CN104599002A - Order value predicting method and equipment - Google Patents
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Abstract
An embodiment of the invention discloses an order value predicting method and equipment. The method includes acquiring characteristics relevant to an order value of historical orders; generating a mapping model of the characteristics and order value; on the basis of the mapping model and fresh order data, predicating the order values of fresh orders. According to the method, the problem that in the prior art, according to a taxi system, system order data are difficult to predict sensitively and accurately in a manner of being independent to a map can be solved.
Description
Technical field
Embodiment of the present disclosure relates to system prediction field, is specifically related to a kind of method and apparatus that order is worth of predicting.
Background technology
At present, along with the raising of people's living standard, taxi use amount is increasing, and urban taxi has become the important vehicles.And universal along with smart machine particularly intelligent navigation and smart mobile phone, the use of taxi system platform is more and more general, and brings great convenience to the trip of people.
In taxi system platform, owing to there is problem of information asymmetry between taxi driver and passenger, therefore need to user under the value of order estimate, wherein order is worth and comprises mileage and pricing information.Mileage and the pricing information of existing taxi system depend on map software usually, therefore higher to the requirement of map datum.And the Prediction System based on map datum cannot consider the impact of other unpredictable factor, the complex situations such as such as working day, peak period, core road section traffic volume situation and weather.Therefore, designing and research and develop a kind of is necessary to the order in taxi system independent of the method that map datum is accurately estimated.
Summary of the invention
Embodiment of the present disclosure aims to provide a kind of method and apparatus that order is worth of predicting, the taxi system in correlation technique that can solve to be difficult to independent of map sensitively, the problem of Prediction System order data exactly.
According to an aspect of the present disclosure, provide a kind of method that order is worth of predicting, comprising: obtain and be worth with order the feature be associated in History Order; Generate the mapping model that described feature and described order are worth; And based on the data of described mapping model and new order, predict that the order of described new order is worth.
In one embodiment, the method also comprises: utilize the data of new order to upgrade described mapping model.
In one embodiment, the described feature be associated that is worth with order comprises following at least one: user's order starting point longitude, user's order starting point latitude, user's order terminal longitude, user's order terminal latitude and user's order start time.
In one embodiment, based on the data of described mapping model and new order, predict that the order of described new order is worth and comprise: that extracts described new order is worth with order the feature be associated; And based on the feature of described new order and described mapping model, determine that the order of described new order is worth.
In one embodiment, based on the data of described mapping model and new order, predict that the order of described new order is worth and comprise: the order of described new order is worth the mean value that the order that is defined as the History Order be associated with described new order is worth.
In one embodiment, based on the data of described mapping model and new order, predict that the order of described new order is worth and comprise: the order of described new order is worth the weighted mean value that the order that is defined as the History Order be associated with described new order is worth.
In one embodiment, when the distance metric between described new order and the feature of History Order is less than threshold value, determine that described History Order is associated with described new order.
In one embodiment, described distance metric comprises mahalanobis distance and Euclidean distance.
In one embodiment, described threshold value is set according to Gaussian distribution.
According to the another aspect of embodiment of the present disclosure, propose a kind of for predicting the equipment that order is worth.This equipment comprises: acquisition device, is configured to obtain be worth with order the feature be associated in History Order; Generating apparatus, is configured to generate the mapping model that described feature and described order are worth; And prediction unit, be configured to the data based on described mapping model and new order, predict that the order of described new order is worth.
In one embodiment, this equipment also comprises: updating device, is configured to utilize the data of new order to upgrade described mapping model.
In one embodiment, the described feature be associated that is worth with order comprises following at least one: user's order starting point longitude, user's order starting point latitude, user's order terminal longitude, user's order terminal latitude and user's order start time.
In one embodiment, described prediction unit comprises: extraction module, and what be configured to extract described new order is worth with order the feature be associated; And determination module, be configured to the feature based on described new order and described mapping model, determine that the order of described new order is worth.
In one embodiment, described prediction unit is configured to: the order of described new order is worth the mean value that the order that is defined as the History Order be associated with described new order is worth.
In one embodiment, described prediction unit is configured to: the order of described new order is worth the weighted mean value that the order that is defined as the History Order be associated with described new order is worth.
In one embodiment, prediction unit is also configured to: when the distance metric between described new order and the feature of History Order is less than threshold value, determines that described History Order is associated with described new order.
