CN111080207A

CN111080207A - Order processing method, device, equipment and storage medium

Info

Publication number: CN111080207A
Application number: CN201911366358.5A
Authority: CN
Inventors: 余威; 陈思锦
Original assignee: Beijing Missfresh Ecommerce Co Ltd
Current assignee: Beijing Missfresh Ecommerce Co Ltd
Priority date: 2019-12-26
Filing date: 2019-12-26
Publication date: 2020-04-28

Abstract

The application discloses an order processing method, an order processing device, order processing equipment and a storage medium, and belongs to the technical field of computers. The method comprises the steps of obtaining a plurality of orders to be processed, determining at least one target distribution task based on position information carried by each order and inter-order distribution time length among the orders, wherein one target distribution task is used for indicating at least one target order to be distributed in one distribution process, determining an order set based on commodity matching degree among the orders, and distributing the order set to a target order picker, wherein the commodity matching degree between commodities included in any order in the order set and commodities included in the target orders meets target conditions. In the process, the orders are combined, the distribution task is determined, the distribution efficiency is improved, the orders are collected based on the commodity information in each order, the order collection is carried out once, the order collection efficiency is improved, and the order processing efficiency is improved in the aspects of distribution and order collection.

Description

Order processing method, device, equipment and storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to an order processing method, apparatus, device, and storage medium.

Background

With the development of e-commerce industry and the popularization of online shopping, the order quantity of each merchant is larger and larger, and the order processing pressure is also larger and larger, so that for e-commerce, how to improve the efficiency of each link in the order processing process, for example, the picking speed and the distribution speed are improved, so as to reduce the order processing time, and to deliver goods in time is an important research direction at present.

Disclosure of Invention

The embodiment of the application provides an order processing method, an order processing device, order processing equipment and a storage medium, and order processing efficiency can be improved. The technical scheme is as follows:

in one aspect, an order processing method is provided, and the method includes:

acquiring a plurality of orders to be processed;

determining at least one target distribution task based on the position information carried by each order and the inter-order distribution time length among the orders, wherein the target distribution task is used for indicating at least one target order to be distributed in one distribution process;

determining an order set based on the commodity matching degree between the commodities included in any one of the target orders and the commodities included in each one of the orders, wherein the commodity matching degree between the commodities included in any one of the orders in the order set and the commodities included in the target orders meets target conditions;

the set of orders is assigned to the target picker based on the item information for each order in the set of orders.

In a possible implementation manner, the determining at least one target delivery task based on the location information carried by each of the orders and the inter-order delivery time length between each of the orders includes:

determining at least one candidate delivery task based on the position information of each order and the delivery time length among the orders, wherein one candidate delivery task corresponds to at least one order;

determining the task remaining time of each candidate delivery task based on the delivery time of the order delivered by each candidate delivery task;

determining the candidate distribution tasks with the task remaining duration less than the duration threshold as the target distribution tasks;

and determining at least one target order based on at least one order corresponding to each target delivery task.

In one possible implementation, before determining at least one candidate delivery task based on the location information of each of the orders and the inter-order delivery duration, the method further includes:

constructing a characteristic vector of each order based on the position information, the commodity information, the delivery time, the environment information corresponding to the position information and the distance information among the orders of each order;

inputting the characteristic vector of each order into an order distribution time prediction model, and determining the inter-order distribution time length between each order by the order distribution time prediction model based on each characteristic vector.

In one possible implementation manner, the determining the at least one target order based on the at least one order corresponding to each target delivery task includes:

determining each order corresponding to each target distribution task as a first candidate order;

determining at least one order matched with each first candidate order in the target delivery task as a second candidate order based on the position information and the weight information of each order;

determining at least one of the first candidate order and the second candidate order as the target order.

In one possible implementation manner, the determining, as the second candidate order, at least one of the orders that matches the first candidate order in the target delivery task based on the location information and the weight information of each of the orders includes:

determining a distribution time length change value corresponding to the target distribution task when any order is added to the target distribution task based on the position information of any order;

determining the position matching degree of each order and each first candidate order based on the position information of each order and the position information of each first candidate order;

and taking at least one order matched with each first candidate order as the second candidate order based on the distribution time length change value, the position matching degree and the weight information.

In a possible implementation manner, after determining an order set based on a product matching degree between the product included in any of the target orders and the product included in each of the orders, the method further includes:

and determining a picking sequence and a picking path corresponding to each commodity in any order based on at least one commodity identifier carried by any order in the order set and the storage position information of each commodity.

In one possible implementation, the assigning the set of orders to the target picker based on the item information for each order in the set of orders includes:

and determining the target order picker corresponding to the order set based on at least one commodity identification carried by each order in the order set, the storage position information of each commodity and the historical order picking data of each order picker.

In one aspect, an order processing apparatus is provided, the apparatus comprising:

the acquisition module is used for acquiring a plurality of orders to be processed;

the distribution task determining module is used for determining at least one target distribution task based on the position information carried by each order and the inter-order distribution time length among the orders, wherein the target distribution task is used for indicating at least one target order to be distributed in one distribution process;

the order set determining module is used for determining an order set based on the commodity matching degree between the commodities included in any one of the target orders and the commodities included in each one of the orders, wherein the commodity matching degree between the commodities included in any one of the orders in the order set and the commodities included in the target orders meets target conditions;

and the distribution module is used for distributing the order set to the target order picker based on the commodity information of each order in the order set.

