CN111126843B

CN111126843B - Task processing method, system, electronic device and nonvolatile storage medium

Info

Publication number: CN111126843B
Application number: CN201911346935.4A
Authority: CN
Inventors: 张雪岩
Original assignee: Rajax Network Technology Co Ltd
Current assignee: Rajax Network Technology Co Ltd
Priority date: 2019-12-24
Filing date: 2019-12-24
Publication date: 2021-01-26
Anticipated expiration: 2039-12-24
Also published as: CN111126843A

Abstract

The embodiment of the invention relates to the technical field of information processing, and discloses a task processing method, a task processing system, electronic equipment and a nonvolatile storage medium. The task processing method comprises the following steps: the engine server determines a task upper limit value according to the picking pressure and/or the distribution pressure acquired from the pressure server; the task server acquires the similarity between a preset reference task and each referenced task in each task according to the characteristic data and the classification condition of each task of the target object; screening out referenced tasks with similarity greater than a preset threshold value with the reference tasks, dividing the screened referenced tasks and the reference tasks into a task set, sending picking data of the tasks in each task set to a picking client in a centralized manner, sending distribution data of the tasks in the task sets which finish picking to a distribution client in a centralized manner, wherein the number of the tasks in each task set is less than or equal to the upper limit value of the tasks; the task allocation efficiency can be improved, and the task completion speed can be increased.

Description

Task processing method, system, electronic device and nonvolatile storage medium

Technical Field

The present invention relates to the field of information processing technologies, and in particular, to a method and a system for processing a task, an electronic device, and a non-volatile storage medium.

Background

At present, due to the fact that chain supermarkets are different in commodity types, large in task density, distribution modes and the like, special people are required to be arranged to package commodities and distribute logistics. In the related art, the order sorting and distribution are carried out according to the order, namely, the order is picked and distributed by a single order, and after the order is placed by a user, the order is sorted by packing personnel in a warehouse and then distributed to distribution personnel.

However, the inventors found that at least the following problems exist in the related art: when picking the goods to the task under the user, if a single order picks the goods and delivers, the picking cost in the storehouse and the delivery time cost of logistics are greatly increased, and the picking efficiency and the delivery efficiency are lower.

Disclosure of Invention

Embodiments of the present invention provide a task processing method, a task processing system, an electronic device, and a non-volatile storage medium, which can reduce picking and distribution costs, improve task allocation efficiency, and accelerate task completion.

In order to solve the above technical problem, an embodiment of the present invention provides a task processing method, including: the pressure server calculates the picking pressure and/or the distribution pressure of the target object; the engine server acquires the picking pressure and/or the distribution pressure from the pressure server and determines a task upper limit value according to the picking pressure and/or the distribution pressure; the task server receives each task of the target object and acquires the characteristic data of each task; the task server acquires the similarity between a preset reference task and each referred task except the reference task in each task according to the characteristic data and the classification condition; the task server screens out the referenced tasks with the similarity greater than a preset threshold value with the reference task, and divides the screened referenced tasks and the reference task into a task set; the task server acquires the task upper limit value from the engine server and controls the number of tasks in each task set to be smaller than or equal to the task upper limit value; the task server sends the picking data of the tasks in each task set to the picking client side in a centralized manner, and sends the distribution data of the tasks in the task sets which finish picking to the distribution client side in a centralized manner after receiving picking completion information returned by the picking client side.

An embodiment of the present invention further provides a task processing system, including: : a pressure server for calculating a picking pressure and/or a delivery pressure of the target object; the engine server is used for acquiring the picking pressure and/or the distribution pressure from the pressure server and determining a task upper limit value according to the picking pressure and/or the distribution pressure; the task server is used for receiving each task of the target object and acquiring the characteristic data of each task; according to the feature data and the classification conditions, acquiring the similarity between a preset reference task and each referenced task except the reference task in each task; screening out the referenced tasks with the similarity greater than a preset threshold value with the reference task, and dividing the screened referenced tasks and the reference task into a task set; and sending the picking data of the tasks in each task set to a picking client in a centralized manner, and sending the distribution data of the tasks in the task sets which finish picking to a distribution client in a centralized manner after receiving picking completion information returned by the picking client. And the task server acquires the task upper limit value from the engine server and controls the number of tasks in each task set to be less than or equal to the task upper limit value.

Embodiments of the present invention further provide an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor executes the program to perform: calculating a picking pressure and/or a dispensing pressure of the target object; receiving each task of the target object and acquiring characteristic data of each task; according to the feature data and the classification conditions, acquiring the similarity between a preset reference task and each referenced task except the reference task in each task; screening out the referenced tasks with the similarity greater than a preset threshold value with the reference task, and dividing the screened referenced tasks and the reference task into a task set; wherein the number of tasks in each task set is less than or equal to the task upper limit value; and sending the picking data of the tasks in each task set to a picking client in a centralized manner, and sending the distribution data of the tasks in the task sets which finish picking to a distribution client in a centralized manner after receiving picking completion information returned by the picking client.

Embodiments of the present invention also provide a non-volatile storage medium for storing a computer-readable program for a computer to execute the task processing method as described above.

Compared with the prior art, the implementation mode of the invention has the main differences and the effects that: the engine server obtains the picking pressure and/or the distribution pressure calculated by the pressure server through the interaction among different servers, namely the pressure server calculates the picking pressure and/or the distribution pressure of the target object, so as to determine the task upper limit value. And the task server acquires the task upper limit value determined by the task server from the engine server, so that the number of tasks in each task set is controlled to be less than or equal to the task upper limit value when the tasks are classified. The picking pressure and/or the distribution pressure can reflect the current overall warehousing efficiency, namely the picking efficiency and the distribution efficiency of the target objects to a certain extent. Therefore, the engine server can reasonably determine the task upper limit value according to the picking pressure and/or the distribution pressure, namely the overall warehousing efficiency is considered, so that the number of tasks in each task set is reasonably limited. When the task server carries out task classification, the received feature data of each task of the target object is obtained, the similarity between a preset reference task and each referenced task except the reference task in each task is obtained according to the feature data and the classification conditions, the referenced task with the similarity larger than a preset threshold value with the reference task is screened out, and the screened referenced task and the reference task are divided into a task set. Namely, the task server obtains the similarity between the tasks based on the characteristic data and the classification conditions, and divides the tasks with high similarity into a task set, thereby facilitating the reasonable classification of the tasks. The task server sends the picking data of the tasks in each task set to the picking client in a centralized manner, and sends the distribution data of the tasks in the task sets completing picking to the distribution client after receiving picking completion information returned by the picking client, namely, the picking data and the distribution data of the tasks in each task set are sent in a centralized manner, so that the distribution efficiency of the tasks is improved, the tasks in each task set are conveniently and centrally picked and distributed, the warehousing efficiency is improved to a certain extent, the task completion speed is increased, and the picking and distribution cost is reduced.

