Vehicle demand prediction method, device and equipment

Abstract

The invention relates to a vehicle demand prediction method, device and equipment, and the method comprises the steps: obtaining a vehicle operation rule of a vehicle of a preset type; flights meeting flight conditions in the vehicle operation rules are screened out from the predicted flights, and flight information of the screened flights is obtained; and according to operation conditions in the vehicle operation rule and the flight information of the screened flights, determining a first demand number of vehicles of a preset type in a preset time period.

Description

Technical Field

[0001] This disclosure relates to the field of aviation-related vehicle planning, and more particularly to a vehicle demand forecasting method, apparatus, device, computer-readable storage medium, and computer program product. Background Technology

[0002] When flights take off and land at the airport, a large number of vehicles are needed for coordination and maintenance. These vehicles include special vehicles such as shuttle buses, water trucks, sewage trucks, garbage trucks, lift trucks, conveyor trucks, tractor trucks, passenger boarding stairs, power supply trucks, gas supply trucks, and air conditioning trucks.

[0003] With the increasing prevalence of air travel and the development of aviation services, the number of vehicles required by each airport to support and maintain flights is gradually changing. Forecasting the required number of vehicles for airports is essential for better vehicle preparation and scheduling. Currently, vehicle demand forecasting often employs methods based on direct empirical estimation or simple linear fitting. Summary of the Invention

[0004] According to a first aspect of this disclosure, a vehicle demand forecasting method is provided, comprising: obtaining vehicle operation rules for a preset type of vehicle; filtering flights that meet the flight conditions in the vehicle operation rules from the expected flights, and obtaining flight information of the filtered flights; and determining a first demand quantity of the preset type of vehicle within a preset time period based on the operation conditions in the vehicle operation rules and the flight information of the filtered flights.

[0005] In some embodiments, the operating conditions include the operating time required for a vehicle of a preset type to perform the operation and the window time range in which a vehicle of a preset type can perform the operation.

[0006] In some embodiments, determining the first demand quantity of a preset type of vehicle within a preset time period based on the operation conditions in the vehicle operation rules and the flight information of the selected flights includes: reading from the flight information of the selected flights, or determining, based on the flight information of the selected flights and the window time range, the window time period in which each flight can perform operations and the second demand quantity of the preset type of vehicle required for performing operations on each flight; for each sub-time period within the preset time period, determining the third demand quantity of the preset type of vehicle based on the window time period that at least partially overlaps with the sub-time period, the second demand quantity associated with the window time period, and the operation duration, wherein the preset time period is divided into one or more sub-time periods, and adjacent sub-time periods are consecutive and do not overlap; and determining the largest third demand quantity among the third demand quantities determined for all sub-time periods as the first demand quantity.

[0007] In some embodiments, the start and / or end times of each window period are the endpoints of the corresponding sub-periods. When the operation duration is equal to the window period, in sub-periods that at least partially overlap with multiple window periods, the third demand quantity is the sum of the second demand quantities of vehicles required by the multiple window periods; or when the operation duration is less than the window period, in sub-periods that at least partially overlap with multiple window periods, the third demand quantity is less than or equal to the sum of the second demand quantities of vehicles required by the multiple window periods.

[0008] In some embodiments, the preset type of vehicles includes a first class of vehicles, which are configured to perform a first operation for a flight and a second operation for a vehicle; wherein the start and / or end time of each window period is the endpoint of the corresponding sub-period, and the end time of each first operation and / or the end time of each second operation is the endpoint of the corresponding sub-period; determining the first demand quantity of the preset type of vehicles within the preset period based on the operation conditions in the vehicle operation rules and the flight information of the selected flights further includes: obtaining the first number of first operations that the first class of vehicles can perform at most before performing the second operation and the second operation duration required to perform the second operation; for each sub-period within the preset period, determining the number of first operations that each first class of vehicles has performed since the last second operation before that sub-period; determining the number of first class vehicles whose number of first operations performed is equal to the first number as the number of second operations to be performed, and determining the updated third demand quantity of the preset type of vehicles based on the window period that at least partially overlaps with the sub-period, the second demand quantity associated with the window period, the operation duration, the number of second operations to be performed, and the second operation duration.

[0009] In some embodiments, the preset type of vehicles further includes a second type of vehicles, which are configured to perform only the first operation for flights; determining the first demand quantity of the preset type of vehicles within a preset time period based on the operation conditions in the vehicle operation rules and the flight information of the selected flights includes: calculating, within the preset time period, the first demand quantity of only the first type of vehicles as the fourth demand quantity and the first demand quantity of only the second type of vehicles as the fifth demand quantity, and using the ratio of the fourth demand quantity to the fifth demand quantity as the replacement ratio; and determining the number of first type vehicles required to replace the second type of vehicles in the preset type of vehicles with first type vehicles based on the replacement ratio.

[0010] In some embodiments, the first type of vehicle is an electric vehicle, and obtaining the first number of first operations that the first type of vehicle can perform at most before performing the second operation includes: obtaining the average power consumption and minimum power limit of the first operation; and determining the first number based on the quotient obtained by dividing the difference between the total power consumption and the minimum power limit by the average power consumption of the operation.

[0011] In some embodiments, the product of the average power consumption of the operation and the temperature influence coefficient is used as the updated average power consumption of the operation, wherein the temperature influence coefficient is greater than 1 when the temperature is not within a predetermined threshold range.

