CN105096166A - Method and device for order allocation - Google Patents
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- CN105096166A CN105096166A CN201510537192.4A CN201510537192A CN105096166A CN 105096166 A CN105096166 A CN 105096166A CN 201510537192 A CN201510537192 A CN 201510537192A CN 105096166 A CN105096166 A CN 105096166A
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Abstract
The invention provides a method for order allocation. The method comprises the following steps of after receiving a cab hailing request of UE (User Equipment), generating an order according to the cab hailing request; obtaining at least one terminal in an order broadcasting range according to starting of the order; aiming at each obtained terminal, determining distance between the current position of the terminal and the starting of the order; obtaining order capturing probability of the terminal according to the distance and the current time information through a preset order capturing probability prediction model; and allocating the order according to the order capturing probability of at least one terminal. The invention further provides a device for order allocation, comprising an order generation unit, a terminal screening unit, a distance determining unit, an order capturing probability prediction unit and an order allocation unit. According to the invention, in an order allocation stage, the order broadcasting range of different area and different period of time is limited, thus, waiting time of passengers and deadhead time of drivers are reduced effectively, and experience of the passengers and the drivers is improved.
Description
Technical field
The present invention relates to computer processing technology field, particularly relate to a kind of method and device of Order splitting.
Background technology
At present, the use of system of calling a taxi is more and more general, and passenger can pass through subscriber equipment (UserEquipment is called for short UE) easily, and upper system of calling a taxi of installing issues the request of calling a taxi, and system of calling a taxi generates order further and distributed by order.
In existing order allocation method, what system of calling a taxi was selected according to its current location according to driver listens single scope to carry out order to broadcast single and distribute, and driver often will listen single scope to be set to maximumly to listen single scope to select for oneself to obtain more orders.But this distribution method is while giving the maximum right to choose of driver, but the more time cost that made passenger pay (strike a bargain from wait and reach to driver), especially it is particularly evident time traffic congestion situation is inconsistent, as Beijing 6 pm and evening 11 point, jam situation is completely different, if driver is distance passenger 2km equally, then driver receives the time of passenger is different completely, if passenger waiting time is long, then driver's sky sail the time corresponding also can be longer, department can be affected and take advantage of experience, order probability of transaction can be affected simultaneously.
Summary of the invention
For the defect of prior art, the invention provides a kind of method and device of Order splitting, by the Order splitting stage, single scope of broadcasting of zones of different different time sections is limited, minimizing passenger waiting time and driver's sky sail the time effectively, and lifting department takes advantage of experience.
First aspect, the invention provides a kind of method of Order splitting, the method comprises:
After the request of calling a taxi receiving user equipment (UE), generate order according to the described request of calling a taxi; And,
According to the departure place of described order, obtain at least one terminal broadcast in single scope of described order;
For each terminal obtained, determine the distance of the departure place of this terminal current location and described order;
Adopt the competition for orders probability prediction model set up in advance, obtain the competition for orders probability of this terminal according to described distance and current temporal information;
According to the competition for orders probability of at least one terminal described, described order is distributed;
Wherein, be benchmark with departure place, in preset time period order competition for orders probability be greater than the scope of predetermined threshold value be described order broadcast single scope.
Preferably, the described departure place according to described order, before obtaining at least one terminal broadcasting in single scope of described order, the method also comprises:
Obtain the History Order data in the first preset time period in predeterminable area;
According to the temporal information of described History Order data and current order, that determines current order broadcasts single scope.
Preferably, the method also comprises:
Using described History Order data as characteristic, adopt linear regression model (LRM) to train described characteristic, obtain competition for orders probability prediction model;
Wherein, described History Order data comprise: the conclusion of the business distance of each conclusion of the business order, closing time, terminal reach the consuming time of order departure place; The competition for orders distance of cancelling an order after each response, the competition for orders time, the cancellation time and the time of cancelling an order terminal apart from the distance of order departure place.
Preferably, described linear regression model (LRM) is this special regression model or supporting vector machine model of logic.
Preferably, the method also comprises:
According to the order data of Real-time Obtaining on line, adopt machine learning algorithm, described competition for orders probability prediction model is optimized.
Preferably, the competition for orders probability of at least one terminal described in described basis, distributes described order, comprising:
If at least one terminal described comprises a terminal, then send described order to described terminal; Or,
If at least one terminal described comprises multiple terminal, then according to the competition for orders probability order from big to small of described multiple terminal, send described order successively to described multiple terminal.
