CN115619502A - Network retail order management scheduling method and system - Google Patents
Network retail order management scheduling method and system Download PDFInfo
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Abstract
The invention provides a network retail order management scheduling method and a network retail order management scheduling system, wherein the method comprises the following steps: step S1: acquiring new order information of a target commodity which needs to be issued by a merchant on a network retail platform; step S2: acquiring preset warehouse distribution information corresponding to the target commodity; and step S3: carrying out warehouse delivery scheduling on the basis of the new order information and the warehouse distribution information; wherein the new order information comprises: customer address and number of goods; the warehouse distribution information includes: and a plurality of groups of warehouses, warehouse addresses and commodity margins which are in one-to-one correspondence. According to the network retail order management scheduling method and system, when a new order is provided for a commodity which needs to be issued by a merchant, the warehouse is automatically dispatched for generation without docking with an issuing party, so that the situation of error in modern delivery docking is avoided, the issuing party does not need to determine a proper delivery warehouse according to a customer address in a near principle, and convenience is greatly improved.
Description
Technical Field
The invention relates to the technical field of network retail, in particular to a network retail order management scheduling method and system.
Background
Currently, some merchants in network retail adopt a mode of replacing a sending party with a sending party [ for example: the merchant does not stock up or pack, and contacts with the commodity manufacturer when an order is made, and the order is issued by the manufacturer; another example is: and (4) the merchant stores the goods without packaging, transports the goods stored to the warehouse of the generation party, contacts with the generation party when an order is made, and delivers the goods in the warehouse by the generation party to the customer. However, when a new order is provided by a merchant, the merchant needs to be in butt joint with a generation party and send order information to the generation party, which is relatively complicated, and particularly, when only part of goods sold by the merchant needs to be generated, generation and butt joint errors may be caused. In addition, the number of warehouses of the forwarder is often multiple, and the forwarder needs to determine a proper delivery warehouse according to the customer address in a near principle, so that convenience is reduced.
Therefore, a solution is needed.
Disclosure of Invention
One of the purposes of the invention is to provide a network retail order management scheduling method, when a new order is provided for a commodity which needs to be sent by a merchant, warehouse sending and scheduling are automatically carried out, the generation and sending party does not need to be in butt joint with the generation and sending party, the condition that the modern sending and the butt joint are in error is avoided, the generation and sending party does not need to determine a proper delivery warehouse in a near principle according to a customer address, and convenience is greatly improved.
The embodiment of the invention provides a network retail order management scheduling method, which comprises the following steps:
step S1: acquiring new order information of a target commodity which needs to be issued on a network retail platform by a merchant;
step S2: acquiring preset warehouse distribution information corresponding to the target commodity;
and step S3: carrying out warehouse delivery scheduling on the basis of the new order information and the warehouse distribution information;
wherein the new order information comprises: customer address and number of items;
the warehouse distribution information includes: and a plurality of groups of warehouses, warehouse addresses and commodity margins which are in one-to-one correspondence.
Preferably, the step S1: acquiring new order information of target goods which need to be issued by a merchant on a network retail platform, wherein the new order information comprises the following steps:
acquiring a plurality of on-sale commodities of a merchant in an online shop on a network retail platform;
acquiring a preset commodity generation contract corresponding to a merchant;
determining target commodities needing to be issued from the commodities on sale based on the commodity issuing contract;
obtaining order information of the non-delivery commodities of the online shop;
determining new order information for the target item from the non-shipped item order information.
Preferably, the step S3: based on the new order information and the warehouse distribution information, carrying out warehouse delivery scheduling, comprising:
taking the warehouse corresponding to the commodity allowance larger than or equal to the commodity quantity as a target warehouse;
determining a delivery logistics route between the warehouse address corresponding to the target warehouse and the customer address from a preset delivery logistics route library;
taking the target warehouse corresponding to the shortest delivery logistics route as a delivery warehouse;
generating a picking and delivering proxy task according to the customer address and the quantity of the target commodity based on a preset picking and delivering proxy task generating template;
and delivering the picking agent sending task to a preset task sending node corresponding to the delivery warehouse.