[0012] In some embodiments, flight conditions include at least one of the following: arrival and departure status of flights served by vehicles of a preset type, aircraft type, operating area, operating time period, and historical demand data.

[0013] In some embodiments, the type of vehicle is determined based on at least one of the vehicle's job type, job area, and vehicle model.

[0014] In some embodiments, determining the first demand quantity of vehicles of a preset type based on the operating conditions in the vehicle operation rules and the flight information of the selected flights further includes: multiplying the first demand quantity by a redundancy coefficient as the updated first demand quantity of vehicles of the preset type.

[0015] According to a second aspect of this disclosure, a vehicle demand forecasting device is provided, comprising: a rule acquisition module, a flight screening module, and a demand determination module. The rule acquisition module is configured to acquire vehicle operation rules for a preset type of vehicle; the flight screening module is configured to screen flights from expected flights that meet the flight conditions in the vehicle operation rules and acquire the flight information of the screened flights; the demand determination module is configured to determine a first demand quantity for the preset type of vehicle based on the operation conditions in the vehicle operation rules and the flight information of the screened flights.

[0016] According to a third aspect of this disclosure, a vehicle demand forecasting device is provided, comprising: a memory and a processor coupled to the memory; wherein the memory is configured to store instructions; when the instructions are executed by the processor, the operation of the aforementioned vehicle demand forecasting method is implemented.

[0017] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having instructions stored thereon, wherein the instructions, when executed by a processor, implement the operation of the aforementioned vehicle demand forecasting method.

[0018] According to a fifth aspect of this disclosure, a computer program product is provided, including instructions that, when executed by a processor, implement the operation of the aforementioned vehicle demand forecasting method.

[0019] Other features and advantages of this disclosure will become clearer from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0020] The accompanying drawings, which form part of this specification, illustrate embodiments of this disclosure and, together with the specification, serve to explain the principles of this disclosure.

[0021] This disclosure will become clearer with reference to the accompanying drawings and the following detailed description, wherein:

[0022] Figure 1 A flowchart illustrating a vehicle demand forecasting method according to an exemplary embodiment of the present disclosure is shown;

[0023] Figure 2 It shows Figure 1 A flowchart illustrating step S120 in the process;

[0024] Figure 3 A flowchart illustrating a vehicle demand forecasting method according to an exemplary embodiment of the present disclosure is shown;

[0025] Figure 4 A schematic diagram of a vehicle demand forecasting process according to a specific embodiment of the present disclosure is shown;

[0026] Figure 5 A schematic diagram of a vehicle demand forecasting process according to another specific embodiment of the present disclosure is shown;

[0027] Figure 6 A schematic diagram of a vehicle demand forecasting process according to yet another specific embodiment of the present disclosure is shown;

[0028] Figure 7 An example block diagram of a vehicle demand prediction device according to some embodiments of the present disclosure is shown;

[0029] Figure 8 An example block diagram of a vehicle demand prediction device according to some embodiments of the present disclosure is shown.

[0030] Note that in the embodiments described below, the same reference numerals are sometimes used across different figures to denote the same parts or parts having the same function, and repeated descriptions are omitted. In this specification, similar reference numerals and letters are used to denote similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0031] For ease of understanding, the positions, dimensions, and extents of the structures shown in the accompanying drawings and other materials may not represent actual positions, dimensions, and extents. Therefore, the disclosed invention is not limited to the positions, dimensions, and extents disclosed in the accompanying drawings and other materials. Furthermore, the drawings are not necessarily drawn to scale, and some features may be enlarged to show details of specific components. Detailed Implementation

[0032] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0033] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use. Those skilled in the art will understand that they merely illustrate exemplary ways that can be used to implement this disclosure, and are not exhaustive.

[0034] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0035] The inventors noted that current forecasts of vehicle demand are usually based on experience, such as making linear estimates based on past vehicle demand, which makes it difficult to make corresponding adjustments based on the density of flight schedules. Moreover, different vehicles have different actual working times but use the same forecasting rules, so the results of forecasting vehicle demand are unreasonable and cannot meet the needs of actual vehicle preparation and scheduling.

[0036] To address the aforementioned issues, this disclosure proposes a vehicle demand forecasting method.

[0037] In one exemplary embodiment of this disclosure, such as Figure 1 As shown, the vehicle demand forecasting method may include: step S100, obtaining vehicle operation rules for a preset type of vehicle; step S110, filtering out flights that meet the flight conditions in the vehicle operation rules from the expected flights, and obtaining the flight information of the filtered flights; and step S120, determining the first demand quantity of the preset type of vehicle within a preset time period based on the operation conditions in the vehicle operation rules and the flight information of the filtered flights.

[0038] In some embodiments, the type of vehicle can be determined based on at least one of the vehicle's operation type, operation area, and vehicle model. In other words, if two vehicles have different operation types, operation areas, or vehicle models, then these two vehicles can be classified into different types. Vehicles can be categorized according to their operation type as shuttle buses, water trucks, sewage trucks, garbage trucks, elevator trucks, conveyor trucks, tractor trucks, passenger boarding stairs, power supply trucks, gas supply trucks, air conditioning trucks, etc. Vehicle models can include different models for vehicles performing the same operation type; for example, passenger boarding stairs may have different sizes to accommodate the door heights of aircraft at different altitudes. The operation area of ​​a vehicle can be global, such as supporting related operations throughout the entire airport, or it can be zoned, such as supporting related operations only in a designated area within the airport. In some embodiments, the initial demand quantity for each type of vehicle can be predicted separately; for example, predictions can be made separately for vehicles with different operation types, different models, and different operation areas. In a specific example, the prediction results for vehicles operating in different areas can be summed to obtain a global prediction result for the entire airport.

