CN109685276B - Order processing method and device, electronic equipment and computer readable storage medium - Google Patents
Order processing method and device, electronic equipment and computer readable storage medium Download PDFInfo
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Abstract
The embodiment of the disclosure discloses an order processing method, an order processing device, electronic equipment and a computer readable storage medium, wherein the method comprises the following steps: acquiring order data, distribution data and current objective data of a current order; respectively extracting and splicing the characteristics of the order data, the distribution data and the current objective data to obtain a multi-dimensional current order characteristic vector; and obtaining the estimated overtime information of the current order according to the characteristic vector of the current order and a preset overtime prediction model. According to the scheme, the multi-dimensional information of the representative service scene of the current order and the preset timeout prediction model can be utilized, and the estimated timeout information of the current order for guiding whether the order to be appended is appended to the delivery personnel can be accurately obtained, so that the order quantity distributed to each delivery personnel is dynamically adjusted, and the delivery efficiency is improved.
Description
Technical Field
The present disclosure relates to the field of instant distribution technologies, and in particular, to an order processing method and apparatus, an electronic device, and a computer-readable storage medium.
Background
With the improvement of living standard of people, various take-out services are produced. Such as fast food takeaway, supermarket merchandise takeaway, flower takeaway, etc.
For take-away services, an upper limit on the amount of orders is typically set for each deliverer based on the maximum amount of orders that the deliverer who delivers take-away has historically not timed out each day. When the amount of orders for the delivery personnel on the day exceeds the upper limit, no more orders are added to the delivery personnel.
However, the order is only allocated to the delivery personnel according to the back order quantity, and the consideration range is single, so that the time-out is serious after the order is allocated.
Disclosure of Invention
The embodiment of the disclosure provides an order processing method and device, electronic equipment and a computer-readable storage medium.
In a first aspect, an embodiment of the present disclosure provides an order processing method.
Specifically, the order processing method includes:
acquiring order data, distribution data and current objective data of a current order; the current objective data comprises environmental information or non-human factors of current order distribution, and the current order comprises an undelivered order and an order to be added of a distributor;
respectively extracting and splicing the features of the order data, the distribution data and the current objective data to obtain a multi-dimensional current order feature vector;
and obtaining the estimated overtime information of the current order according to the characteristic vector of the current order and a preset overtime prediction model.
With reference to the first aspect, in a first implementation manner of the first aspect, the method further includes:
and if the estimated overtime information of the current order indicates that the current order is not overtime, distributing the order to be added to the delivery personnel.
With reference to the first aspect, in a second implementation manner of the first aspect, the current order data includes: at least one of order content data, merchant data, and user data;
the delivery data includes: at least one of a distributor level, a distribution timeout condition, a distribution vehicle;
the current objective data includes: at least one of environmental information and distribution period traffic congestion conditions.
With reference to the first aspect, the first implementation manner of the first aspect, and the second implementation manner of the first aspect, in a third implementation manner of the first aspect, the obtaining a multidimensional current order feature vector after respectively performing feature extraction and splicing on the current order data, the delivery data, and the current objective data includes:
performing feature extraction on the current order data to obtain a first feature vector;
performing feature extraction on the distribution data to obtain a second feature vector;
extracting features of the current objective data to obtain a third feature vector;
and splicing the first feature vector, the second feature vector and the third feature vector to obtain the multi-dimension-based current order feature vector.
With reference to the first aspect, in a fourth implementation manner of the first aspect, before obtaining the estimated timeout information of the current order according to the order feature vector and a preset timeout prediction model, the method further includes:
and acquiring the preset timeout prediction model.
With reference to the first aspect and the fourth implementation manner of the first aspect, in a fifth implementation manner of the first aspect, the obtaining the preset timeout prediction model includes:
acquiring historical order data, historical distribution data, historical objective data and corresponding historical overtime information;
respectively extracting and splicing the features of the historical order data, the historical distribution data and the historical objective data to obtain a multi-dimensional historical order feature vector;
and performing deep neural network model training by adopting the multi-dimension-based historical order characteristic vector, the historical overtime information and a preset initial model to obtain the preset overtime prediction model.
With reference to the first aspect and the second implementation manner of the first aspect, in a sixth implementation manner of the first aspect, the order content data includes: at least one of a delivery address, an order number, an order placement time, a desired time, an ordered item;
the merchant data includes: at least one of a merchant address and a merchant order-giving time;
the user data includes: at least one of user floor, user elevator information.
In a second aspect, an order processing apparatus is provided in an embodiment of the present disclosure.
