Energy filling queuing duration estimation method and device based on multivariate big data

Abstract

The invention discloses an energy filling queuing duration estimation method based on multivariate big data. The method comprises the following steps: acquiring the position of each of a plurality of vehicles and the position of a target station; determining the vehicles within the preset distance of the target station in the plurality of vehicles as vehicles in the area; acquiring historical driving data of each vehicle in the vehicles in the area; determining queuing vehicles in the vehicles in the area based on the historical driving data; acquiring a predefined fuel loading time length of each vehicle in the queuing vehicles; determining the number of available fuel guns of the target station; and determining the vehicle queuing duration of the target station based on the number of the available fuel guns, the historical driving data of each vehicle in the queuing vehicles and the predefined fuel loading duration.

Description

Technical Field

[0001] The present disclosure relates to a method and a device for estimating the length of a vehicle queue, and more specifically, to a method and a device for estimating the length of an energy refueling queue based on multivariate big data. Background Art

[0002] Hydrogen fuel cell vehicles are increasingly being used due to their significant advantages in environmental protection, energy efficiency and ease of use. However, due to the limited number of hydrogen refueling stations currently in use and the need to improve the stability of the hydrogen fuel supply chain, it is beneficial to know the status of vehicles queuing at hydrogen refueling stations in real time. Summary of the invention

[0003] The present disclosure provides a method and device for estimating the length of vehicle queues. The method and device enable real-time knowledge of the number of vehicles queuing at a hydrogen refueling station, the length of vehicle queues, and other conditions, which is beneficial for the hydrogen refueling station to control the supply and demand progress of hydrogen fuel, and is also beneficial for vehicles to make flexible decisions based on the length of vehicle queues.

[0004] One aspect of the present disclosure provides a method for estimating vehicle queue duration, the method comprising: obtaining a position of each vehicle among a plurality of vehicles and a position of a target station; determining vehicles among the plurality of vehicles that are within a predetermined distance of the target station as vehicles within an area; obtaining historical driving data of each vehicle among the vehicles within the area; determining queued vehicles among the vehicles within the area based on the historical driving data; obtaining a predefined fuel loading duration for each vehicle among the queued vehicles; determining the number of available fuel guns at the target station; and determining the vehicle queue duration at the target station based on the number of available fuel guns, the historical driving data of each vehicle among the queued vehicles, and the predefined fuel loading duration.

[0005] One aspect of the present disclosure provides a device for estimating the duration of a vehicle queue, the device comprising: a memory having instructions stored thereon; a processor coupled to the memory, and when the instructions are executed by the processor, the device executes a method for estimating the duration of a vehicle queue.

[0006] Another aspect of the present disclosure provides a computer-readable medium having instructions stored thereon, and when the instructions are executed by a processor, the processor executes the aforementioned vehicle queue duration estimation method.

[0007] Another aspect of the present disclosure provides a computer program product having instructions stored thereon. When the instructions are executed by a processor, the processor is caused to execute the aforementioned vehicle queue duration estimation method.

[0008] The vehicle queue duration estimation method according to the embodiment of the present disclosure uses the historical driving data and historical fuel data of the vehicle to identify the queued vehicles, and uses the predefined fuel loading time and historical data of the vehicle to estimate the vehicle queue duration of the target station, so that the number of queued vehicles and the estimated queue duration of the target station can be known in real time, which is beneficial for the target station to better manage traffic flow, and also beneficial for the vehicle to know the vehicle queue duration in advance, thereby providing a better customer experience. In addition, the data obtained by executing this method is also beneficial for the target station to understand the distribution of the number of queued vehicles in each time period, and then flexibly adjust its operating decisions according to this distribution, such as adding staff or opening more fuel nozzles during peak hours, conducting promotional activities during off-peak hours, expanding facilities, etc. This method and device are not only applicable to hydrogen refueling stations, but also to supply stations for other fuels. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The various aspects, features and advantages of the present disclosure will become clearer and easier to understand through the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0010] Figure 1 A schematic application scenario of the method and device for estimating vehicle queue duration according to an embodiment of the present disclosure is shown;

[0011] Figure 2 A schematic flow chart showing a method for estimating vehicle queue duration according to an embodiment of the present disclosure;

[0012] Figure 3 Show Figure 2 A schematic flow chart of step S240 in FIG.

[0013] FIG. 4A to FIG. 4D An example is shown in which the vehicle queue duration estimation method according to the present disclosure is implemented; and

[0014] Figure 5 A schematic block diagram of a vehicle queue duration estimation device according to an embodiment of the present disclosure is shown.