In one embodiment, described distance metric comprises mahalanobis distance and Euclidean distance.
In one embodiment, prediction unit is also configured to set described threshold value according to Gaussian distribution.
Accompanying drawing explanation
Accompanying drawing described herein is used to provide further understanding of the disclosure, and form a application's part, schematic description and description of the present disclosure, for explaining the disclosure, is not formed improper restriction of the present disclosure.In the accompanying drawings:
Fig. 1 be a diagram that the process flow diagram of the method be worth according to the prediction order of embodiment of the present disclosure;
Fig. 2 be a diagram that realize according to Fig. 1 for predicting the system architecture schematic diagram that order is worth; And
Fig. 3 be a diagram that the structured flowchart of the equipment be worth according to the prediction order of embodiment of the present disclosure.
Embodiment
It should be noted that, when not conflicting, the embodiment in the application and the feature in embodiment can combine mutually.Below with reference to the accompanying drawings and describe the disclosure in detail in conjunction with the embodiments.
Fig. 1 be a diagram that the process flow diagram of the method 100 be worth according to the prediction order of embodiment of the present disclosure, comprising following step S101 to step S103.
As shown in Figure 1, in step S101, obtain and be worth with order the feature be associated in History Order.In step S102, generate the mapping model that described feature and described order are worth.And, in step S103, based on the data of described mapping model and new order, predict that the order of described new order is worth.In an example, order is worth and comprises mileage and pricing information
In existing taxi platform system, owing to using the quantity of user increasing, taxi platform system has added up a large amount of sequence informations.Sequence information comprises the various information such as departure place, destination, departure time, mileage, price.Therefore, taxi platform system can by the History Order data founding mathematical models obtained.
In step S101 place, be worth with order the feature be associated and comprise following at least one: user's order starting point longitude, user's order starting point latitude, user's order terminal longitude, user's order terminal latitude and user's order start time etc.
The set of order feature can represent with following formula,
O
i={ sx
i, sy
i, ex
i, ey
i, t
i(formula 1)
Wherein, i=1,2,3 ... n.O
irepresent the characteristic set of i-th order; Sx represents order starting point longitude; Sy represents order starting point latitude; Ex represents order terminal longitude; Ey represents that order terminal latitude and t represent the order start time.
In step S101 place, after the feature obtaining a large amount of order, set up the mapping model that feature and described order are worth, such as, set up the mapping relations { D between the mileage of this order and the feature of this order respectively
i, O
i, and the mapping relations (P between order price and the feature of this order
i, O
i, wherein D
irepresent the mileage of i-th order, P
irepresent the price of i-th order, O
irepresent the feature of i-th order.
And method 100 can also utilize the data of new order to upgrade mapping model.In one embodiment, step S101 and S102 can carry out by off-line the process of historical data.
In step s 103, new order inputs to taxi platform system, and taxi system needs to predict the value of new order.Value wherein based on the data prediction new order of mapping model and new order comprises: that extracts described new order is worth with order the feature be associated; And based on the feature of described new order and described mapping model, determine that the order of described new order is worth.
In one example, the feature extraction of the new order of user's transmission is:
O
p={ sx
p, sy
p, ex
p, ey
p, t
p(formula 2)
O
prepresent the characteristic set of new order; Sx represents order starting point longitude; Sy represents order starting point latitude; Ex represents order terminal longitude; Ey represents that order terminal latitude and t represent the order start time.
In one embodiment, based on the data of mapping model and new order, predict that the order of described new order is worth and comprise: the order of described new order is worth the mean value that the order that is defined as the History Order be associated with described new order is worth.
Such as, the mileage of new order and price are shown below respectively:
Wherein D
prepresent the mileage of new order, P
prepresent the price of new order, O
prepresent the feature of new order, O
jrepresent the feature of a jth order, D
jrepresent the mileage of a jth order, P
jrepresent the price of a jth order, ω (O
p, O
j) represent distance metric, wherein distance metric can comprise mahalanobis distance and Euclidean distance, and W is constant value.
Euclidean distance is the distance definition usually adopted, and refers to the actual distance between two points in m-dimensional space, or the natural length of vector (namely this point is to the distance of initial point).Euclidean distance in two and three dimensions space is exactly the actual range between 2.
