CN115293713A

CN115293713A - Order data processing method, device, equipment and storage medium

Info

Publication number: CN115293713A
Application number: CN202211221633.6A
Authority: CN
Inventors: 栗志; 李超; 李培杰; 舒大克
Original assignee: Ali Health Technology China Co ltd
Current assignee: Ali Health Technology China Co ltd
Priority date: 2022-10-08
Filing date: 2022-10-08
Publication date: 2022-11-04

Abstract

The embodiment of the specification provides an order data processing method, an order data processing device, order data processing equipment and a storage medium. The method comprises the following steps: receiving a goods order; the goods order is split into a first sub-order comprising first goods information and a second sub-order comprising second goods information; the time consumption of the processing flow of the first goods information is greater than that of the processing flow of the second goods information; placing the second sub-order into an order pool; the order pool comprises a plurality of orders which are not yet executed to be delivered out of the warehouse; under the condition that the first sub-order executes the ending processing flow, a second sub-order is obtained based on the matching of the first sub-order in the order pool; and performing the goods delivery based on the goods order combined by the first sub-order and the second sub-order. After the second sub-order with less time-consuming processing flow in the same goods order waits for the first sub-order with more time-consuming processing flow to be executed, the first sub-order and the second sub-order are combined to execute goods delivery, so that the warehouse delivery pressure is relieved to a certain extent.

Description

Order data processing method, device, equipment and storage medium

Technical Field

Embodiments in this specification relate to the field of warehouse management, and in particular, to an order data processing method, apparatus, device, and storage medium.

Background

Currently, some goods require a certain amount of audit time before being sold. Therefore, under the condition that one goods order comprises goods needing to be checked and goods not needing to be checked, the goods needing to be checked and the goods not needing to be checked are split into two orders for delivery. Specifically, the order of the goods which do not need to be checked can be issued to the warehouse for delivery. After the goods to be checked are checked, issuing the checked goods order to a warehouse for delivery.

Therefore, multiple warehouses are required for goods of the same order, which increases the pressure of warehouse shipment.

Disclosure of Invention

In view of the above, embodiments of the present disclosure are directed to providing an order data processing method, apparatus, device and storage medium, so as to reduce the pressure of warehouse shipment to some extent.

One embodiment of the present specification provides an order data processing method. The method comprises the following steps: receiving a goods order; the goods order is split into a first sub-order comprising first goods information and a second sub-order comprising second goods information; the time consumption of the processing flow of the first goods information is greater than that of the processing flow of the second goods information; placing the second sub-order into an order pool; wherein the order pool comprises a plurality of orders which are not yet executed to deliver the goods; under the condition that the processing flow is finished when the first sub-order is executed, matching the first sub-order in the order pool to obtain a second sub-order; performing a shipment of goods based on the goods order in which the first sub-order and the second sub-order are merged.

One embodiment of the present specification provides an order data processing apparatus. The device comprises: a receiving module for receiving a goods order; the goods order is split into a first sub-order comprising first goods information and a second sub-order comprising second goods information; the time consumption of the processing flow of the first goods information is longer than that of the processing flow of the second goods information; the adding module is used for placing the second sub-order into an order pool; wherein the order pool comprises a plurality of orders which are not yet executed to deliver the goods; the matching module is used for matching the first sub-order in the order pool to obtain a second sub-order under the condition that the processing flow is finished when the first sub-order is executed; and the execution module is used for executing the delivery of goods based on the goods orders combined by the first sub-orders and the second sub-orders.

In the embodiments provided by the present description, the second sub-order with a processing flow consuming less time in the same item order waits for the first sub-order with a consuming more time to be executed after the processing flow of the first sub-order is completed, and then the first sub-order and the second sub-order are combined to execute the delivery of the item, so that the delivery pressure of the warehouse is reduced to a certain extent.

Drawings

Fig. 1 is a schematic diagram illustrating an application scenario example of an order data processing method according to an embodiment of the present specification.

Fig. 2 is a schematic diagram illustrating a system architecture of an order data processing method according to an embodiment of the present disclosure.

Fig. 3 is a schematic diagram illustrating a flow of an order data processing method according to an embodiment of the present specification.

Fig. 4 is a schematic diagram of an order data processing device according to an embodiment of the present specification.

FIG. 5 is a schematic diagram of a computer device according to one embodiment of the present description.

Detailed Description

SUMMARY

In the related art, a part of goods needs to be audited before being sold. After the audit is confirmed, the goods can be sold to the corresponding consumer. For example, prescription drugs for the medical field. The consumer is required to go through the review of the pharmacist after ordering. After confirmation by the pharmacist, the warehouse can deliver the prescribed drug to the consumer. For drugs that do not require auditing, a warehouse may be instructed directly to deliver the drug to a customer.

Therefore, in the case where one item order includes an item that needs to be checked and an item that does not need to be checked, the item that needs to be checked and the item that does not need to be checked are split into two different orders for shipment in the related art. For example, after receiving an order with a prescription drug and an over-the-counter drug. The sales system may split the order into a sub-order that includes prescription drugs and a sub-order that includes non-prescriptions. The sales system may then issue a warehouse out command based on the sub-order for the over-the-counter medication. The sales system may also provide sub-orders including prescription drugs to pharmacists for review. After the pharmacist has verified, the sales system will issue a delivery order to the warehouse based on the sub-orders that include the prescribed medication. Thus, for orders placed by the same customer. The sales system needs to be split into multiple sub-orders. According to different time lengths to be checked, a warehouse delivery instruction is issued to the warehouse based on the sub-orders at different times. To a certain extent, the capacity of the warehouse is wasted, and the pressure of warehouse delivery is increased.

