimum battery level for each electric shuttle bus, the electronic equipment identifies at least one electric shuttle bus with a remaining battery level greater than the minimum battery level from among the idle electric shuttle buses. Then, in ascending order of remaining battery level, it identifies at least one first shuttle bus closest to each shuttle task (specifically, closest to the task's starting point). This ensures that electric shuttle buses with low battery levels complete their tasks first and are thus prioritized for charging. This allows low-battery electric shuttle buses to receive more power from the photovoltaic modules, ensuring the completion of shuttle tasks while avoiding the problem of high-battery electric shuttle buses repeatedly charging, thereby improving the energy utilization rate of the photovoltaic modules.

[0073] A preset power threshold is stored in the electronic device. The preset power threshold can be 100%, 90%, etc., and this embodiment does not limit the value of the preset power threshold. Since the electric shuttle vehicles other than the first shuttle vehicle with remaining power less than or equal to the preset power threshold and in an idle state are designated as the second electric shuttle vehicles, all idle electric shuttle vehicles whose power has not reached the preset power threshold enter the charging queue for charging. Under the premise of ensuring the completion of the shuttle task, it can be ensured that each electric shuttle vehicle makes full use of the photovoltaic module's electrical energy as much as possible, further improving the utilization rate of the photovoltaic module's electrical energy.

[0074] Step 103: If the target time is reached at the current time, the first shuttle bus is dispatched to perform the corresponding shuttle task; based on the available photovoltaic power generation and charging pile status information at the target time, the second shuttle bus is dispatched to the charging pile for charging in order of the remaining power of the second shuttle bus from low to high; the first shuttle bus that has completed the shuttle task and the second shuttle bus that has completed charging are in an idle state.

[0075] Dispatching the first shuttle bus to perform the corresponding shuttle task includes: for each shuttle task, dispatching at least one first shuttle bus corresponding to the shuttle task to run from the vehicle position indicated by the vehicle status information to the task start point, while the vehicle status information of at least one first shuttle bus indicates that a shuttle task is being performed; after each first shuttle bus reaches the full load state, it runs to the task end point, and after reaching the empty load state, the vehicle status information indicates that the first shuttle bus is in an idle state.

[0076] Optionally, the dispatching of at least one first shuttle vehicle corresponding to the shuttle task from its vehicle location to the task start point can be achieved by sending a departure command to at least one first shuttle vehicle. This departure command includes the task start point, task end point, task path, and the path from the vehicle location to the task start point, so that the driver of the first shuttle vehicle can control the first shuttle vehicle to complete the shuttle task according to the departure command. Alternatively, it can be achieved by sending operating parameters to the first shuttle vehicle according to the task start point, task end point, task path, and the path from the vehicle location to the task start point, so that the first shuttle vehicle can automatically drive according to the operating parameters to complete the shuttle task. This embodiment does not limit the way the first shuttle vehicle completes the shuttle task.

[0077] The available photovoltaic (PV) power generation at the target time refers to the portion of the electricity generated by the PV modules at that time that can be transmitted to charging stations to power the electric shuttle buses. It should be noted that the electricity generated by the PV modules at the target time may not only be used for charging the electric shuttle buses but may also be used for purposes such as providing lighting for the airport. Therefore, the available PV power generation may not be equal to the electricity generated by the PV modules.

[0078] For example, the available photovoltaic power generation at the target time is predicted based on a pre-defined power generation prediction model. Optionally, the power generation prediction model can be implemented in ways including but not limited to one of the following:

[0079] The first method involves inputting meteorological information at the target time and geographical information of the photovoltaic modules into a clear-sky irradiance model to determine the irradiance information. Then, the irradiance information output from the clear-sky irradiance model, along with the module temperature of the photovoltaic modules at the target time, is input into a single-diode temperature model. Finally, the active power output from the single-diode temperature model is used to determine the predicted power generation of the photovoltaic modules. In other words, the power generation prediction model includes both a clear-sky irradiance model and a single-diode temperature model.