Specifically, the order processing apparatus includes:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is configured to acquire order data, delivery data and current objective data of a current order; the current objective data comprises environmental information or non-human factors of current order distribution, and the current order comprises an undelivered order and an order to be added of a distributor;
the extraction and splicing module is configured to respectively perform feature extraction and splicing on the order data, the distribution data and the current objective data to obtain a multi-dimensional current order feature vector;
and the prediction module is configured to obtain the estimated timeout information of the current order according to the current order feature vector and a preset timeout prediction model.
With reference to the second aspect, in a first implementation manner of the second aspect, the apparatus further includes an additional module:
the adding module is configured to allocate the order to be added to the delivery personnel if the estimated timeout information of the current order indicates that the current order is not timed out.
With reference to the second aspect, in a second implementation manner of the second aspect, the current order data includes: at least one of order content data, merchant data, and user data;
the delivery data includes: at least one of a distributor level, a distribution timeout condition, a distribution vehicle;
the current objective data includes: at least one of environmental information and distribution period traffic congestion conditions.
With reference to the second aspect, the first implementation manner of the second aspect, and the second implementation manner of the second aspect, in a third implementation manner of the second aspect, the extracting and splicing module includes:
the first extraction submodule is configured to perform feature extraction on the current order data to obtain a first feature vector;
the second extraction submodule is used for extracting the characteristics of the distribution data to obtain a second characteristic vector;
the third extraction submodule is used for extracting the features of the current objective data to obtain a third feature vector;
and the splicing submodule is configured to splice the first feature vector, the second feature vector and the third feature vector to obtain the multi-dimensional current order feature vector.
With reference to the second aspect, in a fourth implementation manner of the second aspect, the apparatus further includes a second obtaining module:
the second obtaining module is configured to obtain the preset timeout prediction model.
With reference to the second aspect, in a sixth implementation manner of the second aspect, the second obtaining module includes:
the acquisition submodule is configured to acquire historical order data, the historical distribution data, the historical objective data and corresponding historical timeout information;
the extraction and splicing sub-module is configured to respectively perform feature extraction and splicing on the historical order data, the historical distribution data and the historical objective data to obtain a multi-dimensional historical order feature vector;
and the generation submodule is configured to perform deep neural network model training by adopting the multi-dimension-based historical order feature vector, the historical timeout information and a preset initial model to obtain the preset timeout prediction model.
With reference to the second aspect and the second implementation manner of the second aspect, in a seventh implementation manner of the second aspect, the order content data includes: at least one of a delivery address, an order number, an order placement time, a desired time, an ordered item;
the merchant data includes: at least one of a merchant address and a merchant order-giving time;
the user data includes: at least one of user floor, user elevator information.
In a third aspect, the disclosed embodiments provide an electronic device, including a memory and a processor, where the memory is used to store one or more computer instructions that are executed by the processor to implement the method steps in the first aspect.
In a fourth aspect, the disclosed embodiments provide a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the method steps of the first aspect described above.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
according to the technical scheme, orders which are not delivered by delivery personnel and orders to be added are used as current orders, feature extraction and splicing are carried out on the obtained order data and delivery data of the current orders and current objective data representing environmental information and non-human factors where the orders are currently delivered, a multi-dimensional current order feature vector is obtained, timeout prediction is carried out on the obtained multi-dimensional current order feature vector by using a preset timeout prediction model, and timeout information of the current orders is obtained. According to the technical scheme, the estimated overtime information of the current order for guiding whether the order to be appended is appended to the delivery personnel can be accurately obtained by utilizing the multidimensional information of the representative service scene of the current order and the preset overtime prediction model, so that the order quantity distributed to each delivery personnel is dynamically adjusted, and the delivery efficiency is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments when taken in conjunction with the accompanying drawings. In the drawings:
FIG. 1 illustrates a flow diagram of an order processing method according to an embodiment of the present disclosure;
FIG. 2 shows a flow diagram of an order processing method according to another embodiment of the present disclosure;
FIG. 3 shows a flow chart of step S102 of the order processing method according to the embodiment shown in FIG. 1;
FIG. 4 shows a schematic diagram of feature vector stitching;
FIG. 5 illustrates a flow diagram of an order processing method according to yet another embodiment of the present disclosure;
FIG. 6 shows a flowchart of step S105 of the order processing method according to the embodiment shown in FIG. 5;
FIG. 7 shows a block diagram of an order processing apparatus according to an embodiment of the present disclosure;
fig. 8 is a block diagram showing the structure of an order processing apparatus according to another embodiment of the present disclosure;
FIG. 9 is a block diagram of the extraction and concatenation module 602 of the order processing apparatus according to the embodiment shown in FIG. 7;
fig. 10 is a block diagram showing the structure of an order processing apparatus according to still another embodiment of the present disclosure;
FIG. 11 illustrates a second acquisition module of the order processing apparatus according to the embodiment shown in FIG. 10
605 structural block diagram;
FIG. 12 shows a block diagram of an electronic device according to an embodiment of the present disclosure;
FIG. 13 is a block diagram of a computer system suitable for use in implementing an order processing method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. Also, for the sake of clarity, parts not relevant to the description of the exemplary embodiments are omitted in the drawings.
