CN111612385A - Method and device for clustering to-be-delivered articles - Google Patents

Method and device for clustering to-be-delivered articles Download PDF

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CN111612385A
CN111612385A CN201910132026.4A CN201910132026A CN111612385A CN 111612385 A CN111612385 A CN 111612385A CN 201910132026 A CN201910132026 A CN 201910132026A CN 111612385 A CN111612385 A CN 111612385A
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武玉东
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Beijing Jingbangda Trade Co Ltd
Beijing Jingdong Zhenshi Information Technology Co Ltd
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Abstract

The invention discloses a method and a device for clustering articles to be distributed, and relates to the technical field of computers. One embodiment of the method comprises: acquiring splitting information of an order, wherein the splitting information comprises sub orders split according to the order and articles to be delivered included in the sub orders; determining the incidence relation of the articles to be delivered among the sub orders according to the splitting information of the orders, and calculating the incidence degree of the incidence relation; and determining the articles to be delivered which need to be clustered according to the association degree. According to the method and the device, the articles to be delivered which need to be clustered can be determined according to the splitting information of the historical orders, so that the order splitting rate is reduced, the order delivery cost is saved, the task operation is saved, the timeliness control over order delivery is improved, and the customer experience is optimized.

Description

Method and device for clustering to-be-delivered articles
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for clustering items to be distributed, an electronic device, and a computer-readable medium.
Background
At present, one of the production indexes of the e-commerce platform which is mainly concerned in the warehouse production process is the bill dismantling rate. The order splitting rate is the ratio of the split order quantity to the total order quantity, and splitting operation of an order is likely to occur under the following scenes:
(1) the distribution of the warehouse of the articles to be distributed included in the order is different;
(2) the order includes different shippers of the items to be delivered (such as including third party vendors or self-owned shipments);
(3) the customer selects the delivery of goods first;
(4) other splitting scenes, such as the order containing the articles to be delivered which cannot be packed and delivered simultaneously (such as high-value articles to be delivered, large articles to be delivered, food, dangerous articles such as perfume and the like which cannot be packed and delivered together with other articles to be delivered), and different logistics companies have special requirements on the weight or volume of a single package, and splitting is also needed when the weight or volume exceeds the limit of the logistics companies.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
one of the main reasons for order splitting is that the warehouse distribution of the to-be-distributed items included in the order is different, so that the order has to be split into a plurality of independent orders and then the independent orders are delivered by the warehouse, which doubles the order delivery cost, and the processing tasks such as tracking, collecting and processing of order delivery information are increased at the same time, which is not beneficial to controlling the order delivery timeliness, and simultaneously reduces the customer experience.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for clustering to-be-delivered items, which can determine to-be-delivered items to be clustered according to splitting information of a historical order, thereby reducing order splitting rate, saving order delivery cost, saving task operation, improving timeliness control over order delivery, and optimizing customer experience.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method for clustering items to be delivered, including: acquiring splitting information of an order, wherein the splitting information comprises sub orders split according to the order and articles to be delivered included in the sub orders; determining the incidence relation of the articles to be delivered among the sub orders according to the splitting information of the orders, and calculating the incidence degree of the incidence relation; and determining the articles to be delivered which need to be clustered according to the association degree.
Optionally, the association degree comprises one or more of a support degree, a confidence degree and a promotion degree; the support degree is the ratio of the number of orders of the to-be-distributed articles in the incidence relation to the total number of orders; the confidence coefficient is the ratio of the order quantity of the to-be-dispensed articles in the incidence relation and the order quantity of the to-be-dispensed articles in at least one sub order in the incidence relation; the promotion degree is the ratio of the confidence degree and the support degree of the articles to be delivered in the association relation.
Optionally, the method for determining the to-be-dispensed articles to be clustered according to the association degree includes: sorting the incidence relations according to the incidence degrees from high to low; and selecting the articles to be dispensed in the incidence relation with the incidence degree larger than or equal to the first threshold value.