[0080] The clear-sky irradiance model is used to calculate the theoretical maximum irradiance received by the Earth's surface under ideal conditions of no clouds, no precipitation, and only considering the attenuation of atmospheric molecules and aerosols. Optionally, the clear-sky irradiance model can be the Ineichen and Perez clear-sky irradiance model, or the Simplified Solis clear-sky irradiance model, etc. This embodiment does not limit the implementation method of the clear-sky irradiance model.

[0081] A single-diode temperature model is used to convert irradiance information and module temperature into a curve showing the change in active power. For example, the single-diode temperature model can be an algorithm from the Photovoltaic LIBrary (PVlib), and this embodiment does not limit the implementation of the single-diode temperature model.

[0082] Optionally, after the active power is output by the single diode temperature model, a preset value can be subtracted from the active power to obtain the power generation prediction result.

[0083] The second method involves training a machine learning model based on historical meteorological information and historical power generation data to obtain a power generation prediction model.

[0084] Optionally, historical meteorological information includes, but is not limited to: cloud cover, temperature, wind speed, global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI).

[0085] Optionally, machine learning models include, but are not limited to: Autoregressive Integrated Moving Average (ARIMA), Prophet, eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM).

[0086] The third method involves using a clear-sky irradiance model and a single-diode temperature model to determine the theoretical power generation based on historical meteorological information for each historical moment; obtaining the actual power generation corresponding to that historical moment, and determining the residual between the theoretical and actual power generation for each historical moment; inputting the residuals and historical meteorological information from n historical moments into a pre-set machine learning model to obtain the residual prediction values ​​for m historical moments after n historical moments; iteratively updating the model parameters of the machine learning model based on the residuals and residual prediction values ​​from the m historical moments to obtain the residual prediction model. In this case, the clear-sky irradiance model, the single-diode temperature model, and the residual prediction model are combined to obtain the power generation prediction model. The clear-sky irradiance model and the single-diode temperature model are used to determine the theoretical power generation at the target moment, while the residual prediction model is used to determine the residual prediction value at the target moment based on the residuals and historical meteorological information from historical moments before the target moment. The sum of the theoretical power generation and the residual prediction value is the power generation prediction result.

[0087] In other embodiments, the power generation prediction model can be implemented in other ways, which will not be listed here.

[0088] Optionally, since the power generation prediction results output by the power generation prediction model may contain errors, in order to ensure that the charging pile has photovoltaic modules generating electricity when the electric shuttle bus responds to the charging command, a conservative allocation of electricity can be made using the confidence interval of the photovoltaic prediction. Accordingly, when outputting the power generation prediction results, the power generation prediction model will also output the confidence interval of the power generation prediction results. The confidence interval is used to indicate the degree of uncertainty in the power generation prediction results.

[0089] At this point, based on the available photovoltaic power generation and charging pile status information at the target time, before dispatching the second shuttle bus to the charging pile for charging in order of its remaining power from low to high, the following steps are also included:

[0090] Based on a pre-set power generation prediction model, the predicted power generation of photovoltaic modules in the airport at the target time and the confidence interval of the predicted power generation are determined; based on the predicted power generation and the confidence interval, the available photovoltaic power generation is determined.

[0091] Optionally, based on the power generation prediction results and confidence intervals, the available photovoltaic power generation is determined, including: multiplying the power generation prediction results by the lower limit of the confidence interval to obtain the available photovoltaic power generation; or, assigning different weights to different probability values ​​in the confidence interval, determining the power generation prediction results by multiplying the weighted average of the different probability values ​​to obtain the available photovoltaic power generation. This embodiment does not limit the method of determining the available photovoltaic power generation based on the power generation prediction results and confidence intervals.

[0092] The charging pile status information includes: the number of concurrent charging piles, the output power of the charging piles, the output threshold of the charging piles, and the available photovoltaic power generation of each charging pile.

[0093] The concurrent charging capacity of a charging station refers to the maximum number of electric shuttle buses that can be powered simultaneously. Optionally, different charging stations may have the same or different concurrent charging capacities.