Therefore, it is necessary to provide an order data processing method, which combines a first sub-order and a second sub-order to execute the shipment of the goods after the second sub-order with less time consumption of the processing flow of the same goods order waits for the execution of the first sub-order with more time consumption to complete, so as to reduce the warehouse shipment pressure to some extent.

Example of a scene

Referring to FIG. 1, an example scenario of an order data processing system is provided herein. The order data processing system may be deployed at a staging system.

The order data processing system may receive the item order sent by the e-commerce platform. The goods order is attached with the identity of the consumer. The goods order may be used to purchase aspirin and cold infusions. Wherein, aspirin belongs to prescription medicine, and cold granules belong to non-prescription medicine.

Next, the order data processing system may split the goods order into a first sub-order comprising aspirin and a second sub-order comprising cold granules. For the first sub-order, the order data processing system may assign a doctor who made a prescription for the first sub-order and a review pharmacist who reviews the prescription. Then, the order data processing system can obtain the characteristic data of the consumer, the characteristic data of the doctor, the characteristic data of the pharmacist and the characteristic data of the medicine by matching in a database according to the identification of the consumer, the identification of the doctor issuing the prescription, the identification of the checking pharmacist and the medicine identification of the aspirin. Predicting, by using an audit time prediction model, an expected time consumption of the first sub-order being prescribed by the doctor and audit confirmation by the audit pharmacist based on the characteristic data of the consumer, the characteristic data of the doctor, the characteristic data of the pharmacist, and the characteristic data of the medicine, wherein the expected time consumption can be used as an expected waiting time length.

Subsequently, the order data processing system may generate a composite identifier for the first sub-order and the second sub-order based on the characteristic data of the customer, the shipping address of the item order, and the warehouse information corresponding to the item. And the order combination identification of the first sub-order and the second sub-order is the same. The order data processing system may then place the second sub-order into an order pool. The order pool may include a plurality of orders for which the shipment of goods has not yet been executed.

The first sub-order may include a prescription drug that is approved for sale to the consumer by the audit pharmacist for the projected wait period. The order data processing system may then go to an order pool according to the order identifier of the first sub-order to match a second sub-order in the order pool that is the same as the order identifier. In the event that the second sub-order is matched, the order data processing system may issue a shipment instruction to the warehouse system for the goods based on the goods order in which the first sub-order and the second sub-order are merged. And under the condition that the warehouse system receives the goods delivery instruction, the warehouse system can prompt the staff in the warehouse to package the sorted amoxicillin and cold granules into a package for the logistics company to deliver.

System architecture

Referring to fig. 2, an embodiment of the present disclosure provides an order data processing system. The order data processing system may include a client and a server.

The client can be used for receiving real-time data and displaying the real-time data to workers. For example, the client may be used to receive information for orders that are in an order pool. The orders in the order pool may also be arranged to be placed in advance for shipment according to the configuration information of the staff. The client may be an electronic device with network access capabilities. Specifically, for example, the client may be a desktop computer, a tablet computer, a notebook computer, a smart phone, a digital assistant, a smart wearable device, a shopping guide terminal, a television, a smart speaker, a microphone, and the like. Wherein, wearable equipment of intelligence includes but not limited to intelligent bracelet, intelligent wrist-watch, intelligent glasses, intelligent helmet, intelligent necklace etc.. Alternatively, the client may be software capable of running in the electronic device.

The server may be configured to process the order data. The server may also send inventory information to the client. The server may be an electronic device with certain arithmetic processing capability. Which may have a network communication module, a processor, memory, etc. Of course, the server may also refer to software running in the electronic device. The server may also be a distributed server, which may be a system with multiple processors, memory, network communication modules, etc. operating in coordination. Alternatively, the server may be a server cluster formed by several servers. Or, with the development of scientific technology, the server can also be a new technical means capable of realizing the corresponding functions of the specification implementation mode. For example, it may be a new form of "server" implemented based on quantum computing.

Example methods

Referring to fig. 3, an embodiment of the present disclosure provides an order data processing method. The order data processing method can be applied to a server of a middle station system. The order data processing method may include the following steps.

Step S110: receiving a goods order; the goods order is divided into a first sub-order comprising first goods information and a second sub-order comprising second goods information; and the time consumption of the processing flow of the first item information is greater than that of the processing flow of the second item information.

In some cases, part of the goods needs to have their goods information processed before being taken out of the warehouse. The time length for processing the goods information can have a certain difference according to the difference of the goods types, the consumers and other factors. Therefore, in the case where the same order includes a good that needs to be subjected to the goods information processing and includes a good that does not need to be subjected to the goods information processing, the order data processing system may split the goods order into a plurality of sub-orders. For example, the sub-orders may include sub-orders with items that need to be processed for item information and sub-orders that do not need to be processed for item information. According to the time consumed by the processing of the goods information required by the goods corresponding to different sub-orders, the goods can be sequentially taken out of the warehouse after the processing of the goods information of the sub-orders is completed. However, this may cause the same goods order to be split into too many sub-orders, and the goods delivery instruction is issued to the warehouse separately, which may increase the warehouse delivery pressure to some extent.

Therefore, after receiving the goods order including the first sub-order and the second sub-order, the second sub-order with less time consumption of the processing flow of the goods information can be placed in the order pool to wait for the first sub-order to finish the processing flow, and then the first sub-order and the second sub-order are combined to obtain the goods order. Further, the shipment of the goods can be performed based on the goods order, reducing the shipment pressure of the warehouse to some extent. Meanwhile, the number of freight parcels is reduced, and the freight cost can also be reduced to a certain extent.