Preferably, the step S2: before the preset warehouse distribution information corresponding to the target commodity is obtained, the method further comprises the following steps:
acquiring a plurality of operation behaviors generated on the network retail platform within a preset time after a customer purchasing the target commodity purchases the target commodity;
predicting whether the customer will cancel purchasing the target commodity based on the operation behavior;
if yes, temporarily placing the new order information corresponding to the customer;
and if the customer does not cancel the purchase of the target commodity within the preset waiting time, canceling the temporary shelving of the new order information of the customer.
Preferably, predicting whether the customer will cancel purchasing the target product based on the operation behavior includes:
obtaining selling information of the target commodity sold by a merchant on the network selling platform;
generating a prediction trigger condition according to the selling information based on a preset prediction trigger condition generation module;
establishing a time axis, generating time based on the behavior of the operation behavior, and setting the operation behavior on the time axis;
determining whether the operational behavior satisfies the predicted trigger condition;
if so, taking the corresponding operation behavior as a target operation behavior;
taking the operation behavior between the most previous target operation behavior and the last target operation behavior on the time axis as a prediction basis;
based on a preset feature extraction template, carrying out feature extraction on the prediction basis to obtain a plurality of feature values;
predicting whether the customer will cancel purchasing the target product based on the feature value.
Preferably, the predicting whether the customer will cancel purchasing the target product based on the feature value includes:
inputting the characteristic value into a preset purchasing cancellation prediction model, and predicting whether the customer cancels the purchasing of the target commodity;
and/or the presence of a gas in the gas,
constructing a first description vector of the prediction basis based on the characteristic value;
acquiring a preset purchase cancellation prediction library, wherein the purchase cancellation prediction library comprises: a plurality of groups of one-to-one corresponding second description vectors and the requirement of vector matching degree;
carrying out vector matching on the first description vector and any one of the second description vectors to obtain a vector matching degree;
and if the vector matching degree meets the vector matching degree requirement corresponding to the second description vector for matching, predicting that the customer cancels the purchase of the target commodity.
Preferably, the network retail order management scheduling method further includes:
when a customer who purchases the target commodity needs to return goods after receiving the target commodity, acquiring a home-in goods taking time period and a goods taking address input by the customer;
determining a plurality of logistics personnel in a preset range around the pickup address from a preset logistics personnel distribution map corresponding to a city where the customer is located;
acquiring a plurality of future working strokes of the logistics personnel; the working stroke comprises: a starting location, an ending location, and a latest arrival time;
determining a target work journey in which the logistics personnel can conveniently take the target commodity returned by the customer through the pickup address in the home pickup time period from the work journey;
if yes, corresponding to the target logistics personnel of the logistics personnel;
generating an on-door goods taking task according to the on-door goods taking time period, the goods taking address and the target working stroke based on a preset on-door goods taking task generating module;
and pushing the home pick task to the target logistics personnel.
Preferably, the determining, from the work schedule, a target work schedule for the logistics worker to conveniently pick the target product returned by the customer through the pick address in the home pick time period includes:
a connecting line between the starting position and the end position of any work stroke is used as a diameter to make a target circle in the logistics personnel distribution map;
if the goods picking address is in the target circle, taking the corresponding working stroke as a working stroke to be selected;
predicting a first arrival time of the logistics personnel from the starting position of the to-be-selected work journey to the pick-up address by taking the latest arrival time of the last work journey of the to-be-selected work journey as a departure time in the logistics personnel distribution map, and meanwhile predicting a second arrival time of the logistics personnel from the pick-up address to the end position of the to-be-selected work journey by taking the arrival time as the departure time;
and if the first arrival time falls within the upper door goods taking time period and the second arrival time is before the latest arrival time of the to-be-selected working journey, taking the corresponding to-be-selected working journey as a target working journey.
The embodiment of the invention provides a network retail order management scheduling system, which comprises:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring new order information of a target commodity which needs to be issued by a merchant on a network retail platform;
the second acquisition module is used for acquiring preset warehouse distribution information corresponding to the target commodity;
the scheduling module is used for carrying out warehouse delivery scheduling on the basis of the new order information and the warehouse distribution information;
wherein the new order information comprises: customer address and number of goods;
the warehouse distribution information includes: and a plurality of groups of warehouses, warehouse addresses and commodity margins which are in one-to-one correspondence.