In one possible implementation, the delivery task determination module is to:

In one possible implementation, the apparatus further includes:

the vector construction module is used for constructing a characteristic vector of each order based on the position information, the commodity information, the distribution time, the environment information corresponding to the position information and the distance information among the orders of each order;

and the time length determining module is used for inputting the characteristic vector of each order into the order distribution time prediction model, and determining the inter-order distribution time length among the orders by the order distribution time prediction model based on each characteristic vector.

In one possible implementation, the delivery task determination module is to:

In one possible implementation, the order set determination module is to:

In one possible implementation, the allocation module is configured to:

In one aspect, a computer device is provided that includes one or more processors and one or more memories having at least one program code stored therein, the at least one program code being loaded and executed by the one or more processors to perform the operations performed by the order processing method.

In one aspect, a computer-readable storage medium having at least one program code stored therein is provided, the at least one program code being loaded and executed by a processor to implement the operations performed by the order processing method.

According to the technical scheme provided by the embodiment of the application, a plurality of orders to be processed are obtained, at least one target distribution task is determined based on position information carried by each order and inter-order distribution time length among the orders, the target distribution task is used for indicating at least one target order to be distributed in one distribution process, an order set is determined based on commodity matching degree between commodities included in any target order and commodities included in each order, the commodity matching degree between the commodities included in any order in the order set and the commodities included in the target orders meets target conditions, and the order set is distributed to target pickers based on commodity information of each order in the order set. In the process, the orders are combined, the distribution task is determined, the distribution efficiency is improved, the orders are collected based on the commodity information in each order, the order collection is carried out once, the order picking efficiency is improved, and the order processing efficiency is improved in the aspects of distribution and order picking.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a block diagram of an order processing system according to an embodiment of the present application;

fig. 2 is a flowchart of an order processor according to an embodiment of the present application;

FIG. 3 is a schematic diagram of an order processing process provided by an embodiment of the present application;

fig. 4 is a schematic diagram of an order processing apparatus according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of a server according to an embodiment of the present application.

Detailed Description

To make the purpose, technical solutions and advantages of the present application clearer, the following will describe embodiments of the present application in further detail with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The terms "first," "second," and the like in this application are used for distinguishing between similar items and items that have substantially the same function or similar functionality, and it should be understood that "first," "second," and "nth" do not have any logical or temporal dependency or limitation on the number or order of execution.

In order to facilitate understanding of the technical processes of the embodiments of the present application, some terms referred to in the embodiments of the present application are explained below:

inter-order delivery duration: refers to the length of time required for a distributor to arrive at the shipping address indicated by another order from the shipping address of one order.

SKU (Stock Keeping Unit): it is the basic unit of stock in and out metering, and can be in units of pieces, boxes, etc. A SKU may correspond to an individual item, which for a good may be referred to as an individual item when its brand, model, configuration, grade, suit, packaging capacity, unit, date of manufacture, shelf life, use, price, place of manufacture, etc. attributes differ from those of other goods.

Fig. 1 is a block diagram of an order processing system according to an embodiment of the present application. The order processing system 100 includes: a terminal 110 and an order processing platform 140.

The terminal 110 is connected to the order processing platform 110 through a wireless network or a wired network. The terminal 110 may be at least one of a smart phone, a desktop computer, a tablet computer, an e-book reader, an MP3 player (Moving Picture Experts group Audio Layer III, motion Picture Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts group Audio Layer IV, motion Picture Experts compression standard Audio Layer 4), and a laptop computer. The terminal 110 may be installed and run with an application program that supports order processing. The application may be an e-commerce type application or the like.

The terminal 110 is connected to the order processing platform 140 through a wireless network or a wired network.

The order processing platform 140 includes at least one of a server, a plurality of servers, a cloud computing platform, and a virtualization center. Order processing platform 140 is used to provide background services for applications that support order processing. In one possible implementation, the order processing platform 140 undertakes primary processing and the terminal 110 undertakes secondary processing; or, the order processing platform 140 undertakes the secondary processing work, and the terminal 110 undertakes the primary processing work; alternatively, the order processing platform 140 or the terminal 110, respectively, may undertake the processing separately.

In one possible implementation, the order processing platform 140 includes: the system comprises an access server, an order processing server and a database. The access server is used to provide access services for the terminal 110. The order processing server is used for providing background services related to order processing. The order processing server can be one or more. When the order processing servers are multiple, at least two order processing servers exist for providing different services, and/or at least two order processing servers exist for providing the same service, for example, providing the same service in a load balancing manner, which is not limited in the embodiment of the present application. The order processing server can be provided with an order distribution time prediction model, a distribution task construction model, a picking task distribution model and the like.

The terminal 110 may be generally referred to as one of a plurality of terminals, and the embodiment is only illustrated by the terminal 110.