[0039] In some embodiments, vehicle operation rules may include operation conditions and flight conditions. The operation conditions for vehicles may include the operation duration required for a preset type of vehicle to perform the operation and the window time range within which the preset type of vehicle can perform the operation. For example, the window period within which operations can be performed on a corresponding flight can be determined based on the window time range and the corresponding flight's takeoff and landing times. It is important to note that the window time range is set for vehicles, while the window period is set for flights. For example, the window time range may indicate the relationship between the window period and the flight's takeoff and landing times; for instance, the window time range may be from 20 minutes after the flight's landing time to 10 minutes before the flight's departure time, and the window period for the flight can be obtained based on the actual flight's takeoff and landing times. In some embodiments, the window period may generally be set between the flight's previous arrival time and its next departure time, within which vehicles can perform the relevant operations. In other embodiments, some vehicles may begin work before the flight's arrival time to complete preparations, and the start time of the window period may be before the flight's arrival time. In still other embodiments, some vehicles may finish work some time after the flight's departure time to complete finishing work, and the end time of the window period may be after the flight's departure time. For example, a water truck can begin its work some time before the flight's arrival time to replenish the required fresh water, while a sewage truck can empty its sewage after the flight's departure time. The operation duration refers to the actual time required for the vehicle to perform the operation. For example, the operation duration can include the actual interaction time with the flight, the vehicle's preparation time before the operation, and the travel time required for the vehicle to reach different operation locations. Different vehicles may have different operation durations, and the same vehicle may have different operation durations for different types of flights. In some embodiments, the operation duration can be equal to the window period; in other words, the entire window period must be used for operation. For example, a passenger boarding bridge vehicle operates continuously during the window period. In other embodiments, the operation duration can be less than the window period, allowing for a selection of time within the window period for operation to avoid conflicts with other operations. For example, if the water truck's operation duration is short, it only needs to select a time outside the time range used for other operations within the overlapping window period of that operation. That is, multiple operations can be performed within a time range overlapping with multiple window periods.

[0040] Flight conditions may include at least one of the following: arrival / departure status of flights served by a preset type of vehicle, aircraft type, operating area, operating time period, and historical demand data. Specifically, flight conditions determine the specific flights served by that type of vehicle, thus enabling the filtering of these flights to calculate the demand for the preset type of vehicle. Some vehicles are only used on certain flights, therefore, corresponding flight conditions are used to filter out the flights corresponding to these vehicles. The aircraft type of the flight affects the selection of the specific vehicle type. For example, large passenger boarding stairs are used on aircraft with high doors, while small passenger boarding stairs are used on aircraft with low doors. In some embodiments, the vehicle may be in a global support state, in which case all flights on the entire apron are included in the calculation. In other embodiments, the vehicle may be in a zoned support state, in which case the apron is divided into zones, and the vehicle only operates within the designated area during the corresponding time period of the zoned support state. Therefore, in the prediction of the corresponding zone, it is necessary to filter out the flights within that zone. Specifically, historical demand data can include the bridge utilization rate of different flights in the past, and the special needs of certain departure or arrival airports (e.g., the need for defrosting in cold regions, and the need to cope with heavy rain, strong winds, and dense fog in areas with extreme weather). For example, if a shuttle bus is only needed when a flight does not dock at a bridge, historical flight data can be used to infer the future bridge utilization rate, and the decision to activate the shuttle bus can be made based on the bridge utilization rate.

[0041] By acquiring the vehicle operation rules for a preset type of vehicle, the flight conditions of the flights associated with the vehicle to be predicted, as well as the operation conditions of the vehicle to be predicted, can be determined, allowing the prediction to be based on specific vehicle types and associated flights, thus increasing the accuracy of the prediction.

[0042] Then, in step S110, flights that meet the flight conditions in the vehicle operation rules are selected from the expected flights, and the flight information of the selected flights is obtained.

[0043] In some embodiments, all anticipated flights can be determined based on flight schedules or flight dynamics data. Flights meeting the conditions of a preset vehicle type can be filtered based on information matching flight conditions in vehicle operation rules, such as aircraft type, departure and arrival airports, and designated areas on the apron in future flight schedules. Flights meeting the conditions of a preset vehicle type can also be filtered based on historical demand data related to flights (e.g., gate utilization rate). Flight information may include the flight's landing time and / or departure time and / or time on the apron, information for calculating the number of operations to be completed for a second demand quantity, or the required number of each type of vehicle, etc.

[0044] By filtering flights from flight schedules and obtaining flight information, detailed information about the flights corresponding to the vehicles to be predicted can be obtained, making the prediction specific to each relevant flight and increasing the accuracy of the prediction.

[0045] Then, in step S120, the first demand quantity of vehicles of the preset type within the preset time period is determined based on the operating conditions in the vehicle operation rules and the flight information of the selected flights.