Preferably, the acquiring module acquires new order information of a target product that a merchant needs to issue on a network retail platform, and includes:
acquiring a plurality of on-sale commodities of a merchant in an online shop on a network retail platform;
acquiring a preset commodity generation contract corresponding to a merchant;
determining target commodities needing to be issued from the commodities on sale based on the commodity issuing contract;
obtaining order information of the non-delivery commodities of the online shop;
determining new order information for the target item from the order information for the non-shipped item.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a network retail order management scheduling method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a network retail order management scheduling system according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides a network retail order management scheduling method, as shown in fig. 1, comprising the following steps:
step S1: acquiring new order information of a target commodity which needs to be issued on a network retail platform by a merchant;
step S2: acquiring preset warehouse distribution information corresponding to the target commodity;
and step S3: performing warehouse delivery scheduling based on the new order information and the warehouse distribution information;
wherein the new order information comprises: customer address and number of goods;
the warehouse distribution information includes: and the warehouses, warehouse addresses and commodity residuals are in one-to-one correspondence.
The working principle and the beneficial effects of the technical scheme are as follows:
when a customer purchases a target commodity which needs to be issued by a merchant on the network retail platform, new order information is generated for acquisition. The new order information includes the customer address and the quantity of the goods purchased by the customer. The preset warehouse distribution information corresponding to the target commodity comprises a warehouse where the target commodity is stored, a warehouse address of the warehouse and commodity allowance of the target commodity in the warehouse. And performing warehouse delivery scheduling based on the new order information and the warehouse distribution information, for example: and determining a warehouse with the commodity allowance larger than or equal to the quantity of the commodities purchased by the customer, then determining a proper delivery warehouse in the warehouse according to the customer address in a near principle, and scheduling picking personnel, packaging delivery personnel and the like in the delivery warehouse to pick and package delivery according to the new order information.
According to the warehouse delivery scheduling method and system, when a new order is provided for a commodity needing to be delivered by a merchant, the warehouse delivery scheduling is automatically carried out, the warehouse delivery scheduling is not required to be in butt joint with a delivery agent, the condition that the modern delivery is in error is avoided, the delivery agent is not required to determine a proper delivery warehouse according to a customer address and the principle nearby, and convenience is improved to the greatest extent.
In one embodiment, the step S1: acquiring new order information of target goods which need to be issued by a merchant on a network retail platform, wherein the new order information comprises the following steps:
acquiring a plurality of on-sale commodities of a merchant in an online shop on a network retail platform;
acquiring a preset commodity generation contract corresponding to a merchant;
determining target commodities needing to be issued from the commodities on sale based on the commodity issuing contract;
acquiring order information of non-shipped commodities of the online shop;
determining new order information for the target item from the order information for the non-shipped item.
The working principle and the beneficial effects of the technical scheme are as follows:
when a merchant needs commodity distribution instead, the merchant signs a commodity distribution instead contract with a distributor, and the commodity distribution instead contract indicates that the commodity needs distribution instead, so that the target commodity needing distribution is determined from a plurality of commodities sold in an online shop of the merchant on the network retail platform based on the commodity distribution instead contract. The background of the online shop can collect the order information of the undelivered commodities of all the commodities on sale, and the new order information of the target commodity can be determined from the order information of the undelivered commodities. The target commodity needing to be issued by the merchant is determined based on the issuing contract, the new order information of the target commodity is determined from the order information of the non-delivery commodity of the online shop, and the reasonability of obtaining the new order information of the target commodity is improved.
In one embodiment, the step S3: based on the new order information and the warehouse distribution information, warehouse delivery scheduling is carried out, and the method comprises the following steps:
taking the warehouse corresponding to the commodity allowance larger than or equal to the commodity quantity as a target warehouse;
determining a delivery logistics route between the warehouse address corresponding to the target warehouse and the customer address from a preset delivery logistics route library;
taking the target warehouse corresponding to the shortest delivery logistics route as a delivery warehouse;
generating a picking and delivering proxy task according to the customer address and the quantity of the target commodity based on a preset picking and delivering proxy task generating template;
and delivering the picking agent sending task to a preset task sending node corresponding to the delivery warehouse.