[0094] The output threshold of a charging pile includes the maximum power that the charging pile can output; correspondingly, the output power is the output power of the charging pile. Optionally, the output threshold also includes the power supply limit of the charging pile. The power supply limit refers to the maximum active power that the medium-voltage distribution feeder of the charging pile is allowed to carry when feeding power to the grid.

[0095] A photovoltaic module can provide power to at least one charging pile. In this case, the available photovoltaic power generation of the photovoltaic module can be evenly distributed to each charging pile according to the carrying capacity of the charging pile, or it can be distributed to each charging pile according to a preset weight, so as to obtain the available photovoltaic power generation of each charging pile. This embodiment does not limit the distribution method of the available photovoltaic power generation of the photovoltaic module.

[0096] Accordingly, based on the available photovoltaic power generation and charging pile status information at the target time, the second shuttle bus is dispatched to the charging pile for charging in ascending order of remaining battery power, including:

[0097] For each second shuttle bus sorted in order of battery power from low to high, the target charging station is identified from among the charging stations whose status information indicates that the output battery power has not exceeded the output threshold (i.e., the output power of the charging station has not exceeded the maximum output power of the charging station), the output battery power has not exceeded the available photovoltaic power generation of the charging station, and the station is the closest to the second shuttle bus; when the target time is reached, the second shuttle bus is dispatched to the target charging station for charging.

[0098] Optionally, if no target charging station is identified for a given second shuttle bus, the determination of target charging stations for that second shuttle bus and other second shuttle buses following it is stopped. The second shuttle bus is controlled to maintain its current vehicle status or to run to the charging waiting area of ​​the airport. The second shuttle bus is in an idle state until the next target time is reached. The second shuttle buses are then sorted again according to their remaining battery power from low to high. When a target charging station is identified, the vehicle status of the second shuttle bus at the target time is updated to charging status.

[0099] Since the vehicle status information of electric shuttle buses that are charging or performing shuttle duties may also be used for scheduling electric shuttle buses at subsequent target times, the electronic equipment will also predict the remaining battery power of the second shuttle bus that is charging. When the remaining battery power reaches the desired level (such as when the battery power reaches the maximum capacity), charging of the second shuttle bus will be stopped, and the vehicle status will be updated to idle. Optionally, the second shuttle bus that has finished charging can also be scheduled to transfer to the task waiting area. At the same time, the electronic equipment will also predict the remaining battery power of the first shuttle bus that is performing a shuttle duty, for use in determining the first and second shuttle buses at subsequent target times.

[0100] The remaining battery power of an electric shuttle vehicle that is charging or performing a shuttle task can be determined based on a state estimation model or a machine learning model. This embodiment does not limit the method of determining the remaining battery power.

[0101] Optionally, after obtaining the available photovoltaic power generation of the photovoltaic module at the target time, the electronic device can also reduce the charging power of the second shuttle bus in descending order of remaining power when the available photovoltaic power generation is less than or equal to a preset power generation threshold, so as to ensure that the second shuttle bus with low power can replenish power as much as possible, thereby ensuring that as many electric shuttle buses as possible can complete the shuttle task.

[0102] Optionally, the target time period is updated every preset time interval. The updated target time period is located after the target time period before the update by a preset time interval. This triggers the execution of the steps to determine the shuttle task to be performed at the target time within the target time period based on the airport's flight status information within the target time period, and subsequent steps, i.e., steps 101-103 are executed again.

[0103] For example: if the preset duration is 15 minutes, the initial target time period is 7:00-8:00 every day, and step 101 is triggered for the first time at 6:00, then at 6:00, the shuttle task, the first shuttle vehicle to perform the shuttle task, and the second shuttle vehicle that needs charging are determined for each target time within 7:00-8:00. At 6:15, the target time period is updated to 7:15-8:15, and the shuttle task, the first shuttle vehicle to perform the shuttle task, and the second shuttle vehicle that needs charging are predicted for each target time within 7:15-8:15. At 6:30, the target time period is updated to 7:30-8:30, and the shuttle task, the first shuttle vehicle to perform the shuttle task, and the second shuttle vehicle that needs charging are predicted for each target time within 7:30-8:30. This cycle continues until the photovoltaic modules stop generating electricity, or a stop scheduling command is received.