[0015] Specific embodiment

[0016] The present disclosure will be described in detail below with reference to exemplary embodiments of the present disclosure. However, the present disclosure is not limited to the embodiments described herein, and it can be implemented in many different forms. The described embodiments are only used to make the present disclosure thorough and complete, and to fully convey the concept of the present disclosure to those skilled in the art. The features of the various embodiments described can be combined or replaced with each other, unless explicitly excluded or should be excluded according to the context.

[0017] As mentioned above, since the number of hydrogen refueling stations currently in use is limited and the stability of the hydrogen fuel supply chain needs to be improved, it is beneficial to know the status of vehicles queuing at hydrogen refueling stations in real time. Thanks to the development of communication and sensing technology, it is not difficult to obtain multivariate big data related to vehicles and fuel refueling stations such as vehicle location, vehicle driving route, vehicle fuel consumption, refueling station flow, nearby traffic conditions, weather conditions, etc. from various channels. The present disclosure provides a method and device for estimating the length of vehicle queues at energy refueling stations based on such multivariate big data.

[0018] Figure 1 A schematic application scenario of the method and apparatus for estimating vehicle queue duration according to an embodiment of the present disclosure is shown.

[0019] refer to Figure 1 , the target station 10 may be a station that provides fuel supply for vehicles, including but not limited to stations that provide hydrogen, gasoline, diesel, natural gas, liquefied petroleum gas, synthetic natural gas, hydrogenated natural gas and other fuels for vehicles. For ease of explanation, the target station 10 is taken as an example of a hydrogen refueling station below. A vehicle queue duration estimation device 20 according to an embodiment of the present disclosure is configured at the target station 10. It can be detected that there are multiple hydrogen fuel cell vehicles near the target station 10 (for example, through tools such as maps), and these vehicles may include trucks, passenger cars, commercial vehicles, etc. The vehicle queue duration estimation device 20 can identify which of these vehicles are vehicles queuing for hydrogen refueling by executing the vehicle queue duration estimation method 200, and estimate the vehicle queue duration at the target station 10 accordingly. In addition, vehicle 20 can also broadcast or send information containing the estimated queue time to vehicles that wish to know the queue time. For example, the information can be various forms of information such as voice, text or images indicating contents such as "There are currently 10 vehicles queuing at target station 10, and the queue is expected to take 60 minutes", "There are currently 20 vehicles queuing at target station 10", "The queue at target station 10 is expected to take 60 minutes", etc.

[0020] Figure 2 A schematic flow chart of a method for estimating vehicle queue duration according to an embodiment of the present disclosure is shown.

[0021] refer to Figure 2 The vehicle queue duration estimation method 200 may include steps S210 to S290, wherein the dashed boxes represent optional steps. The method 200 may be executed by a vehicle queue duration estimation device 20 (hereinafter referred to as device 20) configured at the target station 10.

[0022] In step S210, the apparatus 20 may obtain the position of each of the plurality of vehicles and the position of the target station 10. For example, the plurality of vehicles may include Figure 1The vehicles V1 to Vm are shown in FIG. The position may refer to a position determined by longitude and latitude. In this step, the device 20 may obtain the positions of the vehicle and the target station from a map application, a geographic information system (GIS) or other devices capable of storing or identifying longitude and latitude of physical objects.

[0023] In step S220, the device 20 may determine that a vehicle located within a predetermined distance R of the target station 10 among the multiple vehicles V1 to Vm is a vehicle in the area. In this step, the predetermined distance R represents a distance that can be considered to have entered the range of the target station 10. The predetermined distance R can be determined based on the geographical conditions of the target station 10, such as the area, topography, and the like. In this step, the device 20 may calculate the distance between the position of each of the multiple vehicles V1 to Vm and the position of the target station 10. The distance between each vehicle and the target station may be achieved based on any existing or future technology that can calculate the distance between two points. For example, the GPS longitude data and dimension data of the vehicle may be extracted from the historical driving data of the vehicle, and then combined with the GPS longitude data and dimension data of the target station for calculation. For example, the distance between the vehicle and the target station can be calculated as follows: z*arccos(sin(y1)*sin(y2)+cos(y1)*cos(y2)*cos(x1-x2)), where z is the radius of the earth, with an average of 6370km, the longitude and latitude of the vehicle are x1 and y1, respectively, and the longitude and latitude of the target station are x2 and y2, respectively. If the calculated distance between the vehicle and the target station 10 is less than or equal to the predetermined distance R, the vehicle is marked as in, indicating that the vehicle is confirmed as a vehicle within the area. Conversely, if the calculated distance is greater than the predetermined distance R, the vehicle is marked as Out, indicating that the vehicle is confirmed as a vehicle outside the area. For example, in this step, vehicles V1 to Vg among multiple vehicles V1 to Vm are determined to be vehicles within the area, and vehicles Vg to Vm are determined to be vehicles outside the area.