In the present disclosure, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of the disclosed features, numbers, steps, behaviors, components, parts, or combinations thereof, and are not intended to preclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or combinations thereof may be present or added.
It should be further noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
According to the technical scheme provided by the embodiment of the disclosure, orders which are not delivered by delivery personnel and orders to be added are taken as current orders, feature extraction and splicing are carried out on the obtained order data and delivery data of the current orders and current objective data representing environmental information and non-human factors where the orders are currently delivered, a multi-dimensional current order feature vector is obtained, a preset timeout prediction model is used for carrying out timeout prediction on the obtained multi-dimensional current order feature vector, and the timeout information of the current orders is obtained. According to the technical scheme, the estimated overtime information of the current order for instructing whether the order to be appended is appended to the delivery personnel can be accurately obtained by utilizing the multidimensional information of the representative service scene of the current order and the preset overtime prediction model, so that the order quantity distributed to each delivery personnel is dynamically adjusted, and the working efficiency of the delivery personnel is improved.
According to the technical scheme provided by the embodiment of the disclosure, a flow chart of an order processing method according to an embodiment of the disclosure is shown in fig. 1. As shown in fig. 1, the order processing method includes the following steps S101 to S103:
in step S101, order data, delivery data, and current objective data of a current order are acquired;
in step S102, after feature extraction and splicing are respectively carried out on order data, distribution data and current objective data, a multi-dimensional current order feature vector is obtained;
in step S103, according to the current order feature vector and the preset timeout prediction model, the estimated timeout information of the current order is obtained.
As mentioned above, various take-out services have been produced as the living standard of people increases. Such as fast food takeaway, supermarket merchandise takeaway, flower takeaway, etc. For take-away services, an upper limit on the amount of orders is typically set for each deliverer based on the maximum amount of orders that the deliverer who delivers take-away has historically not timed out each day. When the amount of orders for the delivery personnel on the day exceeds the upper limit, no more orders are added to the delivery personnel. However, the orders are only allocated to the delivery personnel according to the back order quantity, and complicated business scenarios such as the distance between two orders and the time period corresponding to the orders are not considered, so that the orders are not allocated reasonably, the delivery timeout is serious, and the delivery efficiency is reduced.
The quantity of the orders is the quantity of the orders which are not delivered by the distributor currently; the dispatch timeout is when the dispatcher has not reached the specified time.
In view of the above drawbacks, in this embodiment, an order processing method is provided, where an order that is not delivered by a delivery person and an order to be added are used as a current order, feature extraction and splicing are performed on obtained order data and delivery data of the current order, and current objective data representing environmental information and non-human factors where the current delivery is performed, so as to obtain a current order feature vector based on multiple dimensions, and an timeout prediction is performed on the obtained current order feature vector based on multiple dimensions by using a preset timeout prediction model, so as to obtain timeout information of the current order. According to the technical scheme, the pre-estimated timeout information for guiding whether the order to be appended is appended to the current order of the delivery personnel (the order which is not delivered and the order to be appended of the delivery personnel) can be accurately obtained by utilizing the multidimensional information of the representative service scene of the current order and the preset timeout prediction model, so that the order quantity distributed to each delivery personnel is dynamically adjusted, and the delivery efficiency is improved.
The preset overtime prediction model represents the corresponding relation between the multidimensional order characteristic vector and the overtime information.
The current orders include orders that are not delivered by the delivery personnel and orders to be added.
Wherein, the order data comprises: at least one of order content data, merchant data, and user data.
Wherein the order content data includes: at least one of a delivery address, an order number, a time to place an order, a desired time, an ordered item.
Wherein the merchant data comprises: at least one of merchant address, merchant order time. For example, the merchant order time may be the merchant meal time.
Wherein the user data includes: at least one of user floor, user elevator information. For example, the user floor information may be that the user lives on the 6 th floor, and the user elevator information is no elevator.