[0104] Optionally, the preset duration is shorter than the target time period. The first time the ferry task is determined can be the initial time of the target time period. This embodiment does not limit the value of the preset duration or the time of predicting the ferry task.

[0105] Optionally, after step 101, in response to the update of flight status information for the target time period, the step of determining the shuttle task to be performed at the target time within the target time period based on the airport's flight status information within the target time period, and subsequent steps, i.e., triggering the execution of steps 101-103.

[0106] Because the flight status information for the target time period is updated, the shuttle mission, the first shuttle bus, and the second shuttle bus for each target time may also be updated. Therefore, when the flight status information for the target time period is updated, re-determining the shuttle mission, the first shuttle bus, and the second shuttle bus based on the flight status information within the target time period (which is the updated flight status information at this time) can ensure accurate scheduling.

[0107] In summary, the airport electric shuttle bus scheduling method in the photovoltaic direct charging scenario provided in this embodiment determines the shuttle bus tasks to be performed at a target time within a target time period based on the airport's flight status information within that period. For each target time, based on the vehicle status information of each electric shuttle bus within the airport, it determines the first shuttle bus to perform the shuttle bus task at the target time and the second shuttle bus to be charged. The first shuttle bus is the electric shuttle bus with the smallest remaining battery power and currently idle, among at least one electric shuttle bus with a remaining battery power greater than the minimum battery power required to perform the shuttle bus task. The second shuttle bus is any electric shuttle bus other than the first shuttle bus. Electric shuttle buses with remaining power less than or equal to a preset power threshold and in an idle state are selected. If the target time is reached at the current time, the first shuttle bus is dispatched to perform the corresponding shuttle task. Based on the available photovoltaic power generation and charging pile status information at the target time, the second shuttle bus is dispatched to the charging pile in ascending order of remaining power. This ensures that electric shuttle buses with low power levels complete their tasks first and are thus prioritized for charging, allowing them to obtain more power from the photovoltaic modules. While ensuring the completion of the shuttle task, this avoids the problem of high-power electric shuttle buses repeatedly charging, improving the utilization rate of photovoltaic module power.

[0108] Meanwhile, since idle electric shuttle vehicles whose power has not reached the preset power threshold enter the charging queue for charging, each electric shuttle vehicle can make full use of the photovoltaic module's power as much as possible while ensuring the completion of the shuttle task, thereby further improving the utilization rate of the photovoltaic module's power.

[0109] The following example illustrates the scheduling method for airport electric shuttle buses in a photovoltaic direct charging scenario provided in this application. Taking the scheduling of 20 electric shuttle buses in an airport as an example, assuming that each electric shuttle bus has a battery capacity of 350kWh, a maximum charging power of 120kW, and an energy consumption of approximately 2.0kWh / km when fully loaded and approximately 1.4kWh / km when unloaded. The airport has two DC fast charging zones, each connected to different photovoltaic sub-arrays (i.e., photovoltaic modules), with a total installed capacity (i.e., the theoretical maximum power generation capacity obtained by adding up all power generation equipment) of approximately 2MWp. On sunny days, it can provide approximately 8–10MWh of usable photovoltaic power. The main power generation period is from 08:00 to 17:00 during the day, with the peak power generation period being from 11:00 to 14:00.