[0024] In step S230, the device 20 can obtain the historical driving data of each vehicle from the vehicles V1 to Vg in the area. As is known to all, there are currently facilities or devices that can record vehicle driving data in real time, such as on-board diagnostic systems (ODB), in-vehicle information systems (IVI), cloud platforms provided by automobile manufacturers or third-party service providers, and vehicle black boxes (EDR). Therefore, the device 20 can obtain the historical driving data of the vehicle by communicating with these facilities or devices that collect vehicle driving data. For each vehicle from the vehicles V1 to Vg, the historical driving data of the vehicle obtained in this step may at least include the historical position data and historical fuel data of the vehicle. The historical position data can indicate the change of the position of the vehicle over time, such as indicating the change of the position of the vehicle over time in the past period of time (e.g., 5 hours, 10 hours, 24 hours, etc.). The historical fuel data can indicate the change of the fuel balance of the vehicle over time, such as indicating the change of the fuel balance of the vehicle over time in the past period of time (e.g., 5 hours, 10 hours, 24 hours, etc.). For example, for a hydrogen fuel cell vehicle, its historical fuel data may indicate changes in the pressure value (eg, in MPa) of the vehicle's hydrogen storage tank over time.

[0025] The acquisition of historical driving data of a vehicle is simple and easy. It can be directly obtained from existing on-board diagnostic systems (ODB), in-vehicle information systems (IVI), cloud platforms, vehicle black boxes (EDR), and other facilities or equipment that can provide real-time vehicle driving data. There is no need to specially configure additional sensor equipment, which saves costs.

[0026] In step S240, the queued vehicles among the vehicles V1 to Vg in the area are determined based on the historical driving data of each vehicle among the vehicles V1 to Vg in the area. It is understandable that not all the vehicles V1 to Vg in the area are vehicles waiting to queue. For example, the vehicles V1 to Vg in the area may also include vehicles that have been loaded with fuel but have not left the target station 10 (for example, vehicles that have just been loaded with fuel, or have been loaded with fuel for a long time but may not have left the target station for other reasons, such as the vehicle user still staying at the target station 10 to eat, rest, etc.), and may also include vehicles that stay at the target station 10 for a long time (for example, broken down vehicles, vehicles that are not intended to be loaded with fuel and are only resting here, etc.). The following is combined with Figure 3 as well as FIG. 4A to FIG. 4D to describe this step.

[0027] Figure 3 Show Figure 2 Schematic flowchart of step S240 in .

[0028] See also Figure 3 , step S240 may include sub-steps S241 to S243.

[0029] In sub-step S241, for each of the vehicles V1 to Vg in the area, the device 20 can determine whether the vehicle is a refueled vehicle based on the historical location data and historical fuel data of the vehicle. In this step, for each of the vehicles V1 to Vg in the area, if the vehicle meets one of the following two conditions, the device 20 can determine the vehicle as a refueled vehicle: (1) The historical fuel data of the vehicle indicates that the vehicle has a fuel loading record and the time difference between the fuel loading end time corresponding to the vehicle's most recent fuel loading record and the current time does not exceed a predetermined time interval; or (2) The historical fuel data of the vehicle indicates that the vehicle has a fuel loading record and the time difference between the fuel loading end time corresponding to its most recent fuel loading record and the current time exceeds a predetermined time interval, but the vehicle's historical location data indicates that the vehicle's most recent entry into the predetermined distance R of the target station 10 is earlier than the fuel loading end time. The predetermined time interval can be predetermined by using a statistical method based on the average time required for a vehicle to leave the target station 10 beyond the predetermined distance R immediately after loading fuel in the past, for example, it may be predetermined to be 5 minutes, 10 minutes, etc.

[0030] For ease of understanding, the following FIG. 4A to FIG. 4D The example shown is used for illustration.

[0031] FIG. 4A to FIG. 4D An example is shown in which the vehicle queue duration estimation method 200 according to the present disclosure is implemented.

[0032] refer to Figure 4A , which is shown in Figure 1 In the scenario, after the device 20 executes step S220, it determines that vehicles V1-V9 are vehicles in the area. As shown in the figure, they include trucks V1 and V5, commercial vehicle V8, and the rest are passenger cars.

