CN111985859A

CN111985859A - Method, computing device and computer-readable storage medium for order scheduling

Info

Publication number: CN111985859A
Application number: CN202011058356.2A
Authority: CN
Inventors: 朱广; 章瑞平; 谢春
Original assignee: Shanghai Ehi Auto Services Co ltd; Nanjing Wenhang Automobile Technology Co ltd
Current assignee: Shanghai Ehi Auto Services Co ltd
Priority date: 2020-09-30
Filing date: 2020-09-30
Publication date: 2020-11-24

Abstract

Embodiments of the present disclosure present methods, computing devices, and computer-readable storage media for order scheduling. According to the method, first information relating to the order is obtained, the first information comprising at least first location information relating to an associated object of the order. A plurality of second information corresponding to a plurality of second objects in the second set of objects is obtained, each of the plurality of second information including at least second location information corresponding to the second object and information related to a degree of fatigue of the second object. Based on the first information and the second information, a second object for the order is selected from a second set of objects. Selecting a target second object for the order from the plurality of second objects such that the selected target second object is in a non-fatigued state, and assigning the order to the target second object. The present disclosure enables efficient, accurate, and safe matching of orders with drivers and vehicles taking orders.

Description

Method, computing device and computer-readable storage medium for order scheduling

Technical Field

Embodiments of the present disclosure relate to computer technology, and more particularly, to methods, computing devices, and computer-readable storage media for order scheduling.

Background

With the increasing popularity of car rental business, the volume of car rental orders is also rapidly increased. In some scenarios, the user wishes to be provided with a driver while renting a vehicle from a rental company. For the scene, after receiving the order of the user, the driver and the vehicle meeting the requirement of the user are distributed to the user according to the order requirement to become an essential weight link in the service. Therefore, how to quickly, efficiently and accurately match drivers and allocate corresponding vehicles according to the requirements of user orders becomes a difficult problem to be solved urgently for a vehicle rental management platform or a scheduling system.

Conventional order scheduling schemes include, for example: orders are allocated based on driver self-order taking and manual ordering. The conventional order scheduling scheme may not be able to match the appropriate driver and vehicle for the order of the user, nor to arrange the order for the driver reasonably, so as to reduce the vehicle empty driving rate of the driver, and ensure driving safety. Thus, conventional order scheduling schemes have difficulty efficiently, accurately, and safely matching orders to drivers and vehicles taking orders.

Disclosure of Invention

The embodiment of the disclosure provides a scheme for order scheduling, which can efficiently, accurately and safely match orders with drivers and vehicles taking orders.

In a first aspect of the disclosure, a method for order scheduling is provided. The method comprises the following steps: first information relating to the order is obtained, the first information comprising at least first location information relating to an associated object of the order. A plurality of second information corresponding to a plurality of second objects in the second set of objects is obtained, each of the plurality of second information including at least second location information corresponding to the second object and information related to a degree of fatigue of the second object. Based on the first information and the second information, a second object for the order is selected from a second set of objects. In response to determining that the selected second object includes a plurality of second objects, selecting a target second object for the order from the plurality of second objects based on at least the first location information, a corresponding plurality of second location information from the plurality of second objects, and a plurality of information relating to a degree of fatigue of the plurality of second objects, such that the selected target second object is in a non-fatigue state, and assigning the order to the target second object.

In a second aspect of the disclosure, a computing device is provided. The apparatus comprises: at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions when executed by the at least one processing unit, cause the apparatus to perform the steps of the method according to the first aspect of the disclosure.

In a third aspect of the disclosure, a computer-readable storage medium is provided, having stored thereon a computer program which, when executed by a machine, implements a method according to the first aspect of the disclosure.

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Drawings

FIG. 1 illustrates a schematic diagram of an environment in which some embodiments of the present disclosure can be implemented.

FIG. 2 illustrates a flow diagram of a method for order scheduling according to some embodiments of the present disclosure.

Fig. 3 illustrates a flow diagram of a method for selecting a target second object from a plurality of second objects, according to some embodiments of the present disclosure.

Fig. 4 shows a flow diagram of a method for selecting a target second object from a plurality of second objects according to further embodiments of the present disclosure.

Fig. 5A and 5B illustrate schematic diagrams of a driver dispatch list table and a driver shift list table according to some embodiments of the present disclosure.

Fig. 6 illustrates a schematic diagram of an example scheduling system, in accordance with some embodiments of the present disclosure.

FIG. 7 shows a schematic block diagram of an example device that may be used to implement embodiments of the present disclosure.

In the drawings, the same or similar reference characters are used to designate the same or similar elements.

Detailed Description

The present disclosure will now be discussed with reference to several example implementations. It should be understood that these implementations are discussed only to enable those of ordinary skill in the art to better understand and thus implement the present disclosure, and are not intended to imply any limitation as to the scope of the present disclosure.

As used herein, the term "include" and its variants are to be read as open-ended terms meaning "including, but not limited to. The term "based on" is to be read as "based, at least in part, on". The terms "one implementation" and "an implementation" are to be read as "at least one implementation". The term "another implementation" is to be read as "at least one other implementation". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.