In addition, the task server obtains the similarity between a reference task preset in each task and each referred task except the reference task according to the feature data and the classification conditions, and the similarity comprises the following steps: the task server determines the classification conditions which are met between the reference task and each referred task according to the characteristic data; the task server acquires a similarity coefficient corresponding to the determined classification condition; and the task server acquires the similarity between the reference task and each referred task according to the similarity coefficient. The method provides a specific way for acquiring the similarity, namely the similarity between the reference task and each referenced task is acquired mainly based on the classification conditions met between the reference task and each referenced task, and the method is favorable for accurately acquiring the similarity between the tasks.

In addition, different grades of classification conditions are set under each kind of classification conditions, and the different grades correspond to different similarity coefficients; the task server determines the classification conditions met between the reference task and each referred task according to the characteristic data, and specifically comprises the following steps: the task server determines the grade of the classification condition which is met between the reference task and each referred task according to the characteristic data; the task server obtains a similarity coefficient corresponding to the determined classification condition, specifically: and the task server acquires a similarity coefficient corresponding to the determined classification condition grade. That is, each classification condition includes classification conditions of different levels, and by further subdividing each classification condition, it is advantageous to further subdivide which level the classification condition satisfied between the reference task and each of the referenced tasks belongs to, thereby more accurately determining the similarity between the reference task and each of the referenced tasks.

In addition, the classification condition includes any one or a combination of the following: the reference task and the referenced task have target commodities in the same picking area, the sum of the number of the target commodities in the reference task and the referenced task is less than the stock number of the target commodities in the same picking area, the distance between the delivery destinations of the reference task and the referenced task is less than the preset distance, and the proximity of the reserved delivery time period of the reference task and the referenced task is more than the preset threshold. A specific form of classification condition is provided, and setting according to actual needs is facilitated.

Drawings

Fig. 1 is a flowchart of a task processing method according to a first embodiment of the present invention;

FIG. 2 is a diagram illustrating the satisfaction of sorting criteria between orders based on the picking area in which the goods are located within the task, according to the first embodiment of the present invention;

FIG. 3 is a diagram for explaining that a classification condition based on the stock quantity of the goods within the task is satisfied between orders according to the first embodiment of the present invention;

FIG. 4 is a diagram for explaining a classification condition between orders satisfying a task-based reserved delivery time period according to the first embodiment of the present invention;

FIG. 5 is a diagram for explaining a classification condition between orders satisfying a task-based delivery destination according to the first embodiment of the present invention;

FIG. 6 is a flowchart of an implementation procedure of step S104 in the second embodiment according to the present invention;

FIG. 7 is a schematic diagram of a task processing system according to a third embodiment of the present invention;

fig. 8 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, it will be appreciated by those of ordinary skill in the art that numerous technical details are set forth in order to provide a better understanding of the present application in various embodiments of the present invention. However, the technical solution claimed in the present application can be implemented without these technical details and various changes and modifications based on the following embodiments. The following embodiments are divided for convenience of description, and should not constitute any limitation to the specific implementation manner of the present invention, and the embodiments may be mutually incorporated and referred to without contradiction.

The first embodiment of the invention relates to a task processing method which is applied to electronic equipment. The electronic device may be: the present embodiment is not particularly limited to this, and the terminal and the server such as a mobile phone and a computer are used. The electronic device may have a virtual server for implementing different functions, and the virtual server in this embodiment may include a pressure server, an engine server, and a task server. Wherein the pressure server may implement the function of calculating the picking pressure and/or the dispensing pressure. The engine server can interact with the pressure server to realize the function of determining the task upper limit value according to the picking pressure and/or the distribution pressure acquired from the pressure server. The task server can interact with the engine server, the number of the tasks in each classified task set is controlled to be smaller than or equal to the upper limit value of the tasks, and the tasks of the target object are reasonably classified, so that the functions of centralized picking and distribution are realized. The following is a detailed description of the implementation details of the task processing method of the present embodiment, and the following is only provided for the convenience of understanding and is not necessary for implementing the present embodiment.

A flowchart of a task processing method according to this embodiment may be as shown in fig. 1, where the method includes:

in step S101, the pressure server calculates a picking pressure and/or a dispensing pressure of the target object.

The target object can be an object which can be displayed to a user on a 020 e-commerce platform, such as an object which needs to be ordered on line, picked and distributed off line, of a supermarket, a fruit and vegetable store and the like. For convenience of explanation, in the present embodiment and the following embodiments, the target object is explained by taking a supermarket as an example, but the present invention is not limited to this. In addition, the task of the target object mentioned below is a task that the target object needs to complete, for example, the task of the supermarket may be a distribution task that the supermarket needs to complete, that is, an order of the supermarket.

In one example, the distribution pressure of the supermarket may be obtained by a pressure server. The pressure server may calculate the delivery pressure in the following manner: and acquiring the sum of the non-final-state single amount of the distribution resources responsible for the order distribution of the supermarket and the order amount which is not distributed to the distribution resources by the supermarket. Wherein, the delivery resource can be the resource that order distribution can be accomplished for rider, unmanned aerial vehicle, intelligent vehicle etc.. The non-finalized singleton refers to the number of orders that have been allocated to the shipped resources, but the shipped resources have not yet completed the shipment. And then acquiring the maximum back order quantity of all the distribution resources responsible for the order distribution of the supermarket, namely the sum of the maximum back order quantity of each distribution resource. The maximum amount of each delivered resource can be pre-stored in the electronic device and can be modified according to actual needs. And finally, taking the proportion of the sum of the obtained non-final-state single amount and the unallocated order amount in the maximum back order amount of all distribution resources as the distribution pressure of the supermarket.

In one example, pickup pressure for a supermarket may be obtained via a pressure server. The pressure server may calculate the picking pressure by: the total of the order quantity of the picking resources which are responsible for picking orders of supermarkets picking orders and the order quantity waiting for picking orders is obtained. The goods picking resources can be resources which can complete order picking by a goods picker, an automatic goods picking device and the like. And then obtaining the maximum picking amount of all picking resources responsible for order picking of the supermarket, namely the sum of the maximum picking amount of all picking resources. The maximum inventory amount for each pick-up resource may be pre-stored in the electronic device and may be modified according to actual needs. And finally, taking the proportion of the sum of the obtained order quantity of the picking and the order quantity waiting for picking in the maximum picking quantity of all picking resources as the picking pressure of the supermarket.