Second aspect, the invention provides a kind of device of Order splitting, and this device comprises:
Order generation unit, for receive user equipment (UE) call a taxi request time, according to described call a taxi request generate order;
Terminal screening unit, for the departure place according to described order, obtains at least one terminal broadcast in single scope of described order;
Distance determining unit, for for each terminal obtained, determines the distance of the departure place of this terminal current location and described order;
Competition for orders probability prediction unit, for adopting the competition for orders probability prediction model set up in advance, obtains the competition for orders probability of this terminal according to described distance and current temporal information;
Order splitting unit, for the competition for orders probability according at least one terminal described, distributes described order;
Wherein, be benchmark with departure place, in preset time period order competition for orders probability be greater than the scope of predetermined threshold value be described order broadcast single scope.
Preferably, this device also comprises broadcasts single scope determining unit, for:
Obtain the History Order data in the first preset time period in predeterminable area;
According to the temporal information of described History Order data and current order, that determines current order broadcasts single scope.
Preferably, this device also comprises prediction model and sets up unit, for:
Using described History Order data as characteristic, adopt linear regression model (LRM) to train described characteristic, obtain competition for orders probability prediction model;
Wherein, described History Order data comprise: the conclusion of the business distance of each conclusion of the business order, closing time, terminal reach the consuming time of order departure place; The competition for orders distance of cancelling an order after each response, the competition for orders time, the cancellation time and the time of cancelling an order terminal apart from the distance of order departure place.
Preferably, described linear regression model (LRM) is this special regression model or supporting vector machine model of logic.
Preferably, described device also comprises model optimization unit, for:
According to the order data of Real-time Obtaining on line, adopt machine learning algorithm, described competition for orders probability prediction model is optimized.
Preferably, described Order splitting unit, for:
If at least one terminal described comprises a terminal, then send described order to described terminal; Or,
If at least one terminal described comprises multiple terminal, then according to the competition for orders probability order from big to small of described multiple terminal, send described order successively to described multiple terminal.
As shown from the above technical solution, the invention provides a kind of method and device of Order splitting, by in the Order splitting stage, single scope of broadcasting of different time sections is limited, preliminary screening is carried out to terminal, and obtain the competition for orders probability of each terminal filtered out further, and according to competition for orders probability, order is distributed, so, driver can listen single distance to diminish, but the time that order strikes a bargain can be shorter, improves order conclusion of the business probability, and can effectively reduce passenger waiting time and driver's sky sails the time, lifting department takes advantage of experience.
Accompanying drawing explanation
In order to be illustrated more clearly in disclosure embodiment or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only embodiments more of the present disclosure, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these figure.
Fig. 1 is the schematic flow sheet of the method for a kind of Order splitting that the disclosure one embodiment provides;
Fig. 2 is the structural representation of the device of a kind of Order splitting that the disclosure one embodiment provides.
Embodiment
Below in conjunction with the accompanying drawing in disclosure embodiment, be clearly and completely described the technical scheme in disclosure embodiment, obviously, described embodiment is only disclosure part embodiment, instead of whole embodiments.Based on the embodiment in the disclosure, those of ordinary skill in the art are not making the every other embodiment obtained under creative work prerequisite, all belong to the scope of disclosure protection.
As shown in Figure 1, the schematic flow sheet of the method for a kind of Order splitting provided for the disclosure one embodiment, the method comprises the steps:
S1: after the request of calling a taxi receiving user equipment (UE), generates order according to the described request of calling a taxi.
Wherein, subscriber equipment (UserEquipment is called for short UE) refers to call service side, as the passenger in vehicles dial-a-cab, and the equipment such as the mobile terminal used or personal computer (PersonalComputer is called for short PC).Such as smart mobile phone, personal digital assistant (PDA), panel computer, notebook computer, vehicle-mounted computer (carputer), handheld device, intelligent glasses, intelligent watch, wearable device, virtual display device or display enhancing equipment (as GoogleGlass, OculusRift, Hololens, GearVR) etc.And the request of calling a taxi that UE sends also comprises: one or more in the information such as the user ID of departure place, destination and described UE.The user ID of UE comprise in the information such as phone number, Identity Code (Identity is called for short id), hardware address (MediaAccessControl is called for short MAC) one or more.
