Vehicle demand prediction method, device and apparatus

Abstract

The present disclosure relates to a vehicle demand prediction method, device and equipment, wherein the vehicle demand prediction method comprises: obtaining a vehicle operation rule of a preset type of vehicle; screening flights that meet flight conditions in the vehicle operation rule from scheduled flights, and obtaining flight information of the screened flights; and determining a first demand quantity of the preset type of vehicle in a preset time period according to operation conditions in the vehicle operation rule and the flight information of the screened flights.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of aviation-related vehicle planning, and in particular to a vehicle demand prediction method, device, equipment, computer readable storage medium and computer program product. BACKGROUND

[0002] When flights are taking off and landing at an airport, a large number of vehicles are needed to cooperate and maintain, including special vehicles such as shuttle buses, water trucks, sewage trucks, garbage trucks, elevators, conveyor trucks, towing trucks, passenger elevator trucks, power supply trucks, gas supply trucks, air conditioning trucks, etc.

[0003] With the popularity of flights and the development of aviation business, the number of vehicles required for each airport to cooperate and maintain flights is gradually changing, and the number of vehicles required by the airport needs to be predicted in order to better complete the task of vehicle preparation and scheduling. At present, when predicting the demand for vehicles, a method based on experience is often used to directly calculate or a method of simple linear fitting is used to calculate. SUMMARY

[0004] According to a first aspect of the present disclosure, a vehicle demand prediction method is provided, comprising: obtaining a vehicle operation rule of a preset type of vehicle; selecting flights that meet the flight conditions in the vehicle operation rule from the predicted flights, and obtaining the flight information of the selected flights; and determining a first demand quantity of the preset type of vehicle in a preset period according to the operation condition in the vehicle operation rule and the flight information of the selected flights.

[0005] In some embodiments, the operation condition includes an operation duration required for the preset type of vehicle to perform the operation and a window time range in which the preset type of vehicle can perform the operation.

[0006] In some embodiments, determining the first demand quantity of the preset type of vehicle in the preset period according to the operation condition in the vehicle operation rule and the flight information of the selected flights includes: reading, from the flight information of the selected flights, or determining, according to the flight information of the selected flights and the window time range, a window period in which each flight can be operated and a second demand quantity of the preset type of vehicle required for each flight to perform the operation in the preset period; for each sub-period in the preset period, determining a third demand quantity of the preset type of vehicle according to the window period at least partially coinciding with the sub-period, the second demand quantity associated with the window period, and the operation duration, wherein the preset period is divided into one or more sub-periods, and adjacent two sub-periods are continuous and non-overlapping; and determining the maximum third demand quantity among the third demand quantities determined for all sub-periods as the first demand quantity.

[0007] In some embodiments, the starting and / or ending time of each window period is the endpoint of the corresponding sub-period, in the case where the job duration is equal to the window period, the third demand quantity is the sum of the second demand quantities of the vehicles required by the plurality of window periods in the sub-period at least partially overlapping with the plurality of window periods; or in the case where the job duration is less than the window period, the third demand quantity is less than or equal to the sum of the second demand quantities of the vehicles required by the plurality of window periods in the sub-period at least partially overlapping with the plurality of window periods.

[0008] In some embodiments, the preset type of vehicle includes a first type of vehicle configured to perform a first job for a flight and a second job for a vehicle; wherein the starting and / or ending time of each window period is the endpoint of the corresponding sub-period, and the ending time of each first job and / or the ending time of each second job is the endpoint of the corresponding sub-period; and determining the first demand quantity of the preset type of vehicle in the preset period according to the job condition in the vehicle job rule and the flight information of the screened flight further comprises: obtaining a first quantity of the first jobs that the first type of vehicle can perform at most before performing the second job and a second job duration required for performing the second job; for each sub-period in the preset period, determining the number of the first jobs that each first type of vehicle has performed after the last second job before the sub-period; determining the number of the first type of vehicles whose number of the first jobs that have been performed is equal to the first quantity as the number of the second jobs that need to be performed, and determining an updated third demand quantity of the preset type of vehicle according to the window period at least partially overlapping with the sub-period, the second demand quantity associated with the window period, the job duration, the number of the second jobs that need to be performed, and the second job duration.

[0009] In some embodiments, the preset type of vehicle further includes a second type of vehicle configured to perform only the first job for a flight; and determining the first demand quantity of the preset type of vehicle in the preset period according to the job condition in the vehicle job rule and the flight information of the screened flight comprises: in the preset period, calculating the first demand quantity of only the first type of vehicle as a fourth demand quantity and the first demand quantity of only the second type of vehicle as a fifth demand quantity, respectively; and taking the ratio of the fourth demand quantity and the fifth demand quantity as a replacement ratio; and determining the number of the first type of vehicles required to replace the second type of vehicles in the preset type of vehicles to the first type of vehicles according to the replacement ratio.

[0010] In some embodiments, the first type of vehicle is an electric vehicle, and obtaining the first quantity of the first jobs that the first type of vehicle can perform at most before performing the second job comprises: obtaining a job average power consumption of the first job and a minimum power limit; and determining the first quantity according to the quotient obtained by dividing the difference between the total power and the minimum power limit by the job average power consumption.