Those skilled in the art will appreciate that the number of terminals described above may be greater or fewer. For example, the number of the terminals may be only one, or several tens or hundreds, or more, and in this case, the order processing system further includes other terminals. The number of terminals and the type of the device are not limited in the embodiments of the present application.

Fig. 2 is a flowchart of an order processor according to an embodiment of the present application, where the method may be applied to the terminal or the server, and both the terminal and the server may be regarded as a computer device, and therefore, the embodiment of the present application is described based on the computer device as an execution subject, referring to fig. 2, the method may specifically include the following steps:

201. a computer device obtains a plurality of orders to be processed.

The computer device can receive orders triggered by various users as orders to be processed. For example, a terminal used by each user may be installed and run with a target application program, where the target application program may be an e-commerce application program, the target application program may support order creation, the terminal may send an order creation instruction triggered by the user to a server corresponding to the target application program, the server performs an order creation step based on the order creation instruction, and sends a created order to the computer device. Of course, the computer device and the server corresponding to the target application program may also be the same device, which is not limited in this embodiment of the application.

In one possible implementation, the computer device may group a plurality of orders received within the target period into a pending order group. The target period may be set by a developer, and is not limited in this embodiment of the application.

202. And the computer equipment determines inter-order delivery time length among the orders based on the position information, the commodity information and the delivery time carried by the orders.

In one possible implementation, the computer device may retrieve orders from a pending order set and determine inter-order delivery durations between the orders. Each order may carry location information, commodity information, delivery time, etc. The position information can be a longitude value and a latitude value of a receiving address in an order; the delivery time may be a commodity delivery time set by a user; the goods information may include goods identification, quantity, weight, category, etc. of the goods selected by the user.

In one possible implementation, the computer device may determine the inter-order delivery duration based on an order delivery time prediction model, and specifically may include the following steps:

step one, the computer device may construct a feature vector of each order based on the location information of each order, the commodity information, the delivery time, the environment information corresponding to the location information, and the distance information between each order.

The environment information corresponding to the location information may include real-time weather at the receiving address, and of course, may also include a traffic congestion degree at the receiving address; the distance information between orders may include manhattan distance between orders, navigation distance between orders. The present embodiment does not limit the specific contents of the environment information and the distance information.

In this embodiment, the computer device may determine the volume of the commodity, the belonging production line, and the like based on the commodity identification, the quantity, the weight, and the category in the commodity information. In a possible implementation manner, different production lines may correspond to different commodity manufacturing durations, for example, different types of commodities such as restaurants and fresh products belong to different production lines, the corresponding commodity manufacturing durations are different, and the commodity manufacturing durations corresponding to the production lines may be set by developers, for example, average manufacturing durations of the commodities such as fresh products and restaurants may be counted to determine the commodity manufacturing durations corresponding to the production lines, which is not limited in this embodiment of the present application.

In one possible implementation manner, the computer device may construct the feature vector by using information, such as a longitude value, a latitude value, production line information, a commodity type, a commodity quantity, a commodity weight, a commodity volume, a distribution time, real-time weather, a manhattan distance between orders, a navigation distance between orders, and the like, corresponding to one order as a plurality of features of the one order. In one possible implementation, the computer device needs to convert the non-numeric characters corresponding to the respective features into numeric characters recognizable by the order delivery time prediction model, and generate the feature vector based on the numeric characters corresponding to the respective features. For example, the computer device may convert each non-numeric character into a numeric character based on a LabelEncoder (Label coding) and numeric mapping method. Specifically, taking the example of converting the number of commodities, the weight of the commodities, and the real-time weather information into numeric characters, where the number of the commodities may be 3, the weight of the commodities may be 2kg, and the real-time weather is snow and rain, the computer device may map the number of the commodities and the weight of the commodities to 3 and 2, respectively, and allocate a numeric number to each weather type based on a tag coding method, for example, "snow and rain" may correspond to the numeric number 5, and then the three items of the quantity of the commodities, the weight of the commodities, and the real-time weather may be mapped to a vector (3, 2, 5).

It should be noted that the above description of the method for converting a non-numeric character string into a numeric character string and the description of the method for constructing a feature vector are only exemplary descriptions, and the embodiment of the present application is not limited to which character conversion method and which feature vector construction method are specifically adopted.

Of course, the computer device may also construct a feature matrix based on information in the order, and represent features of the order through the feature matrix, and the embodiment of the present application does not limit a specific representation manner of features of the order.

Step two, the computer device can input the feature vector of each order into an order distribution time prediction model, and the order distribution time prediction model determines the inter-order distribution time length among the orders based on each feature vector.

The order distribution time prediction model may be a Machine learning model constructed based on an XGboost (eXtreme Gradient enhancement) algorithm and a linear regression algorithm, may also be a Machine learning model constructed based on a Light Gradient Boosting Machine (Light Gradient Boosting tree) algorithm and a linear regression algorithm, and of course, may also be a model constructed based on other algorithms, which is not limited in the embodiment of the present application. Taking the model for predicting order delivery time as an example of a model constructed based on an XGboost algorithm and a linear regression algorithm, in one possible implementation manner, the computer device may construct a tree structure based on characteristics of each order, each tree structure may include at least one leaf node, the computer device may calculate a score corresponding to each leaf node, and predict inter-order delivery time length between two orders based on the score corresponding to each leaf node.

