CN105117777A - Order distributing method and apparatus - Google Patents
Order distributing method and apparatus Download PDFInfo
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- CN105117777A CN105117777A CN201510451956.8A CN201510451956A CN105117777A CN 105117777 A CN105117777 A CN 105117777A CN 201510451956 A CN201510451956 A CN 201510451956A CN 105117777 A CN105117777 A CN 105117777A
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Abstract
The invention provides an order distributing method. The method comprises the following steps of performing conditional distribution on current orders according to order matching conditions corresponding to a terminal and screening out those orders satisfying the order matching conditions, wherein, the current orders are orders to be distributed in a taxiing platform; for each screened order, determining the proximity grade of the order corresponding to the terminal according to a preset strategy; using an order scrambling probability estimating model established in advance to predict the order scrambling probability of the terminal according to the order proximity grade; and according to the order scrambling probability, determining whether to distribute the screened order to the terminal. The invention further provides an order distributing apparatus. The apparatus comprises an order screening unit, a proximity grade determining unit, an order scrambling probability predicting unit and an order distributing unit. The order distributing method and apparatus can effectively reduce deadhead kilometers of drivers and the waiting time of passengers. The user experience can be further 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
Along with the driver and passengers quantity that use software of calling a taxi are increasing, driver very can not ensure the online order of whole day, and many times driver can only abandon order due to temporarily busy, thus travels the thing of the process oneself that goes down.
By the investigation to driver, known for above-mentioned situation, driver is in fact still very burning desire and goes order.Although existing competition for orders mode gives driver and selects right very widely, when occurring above-mentioned condition, driver is still difficult to run into applicable order, when especially becoming appointment from competition for orders, listens single density to reduce, the more difficult order run into required for oneself of driver.
How according to the order wish of driver, realizing that driver mates with the best order of passenger is a urgent problem.
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, carry out the screening of order according to the order wish of driver, ensure the accuracy of Order splitting, effectively reduce driver's deadhead kilometres.
According to an aspect of the present invention, provide a kind of method of Order splitting, the method comprises:
The order matching condition corresponding according to terminal carries out Condition Matching to current order, filters out the order meeting described order matching condition; Described current order is order to be allocated in taxi taking platform;
For each order filtered out, determine the direct route grade of the corresponding described terminal of this order according to preset strategy; And
Adopt the competition for orders probability prediction model set up in advance, the order competition for orders probability of terminal according to the direct route grade forecast of this order;
According to described order competition for orders probability, determine whether this order filtered out to described terminal distribution.
Optionally, described method also comprises:
Obtain the order matching condition that terminal is corresponding;
The obtain manner of described order matching condition, comprising:
The order matching condition that receiving terminal is uploaded, or
Based on the movement state information that described terminal is current, determine according to preset rules the order matching condition that described terminal is corresponding.
Optionally, the described direct route grade determining the corresponding described terminal of this order according to preset strategy, comprising:
Obtain origin and the destination of the described order filtered out;
Obtain the destination that described terminal owning user is current;
The direct route grade of the destination that the corresponding described terminal owning user in origin and destination of described order is current is determined according to preset strategy;
Wherein, described direct route grade comprises through and to reach.
Optionally, described method also comprises:
Obtain terminal History Order data within a predetermined period of time;
Using described History Order data as training data, adopt linear regression model (LRM) to train described training data, obtain described competition for orders probability prediction model;
Wherein, described History Order data comprise the direct route levels characteristic of the corresponding described terminal of each History Order.
Optionally, described linear regression model (LRM) is this special regression model or supporting vector machine model of logic.
Optionally, described 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;
The order data of described Real-time Obtaining comprises the direct route levels characteristic of the corresponding described terminal of this order.
Optionally, according to described order competition for orders probability, determine whether this order filtered out to described terminal distribution, specifically comprise:
Judge whether described order competition for orders probability is greater than predetermined threshold value;
If described order competition for orders probability is greater than predetermined threshold value, then determine that this order meets Order splitting condition, and to this order that described terminal distribution filters out.