In step S102, the engine server obtains the picking pressure and/or the delivery pressure from the pressure server, and determines a task upper limit value according to the picking pressure and/or the delivery pressure.

In one example, the engine server may pre-store the corresponding relationship between the picking pressure and/or the delivery pressure and the task upper limit value; wherein, the larger the picking pressure and/or the distribution pressure, the larger the corresponding task upper limit value can be. That is, the higher the picking pressure and/or dispensing pressure, the more the number of pieces of material is concentrated, and the lower the picking pressure and/or dispensing pressure, the less the number of pieces of material is concentrated. Wherein, the enhanced set number can be understood as increasing the number of orders collected for picking and delivering in each batch, i.e. increasing the number of orders in each task set.

Step S103, the task server receives each task of the target object and acquires the characteristic data of each task.

Specifically, each task is taken as an example of each order in the supermarket, that is, the task server can receive the order submitted by the user in the supermarket, so as to obtain the characteristic data of each received order, and the characteristic data of the order may include: the order form includes the items in the order form, the reserved delivery time of the order form, the delivery destination of the order form, the picking area where the items in the order form are located, the stock quantity of the items in the order form in the picking area, and the like.

And step S104, the task server acquires the similarity between a preset reference task and each referred task except the reference task in each task according to the characteristic data and the classification condition.

Wherein, the classification condition may include any one or a combination of the following: the classification condition based on the picking area where the goods in the task are located, the classification condition based on the stock number of the goods in the task, the classification condition based on the delivery destination of the task, and the classification condition based on the reserved delivery time period of the task. However, the four optional classification conditions are only used as examples in this embodiment, and in a specific implementation, the present invention is not limited to this. The task is exemplified by an order, and the above classification conditions may be classification conditions based on the order, that is, classification conditions based on a picking area where a product in the order is located, classification conditions based on the stock quantity of the product in the order, classification conditions based on a delivery destination of the order, and classification conditions based on an order scheduled delivery time period. The picking area can comprise a store and a front warehouse, the store can be understood as a supermarket area, and the front warehouse can be understood as a picking warehouse.

In one example, the task server may preset one task as a reference task and the rest tasks as referenced tasks in each task. Then, a classification condition that is satisfied between the reference task and each of the referenced tasks is determined based on the feature data. The criterion for determining that the classification condition is satisfied between the reference task and each of the referenced tasks may include any one or a combination of the following: the target commodities located in the same picking area exist in the reference task and the referenced task, the sum of the number of the target commodities in the reference task and the referenced task is smaller than the stock number of the target commodities in the same picking area, the distance between the delivery destinations of the reference task and the referenced task is smaller than a preset distance, and the proximity of the reserved delivery time period of the reference task and the referenced task is larger than a preset threshold. The preset distance and the preset threshold may be set according to actual needs, and this embodiment is not particularly limited to this.

For easy understanding of the classification conditions satisfied between the reference task and each referenced task, reference may be made to the classification diagrams of each order in the supermarket in fig. 2 to 5.

In fig. 2, the store inventory includes fresh food and the front warehouse inventory includes cola. Assume that the order received by the task server is: order a (1 chicken), order b (8 bottles of cola), and order c (7 bottles of cola); and taking the order b as a reference task, and taking the orders a and c as referenced tasks. Since there is a target commodity (cola) in the order b and c located in the same picking area (front bin), it can be determined that the order b and c satisfy the classification condition based on the picking area where the commodity is located within the task. Batch a may be an order set including order a, and batch b may be an order set including orders b and c.

In FIG. 3, the amount of coke stored in the store is 30 bottles, and the amount of coke stored in the front warehouse is 14 bottles. Assume that the order received by the task server is: order a (7 bottles of cola), order b (8 bottles of cola); and taking the order a as a reference task and the order b as a referenced task. Since the sum (7+8 — 15) of the numbers of the target goods (coke) in the order b and the order a is smaller than the stock number (30) of the target goods in the same picking area (store), it can be determined that the orders b and a satisfy the classification condition based on the stock number of the goods within the task. It is worth mentioning that if the amount of coke in the front warehouse also exceeds 15 bottles, i.e. the amount of coke in the front warehouse and the store is enough to satisfy the sum of the amount of coke in order b and order a, then the store can be selected to pick the order. Wherein, the batch a may be an order collection including orders a and b.

In fig. 4, the store inventory includes fresh food and the front end store inventory includes cola. Assume that the order received by the task server is: order a (7 bottles of cola, reserved delivery time period 9: 00-9: 30), order b (8 bottles of cola, reserved delivery time period 7: 30-8: 00), and order c (1 chicken, reserved delivery time period 7: 45-8: 15); and taking the order b as a reference task, and taking the orders a and c as referenced tasks. Since the reserved delivery time period of order b and the reserved delivery time period of order c are close, it can be determined that orders b and c satisfy the classification condition of the reserved delivery time period based on the task, and the reserved delivery time period of order a is separated from the reserved delivery time period of order b by a long time, it can be determined that orders b and a do not satisfy the classification condition of the reserved delivery time period based on the task. Batch a may be an order set including order a, and batch b may be an order set including orders b and c.

In fig. 5, it is assumed that the order received by the task server is: the orders a, b and c are distributed in the front bins, and commodities in the 3 orders are distributed in the front bins; the delivery destinations of the orders a, b and c are the positions of the user a, the user b and the user c in the figure respectively. And taking the order b as a reference task, and taking the orders a and c as referenced tasks. In fig. 5, the locations of the user a and the user b are located in the same area of interest (AOI), where the area of interest is an area-like geographic entity in the map data, and it is indicated that the locations of the user a and the user b are closer, that is, the distribution destinations of the orders a and b are closer, it may be determined that the orders a and b satisfy the classification condition of the distribution destinations based on the task. And the delivery destinations of the orders c and b do not fall on the same AOI, that is, the delivery destinations of the orders c and b are far away, it may be determined that the orders c and b do not satisfy the classification condition of the task-based delivery destinations. In a specific implementation, it may be determined that the orders a and b satisfy the classification condition of the delivery destination based on the task if the positions of the users a and b are close to each other, but this embodiment is not limited thereto. Batch a may be an order set including orders a and b, and batch b may be an order set including order c.