As mentioned previously, in conventional order scheduling schemes, there are two methods of scheduling matching drivers and vehicles for a user's order (e.g., a rental car service order): the driver preempts the order and arranges the order manually.

The driver order grabbing mode is that the driver asks the order after the order appears. However, when the order grabbing mode is used, there may be a case where no one takes an order or the driver takes an order excessively. For example, an unmanned pick-up situation may occur when the pick-up location in an order specified by the user is remote from the passenger drop-off location in the previous order assigned by the driver (e.g., the previous order is in the downtown area, while the order currently to be assigned is in a more remote area). In addition, the fatigue condition of the driver may not be considered when the driver is rushing the order, so that the driver who is rushing the order is fatigued to drive when the driver executes the order, and potential safety hazards are brought.

The manual order dispatching mode is a mode of dispatching and arranging orders according to the orders and the conditions of drivers in advance after the orders appear. When using this sort approach, the sort worker matches the driver for each order, e.g., based on predetermined shift information and driver travel distance, etc.). One of the disadvantages of using this model is that ordering is not only time consuming, labor intensive, inefficient, but also difficult to dynamically match based on the driver's existing and current order fulfillment.

In response to the above problems, as well as other potential problems, embodiments of the present disclosure provide a method for order scheduling. The method comprises the following steps: first information related to the order is obtained, and the first information at least comprises first position information where an associated object of the order is located. Then, a plurality of pieces of second information corresponding to a plurality of drivers in the set of drivers is acquired, each of the plurality of pieces of second information including at least second position information corresponding to the driver and information relating to a degree of fatigue of the driver. Thereafter, a driver for the order is selected from the set of drivers based on the first information and the second information. Then, in response to determining that the selected driver includes a plurality of drivers, a target driver for the order is selected from the plurality of drivers to place the selected target driver in a non-fatigued state based on at least the first location information, a plurality of second location information corresponding to the plurality of drivers, and a plurality of information relating to a degree of fatigue of the plurality of drivers. Finally, the order is distributed to the target driver.

In this way, it is possible to avoid fatigue driving of the driver matched to the order while executing the order, while reducing the vehicle empty rate, and therefore, the present disclosure is able to efficiently, accurately, and safely match the order with the driver and the vehicle taking the order.

FIG. 1 illustrates an example diagram of an environment in which some embodiments of the present disclosure can be implemented. For ease of discussion, the following will use the car rental order as an example of an order and the driver as an example of the second object. It should be understood, however, that this is done merely for convenience in explaining the concepts of the embodiments of the present disclosure and is not intended to limit the application scenarios or scope of the present disclosure in any way.

As shown in FIG. 1, environment 100 includes an order 110, a driver 120, a vehicle 130 of driver 120, and a scheduling system 140. The information in the order 110 may include: the number of people traveling, the requirements on license plates (e.g., Beijing license plate, Shanghai license plate), the requirements on suppliers (the suppliers the drivers belong to), the requirements on the language ability of the drivers, the requirements on the types of the vehicles to be rented, and the like.

In some embodiments, the user may send the order 110 to the dispatch system 140 (e.g., without limitation, a back office system of a rental company) in advance of a period of time (e.g., days or weeks in advance, etc.) to reserve the vehicle. In addition, the user may issue the order 110 to the scheduling system 140 in a variety of ways (e.g., email, phone, fax, web page, cell phone application, etc.). In other embodiments, the user may place an order to reserve the vehicle to the dispatch system 140 in other ways as well, allowing the vehicle to pick up the user/passenger immediately at the user's location or at a location designated by the user.

It is understood that the user placing the order 110 may or may not be the same person as the passenger who actually enjoys the order service. For example, for a large customer, it may be that a department assistant book vehicles for other colleagues in their department. The scope of protection of the present application is not limited in this respect.

The driver 120 may include a plurality of drivers 120-1, 120-2, 120-3 … … 120-N (hereinafter collectively or individually referred to as drivers 120 for ease of description). Each driver 120 is equipped with a respective vehicle 130. As such, a plurality of drivers 120-1, 120-2, 120-3 … … 120-N will be equipped with respective vehicles 130-1, 130-2, 130-3 … … 130-N (hereinafter collectively or individually referred to as vehicles 130 for ease of description). The driver 120 may belong to a different entity (e.g., a rental company or a third party service provider, etc.).

It should be appreciated that the driver 120 is not limited to the vehicle driver 120, i.e., the vehicle it drives may also be other types of vehicles, depending on the application scenario, and the scope of the present disclosure is not limited in this respect.

The vehicle 130 may be various types of vehicles 130, such as a pure electric vehicle (BEV), a Hybrid Electric Vehicle (HEV), and so on. The vehicle 130 may also be classified according to its model, such as a top-grade model, a commercial model, a general model, and the like. The vehicles 130 may also be ranked according to their model, such as a first-star model, a second-star model, a third-star model, and so on.

The scheduling system 140 may be used to obtain information about the order 110 and corresponding information to obtain drivers 120 in the set of drivers 120. In one example, the scheduling system 140 may be used to match the appropriate target driver 150 for the order 110. In another example, business system 140 may also be used to generate a respective ranking table for each driver 120.