Optionally, when in the order filtered out, exist multiple when meeting the order of Order splitting condition, described method also comprises:
According to order competition for orders probability order from big to small, meet the order of Order splitting condition to described terminal distribution.
According to another aspect of the present invention, provide a kind of device of Order splitting, this device comprises:
Order screening unit, carries out Condition Matching for the order matching condition corresponding according to terminal to current order, filters out the order meeting described order matching condition; Described current order is order to be allocated in taxi taking platform;
Level de-termination unit by the way, for each order selected for described order screening sieve unit, determines the direct route grade of the corresponding described terminal of this order according to preset strategy;
Competition for orders probability prediction unit, for adopting the competition for orders probability prediction model set up in advance, the order competition for orders probability of terminal according to the direct route grade forecast of this order;
Order splitting unit, for the order competition for orders probability doped according to described competition for orders probability prediction unit, determines whether this order filtered out to described terminal distribution.
Optionally, described device also comprises:
Matching condition acquiring unit, for obtaining order matching condition corresponding to terminal;
Described matching condition acquiring unit, comprising: receiver module or determination module;
Described receiver module, for the order matching condition that receiving terminal is uploaded;
Described determination module, for based on the current movement state information of described terminal, determines according to preset rules the order matching condition that described terminal is corresponding.
Optionally, described direct route level de-termination unit comprises:
Order Address acquisition module, for obtaining origin and the destination of the described order filtered out;
Terminal address acquisition module, for obtaining the current destination of described terminal owning user;
Grade determination module by the way, the direct route grade of the destination that the corresponding described terminal owning user in origin and destination for determining described order according to preset strategy is current;
Wherein, described direct route grade comprises through and to reach.
Optionally, described device also comprises:
Historical data acquiring unit, for obtaining terminal History Order data within a predetermined period of time;
Forecast model sets up unit, for using described History Order data as training data, adopt linear regression model (LRM) described training data is trained, obtain described competition for orders probability prediction model;
Wherein, described History Order data comprise the direct route levels characteristic of the corresponding described terminal of each History Order.
Optionally, described linear regression model (LRM) is this special regression model or supporting vector machine model of logic.
Optionally, described forecast model sets up unit, also for the order data according to Real-time Obtaining on line, adopts machine learning algorithm, is optimized described competition for orders probability prediction model;
Wherein, the order data of described Real-time Obtaining comprises the direct route levels characteristic of the corresponding described terminal of this order.
Optionally, described Order splitting unit, comprising:
Judge module, for judging whether described order competition for orders probability is greater than predetermined threshold value;
Distribution module, for when the judged result of described judge module be described order competition for orders probability be greater than predetermined threshold value time, determine that this order meets Order splitting condition, and to this order that described terminal distribution filters out.
Optionally, described distribution module, also in the order that filter out, exists multiple when meeting the order of Order splitting condition, according to order competition for orders probability order from big to small, meets the order of Order splitting condition to described terminal distribution.
As shown from the above technical solution, the invention provides a kind of method and device of Order splitting, by driver, the matching condition of order is limited, order wish according to driver carries out preliminary screening at the Order splitting initial stage, and carry out order competition for orders probabilistic forecasting for results of preliminary screening, to carry out the distribution of order based on order competition for orders probability, realize order and precisely push, effective minimizing driver's deadhead kilometres and passenger waiting time, thus promote Consumer's 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 schematic flow sheet of the method for a kind of Order splitting that another embodiment of the disclosure provides;
Fig. 3 is the structural representation of the device of a kind of Order splitting that the disclosure one embodiment provides;
Fig. 4 is the structural representation of the device of a kind of Order splitting that another embodiment of the disclosure 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.
Those skilled in the art of the present technique are appreciated that unless expressly stated, and singulative used herein " ", " one ", " described " and " being somebody's turn to do " also can comprise plural form.Should be further understood that, the wording used in instructions of the present invention " comprises " and refers to there is described feature, integer, step, operation, element and/or assembly, but does not get rid of and exist or add other features one or more, integer, step, operation, element, assembly and/or their group.