In this embodiment, the item order may represent an order placed by a consumer to the e-commerce platform for purchasing an item. The order may include an order number, information of a customer who places the order, information of a goods delivery address, generation time, and the like. Wherein the item order may include a first sub-order and a second sub-order. Wherein the first sub-order may include first item information. The second sub-order may include second item information. The first item information and the second item information may have different information processing flows. The information processing flow may represent an audit flow of the goods information, a reservation flow of the goods, a purchase flow, and the like. Accordingly, the time consumption of the processing flow of the first item information may be greater than the time consumption of the processing flow of the second item information.

For example, the item order may represent an order to purchase a pharmaceutical. The first item information may indicate drug information pertaining to a prescribed drug. The second item information may represent drug information pertaining to an over-the-counter drug. Accordingly, the first sub-order may represent an order for a prescription drug obtained after splitting the stock order. The second sub-order may represent an over-the-counter order resulting from splitting the item order. Of course, the first item information may also indicate that other items need to be audited. For example, the first item information may represent an item that requires an audit of consumer credit prior to purchase. Accordingly, the item represented by the second item information may not require auditing. Or, because the audit processes are different, the second item information may represent an item whose audit processing process consumes less time than the audit processing process of the first item information.

In some embodiments, the first item information may represent a plurality of items requiring audit. Wherein, the auditing time of the plurality of goods needing to be audited can be different. Of course, in some embodiments, a plurality of items to be audited may be split into a plurality of sub-orders.

The method for receiving the goods order can be that the server of the e-commerce platform sends the goods order to the middle platform system after receiving the goods order. An order data processing system deployed at the staging system may receive the item order. Of course, the goods order may also be written into the order queue by the e-commerce platform, and the order data processing system reads the goods order from the order queue.

Step S120: placing the second sub-order into an order pool; wherein the order pool comprises a plurality of orders which are not yet executed to carry out the goods delivery.

In some cases, the time consumption of the processing flow of the first item information is larger than that of the processing flow of the second item information. Thus, the second sub-order may be placed in stock before the first sub-order. The address, customer, and warehouse where the goods are located may be the same for the same order for the goods. The same goods order is split into a plurality of sub-orders and then is taken out of the warehouse respectively, and the warehouse delivery pressure is increased to a certain extent. Therefore, the second sub-order with less time-consuming second goods information processing flow can be added into the order pool, so that after the first goods information included in the first sub-order is processed, warehouse discharge is performed based on the goods order after the first sub-order and the second sub-order are combined, and the warehouse discharge frequency can be reduced to a certain extent. The pressure of warehouse delivery is reduced to a certain extent. In addition, the resource wasted by packaging and wrapping can be saved to a certain extent.

The order pool may represent a collection of orders that temporarily hold orders that have not yet been placed for shipment. In some embodiments, orders in the order pool may have an order relationship. For example, orders in the order pool may be ordered according to the generation time of the orders in the order pool. Alternatively, orders in the order pool may be ordered according to the length of time the orders are placed in the order pool. Of course, the order of the orders in the order pool may also represent the order of the warehouse-out process for the orders. Correspondingly, the method for placing the second sub-order into the order pool may also be a method for placing the second sub-order into the order pool according to a preset sorting rule.

Of course, in some embodiments, orders in the order pool may not have a sequential relationship. Correspondingly, the method for placing the second sub-order into the order pool can also directly add the second sub-order to the order set represented by the order pool.

Step S130: and under the condition that the processing flow is finished when the first sub-order is executed, matching the second sub-order in the order pool based on the first sub-order to obtain the second sub-order.

In some cases, after the first sub-order execution ends the process flow, a second sub-order corresponding to the first sub-order may be matched in the order pool to perform the stock-out based on the stock order in which the first and second sub-orders are merged.

When the processing flow indicates an audit flow of the first item information of the first sub-order, the condition that the processing flow is finished by executing the first sub-order may indicate that the audit flow of the first item information passes. Further, the second sub-order may be derived based on the matching of the first sub-order in the order pool. In some embodiments, the process flow may also represent an item waiting flow. For example, after a portion of the goods is purchased by the consumer, the merchant may need to confirm the purchase order with the supplier before the goods can be sold to the consumer. The process flow may also represent the flow of information confirmation between the merchant and the provider. Correspondingly, after the supplier confirms the purchasing information, the first sub-order execution ends the processing flow. Further, the second sub-order is obtained based on the matching of the first sub-order in the order pool.

The method for obtaining the second sub-order based on the matching of the first sub-order in the order pool may be based on order numbers. Specifically, the first sub-order and the second sub-order may have sub-order numbers, and may be accompanied by parent order number information. The corresponding second sub-order may be matched based on the parent order number attached to the order number of the first sub-order. Wherein the parent order number may represent an order number of a goods order comprising the first child order and the second child order. For example, the master order number representing the item order may be 202208270001. The sub-order number of the first sub-order may be denoted as 2022082700001-1. The order number of the second sub-order may be denoted as 2022082700001-2. Therefore, based on the information of the master order number included in the sub-order number, a second sub-order can be obtained by matching.

In some embodiments, the order data processing system may generate the same order-closing identification for the first sub-order and the second sub-order in advance. Correspondingly, the method for obtaining the second sub-order based on the matching of the first sub-order in the order pool may be based on order combination identification. The order-closing mark can be generated through information such as a user, an address and a warehouse of the goods order.