Then, the task server can obtain a similarity coefficient corresponding to the determined classification condition; the electronic device may pre-store a corresponding relationship between the classification condition and the similarity coefficient, where the corresponding relationship may be set according to actual needs, and this embodiment is not particularly limited to this.

And finally, the task server can acquire the similarity between the reference task and each referenced task according to the similarity coefficient. When the preset classification condition is one, if the classification condition is satisfied between the reference task and the referenced task, the similarity coefficient corresponding to the classification condition may be used as the similarity between the reference task and the referenced task. If the classification condition is not satisfied between the reference task and the referenced task, the similarity between the reference task and the referenced task can be considered to be 0. When the preset classification conditions are multiple, the similarity coefficients corresponding to the classification conditions which are met between the reference task and the referenced task can be summed. And then taking the sum of the similarity coefficients as a numerator, taking the number of the preset classification conditions as a denominator, and taking the quotient of the numerator and the denominator as the similarity between the reference task and the referenced task.

In an example, each task still takes each order in the supermarket as an example, and this step can be understood as obtaining the similarity between the preset reference order in each order and each referenced order except the reference order according to the feature data and the classification condition of each order. The preset reference order is any one of orders in the supermarket, and each order can be used as the reference order, so that the similarity between the preset reference order and each other referenced order can be obtained when the preset reference order is used as the reference order.

Step S105, the task server screens out the referenced tasks with the similarity greater than a preset threshold value with the reference task, and divides the screened referenced tasks and the reference tasks into a task set.

The preset threshold may be set according to actual needs, which is not specifically limited in this embodiment, and the reference task and the referenced task having a large similarity to the referenced task are divided into a task set. And the task server acquires the task upper limit value from the engine server and controls the number of the tasks in each task set to be less than or equal to the task upper limit value.

In one example, the tasks are still illustrated as orders from a supermarket. One order can be selected from the orders as a reference order in advance, the similarity between the reference order and other referenced orders is obtained, referenced orders with the similarity larger than a preset threshold value with the reference order are screened out, and the screened referenced orders and the reference order are divided into a task set. And then removing the orders which are already classified into the task set, reselecting one order as a reference order, acquiring the similarity between the reselected reference order and each other reference order, screening the reference orders with the similarity to the reselected reference order being greater than a preset threshold value, and classifying the screened reference orders and the reselected reference orders into the task set. And according to the mode, completing the classification of all orders in sequence.

Referring to FIG. 2, batch a may be a division of order a into one set of tasks and batch b may be a division of orders b, c into another set of tasks. The meaning of the set batch a and the set batch b appearing in fig. 3 to fig. 5 is similar to that in fig. 2, and is not repeated herein to avoid repetition.

And step S106, the task server sends the picking data of the tasks in each task set to the picking client in a centralized manner, and sends the distribution data of the tasks in the task sets which finish picking to the distribution client in a centralized manner after receiving picking completion information returned by the picking client.

In particular, it is understood that the tasks in each set of tasks are collectively picked and distributed. For example, the task server may first send the picking data of the tasks in the task set to the picking client in a centralized manner, so that the picking resources corresponding to the picking client perform centralized picking on the commodities in each task in the task set. The goods picking resources can be goods pickers, goods picking robots and the like, and the goods picking client can be mobile phones, computers and other devices used by the goods picking resources. After picking, the picking client can return picking completion information to the task server, and the task server sends the delivery data of the tasks in the picking task set to the delivery client in a centralized manner, i.e. the delivery resources are called in a centralized manner for the tasks in the task set, and the delivery resources deliver the tasks in the task set in a centralized manner.

For example, the task server sends the picking data of the tasks in the task set 1 to the picking client corresponding to the picking resource 1, and sends the picking data of the tasks in the task set 2 to the picking client corresponding to the picking resource 2. The goods picking resource 1 sorts the goods in the tasks in the task set 1, and the goods picking resource 2 sorts the goods in the tasks in the task set 2. After picking the goods, the picking client corresponding to the picking resources 1 feeds back information that the picking of the tasks in the task set 1 is completed to the task server, and then the task server calls the distribution resources to receive orders for the tasks in the task set 1 in a centralized manner, so that the tasks in the task set 1 are distributed in a centralized manner. After the picking client corresponding to the picking resource 2 feeds back the information that the picking of the tasks in the task set 2 is completed to the task server, the task server calls the distribution resource to receive orders for the tasks in the task set 2 in a centralized manner, so that the tasks in the task set 2 are distributed in a centralized manner. In the present embodiment, the division of each task of the target object into the two task sets is merely an example, and the specific implementation is not limited to this. In a specific implementation, the number of picking resources for picking up the order items in one task set may be 1 or more, and this embodiment is not particularly limited to this.

In a specific implementation, different task sets may correspond to different picking areas, and when the picking data is sent to the picking client, the picking data can be sent to the picking clients distributed in the picking areas according to the picking areas corresponding to the task sets. For example, referring to fig. 2, the batch a where the order a is located is denoted as a task set 1, the batch b where the orders b and c are located is denoted as a task set 2, the picking area of the task set 1 is a store, and the picking area of the task set 2 is a front warehouse. The task server may send the pick data for the tasks in the task set 1 to pick clients distributed in the store and the pick data for the tasks in the task set 2 to pick clients distributed in the pre-warehouse. Similarly, when sending the delivery data, the delivery data of the tasks in the task set 1 may be sent to delivery clients distributed near the store, and the delivery data of the tasks in the task set 2 may be sent to delivery clients distributed near the front warehouse.

Taking an application scenario of picking and delivering orders in a supermarket as an example, it is assumed that the order collection upper limit (task upper limit) determined according to the obtained picking pressure and/or delivering pressure of the supermarket is 7 orders. Then it is ensured that the number of orders in each order set (task set) does not exceed 7 orders when sorting orders for a supermarket. The following description specifically exemplifies the classification of each order in a supermarket:

assume that the classification conditions include: the target goods in the same picking area exist in the reference order and the referenced order (condition 1), the sum of the number of the target goods in the reference order and the referenced order is less than the stock number of the target goods in the same picking area (condition 2), the distance between the delivery destinations of the reference order and the referenced order is less than 1 kilometer (condition 3), and the interval between the reserved delivery time periods of the reference order and the referenced order is less than 20 minutes (condition 4). The similarity coefficients corresponding to the 4 classification conditions are all 0.9, and of course, in this example, the similarity coefficients corresponding to the 4 classification conditions are only taken as an example, which is not limited in specific implementation and can be set according to actual needs. In the following, 4 orders in a supermarket are classified as an example, and as the characteristic data of the 4 orders in the following table 1, it should be noted that the embodiment only takes 4 orders as an example for convenience of description, but the number of orders in a specific implementation is usually much more than 4.