[0110] The electronic device first acquires flight status information for the target time period (08:00–10:00) at 7:30 AM daily, determines the shuttle bus tasks to be performed at the target times within this time period, and, based on the actual vehicle status information of the 20 electric shuttle buses at 7:30 AM, determines the vehicle status information for each target time within 08:00–10:00. Based on this vehicle status information, it determines the first shuttle bus to perform the shuttle bus task for each target time and the second shuttle bus to be charged. For example, for the first shuttle bus task at 8:05 AM, among the idle electric shuttle buses with remaining battery power greater than the minimum battery power, the electronic device prioritizes dispatching the first shuttle bus with the lowest remaining battery power and closest to the task's starting point to perform the task. At the same time, the remaining idle second shuttle buses are added to the charging queue in ascending order of battery power. At 8:05 AM, neither the first shuttle bus nor the second shuttle buses in the charging queue that can be charged using photovoltaic modules are idle.

[0111] The target time period is updated every 10 minutes. The second target time period is updated with flight status information from 08:10 to 10:10. The shuttle bus tasks to be performed at the target times within the target time period are determined. Based on the actual vehicle status information of the 20 electric shuttle buses at 7:30, the vehicle status information for each target time within 08:10 to 10:10 is determined. Based on this vehicle status information, the first shuttle bus to perform the shuttle bus task for each target time and the second shuttle bus to be charged are determined. This cycle continues until the power generation period from 08:00 to 17:00 is updated.

[0112] When the target time of 9:00 is reached, the first shuttle bus corresponding to the 9:00 shuttle task will be dispatched to perform the shuttle task, and the second shuttle bus corresponding to 9:00 will be dispatched to the nearest charging station to charge in order of battery level from low to high.

[0113] Through the above dynamic scheduling, the electricity generated by the photovoltaic modules is basically continuously absorbed throughout the peak period, the on-time rate of the mission vehicles remains at a high level, the electric shuttle buses with low remaining power can be recharged in time before the next mission, and the electric shuttle buses with high remaining power will not occupy charging resources for a long time.

[0114] When a flight is delayed, the electronic equipment receives the updated flight information and adjusts the shuttle mission, the first shuttle bus, and the second shuttle bus based on the updated flight status information. When the available photovoltaic power generation of the photovoltaic modules is less than or equal to the preset power generation threshold, the charging power of the second shuttle bus is reduced in descending order of remaining power to ensure that the second shuttle bus with low power can replenish its power as much as possible, thereby ensuring that as many electric shuttle buses as possible can complete the shuttle mission.

[0115] The scheduling method for airport electric shuttle buses in the direct-charge scenario provided in this application, in continuous operation tests over several days, has achieved a significant improvement in photovoltaic utilization, a reduction in curtailment rate, and a decrease in grid power consumption compared to traditional fixed-time charging scheduling. Furthermore, it maintains on-time performance even under uncertain conditions such as flight delays and weather fluctuations. For example, on a clear day, photovoltaic utilization increased from approximately 62% to approximately 85%, grid power consumption decreased by approximately 17%, the delay rate dropped to below 0.5%, the remaining power distribution of the electric shuttle buses became more balanced, and the empty mileage and charging waiting time significantly decreased, improving the overall economic efficiency and resilience of the operation.

[0116] Figure 2 A block diagram of a dispatching device for an airport electric shuttle bus in a photovoltaic direct charging scenario according to an embodiment of the present disclosure is shown. Figure 2 As shown, the device includes: a task determination module 210, a task allocation module 220, and a vehicle scheduling module 230.

[0117] The task determination module 210 is used to determine the shuttle task that needs to be performed at a target time within the target time period based on the airport's flight status information within the target time period.

[0118] The task allocation module 220 is used to determine, for each target time, a first shuttle bus to perform the shuttle task at that target time and a second shuttle bus to be charged, based on the vehicle status information of each electric shuttle bus in the airport. The first shuttle bus is at least one electric shuttle bus with the smallest remaining battery level and currently idle, among at least one electric shuttle bus with a remaining battery level greater than the minimum battery level required to perform the shuttle task. The second shuttle bus is any electric shuttle bus other than the first shuttle bus with a remaining battery level less than or equal to a preset battery level threshold and currently idle. The idle state refers to a state where the shuttle bus is not charging and is not performing a shuttle task.