Those skilled in the art of the present technique are appreciated that unless otherwise defined, and all terms used herein (comprising technical term and scientific terminology), have the meaning identical with the general understanding of the those of ordinary skill in field belonging to the present invention.Should also be understood that those terms defined in such as general dictionary, should be understood to that there is the meaning consistent with the meaning in the context of prior art, unless and by specific definitions, otherwise can not explain by idealized or too formal implication.
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:
S11, the order matching condition corresponding according to terminal carry out Condition Matching to current order, filter out the order meeting described order matching condition; Described current order is order to be allocated in taxi taking platform;
In practical application, taxi taking platform generates corresponding order according to the described request of calling a taxi after receiving the request of calling a taxi that subscriber equipment (UserEquipment, be called for short UE) sends.Wherein, 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.Taxi taking platform asks according to calling a taxi the departure place information that comprises, is automatically pushed to the terminal of the taxi driver within preset range in certain hour length, driver can by this terminal carry out a key rob should, and and passenger keep in touch.
In the present embodiment, when taxi taking platform according to described in the request of calling a taxi generate after corresponding order, taxi taking platform the order matching condition corresponding according to terminal can carry out Condition Matching to current order, filters out and meets order to be allocated in the taxi taking platform of described order matching condition to terminal distribution.By driver, the matching condition of order is limited, and carry out preliminary screening according to the order wish of driver from the Order splitting initial stage, realize order and precisely push, effectively reduce driver's deadhead kilometres and passenger waiting time, thus promote the advantage of Consumer's Experience.
Wherein, order matching condition comprises the condition such as matching range, match time of order.Concrete, the matching range of order can be determined the destination current according to driver user belonging to the current residing geographic position of city, city and terminal belonging to terminal, current motion state and terminal; Match time can be determined in the arrangement of time current according to driver user belonging to terminal.
S12, for each order filtered out, determine the direct route grade of the corresponding described terminal of this order according to preset strategy;
Concrete, the described direct route grade determining the corresponding described terminal of this order according to preset strategy, comprises following not shown step further:
S121, the origin obtaining the described order filtered out and destination;
S122, obtain the current destination of described terminal owning user;
The direct route grade of the destination that the corresponding described terminal owning user in S123, the origin determining described order according to preset strategy and destination is current;
In the present embodiment, described direct route grade comprises through and to reach.In actual applications, the concrete setting of grade can set more accurately according to user's request and divide by the way, such as, the road surface distance that visible grade by the way travels according to driver user's actual needs, and/or, the time etc. that actual travel needs is divided into A, B, C, D, E five grades, wherein, the road surface distance that driver user's actual needs travels, and/or, the time that actual travel needs, be the preset strategy of the direct route grade for determining the corresponding described terminal of order.
Wherein, for the information of order, include but not limited to following content: the origin of order, the destination of order, passenger are ready that the additional cost of supplementary payments, passenger are ready whether the time of wait, passenger carry heavy luggage etc.Wherein, for the origin of order, both its subscriber equipment can be used to start the software passenger that calls a taxi by passenger to hold and key in or say, also can via the positioning system in the subscriber equipment of passenger, such as GPS, architecture system etc., the locating information obtained is determined, can also determine via other information in appropriate circumstances, wherein, these other information 2 D code information etc. that can include but not limited to bus stop, subway station, specific crossing and specific buildings and put up at these position places.
In the present embodiment, by obtaining the origin of each order meeting order matching condition corresponding to terminal and the destination of order that filter out from the information of each order, and the current stroke of the terminal owning user uploaded according to terminal obtains the destination of this terminal owning user, then determines the direct route grade of the destination that the corresponding described terminal owning user in origin and destination of each order is current according to preset strategy.
The competition for orders probability prediction model that S13, employing are set up in advance, the order competition for orders probability of terminal according to the direct route grade forecast of this order;
In this step, according to the direct route grade of the corresponding present terminal of each order determined in step S12, and the direct route levels characteristic of foundation order, prediction terminal is relative to the competition for orders probability of this order.
S14, according to described order competition for orders probability, determine whether this order filtered out to described terminal distribution.