FIG. 2 illustrates a flow diagram of a method 200 for order scheduling according to some embodiments of the present disclosure. It should be understood that method 200 may also include additional acts not shown and/or may omit acts shown, as the scope of the disclosure is not limited in this respect. For ease of illustration, the method 200 is described with reference to FIG. 1.

At step 210, the rental company's dispatch system 140 can obtain information related to the order 110, including at least location information related to the associated object of the order 110.

In some embodiments, the location information related to the associated object of the order 110 (e.g., the user in the order 110 that needs to be picked up) may include the boarding location and the disembarking location of the passenger. In addition, the information may also include the location of the user-planned stop. This information will help the dispatch system 140 assign the appropriate vehicle 130 to the user.

In some embodiments, as mentioned above, the information related to the order 110 may also include the number of people on the trip, the license plate (e.g., beijing license plate, shanghai license plate), the requirements for the supplier (e.g., the supplier to which the driver 120 belongs), the requirements for the language capabilities of the driver 120, the requirements for the vehicle type to be rented, and the like.

At step 220, the leasing company's dispatch system 140 also obtains a plurality of information corresponding to a plurality of drivers 120 in the set of drivers. Each of these pieces of information includes at least position information corresponding to the driver 120 and information relating to the degree of fatigue of the driver 120. Further, each of these pieces of information may also include the driver 120 work time (e.g., early shift or late shift, driver 120 leave time, etc.), attribute information of the driver's 120 vehicle (e.g., the driver's 120 model, license plate information such as beijing license plate, shanghai license plate, etc.), star rating of the driver 120 (order star rating requirement), etc.

At step 230, the scheduling system 140 selects a driver 120 for the order 110 from the set of drivers based on the information related to the order 110 and a corresponding plurality of information for a plurality of drivers 120 in the set of drivers. In one example, the scheduling system 140 may first exclude out-of-condition drivers 120 based on the particular requirements of the order 110. In another example, the scheduling system 140 may select some eligible drivers 120 based on some base requirements in the order 110.

At step 240, if the scheduling system 140 determines that the selected driver 120 includes a plurality of drivers 120, proceed to step 250, a target driver 150 for the order 110 is selected from the plurality of drivers 120 based on at least the location information related to the associated object of the order 110 (e.g., the user in the order 110 that needs to be picked up), the respective plurality of location information of the plurality of drivers 120, and the plurality of information related to the degree of fatigue of the plurality of drivers 120, to place the selected target driver 150 in a non-fatigue state.

In some embodiments, the corresponding location information of the driver 120 may include an ending location of a previous order that has been assigned to the driver 120 (e.g., a drop-off location of a passenger of the previous order). The location information may also include a starting location for the next order that has been assigned to the driver 120 (e.g., the boarding location for the passenger for the next order). In addition, the information may also include a starting location of the driver 120 each day (e.g., a fixed address of the driver 120, etc.).

In other embodiments, each of the respective plurality of information for the plurality of drivers 120 may further include information related to a degree of fatigue of the driver 120. The information may include, for example, the total work hours of the driver 120 on the day, the continuous work hours of the driver 120, the interval rest hours of the driver 120, the overnight rest hours of the driver 120, and so forth.

At step 260, the order 110 is assigned to the target driver 150 such that the driver 120 obtains the order information assigned thereto to provide the corresponding service to the passenger.

By the above method, the scheduling system 140 can not only select the driver 120 meeting the requirement of the order 110 of the user to provide service according to the requirement of the order 110 of the user, but also can exclude the driver 120 in a fatigue state or to be in a fatigue state according to the fatigue degree of the driver 120, so that the target driver 150 assigned to the order 110 of the user can provide service to the passenger in a good state to avoid fatigue driving. In addition, since the position information about the associated object of the order 110 (e.g., the passenger in the order 110 that needs to be taken) and the position information about the driver 120 are taken into consideration, the order 110 can be more reasonably arranged based on various position information, reducing the empty rate of the vehicle.

The present application will now be described with reference to some more specific exemplary embodiments.

In some embodiments, the driver 120 may first be screened according to the particular needs in the order placed by the user. For example, whether the user has a specified dispatch system 140 or vendor. When such an order 110 is found to have some special requirements, such an order 110 may be excluded for additional scheduling. For example, such orders 110 may be manually dispatched to meet the particular needs of the user.

In other embodiments, the driver 120 may also be selected from a set of drivers based on some other information. As described above, information about the order 110 will be obtained from the order 110, and the information about the order 110 may include the number of people on trip, the number plate (e.g., beijing number plate, shanghai number plate), the requirements for the supplier (e.g., the supplier to which the driver 120 belongs), the requirements for the language ability of the driver 120, the requirements for the vehicle type to be rented, and the like. Thus, a suitable driver 120 may also be selected from the set of drivers based on this information. Specific examples of selecting a suitable driver 120 based on these parameters will be given below.