[0046] In some embodiments, such as Figure 2 As shown, step S120 may include: step S122, reading from the flight information of the selected flights, or determining based on the flight information of the selected flights and the window time range, to obtain the window time period in which each flight can perform operations and the second demand quantity of the preset type of vehicles required for performing operations on each flight within a preset time period; step S124, for each sub-time period within the preset time period, determining the third demand quantity of the preset type of vehicles based on the window time period that at least partially overlaps with the sub-time period, the second demand quantity associated with the window time period, and the operation duration; and step S126, determining the largest third demand quantity among the third demand quantities determined for all sub-time periods as the first demand quantity.

[0047] If the flight information contains a window period and / or a second demand quantity, the flight information can be read directly to obtain the window period and / or the second demand quantity. If the flight information does not contain a window period and / or a second demand quantity, the window period can be calculated based on the takeoff time, landing time, time on the apron, and the window time range in the vehicle operation rules that allows the operation to be carried out, and / or the second demand quantity can be calculated based on the operation volume, operation location, and other data recorded in the flight information.

[0048] In step S124, for each sub-period within a preset time period, a third demand quantity for a preset type of vehicle is determined based on a window period that at least partially overlaps with the sub-period, a second demand quantity associated with the window period, and a working duration. The preset time period is divided into one or more sub-periods, and adjacent sub-periods are continuous and do not overlap.

[0049] In some embodiments, sub-time periods can be divided in any way, and the lengths of different sub-time periods can be the same or different. Sub-time periods can use the start and / or end points of each window time period as the endpoints for division. The more endpoints used for division of sub-time periods, the higher the prediction accuracy; the fewer endpoints used for division of sub-time periods, the faster the prediction speed.

[0050] Figure 4 and Figure 5An example schematic diagram of a vehicle demand forecasting process according to some embodiments of the present disclosure is shown. Figure 4 and Figure 5 As shown, the horizontal axis represents time, from time t0 to time t1, and the vertical axis represents the number of vehicles required. Dashed boxes represent the operation window. Solid boxes represent the operation duration. Within the corresponding preset time period, the number of vehicles needed to perform the operation is predicted. In these embodiments, each flight in all flights requires only one vehicle of the preset type, i.e., the second required quantity is always 1, and the length of the operation window corresponding to each flight remains consistent. However, in other embodiments not shown, a flight may require one or more vehicles of the preset type, and the length of the operation window corresponding to different flights may differ; this is not limited.

[0051] like Figure 4 As shown, the operation durations of operations 1100, 1102, 1104, 1106, and 1108 are equal to their respective window periods. The window period for operation 1100 starts at time t0, for operation 1106 at time t2, for operations 1102 and 1108 at time t3, and for operation 1104 at time t4. The starting point of each window period is used as the boundary for defining sub-periods. Since the operation duration equals the window period, the entire window period must be used for operations. Therefore, the sum of the second demand for vehicles in the window periods overlapping with these sub-periods is the third demand for these sub-periods. In sub-periods t0-t2 and t4-t1, there is only one window period corresponding to operations 1100 or 1104, respectively; therefore, the third demand for both of these sub-periods is 1. In sub-time period t2-t3, since there are two window periods corresponding to jobs 1100 and 1106, the third demand quantity for this sub-time period is 2. In sub-time period t3-t4, since there are three window periods corresponding to jobs 1102, 1106, and 1108, the third demand quantity for this sub-time period is 3.

[0052] like Figure 5As shown, the operation durations of tasks 1200, 1202, 1204, 1206, 1208, and 1210 are shorter than their respective window periods. The window periods for tasks 1200 and 1202 run from time t0 to time t6, for task 1204 from time t5 to time t7, for task 1206 from time t8 to time t10, and for tasks 1208 and 1210 from time t9 to time t11. The start and end points of each window period are used as the boundaries for defining sub-periods. Because the operation durations are shorter than the window periods, a period of equal duration can be selected within the window period based on the status of other tasks. Therefore, the third demand for vehicles in a sub-period can be less than or equal to the sum of the second demand for vehicles in window periods that overlap with these sub-periods. Within sub-time periods t0-t5, although there are two window periods corresponding to operations 1200 and 1202, the durations of sub-time periods t0-t5 and t5-t6 are long enough for one vehicle to complete both operations 1200 and 1202 within these two sub-time periods. Therefore, the third demand quantity for sub-time period t0-t5 is 1. Within sub-time periods t5-t6, although there are three window periods corresponding to operations 1200, 1202, and 1204, the total duration of sub-time periods t0-t5, t5-t6, and t6-t7 is long enough for all three operations 1200, 1202, and 1204 to be completed within these three sub-time periods. Therefore, the third demand quantity for sub-time period t5-t6 is 1. Within sub-time periods t6-t7, there exists a window time corresponding to task 1204. Sub-time periods t5-t6, with tasks 1200 and 1202 already in place, are insufficient to complete task 1204. Task 1204 needs to be completed within sub-time periods t6-t7. Therefore, the third demand quantity for sub-time periods t6-t7 is 1. Within sub-time periods t8-t9, there exists a window time corresponding to task 1206. Sub-time periods t8-t9 and t9-t10 are sufficient to complete task 1206. Therefore, the third demand quantity for sub-time periods t8-t9 is 1. Within sub-time periods t9-t10, there are three window time periods corresponding to tasks 1206, 1208, and 1210. Since the sum of sub-time periods t8-t9, t9-t10, and t10-t11 is insufficient, it is not long enough for one vehicle to complete tasks 1206, 1208, and 1210 within these sub-time periods. However, one vehicle is sufficient to complete operations 1206 and 1208 within sub-time periods t8-t9 and t9-t10. Therefore, only two vehicles are needed to complete operations 1206, 1208, and 1210. Thus, the third demand quantity for sub-time period t9-t10 is 2.Within sub-periods t10-t11, although there are two window periods corresponding to jobs 1208 and 1210, jobs 1208 and 1210 are already sufficiently completed within sub-periods t9-t10. Therefore, the quantity of the third demand in sub-periods t10-t11 is 0. Within sub-periods t11-t1, since no job window periods overlap with it, the quantity of the third demand is also 0.