It should be noted that the above description of determining the inter-order delivery time length by using the order delivery time prediction model is only an exemplary description, and the embodiment of the present application does not limit which time prediction model is specifically used and a specific method for determining the inter-order delivery time length.

In one possible implementation, in the training phase of the order delivery time prediction model, the computer device may filter out features participating in predicting the delivery time between orders, and filter out useless features, so as to improve the accuracy of the model prediction result. For example, the computer device may combine various types of information in an order, that is, combine features of a corresponding order to obtain a plurality of feature combinations, predict inter-order delivery duration corresponding to one feature combination through a decision tree, calculate an error value between a prediction result and a correct result of the decision tree, retain at least one feature combination with a smaller error value, and use a feature in another feature combination as a useless feature. It should be noted that the above description of the filtering method for the useless features is only an exemplary description, and the embodiment of the present application does not limit which method is specifically used to filter the useless features.

203. The computer device determines at least one target delivery task.

In an embodiment of the present application, the computer device may determine at least one candidate delivery task based on the location information of each order and the inter-order delivery duration, where the at least one candidate delivery task corresponds to the at least one order. In one possible implementation, the computer device may determine the at least one candidate delivery task based on a delivery path planning model, for example, the computer device may determine vertices based on the location information of each order and the location information of the delivery warehouse, determine a weight between two vertices based on an inter-order delivery duration, combine orders without timing out the orders, and determine the at least one candidate delivery task such that the delivery duration corresponding to each candidate delivery task may be less than a target threshold. The target threshold may be set by a developer, and the delivery route planning model may be a TSP (travel salesman problem) model.

It should be noted that the above description of determining the candidate delivery tasks based on the delivery path planning model is only an exemplary description, and the embodiment of the present application does not limit which delivery path planning model is specifically applied and a specific method for determining the candidate delivery tasks.

In one possible implementation, the computer device may determine the task remaining time for each of the candidate delivery tasks based on the delivery time of the order delivered by each of the candidate delivery tasks, for example, the computer device may calculate the order remaining time for each of the candidate delivery tasks, and use the shortest order remaining time as the task remaining time for the one candidate delivery task. The computer device may determine the candidate delivery task with the task remaining duration less than the duration threshold as the target delivery task. The duration threshold may be set by a developer, and this is not limited in this embodiment of the application.

204. The computer equipment determines at least one target order based on at least one order corresponding to each target delivery task.

In a possible implementation manner, the process of determining the target order may specifically include the following steps:

step one, the computer device may determine each order corresponding to each target delivery task as a first candidate order.

And step two, the computer equipment can determine at least one order matched with each first candidate order in the target delivery task as a second candidate order based on the position information and the weight information of each order.

The order may belong to the same pending order group as the first candidate order, or may belong to different pending order groups.

In one possible implementation manner, the computer device may determine, based on the location information of any one of the orders, a delivery duration change value corresponding to the target delivery task when any one of the orders is added to the target delivery task; determining the position matching degree of each order and each first candidate order based on the position information of each order and the position information of each first candidate order, namely the forward degree of each order and each first candidate order; and then taking at least one order matched with each first candidate order as the second candidate order based on the distribution time length change value, the position matching degree and the weight information. Wherein the weight information is positively correlated with the urgency of the order, and in one possible implementation, the weight information may be expressed as a product of a reciprocal of a remaining duration of the order and a transport pressure coefficient in the delivery warehouse; the position matching degree may be obtained by encoding a latitude and longitude value indicated by the position information of the order through a GeoHash (Ceo hash) algorithm, and determining the forward degree between each order and each first candidate order based on the encoding result, which is of course, the position matching degree may also be determined based on other algorithms, which is not limited in this embodiment of the present application.

In a possible implementation manner, the above-mentioned process of determining the second candidate order may be implemented based on a bipartite graph and a minimum cost flow algorithm, the computer device may construct a bipartite graph of orders and delivery tasks based on each order and each target delivery task, in the bipartite graph of orders and delivery tasks, one order and one target delivery task may correspond to a path weight, and the path weight may be determined by the computer device through weighted summation of the delivery duration change value, the position matching degree, and the weight information. The computer device may solve the bipartite graph of the order and the delivery tasks based on a minimum cost flow algorithm, match the order with the target delivery tasks based on the respective path weights, and minimize a total of the path weights corresponding globally, thereby determining the order matched with the respective target tasks, that is, determining the order matched with the respective first candidate orders in the target delivery tasks as the second candidate orders. Of course, the computer device may also solve the bipartite graph based on other algorithms, which is not limited in the embodiment of the present application.

Step three, the computer device can determine at least one first candidate order and at least one second candidate order as the target order.