The working principle and the beneficial effects of the technical scheme are as follows:
the commodity surplus of the warehouse should be sufficient for shipment, and therefore, the warehouse corresponding to the commodity surplus equal to or greater than the commodity number is taken as the target warehouse. Delivery logistics routes of logistics [ express delivery companies and logistics companies, etc. ] do not go directly from a delivery location to a receiving location, for example: the express delivered to Shanghai from Jiangsu salt city can be transferred in Huai' an, so that a preset delivery logistics route library is introduced, a large number of delivery logistics routes from delivery places to receiving places are arranged in the delivery logistics route library, delivery logistics routes between warehouse addresses and client addresses corresponding to target warehouses are determined, and the target warehouse corresponding to the shortest delivery logistics route is used as a delivery warehouse. The reasonability and the determination efficiency of the delivery warehouse are improved. The preset picking and delivering replacement task generation template is as follows: "please arrange the generation: target item xx, address: xx. ". And generating a goods picking and sending proxy task according to the customer address and the quantity of the target goods based on the goods picking and sending proxy task generating template. The preset task issuing node corresponding to the delivery warehouse is a network node and is in communication butt joint with terminals (such as mobile phones and PDA equipment) used by personnel responsible for delivery in the delivery warehouse. And delivering the goods picking and delivery substitute task to the task delivery node, and enabling the corresponding personnel to check and see the delivery substitute task. And the scheduling efficiency is improved.
In one embodiment, the step S2: before the preset warehouse distribution information corresponding to the target commodity is obtained, the method further comprises the following steps:
acquiring a plurality of operation behaviors generated on the network retail platform within a preset time after a customer purchasing the target commodity purchases the target commodity;
predicting whether the customer will cancel purchasing the target commodity based on the operation behavior;
if yes, temporarily placing the new order information corresponding to the customer;
and if the customer does not cancel the purchase of the target commodity within the preset waiting time, canceling the temporary shelving of the new order information of the customer.
The working principle and the beneficial effects of the technical scheme are as follows:
in order to improve the delivery efficiency and avoid subsequent delivery order accumulation, delayed delivery and the like, particularly when the network selling platform is used for promoting activities (such as 'double eleven'), generally, a warehouse can arrange packaging delivery immediately after receiving a delivery task. However, during this time, the customer may cancel the order [ e.g.: the order is cancelled after being compared with other commodities of the same type, other commodities of the same type are taken, and if delivery is arranged, the problems that the packing box cannot be reused, printed express bills are wasted, resources of picking and packing personnel or equipment are wasted and the like can be caused. Thus, the time preset after the customer purchase is obtained [ e.g.: 15 minutes) a number of operational activities that are generated on the internet retail platform [ activities generated by browsing merchandise, for example: price of browsing merchandise, type of browsing merchandise, number and duration of browsing detailed pages of merchandise, etc.). Based on the operation behavior, it is predicted whether the customer will cancel purchasing the target item [ for example: and judging whether the user compares the prices of the same type of commodities, if so, canceling the order possibly, and if so, temporarily holding the new order information of the customer and temporarily not arranging delivery. If the customer is waiting for a predetermined amount of time [ e.g.: and within 20 minutes), the target commodity is not purchased, the temporary shelving is cancelled, and the delivery is scheduled. The method and the system avoid a series of problems caused by the fact that the customer cancels the order when the delivery is arranged, and have high applicability.
In one embodiment, predicting whether the customer will cancel purchasing the target good based on the operational behavior comprises:
obtaining selling information of a merchant for selling the target commodity on the network selling platform;
generating a prediction trigger condition according to the selling information based on a preset prediction trigger condition generation module;
establishing a time axis, generating time based on the behavior of the operation behavior, and setting the operation behavior on the time axis;
determining whether the operational behavior satisfies the predicted trigger condition;
if so, taking the corresponding operation behavior as a target operation behavior;
taking the operation behavior between the most previous target operation behavior and the last target operation behavior on the time axis as a prediction basis;
based on a preset feature extraction template, carrying out feature extraction on the prediction basis to obtain a plurality of feature values;
predicting whether the customer will cancel purchasing the target product based on the feature value.