[0053] In other embodiments, a time-sequential pre-allocation method can be used to complete tasks similar to... Figure 4 and Figure 5 The method shown. That is, in Figure 4 In the process, at time t0, task 1100 is pre-assigned to vehicle 1. At time t2, since vehicle 1 has already been pre-assigned task 1100, task 1106 is pre-assigned to vehicle 2. At time t3, since vehicle 2 has already been pre-assigned task 1106, tasks 1102 and 1108 are pre-assigned to vehicles 1 and 3, respectively. At time t4, since no vehicles have been pre-assigned tasks, task 1104 is pre-assigned to vehicle 1. Figure 5 In the process, during the time intervals t0-t6, tasks 1200 and 1202 are pre-assigned to vehicle 1. During the time intervals t5-t7, since there is still enough time to complete task 1204 after tasks 1200 and 1202 are finished, task 1204 is pre-assigned to vehicle 1. During the time intervals t8-t10, task 1206 is pre-assigned to vehicle 1. During the time intervals t9-t11, since there is not enough time for one vehicle to complete tasks 1208 and 1210 after task 1206 is finished, tasks 1208 and 1210 are pre-assigned to vehicle 1 and vehicle 2, respectively. The maximum number of vehicles required to complete the pre-assignment within the corresponding sub-time interval is used as the third demand quantity for the sub-time interval through this pre-assignment method.

[0054] Back Figure 2 In step S126, the largest third demand quantity among all the third demand quantities determined for all sub-time periods is determined as the first demand quantity. This process is repeated for all sub-time periods, and the largest third demand quantity among all sub-time periods is taken as the first demand quantity for the entire preset time period. Figure 4 In the embodiment, the largest third demand quantity is 3 for the sub-time period t3-t4, therefore the first demand quantity for the preset time period t0-t1 is 3. For example... Figure 5 In the embodiment, the largest third demand quantity is 2 of the sub-time period t9-t10, therefore the first demand quantity of the preset time period t0-t1 is 2.

[0055] This method improves the accuracy of vehicle demand forecasting by determining the required number of vehicles based on flight information and vehicle operation rules. It is particularly adaptable to situations where multiple operations are concentrated and vehicles have different operation rules, thereby making vehicle preparation and scheduling more scientific and improving production efficiency and vehicle utilization.

[0056] With the development of new energy vehicles (e.g., electric vehicles), many airport vehicles are gradually being replaced by electric vehicles. However, electric vehicles require dedicated time for charging after performing a certain number of operations. Therefore, the following embodiments are proposed. In other embodiments, the first type of vehicle is a vehicle that needs to perform additional operations such as charging, while the second type of vehicle is a vehicle that only needs to perform flight operations. Step S120 may further include: Step S128, determining the number of first operations performed by each first type of vehicle after the last second operation before the sub-period; and determining the updated third demand quantity for vehicles of the preset type based on the window period that at least partially overlaps with the sub-period, the number of second demands associated with the window period, the operation duration, and the number of vehicles needing to perform second operations.

[0057] In step S128, the first task is a task for the flight, while the second task is a task for the vehicle itself. A first-class vehicle is configured to perform both the first and second tasks. Step S128 includes: wherein the start and / or end time of each window period is the endpoint of the corresponding sub-period, and the end time of each first task and / or each second task is the endpoint of the corresponding sub-period; obtaining a first number of first tasks that a first-class vehicle can perform at most before performing the second task, and the second task duration required to perform the second task; for each sub-period within a preset time period, determining the number of first tasks already performed by each first-class vehicle after the end of the last second task before the start of the sub-period; determining the number of first-class vehicles whose number of performed first tasks equals the first number as the number of second tasks to be performed, and determining a third demand number for vehicles of the preset type based on window periods that at least partially overlap with the sub-period, the second demand number associated with the window period, the task duration, the number of second tasks to be performed, and the second task duration; specifically, the process of updating the third demand number for vehicles of the preset type will refer to... Figure 6 Provided.

[0058] Figure 6 An example schematic diagram of a vehicle demand forecasting process according to another embodiment of this disclosure is shown. Time and task markers are used to... Figure 4 and Figure 5 The same markers are given. For example... Figure 6As shown, there are three first operations for flights, 1300, 1302, and 1304. Their operation duration is equal to the operation window period, and their window periods start from time t0, t12, and t13, respectively. Figure 6 The example embodiment shown is a type 1 vehicle that, in addition to the first operation, also needs to perform a second operation for the vehicle. Before each second operation, the type 1 vehicle can perform the first operation twice. The second operation of the type 1 vehicle and its duration are given by operation 1411. The starting point of each window period is used as the dividing line for defining sub-periods. In sub-periods t0-t12 and t12-t13, ... Figure 4 Similar to the previous implementation, a vehicle is needed to cover the second demand quantity of the tasks within the corresponding sub-period. In response to the fact that the first type of vehicle has already been pre-assigned two first tasks, first task 1300 and first task 1302, at time t13, the first type of vehicle is pre-assigned to perform the second task 1411. The time t14, when the second task 1411 is expected to be completed, is also used as the dividing line for the sub-period. In the sub-period t13-t14, there is a window period for the first task 1304, and simultaneously, a vehicle is pre-assigned to perform the second task 1411. Since the first task 1304 and the second task 1411 cannot be completed simultaneously within this sub-period, the third demand quantity for this sub-period is 2. In the sub-period t14-t1, only the window period for the first task 1304 exists, therefore the third demand quantity for this sub-period is 1. Similarly, in the entire preset time period t0-t1, the updated maximum third demand quantity is 2 of the sub-time period t13-t14. Therefore, the first demand quantity in the preset time period t0-t1 is 2.