The method of the Order splitting that the embodiment of the present invention provides, by driver, the matching condition of order is limited, order wish according to driver carries out preliminary screening at the Order splitting initial stage, and carry out order competition for orders probabilistic forecasting for results of preliminary screening, to carry out the distribution of order based on order competition for orders probability, realize order precisely to push, effectively reduce driver's deadhead kilometres and passenger waiting time, thus promote Consumer's Experience.
Those skilled in the art are to be understood that, subscriber equipment (the UserEquipment mentioned in the embodiment of the present invention, be called for short UE) refer to call service side, as the passenger in vehicles dial-a-cab, 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.
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.
In the present embodiment, in order to distinguish passenger and driver, user equipment (UE) and terminal is adopted to represent the equipment such as the mobile terminal that passenger and driver hold respectively respectively.
Further, the distribution method of the order provided in another embodiment of the present disclosure, as shown in Figure 2, before step S11, also comprises step S10:
The order matching condition that S10, acquisition terminal are corresponding;
Concrete, the obtain manner of the order matching condition in step S10, comprises further:
The order matching condition that receiving terminal is uploaded, or
Based on the movement state information that described terminal is current, determine according to preset rules the order matching condition that described terminal is corresponding.
In the present embodiment, provide the obtain manner of two kinds of order matching conditions, (1) is by receiving driver directly by order matching condition that terminal is uploaded; (2) movement state information that monitoring terminal is current, as travel speed, travel direction etc., based on the movement state information that terminal is current, determines according to preset rules the order matching condition that described terminal is corresponding.
Wherein, the preset rules in the obtain manner of the second order matching condition, can carry out rule settings according to driver is acceptable apart from upper gap and lead time.
Further, the distribution method of order that the present embodiment provides also comprises following not shown step:
A01, acquisition terminal History Order data within a predetermined period of time;
A02, using described History Order data as training data, adopt linear regression model (LRM) described training data is trained, obtain described competition for orders probability prediction model;
Wherein, described History Order data comprise the direct route levels characteristic of the corresponding described terminal of each History Order.
In the present embodiment, linear regression model (LRM) can be following in one: logic this special regression model, supporting vector machine model.
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 when predictive variable X=x, the following formula of probability of target variable Y=1 represents:
P(Y＝1|X＝x)＝1/(1+exp(-w*x)
When predictive variable X=x, the following formula of probability of target variable Y=0 represents:
P(Y＝0|X＝x)＝1-1/(1+exp(-w*x)
Wherein, x represents predictive variable, and y represents target variable, and y=1 represents and is predicted as competition for orders, and y=0 represents and is predicted as not competition for orders, and w represents model parameter.
Concrete, can by History Order data (such as, one or more in order correlated characteristic, driver's correlated characteristic, order and driver's correlated characteristic etc. when broadcasting list) be taken into predictive variable X, and will newly initiate the competition probability of order as target variable Y.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 probability of current order to be allocated.In practice process, whether can also be robbed relevant feature by constantly adding new order of initiating, constantly being improved the accuracy of this special regression model of logic.
The distribution method of the order that the present embodiment provides, after steps A 02, comprises following not shown step further:
A03, order data according to Real-time Obtaining on line, adopt machine learning algorithm, be optimized described competition for orders probability prediction model;
Wherein, the order data of Real-time Obtaining comprises the direct route levels characteristic of the corresponding described terminal of this order.
In the present embodiment, order 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, driver's correlated characteristic, order are become predictive variable with various feature extractions such as driver's correlated characteristics, using driver, 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 Probabilistic Prediction Model.Real-time calculation stages on line: by model use on line, calculates the direct route grade of the corresponding described terminal of the order extracted in real time, adopts machine learning algorithm, be optimized the described forecast 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, to precisely estimating of driver's order probability.
In the present embodiment, step S14 specifically comprises following not shown step:
S141, judge whether described order competition for orders probability is greater than predetermined threshold value;
S141, when described order competition for orders probability is greater than predetermined threshold value, determine that this order meets Order splitting condition, and to this order that described terminal distribution filters out.