[0033] After executing step 230 to obtain the historical driving data of vehicles V1-V9 in the area, the device 20 can identify the most recent fuel loading record of each of these vehicles according to the historical fuel data in the historical driving data. For any vehicle in the area of ​​vehicles V1-V9, if there are two adjacent fuel data in the historical fuel data of the vehicle indicating that its fuel balance increases by more than a predetermined increment over time, the device 20 can be considered that the historical fuel data indicates that the vehicle has a fuel loading record. It is easy to understand that the vehicle is required to turn off the engine when loading gas, and the facilities or equipment (such as the vehicle information system) configured to collect vehicle driving data in the vehicle stop collecting the fuel balance value. When the vehicle is required to drive away from the gas loading gun when the gas is loaded, the vehicle restarts so that the facilities or equipment configured to collect vehicle driving data in the vehicle start to collect the fuel balance value again. In this way, each time the vehicle loads gas, there will inevitably be an increase of more than a predetermined increment between the gas balance values ​​recorded by the two adjacent fuel data. The recording time of the fuel data that is earlier in time in the two adjacent fuel data can be determined as the start time of fuel loading, and the recording time of the fuel data that is later in time can be determined as the end time of fuel loading. For example, for a hydrogen fuel cell vehicle of a certain specification, the predetermined increment can be 2Mpa, that is, each time the hydrogen fuel cell vehicle is loaded with hydrogen, the hydrogen remaining value of the hydrogen storage tank recorded in a later fuel data in time will increase by at least 2Mpa compared to the hydrogen remaining value of the hydrogen storage tank recorded in an earlier fuel data in time.

[0034] After executing step 230 to obtain the historical driving data of vehicles V1-V9 in the area, the device 20 can identify the time when each of these vehicles last entered the target station 10 according to the historical position data in the historical driving data. It is easy to understand that if a vehicle has a fuel loading record, its last fuel loading record may be formed by the vehicle's latest entry into the target station 10 to load fuel, but it may also be formed by the vehicle's previous entry into the target station 10 or other stations to load fuel. These two situations can be distinguished by comparing the time when the vehicle last entered the target station 10 with the time when the last fuel was finished.

[0035] As an example, the first three rows in Table 1 below show the last fuel loading end time of each vehicle V1-V9 determined based on the historical fuel data of the vehicles V1-V9 and the last entry time of each vehicle V1-V9 into the target station 10 determined based on the historical position data of the vehicles V1-V9. Assuming that the current time is 21:00, the fourth row of Table 1 shows the time difference between the last entry time of each vehicle into the target station and the current time.

[0036] Table 1

[0037]

[0038]

[0039] Assume that the predetermined time interval in the aforementioned conditions (1) and (2) is predetermined to be 10 minutes. As can be seen from Table 1, the time difference between the last fuel loading end time of vehicles V1 and V3 and the current time does not exceed 10 minutes, which indicates that vehicles V1 and V3 are basically vehicles that have just been loaded with fuel. Therefore, the device 20 can determine vehicles V1 and V3 as refueled vehicles based on the aforementioned condition (1). Although the time difference between the last loading end time and the current time of vehicles V2, V4 and V5 exceeds 10 minutes, the time they last entered the target station 10 is earlier than their last fuel loading end time, which indicates that vehicles V2, V4 and V5 are basically vehicles that have been loaded with fuel for a long time but may not have left the target station 10 for other reasons. Therefore, the device 20 can determine vehicles V2, V4 and V5 as refueled vehicles based on the aforementioned condition (2). Figure 4B As shown, vehicles V2, V4, and V5 are marked as "added".

[0040] Reference Figure 3 , in sub-step S242, for each of the vehicles in the area that are not determined as refueled vehicles, the device 20 can determine whether the vehicle is a long-term stay vehicle based on the historical location data of the vehicle. It is easy to understand that the vehicles that are not determined as refueled vehicles include vehicles in the queue and long-term stay vehicles (for example, broken vehicles, vehicles that are not intended to load fuel and are just resting here, etc.). Long-term stay vehicles can be determined by judging whether there are vehicles that enter the target station 10 later than it and have completed fuel loading. Therefore, in this step, for each of the vehicles in the area that are not determined as refueled vehicles, if the time when the vehicle last entered the target station 10 within the predetermined distance R is earlier than the time when any of the vehicles that have been determined as refueled vehicles in step S241 last entered the target station 10 within the predetermined distance R, then the vehicle can be determined as a long-term stay vehicle.