Wherein the distribution data includes: a distributor level, a distribution timeout condition, a distribution vehicle. For example, the distributor rating may be: primary, intermediate, and advanced. The delivery timeout condition may be: the dispatching personnel overtime the day order data volume. The dispensing means may be: tricycle, electric motor car, etc., the embodiment of this disclosure is not limiting.
Wherein, the current objective data includes: at least one of environmental information and distribution period traffic congestion conditions. For example, the environmental information may be a current weather condition, a road condition, a natural disaster, a human disaster, and the like.
The objective data herein characterizes factors that are not considered to change and the disclosed embodiments are not limiting.
The preset overtime prediction model represents the corresponding relation between the multidimensional order characteristic vector and the overtime information. For example, the correspondence relationship may be in the form of a table, an index, or a function.
It should be noted that, in this embodiment, the specific embodiment forms of the order data, the distribution data, the current objective data and the corresponding relationship are not limited
In an optional implementation manner of this embodiment, as shown in fig. 2, after step S103, that is, after obtaining the estimated timeout information of the current order according to the current order feature vector and the preset timeout prediction model, the order processing method provided in the embodiment of the present disclosure further includes the following step S104:
and S104, if the estimated overtime information of the current order indicates that the order is not overtime, distributing the order to be added to the distribution personnel.
Correspondingly, if the estimated overtime information of the current order represents that the current order is overtime, the order to be added is not distributed to the distribution personnel.
In this embodiment, if the timeout information of the current order indicates that the order is not timed out, it is described that the order to be added is added to the delivery staff, and the delivery staff can deliver the order which is not delivered and the order to be added to the delivery staff on time. Based on this, the order to be added can be added to the delivery person. On the contrary, if the timeout information of the current order represents that the order is overtime, it indicates that the order to be added is added to the delivery personnel, and the delivery personnel cannot deliver at least one of the order which is not delivered and the order to be added to the delivery personnel on time. Based on this, the order to be added is not added to the delivery person.
In an optional implementation manner of this embodiment, as shown in fig. 3, the step S102, namely, after performing feature extraction and splicing on the order data, the distribution data, and the current objective data, obtaining a multi-dimensional current order feature vector, includes steps S201 to S204:
in step S201, performing feature extraction on current order data to obtain a first feature vector;
in step S202, feature extraction is performed on the distribution data to obtain a second feature vector;
in step S203, performing feature extraction on the current objective data to obtain a third feature vector;
in step S204, the first feature vector, the second feature vector, and the third feature vector are spliced to obtain a multi-dimensional current order feature vector.
In this embodiment, the feature extraction is performed on the order data, the delivery data, and the current objective data of the current order, so that the validity of the data can be improved, the calculation amount can be reduced, and the accuracy and the speed of obtaining the estimated timeout information of the current order can be improved.
The existing characteristic extraction mode can be adopted for carrying out characteristic extraction on the order data, the distribution data and the current objective data of the current order. For example, the order data, the delivery data and the current objective data are subjected to convolution processing by using a convolution neural network, so that the order data, the delivery data and the current objective data can be subjected to feature extraction, and effective dimension reduction can be performed. The convolutional neural network is a feed-forward neural network and consists of one or more convolutional layers and a top full-communication layer, and also comprises an associated weight and a pooling layer.
In addition, when the feature vector is spliced, the first feature vector, the second feature vector, and the third feature vector may be spliced in an arbitrary order. For example, a first feature vector is placed first, a second feature vector is spliced after the first feature vector, and a third feature vector is spliced after the second feature vector. The purpose of stitching is to combine a plurality of feature vectors into one feature vector.
It should be noted that, in the embodiment of the present disclosure, one distributor may correspond to many orders in one day, that is, for one distributor, within one objective condition, may correspond to feature vectors corresponding to a plurality of order data.
For example, as shown in fig. 4, assuming that the first feature vector includes order features and merchant features, the second feature vector is rider features, and the third feature vector is objective features, then one rider feature concatenates one objective feature, and n order features and n merchant features, where the order features and the merchant features correspond to each other one by one.
In an optional implementation manner of this embodiment, as shown in fig. 5, before the step S103, that is, before obtaining the estimated timeout information of the current order according to the order feature vector and the preset timeout prediction model, the method includes the step S105:
and S105, acquiring a preset timeout prediction model.
In this embodiment, the pre-estimated data basis of the pre-estimated timeout information of the current order is obtained by obtaining the preset timeout prediction model.
The preset timeout prediction model may be obtained based on deep learning.