TABLE 1

The goods picking area of the supermarket is divided into a store and a front-end bin, wherein the coke in the store is stored in both the store and the front-end bin, the storage of the coke in the store is 30 bottles, and the storage of the coke in the front-end bin is 14 bottles. The chickens are only stored in the store, and the number of the stored chickens is 10. The oil and the flour are stored in the store and the front-end bin, the inventory of the oil and the flour in the store is 10, and the inventory of the oil and the flour in the front-end bin is 20. The distance between the cell A and the cell C is less than 1 kilometer, and the distance between the cell A and the cell C is greater than 1 kilometer. The distance between the cell B and the cell D is less than 1 kilometer, and the distance between the cell B and the cell D and A, C are both greater than 1 kilometer.

In the 4 orders in table 1, first, order 1 is taken as a reference order, and orders 2, 3, and 4 are taken as referenced orders, and the similarity between order 1 and orders 2, 3, and 4 is obtained respectively. As can be seen from table 1, the classification conditions satisfied by order 1 and order 2 are 0, and it can be seen that the similarity between order 1 and order 2 is 0. If the classification conditions satisfied by the order 1 and the order 3 are 4, the similarity between the order 1 and the order 3 is:

(0.9+0.9+0.9+0.9+)÷4＝0.9

the classification conditions satisfied by order 1 and order 4 are 0, and it can be seen that the similarity between order 1 and order 4 is 0. It can be seen that the similarity between order 1 and order 2 is very high, and order 1 and order 3 can be grouped into 1 order set, which is denoted as set 1. Then, order 2 of order 2 and order 4 that have not been classified is taken as a reference order, and order 4 is taken as a referenced order, although in a specific implementation, if there are other unclassified orders, other unclassified orders will also be taken as referenced orders of order 2. As can be seen from table 1, the number of classification conditions that are satisfied between the order 2 and the order 4 is 4, and it can be seen that the similarity between the order 2 and the order 4 is the same as the similarity between the previous order 1 and the previous order 3, that is, 0.9, and it can be seen that the similarity between the order 2 and the order 4 is extremely high, the order 2 and the order 4 are classified into an order set, which is denoted as a set 2. It should be noted that, since this example is only an example in which 4 orders in a supermarket are taken as an example, when a plurality of orders in the supermarket are classified in a specific implementation, it is ensured that the number of orders in each order set does not exceed the order-collection upper limit value, that is, the order-collection upper limit value initially determined in this example is 7 orders. And finally, after the orders in the supermarket are classified, the orders in each order set are collected and delivered in a centralized mode, namely, the orders in the set 1 are collected and delivered in a centralized mode, and the orders in the set 2 are collected and delivered in a centralized mode. For example, a picker picks orders 1 and 3 simultaneously, and a delivery person is called to deliver orders 1 and 3 simultaneously.

The above examples in the present embodiment are only for convenience of understanding, and do not limit the technical aspects of the present invention.

Compared with the prior art, in the embodiment, the order picking pressure and/or the distribution pressure of the target object are/is calculated through interaction among different servers, namely, the pressure server calculates the order picking pressure and/or the distribution pressure of the target object, and the engine server obtains the calculated order picking pressure and/or the distribution pressure from the pressure server, so that the task upper limit value is determined. And the task server acquires the task upper limit value determined by the task server from the engine server, so that the number of tasks in each task set is controlled to be less than or equal to the task upper limit value when the tasks are classified. The picking pressure and/or the distribution pressure can reflect the current overall warehousing efficiency, namely the picking efficiency and the distribution efficiency of the target objects to a certain extent. Therefore, the engine server can reasonably determine the task upper limit value according to the picking pressure and/or the distribution pressure, namely the overall warehousing efficiency is considered, so that the number of tasks in each task set is reasonably limited. When the task server carries out task classification, the received feature data of each task of the target object is obtained, the similarity between a preset reference task and each referenced task except the reference task in each task is obtained according to the feature data and the classification conditions, the referenced task with the similarity larger than a preset threshold value with the reference task is screened out, and the screened referenced task and the reference task are divided into a task set. Namely, the task server obtains the similarity between the tasks based on the characteristic data and the classification conditions, and divides the tasks with high similarity into a task set, thereby facilitating the reasonable classification of the tasks. The task server sends the picking data of the tasks in each task set to the picking client in a centralized manner, and sends the distribution data of the tasks in the task sets completing picking to the distribution client after receiving picking completion information returned by the picking client, namely, the picking data and the distribution data of the tasks in each task set are sent in a centralized manner, so that the distribution efficiency of the tasks is improved, the tasks in each task set are conveniently and centrally picked and distributed, the warehousing efficiency is improved to a certain extent, the task completion speed is increased, and the picking and distribution cost is reduced.

It is worth mentioning that by classifying each task of the target object, dividing different task sets, and performing centralized picking and distribution of tasks in one task set, the picking number per 5min is increased from 15/person to 24/person, that is, the picking efficiency is improved. The average quantity of the delivered goods of the distribution resources at each time is improved from 2 to 3.2, namely, the distribution efficiency is improved. The picking time is shortened from 12min on average to 8min on average, namely, the picking time of the average order is shortened. The distribution time length is shortened from 28min on average to 23min on average, namely the distribution time length of the average single is shortened. It can be seen that the total time for picking and delivering from 40min to 31min is shortened, i.e. the time for completing the single is shortened.

The following description specifically describes implementation details of the task processing method of the present embodiment, and the following description is provided only for the sake of easy understanding and is not necessary for implementing the present solution.

Fig. 6 may be referred to as a flowchart of an implementation process of step S104 in this embodiment, and the implementation process includes:

in step S201, the task server determines the grade of the classification condition satisfied between the reference task and each of the referenced tasks according to the feature data.

