Information processing method, device, electronic device and storage medium

Abstract

The embodiments of the present application disclose an information processing method, an apparatus, an electronic device, and a storage medium, which obtain a first order from a target customer, determine a first target time for processing the first order based on the first order; determine a probability of obtaining a second order from the target customer within the first target time based on the first order; determine the cost of processing the first order and the second order within the first target time based on the second order and the probability, so as to determine whether to perform aggregate processing on the first order and the second order within the first target time.

Description

Technical Field

[0001] The present application relates to the field of information processing technology, and more specifically, to an information processing method, device, electronic device and storage medium. Background Art

[0002] In the supply chain, when goods in the warehouse are shipped out according to customer orders, people manually judge whether orders from the same customer should be aggregated based on experience, which makes order processing inefficient and leads to low efficiency in shipping goods out. Summary of the invention

[0003] The present application provides an information processing method, device, electronic device and storage medium, including the following technical solutions:

[0004] An information processing method, the method comprising:

[0005] Obtaining a first order from a target customer, and determining a first target time for processing the first order based on the first order;

[0006] Determining, based on the first order, a probability of obtaining a second order from the target customer within the first target time period;

[0007] The cost of processing the first order and the second order within the first target time period is determined based on the second order and the probability to determine whether to perform aggregate processing on the first order and the second order within the first target time period.

[0008] The above method preferably further comprises:

[0009] When it is determined that the first order and the second order are to be aggregated within the first target time period, if a second order of the target customer is obtained within the first target time period, outputting first prompt information;

[0010] The first prompt information indicates that the items reserved in the first order and the second order are aggregated into the same package so that the package can be shipped out within the first target time.

[0011] In the above method, preferably, determining the probability of obtaining the second order from the target customer within the first target time period based on the first order includes:

[0012] Standardizing the destination address in the first order to obtain a standard order with a standard address;

[0013] Processing the standard order through a preset prediction engine to obtain the probability of obtaining a second order from the target customer in each time period within the first target time period;

[0014] The prediction engine is trained through historical standard orders of various customers.

[0015] In the above method, preferably, determining the cost of processing the first order and the second order within the first target time based on the second order and the probability includes:

[0016] Determine the processing cost corresponding to any time period according to the probability of obtaining the second order of the target customer in any time period within the first target time period and the cost required for whether the items reserved by the order obtained in any time period and thereafter are shipped out of the warehouse in any time period within the first target time period and thereafter;

[0017] Obtaining the cost of processing the first order and the second order within the first target time period according to the processing costs corresponding to each time period within the first target time period;

[0018] If the time period during which the items reserved for the first order are to be shipped out when the cost is minimum and the time period during which the items reserved for the second order obtained in any time period are to be shipped out are the same time period within the first target time period, it is determined that the first order and the second order are to be aggregated within the first target time period.

[0019] The above method preferably further comprises:

[0020] After determining that the target item is to be shipped out of the warehouse, a target record is added for the target item, wherein the target record records the quantity of the target item, the destination address, and time information representing the storage time of the target item in the warehouse;

[0021] The target record is used to learn a prediction model, and the prediction model is used to predict the destination addresses of the same batch of items stored in the warehouse, the quantity of items required for each destination address, and the storage time in the warehouse.

[0022] The above method preferably further comprises:

[0023] If a target item storage instruction is obtained, the attribute information of the target item is processed by the prediction model to obtain each destination address of the target item, the quantity of the target item required for each destination address, and the storage time in the warehouse;

[0024] The target items are classified and stored in the warehouse according to the destination addresses of the target items, the quantity of the target items required for each destination address, and the storage time in the warehouse.

[0025] In the above method, preferably, the step of classifying and storing the target items in the warehouse according to the destinations of the target items, the quantity of the target items required at each destination, and the storage time in the warehouse comprises:

[0026] storing target objects of different storage times at different locations in the warehouse; wherein the distance between the target object and the warehouse exit is positively correlated with the storage time of the target object;

[0027] Place target items with the same storage time but different destination addresses in different locations.