In one example, a user placing order 110 may provide information regarding the number of trips in order 110, such that scheduling system 140 may schedule a suitable model of vehicle 130 (e.g., 7, 52, etc.) based on the number of trips provided by the user. Accordingly, the respective information of the drivers 120 in the driver set includes the number of people that each driver 120 can board the vehicle 130 owned by at this time, and thus the respective kinds of vehicles 130 can be matched based on the requirement for the number of trips in the order 110.

In one example, when the order 110 requires that the driver 120 be able to provide services in a foreign language (e.g., english, french, spanish, etc.), only drivers 120 with the corresponding language capabilities will be selected.

In another example, there may be a request in the order 110 for the home location of the license plate in the city or region where the passenger's location needs to be picked up. For example, a location where a passenger is to arrive or pass requires a particular type of license plate (e.g., beijing license plate, shanghai license plate, etc.), such that it is desirable to arrange vehicles 130 that meet the requirements of that license plate according to the requirements. In such an example, the license plate information of the vehicle 130 currently owned by the driver 120 may be included in the respective information of the drivers 120 in the driver set. Thus, if it is determined that the vehicle 130 currently owned by the driver 120 cannot meet the requirements of the license plate, the order 110 will not be matched to such driver 120.

In some examples, the order 110 may be from an enterprise user. In one example, some enterprise users may have a corresponding driver blacklist, that is, a blacklisted driver 120 will not be able to be matched to an order 110 from that enterprise user.

In other examples, requirements for a vehicle type may be included in the order 110. Accordingly, the information of the vehicle type of the driver 120 will be included in the respective information of the drivers 120 in the driver set. Thus, for example, if certain orders 110 from a user may require rental of a business vehicle model, a driver 120 having that vehicle model may be matched for the user based on this requirement in the order 110. Alternatively or additionally, in some examples, when there are no drivers 120 in the set of drivers that meet the user's requirements for a vehicle type, drivers 120 with more advanced vehicle types may be assigned to the order 110, thereby at least meeting the user's minimum requirements for the vehicle type and avoiding matching the order 110 with drivers 120 corresponding to vehicles 130 that do not meet the requirements.

In some embodiments, the driver 120 may also be selected for the order 110 based on the obtained respective information of the drivers 120 in the set of drivers. In some embodiments, the respective information of the driver 120 may include the working time of the driver 120 (e.g., early shift or late shift, time the driver 120 asks for leave, etc.), attribute information of the vehicle of the driver 120 (e.g., the model of the driver 120, information of the license plate of the vehicle 130 owned by the driver 120 (beijing license plate, shanghai license plate)), the star rating of the driver 120 (order star rating requirement), etc.

The shift information of the driver 120 may include whether the driver 120 is an early shift or a late shift. For example, if the driver 120 is currently scheduled for an early shift (e.g., 5:00-17: 00), then an order 110 for the evening (e.g., 8 o' clock late) will not be matched to the driver 120 for that early shift. Further, the leave time of the driver 120 may include whether the driver 120 is currently leaving. Thus, the driver 120 may be selected from the set of drivers based on the work hours of the driver 120 and based on the start time and end time of the corresponding rental car in the order 110.

Alternatively or additionally, in one example, the driver 120 may also be selected from the set of drivers based on the home city of the driver 120. For example, the driver 120 may be selected from the set of drivers considering whether the previous and subsequent orders of the driver 120 belong to the same city. For example, if the ending location in the previous order for driver 120 (i.e., the passenger's drop-off location in the previous order) is city a and the starting location for the current order 110 is another city B that is further away from city a, then this driver 120 at city a will not be selected for the order 110.

In another example, each driver 120 may be ranked such that an order 110 that requires driver star rating may be matched to the appropriate driver 120 based on the star rating of the driver 120 to meet the passenger's requirements for quality of service.

It should be appreciated that the driver 120 for the order 110 may also be selected from the set of drivers based on other information in the order 110, and the scope of the present application is not limited in this respect.

In some embodiments, in response to the drivers 120 selected based on the above manner including a plurality of drivers 120, a target driver 150 for the order 110 may also be selected from the plurality of drivers 120 based on location information related to an associated object of the order 110 (e.g., a passenger in the order 110 that needs to be picked up), a corresponding plurality of location information from the plurality of drivers, and a plurality of information related to a degree of fatigue of the plurality of drivers 120.

In some examples, drivers 120 who may be tired of driving may be excluded from being matched to the current order 110 based on information about the degree of fatigue of the drivers 120. For example, the information about the degree of fatigue of the driver 120 may include the total work period of the driver 120 on the day, the continuous work period of the driver 120, the intermittent rest period of the driver 120, and the overnight rest period of the driver 120.

A driver 120 who is likely to be fatigued may be excluded based on one or more of these pieces of information regarding the degree of fatigue of the driver 120. Although some specific examples will be given below regarding filtering the drivers 120 of the set of drivers 120 based on information related to the degree of fatigue of the drivers 120, it should be understood that the drivers 120 may also be selected from the set of drivers based on other information or sets of information related to the fatigue of the drivers 120, and the scope of the present application is not limited in this respect.