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

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

[0013] In some embodiments, the type of the vehicle is determined according to at least one of a job category of the vehicle, a job area, and a vehicle model.

[0014] In some embodiments, determining the first demand quantity of the preset type of vehicle according to the job conditions in the vehicle job rule and the flight information of the screened flight further includes: taking the product of the first demand quantity and a redundancy coefficient as an updated first demand quantity of the preset type of vehicle.

[0015] According to a second aspect of the present disclosure, a vehicle demand prediction device is provided, including: a rule acquisition module, a flight screening module, and a demand determination module. The rule acquisition module is configured to acquire a vehicle job rule of a preset type of vehicle; the flight screening module is configured to screen a flight that meets flight conditions in the vehicle job rule from scheduled flights, and acquire flight information of the screened flight; and the demand determination module is configured to determine a first demand quantity of the preset type of vehicle according to job conditions in the vehicle job rule and the flight information of the screened flight.

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

[0017] According to a fourth aspect of the present disclosure, a computer readable storage medium is provided, having instructions stored thereon, wherein the instructions are executed by a processor to implement the operations of the aforementioned vehicle demand prediction method.

[0018] According to a fifth aspect of the present disclosure, a computer program product is provided, including instructions, which when executed by a processor, implement the operations of the aforementioned vehicle demand prediction method.

[0019] Other features and advantages of the present disclosure will become more apparent from the following detailed description of exemplary embodiments thereof, taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0021] The present disclosure can be more clearly understood and appreciated from the following detailed description, taken in conjunction with the following drawings of which:

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

[0023] Figure 2 A flowchart of step S120 in Figure 1 is shown;

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

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

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

[0027] Figure 6 A schematic diagram of a vehicle demand prediction 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 apparatus 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 following embodiments, the same reference numerals are sometimes used across different drawings to indicate the same or similar parts or parts having the same function, and repeated description thereof is omitted. In this specification, like numbers and letters designate like items, and once an item is defined in one drawing, that item need not be further discussed in subsequent drawings.

[0031] For ease of understanding, the positions, sizes, ranges, and the like of the structures shown in the drawings and the like are sometimes not actual positions, sizes, ranges, and the like. Therefore, the disclosed application is not limited to the positions, sizes, ranges, and the like disclosed in the drawings and the like. Further, the drawings are not necessarily drawn to scale, and some features can be exaggerated to show specific components in detail. DETAILED DESCRIPTION

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

[0033] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the scope of the present disclosure and its applications or uses.

[0034] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein, but should be considered part of the specification where appropriate.

[0035] The inventors have noticed that the current prediction of vehicle demand is usually based on experience, for example, a linear prediction is made according to the past vehicle demand, it is difficult to make corresponding adjustment according to the density of flight arrangement, and different vehicles have different actual working time but use the same prediction rule, so the result of predicting vehicle demand has unreasonable places and cannot adapt to the needs of actual vehicle preparation and scheduling work.

[0036] To solve the above problems, the present disclosure provides a vehicle demand prediction method.

[0037] In an exemplary embodiment of the present disclosure, as shown in Figure 1 The vehicle demand prediction method can include: step S100, obtaining a vehicle operation rule of a preset type of vehicle; step S110, screening flights that meet the flight conditions in the vehicle operation rule from the predicted flights, and obtaining flight information of the screened flights; and step S120, determining a first demand quantity of the preset type of vehicle within a preset period according to the operation conditions in the vehicle operation rule and the flight information of the screened flights.

[0038] In some embodiments, the type of a vehicle can be determined according to at least one of a work category, a work area, and a vehicle model of the vehicle. In other words, if two vehicles have different work categories, work areas, or vehicle models, the two vehicles can be classified into different types. Vehicles can be classified into different types according to work categories, such as shuttle vehicles, water cleaning vehicles, sewage vehicles, garbage vehicles, lifting vehicles, transfer vehicles, towing vehicles, passenger stair vehicles, power supply vehicles, gas supply vehicles, air conditioning vehicles, etc. Vehicle models can include different models of vehicles that are used for the same work category, such as passenger stair vehicles that have different sizes to accommodate different heights of cabin doors of airplanes. The work area of a vehicle can be global, such as for vehicles that are used to support relevant work in the entire airport, or the work area of a vehicle can be zonal, such as for vehicles that are used to support relevant work in a certain area of the airport. In some embodiments, the first demand quantity of vehicles of a type can be predicted separately for vehicles of different types, such as for vehicles of different work categories, different models, different work areas. In a specific example, the prediction results for vehicles of different zonal work can be added to obtain a global prediction result for the entire airport.