Mahalanobis distance is the covariance distance representing data.It is the method for the similarity of a kind of effective calculating two unknown sample collection.Consider that contacting between various characteristic (such as: the information that origin information Hui Dai vostro account order start time of order is relevant with Euclidean distance unlike it, because both are related) and be yardstick irrelevant (scale-invariant), namely independent of measurement scale.
Can be found out by (formula 5), when the distance metric between new order and the feature of History Order is less than setting threshold value, judge that described History Order is associated with described new order, namely be calculated the value of new order by these orders be associated.W value wherein in (formula 5) can be set by Gaussian distribution, namely judges that the data value of above-mentioned mahalanobis distance or the distribution of Euclidean distance maximum probability is to set W.
Can be found out in this embodiment by (formula 3)-(formula 5), be 1 for the equal value of the order data be associated with new order, adopts mileage and the price of its mean value calculation new order.
In some cases, because History Order differs comparatively large with the correlation degree of new order, therefore consider that weighted value can make this prediction more accurate.Such as after the mahalanobis distance judged between new order and the feature of History Order or Euclidean distance are less than setting threshold value, when can be set in its mahalanobis distance or Euclidean distance be different value, get different weighted values, such as when History Order starting point and terminal are identical with new order, its weighted value is made to be maximum.Consider that different weighted values can make order forecasting more accurate.
Therefore, in one embodiment, order is worth the mileage of History Order and the weighted mean value of price that are defined as being associated with described new order.
Fig. 2 be a diagram that realize according to Fig. 1 for predicting the system architecture schematic diagram that order is worth.As shown in the figure, user is called a taxi by taxi platform system 201, accumulated history order data.Therefore a large amount of History Order data are comprised at order collective data 202.
According to an embodiment of the present disclosure, prognoses system 203 comprises for predicting the system that order is worth.This prognoses system 203 is predicted the mileage of new order and price in conjunction with History Order data and new order data, and forecasting process comprises: obtain and be worth with order the feature be associated in History Order; Generate the mapping model that described feature and described order are worth; And based on the data of described mapping model and new order, predict that the order of described new order is worth.
The mileage predicted by new order of this prognoses system 203 and price feed back to user subsequently.
Fig. 3 be a diagram that the structured flowchart of the equipment 300 be worth according to the prediction order of embodiment of the present disclosure.
This equipment 300 comprises acquisition device 301, is configured to obtain be worth with order the feature be associated in History Order; Generating apparatus 302, is configured to generate the mapping model that described feature and described order are worth; And prediction unit 303, be configured to the data based on described mapping model and new order, predict that the order of described new order is worth.
In one embodiment, this equipment 300 also comprises: updating device is configured to utilize the data of new order to upgrade described mapping model.
In one embodiment, be worth the feature be associated with order and comprise following at least one: user's order starting point longitude, user's order starting point latitude, user's order terminal longitude, user's order terminal latitude and user's order start time.
In one embodiment, described prediction unit 303 comprises: extraction module, and what be configured to extract described new order is worth with order the feature be associated; And determination module, be configured to the feature based on described new order and described mapping model, determine that the order of described new order is worth.
In one embodiment, described prediction unit 303 is configured to the order of described new order to be worth the mean value that the order that is defined as the History Order be associated with described new order is worth further.
In one embodiment, described prediction unit 303 is configured to the order of described new order to be worth the weighted mean value that the order that is defined as the History Order be associated with described new order is worth further.
In one embodiment, described prediction unit 303 is configured to further: when the distance metric between described new order and the feature of History Order is less than threshold value, determines that described History Order is associated with described new order.
In one embodiment, described distance metric comprises mahalanobis distance and Euclidean distance.
In one embodiment, described prediction unit 303 is configured to set described threshold value according to Gaussian distribution further.
Obviously, those skilled in the art should be understood that, above-mentioned of the present disclosure each module or each step can realize with general calculation element, they can concentrate on single calculation element, or be distributed on network that multiple calculation element forms, alternatively, they can realize with the executable program code of calculation element, thus they storages can be performed by calculation element in the storage device, or they are made into each integrated circuit modules respectively, or the multiple module in them or step are made into single integrated circuit module to realize.Like this, the disclosure is not restricted to any specific hardware and software combination.
The foregoing is only preferred embodiment of the present disclosure, be not limited to the disclosure, for a person skilled in the art, the disclosure can have various modifications and variations.All within spirit of the present disclosure and principle, any amendment done, equivalent replacement, improvement etc., all should be included within protection domain of the present disclosure.