The working principle and the beneficial effects of the technical scheme are as follows:
the selling information of the target commodity includes a selling price and a commodity type. The preset prediction trigger condition generation module is as follows: the forecast trigger condition is that browsing other commodities of the same commodity type as the target commodity and/or browsing and target commodities are similar in function or can be replaced [ for example: other commodities of the sweeping robot, a dust collector, a facial cleaning towel, a towel and the like and/or other commodities of the same type as the target commodity and the selling price of other commodities are close to the selling price of the target commodity (the absolute value of the difference is less than a certain value) and/or the selling price of other commodities which are functionally similar or replaceable with the target commodity is close to the selling price of the target commodity. And generating a prediction trigger condition according to the selling information based on the prediction trigger condition generation module. And if the operation behavior meets the prediction trigger condition, the operation behavior is explained to reflect that the customer may cancel the order as the target operation behavior. In general, during the time that the customer generates the target operational behavior, other operational behaviors may also be generated for whether or not the order prediction will be cancelled, such as: looking at the store qualification of the merchant, the customer is shown not to be completely successful in purchasing from the merchant. Therefore, the operation behavior between the most previous target operation behavior and the last target operation behavior on the time axis is used as the prediction basis. Extracting a characteristic value of a prediction basis, wherein the characteristic value comprises: the total browsing times and the total browsing time of other commodities of the same commodity type as the target commodity are browsed, the total browsing times and the total browsing time of other commodities with similar or replaceable functions with the target commodity, the total number of other commodities with similar or replaceable functions with the target commodity and the selling price of other commodities with similar or replaceable functions with the target commodity, the total number of other commodities with similar or replaceable functions with the target commodity and the selling price of the target commodity and the like. And performing prediction based on the characteristic value. And a forecasting triggering condition is introduced, and the screened forecasting basis can be used as a forecasting basis for whether the user cancels order forecasting, so that the working efficiency of the system and the accuracy of forecasting basis selection are improved.
In one embodiment, the predicting whether the customer will cancel purchasing the target product based on the feature value includes:
inputting the characteristic value into a preset purchase cancellation prediction model, and predicting whether the customer cancels purchase of the target commodity;
and/or the presence of a gas in the gas,
constructing a first description vector of the prediction basis based on the characteristic value;
acquiring a preset cancel purchase prediction library, wherein the cancel purchase prediction library comprises: a plurality of groups of one-to-one corresponding second description vectors and the requirement of vector matching degree;
carrying out vector matching on the first description vector and any one of the second description vectors to obtain a vector matching degree;
and if the vector matching degree meets the vector matching degree requirement corresponding to the second description vector for matching, predicting that the customer cancels the purchase of the target commodity.
The working principle and the beneficial effects of the technical scheme are as follows:
there are two ways to predict whether the customer will cancel purchasing the target product based on the feature value: 1. a preset cancellation purchase prediction model is input. Cancellation of purchase prediction model to take advantage of the characteristic values that a customer may cancel an order in large numbers of reactions [ for example: the total browsing times and the total browsing time of other commodities of the same commodity type as the target commodity are more, and the result shows that the user is in entanglement or comparison. 2. Introducing a second description vector constructed by characteristic values reflecting that the customer may cancel the order in a vector form and a vector matching degree requirement, wherein the vector matching degree requirement is a requirement that the vector matching between the first description vector and the second description vector according to prediction when the customer is predicted to cancel the order should meet, for example: the vector matching degree is greater than or equal to 85. And carrying out vector matching on the first description vector and any second description vector according to the prediction to obtain the matching degree, and if the matching degree meets the requirement of the corresponding vector matching degree, indicating that the user may cancel the order. Two modes are introduced to predict whether the customer will cancel the purchase of the target commodity based on the characteristic values, so that the applicability of the system is improved, and the prediction efficiency of whether the customer will cancel the order prediction is further improved.
In one embodiment, the network retail order management scheduling method further comprises:
when a customer who purchases the target commodity needs to return goods after receiving the target commodity, acquiring a home pick time period and a pick address input by the customer;
determining a plurality of logistics personnel in a preset range around the pickup address from a preset logistics personnel distribution map corresponding to a city where the customer is located;
acquiring a plurality of future working strokes of the logistics personnel; the working stroke comprises: a starting position, an ending position, and a latest arrival time;
determining a target work journey in which the logistics personnel can conveniently take the target commodity returned by the customer through the pickup address in the home pickup time period from the work journey;
if yes, corresponding to the target logistics personnel of the logistics personnel;
based on a preset home pick task generation module, generating a home pick task according to the home pick time period, the pick address and the target working stroke;
and pushing the home pick task to the target logistics personnel.