In an optional implementation manner of this embodiment, as shown in fig. 6, the step S105 of obtaining the preset timeout prediction model includes steps S301 to S303:
s301, obtaining historical order data, historical distribution data, historical objective data and corresponding historical overtime information;
s302, respectively extracting and splicing the features of the historical order data, the historical distribution data and the historical objective data to obtain a multi-dimensional historical order feature vector;
s303, deep neural network model training is carried out by adopting the multi-dimensional historical order characteristic vector, the historical overtime information and the preset initial model, and a preset overtime prediction model is obtained.
In this embodiment, deep neural network training is performed by using historical order data, historical distribution data, historical objective data, and corresponding historical timeout information to obtain a preset timeout prediction model. The deep neural network can automatically extract, combine and convert characteristics, and can obtain extremely strong nonlinear fitting capacity through multiple nonlinear transformations, and can map any complex nonlinear relation. Has strong robustness, memory ability and strong learning ability. In addition, the deep neural network has the advantages of simple learning rule, high efficiency, strong plasticity, universality and easy expansion. Therefore, the preset timeout prediction model obtained by training in the embodiment can accurately predict the timeout of the order, so as to provide a calculation basis for obtaining accurate estimated timeout information of the current order.
The historical order data, the historical delivery data and the historical objective data refer to order data, delivery data and objective data of delivered orders. The historical timeout information characterizes whether the historical order has timed out and/or the length of time that has timed out. The historical order data, the historical delivery data, and the historical objective data are the same as those described above, and may be different from the above listed data, and are not described again here.
It should be noted that the deep neural network is one of deep learning, and deep learning is a branch of machine learning, and is an algorithm that attempts to perform high-level abstraction on data using multiple processing layers including complex structures or composed of multiple nonlinear transformations.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods.
Fig. 7 shows a block diagram of an order processing apparatus according to an embodiment of the present disclosure, which may be implemented as part or all of an electronic device by software, hardware, or a combination of both. As shown in fig. 7, the order processing apparatus includes:
a first obtaining module 601, configured to obtain order data, delivery data, and current objective data of a current order; the current objective data comprises environmental information or non-human factors of the current order during distribution, and the current order comprises an undelivered order and an order to be added of a distributor;
the extraction and splicing module 602 is configured to perform feature extraction and splicing on the order data, the distribution data and the current objective data respectively to obtain a multi-dimensional current order feature vector;
the prediction module 603 is configured to obtain estimated timeout information of the current order according to the current order feature vector and a preset timeout prediction model; the preset overtime prediction model represents the corresponding relation between the multidimensional order characteristic vector and the overtime information.
As mentioned above, various take-out services have been produced as the living standard of people increases. Such as fast food takeaway, supermarket merchandise takeaway, flower takeaway, etc. For take-away services, an upper limit on the amount of orders is typically set for each deliverer based on the maximum amount of orders that the deliverer who delivers take-away has historically not timed out each day. When the amount of orders for the delivery personnel on the day exceeds the upper limit, no more orders are added to the delivery personnel. However, the orders are only allocated to the delivery personnel according to the back order quantity, and complicated business scenarios such as the distance between two orders and the time period corresponding to the orders are not considered, so that the orders are not allocated reasonably, the delivery timeout is serious, and the delivery efficiency is reduced.
The quantity of the orders is the quantity of the orders which are not delivered by the distributor currently; the dispatch timeout is when the dispatcher has not reached the specified time.
In view of the above drawbacks, in this embodiment, an order processing apparatus is provided, where an order that is not delivered by a delivery person and an order to be added are taken as a current order, and a splicing module 602 performs feature extraction and splicing on order data and delivery data of the current order, which are acquired by a first acquisition module 601, and current objective data representing environmental information and non-human factors where the current order is currently delivered, so as to obtain a multidimensional current order feature vector, and a prediction module 603 performs timeout prediction on the obtained multidimensional current order feature vector by using a preset timeout prediction model, so as to obtain timeout information of the current order. According to the technical scheme, the pre-estimated timeout information for guiding whether the order to be appended is appended to the current order of the delivery personnel (the order which is not delivered and the order to be appended of the delivery personnel) can be accurately obtained by utilizing the multidimensional information of the representative service scene of the current order and the preset timeout prediction model, so that the order quantity distributed to each delivery personnel is dynamically adjusted, and the delivery efficiency is improved.
It should be noted that the order processing apparatus may be an order processing platform, and the embodiment of the disclosure is not limited.
The current orders include orders that are not delivered by the delivery personnel and orders to be added.