It should be noted that, in the above step 202, step 203, and step 204, at least one target distribution task is determined based on the position information carried by each order and the inter-order distribution time length between each order, and one target distribution task is used to indicate at least one target order to be distributed in one distribution process. The distribution time length is determined based on the position information of each order, each order is combined, distribution tasks are further determined, the time consumed by each distribution task can be reduced, the distribution efficiency is improved, the distribution tasks with urgent time limits are screened out to be used as target distribution tasks, the following integrated single step is executed based on each order in the target distribution tasks, the processing efficiency of urgent orders is improved, and the orders can be guaranteed to be completed on time.

205. The computer equipment determines an order set based on the commodity matching degree between the commodity included in any target order and the commodity included in each order, wherein the commodity matching degree between the commodity included in any order in the order set and the commodity included in the target order meets the target condition.

In the embodiment of the present application, an example of calculating the matching degree of the goods between one target order and one pending order is described. In a possible implementation manner, first, the computer device may obtain similarity values between the commodities included in the one to-be-processed order and the commodities included in the target order, for example, correlation scores between each commodity in the to-be-processed order and each commodity in the target order may be calculated based on a BM25(best match) algorithm, and the correlation scores are weighted and summed to obtain a similarity value between the two orders. Then, the computer device may determine a bin matching degree value between the two orders based on the bin information of the goods included in the one to-be-processed order and the bin information of the goods included in the target order, for example, the computer device may plan a picking path of each order based on an a-x algorithm, determine a forward bin set, and determine a bin matching degree value between the two orders based on the matching degree between the bin information of the goods included in the to-be-processed order and the bin in the forward bin set. The storage position information may be a storage position number of a storage position where the commodity is located. Finally, the computer device may perform weighted summation on the similarity value and the stock location matching degree value to obtain the commodity matching degree between the two orders, where weights corresponding to the similarity value and the stock location matching degree value may be set by a developer, which is not limited in the embodiment of the present application.

In the embodiment of the present application, an execution sequence of obtaining similarity values between the orders and then obtaining the bin matching degree values between the orders is used for description, but in some possible embodiments, the step of obtaining the bin matching degree values between the orders may be executed first, and then the step of obtaining the similarity values between the orders is executed, or the two steps are executed simultaneously, which is not limited in this application.

When the matching degree of the goods between the target order and any order meets the target condition, the any order and the target order can be collected, that is, the any order is added to the order collection corresponding to the target order. The target condition may be set by a developer, and is not limited in this embodiment of the application. For example, the order with the largest matching degree of the goods may be set to be collected with the target order, or at least one order with a matching degree greater than a threshold value of the matching degree may be set to be collected with the target order. Wherein the threshold value of the matching degree can be set by a developer.

206. The computer device assigns a set of orders to the targeted picker based on the item information for each order in the set of orders.

And the computer equipment determines a target order picker corresponding to the order set based on at least one commodity identification carried by each order in the order set, the storage position information of each commodity and historical order picking data of each order picker. For example, the assignment of picking tasks may be based on the gender, height, weight and location of the goods inventory of the individual picker. In one possible implementation, the computer device may allocate a set of orders based on an order allocation model. Taking the order distribution model as a model constructed based on a neural network and a bipartite graph matching algorithm as an example for explanation, in the embodiment of the application, an order set may correspond to a picking task, the neural network may be a model trained based on historical picking data of pickers and figure information of the pickers, the computer device may input data such as commodity identification, stock level information and the like into the neural network model, and the neural network predicts picking time lengths required by each picker to complete each picking task. The historical picking data of the picker can include the picking time of the picker to execute each picking task, the picking time of each commodity, and the like, and the portrait information of the picker can include the sex, the height and the like of the picker. The computer device can construct a bipartite graph of order sets and order pickers, one order set and one order picker can correspond to one edge, the weight of each edge can be the reciprocal of the order picking duration corresponding to the order picker, the computer device can solve the bipartite graph of the order sets and the order picking tasks based on a KM (Kuhn-Munkras, Kuen-Monklas) algorithm, the order picking tasks and the order pickers are matched based on the weight of each edge, and when the total weight of the overall situation corresponding to any matching scheme is the maximum, the order pickers corresponding to the order sets in any matching scheme are determined to be target order pickers.

Of course, the computer device may also solve the bipartite graph based on other algorithms, which is not limited in the embodiment of the present application.

In the embodiment of the present application, after the computer device determines the orders included in one order set, the picking order and picking path of each item in the order set can be planned. In one possible implementation manner, the computer device may determine a picking order and a picking path corresponding to each item in any order based on at least one item identifier carried by any order in the order set and the storage location information of each item. In a possible implementation manner, the computer device may determine the picking order and the picking path corresponding to the order set based on two layers of a algorithms, and specifically, the computer device may solve the shortest picking path of the picking task through a first layer a algorithm based on the information of the positions in the distribution warehouse and the information of the positions corresponding to the respective commodities in the order set, determine the picking order corresponding to the order set, and determine the traveling route from the current commodity position to the next commodity position through a second layer a algorithm, that is, determine the picking path.