[0028] An information processing device, comprising:

[0029] A first determining module, configured to obtain a first order from a target customer and determine a first target duration for processing the first order based on the first order;

[0030] A second determining module, configured to determine, based on the first order, a probability of obtaining a second order from the target customer within the first target time period;

[0031] The third determination module is used to determine the cost of processing the first order and the second order within the first target time based on the second order and the probability, so as to determine whether to aggregate the first order and the second order within the first target time.

[0032] An electronic device, comprising:

[0033] Memory, used to store programs;

[0034] A processor is used to call and execute the program in the memory, and implement each step of the information processing method as described above by executing the program.

[0035] A readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of any of the above information processing methods.

[0036] Through the above scheme, it can be known that the information processing method, device, electronic device and storage medium provided by the present application obtain the first order of the target customer, determine the first target time for processing the first order based on the first order; determine the probability of obtaining the second order of the target customer within the first target time based on the first order; determine the cost of processing the first order and the second order within the first target time based on the second order and the probability, so as to determine whether to aggregate the first order and the second order within the first target time. After obtaining the first order of the target customer, the present application determines the first target time for processing the first order based on the first order, and the probability of obtaining the second order of the target customer within the first target time, determines the cost of processing the first order and the second order within the first target time based on the second order and the probability, so as to determine whether to aggregate the first order and the second order within the first target time, thereby realizing automatic judgment of order aggregation processing and improving order processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 A flowchart of an implementation of the information processing method provided in the embodiment of the present application;

[0039] Figure 2 A flowchart for implementing the method of determining the probability of obtaining a second order from a target customer within a first target time period based on a first order provided in an embodiment of the present application;

[0040] Figure 3 A flowchart for implementing the method of determining the cost of processing the first order and the second order within the first target time period based on the second order and the probability provided in an embodiment of the present application;

[0041] Figure 4 A schematic diagram of the structure of an information processing device provided in an embodiment of the present application;

[0042] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0043] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims and the above drawings are used to distinguish similar parts, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated herein. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] like Figure 1 As shown, a flowchart of an implementation of the information processing method provided in an embodiment of the present application may include:

[0046] Step S101: Obtain a first order from a target customer, and determine a target duration for processing the first order based on the first order (referred to as a first target duration).

[0047] The target time for processing the first order refers to the time limit for processing the first order starting from the moment the first order is received. In other words, the processing of the first order must be completed within the first target time.

[0048] As an example, different customers have different requirements for the time limit for processing their orders. Based on this, the first target time for processing the first order can be determined according to the customer identifier in the first order. For example, the correspondence between the customer identifier and the target time can be preset. After obtaining the first order, the customer identifier in the first order is obtained, and the target time corresponding to the customer identifier is used as the first target time.

[0049] As an example, the processing time limit for orders in different seasons may be different. For example, in some seasons, there are more orders and the processing is slower, so the order processing time will be longer. In this case, the first target time can be longer. In other seasons, there are fewer orders and the order processing speed is faster. The order processing time will be shorter. In this case, the first target time can be shorter. Based on this, the first target time for processing the first order can be determined according to the time when the first order is received. For example, the corresponding relationship between the season and the target time can be preset, and then the current season can be determined according to the time when the first order is received, and then the target time corresponding to the season is determined as the first target time.

[0050] As an example, for any order, the target time for processing the first order is the first target time, that is, the processing time limit for all orders is the same.

[0051] Step S102: Determine the probability of obtaining a second order from a target customer within a first target time period based on the first order.

[0052] After obtaining the first order of the target customer, the present application determines the probability of the target customer placing an order again within the first target time period based on the first order. For the convenience of description, the order placed again by the target customer within the first target time period is recorded as the second order.

[0053] The second order of the target customer refers to an order having the same customer identifier and destination address as the first order.

[0054] As an example, the first target duration may be taken as a time period, and the probability of obtaining a second order from the target customer within the first target duration may be determined based on the first order.

[0055] As an example, the first target duration can be divided into multiple time periods, and the probability of obtaining the second order of the target customer in each time period within the first target duration is determined based on the first order. The division of time periods can be determined based on the time points when the logistics company picks up the goods from the warehouse. For example, if the logistics company picks up the goods from the warehouse at 13:00 and 20:00 every day, the time period can be divided into (13:00-20:00] on the same day, and from 20:00 of the previous day to 13:00 of the next day, that is, (20:00-13:00].