[0039] In some embodiments, the vehicle operation rules can include operation conditions and flight conditions. Among them, the operation conditions for the vehicle can include an operation duration required for a preset type of vehicle to perform an operation and a window time range in which a preset type of vehicle can perform an operation. For example, a window period in which a corresponding flight can be operated can be determined according to the window time range and the take-off and landing time of the corresponding flight, etc. It should be noted that the window time range here is set for the vehicle, and the window period is set for the flight. For example, the window time range can indicate the relationship between the window period and the take-off and landing time of the flight, for example, the window time range is from 20 minutes after the landing time of the flight to 10 minutes before the take-off time of the flight, and then according to the actual take-off and landing time of the flight, the window period for the flight can be obtained. In some embodiments, the window period can generally be set between the previous arrival time and the next take-off time of the flight, and the vehicle can perform the related operation within the window period. In other embodiments, some vehicles can start working in advance before the arrival time of the flight to complete the preparation work, and the starting time of the window period can be before the arrival time of the flight. In still other embodiments, some vehicles can end the operation a period of time after the take-off time of the flight to complete the finishing work, and the end time of the window period can be after the take-off time of the flight. For example, the water truck can start the operation a period of time before the arrival time of the flight to fill the required water, and the sewage truck can go to clean the sewage in the vehicle to end the operation after the take-off time of the flight. The operation duration refers to the time required for the vehicle to actually perform the operation, for example, the operation duration can include the actual interaction operation duration with the flight, the preparation operation duration of the vehicle itself before the operation, and the travel duration of the vehicle to different operation positions, etc. The operation duration of different vehicles can be different, and the operation duration of the same vehicle for different types of flights can also be different. 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, the passenger elevator truck is working all the time within the window period. In other embodiments, the operation duration can be less than the window period, and then a period of time can be selected for operation within the window time range to avoid conflicts with other operations. For example, the operation duration of the water truck is very short, and then only a period of time is selected for operation within the time range of the window period of the operation, which is outside the time range of the same vehicle for other operations. That is, within the time range coinciding with multiple window periods, multiple operations can be performed.

[0040] The flight conditions can include at least one of the status of the departure and arrival of the flights served by the preset type of vehicle, the flight model, the operation area, the operation period, and the historical demand data. Specifically, the flight conditions determine the specific flights served by the type of vehicle, so that the flights can be filtered to calculate the demand of the preset type of vehicle. Some vehicles are only enabled in part of the flights, so the corresponding flight conditions are used to filter the flights corresponding to these vehicles. The model of the flight affects the selection of the specific vehicle type. For example, the large passenger stairs are used on the high door model, and the small passenger stairs are used on the low door model. In some embodiments, the vehicle can be in a global guarantee state, in which all flights of the entire apron are included in the calculation. In other embodiments, the vehicle can be in a partition guarantee state, in which the apron is partitioned, and the vehicle only operates in the designated area during the corresponding period of the partition guarantee state. Therefore, in the prediction of the corresponding partition, the flights in the partition need to be filtered. Specifically, the historical demand data can include the bridge approach rate of different flights in the past history, special demand of some departure or arrival airports (for example, the demand for defrosting in cold areas, the demand for weather such as heavy rain, strong wind, and heavy fog in areas with extreme weather), etc. For example, a trolley bus is only needed when a flight does not approach the bridge, and historical data of airport flights can be used to infer the future bridge approach rate of the flight to determine whether to enable the trolley bus.

[0041] By obtaining the vehicle operation rules of the preset type of vehicle, the flight conditions of the flights associated with the vehicle to be predicted can be determined, and the operation conditions of the vehicle to be predicted can be determined, so that the prediction can be based on the specific vehicle type and the associated flight, and the accuracy of the prediction is increased.

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

[0043] In some embodiments, all scheduled flights can be determined according to the flight plan or dynamic data of the flight. The flights that meet the conditions of the preset type of vehicle can be filtered according to the information such as the model of the flight, the take-off and landing airport, and the scheduled area in the apron in the future flight plan that matches the flight conditions in the vehicle operation rules, and the flights that meet the conditions of the preset type of vehicle can also be filtered according to the historical demand data related to the flight (such as the bridge approach rate). The flight information can include the landing time and / or take-off time and / or period in the apron of the flight, the information required to complete the operation for calculating the second demand quantity, or the number of each type of vehicle required, etc.

[0044] By screening the flights from the flight plan and obtaining the flight information, the detailed information of the flight corresponding to the vehicle to be predicted can be obtained, so that the prediction can be specific to each related flight, and the accuracy of the prediction is increased.

[0045] Then, in step S120, according to the operation condition in the vehicle operation rule and the flight information of the screened flight, the first demand quantity of the preset type of vehicle in the preset period is determined.

[0046] In some embodiments, as shown in Figure 2 Step S120 can include: step S122, reading or determining, from the flight information of the screened flight, the window period in which each flight can be operated and the second demand quantity of the preset type of vehicle required for operating each flight in the preset period; step S124, for each sub-period in the preset period, determining the third demand quantity of the preset type of vehicle according to the window period at least partially coinciding with the sub-period, the second demand quantity associated with the window period and the operation duration; and step S126, determining the maximum third demand quantity among the third demand quantities determined for all sub-periods as the first demand quantity.

[0047] Wherein, where the window period and / or the second demand quantity exist in the flight information, the flight information can be directly read to obtain the window period and / or the second demand quantity; and where the window period and / or the second demand quantity do not exist in the flight information, the window period can be calculated according to the take-off time, the landing time, the time on the apron in the flight information and the window time range in which the operation can be performed in the vehicle operation rule, and / or the second demand quantity can be calculated according to the operation amount, the operation position and other data recorded in the flight information.