In the embodiment of the present application, it is necessary to digitize information related to order processing, such as delivery warehouse information, delivery information, and the like, to take the delivery warehouse information as an example, attributes of the corresponding positions of the SKUs may be added to the SKUs, the number of the position in each position in the delivery warehouse may be stored in the computer device, the computer device may further store average production time, historical picking data, and the like corresponding to each SKU, and the historical picking data may include picking time of each picking task, an identity of a picker, a product identifier included in the picking task at this time, a product quantity, a product volume, a product weight, and the like. Taking the example of performing data on the delivery information, the data such as the delivery duration and whether to fulfill on time of each delivery task may be stored in the computer device, the computer device may further store the delivery time, the delivery distance, the weather, the identification of the deliverer, the order information of each order in the present delivery task, and the like of each delivery task, and the order information may include location information, SKU number, weight, volume, and the like. The computer device may perform order processing steps based on the digitized information.

Referring to fig. 3, fig. 3 is a schematic diagram of an order processing process provided in an embodiment of the present application, where the computer device may store received orders in an order pool 301 to be processed, each order may carry location information, delivery time, SKU information, and the like, the computer device may screen out a target order 304 based on an order delivery time prediction model 302, a delivery route planning model 303, and a minimum cost flow algorithm, determine a similarity value 305 between the target order and each order to be processed based on a BM25 algorithm, determine a bin matching degree value 306 between two orders based on an a x algorithm, perform weighted summation on the similarity value 305 and the bin matching degree value 306, complete an order collection 307, perform picking route planning on the order collection 307 based on a two-layer a x algorithm, and perform a neural network and a KM algorithm, a picker is assigned to the order set. The goods picking process in the delivery warehouse is optimized by combining deep learning, machine learning and graph theory algorithms. On one hand, the picking efficiency is improved through a picking order collection algorithm, a path planning algorithm, a matching algorithm and the like; on the other hand, the distribution efficiency of a distributor in the order distribution process is combined when picking the order, the optimization of the whole order processing process is realized, and the performance efficiency is improved to the maximum extent.

All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.

Fig. 4 is a schematic diagram of an order processing apparatus provided in an embodiment of the present application, and referring to fig. 4, the apparatus may include:

an obtaining module 401, configured to obtain multiple orders to be processed;

a distribution task determining module 402, configured to determine at least one target distribution task based on the location information carried in each order and inter-order distribution time length between the orders, where the target distribution task is used to indicate at least one target order to be distributed in a distribution process;

an order set determining module 403, configured to determine an order set based on a commodity matching degree between a commodity included in any one of the target orders and a commodity included in each of the orders, where the commodity matching degree between the commodity included in any one of the orders in the order set and the commodity included in the target order meets a target condition;

an assigning module 404 for assigning the set of orders to the target picker based on the commodity information for each order in the set of orders.

In one possible implementation, the delivery task determination module 402 is configured to:

In one possible implementation, the apparatus further includes:

In one possible implementation, the order set determination module 403 is configured to:

In one possible implementation, the assignment module 404 is configured to:

The device provided by the embodiment of the application determines at least one target distribution task by obtaining a plurality of orders to be processed, based on position information carried by each order and inter-order distribution time length among the orders, wherein one target distribution task is used for indicating at least one target order to be distributed in one distribution process, determines an order set based on commodity matching degree between commodities included in any one target order and commodities included in each order, the commodity matching degree between the commodities included in any one order in the order set and the commodities included in the target orders meets target conditions, and distributes the order set to target pickers based on commodity information of each order in the order set. The order processing device is applied to combine orders, determine distribution tasks and improve distribution efficiency, the orders are collected based on commodity information in each order, one order is collected to pick the goods once so as to improve the goods picking efficiency, and then the order processing efficiency is improved in the aspects of distribution and goods picking.

It should be noted that: in order processing, the order processing apparatus provided in the above embodiment is only illustrated by dividing the functional modules, and in practical applications, the function distribution may be completed by different functional modules according to needs, that is, the internal structure of the apparatus is divided into different functional modules to complete all or part of the functions described above. In addition, the order processing apparatus and the order processing method provided in the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments and are not described herein again.

The computer device provided by the above technical solution can be implemented as a terminal or a server, for example, fig. 5 is a schematic structural diagram of a terminal provided in the embodiment of the present application. The terminal 500 may be: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, motion video Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, motion video Experts compression standard Audio Layer 4), a notebook computer, or a desktop computer. Terminal 500 may also be referred to by other names such as user equipment, portable terminal, laptop terminal, desktop terminal, and the like.

In general, the terminal 500 includes: one or more processors 501 and one or more memories 502.

The processor 501 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 501 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 501 may also include a main processor and a coprocessor, where the main processor is a processor for processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 501 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, processor 501 may also include an AI (Artificial Intelligence) processor for processing computational operations related to machine learning.

Memory 502 may include one or more computer-readable storage media, which may be non-transitory. Memory 502 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 502 is used to store at least one program code for execution by processor 501 to implement the order processing method provided by the method embodiments herein.

In some embodiments, the terminal 500 may further optionally include: a peripheral interface 503 and at least one peripheral. The processor 501, memory 502 and peripheral interface 503 may be connected by a bus or signal lines. Each peripheral may be connected to the peripheral interface 503 by a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 504, display screen 505, camera assembly 506, audio circuitry 507, positioning assembly 508, and power supply 509.