Further, when in the order filtered out, exist multiple when meeting the order of Order splitting condition, the method for the Order splitting that the present embodiment proposes, also comprises:
According to order competition for orders probability order from big to small, meet the order of Order splitting condition to described terminal distribution.
In practical application, passenger sends order according to oneself trip requirements, and driver chooses order according to the carrying demand of oneself, and usual driver can set the destination oneself thought.For this reason, the method of the Order splitting of the present embodiment, by driver, the matching condition of order is limited, order wish according to driver carries out preliminary screening at the Order splitting initial stage, and carry out order competition for orders probabilistic forecasting for results of preliminary screening, to carry out the distribution of order based on order competition for orders probability, realize order and precisely push, effective minimizing driver's deadhead kilometres and passenger waiting time, thus promote Consumer's Experience.
As shown in Figure 3, still another embodiment provides a kind of structural representation of device of Order splitting for the disclosure, this device comprises: order screening unit 201, by the way level de-termination unit 202, competition for orders probability prediction unit 203 and Order splitting unit 204, wherein:
Described order screening unit 201, carries out Condition Matching for the order matching condition corresponding according to terminal to current order, filters out the order meeting described order matching condition; Described current order is order to be allocated in taxi taking platform;
Described direct route level de-termination unit 202, for each order selected for described order screening sieve unit, determines the direct route grade of the corresponding described terminal of this order according to preset strategy;
Described competition for orders probability prediction unit 203, for adopting the competition for orders probability prediction model set up in advance, the order competition for orders probability of terminal according to the direct route grade forecast of this order;
Described Order splitting unit 204, for the order competition for orders probability doped according to described competition for orders probability prediction unit, determines whether this order filtered out to described terminal distribution.
The device of the Order splitting that the embodiment of the present invention provides, by driver, the matching condition of order is limited, order wish according to driver carries out preliminary screening at the Order splitting initial stage, and carry out order competition for orders probabilistic forecasting for results of preliminary screening, to carry out the distribution of order based on order competition for orders probability, realize order precisely to push, effectively reduce driver's deadhead kilometres and passenger waiting time, thus promote Consumer's Experience.
Further, the distributor of the order provided in another embodiment of the present disclosure, as shown in Figure 4, also comprises matching condition acquiring unit 200;
Concrete, described matching condition acquiring unit 200, for obtaining order matching condition corresponding to terminal;
Wherein, described matching condition acquiring unit 200, comprises further: receiver module or determination module;
Described receiver module, for the order matching condition that receiving terminal is uploaded;
Described determination module, for based on the current movement state information of described terminal, determines according to preset rules the order matching condition that described terminal is corresponding.
Concrete, described direct route level de-termination unit 202, comprises Order Address acquisition module, terminal address acquisition module and direct route grade determination module further, wherein:
Described Order Address acquisition module, for obtaining origin and the destination of the described order filtered out;
Described terminal address acquisition module, for obtaining the current destination of described terminal owning user;
Described direct route grade determination module, the direct route grade of the destination that the corresponding described terminal owning user in origin and destination for determining described order according to preset strategy is current;
Wherein, described direct route grade comprises through and to reach.
In the present embodiment, the above order assigned unit also comprises unshowned historical data acquiring unit and forecast model in Fig. 3 and sets up unit, wherein;
Described historical data acquiring unit, for obtaining terminal History Order data within a predetermined period of time;
Described forecast model sets up unit, for using described History Order data as training data, adopt linear regression model (LRM) described training data is trained, obtain described competition for orders probability prediction model;
Wherein, described History Order data comprise the direct route levels characteristic of the corresponding described terminal of each History Order.
Wherein, described linear regression model (LRM) can be following in one: logic this special regression model, supporting vector machine model.
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 when predictive variable X=x, the following formula of probability of target variable Y=1 represents:
P(Y＝1|X＝x)＝1/(1+exp(-w*x)
When predictive variable X=x, the following formula of probability of target variable Y=0 represents:
P(Y＝0|X＝x)＝1-1/(1+exp(-w*x)
Wherein, x represents predictive variable, and y represents target variable, and y=1 represents and is predicted as competition for orders, and y=0 represents and is predicted as not competition for orders, and w represents model parameter.