Wherein, the order data comprises: at least one of order content data, merchant data, and user data.
Wherein the order content data includes: at least one of a delivery address, an order number, a time to place an order, a desired time, an ordered item.
Wherein the merchant data comprises: at least one of merchant address, merchant order time. For example, the merchant order time may be the merchant meal time.
Wherein the user data includes: at least one of user floor, user elevator information. For example, the user floor information may be that the user lives on the 6 th floor, and the user elevator information is no elevator.
Wherein the distribution data includes: a distributor level, a distribution timeout condition, a distribution vehicle. For example, the distributor rating may be: primary, intermediate, and advanced. The delivery timeout condition may be: the dispatching personnel overtime the day order data volume. The dispensing means may be: tricycle, electric motor car, etc., the embodiment of this disclosure is not limiting.
Wherein, the current objective data includes: at least one of environmental information and distribution period traffic congestion conditions. For example, the environmental information may be a current weather condition, a road condition, a natural disaster, a human disaster, and the like.
The objective data herein characterizes factors that are not considered to change and the disclosed embodiments are not limiting.
The preset overtime prediction model represents the corresponding relation between the multidimensional order characteristic vector and the overtime information. For example, the correspondence relationship may be in the form of a table, an index, or a function.
It should be noted that, in this embodiment, the specific embodiment forms of the order data, the distribution data, the current objective data and the corresponding relationship are not limited
In an optional implementation manner of this embodiment, as shown in fig. 8, the order processing module further includes an additional module 604:
the appending module 604 is configured to assign the order to be appended to the delivery person if the estimated timeout information of the current order indicates that the current order is not timed out.
Correspondingly, the appending module 604 is further configured to not allocate the order to be appended to the delivery personnel if the estimated timeout information of the current order indicates that the current order is overtime.
In this embodiment, if the timeout information of the current order indicates that the order is not timed out, it is described that the order to be added is added to the delivery staff, and the delivery staff can deliver the order which is not delivered and the order to be added to the delivery staff on time. Based on this, the adding module 604 can add the order to be added to the delivery person. On the contrary, if the timeout information of the current order represents that the order is overtime, it indicates that the order to be added is added to the delivery personnel, and the delivery personnel cannot deliver at least one of the order which is not delivered and the order to be added to the delivery personnel on time. Based on this, the adding module 604 may not add the order to be added to the delivery person.
In an optional implementation manner of this embodiment, as shown in fig. 9, the extraction and concatenation module 602 includes:
the first extraction submodule 701 is configured to perform feature extraction on current order data to obtain a first feature vector;
a second extraction submodule 702 configured to perform feature extraction on the distribution data to obtain a second feature vector;
a third extraction sub-module 703, configured to perform feature extraction on the current objective data to obtain a third feature vector;
and the splicing submodule 704 is configured to splice the first feature vector, the second feature vector and the third feature vector to obtain a multi-dimensional current order feature vector.
In this embodiment, the first extraction sub-module 701, the second extraction sub-module 702, and the third extraction sub-module 703 respectively perform feature extraction on order data, delivery data, and current objective data of the current order, so that the validity of the data can be improved, the computation amount can be reduced, and the accuracy and speed of obtaining the estimated timeout information of the current order can be improved.
The existing characteristic extraction mode can be adopted for carrying out characteristic extraction on the order data, the distribution data and the current objective data of the current order. For example, the order data, the delivery data and the current objective data are subjected to convolution processing by using a convolution neural network, so that the order data, the delivery data and the current objective data can be subjected to feature extraction, and effective dimension reduction can be performed.
In addition, the stitching sub-module 704 may stitch the first feature vector, the second feature vector, and the third feature vector in an arbitrary order when performing the feature vector stitching. For example, a first feature vector is placed first, a second feature vector is spliced after the first feature vector, and a third feature vector is spliced after the second feature vector. The purpose of stitching is to combine a plurality of feature vectors into one feature vector.
It should be noted that, in the embodiment of the present disclosure, one distributor may correspond to many orders in one day, that is, for one distributor, within one objective condition, may correspond to feature vectors corresponding to a plurality of order data.
For example, as shown in fig. 4, assuming that the first feature vector includes order features and merchant features, the second feature vector is rider features, and the third feature vector is objective features, then one rider feature concatenates one objective feature, and n order features and n merchant features, where the order features and the merchant features correspond to each other one by one.
In an optional implementation manner of this embodiment, as shown in fig. 10, the order processing module further includes a second obtaining module 605:
a second obtaining module 605 configured to obtain a preset timeout prediction model.