[0056] Step S103: Determine the cost of processing the first order and the second order within the first target time based on the second order and the above probability, so as to determine whether to perform aggregate processing on the first order and the second order within the first target time.

[0057] The second order also includes information about the items ordered by the target customer. Therefore, the cost of processing the first order and the second order within the first target time period can be determined based on the item information in the second order and the above-mentioned probability, so as to determine whether to aggregate the first order and the second order within the first target time period based on the cost.

[0058] In the case where the first target duration is divided into a plurality of time periods, it can also be determined based on the above cost whether to perform aggregation processing on the first order and the second order in each time period within the first target duration.

[0059] Aggregating the first order and the second order means aggregating the items reserved in the first order and the items reserved in the second order into the same package, so as to ship the package out of the warehouse within the first target time.

[0060] Optionally, when determining to aggregate the first order and the second order within the first target time period, a prompt message can also be output to prompt the order processing personnel that the first order can be aggregated with other orders, and the first order can be temporarily not processed, and the first order and the second order can be processed after the second order is obtained.

[0061] The information processing method provided in the embodiment of the present application determines, after obtaining the first order from the target customer, a first target time for processing the first order based on the first order, and a probability of obtaining the second order from the target customer within the first target time, and determines the cost of processing the first order and the second order within the first target time based on the second order and the probability, so as to determine whether to perform aggregate processing on the first order and the second order within the first target time, thereby realizing automatic judgment of order aggregate processing and improving order processing efficiency.

[0062] Furthermore, the information processing method provided in the embodiment of the present application may also include:

[0063] When it is determined that the first order and the second order are to be aggregated within the first target time period, if a second order from the target customer is obtained within the first target time period, a first prompt message is output.

[0064] The first prompt information indicates that the items reserved in the first order and the second order are aggregated into the same package so that the package can be shipped out within the first target time.

[0065] That is, after obtaining the second order of the target customer within the first target time, the user is prompted to process the first order and to aggregate the first order and the second order. After seeing the first prompt information, the order processing personnel can pack the items reserved in the first order and the items reserved in the second order into the same package, so as to ship the package out of the warehouse within the first target time.

[0066] When the first target duration is divided into multiple time periods, the end time point of each time period is the time point for delivery. Based on this, the first prompt information may indicate that the package is delivered during the target time period within the first target duration; wherein the target time period is the time period for obtaining the second order, or the target time period is later than the time period for obtaining the second order. Delivery of the package during the target time period means that the package is delivered at the end time point of the target time period (when the logistics company comes to pick up the package).

[0067] As an example, if the time period for obtaining the second order is the time period for aggregating the first order and the second order based on the above-mentioned cost, the first prompt information may indicate that the first order and the second order are shipped out during the time period for obtaining the second order.

[0068] As an example, if the time period for obtaining the second order is earlier than the time period for aggregate processing of the first order and the second order determined based on the above cost (for example, the first order, the second order and the third order are aggregated and processed based on the above cost, and the time period for aggregate processing is the next time period of the time period for obtaining the second order), then the first prompt information may indicate that the first order, the second order, and the third order can be processed in the next time period of the time period for obtaining the second order, and please wait for the third order from the target customer.

[0069] Optionally, when it is determined that the first order and the second order are to be aggregated and processed within the first target time period, if the second order of the target customer is not obtained when the second target time period is reached, a second prompt message is output, the second prompt message instructing to process the first order so as to pack and ship the items reserved by the first order within the target time period, wherein the end time point of the second target time period is within the target time period.

[0070] That is to say, after entering the target time period, before reaching the end time point of the target time period, if the second order from the target customer has not been obtained, a second prompt message is output to prompt the order processor to process the first order and no longer aggregate the first order with other orders.

[0071] Optionally, when it is determined that the first order and the second order will not be aggregated within the first target time period, a third prompt message is output, which instructs the first order to be processed so as to pack and ship out the items ordered for the first order within the time period when the first order is obtained, or to pack and ship out the items ordered for the first order before the end of the first target time period.