[0048] In step S124, for each sub-period in the preset period, the third demand quantity of the preset type of vehicle is determined according to the window period at least partially coinciding with the sub-period, the second demand quantity associated with the window period and the operation duration, wherein the preset period is divided into one or more sub-periods, and adjacent two sub-periods are continuous and non-overlapping with each other.

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

[0050] Figure 4 and Figure 5An example schematic diagram of a vehicle demand prediction process according to some embodiments of the present disclosure is shown. As Figure 4 and Figure 5 shown, where the horizontal axis represents time, the time shown is from time t0 to time t1, and the vertical axis represents the number of vehicles needed. The dashed boxes represent the window periods of the jobs. The solid boxes represent the job durations of the jobs. In these embodiments, each of the flights needs only one vehicle of a preset type, i.e., the second demand number is 1 for all flights, and the lengths of the window periods corresponding to the jobs of the flights remain the same. But in other embodiments not shown, a flight can need one or more vehicles of a preset type, and the lengths of the window periods corresponding to the jobs of different flights can not be the same, which is not limited.

[0051] As Figure 4 shown, where the horizontal axis represents time, the time shown is from time t0 to time t1, and the vertical axis represents the number of vehicles needed. The dashed boxes represent the window periods of the jobs. The solid boxes represent the job durations of the jobs. In these embodiments, each of the flights needs only one vehicle of a preset type, i.e., the second demand number is 1 for all flights, and the lengths of the window periods corresponding to the jobs of the flights remain the same. But in other embodiments not shown, a flight can need one or more vehicles of a preset type, and the lengths of the window periods corresponding to the jobs of different flights can not be the same, which is not limited.

[0052] As Figure 5As shown, the job durations of jobs 1200, 1202, 1204, 1206, 1208, 1210 are less than the window periods of the jobs. The window periods of jobs 1200 and 1202 both start at time to and end at time t6, the window period of job 1204 starts at time t5 and ends at time t7, the window period of job 1206 starts at time t8 and ends at time tio, and the window periods of jobs 1208 and 1210 both start at time t9 and end at time tn. The start and end of each window period are used as the dividing boundaries for dividing the sub-periods. Since the job durations are less than the window periods of the jobs, a time period with a duration equal to the job duration can be selected for the job within the window period according to the status of other jobs, so the third vehicle demand quantity of a sub-period can be less than or equal to the sum of the second vehicle demand quantities of the window periods that coincide with the sub-period. In sub-period to-t5, although there are two window periods corresponding to jobs 1200 and 1202, since the time of sub-period to-t5 and sub-period t5-t6 is long enough, one vehicle is enough to complete jobs 1200 and 1202 in these two sub-periods, so the third demand quantity of sub-period to-t5 is 1. In sub-period t5-t6, although there are three window periods corresponding to jobs 1200, 1202 and 1204, since the sum of the time of sub-period to-t5, sub-period t5-t6 and sub-period t6-t7 is long enough, one vehicle is enough to complete jobs 1200, 1202 and 1204 in these three sub-periods, so the third demand quantity of sub-period t5-t6 is 1. In sub-period t6-t7, there is one window period corresponding to job 1204, and sub-period t5-t6 is not enough to complete job 1204 in the presence of jobs 1200 and 1202, job 1204 needs to be completed in sub-period t6-t7, so the third demand quantity of sub-period t6-t7 is 1. In sub-period t8-t9, there is one window period corresponding to job 1206, and sub-period t8-t9 and sub-period t9-tio are enough to complete job 1206, so the third demand quantity of sub-period t8-t9 is 1. In sub-period t9-tio, there are three window periods corresponding to jobs 1206, 1208 and 1210, since the sum of sub-periods t8-t9, t9-tio and tio-tii is not long enough for one vehicle to complete jobs 1206, 1208 and 1210 in these sub-periods. But one vehicle is enough to complete jobs 1206 and 1208 in sub-periods t8-t9 and t9-tio, so only two vehicles are needed to complete jobs 1206, 1208 and 1210, so the third demand quantity of sub-period t9-tio is 2.In the sub-period t10-t11, although there are two window periods corresponding to jobs 1208 and 1210, jobs 1208 and 1210 have been completed enough in the sub-period t9-t10, thus the third demand quantity of the sub-period t10-t11 is 0. In the sub-period t11-t1, since there is no window period of a job coinciding with it, thus the third demand quantity is 0.

[0053] In some other embodiments, the method shown in Figure 4 and Figure 5 may be completed in a time-sequential pre-assignment manner. That is, in Figure 4 , at time t0, job 1100 is pre-assigned to vehicle 1. At time t2, since vehicle 1 has been pre-assigned job 1100, job 1106 is pre-assigned to vehicle 2. At time t3, since vehicle 2 has been pre-assigned job 1106, jobs 1102 and 1108 are pre-assigned to vehicle 1 and vehicle 3. At time t4, since no vehicle has been pre-assigned a job, job 1104 is pre-assigned to vehicle 1. In Figure 5 , in the period t0-t6, jobs 1200 and 1202 are pre-assigned to vehicle 1. In the period t5-t7, since there is still enough time to complete job 1204 after the completion of jobs 1200 and 1202, job 1204 is pre-assigned to vehicle 1. In the period t8-t10, job 1206 is pre-assigned to vehicle 1. In the period t9-t11, since there is not enough time for a vehicle to complete jobs 1208 and 1210 after the completion of job 1206, jobs 1208 and 1210 are pre-assigned to vehicle 1 and vehicle 2 respectively. The maximum number of vehicles required to complete the pre-assignment in the corresponding sub-period is taken as the third demand quantity of the sub-period by the form of pre-assignment.