In this embodiment, the second obtaining module 605 obtains the preset timeout prediction model, which is a basis for obtaining the prediction data of the prediction timeout information of the current order.
The preset timeout prediction model may be obtained based on deep learning.
In an optional implementation manner of this embodiment, as shown in fig. 11, the second obtaining module 605 includes:
an acquisition sub-module 801 configured to acquire historical order data, historical delivery data, historical objective data, and corresponding historical timeout information;
the extraction and splicing submodule 802 is configured to perform feature extraction and splicing on the historical order data, the historical distribution data and the historical objective data respectively to obtain a multi-dimensional historical order feature vector;
the generating submodule 803 is configured to perform deep neural network model training by using the multi-dimensional historical order feature vector, the historical timeout information, and the preset initial model, so as to obtain a preset timeout prediction model.
In this embodiment, the extraction and concatenation sub-module 802 is used to perform feature extraction and concatenation on the historical order data, the historical delivery data, the historical objective data, and the corresponding historical timeout information, which are acquired by the acquisition sub-module 801, and the generation sub-module 803 is used to perform deep neural network training on the extraction and concatenation results, so as to obtain a preset timeout prediction model. The deep neural network can automatically extract, combine and convert characteristics, and can obtain extremely strong nonlinear fitting capacity through multiple nonlinear transformations, and can map any complex nonlinear relation. Has strong robustness, memory ability and strong learning ability. In addition, the deep neural network has the advantages of simple learning rule, high efficiency, strong plasticity, universality and easy expansion. Therefore, the preset timeout prediction model obtained by training in the embodiment can accurately predict the timeout of the order, so as to provide a calculation basis for obtaining accurate estimated timeout information of the current order.
The historical order data, the historical delivery data and the historical objective data refer to order data, delivery data and objective data of delivered orders. The historical timeout information characterizes whether the historical order has timed out and/or the length of time that has timed out. The historical order data, the historical delivery data, and the historical objective data are the same as those described above, and may be different from the above listed data, and are not described again here.
It should be noted that the deep neural network is one of deep learning, and deep learning is a branch of machine learning, and is an algorithm that attempts to perform high-level abstraction on data using multiple processing layers including complex structures or composed of multiple nonlinear transformations.
The present disclosure also discloses an electronic device, fig. 12 shows a block diagram of an electronic device according to an embodiment of the present disclosure, and as shown in fig. 12, the electronic device 1100 includes a memory 1101 and a processor 1102; wherein the content of the first and second substances,
the memory 1101 is used to store one or more computer instructions that are executed by the processor 1102 to implement any of the method steps described above.
It should be noted that the electronic device may be an order processing platform, such as a background server of an order application, and the embodiments of the present disclosure are not limited thereto.
FIG. 13 is a block diagram of a computer system suitable for use in implementing an order processing method according to an embodiment of the present disclosure.
As shown in fig. 13, the computer system 1200 includes a Central Processing Unit (CPU)1201, which can execute various processes in the above-described embodiments according to a program stored in a Read Only Memory (ROM)1202 or a program loaded from a storage section 1208 into a Random Access Memory (RAM) 1203. In the RAM1203, various programs and data necessary for the operation of the system 1200 are also stored. The CPU1201, ROM1202, and RAM1203 are connected to each other by a bus 1204. An input/output (I/O) interface 1205 is also connected to bus 1204.
The following components are connected to the I/O interface 1205: an input section 1206 including a keyboard, a mouse, and the like; an output portion 1207 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 1208 including a hard disk and the like; and a communication section 1209 including a network interface card such as a LAN card, a modem, or the like. The communication section 1209 performs communication processing via a network such as the internet. A driver 1210 is also connected to the I/O interface 1205 as needed. A removable medium 1211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 1210 as necessary, so that a computer program read out therefrom is mounted into the storage section 1208 as necessary.
In particular, the above described methods may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a medium readable thereby, the computer program comprising program code for performing the order processing method. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 1209, and/or installed from the removable medium 1211.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, a program segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present disclosure may be implemented by software or hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.
As another aspect, the present disclosure also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus in the above-described embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the present disclosure.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (14)
1. An order processing method, comprising:
acquiring order data, distribution data and current objective data of a current order of a distributor; the current objective data comprises environmental information or non-human factors of current order distribution, and the current order comprises an undelivered order and an order to be added of the distribution personnel;
respectively extracting and splicing the features of the order data of the current order of the distribution personnel, the distribution data and the current objective data to obtain a multi-dimensional current order feature vector;
obtaining estimated overtime information of the current order of the distribution personnel according to the current order feature vector and a preset overtime prediction model;
and if the estimated overtime information of the current order of the delivery personnel indicates that the current order is not overtime, distributing the order to be added to the delivery personnel.