[0072] That is to say, if it is determined that the first order and the second order will not be processed in an aggregated manner within the first target time period, the order processor only needs to pack and ship out the items reserved for the first order before the end of the first target time period.

[0073] In an optional embodiment, a flowchart for implementing the above-mentioned determination of the probability of obtaining a second order from a target customer within a first target time period based on the first order is as follows: Figure 2 As shown, it may include:

[0074] Step S201: Standardize the destination address in the first order to obtain a standard order with a standard address.

[0075] In actual scenarios, there may be different orders from the same customer with different text descriptions of the destination address, but they express the same address. For example, "Building A, Haidian District, Beijing" and "Building A, Haidian District, Beijing" express the same address. For another example, Baoding City B Hospital has two campuses, the main hospital is in Lianchi District, and the west hospital is in Jingxiu District. Then "Baoding City Jingxiu District B Hospital" and "Baoding City Jingxiu District B Hospital (West Hospital)" both express Baoding City B Hospital West Hospital. In this case, due to the different text descriptions of the address, if the address is not standardized, the prediction engine may think that they are two different addresses. When learning, the prediction engine may identify orders from the same customer as orders from different customers, which will affect the probability of the prediction engine predicting that the customer will place an order again. Therefore, in order to ensure the effectiveness of the prediction engine learning and the accuracy of the prediction, this application will standardize the different text contents representing the same address to obtain a standard address representing the same address. For example, "Building A, Haidian District, Beijing" and "Building A, Haidian District, Beijing" are standardized into "Building A, Haidian District, Beijing", and "Hospital B, Jingxiu District, Baoding City" and "Hospital B, Jingxiu District, Baoding City (West Hospital)" are standardized into "Hospital B, Jingxiu District, Baoding City West Hospital".

[0076] As an example, a correspondence between a standard address and an address in an order can be established. When an order is received, the standardized address corresponding to the address in the order can be obtained based on the correspondence, and the obtained standardized address can be used to replace the address in the order to obtain a standard order with a standard address.

[0077] As an example, if the corresponding standardized address is not obtained through the above correspondence, the semantic similarity between the address in the order and each standardized address can be obtained, and the standardized address with a semantic similarity greater than a threshold and the greatest semantic similarity to the address in the order is determined as the standardized address corresponding to the address in the order. If there is no standardized address with a semantic similarity greater than the threshold, it means that the address in the order is a new address, and the address in the order can be directly used as the standardized address.

[0078] Step S202: Processing the standard order through a preset prediction engine to obtain the probability of obtaining the second order from the target customer in each time period within the first target time period.

[0079] The prediction engine is trained through historical standard orders of each customer.

[0080] As an example, a standard order may be input into a prediction engine to obtain the probability of obtaining a second order from a target customer in each time period within the first target duration.

[0081] In an optional embodiment, the input of the prediction engine is the historical standard orders of each customer, and the training process of the prediction engine may include:

[0082] For any customer, the prediction engine processes the historical standard orders of the any customer, and obtains the first probability of obtaining the order of the any customer again in each time period within the first target time length for processing the historical standard orders of the any customer, and the second probability of the any customer placing an order again for each item that has been booked in each time period; the first probability represents whether the order of the any customer is obtained again in each time period; the second probability represents the items booked by placing an order again in each time period. As an example, for each time period, the item corresponding to the maximum second probability in the time period can be determined as the item booked by the any customer placing an order again in the time period.

[0083] The parameters of the prediction engine are updated with the goal that whether an order from any customer is obtained again in each time period represented by the first probability is close to whether an order from any customer is obtained again in each time period in the historical records, and the items ordered again in each time period represented by the second probability are close to the items ordered again in each time period in the historical records, until the training end conditions are met.

[0084] Optionally, the prediction engine may obtain the first probability and the second probability in one of the following ways:

[0085] Optionally, for the historical standard orders of any customer, the prediction engine may perform feature extraction on the historical standard orders of any customer through a first feature extraction module to obtain a first feature, process the first feature through the first prediction module to obtain a first probability of obtaining the order of any customer again in each time period within the first target time length for processing the historical standard orders of any customer, and process the first feature through a second prediction module to obtain a second probability of any customer placing an order again for each item that has been booked in each time period.