[0041] Continuing with the previous example, refer to Figure 4B, in step S241, vehicles V1 to V5 are determined as refueled vehicles, and vehicles V6 to V9 are not determined as refueled vehicles. The time when vehicle V7 last entered the target station 10 within the predetermined distance R is 15:30, which is earlier than the time when any of the refueled vehicles V1 to V5 last entered the target station 10 within the predetermined distance R, indicating that the vehicles that entered the target station 10 later than vehicle V7 have completed fuel loading. Therefore, the device 20 does not believe that vehicle V7 goes to the target station 10 to load fuel, and thus determines vehicle V7 as a long-term stay vehicle. Figure 4C As shown, vehicle V7 is marked as "stayed".

[0042] Reference Figure 3 In sub-step S243, the device 20 may determine the vehicles in the area other than the refueled vehicles and the long-stayed vehicles as queued vehicles.

[0043] Continuing with the above example, in addition to the refueled vehicles V1 to V5 and the long-stayed vehicle V7, the remaining vehicles V6, V8 and V9 are among the vehicles V1 to V9 in the area. Figure 4D As shown, vehicles V6, V8, and V9 are marked as "queued".

[0044] Thus, the queue duration estimation method 200 according to the embodiment of the present disclosure can screen out queued vehicles from vehicles in the area of ​​the target station 10 only with the historical driving data of the vehicles. Since the historical driving data of the vehicles is easy to obtain, the method is easy to implement and has low cost.

[0045] Reference Figure 2 In step S250, the device 20 may obtain the predefined fuel loading duration of each vehicle in the queue. In this step, the predefined fuel loading duration of the vehicle indicates the time that the vehicle will take from the end of the queue to the end of the fuel recording. It is easy to understand that the vehicle basically undergoes the following operations from the end of the queue to the end of the fuel recording: the driver drives the vehicle from the queue position to the fuel gun mouth, the vehicle is turned off, the waiter inserts the gun, the storage container is filled with fuel, the waiter takes the gun, etc. Therefore, the predefined fuel loading duration of the vehicle may include two durations, wherein the first duration includes the duration of fuel being loaded to fill the fuel storage container, and the second duration includes the duration of the driver driving the vehicle from the queue position to the fuel gun mouth, the vehicle is turned off, the waiter inserts the gun, and the waiter takes the gun. The first duration is related to the specifications of the vehicle, more specifically, to the capacity of the fuel storage container configured by the vehicle, so the first duration can be obtained by querying the vehicle specifications. The second duration is a fixed value, and an empirical value can be summarized based on relevant historical data.

[0046] For example, FIG. 4A to FIG. 4DThe vehicles V1, V8 and V9 shown in the figure are a hydrogen fuel cell truck, a hydrogen fuel cell commercial vehicle and a hydrogen fuel cell passenger car, respectively, and their hydrogen storage tank specifications indicate that it takes 8 minutes, 10 minutes and 5 minutes to fill their hydrogen storage tanks, respectively. Therefore, in this example, the first duration of the predefined fuel loading time of vehicle V1 is 8 minutes, the first duration of the predefined fuel loading time of vehicle V8 is 10 minutes, and the first duration of the predefined fuel loading time of vehicle V9 is 5 minutes. Assuming that the empirical value of the second duration is 4 minutes, the predefined fuel loading times of vehicles V1, V8 and V9 are 12 minutes, 14 minutes and 9 minutes, respectively.

[0047] In step S260, the device 20 may determine the number of available fuel guns of the target station. There are various ways to determine the number of available fuel guns of the target station 10.

[0048] For example, the target station 10 may be configured with a sensor associated with the switch of each available fuel gun, which sensor itself or via other communication devices can communicate with the device 20, so that the device 20 can know from the sensor whether each fuel gun is currently open or closed, thereby determining the number of available fuel guns.

[0049] For another example, the device 20 may also obtain historical usage data about each fuel gun of the target station 10 (for example, obtained from the data center of the target station 10), and the historical usage data can indicate the number of fuel guns put into use every day at the target station 10. The device 20 can then round off the average number of fuel guns put into use within a predetermined number of days to determine the number of available fuel guns. For example, given that the target station 10 is configured with 3 fuel guns, in the past week, 2 fuel guns were opened on Monday and Tuesday, Thursday and Friday, 3 fuel guns were opened on Wednesday due to promotional activities, and 3 fuel guns were also opened on weekends due to more vehicles traveling, so the number of available fuels N can be determined as N=round(1 / 7*(2*4+3*3))=2, where round means rounding. In addition, it should be understood that as long as there are fuel guns open, the number of available fuel guns N must be greater than or equal to 1. In this way, only the historical data of the fuel guns is required, avoiding the configuration of sensors associated with the switches of the fuel guns, saving costs.