[0059] In some embodiments, the first type of vehicle is an electric vehicle, the first operation is a flight operation, and the second operation is a charging operation. The average power consumption and minimum power limit for each flight operation are obtained through historical data and vehicle parameters. Subtracting the minimum power limit from the total power consumption and dividing by the average power consumption, followed by rounding and / or adding redundancy calculations, yields the number of flight operations that can be performed before each charging operation. In some embodiments, since temperature affects battery power consumption, the average power consumption of each flight operation is multiplied by a temperature impact coefficient to obtain an updated average power consumption for each flight operation. The temperature impact coefficient is greater than 1 when the temperature exceeds a predetermined threshold range, and can be equal to 1 when the temperature is within the predetermined threshold range. For example, during periods when the temperature is below the predetermined threshold, such as November to March each year, the temperature impact coefficient for that period is determined based on historical data on the effects of low temperatures.

[0060] By separately arranging and calculating the second operations for the first type of vehicles that require a second operation, the preparation and scheduling of vehicles becomes more scientific, thereby improving production efficiency and vehicle utilization.

[0061] Figure 3 An example flowchart of a demand forecasting method for both Class 1 and Class 2 vehicles, according to some embodiments of the present disclosure, is shown.

[0062] like Figure 3 As shown, the vehicle demand forecasting method includes: step S202, calculating the first demand quantity of only the first type of vehicles as the fourth demand quantity; step S204, calculating the first demand quantity of only the second type of vehicles as the fifth demand quantity; step S206, determining the replacement ratio; and step S208, determining the replacement quantity based on the replacement ratio.

[0063] In step S202, the first demand quantity is calculated based on the first type of vehicles that only require the second operation, resulting in the fourth demand quantity of the first type of vehicles. In step S204, the first demand quantity is calculated based on the second type of vehicles that only do not require the second operation, resulting in the fifth demand quantity of the second type of vehicles. In step S206, the replacement ratio of the two types of vehicles is determined. In some embodiments, the fourth demand quantity of the first type of vehicles is divided by the fifth demand quantity of the second type of vehicles to obtain the replacement ratio of the two types of vehicles. In step S208, the number of first type of vehicles required for replacement is determined based on the replacement ratio. In some embodiments, the first type of vehicles are electric vehicles, the second type of vehicles are conventional energy vehicles, and the second operation is charging. The number of conventional energy vehicles available during the predicted period is obtained by subtracting the number of conventional energy vehicles expected to be scrapped from the number of existing conventional energy vehicles. The number of conventional energy vehicles required is subtracted from the number of available conventional energy vehicles to obtain the number of conventional energy vehicles that need to be replaced by electric vehicles. After multiplying the number of conventional energy vehicles that need to be replaced by electric vehicles by the replacement ratio, and then performing rounding and / or adding redundancy, the required number of electric vehicles can be obtained. For example, if the fifth demand quantity is 20 when only traditional energy vehicles are available, and the fourth demand quantity is 28 when only electric vehicles are available, then the replacement ratio is 1.4. When there is a shortage of 5 traditional energy vehicles, 7 electric vehicles are needed to fill the gap. In this way, the forecasting process can be smoothly transitioned during the replacement of traditional energy vehicles with new energy vehicles, reducing resource waste.

[0064] The vehicle demand forecasting method disclosed herein improves the accuracy of vehicle demand forecasting by determining the number of vehicles needed based on flight information and vehicle operation rules. It is particularly adaptable to situations where multiple operations are concentrated and vehicles have different operation rules, thereby making vehicle preparation and scheduling more scientific and improving production efficiency and vehicle utilization.

[0065] Considering that temporary vehicle breakdowns and unexpected backlogs due to distance or delays during scheduling may occur, resulting in insufficient vehicle numbers, in some embodiments, the required vehicle number is multiplied by a redundancy factor to obtain the final required vehicle number. The redundancy factor can cover vehicle shortages caused by unforeseen circumstances.

[0066] In the vehicle demand forecasting method disclosed herein, vehicle operation rules for a preset type of vehicle can be obtained; then, flights that meet the flight conditions in the vehicle operation rules are selected from the expected flights, and the flight information of the selected flights is obtained; and based on the operation conditions in the vehicle operation rules and the flight information of the selected flights, the first demand quantity of the preset type of vehicle within a preset time period is determined, thus enabling more accurate forecasting of specific preset type vehicles based on the expected flight situation. Furthermore, in some embodiments, within the preset time period, the first demand quantity containing only type 1 vehicles can be calculated as the fourth demand quantity, and the first demand quantity containing only type 2 vehicles can be calculated as the fifth demand quantity, with the ratio of the fourth demand quantity to the fifth demand quantity used as the replacement ratio; and based on the replacement ratio, the number of type 1 vehicles required to replace type 2 vehicles with type 1 vehicles in the preset type of vehicle is determined, thereby achieving accurate forecasting during the replacement process of different types of vehicles.