[0119] The vehicle dispatch module 230 is used to dispatch the first shuttle bus to perform the corresponding shuttle task when the target time is reached at the current time; based on the available photovoltaic power generation and charging pile status information at the target time, the second shuttle bus is dispatched to the charging pile for charging in the order of the remaining power of the second shuttle bus from low to high; the first shuttle bus that has completed the shuttle task and the second shuttle bus that has completed charging are in an idle state.

[0120] Optionally, the vehicle dispatching module 230 is used for:

[0121] For each second shuttle bus sorted in order of battery power from low to high, the target charging station is determined from each charging station whose status information indicates that the output battery power does not exceed the output threshold, the output battery power does not exceed the available photovoltaic power generation of the charging station, and is the closest to the second shuttle bus.

[0122] The second shuttle bus will be dispatched to the target charging station for charging.

[0123] Optionally, the device further includes: a power generation prediction module, used for:

[0124] Based on the available photovoltaic power generation and charging pile status information at the target time, before dispatching the second shuttle bus to the charging pile for charging in order of the remaining power of the second shuttle bus from low to high, the predicted power generation result of the photovoltaic modules in the airport at the target time and the confidence interval of the predicted power generation result are determined based on a preset power generation prediction model.

[0125] Based on the power generation prediction results and the confidence interval, the available photovoltaic power generation is determined.

[0126] Optionally, the task allocation module 220 is used for:

[0127] Based on the task distance of the shuttle mission and the energy consumption per unit distance of the electric shuttle vehicle, the minimum amount of electricity required to complete the shuttle mission is determined.

[0128] From at least one electric shuttle bus whose remaining battery level, as indicated by the vehicle status information, is greater than the minimum battery level, the first shuttle bus closest to the shuttle task is determined in ascending order of remaining battery level.

[0129] Optionally, the task allocation module 220 is specifically used for:

[0130] From a pre-established energy consumption table, obtain the energy consumption per unit distance that matches the vehicle status information; wherein, the energy consumption table is used to indicate the energy consumption per unit distance corresponding to different vehicle status information;

[0131] The task energy consumption is obtained by multiplying the task distance by the energy consumption per unit distance.

[0132] The minimum power consumption is determined based on the quotient of the task energy consumption and the battery capacity of the electric shuttle vehicle, as well as a preset safety redundancy value.

[0133] Optionally, the device further includes: a task update module, used for:

[0134] Every preset time interval, the target time period is updated. The updated target time period is located after the original target time period by a preset time interval. This triggers the execution of the steps to determine the shuttle task to be performed at the target time within the target time period based on the airport's flight status information within the target time period, and subsequent steps.

[0135] Optionally, the task update module is further configured to:

[0136] In response to an update of flight status information for the target time period, the steps of determining the shuttle task to be performed at the target time within the target time period based on the airport's flight status information within the target time period, and subsequent steps, are triggered.

[0137] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0138] This disclosure also provides a scheduling device for airport electric shuttle buses in a photovoltaic direct charging scenario, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0139] This disclosure also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0140] This disclosure also provides a computer program product, including a computer program or a non-volatile computer-readable storage medium carrying the computer program, wherein the computer program, when executed by a processor, implements the steps of the above method.

[0141] Figure 3 This is a block diagram illustrating a scheduling device 1900 for an airport electric shuttle bus in a photovoltaic direct-charging scenario, according to an exemplary embodiment. For example, device 1900 can be provided as a server or terminal device. (Refer to...) Figure 3The apparatus 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0142] Device 1900 may also include a power supply component 1926 configured to perform power management of device 1900, a wired or wireless network interface 1950 configured to connect device 1900 to a network, and an input / output interface 1958 (I / O interface). Device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TM macOS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0143] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of the device 1900 to perform the above-described method.

[0144] Computer-readable storage media can be tangible devices capable of holding and storing programs / instructions used by instruction execution devices. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0145] The computer program (or computer-readable program instructions) described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage medium in the respective computing / processing device.

[0146] The computer program (or computer program instructions) used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions to implement various aspects of this disclosure.