Claims (18)
1. predict and comprise the method that order is worth:
Obtain and be worth with order the feature be associated in History Order;
Generate the mapping model that described feature and described order are worth; And
Based on the data of described mapping model and new order, predict that the order of described new order is worth.
2. method according to claim 1, also comprises:
The data of new order are utilized to upgrade described mapping model.
3. method according to claim 1 and 2, the wherein said feature be associated that is worth with order comprises following at least one:
User's order starting point longitude, user's order starting point latitude, user's order terminal longitude, user's order terminal latitude and user's order start time.
4. method according to claim 1 and 2, wherein based on the data of described mapping model and new order, predict that the order of described new order is worth and comprise:
That extracts described new order is worth with order the feature be associated; And
Based on feature and the described mapping model of described new order, determine that the order of described new order is worth.
5. method according to claim 1, wherein based on the data of described mapping model and new order, predict that the order of described new order is worth and comprise:
The order of described new order is worth the mean value that the order that is defined as the History Order be associated with described new order is worth.
6. method according to claim 1, wherein based on the data of described mapping model and new order, predict that the order of described new order is worth and comprise:
The order of described new order is worth the weighted mean value that the order that is defined as the History Order be associated with described new order is worth.
7. the method according to claim 5 or 6, wherein
When distance metric between described new order and the feature of History Order is less than threshold value, determine that described History Order is associated with described new order.
8. method according to claim 7, wherein said distance metric comprises mahalanobis distance and Euclidean distance.
9. method according to claim 7, wherein sets described threshold value according to Gaussian distribution.
10. predict and comprise the equipment that order is worth:
Acquisition device, is configured to obtain and is worth with order the feature be associated in History Order;
Generating apparatus, is configured to generate the mapping model that described feature and described order are worth; And
Prediction unit, is configured to the data based on described mapping model and new order, predicts that the order of described new order is worth.
11. equipment according to claim 10, also comprise:
Updating device, is configured to utilize the data of new order to upgrade described mapping model.
12. equipment according to claim 10 or 11, the wherein said feature be associated that is worth with order comprises following at least one:
User's order starting point longitude, user's order starting point latitude, user's order terminal longitude, user's order terminal latitude and user's order start time.
13. equipment according to claim 10 or 11, wherein said prediction unit comprises:
Extraction module, what be configured to extract described new order is worth with order the feature be associated; And
Determination module, is configured to the feature based on described new order and described mapping model, determines that the order of described new order is worth.
14. equipment according to claim 10, wherein said prediction unit is configured to:
The order of described new order is worth the mean value that the order that is defined as the History Order be associated with described new order is worth.
15. equipment according to claim 10, wherein said prediction unit is configured to:
The order of described new order is worth the weighted mean value that the order that is defined as the History Order be associated with described new order is worth.
16. equipment according to claims 14 or 15, wherein prediction unit is also configured to:
When distance metric between described new order and the feature of History Order is less than threshold value, determine that described History Order is associated with described new order.
17. equipment according to claim 16, wherein said distance metric comprises mahalanobis distance and Euclidean distance.
18. equipment according to claim 16, wherein prediction unit is also configured to set described threshold value according to Gaussian distribution.
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| CN201510079224.0A CN104599002B (en) | 2015-02-13 | 2015-02-13 | Method and equipment for predicting order value |
| SG11201704715YA SG11201704715YA (en) | 2014-12-09 | 2015-12-09 | User maintenance system and method |
| US15/533,994 US20170364933A1 (en) | 2014-12-09 | 2015-12-09 | User maintenance system and method |
| GB1709115.8A GB2547395A (en) | 2014-12-09 | 2015-12-09 | User maintenance system and method |
| PCT/CN2015/096820 WO2016091173A1 (en) | 2014-12-09 | 2015-12-09 | User maintenance system and method |
| PH12017501080A PH12017501080A1 (en) | 2014-12-09 | 2017-06-08 | User maintenance system and method |
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| CN109508461A (en) * | 2018-12-29 | 2019-03-22 | 重庆猪八戒网络有限公司 | Order price prediction technique, terminal and medium based on Chinese natural language processing |
| CN113139803A (en) * | 2021-04-08 | 2021-07-20 | 北京京东振世信息技术有限公司 | Order information generation method and device, electronic equipment and computer readable medium |
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