The working principle and the beneficial effects of the technical scheme are as follows:
after receiving goods, if the goods can be returned due to dissatisfaction and the like, at present, most goods returned are goods taken by couriers at home, and the customers input the time period of goods taking at home and goods taking addresses. The preset logistics personnel distribution map corresponding to the city where the client is located is a city map marked with real-time positions of logistics personnel (couriers and the like) in the city. And determining a plurality of logistics personnel in a preset range around the goods taking address (a circular range with the goods taking address as a circle center and the diameter length of 2 kilometers) from the logistics personnel distribution map. And acquiring a plurality of future working strokes of the logistics personnel. And determining a target working journey of the logistics worker for conveniently taking the goods of the target goods returned by the customer through the goods taking address in the home goods taking time period from the working journey, and if so, taking the target working journey as the target working journey. The preset home delivery task generation module is as follows: "Chige you Hao! You can go to xxxx (goods picking address) to pick up goods when xxxx (target work journey) is carried out, the time is sufficient, and the arrival time period is xxxx (getting-on-door goods picking time period). The home pick task generation module generates a home pick task according to a home pick time period, a pick address and the target working travel and pushes the home pick task to target logistics personnel. Generally, the goods returned to the home and taken are mostly arranged on the next day, so that the couriers can reasonably arrange the goods returned on the next morning according to the working conditions of the couriers, such as a dispatching route, a goods taking route and the like, therefore, the goods returned are mostly finished on the next day, the goods returning efficiency is low, and the goods turnover operation of merchants is influenced to a certain extent. According to the embodiment of the invention, the target logistics personnel convenient for halfway goods taking are reasonably determined according to the working schedule of the logistics personnel, the time period of getting goods at home and the goods taking address input by the client, and the logistics personnel is scheduled to take the goods halfway.
In one embodiment, the determining, from the work schedule, a target work schedule for the logistics personnel to conveniently pick the target commodity returned by the customer through the pick address in the home pick time period includes:
a connecting line between the starting position and the end position of any work stroke is used as a diameter to make a target circle in the logistics personnel distribution map;
if the goods picking address is in the target circle, taking the corresponding working stroke as a working stroke to be selected;
predicting a first arrival time of the logistics personnel from the starting position of the to-be-selected work journey to the pick-up address by taking the latest arrival time of the last work journey of the to-be-selected work journey as a departure time in the logistics personnel distribution map, and meanwhile predicting a second arrival time of the logistics personnel from the pick-up address to the end position of the to-be-selected work journey by taking the arrival time as the departure time;
and if the first arrival time falls within the pick-up time period and the second arrival time is before the latest arrival time of the to-be-selected working journey, taking the corresponding to-be-selected working journey as a target working journey.
The working principle and the beneficial effects of the technical scheme are as follows:
and taking a connecting line between the initial position and the final position as a diameter in the logistics personnel distribution map as a target circle, and if the goods taking address is in the target circle, indicating that logistics personnel can not bypass too much in the working process if taking goods midway, and taking the logistics personnel as a working process to be selected. The method for predicting the first arrival time from the starting position of the work journey to the pick-up address by using the latest arrival time of the last work journey of the work journey to be selected (the latest time for completing the last work journey) as the starting time (arrival time prediction) belongs to the field of the prior art, and comprises the following steps: the navigation App displays the expected time of arrival when planning the path. And predicting a second arrival time of the logistics personnel from the pick-up address to the destination position of the to-be-selected work journey by taking the arrival time as the departure time. If the first arrival time is within the upper pick-up time period and the second arrival time is before the latest arrival time of the to-be-selected work journey, the logistics personnel can pick up goods within the time period desired by the customer when executing the to-be-selected work journey without delaying the logistics personnel to execute the work journey, and the logistics personnel are taken as the target work journey. According to the embodiment of the invention, the to-be-selected work flow that logistics personnel cannot detour is determined based on the target circle, whether the logistics personnel can take goods in midway is determined based on the arrival time prediction, and the prediction accuracy and the prediction rationality are improved.