Step S140: performing a shipment of goods based on the goods order in which the first sub-order and the second sub-order are merged.

In some cases, the shipment of the item may be performed based on the consolidated item order of the first sub-order and the second sub-order.

Performing the shipment of the goods based on the goods order in which the first sub-order and the second sub-order are merged may represent issuing the goods order including the first sub-order and the second sub-order to a warehouse. Accordingly, the warehouse may pack the goods represented by the first goods information of the first sub-order and the goods represented by the second goods information of the second sub-order into the same package for logistics. The parcel picking and delivering device can avoid the situation that the same consumer picks goods for multiple times to a certain extent, can improve the experience of the consumer, reduces the warehouse delivery pressure to a certain extent, and reduces the logistics cost.

In some embodiments, the order data processing method may further include: determining the predicted time consumption of the processing flow of the first goods information; and when the time consumed for executing the processing flow by the first sub-order is larger than the predicted time consumed, executing the delivery of the goods based on the first sub-order and the second sub-order respectively.

In some cases, the processing flow of the first item information may be relatively long. For example, where the process flow of the first item information represents an audit flow, the auditor finds that the audit information is problematic, which may take a long time for further processing. If the second sub-order is always in the order pool and waits for the execution of the processing flow of the first item information of the first sub-order, the item of the item order combined by the first sub-order and the second sub-order is further taken out, which may cause that the item represented by the second item information of the second sub-order cannot be timely delivered to the consumer, and bring about poor shopping experience to the consumer. Moreover, placing the second sub-order into the order pool for waiting also means that the goods represented by the second goods information are always in the warehouse, which also increases the warehouse pressure to a certain extent. Therefore, in order to better balance the warehouse delivery pressure and the warehouse storage pressure, the estimated time consumption of the processing flow of the first item information can be determined, and when the time consumption for executing the processing flow by the first sub-order is greater than the estimated time consumption, the item delivery is executed respectively based on the first sub-order and the second sub-order, namely, the items represented by the first item information and the items represented by the second item information are respectively packed and delivered, so that the warehouse delivery pressure and the warehouse storage pressure can be better balanced.

According to the method for determining the expected time consumption of the processing flow of the first item information, a worker can set an expected time consumption threshold value for the items represented by different first item information in advance. Binding the projected elapsed time threshold to a second sub-order. And in the case that the waiting time of the second sub-order in the order pool is greater than the estimated time-consuming threshold value, moving the second sub-order out of the order pool and carrying out the delivery of the goods based on the second sub-order. Of course, the method for determining the predicted time consumption of the processing flow of the first item information may also be a method for predicting the predicted time consumption based on a mathematical model to obtain the predicted time consumption.

In some embodiments, in the case that the waiting time of the second sub-order is longer than the expected time, the orders in the order pool may be sorted according to a time length that the waiting time of the second sub-order in the order pool exceeds the expected time. And further, sequentially carrying out warehouse discharge according to the warehouse discharge capacity. This also means that in some cases, the second sub-order may not immediately perform the shipment if the waiting time of the second sub-order exceeds the expected time consumption. Specifically, for example, the warehouse may be configured based on the staff, and the regular order may be set to preferentially execute the shipment of the goods. The regular order may represent an order that does not include a process flow requiring execution of the item information in the item order. For example, the regular order may be an order that includes only non-prescription drugs that do not require review by a pharmacist. The warehouse may have a capacity of 1000 units in a period based on the warehouse construction conditions. During the time period, the quantity generated for a conventional order may be 950, so that the order data processing system may preferentially execute the shipment of goods for 950 conventional orders. Accordingly, only 50 items can be offered for shipment for orders in the order pool. In the case where the waiting time periods for 51 orders in the order pool all exceed the predicted elapsed time, the delivery of the goods may be preferentially performed based on the order with the greater difference according to the difference between the waiting time period and the predicted elapsed time, that is, the waiting time period for the order in the order pool exceeding the predicted elapsed time.

The performing of the shipment of the goods based on the first sub-order and the second sub-order, respectively, may represent that the first sub-order and the second sub-order may be separated for logistics in two packages in case the time taken for the first sub-order to perform the process flow is greater than the expected time taken.

In some embodiments, the step of determining a projected time consumption of the process flow of the first good information comprises: predicting the predicted time consumption of the processing flow of the first goods information according to the characteristic data of the first party issuing the goods order and the characteristic data of the second party executing the processing flow of the first goods information.

In some cases, the expected time consumption of the process flow of the item information for different items may be different. Different consumers and different persons involved in the process flow may also have some impact on the expected time consumption of the process flow of the good information. Thus, the predicted elapsed time of the processing flow of the first item information can be predicted based on the characteristic data of the first party placing the item order and the characteristic data of the second party performing the processing flow of the first item information. The accuracy of the predicted time consumption of the processing flow of the predicted first good information can be improved to a certain extent.

The characteristic data of the first party placing the order for the good may include historical behavioral data of the first party. Wherein the first party may represent a consumer. In some embodiments, the historical behavioral data may include a record of a consumer historically purchasing the item or building a record related to the item. In the case where the item represents a pharmaceutical, the characteristic data of the first party may further include physical condition data or the like provided by the consumer.

The characteristic data of the second party performing the processing flow of the first item information may represent behavior data of the second party performing the processing flow. The second party may also be different according to the different processing flows of the goods information of different goods. For example, where the item represents a prescription drug, the second party may represent a reviewing pharmacist. Correspondingly, the characteristic data of the pharmacist can comprise the grade of the pharmacist, the historical review time and the like. Where the process flow represents a procurement flow, the second party may represent a procurement staff. Accordingly, the characteristic data of the purchasing personnel can comprise the seniority of the purchasing personnel, the historical purchasing efficiency and the like.