[0054] Returning to Figure 2 , in step S126, the maximum third demand quantity among the third demand quantities determined for all sub-periods is determined as the first demand quantity. All sub-periods are traversed, and the maximum third demand quantity among all sub-periods is taken as the first demand quantity of the entire pre-set period. As in the embodiment of Figure 4 , the maximum third demand quantity is 3 of the sub-period t3-t4, thus the first demand quantity of the pre-set period t0-t1 is 3. As in the embodiment of Figure 5 , the maximum third demand quantity is 2 of the sub-period t9-t10, thus the first demand quantity of the pre-set period t0-t1 is 2.

[0055] The method determines the required number of vehicles based on flight information and vehicle operation rules, improves the accuracy of vehicle demand prediction, especially has better adaptability to the case that multiple operations are concentrated and vehicles have different operation rules, so that the preparation and scheduling arrangement of vehicles are more scientific, and the production efficiency and vehicle utilization rate are improved.

[0056] With the development of new energy vehicles (for example, electric vehicles), many airport vehicles are gradually replaced by electric vehicles, and electric vehicles need to spend special time for charging after performing a certain number of operations. Therefore, the following embodiments are proposed. In some other embodiments, the first type of vehicle is a vehicle that needs to additionally perform additional operations such as charging operations, and the second type of vehicle is a vehicle that only needs to perform flight operations. Step S120 can further include: step S128, determining the number of first operations that each first type of vehicle has performed after the last second operation before the sub-period; determining an updated third required number of vehicles of the preset type according to the window period at least partially coinciding with the sub-period, the second required number associated with the window period, the operation duration, and the number of vehicles required to perform the second operation.

[0057] In step S128, the first operation is an operation for a flight, and the second operation is an operation for the vehicle itself, and the first type of vehicle is configured to perform the first operation and the second operation. Step S128 includes: wherein the time at which each window period starts and / or ends is the endpoint of the corresponding sub-period, and the time at which each first operation ends and / or the time at which each second operation ends is the endpoint of the corresponding sub-period; obtaining a first number of first operations that each first type of vehicle can perform at most before performing a second operation and a 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 type of vehicle has performed after the end of the last second operation before the start of the sub-period; determining the number of first type vehicles whose number of first operations performed is equal to the first number as the number of second operations required to be performed, and determining an updated third required number of vehicles of the preset type according to the window period at least partially coinciding with the sub-period, the second required number associated with the window period, the operation duration, and the number of second operations required to be performed and the second operation duration. Specifically, the process of updating the third required number of vehicles of the preset type will be given with reference to Figure 6

[0058] Figure 6 An example schematic diagram of a vehicle demand prediction process according to another embodiment of the present disclosure is shown. The labels of time and operations are given in the same way as Figure 4 and Figure 5 Figure 6 ​​As shown, there are three first jobs 1300, 1302, 1304 for flights, and their job durations are equal to the window periods of the jobs, and their window periods start at time t0, t12, t13 respectively. Figure 6 The example embodiment shown is a first type of vehicle, and in addition to the first jobs, the second jobs for the vehicle also need to be performed, and the first type of vehicle can perform two first jobs before each second job. The second job of the first type of vehicle and its second job duration are given by job 1411. The start of each window period is used as the division boundary for demarcating sub-periods. In the sub-periods t0-t12, t12-t13, there is a window period of the first job 1300, and there is also a vehicle pre-assigned to need to perform the second job 1411, and the first job 1300 and the second job 1411 cannot be completed in the sub-periods at the same time, so the third demand quantity of the sub-periods is 2. In the sub-period t14-t1, there is only a window period of the first job 1304, so the third demand quantity of the sub-period is 1. Similarly, in the entire preset period t0-t1, the updated maximum third demand quantity is 2 of the sub-period t13-t14, so the first demand quantity of the preset period t0-t1 is 2. Figure 4 The example embodiment shown is a first type of vehicle, and in addition to the first jobs, the second jobs for the vehicle also need to be performed, and the first type of vehicle can perform two first jobs before each second job. The second job of the first type of vehicle and its second job duration are given by job 1411. The start of each window period is used as the division boundary for demarcating sub-periods. In the sub-periods t0-t12, t12-t13, there is a window period of the first job 1300, and there is also a vehicle pre-assigned to need to perform the second job 1411, and the first job 1300 and the second job 1411 cannot be completed in the sub-periods at the same time, so the third demand quantity of the sub-periods is 2. In the sub-period t14-t1, there is only a window period of the first job 1304, so the third demand quantity of the sub-period is 1. Similarly, in the entire preset period t0-t1, the updated maximum third demand quantity is 2 of the sub-period t13-t14, so the first demand quantity of the preset period t0-t1 is 2.