[0147] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0148] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0149] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0150] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0151] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A scheduling method for airport electric shuttle buses in a photovoltaic direct charging scenario, characterized in that, The method includes: Based on the airport's flight status information within the target time period, determine the shuttle task that needs to be performed at the target time within the target time period; For each target time, based on the vehicle status information of each electric shuttle bus within the airport, a first shuttle bus to perform the shuttle task at the target time and a second shuttle bus to be charged are determined. The first shuttle bus is at least one electric shuttle bus with the smallest remaining battery level and currently idle, among those with a remaining battery level greater than the minimum battery level required to perform the shuttle task. The second shuttle bus is any electric shuttle bus other than the first one with a remaining battery level less than or equal to a preset battery threshold and currently idle. The idle state refers to a state where the shuttle bus is not charging and is not performing a shuttle task. When the target time is reached at the current time, the first shuttle bus is dispatched to perform the corresponding shuttle task; based on the available photovoltaic power generation and charging pile status information at the target time, the second shuttle bus is dispatched to the charging pile for charging in order of the remaining power of the second shuttle bus from low to high; the first shuttle bus that has completed the shuttle task and the second shuttle bus that has completed charging are in an idle state.

2. The method according to claim 1, characterized in that, The step of scheduling the second shuttle bus to the charging station for charging, based on the available photovoltaic power generation and charging pile status information at the target time and in ascending order of remaining battery power, includes: For each second shuttle bus sorted in order of battery power from low to high, the target charging station is determined from each charging station whose status information indicates that the output battery power does not exceed the output threshold, the output battery power does not exceed the available photovoltaic power generation of the charging station, and is the closest to the second shuttle bus. The second shuttle bus will be dispatched to the target charging station for charging.

3. The method according to claim 1, characterized in that, Before dispatching the second shuttle bus to the charging station for charging based on the available photovoltaic power generation and charging pile status information at the target time, according to the order of the remaining power of the second shuttle bus from low to high, the following steps are also included: Based on a preset power generation prediction model, the predicted power generation of the photovoltaic modules in the airport at the target time and the confidence interval of the predicted power generation are determined. Based on the power generation prediction results and the confidence interval, the available photovoltaic power generation is determined.

4. The method according to claim 1, characterized in that, The step of determining the first shuttle bus to perform the shuttle mission at the target time based on the vehicle status information of each electric shuttle bus within the airport includes: Based on the task distance of the shuttle mission and the energy consumption per unit distance of the electric shuttle vehicle, the minimum amount of electricity required to complete the shuttle mission is determined. From at least one electric shuttle bus whose remaining battery level, as indicated by the vehicle status information, is greater than the minimum battery level, the first shuttle bus closest to the shuttle task is determined in ascending order of remaining battery level.

5. The method according to claim 4, characterized in that, The determination of the minimum electricity required to complete the ferry task based on the task distance and the energy consumption per unit distance of the electric shuttle vehicle includes: From a pre-established energy consumption table, obtain the energy consumption per unit distance that matches the vehicle status information; wherein, the energy consumption table is used to indicate the energy consumption per unit distance corresponding to different vehicle status information; The task energy consumption is obtained by multiplying the task distance by the energy consumption per unit distance. The minimum power consumption is determined based on the quotient of the task energy consumption and the battery capacity of the electric shuttle vehicle, as well as a preset safety redundancy value.

6. The method according to claim 1, characterized in that, The method further includes: Every preset time interval, the target time period is updated. The updated target time period is located after the original target time period by a preset time interval. This triggers the execution of the steps to determine the shuttle task to be performed at the target time within the target time period based on the airport's flight status information within the target time period, and subsequent steps.

7. The method according to claim 1, characterized in that, The method further includes: In response to an update of flight status information for the target time period, the steps of determining the shuttle task to be performed at the target time within the target time period based on the airport's flight status information within the target time period, and subsequent steps, are triggered.

8. A scheduling device for airport electric shuttle buses in a photovoltaic direct charging scenario, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program, or a non-volatile computer-readable storage medium carrying a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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