In one example, the driver 120 may be selected from the set of drivers based on the total service duration of the driver 120 on the day. In such an example, the current order 110 may be similarly placed into the driver's 120 work list for the current day to calculate the driver's 120 total service duration for the current day. The total service duration of the driver 120 for the current day may be calculated according to the following formula (1).

Driver 120 total service duration on the day = tail single getting-off time-head single getting-on time on the same day- - - (1)

Wherein the time of the day tail order disembarkation represents the time of the passenger disembarkation of the last order scheduled by the driver 120 on the day. The first order pick-up time represents the pick-up time of the passenger for the first order scheduled by the driver 120 for the current day.

Specifically, for example, the driver 120 has been scheduled to have a first order get-on time of nine am and a scheduled tail order get-off time of three pm. Additionally, the order 110 currently to be scheduled is from three to four and a half pm (i.e., the expected time to alight is four and a half pm), then when this order is simulated for the driver 120, the total service duration for the driver on the day is seven and a half hours between nine am and four and a half pm.

In another example, the driver 120 has been scheduled with a first order get-on time of nine am and a scheduled tail order get-off time of three pm. In addition, the order 110 to be scheduled currently is twelve am to one pm (i.e., the expected time to alight is one pm), then when this order is simulated for the driver 120, the total service duration of the driver on the day is six hours between nine am and three pm.

Thus, fatigue verification may be performed for the driver 120 based on the calculated total service duration of the driver 120 on the day. For example, a fatigue state threshold (such as 8 hours, 13 hours, etc.) may be predetermined. If the then-current total service duration for the driver 120 exceeds this predetermined fatigue state threshold, then the driver 120 is deemed to be in a state of fatigue and such a driver 120 is not selected for the order 110. It will be appreciated that the total length of time the driver is currently in service may include the rest period between the scheduled start and end of the driver 120. In other words, the driver 120 may be considered to be in the working service state as long as the driver 120 is currently on standby. For example, where the driver 120 is scheduled to be spaced three hours apart between two orders, and a time (e.g., 1 hour) is required for the driver 120 to rush between the two orders, the driver 120 may have a two hour rest period, and since the driver 120 is on standby (i.e., an order receivable state) for the period of time during which it can rest, the two hours will also be included in the total duration of the then-current standby/service.

Therefore, whether the driver 120 is in a fatigue state can be determined based on the total service time of the driver 120 in the day, so that the driver 120 which is possibly working for a long time at that time is excluded, fatigue driving of the driver 120 is avoided, and safety of passengers in a bus is ensured.

It is understood that the above provides one specific example of how the total service duration of the driver 120 in the day can be calculated, and the total service duration of the driver 120 in the day can also be calculated based on other conceivable ways, and the scope of the present application is not limited in this respect.

Alternatively or additionally, in other embodiments, the driver 120 may also be selected based on the duration of continuous operation of the driver 120. For example, it may be preset that the continuous operation time period of the driver 120 cannot exceed a threshold value (for example, four hours), so that the continuous order driving time of the driver 120 and the empty driving time (for example, the driving time period from the end position of the previous order to the start position of the next order) may be estimated, thereby estimating the continuous driving time of the driver 120. It is to be appreciated that the continuous order driving time of the driver 120 may be obtained in various manners, and the scope of the present application is not limited in this respect.

Alternatively or additionally, in some embodiments, the driver 120 may be selected based on the interval rest period of the driver 120. For example, after the ordering is simulated as described above, it may be determined whether the driver 120 has enough rest time (e.g., half an hour) between the front and back orders, so as to ensure that the driver 120 does not drive for too long, ensure the rest of the driver 120, and avoid fatigue driving of the driver 120. Alternatively or additionally, the time that the driver is running between two orders will be estimated and planed from the time intervals of the two orders before and after to more accurately estimate the interval rest period of the driver.

Alternatively or additionally, in other examples, driver 120 may also be selected based on the overnight rest period of driver 120. In one example, it may be that the last order of the day before the driver 120 ends later, in which case scheduling the driver 120 with an order 110 earlier the next day may be avoided as much as possible to ensure that the driver 120 has sufficient sleep. For example, it may be preconfigured that the driver 120 needs to be eleven hours from the last order of the previous day, so that if the time of work taken by the driver 120 the previous day is twelve pm, then the driver 120 is not scheduled with an order 110 that was ten am the next day before, to avoid fatigue driving.

It is to be appreciated that the above-described manners of avoiding fatigue of driver 120 may also be used in combination, and the scope of the present application is not limited in this respect.

In some embodiments, the driver 120 may be selected based on a single time interval. In one example, a time interval limit may be set such that the time interval between the rear single get-on time and the front single get-off time does not exceed a threshold (e.g., two hours) and/or is not less than an hour. Thus, if the start time of the order 110 (i.e., the time that the passenger gets on the vehicle) and the end time of the previous order that has been assigned to the driver 120 (i.e., the time that the passenger of the previous order gets off the vehicle) exceed 2 hours, the current order 110 will not be assigned to such driver 120. In this way, front and rear units can be connected in series, and the driver 120 can be reasonably scheduled for work time by considering the time of front and rear orders, thereby reducing the idling rate.