[0059] In some embodiments, the first type of vehicle is an electric vehicle, the first job is a flight job, and the second job is a charging job. The average power consumption of each flight job and the minimum power limit are obtained through historical data and vehicle parameters. The number of flight jobs that can be performed before each charging job is obtained by dividing the total power minus the minimum power limit by the average power consumption, and then rounding and / or adding a redundancy amount. In some embodiments, since temperature affects battery power consumption, multiply the average power consumption of each flight job by a temperature influence coefficient to obtain the updated average power consumption of each flight job, the temperature influence coefficient is greater than 1 when the temperature is outside a predetermined threshold range, and can be equal to 1 when the temperature is within the predetermined threshold range. For example, during a period when the temperature is below a predetermined threshold, such as from November to March each year, the temperature influence coefficient for that period is determined based on historical data under low temperature influence.

[0060] By separately scheduling and calculating the second jobs of the first type of vehicle that need the second jobs, the preparation and scheduling of the vehicles are more scientific, and the production efficiency and vehicle utilization rate are improved.

[0061] Figure 3 An example flowchart of a demand prediction method for both first-type vehicles and second-type vehicles is shown.

[0062] As shown in Figure 3 the vehicle demand prediction method includes: step S202, calculating a first demand quantity of only the first-type vehicles as a fourth demand quantity; step S204, calculating a first demand quantity of only the second-type vehicles as a fifth demand quantity; step S206, determining a replacement ratio; and step S208, determining a number of replacements according to the replacement ratio.

[0063] In step S202, the first demand quantity is calculated for only the first-type vehicles that need a second operation, obtaining a fourth demand quantity of the first-type vehicles. In step S204, the first demand quantity is calculated for only the second-type vehicles that do not need the second operation, obtaining a fifth demand quantity of the second-type 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 vehicles is divided by the fifth demand quantity of the second-type vehicles, obtaining the replacement ratio of the two types of vehicles. In step S208, the number of the first-type vehicles needed for replacement is determined according to the replacement ratio. In some embodiments, the first-type vehicles are electric vehicles, the second-type vehicles are conventional energy vehicles, the second operation is charging, and the number of conventional energy vehicles available in the prediction period is obtained by subtracting the number of vehicles expected to be scrapped from the existing number of conventional energy vehicles. The number of conventional energy vehicles that need to be replaced by electric vehicles is obtained by subtracting the number of available conventional energy vehicles from the demand vehicle quantity of conventional energy vehicles. The number of electric vehicles needed is obtained by multiplying the number of conventional energy vehicles that need to be replaced by electric vehicles by the replacement ratio, and then rounding and / or adding a redundancy, etc. For example, the fifth demand quantity of only conventional energy vehicles is 20, and the fourth demand quantity of only electric vehicles is 28, so the replacement ratio is 1.4. When there are 5 conventional energy vehicles missing, 7 electric vehicles are needed to fill the gap. In this way, the prediction process can be smoothly transitioned during the replacement process of conventional energy vehicles and new energy vehicles, reducing the waste of resources.

[0064] The vehicle demand prediction method of the present disclosure improves the accuracy of vehicle demand prediction by determining the demand quantity of vehicles based on flight information and vehicle operation rules, especially for cases where multiple operations are concentrated and vehicles have different operation rules, thus making the preparation and scheduling of vehicles more scientific and improving production efficiency and vehicle utilization.

[0065] In some embodiments, the obtained number of required vehicles is multiplied by a redundancy factor to obtain a final number of required vehicles. The redundancy factor can cover the shortage of vehicles due to unexpected situations.

[0066] In the vehicle demand prediction method of the present disclosure, a vehicle operation rule of a preset type of vehicle can be obtained; then, flights meeting flight conditions in the vehicle operation rule are screened from the scheduled flights, and flight information of the screened flights is obtained; and a first demand quantity of the preset type of vehicle in a preset period is determined according to operation conditions in the vehicle operation rule and the flight information of the screened flights, so that the specific preset type of vehicle can be more accurately predicted according to the scheduled flight conditions. Further, in some embodiments, the first demand quantity of only the first type of vehicle can be calculated as a fourth demand quantity and the first demand quantity of only the second type of vehicle can be calculated as a fifth demand quantity in the preset period, and a ratio of the fourth demand quantity to the fifth demand quantity is taken as a replacement ratio; and the quantity of the first type of vehicle required to replace the second type of vehicle in the preset type of vehicle is determined according to the replacement ratio, so as to realize accurate prediction in the replacement process of different types of vehicles.

[0067] According to another aspect of the present disclosure, a vehicle demand prediction device is also provided. As shown in Figure 7 The vehicle demand prediction device 300 can include a rule obtaining module 302, a flight screening module 304, and a demand determining module 306. The rule obtaining module 302 can be configured to obtain a vehicle operation rule of a preset type of vehicle. The flight screening module 304 can be configured to screen flights meeting flight conditions in the vehicle operation rule from the scheduled flights, and obtain flight information of the screened flights. The demand determining module 306 can be configured to determine a first demand quantity of the preset type of vehicle according to operation conditions in the vehicle operation rule and the flight information of the screened flights. The vehicle demand prediction device 300 can be configured to perform the method according to any of the preceding embodiments of the present disclosure.