S2: according to the departure place of described order, obtains at least one terminal broadcast in single scope of described order;
Wherein, be benchmark with departure place, in preset time period order competition for orders probability be greater than the scope of predetermined threshold value be current order broadcast single scope.For example, in order to increase order probability of transaction, predetermined threshold value can be set to 100%, then each order of (as yesterday) broadcasts in single scope at this that to broadcast single be 100% by the probability of competition for orders in preset time period.
Specifically, single scope is broadcast at current point in time according to the order history data determination current order in preset time period, according to broadcasting single scope (maximum broadcast single distance), preliminary screening is carried out to terminal, system of calling a taxi only sends this sequence information to the terminal broadcast in single scope, the driver of most suitable order is listened to carry out optimum distance order coupling to setting, thus reduce the stand-by period of passenger, save the closing time of order.
Those skilled in the art are to be understood that, the terminal mentioned in the embodiment of the present invention is for providing service side, as the driver in vehicles dial-a-cab, the equipment such as the mobile terminal for order used or personal computer (PersonalComputer is called for short PC).Such as smart mobile phone, personal digital assistant (PDA), panel computer, notebook computer, vehicle-mounted computer (carputer), handheld device, intelligent glasses, intelligent watch, wearable device, virtual display device or display enhancing equipment (as GoogleGlass, OculusRift, Hololens, GearVR) etc.
S3: for each terminal obtained, determine the distance of the departure place of this terminal current location and described order.
Specifically, terminal obtains its current location according to location technology and is sent to the system of calling a taxi, and system-computed of calling a taxi obtains the distance between terminal current location and order departure place.
S4: adopt the competition for orders probability prediction model set up in advance, obtain the competition for orders probability of this terminal according to described distance and current temporal information.
Specifically, according to the distance that step S3 obtains, and current temporal information, the competition for orders probability of this terminal to this order is predicted.Can also be worth by the distance between this UE departure place and destination, this order, traffic information etc. carries out one-step prediction to competition for orders probability.As can be seen here, the competition for orders probability of terminal on this order is subject to the impact of distance and current time information etc., and current time information can reflect the feature such as peak period or flat peak phase, if be peak period in the morning 8 o'clock to 9 o'clock, it is on broadcasting single scope and competition for orders probability is certain to affect to some extent; And distance is nearer, competition for orders probability is then corresponding higher.
S5: according to the competition for orders probability of at least one terminal described, described order is distributed.
Present embodiments provide a kind of method of Order splitting, by in the Order splitting stage, single scope of broadcasting of different time sections is limited, preliminary screening is carried out to terminal, and obtain the competition for orders probability of each terminal filtered out further, and according to competition for orders probability, order is distributed, so, single distance can be listened to diminish according to driver, the time that order strikes a bargain can be shorter, improve order conclusion of the business probability, and can effectively reduce passenger waiting time and driver's sky sails the time, lifting department takes advantage of experience.
In the present embodiment, before step S2, the method also comprises:
S00: obtain the History Order data in the first preset time period in predeterminable area;
Wherein, History Order data comprise: the conclusion of the business distance of each conclusion of the business order, closing time, terminal reach the consuming time etc. of order departure place; The competition for orders distance of cancelling an order after each response, the competition for orders time, the cancellation time and the time of cancelling an order terminal apart from the distance etc. of order departure place.
It should be noted that, above-mentioned predeterminable area represents geographic area, as different cities, or the zones of different in same city.
S01: according to the temporal information of described History Order data and current order, that determines current order broadcasts single scope.
Specifically, to History Order data analysis, as by the hour, subregion statistical history order data, order can be obtained and broadcast single distance (broadcasting single scope) at the maximum of zones of different Different periods, and further according to the temporal information, departure place information etc. of current order, that just can determine current order broadcasts single scope.The maximum of different cities Different periods can be obtained according to step S00 to S01 and broadcast single distance.
It should be noted that, constantly should broadcast single scope and upgrade dynamically, namely according to new initiating whether order is robbed, competition for orders distance, feature that competition for orders time and competition for orders region etc. are relevant, re-start estimate broadcasting single scope.Broadcasting single scope and can estimate according to the History Order data of yesterday as the order that receives today.
As can be seen here, in the present embodiment, order only sends to the maximum terminal broadcast in single distance (broadcasting single scope) estimated and obtain, and namely screen terminal according to broadcasting single scope, what prevent terminal listens single scope excessive, thus reduce the stand-by period of passenger, save the closing time of order.
Further, the method also comprises:
S02: using described History Order data as characteristic, adopts linear regression model (LRM) to train described characteristic, obtains competition for orders probability prediction model.