In the above example, if there is a mid-stop condition in the previous order assigned to the driver 120, a certain time interval (e.g., half an hour) may be added to the previous order time interval. In one embodiment, the user may provide information about whether there is a stopover condition at the time of order placement. Alternatively, when the single passenger temporarily decides to make a stop halfway through the ride, information about whether the order 110 has a stop halfway can be obtained by way of feedback from the driver 120. In this way, the order 110 may be more reasonably scheduled for the driver 120 in time.

In some examples, the target driver 150 may also be selected based on a distance between a front order drop-off location and a rear order pick-up location. For example, when the distance from the getting-off position of the previous order assigned to the driver 120 to the getting-on position of the order 110 to be currently assigned exceeds a certain threshold (e.g., a distance greater than 35 km), the driver 120 is not assigned to the order 110. In this way, the driver 120 can be reasonably placed with the order 110, avoiding excessive empty travel distance between the front and back orders for the driver 120.

In some examples, the target driver 150 may be further selected from the plurality of drivers 120 in the following manner. For example, the target distance may be determined for each driver according to different situations, so that the plurality of target distances determined for the plurality of drivers 120 may be ranked, so that the corresponding driver 120 with the smallest target distance (i.e., the front-rear single-distance closest) is determined as the target driver 150. In this way, the closest driver 120 may be assigned to the current order 110.

For example, determining a target distance for each driver from case to case includes determining whether the order 110 currently to be matched, if assigned to this driver, is the first order of the driver 120 for the current day. If so, a distance between the origin of the driver 120 (e.g., the place where the driver 120 resides) and the starting location of the order 110 to be matched is determined to be the target distance.

In one example, the target distance may be calculated by a latitude and longitude direct distance. This distance may also be determined in other ways that are conceivable, and the scope of protection of the present application is not limited in this respect.

If the order 110 to be currently matched (hereinafter referred to as the current order 110) is not the first order of the driver 120, that is, there are orders whose start times are earlier than the order 110 to be currently matched among other orders to which the driver 120 has been assigned, in this case, the target distance is determined separately in three cases. In addition, the time interval between the previous order and the current order 110 of the driver 120 and the average speed can be obtained, so as to determine whether the driver 120 has enough time to reach the starting position of the current order 110 after finishing the previous order and the passenger getting to the current order 110 according to the following formula (2):

target distance < = single interval time before and after target speed (2)

Wherein the target distance may be determined according to the situation of the currently assigned order 110 of the driver 120, which will be described in detail below; the previous and subsequent order interval time is the interval time between the end time of the previous order (e.g., the estimated end time) and the start time of the current order 110; the speed is the estimated speed of the driver 120 on the way to the next passenger after finishing the previous order.

Specifically, the target distance may be determined according to three different situations: (1) the current order 110 has a leading order and no trailing order, (2) the current order 110 has a leading order and also a trailing order, and (3) the current order 110 has a trailing order and no leading order, thereby determining whether the driver 120 is in time to receive the passenger if he is assigned the current order 110.

In the first case where the current order 110 has a preceding order and no succeeding order, the distance between the starting position of the current order 110 (i.e., the getting-in position of the passenger of the current order 110) and the ending position of the preceding order (i.e., the getting-off position of the passenger of the preceding order) may be determined as the target distance, so that it is determined based on the target distance whether the driver 120 has enough time to reach the starting position of the current order 110 after the ending of the preceding order to be able to timely get in the passenger of the current order 110 if the current order 110 is assigned to the driver 120.

In the case of a front order and a back order for the current order 110, the following two distances will be determined: the distance between the start position of the current order 110 (i.e., the boarding position of the passenger for the current order 110) and the previous order ending position (i.e., the disembarking position of the passenger for the previous order), and the distance between the ending position of the current order 110 (i.e., the disembarking position of the passenger for the current order 110) and the next order ending position (i.e., the boarding position of the passenger for the next order). After the above two distances are determined, the larger value of the two distances is determined as the target distance.

In the case where there is a back order and no front order for the current order 110, the distance between the end position of the current order 110 (i.e., the alighting position of the passenger for the current order 110) and the end position of the next order (i.e., the boarding position of the passenger for the next order) may be determined as the target distance.

In some embodiments, the target speed may be determined in the following manner. For example, an average speed between the latitudes and longitudes of two points (i.e., two points of the front single-end position and the rear single-start position) may be queried according to a third-party interface, and this speed may be determined as the target speed.

Alternatively or additionally, the target speed may be further determined by the following equation (3):

target speed = mean speed x position-dependent scaling factor (3)

Wherein the average speed may be an average speed as described above; the position-related scaling factor may be, for example, whether the geographic position between the two units is in a urban area or a suburban area determined according to the geographic fence, so as to determine the corresponding scaling factor. For example, when the geographic location is a downtown area, a proportionality coefficient (e.g., 150%) related to the location may be determined by considering a degree of congestion of downtown traffic. When the geographic location is suburban, the location-related scaling factor may be determined to be a lower value (e.g., 110%) than urban. In addition, if the end position of the previous order that has been allocated by the driver 120 is in a suburban area and the start position of the current order 110 is in an urban area, the scaling factor may be determined in a mixed manner or the corresponding scaling factor of the urban area may be directly determined as the scaling factor related to the position. Thus, the driver 120 can be arranged in a more rational manner taking into account not only the possible driving speed of the driver 120, but also the degree of congestion of the traffic when matching the driver 120 to the order 110.