[0067] According to another aspect of this disclosure, a vehicle demand forecasting device is also provided. For example... Figure 7 As shown, the vehicle demand forecasting device 300 may include a rule acquisition module 302, a flight screening module 304, and a demand determination module 306. The rule acquisition module 302 may be configured to acquire vehicle operation rules for a preset type of vehicle. The flight screening module 304 may be configured to filter flights from the expected flights that meet the flight conditions in the vehicle operation rules and acquire the flight information of the filtered flights. The demand determination module 306 may be configured to determine a first demand quantity of vehicles of the preset type based on the operation conditions in the vehicle operation rules and the flight information of the filtered flights. The vehicle demand forecasting device 300 may be configured to execute the method described according to any of the foregoing embodiments of this disclosure.

[0068] like Figure 8As shown, this disclosure also provides a vehicle demand forecasting device 400, which may include one or more processors 402 and a memory 404 storing computer-executable instructions, which, when executed by the one or more processors, cause the one or more processors to perform the methods described according to any of the foregoing embodiments of this disclosure. The one or more processors 402 may be, for example, a central processing unit (CPU) of the vehicle demand forecasting device 400. The one or more processors 402 may be any type of general-purpose processor, or may be a processor specifically designed for vehicle forecasting / scheduling, such as an application-specific integrated circuit (“ASIC”). The memory 404 may include various computer-readable media accessible by the one or more processors 402. In various embodiments, the memory 404 described herein may include volatile and non-volatile media, removable and non-removable media. For example, the memory 404 may include any combination of: random access memory (“RAM”), dynamic RAM (“DRAM”), static RAM (“SRAM”), read-only memory (“ROM”), flash memory, cache memory, and / or any other type of non-transient computer-readable media. The memory 404 may store instructions that, when executed by the processor 402, cause the processor 402 to execute the method described according to any of the foregoing embodiments of this disclosure.

[0069] This disclosure also provides a non-transient storage medium having computer-executable instructions stored thereon, which, when executed by a computer, cause the computer to perform the methods described according to any of the foregoing embodiments of this disclosure.

[0070] This disclosure also provides a computer program product that may include instructions that, when executed by a processor, can implement the methods described according to any of the foregoing embodiments of this disclosure. The instructions may be any set of instructions that will be executed directly by one or more processors, such as machine code, or any set of instructions that will be executed indirectly, such as a script. The instructions may be stored in an object code format for direct processing by one or more processors, or stored in any other computer language, including scripts or sets of independent source code modules that are interpreted on demand or compiled in advance.

[0071] The foregoing has described one or more exemplary embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. A typical implementation device is a server system. Of course, this disclosure does not exclude the possibility that, with the future development of computer technology, the computer implementing the functions of the above embodiments may be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a game console, a tablet computer, a wearable device, or any combination thereof.

[0073] The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, product, or apparatus. Without further limitation, the presence of other identical or equivalent elements in the process, method, product, or apparatus that includes said elements is not excluded. For example, the use of terms such as "first" or "second" to denote names does not indicate any particular order.

[0074] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, when implementing one or more embodiments of this disclosure, the functions of each module can be implemented in one or more software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0075] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will 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 program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0076] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0077] Those skilled in the art will understand that one or more embodiments of this disclosure may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of this disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] One or more embodiments of this disclosure can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this disclosure can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.

[0079] The same or similar parts between the various embodiments of this disclosure can be referred to mutually, and each embodiment focuses on describing the differences from other embodiments. In particular, for the apparatus embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments. In the description of this disclosure, the reference to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," "exemplary," etc., means that the specific feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of this disclosure. In this disclosure, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this disclosure and the features of different embodiments or examples.

[0080] Additionally, when used in this disclosure, the terms “here,” “above,” “below,” “below,” “in the following,” “overall,” and similar terms should refer to the entirety of this disclosure and not any particular part thereof. Furthermore, unless expressly stated otherwise or otherwise understood in the context in which they are used, conditional language used herein, such as “may,” “possibly,” “for example,” “like,” etc., is generally intended to express that certain embodiments include, while other embodiments do not, certain features, elements, and / or states. Therefore, such conditional language is not generally intended to imply that one or more embodiments require features, elements, and / or states in any way, or whether such features, elements, and / or states are included or performed in any particular embodiment.

[0081] The above description is merely an embodiment of one or more embodiments of this disclosure and is not intended to limit the scope of the one or more embodiments of this disclosure. Various modifications and variations can be made to the one or more embodiments of this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of the claims.

Claims

1. A vehicle demand forecasting method, comprising: Obtain vehicle operation rules for preset vehicle types; Select flights that meet the flight conditions in the vehicle operation rules from the expected flights, and obtain the flight information of the selected flights; as well as Based on the operating conditions in the vehicle operation rules and the flight information of the selected flights, determine the first required number of vehicles of the preset type within the preset time period.

2. The vehicle demand forecasting method according to claim 1, wherein, The operating conditions include the operating time required for the vehicle of the preset type to perform the operation and the window time range within which the vehicle of the preset type can perform the operation.