Wherein, each kind of classification condition is provided with classification conditions of different grades, and different grades correspond to different similarity coefficients. For example, the classification conditions having different levels set under the classification condition based on the reserved delivery time period of the task may include: the scheduled delivery time period of the reference task and the referenced task is identical (level 1). The reference task partially coincides with the scheduled delivery time period of the referenced task (level 2). The reference task and the scheduled delivery time period of the referenced task do not coincide, but are closely spaced (level 3). The scheduled delivery time periods of the reference task and the referenced task do not coincide and are far apart (level 4). The classification conditions having different levels set under the classification condition based on the delivery destination of the task may include: the distance separating the reference task from the delivery destination of the referenced task is less than 300 meters (level 1). The distance separating the reference task from the delivery destination of the referenced task is between 300 and 600 meters (level 2). The distance separating the reference task from the delivery destination of the referenced task is between 600 meters and 1 km (level 3). The distance separating the reference task from the delivery destination of the referenced task is between 1 km and 1.5 km (level 4). The sorting conditions having different grades set based on the sorting conditions of the picking area where the goods are located within the task may include: all items in the reference and referenced tasks are located in the same pick-up area (level 1). The reference task is located in the same pick-up area (level 2) as 70% of the items in the referenced task. The reference task is located in the same pick-up area (level 3) as 50% of the items in the referenced task. The reference task is located in the same pick-up area (level 4) as 30% of the items in the referenced task. It should be noted that the classification conditions of the above-mentioned levels are only specific descriptions for easy understanding, and the specific implementation is not limited thereto.

Specifically, the task server may analyze the acquired feature data of each task, such as a degree of closeness of the reserved delivery time period of each task, a distance between delivery destinations of each task, a picking area where goods in each task are located, and an inventory number of the goods in each task in different picking areas. Thereby determining the level of classification conditions that are satisfied between the reference task and each of the referenced tasks.

In step S202, the task server acquires a similarity coefficient corresponding to the level of the determined classification condition.

Wherein, the task server can pre-store the corresponding relation between the grade of the classification condition and the similarity coefficient. For example, the similarity coefficients from level 1 to level 4 may become smaller and smaller, indicating that the classification condition with the higher level corresponds to lower similarity between two tasks. The similarity coefficients corresponding to the levels of the different types of classification conditions may be the same or different. For example, the level 1 under the classification condition based on the reserved delivery time period of the task may be the same as or different from the similarity coefficient of the level 1 under the classification condition based on the delivery destination of the task.

Specifically, the task server may obtain the similarity coefficient corresponding to the level of the determined classification condition according to a pre-stored correspondence. It will be appreciated that if there are 4 different categories of classification conditions, the determined similarity coefficients should be 4, i.e. each classification condition would correspond to a similarity coefficient.

Step S203, the task server obtains the similarity between the reference task and each referenced task according to the similarity coefficient.

Specifically, if there is a classification condition, the similarity coefficient corresponding to the determined level of the classification condition is the similarity between the reference task and each of the referenced tasks. For example, if the classification condition corresponding to the level 4 is satisfied by the reference task and each of the referenced tasks, the similarity coefficient corresponding to the level 4 is used as the similarity between the reference task and the referenced task. If the classification condition corresponding to any one grade is not met by the reference task and the referenced task, the similarity between the reference task and the referenced task is 0. If there are multiple classification conditions, for example, 4 classification conditions, the determined 4 classification conditions all correspond to one similarity coefficient, and the 4 similarity coefficients may be averaged, and the average value is used as the similarity between the reference task and each of the referenced tasks.

Similarly, taking an application scenario of picking and delivering orders in a supermarket as an example, the similarity between the orders obtained in the embodiment is exemplified as follows:

the similarity coefficients corresponding to level 1 to level 4 are assumed to be: 0.95, 0.85, 0.7 and 0.5. In table 1, it is assumed that the distance between the a cell and the C cell is 200 meters, and the distance between the B cell and the D cell is 500 meters. Referring to the characteristic data of each order in table 1 and the related description of the grades of the different kinds of classification conditions described in step S301, it can be analyzed that: the sort condition based on the delivery destination satisfied by order 1 and order 3 is specifically level 1. The sort condition based on the delivery destination satisfied by order 2 and order 4 is specifically level 2. The classification condition based on the reserved time period satisfied by order 1 and order 3 is specifically level 2, and the classification condition based on the reserved time period satisfied by order 2 and order 4 is specifically level 2. The sorting condition satisfied by order 1 and order 3 is specifically level 1 based on the order and the picking area where the goods in the order are located. The sorting condition satisfied by order 2 and order 4 is specifically level 1 based on the order and the picking area where the goods in the order are located. The preset sorting conditions are assumed to be the three types described above, namely, the sorting condition based on the delivery destination, the sorting condition based on the reserved time period, and the sorting condition based on the picking area where the product in the order is located. Then, the calculated similarity between order 1 and order 3 is: (0.95+0.85+0.95) ÷ 3 ═ 0.92. The calculated similarity between order 2 and order 4 is: (0.85+0.85+0.95) ÷ 3 ═ 0.88.

Compared with the prior art, the method and the device have the advantages that each classification condition is further subdivided, the classification condition which is met between the reference task and each referenced task belongs to which grade is further subdivided, and therefore the similarity between the reference task and each referenced task is more accurately determined.

The steps of the above methods are divided for clarity, and the implementation may be combined into one step or split some steps, and the steps are divided into multiple steps, so long as the same logical relationship is included, which are all within the protection scope of the present patent; it is within the scope of the patent to add insignificant modifications to the algorithms or processes or to introduce insignificant design changes to the core design without changing the algorithms or processes.

A third embodiment of the present invention relates to a task processing system, as shown in fig. 7, including: a pressure server 301 for calculating a picking pressure and/or a delivery pressure of a target object; an engine server 302, configured to obtain the picking pressure and/or the delivery pressure from the pressure server 301, and determine a task upper limit value according to the picking pressure and/or the delivery pressure; the task server 303 is configured to receive each task of the target object and obtain feature data of each task; according to the feature data and the classification conditions, acquiring the similarity between a preset reference task and each referenced task except the reference task in each task; screening out the referenced tasks with the similarity greater than a preset threshold value with the reference task, and dividing the screened referenced tasks and the reference task into a task set; the picking data of the tasks in each task set are sent to a picking client side in a centralized mode, and after picking completion information returned by the picking client side is received, the distribution data of the tasks in the task sets which complete picking are sent to a distribution client side in a centralized mode; and the task server acquires the task upper limit value from the engine server and controls the number of tasks in each task set to be less than or equal to the task upper limit value.

In one example, the task server 303 obtains, according to the feature data and the classification condition, a similarity between a preset reference task and each referred task except the reference task in each of the tasks, including: the task server 303 determines a classification condition which is satisfied between the reference task and each of the referenced tasks according to the feature data; acquiring a similarity coefficient corresponding to the determined classification condition; and according to the similarity coefficient, obtaining the similarity between the reference task and each referred task.