[0068] As shown in Figure 8As shown, the present disclosure also provides a vehicle demand prediction device 400, which can include one or more processors 402 and a memory 404 storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform the method according to any of the preceding embodiments of the present disclosure. The processor(s) 402 can be, for example, a central processing unit (CPU) of the vehicle demand prediction device 400. The processor(s) 402 can be any type of general purpose processor, or can be a processor specifically designed for vehicle prediction / scheduling, such as an application specific integrated circuit (“ASIC”). The memory 404 can include various computer-readable media that are accessible by the processor(s) 402. In various embodiments, the memory 404 described herein can include volatile and non-volatile media, removable and non-removable media. For example, the memory 404 can include any combination of the following: 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-transitory computer readable media. The memory 404 can store instructions that, when executed by the processor 402, cause the processor 402 to perform the method according to any of the preceding embodiments of the present disclosure.

[0069] The present disclosure also provides a non-transitory storage medium having stored thereon computer-executable instructions that, when executed by a computer, cause the computer to perform the method according to any of the preceding embodiments of the present disclosure.

[0070] The present disclosure also provides a computer program product, which can include instructions that, when executed by a processor, can implement the method according to any of the preceding embodiments of the present disclosure. The instructions can be any set of instructions that directly cause one or more processors to execute, such as machine code, or indirectly cause one or more processors to execute, such as scripts. The instructions can be stored as object code in an object code format that is directly executable by one or more processors, or in any other computer languages, including scripts or collections of independently

[0071] The foregoing description of one or more exemplary embodiments of the present disclosure has been presented. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still accomplish the desired results. Additionally, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

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

[0073] The terms "comprising", "containing", or any other similar term are intended to encompass non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements does not necessarily include those elements only, but can include other elements not expressly listed, or can include elements inherent in such process, method, product, or apparatus. Without more limitations, it does not exclude the presence of other identical or equivalent elements in the process, method, product, or apparatus that includes the elements. For example, if the terms "first", "second", etc. are used to indicate names, they do not necessarily indicate any specific order.

[0074] For the convenience of description, the above apparatus is described in various modules with functions. Of course, when implementing one or more embodiments of the present disclosure, the functions of each module can be implemented in the same or more software and / or hardware, or the modules implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The above described apparatus embodiments are only illustrative, for example, the division of the units is only a logical function division, and in actual implementation, another division mode can be used, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0075] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, apparatus (system) and computer program products according to embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce the apparatus for implementing the functions specified in one or more flows of the flowcharts and / or one or more blocks of the block diagrams.

[0076] These computer program instructions can also be stored in a computer- readable memory 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 memory produce an article of manufacture including instructions which implement the function specified in the flowchart or flowsheets and / or block or blocks of the block diagrams. The computer program instructions can 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 the flowchart or flowsheets and / or block or blocks of the block diagrams.

[0077] Those skilled in the art will appreciate that one or more embodiments of the disclosure can be embodied in the form of entirely hardware embodiments, entirely software embodiments or embodiments combining software and hardware aspects. Furthermore, one or more embodiments of the disclosure can be embodied in the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, CD-ROM, optical storage etc.) having computer usable program code embodied therein.

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

[0079] The same parts and / or features of the various embodiments of the present disclosure can be referred to with the same or similar reference numerals. Each embodiment described herein is intended to serve as a representative example only, and is not intended in any way to limit the overall scope of the present disclosure. Furthermore, the various embodiments of the present disclosure can be used together or in various combinations, as would be understood by one of ordinary skill in the art. In the description of the present disclosure, the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" are intended to mean that a particular feature, structure, material, or characteristic is included in at least one embodiment or example of the present disclosure, and is used in certain instances to

[0080] In addition, the words "herein," "above," "below," "nowhere," "above-mentioned," and words of similar meaning, when used in this disclosure, shall not

[0081] The above description is merely illustrative of the embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. Various modifications and changes can be made by persons of ordinary skill in the art to the embodiments of the present disclosure without departing from the spirit and the scope of the present disclosure. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present disclosure shall be included in the scope of the claims.

Claims

1. A method for predicting vehicle demand, comprising: obtaining a vehicle operation rule of a preset type of vehicle; selecting flights that meet flight conditions in the vehicle operation rule from scheduled flights, and obtaining flight information of the selected flights; and determining a first demand quantity of the preset type of vehicle in a preset period according to operation conditions in the vehicle operation rule and the flight information of the selected flights, wherein the operation conditions include an operation duration required for the preset type of vehicle to perform an operation and a window time range in which the preset type of vehicle can perform the operation; wherein determining the first demand quantity of the preset type of vehicle in the preset period according to the operation conditions in the vehicle operation rule and the flight information of the selected flights comprises: reading, from the flight information of the selected flights, or determining, according to the flight information of the selected flights and the window time range, a window period in the preset period in which each flight can be operated and a second demand quantity of the preset type of vehicle required for each flight to perform the operation, for each sub-period in the preset period, determining a third demand quantity of the preset type of vehicle according to a window period at least partially coinciding with the sub-period, a second demand quantity associated with the window period and the operation duration, wherein the preset period is divided into one or more sub-periods, and adjacent two sub-periods are continuous and non-overlapping with each other, and determining the maximum of the third demand quantities determined for all sub-periods as the first demand quantity.