[0086] Optionally, for the historical standard orders of any customer, the prediction engine may perform feature extraction on the historical standard orders of any customer through a first feature extraction module to obtain a first feature, perform feature extraction on the first feature through a second feature extraction module to obtain a second feature, process the second feature through the first prediction module to obtain a first probability of obtaining the order of any customer again in each time period within the first target time length for processing the historical standard orders of any customer, perform feature extraction on the first feature through a third feature extraction module to obtain a third feature, process the third feature through the second prediction module to obtain a second probability of any customer placing an order again for each item that has been booked in each time period.

[0087] In an optional embodiment, a flowchart for implementing the above-mentioned determination of the cost of processing the first order and the second order within the first target time based on the second order and the probability is as follows: Figure 3 As shown, it may include:

[0088] Step S301: Determine the processing cost corresponding to each time period within the first target time period: Determine the processing cost corresponding to any time period according to the probability of obtaining the second order from the target customer in any time period within the first target time period, and the cost required for whether the items reserved for the order obtained in any of the above time periods and thereafter are shipped out of the warehouse in any of the above time periods and thereafter within the first target time period.

[0089] Among them, if any of the above time periods is the time period for obtaining the first order, then the orders obtained in and after any of the above time periods include the first order and the second order; if any of the above time periods is the time period after the time period for obtaining the first order, then the orders obtained in and after any of the above time periods do not include the first order.

[0090] As an example, assuming that the first target duration includes 4 time periods, which are respectively recorded as the 0th time period, the 1st time period, the 2nd time period and the 3rd time period, the time period in which the first order is received is the 0th time period, that is, the first order is obtained at time 0. In this application, the following information needs to be obtained to determine the processing cost corresponding to each time period in the first target duration:

[0091] The probability c of obtaining the second order from the target customer in the i-th (i=0, 1, 2, 3) time period within the first target duration i .

[0092] The cost (including the transportation price p) required for the items ordered from the i-th time period to the j-th time period (if i=0, the orders obtained from the i-th time period to the j-th time period include the first order and the second order; if i>0, the orders obtained from the i-th time period to the j-th time period include the second order but not the first order) to be shipped out of the warehouse in the j-th time period. ji x ji and the cost of storage in the warehouse q ji ), where p ji represents the freight rate required for the items ordered from the i-th time period to the j-th time period to be shipped out of the warehouse in the j-th time period, x ji The value of x is 0 or 1. ji = 0 means that the items ordered from the i-th time period to the j-th time period are not shipped out in the j-th time period, xji =1 means that the items ordered from the i-th time period to the j-th time period are shipped out in the j-th time period, and the cost of staying in the warehouse is q ji It is proportional to the time the goods are detained in the warehouse. The longer the goods are detained in the warehouse, the higher the cost of detention in the warehouse. For example, a ji =(ji)ε. ε is a preset parameter and can be an empirical value.

[0093] As an example, the processing cost corresponding to the i-th time period (denoted as cost i ) can be calculated as follows:

[0094]

[0095] Step S302: Obtain the cost of processing the first order and the second order within the first target time period according to the processing costs corresponding to each time period within the first target time period.

[0096] As an example, the processing costs corresponding to each time period within the first target duration can be summed to obtain the cost of processing the first order and the second order within the first target duration (denoted as COST), which can be expressed as follows:

[0097]

[0098] Step S303: Determine whether to aggregate the first order and the second order within the first target time period: if the time period when the items reserved by the first order are to be shipped out when the cost is minimized and the time period when the items reserved by the second order obtained from any of the above time periods are to be shipped out are the same time period within the first target time period, determine to aggregate the first order and the second order within the first target time period.

[0099] In which, formula (2)x ji When the value of is different, the value of COST is different, that is, according to x ji The value of COST is different. COST can have multiple values. In this application, the minimum value of COST is x ji The value of is taken as the final value. If there is x j0 =x ji =1, indicating that the first order in the jth time period and the second order obtained in the ith time period are to be aggregated.

[0100] If x does not exist j0 =x ji =1, indicating that the first order does not need to be aggregated with other orders.