The method of predicting the predicted time consumption of the process flow of the first item information may be predicting through a machine learning model. Wherein the machine learning model is obtainable based on historically different feature data of the first party, and feature data of the second party, and time-consuming training of the corresponding process flow. In predicting the predicted time-consuming process flow of the first item information, characteristic data of a first party currently placing the item order and characteristic data of a second party assigned to process the item order may be input for prediction. The machine learning model may be a GBDT model, a neural network, or the like.

In some embodiments, the order data processing method may further include: setting order combination identification for the first sub-order and the second sub-order; the order combination identification of the first sub-order and the second sub-order is the same; the order combination identification of the first sub-order and the second sub-order belonging to different goods orders is different; correspondingly, when the first sub-order is executed and the processing flow is ended, the step of obtaining the second sub-order based on the matching of the first sub-order in the order pool includes: and matching the order pool to obtain the second sub-order based on the order combination identifier of the first sub-order under the condition that the first sub-order executes and ends the processing flow.

In some cases, a method of matching the identification information such as the sub-order number of the first sub-order in the order pool to obtain the second sub-order matched with the identification information may increase a certain error rate. For example, time information may be attached to the order number in general. The waiting time of the orders in the order pool is not longer, so the time information of the order numbers of the orders in the order pool can be converged. Correspondingly, in the matching process, the server can cause certain waste in the process of comparing the order numbers bit by bit, and the matching error is more easily caused. Therefore, the order identification can be generated for the first sub-order and the second sub-order before the second sub-order is placed into the order pool, so as to match the second sub-order in the order pool by using the first sub-order, thereby improving the matching efficiency and the matching accuracy to a certain extent.

The order identification may be used to indicate a first sub-order and a second sub-order belonging to the same item order. The order combining identifier may be represented by a character string, or may be an order combining code represented by a two-dimensional code. The order-closing identifications of the first sub-order and the second sub-order belonging to the same goods order are the same. The order-closing identifications of the first sub-order and the second sub-order belonging to different goods orders are not identical.

The method for setting the order-closing identifier for the first sub-order and the second sub-order may be generated based on the identity information of the customer, the delivery address of the goods order, and the warehouse identifier to which the goods belong. Specifically, the method for setting the order-closing identifier for the first sub-order and the second sub-order may generate an identifier such as a character string or a two-dimensional code after inputting the identity information of the consumer, the delivery address of the goods order, and the warehouse identifier of the warehouse to which the goods belong into the encoding algorithm, and use the identifier as the order-closing identifier. Wherein, the encoding algorithm may be an MD5 algorithm, an AES256 algorithm, or the like.

The method for obtaining the second sub-order by matching in the order pool based on the order combination identifier of the first sub-order may be to perform order combination with the first sub-order by querying the second sub-order with the same order combination identifier.

In some embodiments, the order data processing method may further include: in the absence of a second sub-order in the order pool that matches the first sub-order, performing an item shipment based on the first sub-order.

In some cases, the second sub-order in the order pool that matches the first sub-order may be taken out of stock before the process flow execution of the first sub-order is complete due to the longer time-consuming process flow of the first sub-order. Thus, in the event that there is no second sub-order in the order pool that matches the first sub-order, the shipment of goods may be performed directly based on the first sub-order. Specifically, when there is no second sub-order matching the first sub-order in the order pool, the method may indicate that identification information such as an order number and an order combination number based on the first sub-order does not exist in the order pool, and identification information belonging to the second sub-order matching the identification information does not exist in the order pool.

In some embodiments, the order data processing method may further include: determining the quantity of orders for the warehouse plan to accept the executed goods for shipment within a specified time period; the order quantity is used as the total amount of planned delivery orders; calculating the warehouse-out quantity of the order pool of the orders needing to execute the goods warehouse-out in the order pool according to the total amount of the planned warehouse-out orders; the warehouse outlet quantity of the order pool is not more than the total quantity of the planned warehouse outlet orders; selecting the order pool number of orders to be taken out from the order pool to perform the taking out of the goods based on the orders in the appointed time period.

In some cases, warehouse delivery may be through picking, allocating, rechecking, packing, sorting, stacking, and receiving. Therefore, the warehouse delivery capacity is limited based on different personnel arrangement and warehouse construction of different warehouses. I.e. the capacity of the warehouse has certain limits. For example, in a period of time, the warehouse can only sort and pack the goods included in 10000 orders, and provide them to the logistics company. 10000 packages corresponding to an order may be taken as the output of the warehouse for that time period. Accordingly, the quantity of order packages obtained by the warehouse in the unit time based on the order of the customer to carry out the goods delivery can be used as the output of the warehouse.

Thus, if the wait time for an order in the order pool is longer, on the one hand, it may result in the consumer receiving the item later, which may affect the user experience. On the other hand, it is also possible that orders in the order pool are not issued to the warehouse all the time in one time period, so that the number of orders for which the warehouse performs warehouse-out in part of the time period may be smaller than the tolerable range. Accordingly, in another time period, the orders in the order pool are sent to the warehouse intensively, so that the number of orders taken out from the warehouse can exceed an acceptable range, and further delay of the delivery time of the goods is caused. In addition, the orders in the order pool are always in a waiting state, which also causes the stock overstock of the warehouse and increases the stock pressure of the warehouse. Therefore, the quantity of the orders needing to execute the goods delivery in the order pool can be determined according to the quantity of the orders which can be accepted by the warehouse and delivered to the warehouse within the appointed time period, so that the order combination probability, the warehouse delivery capacity and the consumer experience are balanced to a certain extent.