3. The vehicle demand forecasting method according to claim 2, wherein, Based on the operating conditions in the vehicle operation rules and the flight information of the selected flights, the first required number of vehicles of the preset type within a preset time period is determined, including: Read from the flight information of the selected flights, or determine, based on the flight information of the selected flights and the window time range, the window time in which each flight can be operated and the second required number of vehicles of the preset type for each flight to be operated; For each sub-period within the preset time period, a third demand quantity for vehicles of the preset type is determined based on window periods that at least partially overlap with the sub-period, a second demand quantity associated with the window periods, and the operation duration, wherein the preset time period is divided into one or more sub-periods, and adjacent sub-periods are consecutive and do not overlap; and The largest third demand quantity among all the third demand quantities determined for all sub-time periods is determined as the first demand quantity.

4. The vehicle demand forecasting method according to claim 3, wherein, The start and / or end times of each window period are the endpoints of the corresponding sub-periods. When the operation duration is equal to the window period, in sub-periods that at least partially overlap with multiple window periods, the third demand quantity is the sum of the second demand quantities of vehicles required by the multiple window periods; or When the operation duration is less than the window period, in a sub-period that at least partially overlaps with multiple window periods, the third demand quantity is less than or equal to the sum of the second demand quantities of vehicles required by the multiple window periods.

5. The vehicle demand forecasting method according to claim 3, wherein, The preset type of vehicle includes a first type of vehicle, which is configured to perform a first operation for a flight and a second operation for a vehicle. The start and / or end times of each window period are the endpoints of the corresponding sub-periods, and the end time of each first job and / or the end time of each second job are the endpoints of the corresponding sub-periods; Determining the first demand quantity of the preset type of vehicles within a preset time period, based on the operating conditions in the vehicle operation rules and the flight information of the selected flights, further includes: Obtain the first number of the first type of vehicles that can perform the first task before performing the second task, and the second task duration required to perform the second task; For each sub-period within the preset time period, determine the number of first operations that each type of vehicle has performed after the last second operation before the start of the sub-period; The number of first-type vehicles whose first jobs have been performed equals the first number is determined as the number of second jobs to be performed, and the updated third demand number of the preset type of vehicles is determined based on the window period that at least partially overlaps with the sub-time period, the second demand number associated with the window period, the job duration, the number of second jobs to be performed, and the second job duration.

6. The vehicle demand forecasting method according to claim 5, wherein, The preset type of vehicle also includes a second type of vehicle, which is configured to perform only the first operation for the flight; Based on the operating conditions in the vehicle operation rules and the flight information of the selected flights, the first required number of vehicles of the preset type within a preset time period is determined, including: Within a preset time period, the first demand quantity consisting only of the first type of vehicles is calculated as the fourth demand quantity, and the first demand quantity consisting only of the second type of vehicles is calculated as the fifth demand quantity. The ratio of the fourth demand quantity to the fifth demand quantity is used as the replacement ratio. Based on the replacement ratio, determine the number of Class 1 vehicles required to replace Class 2 vehicles with Class 1 vehicles in the preset vehicle type.

7. The vehicle demand forecasting method according to claim 5, wherein the first type of vehicle is an electric vehicle, and obtaining the first number of the first type of vehicle that can perform the first task at most before performing the second task includes: Obtain the average power consumption and minimum power limit of the first job; The first quantity is determined by dividing the difference between the total power consumption and the minimum power consumption limit by the quotient obtained by the average power consumption of the operation.

8. The vehicle demand forecasting method according to claim 7, wherein, The product of the average power consumption of the operation and the temperature influence coefficient is used as the updated average power consumption of the operation. Where the temperature is not within a predetermined threshold range, the temperature influence coefficient is greater than 1.

9. The vehicle demand forecasting method according to claim 1, wherein, The flight conditions include at least one of the following: The arrival and departure status, aircraft type, operating area, operating time period, and historical demand data of flights served by the preset type of vehicles.

10. The vehicle demand forecasting method according to claim 1, wherein, The type of vehicle is determined based on at least one of the vehicle's operation type, operation area, and vehicle model.

11. The vehicle demand forecasting method according to any one of claims 1-10, further comprising determining the first demand quantity of the preset type of vehicles based on the operating conditions in the vehicle operation rules and the flight information of the selected flights: The product of the first demand quantity and the redundancy coefficient is used as the updated first demand quantity for the preset type of vehicles.

12. A vehicle demand forecasting device, comprising: A rule acquisition module, configured to acquire vehicle operation rules for a preset type of vehicle; The flight screening module is configured to filter flights that meet the flight conditions in the vehicle operation rules from the expected flights, and obtain the flight information of the filtered flights. as well as The demand determination module is configured to determine the first demand quantity of vehicles of the preset type based on the operating conditions in the vehicle operation rules and the flight information of the selected flights.

13. A vehicle demand forecasting device, comprising: A memory configured to store instructions; as well as A processor coupled to the memory, when the instructions are executed by the processor, implements the vehicle demand forecasting method according to any one of claims 1-11.

14. A computer-readable storage medium having instructions stored thereon, wherein, When the instruction is executed by the processor, it implements the operation of the vehicle demand forecasting method according to any one of claims 1-11.

15. A computer program product comprising instructions that, when executed by a processor, implement the vehicle demand forecasting method according to any one of claims 1-11.

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