Concrete, can by History Order data (such as, one or more in order correlated characteristic, driver's correlated characteristic, order and driver's correlated characteristic etc. when broadcasting list) be taken into predictive variable X, and will newly initiate the competition probability of order as target variable Y.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 probability of current order to be allocated.In practice process, whether can also be robbed relevant feature by constantly adding new order of initiating, constantly being improved the accuracy of this special regression model of logic.
Further, described forecast model sets up unit, also for the order data according to Real-time Obtaining on line, adopts machine learning algorithm, is optimized described competition for orders probability prediction model;
Wherein, the order data of described Real-time Obtaining comprises the direct route levels characteristic of the corresponding described terminal of this order.
Further, described Order splitting unit 204, specifically comprises judge module and distribution module, wherein:
Described judge module, for judging whether described order competition for orders probability is greater than predetermined threshold value;
Described distribution module, for when the judged result of described judge module be described order competition for orders probability be greater than predetermined threshold value time, determine that this order meets Order splitting condition, and to this order that described terminal distribution filters out.
Wherein, described distribution module, also in the order that filter out, exists multiple when meeting the order of Order splitting condition, according to order competition for orders probability order from big to small, meets the order of Order splitting condition to described terminal distribution.
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.
In sum, according to above-mentioned disclosure embodiment, the method of a kind of Order splitting provided and device, limited the matching condition of order by driver, the order wish according to driver carries out preliminary screening at the Order splitting initial stage, and carries out order competition for orders probabilistic forecasting for results of preliminary screening, to carry out the distribution of order based on order competition for orders probability, realize order precisely to push, effectively reduce driver's deadhead kilometres and passenger waiting time, thus promote Consumer's Experience.
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 (16)
1. a method for Order splitting, is characterized in that, the method comprises:
The order matching condition corresponding according to terminal carries out Condition Matching to current order, filters out the order meeting described order matching condition; Described current order is order to be allocated in taxi taking platform;
For each order filtered out, determine the direct route grade of the corresponding described terminal of this order according to preset strategy; And
Adopt the competition for orders probability prediction model set up in advance, the order competition for orders probability of terminal according to the direct route grade forecast of this order;
According to described order competition for orders probability, determine whether this order filtered out to described terminal distribution.
2. method according to claim 1, is characterized in that, described method also comprises:
Obtain the order matching condition that terminal is corresponding;
The obtain manner of described order matching condition, comprising:
The order matching condition that receiving terminal is uploaded, or
Based on the movement state information that described terminal is current, determine according to preset rules the order matching condition that described terminal is corresponding.
3. method according to claim 1, is characterized in that, the described direct route grade determining the corresponding described terminal of this order according to preset strategy, comprising:
Obtain origin and the destination of the described order filtered out;
Obtain the destination that described terminal owning user is current;
The direct route grade of the destination that the corresponding described terminal owning user in origin and destination of described order is current is determined according to preset strategy;
Wherein, described direct route grade comprises through and to reach.
4. method according to claim 1, is characterized in that, described method also comprises:
Obtain terminal History Order data within a predetermined period of time;
Using described History Order data as training data, adopt linear regression model (LRM) to train described training data, obtain described competition for orders probability prediction model;
Wherein, described History Order data comprise the direct route levels characteristic of the corresponding described terminal of each History Order.
5. method according to claim 4, is characterized in that, described linear regression model (LRM) is this special regression model or supporting vector machine model of logic.
6. method according to claim 4, is characterized in that, described 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;
The order data of described Real-time Obtaining comprises the direct route levels characteristic of the corresponding described terminal of this order.
7. method according to claim 1, is characterized in that, according to described order competition for orders probability, determines whether this order filtered out to described terminal distribution, specifically comprises:
Judge whether described order competition for orders probability is greater than predetermined threshold value;
If described order competition for orders probability is greater than predetermined threshold value, then determine that this order meets Order splitting condition, and to this order that described terminal distribution filters out.
8. method according to claim 7, is characterized in that, when in the order filtered out, exist multiple when meeting the order of Order splitting condition, described method also comprises:
According to order competition for orders probability order from big to small, meet the order of Order splitting condition to described terminal distribution.