In the present embodiment, described linear regression model (LRM) can be: logic this special regression model or supporting vector machine model.
Because supporting vector machine model efficiency is lower, then below using this special regression model of logic as linear regression training pattern for specific embodiment, technical solution of the present invention is described.
This special (LogisticRegression) model that returns of logic is widely used in two classification problems, and wherein y is (0,1) mark, and namely whether characteristic of correspondence hits; W is weight corresponding to this feature.Pr (y=1|x, w) represents the probability estimated as positive example, and Pr (y=0|x, w) represents the probability estimating negative example, and concrete model is as follows:
Wherein, x represents predictive variable, and y represents target variable, and y=1 represents and is predicted as positive example, and y=0 represents and is predicted as negative example, and w represents weight.In the present embodiment, y=1 represents and is predicted as competition for orders, and y=0 represents and is predicted as not competition for orders, and P (y=1|x, w) represents competition for orders probability, and P (y=0|x, w) is not competition for orders probability.
Concrete, History Order data pick-up can be become predictive variable x, such as, the conclusion of the business of each conclusion of the business order distance, closing time, terminal be reached the consuming time etc. of order departure place; The competition for orders distance of cancelling an order after each response, the competition for orders time, the cancellation time and the time of cancelling an order terminal be all taken into limit x apart from the distance etc. of order departure place, and be P (y=1|x, w) by the competition for orders probability of newly initiating order.By carrying out this special regression model training of logic to the conclusion of the business information of History Order, just can predict the competition for orders probability of different terminals to current order to be allocated.
And further, the method also can comprise the steps:
S03: according to the order data of Real-time Obtaining on line, adopts machine learning algorithm, is optimized described competition for orders probability prediction model.
So, whether being robbed relevant feature by constantly adding new order of initiating, constantly being improved the accuracy of this special regression model of logic.
In the present embodiment, competition for orders probability is estimated to be divided on off-line training and line and is calculated two stages in real time.Off-line training step: order correlated characteristic when broadcasting list, terminal correlated characteristic, order are become predictive variable with various feature extractions such as terminal correlated characteristics, using terminal, whether competition for orders is as target variable, utilize the historical data broadcasting list and competition for orders to carry out model training, obtain competition for orders probability prediction model.Real-time calculation stages on line: by model use on line, calculates the distance of the current order departure place extracted in real time and terminal current location, adopts machine learning algorithm, is optimized the described competition for orders probability prediction model set up in advance.
The method of the Order splitting that the present embodiment provides, uses machine learning algorithm, realizes the self-teaching after line being collected data, precisely estimate terminal competition for orders probability.
In the present embodiment, step S5, specifically comprises the steps:
S51: if at least one terminal described comprises a terminal, then send described order to described terminal; Or,
S52: if at least one terminal described comprises multiple terminal, then according to the competition for orders probability order from big to small of described multiple terminal, send described order successively to described multiple terminal.
In practical application, driver is arranged by terminal and listens single scope, and be often set to maximum scope, then terminal can get order far away, and if competition for orders success, passenger waiting time can be caused longer, and driver's sky to sail the time longer, impact department takes advantage of experience.Therefore, in the present embodiment, first can carry out preliminary screening according to single scope of broadcasting of order to terminal, single scope of listening of driver be limited to some extent, makes driver can only receive nearer order.Then sort according to the competition for orders probability of terminal to current order screened further, according to competition for orders probability order from big to small, order is distributed, raising order probability of transaction, also can reduce passenger waiting time and driver's sky sails the time, and lifting department takes advantage of experience.
As shown in Figure 2, the structural representation of the device of a kind of Order splitting provided for another embodiment of the disclosure, this device comprises: the screening of order generation unit 201, terminal unit 202, distance determining unit 203, competition for orders probability prediction unit 204 and Order splitting unit 205.Wherein:
Order generation unit 201, for receive user equipment (UE) call a taxi request time, according to described call a taxi request generate order.
Terminal screening unit 202, for the departure place according to described order, obtains at least one terminal broadcast in single scope of described order.
Distance determining unit 203, for for each terminal obtained, determines the distance of the departure place of this terminal current location and described order.
Competition for orders probability prediction unit 204, for adopting the competition for orders probability prediction model set up in advance, obtains the competition for orders probability of this terminal according to described distance and current temporal information.
Order splitting unit 205, for the competition for orders probability according at least one terminal described, distributes described order.