[0101] In an optional embodiment, after the ordered items are shipped out of the warehouse, corresponding records may be added for the shipped items. For ease of description, the shipped items are recorded as target items, and the information processing method provided in the embodiment of the present application may also include:

[0102] After determining that the target item is to be shipped out of the warehouse, a target record is added for the target item, in which the target record records the quantity of the target item, the destination address, and time information representing the storage time of the target item in the warehouse.

[0103] The destination address may be the address in the standard order for ordering the target item, that is, the standard address is recorded in the target record.

[0104] The time information can be the time it takes for the target item to enter the warehouse and leave the warehouse. Or,

[0105] The time information is the target item's entry time and exit time, and the difference between the exit time and the entry time is the length of time the target item is stored in the warehouse.

[0106] The target records are used to learn a prediction model, which is used to predict the destination addresses of the same batch of items stored in the warehouse, the number of items required for each destination address, and the storage time in the warehouse.

[0107] After obtaining several target records of items that have been put into storage in multiple batches (items that have been put into storage in the same batch are the same, and items that have been put into storage in different batches are the same or different), all destination addresses of items in multiple batches can be counted based on the several target records. The number of items required for each destination address and the storage time of the required items in the warehouse can also be counted among the items that have been put into storage in the same batch.

[0108] The input of the prediction model can be the attribute information of items (which can be target items or other items) that are put into the warehouse in the same batch, such as the size, content, weight, category and other information of the items. The output of the prediction model is: whether each destination address needs the items that are put into the warehouse in the same batch, whether the quantity of items required by each destination address is one of the preset multiple quantities, and whether the storage time in the warehouse is one of the preset multiple time periods.

[0109] In the training samples used to train the prediction model, each training sample includes the following information: attribute information of items in the same batch of warehouse, and label information, wherein the label information includes: whether each destination address needs the items in the same batch of warehouse, the quantity of items required for each destination address and the required storage time of the items in the warehouse.

[0110] When training the prediction model, the input of the prediction model is the attribute information in the training sample, and the output is the prediction, whether each destination address needs the same batch of items corresponding to the training sample, whether the number of items required for each destination address is one of the preset multiple quantities, and whether the storage time in the warehouse is one of the preset multiple time periods. With the goal of making the output of the prediction model close to the label information in the training sample, the parameters of the prediction model are updated until the training end conditions are met.

[0111] In an optional embodiment, the information processing method provided in the embodiment of the present application may also include:

[0112] If the storage instruction of the target item is obtained, the attribute information of the target item is obtained, and the attribute information may be input by the data input operator.

[0113] When obtaining the warehousing instructions for a batch of target items again, the attribute information of the batch of target items can be processed through the prediction model to obtain the destination addresses of the target items, the quantity of target items required for each destination address and the storage time in the warehouse.

[0114] The target items are classified and stored in the warehouse according to their respective destination addresses, the quantity of the target items required for each destination address, and the storage time in the warehouse.

[0115] As an example, target items with different storage times can be stored in different locations in the warehouse; wherein the distance between the target item and the exit is positively correlated with the storage time of the target item. That is, the longer the storage time of the target item in the warehouse, the farther the target item is from the exit; the shorter the storage time of the target item in the warehouse, the closer the target item is to the exit.

[0116] Place target items with the same storage time but different destination addresses in different locations.

[0117] Optionally, for each destination address that requires target items, the target items of the required quantity for the destination address can be extracted from all the target items that need to be stored in the warehouse, and then the extracted target items can be stored in the target location in the warehouse according to the storage time of the required items for the destination address in the warehouse.

[0118] Corresponding to the method embodiment, the embodiment of the present application further provides an information processing device. A structural schematic diagram of the information processing device provided by the embodiment of the present application is as follows: Figure 4 As shown, it may include:

[0119] A first determining module 401, a second determining module 402 and a third determining module 403; wherein,

[0120] The first determination module 401 is used to obtain a first order from a target customer, and determine a first target time for processing the first order based on the first order;

[0121] The second determination module 402 is used to determine the probability of obtaining a second order from the target customer within the first target time period based on the first order;

[0122] The third determination module 403 is used to determine the cost of processing the first order and the second order within the first target time period based on the second order and the probability, so as to determine whether to perform aggregate processing on the first order and the second order within the first target time period.