[0050] In step S270 , the device 20 may determine the vehicle queue time at the target station based on the number of available fuel guns and the historical driving data of each vehicle in the queue and the predefined fuel loading time.

[0051] In this step, the vehicle queue duration of the target station may include two parts of duration. The first part of duration a is the fuel loading duration of all queued vehicles, and the second part of duration b is the queue waiting duration.

[0052] It is easy to understand that the number of available fuel guns is equivalent to the number of queues of vehicles. Therefore, the device 20 can determine the value obtained by dividing the sum of the predefined fuel loading time of each vehicle in the queue by the number of available fuel guns as the fuel loading time of all queued vehicles, that is, the first part time a. Figure 4A and Figure 4B In the example shown, the queued vehicles include V6, V8 and V9, whose predefined fuel loading durations are 9 minutes, 14 minutes and 9 minutes respectively, and the number of available fuel guns is 2, so the first part duration a=1 / 2*(9+14+9)=16 minutes.

[0053] The second portion of the duration b can be calculated based on the net queue duration of the first vehicle currently in each queue. For each vehicle in the queue, its net queue duration is the value obtained by subtracting its predefined fuel loading duration from the time difference between the current time and the time when it last entered the target station 10 within the predetermined distance R. For example, continuing the above example, Figure 4D As shown, the queued vehicles include V6, V8 and V9. According to the aforementioned Table 1, their last entry times into the target station 10 are 20:35, 20:40 and 20:50 respectively. Since their predefined fuel loading times are 9 minutes, 14 minutes and 9 minutes respectively, and the current time is 21:00, the net queue times of the queued vehicles V6, V8 and V9 are 16 minutes, 6 minutes and 1 minute respectively.

[0054] The device 20 can sort each vehicle in the queue according to the time when it last entered the target station within a predetermined distance. If the number of available fuel guns is N, the second part duration b is determined based on the net queue duration of each vehicle in the first N vehicles after sorting. Determining the second part duration b includes: if N=1, the net queue duration of the first vehicle after sorting is used as the second part duration b; if N=2, if the larger net queue duration of the net queue durations of the first and second vehicles after sorting exceeds twice the smaller net queue duration, the smaller queue duration is determined as the second part duration b, otherwise, the average of the net queue durations of the first and second vehicles is determined as the second part duration b; if N≥3, the median of the net queue durations of the first to Nth vehicles after sorting is determined as the queue waiting duration b.

[0055] For example, continuing the above example, the number of available fuel guns is N=2, and the queued vehicles include V6, V8, and V9, and their last entry times into the target station 10 are 20:35, 20:40, and 20:50, respectively. Sorted by the time they last entered the target station 10, the sorted queues are V6, V8, and V9, respectively. Since the net queue times of vehicle V6 and vehicle V8 are 16 minutes and 6 minutes, respectively, 16 minutes is more than twice 6 minutes, so the second part duration b is determined to be 6 minutes. Based on this, the device 20 can determine that the vehicle queue duration at the target station 10 is the sum of the first part duration a and the second part duration, which is 16+6=22 minutes.

[0056] In this way, the vehicle queue duration estimation method 200 according to the embodiment of the present disclosure can estimate the vehicle queue duration of the target station 10 in real time. The target station 10 can optimize the flow management of the target station and improve the fuel supply strategy based on the estimated vehicle queue duration. In addition, the method 200 can estimate the vehicle queue duration of the target station by using only the historical driving data, historical fuel data and historical fuel gun usage data of the target station, and these data are all existing and easy to obtain, so the method is simple and easy to implement and low cost.

[0057] like Figure 2 The shown queuing duration estimation method may further include step S280.

[0058] In step S280, the device 20 may also send a message including the estimated vehicle queue duration and / or the total number of vehicles in the queue. For example, the device 20 may send a message such as "There are currently 3 vehicles in the queue at the target station 10, and the queue is expected to take 22 minutes", "There are currently 3 vehicles in the queue at the target station 10", "The queue at the target station 10 is expected to take 22 minutes". The message may be sent by broadcasting or in response to a request from a vehicle or other device. For example, in Figure 1 In the example shown, a vehicle Vm located outside a predetermined distance R of the target station 10 intends to load fuel at the target station 10, and it can send a request message requesting the queue time to the target station 10. After receiving this request, the device 20 can send a message including the determined queue time and / or the number of vehicles in the queue to the vehicle Vm. In this way, the vehicle Vm can decide whether to load fuel at the target station 10.