An embodiment of the present invention provides a network retail order management scheduling system, as shown in fig. 2, including:
the system comprises a first acquisition module 1, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring new order information of a target commodity which needs to be issued by a merchant on a network retail platform;
the second obtaining module 2 is configured to obtain preset warehouse distribution information corresponding to the target commodity;
the scheduling module 3 is used for carrying out warehouse delivery scheduling on the basis of the new order information and the warehouse distribution information;
wherein the new order information comprises: customer address and number of goods;
the warehouse distribution information includes: and a plurality of groups of warehouses, warehouse addresses and commodity margins which are in one-to-one correspondence.
In one embodiment, the first obtaining module obtains new order information of a target product that a merchant needs to issue on a network retail platform, and includes:
acquiring a plurality of on-sale commodities of a merchant in an online shop on a network retail platform;
acquiring a preset commodity generation contract corresponding to a merchant;
determining a target commodity needing to be issued from the commodities sold on the basis of the commodity issuing contract;
obtaining order information of the non-delivery commodities of the online shop;
determining new order information for the target item from the non-shipped item order information.
Without departing from the spirit and scope of the present invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A network retail order management scheduling method is characterized by comprising the following steps:
step S1: acquiring new order information of a target commodity which needs to be issued by a merchant on a network retail platform;
step S2: acquiring preset warehouse distribution information corresponding to the target commodity;
and step S3: performing warehouse delivery scheduling based on the new order information and the warehouse distribution information;
wherein the new order information comprises: customer address and number of goods;
the warehouse distribution information includes: and a plurality of groups of warehouses, warehouse addresses and commodity margins which are in one-to-one correspondence.
2. The network retail order management scheduling method of claim 1, wherein the step S1: acquiring new order information of target goods which need to be issued by a merchant on a network retail platform, wherein the new order information comprises the following steps:
acquiring a plurality of on-sale commodities of a merchant in an online shop on a network retail platform;
acquiring a preset commodity generation contract corresponding to a merchant;
determining a target commodity needing to be issued from the commodities sold on the basis of the commodity issuing contract;
obtaining order information of the non-delivery commodities of the online shop;
determining new order information for the target item from the non-shipped item order information.
3. The network retail order management scheduling method of claim 1, wherein the step S3: based on the new order information and the warehouse distribution information, warehouse delivery scheduling is carried out, and the method comprises the following steps:
taking the warehouse corresponding to the commodity allowance larger than or equal to the commodity quantity as a target warehouse;
determining a delivery logistics route between the warehouse address and the customer address corresponding to the target warehouse from a preset delivery logistics route library;
taking the target warehouse corresponding to the shortest delivery logistics route as a delivery warehouse;
generating a picking up and delivering task based on a preset picking up and delivering task generating template according to the customer address and the quantity of the target goods;
and delivering the picking agent sending task to a preset task sending node corresponding to the delivery warehouse.
4. The network retail order management scheduling method of claim 1, wherein the step S2: before the preset warehouse distribution information corresponding to the target commodity is obtained, the method further comprises the following steps:
acquiring a plurality of operation behaviors generated on the network retail platform within a preset time after a customer purchasing the target commodity purchases the target commodity;
predicting whether the customer will cancel purchasing the target commodity based on the operation behavior;
if yes, temporarily placing the new order information corresponding to the customer;
and if the customer does not cancel the purchase of the target commodity within the preset waiting time, canceling the temporary shelving of the new order information of the customer.
5. The method as claimed in claim 4, wherein predicting whether the customer will cancel purchasing the target product based on the operational behavior comprises:
obtaining selling information of the target commodity sold by a merchant on the network selling platform;
generating a prediction trigger condition according to the selling information based on a preset prediction trigger condition generation module;
establishing a time axis, generating time based on the behavior of the operation behavior, and setting the operation behavior on the time axis;
determining whether the operational behavior satisfies the predicted trigger condition;
if so, taking the corresponding operation behavior as a target operation behavior;
taking the operation behavior between the most previous target operation behavior and the last target operation behavior on the time axis as a prediction basis;
based on a preset feature extraction template, carrying out feature extraction on the prediction basis to obtain a plurality of feature values;
and predicting whether the customer will cancel purchasing the target product based on the characteristic value.