In one example, different levels of classification conditions are set under each classification condition, and the different levels correspond to different similarity coefficients; the task server 303 determines, according to the feature data, a classification condition that is satisfied between the reference task and each of the referenced tasks, specifically: the task server 303 determines the grade of the classification condition satisfied between the reference task and each of the referenced tasks according to the feature data; the task server 303 obtains a similarity coefficient corresponding to the determined classification condition, specifically: the task server 303 acquires a similarity coefficient corresponding to the determined level of the classification condition.

In one example, the classification condition includes any one or a combination of the following: the classification condition based on the picking area where the goods in the task are located, the classification condition based on the stock number of the goods in the task, the classification condition based on the delivery destination of the task, and the classification condition based on the reserved delivery time period of the task.

In one example, the classification condition includes: classifying conditions based on the picking area where the goods in the task are located; the picking area comprises: stores and front-end bins.

In one example, the classification condition includes any one or a combination of the following: the reference task and the referenced task have target commodities in the same picking area, the sum of the number of the target commodities in the reference task and the referenced task is less than the stock number of the target commodities in the same picking area, the distance between the delivery destinations of the reference task and the referenced task is less than a preset distance, and the proximity of the reserved delivery time period of the reference task and the referenced task is greater than a preset threshold.

It should be understood that this embodiment is a system example corresponding to the first or second embodiment, and may be implemented in cooperation with the first or second embodiment. The related technical details mentioned in the first or second embodiment are still valid in this embodiment, and are not described herein again to reduce repetition. Accordingly, the related-art details mentioned in the present embodiment can also be applied to the first or second embodiment.

A fourth embodiment of the present invention relates to an electronic apparatus, as shown in fig. 8, including: at least one processor 401; and a memory 402 communicatively coupled to the at least one processor 401; and a communication component 403 communicatively coupled to the scanning device, the communication component 403 receiving and transmitting data under control of the processor 401; wherein the memory 402 stores instructions executable by the at least one processor 401 to perform, by the at least one processor 401: calculating a picking pressure and/or a dispensing pressure of the target object; receiving each task of the target object and acquiring characteristic data of each task; according to the feature data and the classification conditions, acquiring the similarity between a preset reference task and each referenced task except the reference task in each task; screening out the referenced tasks with the similarity greater than a preset threshold value with the reference task, and dividing the screened referenced tasks and the reference task into a task set; wherein the number of tasks in each task set is less than or equal to the task upper limit value; and sending the picking data of the tasks in each task set to a picking client in a centralized manner, and sending the distribution data of the tasks in the task sets which finish picking to a distribution client in a centralized manner after receiving picking completion information returned by the picking client.

Specifically, the electronic device includes: one or more processors 401 and a memory 402, one processor 401 being exemplified in fig. 8. The processor 401 and the memory 402 may be connected by a bus or other means, and fig. 4 illustrates the connection by a bus as an example. Memory 402, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The processor 401 executes various functional applications of the device and data processing, that is, implements the above-described task processing method, by executing nonvolatile software programs, instructions, and modules stored in the memory 402.

The memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store a list of options, etc. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 402 may optionally include memory 402 located remotely from the processor 401, and these remote memories 402 may be connected to external devices through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

One or more modules are stored in the memory 402 and, when executed by the one or more processors 401, perform the task processing method of any of the method embodiments described above.

The product can execute the method provided by the embodiment of the application, has corresponding functional modules and beneficial effects of the execution method, and can refer to the method provided by the embodiment of the application without detailed technical details in the embodiment.

A fifth embodiment of the invention relates to a non-volatile storage medium for storing a computer-readable program for causing a computer to perform some or all of the above method embodiments.

That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific examples for carrying out the invention, and that various changes in form and details may be made therein without departing from the spirit and scope of the invention in practice.

The embodiment of the application provides A1. a task processing method, which comprises the following steps:

the pressure server calculates the picking pressure and/or the distribution pressure of the target object;

the engine server acquires the picking pressure and/or the distribution pressure from the pressure server and determines a task upper limit value according to the picking pressure and/or the distribution pressure;

the task server receives each task of the target object and acquires the characteristic data of each task;

the task server acquires the similarity between a preset reference task and each referred task except the reference task in each task according to the characteristic data and the classification condition;

the task server screens out the referenced tasks with the similarity greater than a preset threshold value with the reference task, and divides the screened referenced tasks and the reference task into a task set; the task server acquires the task upper limit value from the engine server and controls the number of tasks in each task set to be smaller than or equal to the task upper limit value;

the task server sends the picking data of the tasks in each task set to the picking client side in a centralized manner, and sends the distribution data of the tasks in the task sets which finish picking to the distribution client side in a centralized manner after receiving picking completion information returned by the picking client side.

A2. According to the task processing method described in a1, the task server obtains, according to the feature data and the classification condition, a similarity between a reference task preset in each of the tasks and each of the referenced tasks other than the reference task, including:

the task server determines the classification conditions which are met between the reference task and each referred task according to the characteristic data;

the task server acquires a similarity coefficient corresponding to the determined classification condition;

and the task server acquires the similarity between the reference task and each referred task according to the similarity coefficient.

A3. According to the task processing method described in a2, each of the classification conditions is provided with a classification condition of a different level, and the different levels correspond to different similarity coefficients;

the task server determines the classification conditions met between the reference task and each referred task according to the characteristic data, and specifically comprises the following steps:

the task server determines the grade of the classification condition which is met between the reference task and each referred task according to the characteristic data;

the task server obtains a similarity coefficient corresponding to the determined classification condition, specifically:

and the task server acquires a similarity coefficient corresponding to the determined classification condition grade.

A4. The task processing method according to any one of A1-A3, wherein the classification condition comprises any one or combination of the following:

the classification condition based on the picking area where the goods in the task are located, the classification condition based on the stock number of the goods in the task, the classification condition based on the delivery destination of the task, and the classification condition based on the reserved delivery time period of the task.

A5. The task processing method according to a4, wherein the classification condition includes: classifying conditions based on the picking area where the goods in the task are located;

the picking area comprises: stores and front-end bins.

A6. The task processing method according to a1, wherein the classification condition includes any one or combination of the following:

the reference task and the referenced task have target commodities in the same picking area, the sum of the number of the target commodities in the reference task and the referenced task is less than the stock number of the target commodities in the same picking area, the distance between the delivery destinations of the reference task and the referenced task is less than a preset distance, and the proximity of the reserved delivery time period of the reference task and the referenced task is greater than a preset threshold.