9. a device for Order splitting, is characterized in that, this device comprises:
Order screening unit, carries out Condition Matching for the order matching condition corresponding according to terminal to current order, filters out the order meeting described order matching condition; Described current order is order to be allocated in taxi taking platform;
Level de-termination unit by the way, for each order selected for described order screening sieve unit, determines the direct route grade of the corresponding described terminal of this order according to preset strategy;
Competition for orders probability prediction unit, for adopting the competition for orders probability prediction model set up in advance, the order competition for orders probability of terminal according to the direct route grade forecast of this order;
Order splitting unit, for the order competition for orders probability doped according to described competition for orders probability prediction unit, determines whether this order filtered out to described terminal distribution.
10. device according to claim 9, is characterized in that, described device also comprises:
Matching condition acquiring unit, for obtaining order matching condition corresponding to terminal;
Described matching condition acquiring unit, comprising: receiver module or determination module;
Described receiver module, for the order matching condition that receiving terminal is uploaded;
Described determination module, for based on the current movement state information of described terminal, determines according to preset rules the order matching condition that described terminal is corresponding.
11. devices according to claim 9, is characterized in that, described direct route level de-termination unit comprises:
Order Address acquisition module, for obtaining origin and the destination of the described order filtered out;
Terminal address acquisition module, for obtaining the current destination of described terminal owning user;
Grade determination module by the way, the direct route grade of the destination that the corresponding described terminal owning user in origin and destination for determining described order according to preset strategy is current;
Wherein, described direct route grade comprises through and to reach.
12. devices according to claim 9, is characterized in that, described device also comprises:
Historical data acquiring unit, for obtaining terminal History Order data within a predetermined period of time;
Forecast model sets up unit, for using described History Order data as training data, adopt linear regression model (LRM) described training data is trained, obtain described competition for orders probability prediction model;
Wherein, described History Order data comprise the direct route levels characteristic of the corresponding described terminal of each History Order.
13. devices according to claim 12, is characterized in that, described linear regression model (LRM) is this special regression model or supporting vector machine model of logic.
14. devices according to claim 12, is characterized in that, described forecast model sets up unit, also for the order data according to Real-time Obtaining on line, adopt machine learning algorithm, are optimized described competition for orders probability prediction model;
Wherein, the order data of described Real-time Obtaining comprises the direct route levels characteristic of the corresponding described terminal of this order.
15. devices according to claim 9, is characterized in that, described Order splitting unit, comprising:
Judge module, for judging whether described order competition for orders probability is greater than predetermined threshold value;
Distribution module, for when the judged result of described judge module be described order competition for orders probability be greater than predetermined threshold value time, determine that this order meets Order splitting condition, and to this order that described terminal distribution filters out.
16. devices according to claim 15, is characterized in that, described distribution module, also in the order that filter out, exist multiple when meeting the order of Order splitting condition, according to order competition for orders probability order from big to small, meet the order of Order splitting condition to described terminal distribution.
Priority Applications (10)
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| CN201510451956.8A CN105117777A (en) | 2015-07-28 | 2015-07-28 | Order distributing method and apparatus |
| CN202011120410.1A CN112149855A (en) | 2015-07-28 | 2015-07-28 | Order allocation method and device |
| EP16742811.9A EP3252705A4 (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 |
| KR1020177024089A KR20180013843A (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 |
| US15/547,221 US10977585B2 (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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| CN201510451956.8A CN105117777A (en) | 2015-07-28 | 2015-07-28 | Order distributing method and apparatus |
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| CN202011120410.1A Division CN112149855A (en) | 2015-07-28 | 2015-07-28 | Order allocation method and device |
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| CN105117777A true CN105117777A (en) | 2015-12-02 |
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| CN201510451956.8A Pending CN105117777A (en) | 2015-01-29 | 2015-07-28 | Order distributing method and apparatus |
| CN202011120410.1A Pending CN112149855A (en) | 2015-07-28 | 2015-07-28 | Order allocation method and device |
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