[0123] The information processing device provided in the embodiment of the present application, after obtaining the first order from the target customer, determines a first target time for processing the first order based on the first order, and a probability of obtaining the second order from the target customer within the first target time, and determines the cost of processing the first order and the second order within the first target time based on the second order and the probability, so as to determine whether to perform aggregate processing on the first order and the second order within the first target time, thereby realizing automatic judgment of order aggregation processing and improving order processing efficiency.

[0124] In an optional embodiment, the information processing device may further include:

[0125] an output module, configured to output first prompt information if a second order of the target customer is obtained within the first target time period when the first order and the second order are aggregated within the first target time period;

[0126] The first prompt information indicates that the items reserved in the first order and the second order are aggregated into the same package so that the package can be shipped out within the first target time.

[0127] In an optional embodiment, the second determining module 402 is used to:

[0128] Standardizing the destination address in the first order to obtain a standard order with a standard address;

[0129] Processing the standard order through a preset prediction engine to obtain the probability of obtaining the second order of the target customer in each time period within the first target time period;

[0130] The prediction engine is trained through historical standard orders of various customers.

[0131] In an optional embodiment, the third determining module 403 is used to:

[0132] Determine the processing cost corresponding to any time period according to the probability of obtaining the second order of the target customer in any time period within the first target time period and the cost required for whether the items reserved by the order obtained in any time period and thereafter are shipped out of the warehouse in any time period within the first target time period and thereafter;

[0133] Obtaining the cost of processing the first order and the second order within the first target time period according to the processing costs corresponding to each time period within the first target time period;

[0134] If the time period during which the items reserved for the first order are to be shipped out when the cost is minimum and the time period during which the items reserved for the second order obtained in any time period are to be shipped out are the same time period within the first target time period, it is determined that the first order and the second order are to be aggregated within the first target time period.

[0135] In an optional embodiment, the information processing device may further include:

[0136] A recording module, for adding a target record for the target item after determining that the target item is out of the warehouse, wherein the target record records the quantity of the target item, the destination address, and time information representing the storage time of the target item in the warehouse;

[0137] The target record is used to learn a prediction model, and the prediction model is used to predict the destination addresses of the same batch of items stored in the warehouse, the quantity of items required for each destination address, and the storage time in the warehouse.

[0138] In an optional embodiment, the information processing device may further include:

[0139] A prediction module, for, if a target item storage indication is obtained, processing the attribute information of the target item through the prediction model to obtain each destination address of the target item, the quantity of the target item required for each destination address and the storage time in the warehouse;

[0140] The processing module is used to classify and store the target items in the warehouse according to the destination addresses of the target items, the quantity of the target items required for each destination address and the storage time in the warehouse.

[0141] In an optional embodiment, the processing module may be configured to:

[0142] storing target objects of different storage times at different locations in the warehouse; wherein the distance between the target object and the warehouse exit is positively correlated with the storage time of the target object;

[0143] Place target items with the same storage time but different destination addresses in different locations.

[0144] Corresponding to the method embodiment, the present application also provides an electronic device, a structural schematic diagram of the electronic device is shown as follows Figure 5 As shown, it may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4.

[0145] In the embodiment of the present application, the number of the processor 1 , the communication interface 2 , the memory 3 , and the communication bus 4 is at least one, and the processor 1 , the communication interface 2 , and the memory 3 communicate with each other through the communication bus 4 .

[0146] The processor 1 may be a central processing unit CPU, or an application-specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0147] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), etc., such as at least one disk memory.

[0148] The memory 3 stores a program, and the processor 1 can call the program stored in the memory 3, and the program is used to:

[0149] Obtaining a first order from a target customer, and determining a first target time for processing the first order based on the first order;

[0150] Determining, based on the first order, a probability of obtaining a second order from the target customer within the first target time period;

[0151] The cost of processing the first order and the second order within the first target time period is determined based on the second order and the probability to determine whether to perform aggregate processing on the first order and the second order within the first target time period.