Thus, after determining the corresponding target distance for each driver 120 through the above three conditions, it can be determined whether the corresponding target distance can satisfy a position-dependent scaling factor smaller than the target speed based on the above formula (3), thereby further excluding the drivers 120 that do not meet the condition. In this way, drivers 120 of passengers who cannot timely catch up to the start position of the current order 110 to take the current order 110 if this current order 110 is arranged for the driver 120 due to the long distance between the end position of the previous order that has been assigned and the start position of the current order 110 may be excluded. It is also possible to exclude from the plurality of drivers 120 that the driver 120 cannot timely catch up to the start position of the assigned next order to take the passenger if the driver 120 is scheduled with the current order 110 due to the relatively large distance between the end position of the current order 110 and the start position of the next order already assigned to the driver 120. In other words, assigning the current order 110 to such a driver 120 is avoided.

Fig. 3 illustrates a flow diagram of a method 300 for selecting a target second object from a plurality of second objects, in accordance with some embodiments of the present disclosure. It should be understood that method 300 may also include additional acts not shown and/or may omit acts shown, as the scope of the disclosure is not limited in this respect. For ease of illustration, the method 300 is described with reference to FIG. 1.

At step 310, if a driver 120 of the plurality of drivers 120 has a previous order that has been assigned, then at step 320, a first distance difference between a starting location of the order 110 and an ending location of the previous order is determined for the driver 120.

At step 330, if a driver 120 of the plurality of drivers 120 has a next order assigned, then at step 340, a second distance difference between the ending location of the order 110 and the starting location of the next order is determined for the driver 120.

At step 350, if the driver 120 of the plurality of drivers 120 does not have a previous order that has been assigned, then at step 360, a third distance difference between the daily starting position and the first starting position for the driver 120 of the plurality of drivers 120 is determined.

At step 370, the drivers 120 of the plurality of drivers 120 are ranked based on at least one of the first, second, and third distance differences determined for each of the plurality of drivers 120.

In step 380, a target driver 150 is selected from the plurality of drivers 120 based on the results of the ranking.

By adopting the means, the order and the driver can be reasonably matched according to the condition that the driver has previous and subsequent orders.

Fig. 4 illustrates a flow diagram of a method 400 for selecting a target second object from a plurality of second objects, in accordance with some embodiments of the present disclosure. It should be understood that method 400 may also include additional acts not shown and/or may omit acts shown, as the scope of the disclosure is not limited in this respect. For ease of illustration, the method 400 is described with reference to FIG. 1.

Prior to ranking the plurality of drivers 120, an average speed of each driver 120 of the plurality of drivers 120 is determined at step 410. As to the manner of determining the average speed, for example, the average speed of the driving vehicle of each driver 120 between the latitudes and longitudes of two points (i.e., two points of the front single-stop position and the rear single-start position) may be queried according to the third-party interface.

At step 420, attributes associated with the location are determined based on the location information in the order 110 and the corresponding location information for each driver 120.

Regarding the location-related attribute, it may be a location-related scaling factor, for example, it may be determined whether the geographic location between the two preceding and succeeding orders determined according to the geo-fence is in a downtown area or a suburban area, so as to determine the corresponding scaling factor. For example, when the geographic location is a downtown area, a proportionality coefficient (e.g., 150%) related to the location may be determined by considering a degree of congestion of downtown traffic. When the geographic location is suburban, the location-related scaling factor may be determined to be a lower value (e.g., 110%) than urban.

At step 430, a scaling factor corresponding to the average speed of each driver 120 is determined based on the location-related attribute. For example, if the end position of the previous order assigned by the driver 120 is in a suburban area and the start position of the current order 110 is in an urban area, the scaling factor may be determined in a mixed manner or the corresponding scaling factor of the urban area may be directly determined as the location-dependent scaling factor.

At step 440, a target driver 150 is selected from the plurality of drivers 120 based on the average speed of each driver 120 of the plurality of drivers 120 and the corresponding scaling factor.

By adopting the means, the method and the device can avoid delay of a driver for receiving the current menu and the next menu.

Fig. 5A and 5B illustrate schematic diagrams of a driver dispatch list table and a driver shift list table according to some embodiments of the present disclosure. It should be understood that other additional information related to the order 110 or the driver 120, not shown, may also be included in fig. 5A and 5B, and the scope of the present disclosure is not limited in this respect.

As shown in FIG. 5A, in some embodiments, after all orders 110 in the current order list are assigned to respective drivers 120, the scheduling system 140 may generate a driver dispatch list table in various ways for management by the administrator. For example, the driver scheduling list table shown in fig. 3 may display the corresponding drivers 120 to which all orders in the list for this scheduling are allocated.