An embodiment of the present application further provides a b1. a task processing system, including:

a pressure server for calculating a picking pressure and/or a delivery pressure of the target object;

the engine server is used for acquiring the picking pressure and/or the distribution pressure from the pressure server and determining a task upper limit value according to the picking pressure and/or the distribution pressure;

the task server is used for receiving each task of the target object and acquiring the characteristic data of each task; according to the feature data and the classification conditions, acquiring the similarity between a preset reference task and each referenced task except the reference task in each task; screening out the referenced tasks with the similarity greater than a preset threshold value with the reference task, and dividing the screened referenced tasks and the reference task into a task set; the picking data of the tasks in each task set are sent to a picking client side in a centralized mode, and after picking completion information returned by the picking client side is received, the distribution data of the tasks in the task sets which complete picking are sent to a distribution client side in a centralized mode;

and the task server acquires the task upper limit value from the engine server and controls the number of tasks in each task set to be smaller than or equal to the task upper limit value.

An embodiment of the present application further provides c1. an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor executes, when executing the program:

calculating a picking pressure and/or a dispensing pressure of the target object;

receiving each task of the target object and acquiring characteristic data of each task;

according to the feature data and the classification conditions, acquiring the similarity between a preset reference task and each referenced task except the reference task in each task;

screening out the referenced tasks with the similarity greater than a preset threshold value with the reference task, and dividing the screened referenced tasks and the reference task into a task set; wherein the number of tasks in each task set is less than or equal to the task upper limit value;

and sending the picking data of the tasks in each task set to a picking client in a centralized manner, and sending the distribution data of the tasks in the task sets which finish picking to a distribution client in a centralized manner after receiving picking completion information returned by the picking client.

C2. According to the electronic device described in C1, the obtaining, according to the feature data and the classification condition, a similarity between a preset reference task and each referred task other than the reference task in each of the tasks includes:

according to the characteristic data, determining a classification condition which is met between the reference task and each referred task;

acquiring a similarity coefficient corresponding to the determined classification condition;

and according to the similarity coefficient, obtaining the similarity between the reference task and each referred task.

C3. According to the electronic device of C2, each of the classification conditions is provided with a different level of classification conditions, where the different levels correspond to different similarity coefficients;

the determining, according to the feature data, a classification condition that is satisfied between the reference task and each of the referenced tasks specifically includes:

determining the grade of the classification condition which is met between the reference task and each referred task according to the characteristic data;

the obtaining of the similarity coefficient corresponding to the determined classification condition specifically includes:

and acquiring a similarity coefficient corresponding to the determined grade of the classification condition.

C4. The electronic device of any of C1-C3, the classification condition comprising any one or combination of:

C5. The electronic device of C4, the classification condition comprising: classifying conditions based on the picking area where the goods in the task are located;

the picking area comprises: stores and front-end bins.

C6. The electronic device according to C1, wherein the classification condition includes any one or a combination of the following:

A non-volatile storage medium storing a computer-readable program for causing a computer to perform the task processing method according to any one of a1 through a6 is also provided by an embodiment of the present application.

Claims

1. A task processing method, comprising:

the pressure server calculates the picking pressure of the target object;

the engine server acquires the picking pressure from the pressure server and determines a task upper limit value according to the picking pressure;

the task server acquires the similarity between a preset reference task and each referred task except the reference task in each task according to the characteristic data and the classification condition; wherein the classification condition includes: the sorting condition based on the picking area where the goods in the task are located and/or the sorting condition based on the inventory quantity of the goods in the task;

2. The task processing method according to claim 1, wherein the task server obtains a similarity between a preset reference task and each referred task except the reference task in each of the tasks according to the feature data and the classification condition, and includes:

3. The task processing method according to claim 2, wherein different levels of classification conditions are set for each of the classification conditions, and the different levels correspond to different similarity coefficients;

4. A task processing method according to any one of claims 1 to 3, wherein the classification condition further includes:

a classification condition based on a delivery destination of the task, and/or a classification condition based on a scheduled delivery time period of the task.

5. The task processing method according to claim 4, wherein the classification condition includes: classifying conditions based on the picking area where the goods in the task are located;

the picking area comprises: stores and front-end bins.

6. The task processing method according to claim 1, wherein the classification condition includes any one or a combination of:

7. A task processing system, comprising:

the pressure server is used for calculating the picking pressure of the target object;

the engine server is used for acquiring the picking pressure from the pressure server and determining a task upper limit value according to the picking pressure;

the task server is used for receiving each task of the target object and acquiring the characteristic data of each task; according to the feature data and the classification conditions, acquiring the similarity between a preset reference task and each referenced task except the reference task in each task; screening out the referenced tasks with the similarity greater than a preset threshold value with the reference task, and dividing the screened referenced tasks and the reference task into a task set; the picking data of the tasks in each task set are sent to a picking client side in a centralized mode, and after picking completion information returned by the picking client side is received, the distribution data of the tasks in the task set which completes picking are sent to a distribution client side in a centralized mode; wherein the classification condition includes: the sorting condition based on the picking area where the goods in the task are located and/or the sorting condition based on the inventory quantity of the goods in the task;

8. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor executing the program to perform:

calculating the picking pressure of the target object;

according to the feature data and the classification conditions, acquiring the similarity between a preset reference task and each referenced task except the reference task in each task; wherein the classification condition includes: the sorting condition based on the picking area where the goods in the task are located and/or the sorting condition based on the inventory quantity of the goods in the task;

screening out the referenced tasks with the similarity greater than a preset threshold value with the reference task, and dividing the screened referenced tasks and the reference task into a task set; the task number in each task set is less than or equal to the task upper limit value;

9. The electronic device according to claim 8, wherein the obtaining, according to the feature data and the classification condition, a similarity between a preset reference task and each referred task other than the reference task in each of the tasks includes:

10. The electronic device according to claim 9, wherein each of the classification conditions is provided with a different level of classification conditions, and the different levels correspond to different similarity coefficients;

11. The electronic device according to any one of claims 8 to 10, wherein the classification condition comprises any one or a combination of the following:

12. The electronic device of claim 11, wherein the classification condition comprises: classifying conditions based on the picking area where the goods in the task are located;

the picking area comprises: stores and front-end bins.

13. The electronic device of claim 8, wherein the classification condition comprises any one or a combination of the following:

14. A non-volatile storage medium storing a computer-readable program for causing a computer to execute the task processing method according to any one of claims 1 to 6.