2. The method of claim 1,
the order data includes: at least one of order content data, merchant data, and user data;
the delivery data includes: at least one of a distributor level, a distribution timeout condition, a distribution vehicle;
the current objective data includes: at least one of environmental information and distribution period traffic congestion conditions.
3. The method according to claim 1 or 2, wherein the obtaining a multi-dimensional current order feature vector after performing feature extraction and splicing on the order data, the delivery data and the current objective data respectively comprises:
performing feature extraction on the order data to obtain a first feature vector;
performing feature extraction on the distribution data to obtain a second feature vector;
extracting features of the current objective data to obtain a third feature vector;
and splicing the first feature vector, the second feature vector and the third feature vector to obtain the multi-dimension-based current order feature vector.
4. The method of claim 1, wherein before obtaining the estimated timeout information of the current order of the delivery person according to the current order eigenvector and a preset timeout prediction model, the method further comprises:
and acquiring the preset timeout prediction model.
5. The method of claim 4, wherein the obtaining the preset timeout prediction model comprises:
acquiring historical order data, historical distribution data, historical objective data and corresponding historical overtime information;
respectively extracting and splicing the features of the historical order data, the historical distribution data and the historical objective data to obtain a multi-dimensional historical order feature vector;
and performing deep neural network model training by adopting the multi-dimension-based historical order characteristic vector, the historical overtime information and a preset initial model to obtain the preset overtime prediction model.
6. The method of claim 2,
the order content data includes: at least one of a delivery address, an order number, an order placement time, a desired time, an ordered item;
the merchant data includes: at least one of a merchant address and a merchant order-giving time;
the user data includes: at least one of user floor, user elevator information.
7. An order processing apparatus, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is configured to acquire order data, delivery data and current objective data of a current order of a delivery person; the current objective data comprises environmental information or non-human factors of current order distribution, and the current order comprises an undelivered order and an order to be added of the distribution personnel;
the extraction and splicing module is configured to respectively perform feature extraction and splicing on the order data of the current order of the distribution personnel, the distribution data and the current objective data to obtain a multi-dimensional current order feature vector;
the prediction module is configured to obtain estimated timeout information of the current order of the delivery personnel according to the current order feature vector and a preset timeout prediction model;
and the adding module is configured to allocate the order to be added to the delivery personnel if the estimated timeout information of the current order of the delivery personnel indicates that the current order is not overtime.
8. The apparatus of claim 7, wherein the order data comprises: at least one of order content data, merchant data, and user data;
the delivery data includes: at least one of a distributor level, a distribution timeout condition, a distribution vehicle;
the current objective data includes: at least one of environmental information and distribution period traffic congestion conditions.
9. The apparatus of claim 7 or 8, the extraction stitching module comprising:
the first extraction submodule is configured to perform feature extraction on the order data to obtain a first feature vector;
the second extraction submodule is used for extracting the characteristics of the distribution data to obtain a second characteristic vector;
the third extraction submodule is used for extracting the features of the current objective data to obtain a third feature vector;
and the splicing submodule is configured to splice the first feature vector, the second feature vector and the third feature vector to obtain the multi-dimensional current order feature vector.
10. The apparatus of claim 7, further comprising a second acquisition module to:
the second obtaining module is configured to obtain the preset timeout prediction model.
11. The apparatus of claim 10, wherein the second obtaining module comprises:
the acquisition submodule is configured to acquire historical order data, historical distribution data, historical objective data and corresponding historical timeout information;
the extraction and splicing sub-module is configured to respectively perform feature extraction and splicing on the historical order data, the historical distribution data and the historical objective data to obtain a multi-dimensional historical order feature vector;
and the generation submodule is configured to perform deep neural network model training by adopting the multi-dimension-based historical order feature vector, the historical timeout information and a preset initial model to obtain the preset timeout prediction model.
12. The apparatus of claim 8,
the order content data includes: at least one of a delivery address, an order number, an order placement time, a desired time, an ordered item;
the merchant data includes: at least one of a merchant address and a merchant order-giving time;
the user data includes: at least one of user floor, user elevator information.
13. An electronic device comprising a memory and a processor; wherein the content of the first and second substances,
the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method steps of any of claims 1-6.
14. A computer-readable storage medium having stored thereon computer instructions, characterized in that the computer instructions, when executed by a processor, carry out the method steps of any of claims 1-6.
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