The specified time period may represent a time period during which the warehouse is able to perform the shipment of the goods. The warehouse may perform the shipment of the goods during the specified time period. Depending on the staffing arrangement of the warehouse, the warehouse construction, the number of orders that the warehouse can take over to carry out the shipment of the goods in a given time period may have certain limitations. For example, the warehouse may perform shipment of items for items that comprise up to 5000 item orders per hour. The specified time period may represent 11 am 59 to 23 pm 59 minutes. Accordingly, the maximum number of orders for which the warehouse can accept the shipment of the performed goods within the specified time period is 60000.

The total planned out order amount may represent the number of orders the warehouse plans to require to perform the shipment of goods within a specified time period.

The method for determining the quantity of orders of the executed goods which are planned to be taken out of the warehouse within the specified time period can be that the workload of the warehouse within the specified time period is determined according to the preset configuration of the staff of the warehouse, and further, the quantity of the orders of the executed goods which can be taken out of the warehouse, namely the total quantity of the planned out-of-the-warehouse orders, is determined according to the time length of the specified time period. In some embodiments, the method of determining the number of orders for the warehouse to carry out the shipment over a specified time period may also be determined based on the delivery volume provided by the logistics company. For example, in the case where the capacity of the warehouse is larger than the delivery volume provided by the logistics company, the maximum value of the delivery volume that can be provided by the logistics company may be used as the number of orders for the shipment of the executed goods planned to be accepted in the specified time period, that is, the total planned shipment order amount.

The order data processing system may perform the shipment of goods directly after receiving the conventional order. Accordingly, the goods included in a conventional order may occupy a portion of the capacity of the warehouse during the performance of the shipment of the goods. I.e., the total amount of planned outbound orders may include the number of regular orders. Correspondingly, the method for calculating the warehouse-out quantity of the order pool of the orders needing to execute the goods warehouse-out in the order pool according to the planned warehouse-out order total quantity can take the residual quantity as the warehouse-out quantity of the order pool after deducting the conventional order quantity from the planned warehouse-out order total quantity. For example, the total amount of planned shipment orders may be 10000 pieces. The number of regular orders for which goods are to be taken out is 9000 pieces. Accordingly, the shipment of the goods can be preferentially performed on the basis of the regular order. Accordingly, the total number of planned outbound orders for order taking in the order pool may be 1000. Further, 1000 orders may be selected in the order pool for shipment of the item.

Of course, the method for calculating the warehouse-out quantity of the order pool of the orders needing to execute the goods warehouse-out in the order pool according to the planned warehouse-out order quantity can also divide the planned warehouse-out order quantity into the conventional orders and the orders in the order pool according to the preset proportion.

The method for selecting the order with the warehouse outlet quantity in the order pool to execute the warehouse outlet of the goods based on the orders in the specified time period can be that after the orders in the order pool exceed the time length expected to take time, the orders with the warehouse outlet quantity in the order pool with the longer time length exceeding the expected time length are selected, and the warehouse outlet of the goods is executed based on the orders with the warehouse outlet quantity in the order pool. Of course, in some embodiments, the probability that orders in the order pool can be closed can also be calculated through the data model, and the orders are sorted according to the closing probability, the orders with lower probability are taken out from the order pool, and the goods taking out is performed based on the orders in the order pool.

In some embodiments, the specified time period is divided into a plurality of time segments, and the order data processing method may further include: distributing the total amount of the planned delivery orders in the specified time period to the plurality of time segments to obtain the quantity of the planned delivery orders corresponding to different time segments; correspondingly, the step of calculating the warehouse-out quantity of the order pool of the orders needing to execute the warehouse-out of the goods in the order pool according to the total amount of the planned warehouse-out orders comprises the following steps: calculating the warehouse-out quantity of the order pool of the order which needs to execute the warehouse-out of the goods in the time segment according to the planned warehouse-out order quantity corresponding to the time segment; correspondingly, the step of selecting the orders of the order pool warehouse-out quantity in the order pool to carry out warehouse-out of the goods based on the orders in the appointed time period comprises the following steps: selecting orders of the order pool warehouse-out quantity in the order pool to execute warehouse-out of goods based on the orders in the time slice.

In some cases, the designated time period may be divided into a plurality of time segments, and by determining the planned delivery order quantity corresponding to each time segment and the delivery quantity of the order pool in which the delivery of the goods is required to be performed in the time segment, the orders may be issued to the warehouse more finely, so as to avoid the problem that the quantity of the orders for performing the delivery of the goods in one time segment is small, and the quantity of the orders for performing the delivery of the goods in another time segment is large to some extent. In addition, the designated time period is divided into a plurality of time segments, so that the supervision of workers is facilitated.

The method for distributing the total amount of the planned delivery orders in the specified time period to the plurality of time segments to obtain the number of the planned delivery orders corresponding to different time segments may be that the total amount of the planned delivery orders is distributed to the plurality of time segments according to the time length of each time segment. For example, a time slice of longer duration will be assigned a greater number of planned outbound orders. Of course, the method for obtaining the planned warehouse-out order quantity corresponding to different time slices by distributing the planned warehouse-out order quantity of the specified time slice to the plurality of time slices may also be distributed according to the predicted order quantity of each time slice. The projected order quantity may be predicted based on historical order quantities for each time segment.