6. The network retail order management scheduling method of claim 5, wherein predicting whether the customer will cancel purchasing the target product based on the characteristic value comprises:
inputting the characteristic value into a preset purchasing cancellation prediction model, and predicting whether the customer cancels the purchasing of the target commodity;
and/or the presence of a gas in the atmosphere,
constructing a first description vector of the prediction basis based on the characteristic value;
acquiring a preset cancel purchase prediction library, wherein the cancel purchase prediction library comprises: a plurality of groups of one-to-one corresponding second description vectors and the requirement of vector matching degree;
carrying out vector matching on the first description vector and any one of the second description vectors to obtain a vector matching degree;
and if the vector matching degree meets the vector matching degree requirement corresponding to the second description vector for matching, predicting that the customer cancels the purchase of the target commodity.
7. The network retail order management scheduling method of claim 1 further comprising:
when a customer who purchases the target commodity needs to return goods after receiving the target commodity, acquiring a home pick time period and a pick address input by the customer;
determining a plurality of logistics personnel in a preset range around the pickup address from a preset logistics personnel distribution map corresponding to a city where the customer is located;
acquiring a plurality of future working strokes of the logistics personnel; the working stroke comprises: a starting position, an ending position, and a latest arrival time;
determining a target work journey for the logistics personnel to conveniently pick the target commodity returned by the customer through the pick address in the home pick time period from the work journey;
if yes, corresponding to the target logistics personnel of the logistics personnel;
based on a preset home pick task generation module, generating a home pick task according to the home pick time period, the pick address and the target working stroke;
and pushing the home pick task to the target logistics personnel.
8. The network retail order management scheduling method of claim 7, wherein the determining from the work schedule a target work schedule for the logistics personnel to facilitate picking of the target goods returned by the customer via the pick address during the home pick time period comprises:
a connecting line between the starting position and the end position of any one working stroke is used as a diameter to be used as a target circle in the logistics personnel distribution map;
if the goods picking address is in the target circle, taking the corresponding working stroke as a working stroke to be selected;
predicting a first arrival time of the logistics personnel from the starting position of the to-be-selected work journey to the pick-up address by taking the latest arrival time of the last work journey of the to-be-selected work journey as a departure time in the logistics personnel distribution map, and meanwhile predicting a second arrival time of the logistics personnel from the pick-up address to the end position of the to-be-selected work journey by taking the arrival time as the departure time;
and if the first arrival time falls within the pick-up time period and the second arrival time is before the latest arrival time of the to-be-selected working journey, taking the corresponding to-be-selected working journey as a target working journey.
9. A network retail order management scheduling system, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring new order information of a target commodity which needs to be issued by a merchant on a network retail platform;
the second acquisition module is used for acquiring preset warehouse distribution information corresponding to the target commodity;
the scheduling module is used for carrying out warehouse delivery scheduling on the basis of the new order information and the warehouse distribution information;
wherein the new order information comprises: customer address and number of goods;
the warehouse distribution information includes: and the warehouses, warehouse addresses and commodity residuals are in one-to-one correspondence.
10. The system of claim 9, wherein the first obtaining module obtains new order information of target goods that a merchant needs to issue on the network retail platform, and the obtaining module comprises:
acquiring a plurality of on-sale commodities of a merchant in an online shop on a network retail platform;
acquiring a preset commodity generation contract corresponding to a merchant;
determining target commodities needing to be issued from the commodities on sale based on the commodity issuing contract;
obtaining order information of the non-delivery commodities of the online shop;
determining new order information for the target item from the order information for the non-shipped item.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116151935A (en) * | 2023-04-23 | 2023-05-23 | 万联易达物流科技有限公司 | Matching method and system for automatic freight combined payment |
| CN117350612A (en) * | 2023-10-18 | 2024-01-05 | 张家口巧工匠科技服务有限公司 | Selling and picking method, system and storage medium based on online mall |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116151935A (en) * | 2023-04-23 | 2023-05-23 | 万联易达物流科技有限公司 | Matching method and system for automatic freight combined payment |
| CN117350612A (en) * | 2023-10-18 | 2024-01-05 | 张家口巧工匠科技服务有限公司 | Selling and picking method, system and storage medium based on online mall |
| CN117350612B (en) * | 2023-10-18 | 2024-04-19 | 广州市宏意星计算机系统服务有限公司 | Selling and picking method, system and storage medium based on online mall |
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