[0152] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0153] The embodiment of the present application further provides a storage medium, which may store a program suitable for execution by a processor, wherein the program is used to:

[0154] Obtaining a first order from a target customer, and determining a first target time for processing the first order based on the first order;

[0155] Determining, based on the first order, a probability of obtaining a second order from the target customer within the first target time period;

[0156] The cost of processing the first order and the second order within the first target time period is determined based on the second order and the probability to determine whether to perform aggregate processing on the first order and the second order within the first target time period.

[0157] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0158] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0159] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0160] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0161] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0162] It should be understood that in the embodiments of the present application, the various embodiments and features can be combined with each other to solve the aforementioned technical problems.

[0163] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0164] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An information processing method, the method comprising: Obtaining a first order from a target customer, and determining a first target time for processing the first order based on the first order; Determining, based on the first order, a probability of obtaining a second order from the target customer in each time period within the first target duration; The cost of processing the first order and the second order within the first target time period is determined based on the second order and the probability to determine whether to perform aggregate processing on the first order and the second order within the first target time period.

2. The method according to claim 1, further comprising: When it is determined that the first order and the second order are to be aggregated within the first target time period, if a second order of the target customer is obtained within the first target time period, outputting first prompt information; The first prompt information indicates that the items reserved in the first order and the second order are aggregated into the same package so that the package can be shipped out within the first target time.

3. The method according to claim 1, wherein determining the probability of obtaining a second order from the target customer within the first target time period based on the first order comprises: Standardizing the destination address in the first order to obtain a standard order with a standard address; Processing the standard order through a preset prediction engine to obtain the probability of obtaining a second order from the target customer in each time period within the first target time period; The prediction engine is trained through historical standard orders of various customers.

4. The method according to claim 3, wherein determining the cost of processing the first order and the second order within the first target time based on the second order and the probability comprises: Determine the processing cost corresponding to any time period according to the probability of obtaining the second order of the target customer in any time period within the first target time period and the cost required for whether the items reserved by the order obtained in any time period and thereafter are shipped out of the warehouse in any time period within the first target time period and thereafter; Obtaining the cost of processing the first order and the second order within the first target time period according to the processing costs corresponding to each time period within the first target time period; If the time period during which the items reserved for the first order are to be shipped out when the cost is minimum and the time period during which the items reserved for the second order obtained in any time period are to be shipped out are the same time period within the first target time period, it is determined that the first order and the second order are to be aggregated within the first target time period.

5. The method according to any one of claims 2 to 4, further comprising: After determining that the target item is to be shipped out of the warehouse, a target record is added for the target item, wherein the target record records the quantity of the target item, the destination address, and time information representing the storage time of the target item in the warehouse; The target record is used to learn a prediction model, and the prediction model is used to predict the destination addresses of the same batch of items stored in the warehouse, the quantity of items required for each destination address, and the storage time in the warehouse.

6. The method according to claim 5, further comprising: If a target item storage instruction is obtained, the attribute information of the target item is processed by the prediction model to obtain each destination address of the target item, the quantity of the target item required for each destination address, and the storage time in the warehouse; The target items are classified and stored in the warehouse according to the destination addresses of the target items, the quantity of the target items required for each destination address, and the storage time in the warehouse.

7. The method according to claim 6, wherein the target items are classified and stored in the warehouse according to the respective destination addresses of the target items, the quantity of the target items required for each destination address, and the storage time in the warehouse, comprises: storing target objects of different storage times at different locations in the warehouse; wherein the distance between the target object and the warehouse exit is positively correlated with the storage time of the target object; Place target items with the same storage time but different destination addresses in different locations.

8. An information processing device, comprising: A first determining module, configured to obtain a first order from a target customer and determine a first target duration for processing the first order based on the first order; A second determining module, configured to determine, based on the first order, a probability of obtaining a second order from the target customer in each time period within the first target duration; The third determination module is used to determine the cost of processing the first order and the second order within the first target time based on the second order and the probability, so as to determine whether to aggregate the first order and the second order within the first target time.

9. An electronic device, comprising: Memory, used to store programs; A processor is used to call and execute the program in the memory, and implement the various steps of the information processing method as described in any one of claims 1 to 7 by executing the program.

10. A readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the information processing method according to any one of claims 1 to 7 are implemented.

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