In some embodiments, orders in the order pool have a projected wait length; the step of selecting the order pool number of orders to be taken out of the order pool in the order pool to perform the taking out of the goods based on the orders within the specified time period may include: determining an actual wait duration of orders in the order pool for the orders in the order pool; and determining the delivery quantity orders of the order pool in the order pool according to the actual waiting time and the predicted waiting time.

In some cases, the longer the order waiting time in the order pool is, the more the processing flow of the order including the first item information corresponding to the order may be in a problem, which may cause an order failure or a processing time longer than expected. Thus, it is possible to preferentially perform shipment of the goods based on the order waiting longer in the order pool, avoiding the time when the consumer receives the goods too late to some extent. Meanwhile, the storage pressure of the warehouse can be relieved to a certain extent.

The projected wait time period may represent a time period that orders in the order pool are projected to take for an order. Specifically, the expected waiting time may indicate that the processing flow time required by the order including the first item information corresponding to the order in the order pool is consumed. And predicting the time consumption of the information processing flow of the first goods information to obtain the predicted time consumption. The predicted elapsed time may be used as a predicted wait time for an order in the corresponding order pool.

The actual waiting time length may represent a time length for waiting for the order combination after the second sub-order is placed in the order pool. In some embodiments, orders in the order pool may each be maintained for an actual wait period. The method for determining the actual waiting time length of the order in the order pool may be to read the actual waiting time length maintained by the order in the order pool. Of course, in some embodiments, orders in the order pool may have time to be placed in the order pool. Correspondingly, the method for determining the actual waiting time of the order in the order pool may be to calculate the actual waiting time according to the current time and the time of placing the order in the order pool corresponding to the order in the order pool.

The method for selecting the number of orders taken out of the order pool from the order pool according to the actual waiting time length and the predicted waiting time length may be that the orders in the corresponding order pool are taken as the selected orders when the actual waiting time length is greater than the predicted waiting time length. Of course, the number of orders for which the actual wait period is greater than the expected wait period may be the order pool warehouse size. Therefore, for orders with an actual waiting duration less than the predicted waiting duration, the orders may be sorted according to the difference between the actual waiting duration and the predicted waiting duration. Wherein orders with smaller differences are ranked more forward. Further, based on the sorting result, a corresponding number of orders sorted in the top order may be selected for shipment.

In some embodiments, the wait may also be maintained according to configuration information of the staff in the event that none of the actual wait durations for the orders in the order pool exceeds the projected wait duration, and the shipment of goods is performed based on the orders in the event that the actual wait duration exceeds the projected wait duration.

In some embodiments, orders in the order pool have an order relationship; the step of placing the second sub-order into an order pool, comprising: determining the estimated order-closing time of the second sub-order according to the estimated time consumption of the processing flow of the first goods information of the first sub-order; placing the second sub-order into the order pool in accordance with the estimated order time.

In some cases, orders in the order pool may have an order relationship that better maintains the order in which the individual orders perform the shipment.

The method for determining the expected merging time of the second sub-order according to the expected consumed time of the processing flow of the first item information of the first sub-order may be that the current time is delayed from the time point of the expected consumed time to serve as the expected merging time. Of course, in some embodiments, a certain fault-tolerant time may be added to the time point after the current time is delayed by the predicted time to increase the probability of the order.

The placing of the second sub-order into the order pool according to the estimated order combining time may be placing the second sub-order into the order pool according to the estimated order combining time, and attaching a corresponding order sequence. For example, orders of the order pool may be represented by a linked list. Accordingly, the second sub-order may be added to the linked list according to the expected order-closing time of the second sub-order. Of course, in some cases, where a first sub-order matches to a corresponding second sub-order, the second sub-order may be moved out of the order pool to perform the shipment of the consolidated item order. Wherein the consolidated item order may have a higher priority for executing the shipment of the items.

Example apparatus, electronic device, storage medium, and software

Referring to fig. 4, an embodiment of the present disclosure provides an order data processing apparatus. The apparatus may include: the device comprises a receiving module, an adding module, a matching module and an executing module.

A receiving module for receiving a goods order; the goods order is split into a first sub-order comprising first goods information and a second sub-order comprising second goods information; and the time consumption of the processing flow of the first item information is greater than that of the processing flow of the second item information.

The adding module is used for placing the second sub-order into an order pool; wherein the order pool comprises a plurality of orders which are not yet executed to carry out the goods delivery.

And the matching module is used for matching the first sub-order in the order pool to obtain the second sub-order based on the first sub-order when the processing flow is finished after the first sub-order is executed.

And the execution module is used for executing goods delivery based on the goods orders combined by the first sub-orders and the second sub-orders.

Referring to fig. 5, an embodiment of the present disclosure further provides a computer device, where the computer device executes the order data processing method in any of the above embodiments.

The present specification further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a computer, the computer executes the order data processing method in any one of the above embodiments.

The present specification also provides a computer program product containing instructions, and the instructions, when executed by a computer, cause the computer to execute the order data processing method in any one of the above embodiments.

It is understood that the specific examples are set forth herein only to assist those skilled in the art in better understanding the embodiments of the present disclosure and are not intended to limit the scope of the present disclosure.

It should be understood that, in the various embodiments of the present specification, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic, and should not limit the implementation process of the embodiments of the present specification.

It is to be understood that the various embodiments described in the present specification may be implemented individually or in combination, and the embodiments in the present specification are not limited thereto.