Wherein, be benchmark with departure place, in preset time period order competition for orders probability be greater than the scope of predetermined threshold value be current order broadcast single scope.
In the present embodiment, this device also comprises broadcasts single scope determining unit, for:
Obtain the History Order data in the first preset time period in predeterminable area;
According to the temporal information of described History Order data and current order, that determines current order broadcasts single scope.
In the present embodiment, this device also comprises prediction model and sets up unit, for:
Using described History Order data as characteristic, adopt linear regression model (LRM) to train described characteristic, obtain competition for orders probability prediction model;
Wherein said History Order data comprise: the conclusion of the business distance of each conclusion of the business order, closing time, terminal reach the consuming time of order departure place; The competition for orders distance of cancelling an order after each response, competition for orders time, cancellation time and when cancelling terminal apart from the distance of order departure place.
In the present embodiment, described linear regression model (LRM) is this special regression model or supporting vector machine model of logic.
In the present embodiment, described device also comprises model optimization unit, for:
According to the order data of Real-time Obtaining on line, adopt machine learning algorithm, described competition for orders probability prediction model is optimized.
In the present embodiment, described Order splitting unit, for:
If at least one terminal described comprises a terminal, then send described order to described terminal; Or,
If at least one terminal described comprises multiple terminal, then according to the competition for orders probability order from big to small of described multiple terminal, send described order successively to described multiple terminal.
For device embodiment, due to itself and embodiment of the method basic simlarity, so description is fairly simple, relevant part illustrates see the part of embodiment of the method.
Should be noted that, in all parts of system of the present disclosure, the function that will realize according to it and logical partitioning has been carried out to parts wherein, but, the disclosure is not limited to this, can repartition all parts as required or combine, such as, can be single parts by some component combinations, or some parts can be decomposed into more subassembly further.
All parts embodiment of the present disclosure with hardware implementing, or can realize with the software module run on one or more processor, or realizes with their combination.It will be understood by those of skill in the art that the some or all functions that microprocessor or digital signal processor (DSP) can be used in practice to realize according to the some or all parts in the system of disclosure embodiment.The disclosure can also be embodied as part or all equipment for performing method as described herein or device program (such as, computer program and computer program).Realizing program of the present disclosure and can store on a computer-readable medium like this, or the form of one or more signal can be had.Such signal can be downloaded from internet website and obtain, or provides on carrier signal, or provides with any other form.
It should be noted that above-described embodiment is described the disclosure instead of limits the disclosure, and those skilled in the art can design alternative embodiment when not departing from the scope of claims.In the claims, any reference symbol between bracket should be configured to limitations on claims.Word " comprises " not to be got rid of existence and does not arrange element in the claims or step.Word "a" or "an" before being positioned at element is not got rid of and be there is multiple such element.The disclosure can by means of including the hardware of some different elements and realizing by means of the computing machine of suitably programming.In the unit claim listing some devices, several in these devices can be carry out imbody by same hardware branch.Word first, second and third-class use do not represent any order.Can be title by these word explanations.
Above embodiment is only suitable for the disclosure is described; and not to restriction of the present disclosure; the those of ordinary skill of relevant technical field; when not departing from spirit and scope of the present disclosure; can also make a variety of changes and modification; therefore all equivalent technical schemes also belong to category of the present disclosure, and scope of patent protection of the present disclosure should be defined by the claims.
Claims (12)
1. a method for Order splitting, is characterized in that, the method comprises:
After the request of calling a taxi receiving user equipment (UE), generate order according to the described request of calling a taxi; And,
According to the departure place of described order, obtain at least one terminal broadcast in single scope of described order;
For each terminal obtained, determine the distance of the departure place of this terminal current location and described order;
Adopt the competition for orders probability prediction model set up in advance, obtain the competition for orders probability of this terminal according to described distance and current temporal information;
According to the competition for orders probability of at least one terminal described, described order is distributed;
Wherein, be benchmark with departure place, in preset time period order competition for orders probability be greater than the scope of predetermined threshold value be described order broadcast single scope.
2. method according to claim 1, is characterized in that, the described departure place according to described order, and before obtaining at least one terminal broadcasting in single scope of described order, the method also comprises:
Obtain the History Order data in the first preset time period in predeterminable area;
According to the temporal information of described History Order data and current order, that determines current order broadcasts single scope.