The peripheral interface 503 may be used to connect at least one peripheral related to I/O (Input/Output) to the processor 501 and the memory 502. In some embodiments, the processor 501, memory 502, and peripheral interface 503 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 501, the memory 502, and the peripheral interface 503 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

The Radio Frequency circuit 504 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 504 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 504 converts an electrical signal into an electromagnetic signal to transmit, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 504 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuitry 504 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: metropolitan area networks, various generation mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the rf circuit 504 may further include NFC (Near Field Communication) related circuits, which are not limited in this application.

The display screen 505 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 505 is a touch display screen, the display screen 505 also has the ability to capture touch signals on or over the surface of the display screen 505. The touch signal may be input to the processor 501 as a control signal for processing. At this point, the display screen 505 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display screen 505 may be one, providing the front panel of the terminal 500; in other embodiments, the display screens 505 may be at least two, respectively disposed on different surfaces of the terminal 500 or in a folded design; in some embodiments, the display 505 may be a flexible display disposed on a curved surface or a folded surface of the terminal 500. Even more, the display screen 505 can be arranged in a non-rectangular irregular figure, i.e. a shaped screen. The Display screen 505 may be made of LCD (liquid crystal Display), OLED (Organic Light-Emitting Diode), and the like.

The camera assembly 506 is used to capture images or video. Optionally, camera assembly 506 includes a front camera and a rear camera. Generally, a front camera is disposed at a front panel of the terminal, and a rear camera is disposed at a rear surface of the terminal. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize panoramic shooting and VR (Virtual Reality) shooting functions or other fusion shooting functions. In some embodiments, camera assembly 506 may also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.

Audio circuitry 507 may include a microphone and a speaker. The microphone is used for collecting sound waves of a user and the environment, converting the sound waves into electric signals, and inputting the electric signals to the processor 501 for processing, or inputting the electric signals to the radio frequency circuit 504 to realize voice communication. For the purpose of stereo sound collection or noise reduction, a plurality of microphones may be provided at different portions of the terminal 500. The microphone may also be an array microphone or an omni-directional pick-up microphone. The speaker is used to convert electrical signals from the processor 501 or the radio frequency circuit 504 into sound waves. The loudspeaker can be a traditional film loudspeaker or a piezoelectric ceramic loudspeaker. When the speaker is a piezoelectric ceramic speaker, the speaker can be used for purposes such as converting an electric signal into a sound wave audible to a human being, or converting an electric signal into a sound wave inaudible to a human being to measure a distance. In some embodiments, audio circuitry 507 may also include a headphone jack.

The positioning component 508 is used to locate the current geographic position of the terminal 500 for navigation or LBS (location based Service). The positioning component 508 may be a positioning component based on the GPS (global positioning System) in the united states, the beidou System in china, the graves System in russia, or the galileo System in the european union.

Power supply 509 is used to power the various components in terminal 500. The power source 509 may be alternating current, direct current, disposable or rechargeable. When power supply 509 includes a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery may also be used to support fast charge technology.

In some embodiments, terminal 500 also includes one or more sensors 510. The one or more sensors 510 include, but are not limited to: acceleration sensor 511, gyro sensor 512, pressure sensor 513, fingerprint sensor 514, optical sensor 515, and proximity sensor 516.

The acceleration sensor 511 may detect the magnitude of acceleration on three coordinate axes of the coordinate system established with the terminal 500. For example, the acceleration sensor 511 may be used to detect components of the gravitational acceleration in three coordinate axes. The processor 501 may control the display screen 505 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 511. The acceleration sensor 511 may also be used for acquisition of motion data of a game or a user.

The gyro sensor 512 may detect a body direction and a rotation angle of the terminal 500, and the gyro sensor 512 may cooperate with the acceleration sensor 511 to acquire a 3D motion of the user on the terminal 500. The processor 501 may implement the following functions according to the data collected by the gyro sensor 512: motion sensing (such as changing the UI according to a user's tilting operation), image stabilization at the time of photographing, game control, and inertial navigation.

The pressure sensor 513 may be disposed on a side frame of the terminal 500 and/or underneath the display screen 505. When the pressure sensor 513 is disposed on the side frame of the terminal 500, a user's holding signal of the terminal 500 may be detected, and the processor 501 performs left-right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 513. When the pressure sensor 513 is disposed at the lower layer of the display screen 505, the processor 501 controls the operability control on the UI interface according to the pressure operation of the user on the display screen 505. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.

The fingerprint sensor 514 is used for collecting a fingerprint of the user, and the processor 501 identifies the identity of the user according to the fingerprint collected by the fingerprint sensor 514, or the fingerprint sensor 514 identifies the identity of the user according to the collected fingerprint. Upon recognizing that the user's identity is a trusted identity, the processor 501 authorizes the user to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying, and changing settings, etc. The fingerprint sensor 514 may be provided on the front, back, or side of the terminal 500. When a physical button or a vendor Logo is provided on the terminal 500, the fingerprint sensor 514 may be integrated with the physical button or the vendor Logo.