[0059] In this way, executing the vehicle queue duration estimation method 200 according to the embodiment of the present disclosure can also provide the vehicle queue duration and / or the number of queued vehicles at the target station 10 to the vehicles in need, so that the vehicles can arrange their trips more flexibly.

[0060] like Figure 2The shown queuing duration estimation method may further include step S290.

[0061] In step S290, the device 20 can also determine the total amount of fuel currently required based on the sum of the predefined fuel loading durations of all vehicles in the queue and the average fuel loading rate of the fuel guns of the target station 10. For example, continuing the above example, the queued vehicles include V6, V8, and V9, and their predefined fuel loading durations are 9 minutes, 14 minutes, and 9 minutes, respectively. The loading rates of the available fuel guns are all 2 kg / min, so it can be concluded that the total amount of fuel currently required is 64 kg.

[0062] In this way, the vehicle queue duration estimation method 200 according to the embodiment of the present disclosure can also estimate the total amount of fuel required to be supplied by the target station in real time, so that the target station can better control the progress of fuel supply and demand.

[0063] Figure 5 A schematic block diagram of a vehicle queue duration estimation device according to an embodiment of the present disclosure is shown. Figure 5 The device 20 described in the specification can be used to execute the aforementioned vehicle queue duration estimation method 200.

[0064] like Figure 5 As shown, the vehicle queue duration estimation device 20 may include a memory 510 and a processor 520, and the processor 520 may be coupled to the memory 510 via a communication bus 530. The memory 510 stores instructions, and when the instructions are executed by the processor 520, the device 20 executes the vehicle queue duration estimation method 200 described above.

[0065] Examples of processor 510 include microprocessors, microcontrollers, digital signal processors (DSPs), field programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gated logic, discrete hardware circuits, and other suitable hardware that are configured to perform the various functions described in this disclosure.

[0066] The processor 520 may execute software. Software should be broadly understood to mean instructions, instruction sets, codes, code segments, program codes, programs, subroutines, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, programs, functions, etc., functionality, etc., whether software, firmware, middleware, microcode, hardware description language, or otherwise, microcode, hardware description language, etc. The software may be located on the memory 510.

[0067] The memory 510 may be a non-temporary computer-readable medium. For example, non-temporary computer-readable media include magnetic storage devices (such as hard disks, floppy disks, magnetic strips), optical disks (such as compact disks (CDs) or digital versatile disks (DVDs)), smart cards, flash memory devices (such as flash memory cards, flash memory sticks, or key drives), random access memory (RAM), read-only memory (ROM), random access memory (RAM), read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), registers, removable disks, and any other medium suitable for storing software and / or instructions that can be accessed and read by a computer. The memory 510 may be located in the processor 520 or distributed in multiple entities including the processor 520.

[0068] In addition, according to the present disclosure, a computer program product is also provided. The computer program product includes a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium includes program instructions, and the program instructions can be executed by a processor. When executed, the program instructions cause the processor to perform one or more steps in the above-mentioned vehicle queue duration estimation method 200.

[0069] Those skilled in the art should understand that the above-mentioned specific embodiments are merely examples and not limitations, and various modifications, combinations, partial combinations and replacements may be made to the embodiments of the present disclosure according to design requirements and other factors. As long as they are within the scope of the attached claims or their equivalents, they belong to the scope of rights to be protected by the present disclosure.

Claims

1. A method for estimating vehicle queue duration, comprising: obtaining a position of each of the plurality of vehicles and a position of the target station; Determining a vehicle located within a predetermined distance from the target station among the plurality of vehicles as an in-region vehicle; Obtaining historical driving data of each vehicle in the area; Determine queued vehicles among vehicles in the area based on the historical driving data; Obtaining a predefined fuel loading time for each vehicle in the queue; determining the number of available fuel guns at the target station; as well as The vehicle queue duration at the target station is determined based on the number of available fuel guns, the historical driving data of each vehicle in the queue, and the predefined fuel loading duration.

2. The method according to claim 1, wherein: The historical driving data includes: historical position data indicating changes in the position of the vehicle over time; and Historical fuel data indicating changes in the fuel balance of the vehicle over time.

3. The method according to claim 2, wherein: Determining the queued vehicles in the area based on the historical driving data includes: determining a refueled vehicle among vehicles in the area based on the historical location data and the historical fuel data; Determine a long-stayed vehicle among vehicles in the area based on the historical location data; The vehicles other than the refueled vehicles and the long-stayed vehicles among the vehicles in the area are determined as queued vehicles.