3. method according to claim 2, is characterized in that, the method also comprises:
Using described History Order data as characteristic, adopt linear regression model (LRM) to train described characteristic, obtain competition for orders probability prediction model;
Wherein said History Order data comprise: the conclusion of the business distance of each conclusion of the business order, closing time, terminal reach the consuming time of order departure place; The competition for orders distance of cancelling an order after each response, competition for orders time, cancellation time and when cancelling terminal apart from the distance of order departure place.
4. method according to claim 3, is characterized in that, described linear regression model (LRM) is this special regression model or supporting vector machine model of logic.
5. method according to claim 3, is characterized in that, the method also comprises:
According to the order data of Real-time Obtaining on line, adopt machine learning algorithm, described competition for orders probability prediction model is optimized.
6. method according to claim 1, is characterized in that, the competition for orders probability of at least one terminal described in described basis, distributes, comprising described order:
If at least one terminal described comprises a terminal, then send described order to described terminal; Or,
If at least one terminal described comprises multiple terminal, then according to the competition for orders probability order from big to small of described multiple terminal, send described order successively to described multiple terminal.
7. a device for Order splitting, is characterized in that, this device comprises:
Order generation unit, for receive user equipment (UE) call a taxi request time, according to described call a taxi request generate order;
Terminal screening unit, for the departure place according to described order, obtains at least one terminal broadcast in single scope of described order;
Distance determining unit, for for each terminal obtained, determines the distance of the departure place of this terminal current location and described order;
Competition for orders probability prediction unit, for adopting the competition for orders probability prediction model set up in advance, obtains the competition for orders probability of this terminal according to described distance and current temporal information;
Order splitting unit, for the competition for orders probability according at least one terminal described, distributes described order;
Wherein, be benchmark with departure place, in preset time period order competition for orders probability be greater than the scope of predetermined threshold value be described order broadcast single scope.
8. device according to claim 7, is characterized in that, this device also comprises broadcasts single scope determining unit, for:
Obtain the History Order data in the first preset time period in predeterminable area;
According to the temporal information of described History Order data and current order, that determines current order broadcasts single scope.
9. device according to claim 8, is characterized in that, this device also comprises prediction model and sets up unit, for:
Using described History Order data as characteristic, adopt linear regression model (LRM) to train described characteristic, obtain competition for orders probability prediction model;
Wherein, described History Order data comprise: the conclusion of the business distance of each conclusion of the business order, closing time, terminal reach the consuming time of order departure place; The competition for orders distance of cancelling an order after each response, the competition for orders time, the cancellation time and the time of cancelling an order terminal apart from the distance of order departure place.
10. device according to claim 9, is characterized in that, described linear regression model (LRM) is this special regression model or supporting vector machine model of logic.
11. devices according to claim 9, is characterized in that, described device also comprises model optimization unit, for:
According to the order data of Real-time Obtaining on line, adopt machine learning algorithm, described competition for orders probability prediction model is optimized.
12. devices according to claim 7, is characterized in that, described Order splitting unit, for:
If at least one terminal described comprises a terminal, then send described order to described terminal; Or,
If at least one terminal described comprises multiple terminal, then according to the competition for orders probability order from big to small of described multiple terminal, send described order successively to described multiple terminal.
Priority Applications (9)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510537192.4A CN105096166A (en) | 2015-08-27 | 2015-08-27 | Method and device for order allocation |
| KR1020177024089A KR20180013843A (en) | 2015-01-29 | 2016-01-29 | Order allocation system and method |
| PCT/CN2016/072840 WO2016119749A1 (en) | 2015-01-29 | 2016-01-29 | Order allocation system and method |
| US15/547,221 US10977585B2 (en) | 2015-01-29 | 2016-01-29 | Order allocation system and method |
| SG11201706188YA SG11201706188YA (en) | 2015-01-29 | 2016-01-29 | Order allocation system and method |
| EP16742811.9A EP3252705A4 (en) | 2015-01-29 | 2016-01-29 | Order allocation system and method |
| PH12017501364A PH12017501364A1 (en) | 2015-01-29 | 2017-07-28 | Order allocation system and method |
| HK18104774.4A HK1245473A1 (en) | 2015-01-29 | 2018-04-12 | Order allocation system and method |
| US17/227,439 US20210232984A1 (en) | 2015-01-29 | 2021-04-12 | Order allocation system and method |
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| CN201510537192.4A CN105096166A (en) | 2015-08-27 | 2015-08-27 | Method and device for order allocation |
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