The optical sensor 515 is used to collect the ambient light intensity. In one embodiment, the processor 501 may control the display brightness of the display screen 505 based on the ambient light intensity collected by the optical sensor 515. Specifically, when the ambient light intensity is high, the display brightness of the display screen 505 is increased; when the ambient light intensity is low, the display brightness of the display screen 505 is reduced. In another embodiment, processor 501 may also dynamically adjust the shooting parameters of camera head assembly 506 based on the ambient light intensity collected by optical sensor 515.

A proximity sensor 516, also referred to as a distance sensor, is typically disposed on the front panel of the terminal 500. The proximity sensor 516 is used to collect the distance between the user and the front surface of the terminal 500. In one embodiment, when the proximity sensor 516 detects that the distance between the user and the front surface of the terminal 500 gradually decreases, the processor 501 controls the display screen 505 to switch from the bright screen state to the dark screen state; when the proximity sensor 516 detects that the distance between the user and the front surface of the terminal 500 becomes gradually larger, the display screen 505 is controlled by the processor 501 to switch from the breath screen state to the bright screen state.

Those skilled in the art will appreciate that the configuration shown in fig. 5 is not intended to be limiting of terminal 500 and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components may be used.

Fig. 6 is a schematic structural diagram of a server according to an embodiment of the present application, where the server 600 may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 601 and one or more memories 602, where at least one program code is stored in the one or more memories 602, and is loaded and executed by the one or more processors 601 to implement the methods provided by the method embodiments. Of course, the server 600 may also have components such as a wired or wireless network interface, a keyboard, and an input/output interface, so as to perform input and output, and the server 600 may also include other components for implementing the functions of the device, which is not described herein again.

In an exemplary embodiment, a computer readable storage medium, such as a memory including at least one program code executable by a processor to perform the order processing method of the above embodiments, is also provided. For example, the computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

It will be understood by those skilled in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or implemented by at least one program code associated with hardware, where the program code is stored in a computer readable storage medium, such as a read only memory, a magnetic or optical disk, etc.

The above description is only exemplary of the present application and should not be taken as limiting, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. An order processing method, characterized in that the method comprises:

acquiring a plurality of orders to be processed;

determining at least one target distribution task based on the position information carried by each order and the inter-order distribution time length among the orders, wherein one target distribution task is used for indicating at least one target order to be distributed in one distribution process;

determining an order set based on the commodity matching degree between the commodities included in any one of the target orders and the commodities included in each of the orders, wherein the commodity matching degree between the commodities included in any one of the orders in the order set and the commodities included in the target orders meets target conditions;

assigning the set of orders to a target picker based on commodity information for each order in the set of orders.

2. The method according to claim 1, wherein the determining at least one target delivery task based on the position information carried by each order and the inter-order delivery time length between each order comprises:

determining at least one candidate delivery task based on the position information of each order and the delivery time length between orders, wherein one candidate delivery task corresponds to at least one order;

3. The method of claim 2, wherein prior to determining at least one candidate delivery task based on the location information of each of the orders and the inter-order delivery duration, the method further comprises:

constructing a feature vector of each order based on the position information, the commodity information, the distribution time, the environment information corresponding to the position information and the distance information between the orders of each order;

inputting the characteristic vector of each order into an order distribution time prediction model, and determining inter-order distribution time length among the orders by the order distribution time prediction model based on the characteristic vectors.

4. The method of claim 2, wherein said determining said at least one target order based on at least one said order for each said target delivery task comprises:

5. The method of claim 4, wherein determining at least one of the orders matching each of the first candidate orders in the targeted delivery task as a second candidate order based on the location information and the weight information of each of the orders comprises:

determining a distribution time length change value corresponding to the target distribution task when any one order is added to the target distribution task based on the position information of any one order;

6. The method according to claim 1, wherein after determining an order set based on a product matching degree between a product included in any one of the target orders and a product included in each of the orders, the method further comprises:

and determining a picking sequence and a picking path corresponding to each commodity in any order based on at least one commodity identification carried by any order in the order set and the storage position information of each commodity.

7. The method of claim 1, wherein said assigning the set of orders to target pickers based on commodity information for each order in the set of orders comprises:

8. An order processing apparatus, characterized in that the apparatus comprises:

a distribution task determining module, configured to determine at least one target distribution task based on location information carried by each order and inter-order distribution time between the orders, where the target distribution task is used to indicate at least one target order to be distributed in a distribution process;

an order set determining module, configured to determine an order set based on a commodity matching degree between a commodity included in any one of the target orders and a commodity included in each of the orders, where the commodity matching degree between the commodity included in any one of the orders in the order set and the commodity included in the target order meets a target condition;

and the distribution module is used for distributing the order set to a target order picker based on the commodity information of each order in the order set.

9. A computer device comprising one or more processors and one or more memories having at least one program code stored therein, the at least one program code loaded and executed by the one or more processors to perform operations performed by the order processing method of any of claims 1 to 7.

10. A computer-readable storage medium having stored therein at least one program code, the at least one program code being loaded into and executed by a processor to perform operations performed by the order processing method of any of claims 1 to 7.