Unless otherwise defined, all technical and scientific terms used in the embodiments of the present specification have the same meaning as commonly understood by one of ordinary skill in the art to which the present specification belongs. The terminology used in the description presented herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the description. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. As used in the specification embodiments and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It is to be understood that the processor of the embodiments of the present description may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The processor may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component. The various methods, steps, and logic blocks disclosed in the embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present specification may be embodied directly in a hardware decoding processor, or in a combination of hardware and software modules in the decoding processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

It will be appreciated that the memory in the implementations of the specification can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The non-volatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EEPROM (EEPROM), or a flash memory. The volatile memory may be Random Access Memory (RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present specification.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present specification, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

In addition, functional units in the embodiments of the present specification may be integrated into one processing unit, each of the units may exist alone physically, or two or more units may be integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present specification may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present specification. 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, an optical disk, or the like.

The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope disclosed in the present disclosure, and all the changes or substitutions should be covered within the scope of the present disclosure. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

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

receiving a goods order; the goods order is split into a first sub-order comprising first goods information and a second sub-order comprising second goods information; the time consumption of the processing flow of the first goods information is greater than that of the processing flow of the second goods information;

placing the second sub-order into an order pool; wherein the order pool comprises a plurality of orders which are not yet executed to deliver the goods;

under the condition that the processing flow is finished when the first sub-order is executed, matching the first sub-order in the order pool to obtain a second sub-order;

and executing goods delivery based on the goods orders combined by the first sub-orders and the second sub-orders.

2. The method of claim 1, further comprising:

determining the predicted time consumption of the processing flow of the first goods information;

and when the time consumed for executing the processing flow by the first sub-order is larger than the expected time consumed, respectively executing the delivery of the goods based on the first sub-order and the second sub-order.

3. The method of claim 2, wherein the step of determining the expected time consumption of the processing flow of the first item information comprises:

predicting the predicted time consumption of the processing flow of the first goods information according to the characteristic data of the first party issuing the goods order and the characteristic data of the second party executing the processing flow of the first goods information.

4. The method of claim 1, further comprising:

setting order combination identification for the first sub-order and the second sub-order; the order combination identification of the first sub-order and the second sub-order is the same;

correspondingly, when the processing flow is finished by executing the first sub-order, the step of obtaining the second sub-order based on the matching of the first sub-order in the order pool includes:

and matching the order pool to obtain the second sub-order based on the order combination identifier of the first sub-order under the condition that the first sub-order executes and ends the processing flow.

5. The method of claim 1, further comprising:

in the absence of a second sub-order in the order pool that matches the first sub-order, performing an item shipment based on the first sub-order.

6. The method of claim 1, further comprising:

determining the quantity of orders of the executed goods which are planned to be accepted by the warehouse and taken out of the warehouse within a specified time period; the order quantity is used as the total quantity of planned delivery orders;

calculating the warehouse-out quantity of the order pool of the orders needing to execute the goods warehouse-out in the order pool according to the total amount of the planned warehouse-out orders; the warehouse outlet quantity of the order pool is not more than the total quantity of the planned warehouse outlet orders;

selecting the order with the order pool warehouse-out quantity in the order pool to execute warehouse-out of goods based on the order in the appointed time period.

7. The method of claim 6, wherein the specified time period is divided into a plurality of time segments, the method further comprising:

distributing the total amount of the planned delivery orders in the specified time period to the plurality of time segments to obtain the quantity of the planned delivery orders corresponding to different time segments;

correspondingly, the step of calculating the warehouse-out quantity of the order pool of the orders needing to execute the warehouse-out of the goods in the order pool according to the total amount of the planned warehouse-out orders comprises the following steps:

calculating the warehouse-out quantity of the order pool of the order which needs to execute the warehouse-out of the goods in the time segment according to the planned warehouse-out order quantity corresponding to the time segment;

correspondingly, the step of selecting the orders of the order pool warehouse-out quantity in the order pool to carry out warehouse-out of the goods based on the orders in the appointed time period comprises the following steps:

selecting orders of the order pool warehouse-out quantity in the order pool to execute warehouse-out of goods based on the orders in the time slice.

8. The method of claim 6, wherein orders in the order pool have a projected wait length; selecting the order of the order pool warehouse quantity number in the order pool to execute the step of warehouse-out of goods based on the order in the appointed time period, wherein the step comprises the following steps:

determining an actual wait duration of orders in the order pool for the orders in the order pool;

and selecting the orders with the warehouse-out quantity in the order pool according to the actual waiting time length and the predicted waiting time length.

9. The method of claim 1, wherein orders in the order pool have an order relationship; the step of placing the second sub-order into an order pool comprises:

determining the expected order-closing time of the second sub-order according to the expected time consumption of the processing flow of the first goods information of the first sub-order;

placing the second sub-order into the order pool in accordance with the estimated order time.

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

a receiving module for receiving a goods order; the goods order is split into a first sub-order comprising first goods information and a second sub-order comprising second goods information; the time consumption of the processing flow of the first goods information is longer than that of the processing flow of the second goods information;

the adding module is used for placing the second sub-order into an order pool; wherein the order pool comprises a plurality of orders which are not yet executed to deliver the goods;

the matching module is used for matching the first sub-order in the order pool to obtain a second sub-order under the condition that the processing flow is finished when the first sub-order is executed;

and the execution module is used for executing the delivery of goods based on the goods orders combined by the first sub-orders and the second sub-orders.

11. A computer device comprising a memory storing a computer program and a processor implementing the method of any one of claims 1 to 9 when the computer program is executed.

12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 9.