2. The vehicle demand prediction method according to claim 1, wherein the start and / or end time of each window period is the endpoint of the corresponding sub-period, in the case where the operation duration is equal to the window period, in a sub-period at least partially coinciding with a plurality of window periods, the third demand quantity is the sum of the second demand quantities of vehicles required for the plurality of window periods; or in the case where the operation duration is less than the window period, in a sub-period at least partially coinciding with a plurality of window periods, the third demand quantity is less than or equal to the sum of the second demand quantities of vehicles required for the plurality of window periods.

3. The vehicle demand prediction method according to claim 1, wherein the preset type of vehicle includes a first type of vehicle 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 vehicle in the preset period according to the operation conditions in the vehicle operation rule and the flight information of the selected flights further comprises: obtaining a first number of the first operations that the first type of vehicle can perform at most before performing the second operation and a second operation duration required for performing the second operation; for each sub-period in the preset period, determining the number of the first operations that each first type of vehicle has performed after the end of the last second operation before the start of the sub-period. determining a second number of jobs to be performed as a number of jobs already performed by the first type of vehicles equal to the first number, and determining an updated third number of the second number of the preset type of vehicles according to a window period at least partially overlapping with the sub-period, a second number of demands associated with the window period, the job duration, the second number of jobs to be performed, and the second job duration.

4. The vehicle demand prediction method according to claim 3, wherein The preset type of vehicles further includes a second type of vehicles configured to perform only the first job for the flights; determining the first number of the preset type of vehicles in the preset period according to the job condition in the vehicle job rule and the flight information of the filtered flights includes: respectively calculating a first number of only the first type of vehicles as a fourth number of demands and a first number of only the second type of vehicles as a fifth number of demands in the preset period, and taking a ratio of the fourth number of demands and the fifth number of demands as a replacement ratio; determining a number of the first type of vehicles required to replace the second type of vehicles in the preset type of vehicles to the first type of vehicles according to the replacement ratio.

5. The vehicle demand prediction method of claim 3, the first type of vehicles being electric vehicles, and the first number of the first jobs that the first type of vehicles can perform at most before performing the second job includes: obtaining a job average power consumption of the first job and a minimum power limit; determining the first number according to a quotient obtained by dividing a difference between the total power and the minimum power limit by the job average power consumption.

6. The vehicle demand prediction method according to claim 5, wherein multiplying the job average power consumption by a temperature influence coefficient to obtain an updated job average power consumption, wherein the temperature influence coefficient is greater than 1 when the temperature is not within a predetermined threshold range.

7. The vehicle demand prediction method according to claim 1, wherein The flight condition includes at least one of: an arrival and departure state, a flight type, a job area, a job period, and historical demand data of a flight served by the preset type of vehicles.

8. The vehicle demand prediction method according to claim 1, wherein The type of vehicles is determined according to at least one of a job category, a job area, and a vehicle model of the vehicles.

9. The vehicle demand prediction method of any one of claims 1-8, determining the first number of the preset type of vehicles according to the job condition in the vehicle job rule and the flight information of the filtered flights further includes: multiplying the first number of demands by a redundancy coefficient to obtain an updated first number of demands of the preset type of vehicles.

10. A vehicle demand prediction device, comprising: a rule obtaining module configured to obtain a vehicle job rule of a preset type of vehicles; a flight filtering module configured to filter flights meeting a flight condition in the vehicle job rule from scheduled flights and obtain flight information of the filtered flights; and and a demand determining module configured to determine a first demand quantity of the vehicle of the preset type according to a job condition in the vehicle job rule and flight information of the screened flights, wherein the job condition comprises a job duration required for the vehicle of the preset type to perform a job and a window time range in which the vehicle of the preset type can perform a job; wherein the determining the first demand quantity of the vehicle of the preset type in the preset time period according to the job condition in the vehicle job rule and the flight information of the screened flights comprises: reading, from the flight information of the screened flights, or determining, according to the flight information of the screened flights and the window time range, for each flight, a window time period in which the flight can be performed and a second demand quantity of the vehicle of the preset type required for performing the flight, for each sub time period in the preset time period, determining a third demand quantity of the vehicle of the preset type according to a window time period at least partially coinciding with the sub time period, a second demand quantity associated with the window time period and the job duration, wherein the preset time period is divided into one or more sub time periods, and adjacent two sub time periods are continuous and non-overlapping, and determining a maximum third demand quantity among the third demand quantities determined for all the sub time periods as the first demand quantity.

11. A vehicle demand prediction device comprising: a memory configured to store instructions; and a processor coupled to the memory, which, when the instructions are executed by the processor, implements operations of the vehicle demand prediction method according to any one of claims 1-9.

12. A computer readable storage medium having stored thereon instructions, wherein, The instructions, when executed by the processor, implement operations of the vehicle demand prediction method according to any one of claims 1-9.

13. A computer program product comprising instructions, which, when executed by a processor, implement operations of the vehicle demand prediction method according to any one of claims 1-9.

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