4. The method according to claim 3, wherein: Determining a refueled vehicle among vehicles in the area based on the historical location data and the historical fuel data includes: For each vehicle in the area, if the vehicle satisfies the following conditions, it is determined to be a refueled vehicle: The historical fuel data of the vehicle indicates that the vehicle has a fuel loading record and the time difference between the fuel loading end time corresponding to the most recent fuel loading record and the current time does not exceed a predetermined time interval; or The historical fuel data of the vehicle indicates that the vehicle has a fuel loading record and the time difference between the fuel loading end time corresponding to its most recent fuel loading record and the current time exceeds a predetermined time interval, and the historical location data of the vehicle indicates that the time when the vehicle last entered within a predetermined distance of the target station is earlier than the fuel loading end time.

5. The method according to claim 4, wherein: Determining a long-stay vehicle among vehicles in the area based on the historical driving data includes: For each vehicle among the vehicles in the area that are not determined as refueled vehicles, if the time when the vehicle last entered the predetermined distance of the target station is earlier than the time when any of the refueled vehicles last entered the predetermined distance of the target station, the vehicle is determined as a long-term staying vehicle.

6. The method according to claim 3, wherein: For any vehicle in the area, if there are two adjacent fuel data in the historical fuel data of the vehicle indicating that the remaining fuel amount increases over time by more than a predetermined increment, the historical fuel data indicates that the vehicle has a fuel loading record; and The time corresponding to the later fuel data in time of the two adjacent fuel data is determined as the fuel loading end time.

7. The method according to claim 1, wherein: Determining the vehicle queue time at the target station based on the number of available fuel guns and the historical driving data of each vehicle in the queue and a predefined fuel loading time, comprising: determining a value obtained by dividing the sum of the predefined fuel loading durations of each vehicle in the queue by the number of available fuel guns as a first part of the queue duration; Sort each of the vehicles in the queue by the time of the last entry into the target station within a predetermined distance, and determine the second part of the queue time based on the net queue time of each of the first N vehicles after sorting, wherein N is the number of available fuel guns; and The sum of the first part and the second part is determined as the queuing time, The second part is determined based on the net queuing time of the first N vehicles after sorting, including: If N=1, the net queue time of the first vehicle after sorting is taken as the second part; If N=2, then: if the larger net queue time of the sorted first vehicle and the second vehicle exceeds twice the smaller net queue time, the smaller queue time is determined as the second part of the queue time; otherwise, the average of the net queue times of the first vehicle and the second vehicle is determined as the second part; or If N ≥ 3, the median of the net queuing time from the first vehicle to the Nth vehicle after sorting is determined as the second part. For each vehicle in the queue, its net queue time is the value obtained by subtracting its predefined fuel loading time from the time difference between the current time and the time when it last entered the target station within a predetermined distance.

8. The method according to claim 1, further comprising: The total amount of fuel to be supplied is determined according to the sum of the predefined fuel loading durations of all vehicles in the queue and the average fuel loading rate of the fuel guns of the target station.

9. The method according to claim 1, wherein: Determining the number of available fuel guns includes: acquiring historical usage data about the fuel guns of the target station, the historical usage data indicating the number of fuel guns of the target station that are put into use every day; The number of available fuel guns is determined as a value obtained by rounding off the average number of fuel guns put into use within a predetermined number of days.

10. The method according to claim 1, wherein: The predefined fuel loading duration indicates the time it will take for the vehicle to finish fuel loading from the end of the queue.

11. The method according to claim 1, wherein: Determining that a vehicle located within a predetermined distance from the target station among the plurality of vehicles is a vehicle in the area includes: determining a longitude and a latitude of each vehicle in the plurality of vehicles; determining the longitude and latitude of the target station; For each vehicle, determine the distance between the vehicle and the target station as z*arccos(sin(y1)*sin(y2)+cos(y1)*cos(y2)*cos(x1-x2)), where z is the radius of the earth, x1 and y1 are the longitude and latitude of the vehicle, respectively, and x2 and y2 are the longitude and latitude of the target station, respectively; and The vehicles whose distance is less than the predetermined distance are determined as vehicles within the area.

12. The method according to claim 1, further comprising: A message including the determined vehicle queue duration and / or the vehicle number of the queued vehicles is sent.

13. A device for estimating vehicle queue duration, comprising: a memory having instructions stored therein; A processor is coupled to the memory, and when the instructions are executed by the processor, the apparatus is caused to perform the method according to any one of claims 1 to 12.

14. A computer-readable medium having instructions stored thereon, which, when executed by a processor, causes the processor to execute the method according to any one of claims 1 to 12.

15. A computer program product having instructions stored thereon, which, when executed by a processor, causes the processor to perform the method according to any one of claims 1 to 12.

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