As shown in FIG. 5B, in other embodiments, the scheduling system 140 may also generate a driver shift schedule for each driver 120. In this way, the driver 120 may make a reasonable schedule of work after receiving such a shift schedule. For example, FIG. 5B shows information for all

orders

1, 2, 3, M assigned to one driver 120 on a certain day, as well as orders 110.

Fig. 6 illustrates a schematic diagram of an example scheduling system 140, in accordance with some embodiments of the present disclosure. As shown in FIG. 4, all orders, orders that are not scheduled, exception orders, etc. may be displayed by the scheduling system 140. The orders can be automatically arranged in an automatic order distribution mode, and whether the task execution state is successfully displayed for each order arrangement task or not can be further displayed after the orders are automatically distributed. Therefore, the management personnel of the leasing company can conveniently arrange all received orders and process abnormal conditions in time.

Fig. 7 illustrates a schematic block diagram of an example device 700 that may be used to implement embodiments of the present disclosure. For example, the scheduling system 140 as shown in fig. 1 may be implemented by the device 700. As shown, device 700 includes a Central Processing Unit (CPU) 701 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM) 702 or computer program instructions loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The CPU, ROM, and RAM are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in the device 700 are connected to the I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, or the like; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The various processes and processes described above, such as the method 200-400, may be performed by the central processing unit 701. For example, in some embodiments, the method 200-400 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 700 via ROM and/or communications unit 709. When the computer program is loaded into RAM and executed by a CPU, one or more of the acts of method 200 described above may be performed.

The present disclosure may be a method, computing device, and/or computer-readable storage medium. Computer readable storage media has computer readable program instructions embodied thereon for carrying out aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

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

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus, and computer-readable storage media according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods, apparatus, and computer-readable storage media according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for order scheduling, comprising:

obtaining first information related to an order, wherein the first information at least comprises first position information related to an associated object of the order;

obtaining a plurality of second information corresponding to a plurality of second objects in a second object set, each of the plurality of second information including at least second position information corresponding to the second object and information related to a degree of fatigue of the second object;

selecting a second object for the order from the second set of objects based on the first information and the second information;

in response to determining that the selected second object comprises a plurality of second objects, selecting a target second object for the order from the plurality of second objects based on at least the first location information, a plurality of second location information corresponding to respective ones of the plurality of second objects, and a plurality of information relating to a degree of fatigue of the plurality of second objects, such that the selected target second object is in a non-fatigue state; and

assigning the order to the target second object.

2. The method of claim 1, wherein the information related to the degree of fatigue of the second object comprises at least one of:

the total working time of the second object on the day,

the duration of continuous operation of the second object,

the interval rest duration of the second subject, an

A rest period of the second subject at night.

3. The method of claim 1, wherein the first information further comprises at least one of:

attribute information of the vehicle of the target second object,

information indicating language capabilities of the target second object,

a requirement for a second object related to the order.

4. The method of claim 1, wherein the second information further comprises at least one of:

attribute information of a vehicle of the second object,

information indicating language capabilities of the second object, an

And (7) working time.

5. The method of claim 1, wherein the first information further comprises a first start time of the order and a first end time of the order, the second information further comprises time information of the second object, the time information comprising at least one of:

an end time of a previous order that has been assigned to the second object, an

A start time of a next order already assigned to the second object, an

Selecting a target second object from the plurality of second objects further comprises:

in response to there being a previous order assigned to the second object, selecting a target second object for the order from the plurality of second objects further based on the first start time and an end time of the previous order; and

in response to there being a next order assigned to the second object, selecting a target second object for the order from the plurality of second objects further based on the first end time and a start time of the next order.

6. The method of claim 1, wherein the first location information comprises a first start location and a first end location, the second location information comprises at least one of:

an end position of a previous order that has been assigned to the second object,

a starting location of a next order that has been assigned to the second object,

a daily starting position of the second object, an

in response to a second object of the plurality of second objects having a previous order that has been assigned, determining, for the second object, a first distance difference between the first starting location and an ending location of the previous order;

in response to a second object of the plurality of second objects having a next order that has been assigned, determining a second distance difference between the first end position and a start position of the next order for the second object;

in response to a second object of the plurality of second objects not having a previous order that has been allocated, determining a third distance difference between the daily starting position and the first starting position for the second object of the plurality of second objects;

ranking second objects of the plurality of second objects based on at least one of the determined first, second, and third distance differences for each of the plurality of second objects; and

selecting a target second object from the plurality of second objects according to the result of the sorting.

7. The method of claim 6, selecting a target second object from the plurality of second objects further comprising:

determining an average velocity of each of the plurality of second objects prior to sorting the plurality of second objects;

determining an attribute related to a position based on the first position information and the second position information corresponding to each of the second objects;

determining a scaling factor corresponding to the average velocity of each second object based on the location-related attribute; and

selecting a target second object from the plurality of second objects based on the average velocity of each of the plurality of second objects and the corresponding scaling factor.

8. A computing device, comprising:

at least one processing unit; and

at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions when executed by the at least one processing unit, cause the apparatus to perform the steps of the method of any of claims 1 to 7.

9. A computer-readable storage medium, having stored thereon a computer program which, when executed by a machine, implements the method of any of claims 1-7.