Optionally, the method for determining the to-be-dispensed articles to be clustered according to the association degree includes: sorting the incidence relations according to the incidence degrees from high to low; calculating the order splitting rate of the order after the objects to be distributed in the M incidence relations before the sorting are selected for clustering, wherein the order splitting rate is the ratio of the number of the split orders to the total number of the orders; determining whether the order splitting rate is greater than a second threshold; if the order splitting rate is greater than a second threshold value, increasing the number of M progressively until the order splitting rate after the items to be distributed in the M incidence relations before the sorting are selected for clustering is not greater than the second threshold value, and determining the items to be distributed in the M incidence relations as the items to be distributed which need to be clustered.
Optionally, after determining that the items to be delivered in the M association relations are the categories of the items to be delivered that need to be clustered, the method further includes: and determining the quantity of the to-be-delivered articles to be clustered according to the predicted sales volume of the to-be-delivered articles.
To achieve the above object, according to another aspect of the embodiments of the present invention, there is provided an apparatus for clustering items to be delivered, including: the acquisition module is used for acquiring splitting information of an order, wherein the splitting information comprises sub-orders split according to the order and the articles to be delivered contained in the sub-orders; the calculation module is used for determining the incidence relation of the articles to be distributed among the sub orders according to the splitting information of the orders and calculating the incidence degree of the incidence relation; and the clustering module is used for determining the to-be-delivered articles to be clustered according to the association degree.
Optionally, the association degree comprises one or more of a support degree, a confidence degree and a promotion degree; the support degree is the ratio of the number of orders of the to-be-distributed articles in the incidence relation to the total number of orders; the confidence coefficient is the ratio of the order quantity of the to-be-dispensed articles in the incidence relation and the order quantity of the to-be-dispensed articles in at least one sub order in the incidence relation; the promotion degree is the ratio of the confidence degree and the support degree of the articles to be delivered in the association relation.
Optionally, the clustering module is further configured to sort the association relations according to the relevance degrees from high to low; and selecting the articles to be dispensed in the incidence relation with the incidence degree larger than or equal to the first threshold value.
Optionally, the clustering module is further configured to sort the association relations according to the relevance degrees from high to low; calculating the order splitting rate of the order after the objects to be distributed in the M incidence relations before the sorting are selected for clustering, wherein the order splitting rate is the ratio of the number of the split orders to the total number of the orders; determining whether the order splitting rate is greater than a second threshold; if the order splitting rate is greater than a second threshold value, increasing the number of M progressively until the order splitting rate after the items to be distributed in the M incidence relations before the sorting are selected for clustering is not greater than the second threshold value, and determining the items to be distributed in the M incidence relations as the items to be distributed which need to be clustered.
Optionally, the clustering module is further configured to determine the number of the to-be-delivered items that need to be clustered according to the predicted sales volume of the to-be-delivered items.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic apparatus including: one or more processors; a storage device to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement any of a method for clustering items to be delivered.
To achieve the above object, according to still another aspect of embodiments of the present invention, there is provided a computer-readable medium having stored thereon a computer program, which when executed by one or more processors, implements any one of the methods of clustering items to be delivered.
One embodiment of the above invention has the following advantages or benefits: the technical means that the association degree of the to-be-distributed articles among the sub-orders is determined according to the splitting information of the orders and the to-be-distributed articles needing clustering are determined according to the association degree are adopted, so that the technical problems of high order splitting rate and high logistics cost of the orders in the traditional method are solved, the order splitting rate and the logistics cost of the orders are reduced, and the technical effect of customer experience is improved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
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The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic diagram of the main steps of a method of clustering items to be dispensed according to an embodiment of the present invention;
fig. 2 is a schematic diagram of the main steps of the method for determining the items to be dispensed to be clustered according to the relevance, according to the embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the main steps of inter-warehouse transfers according to an embodiment of the present invention;
fig. 4 is a schematic diagram of the main part of an apparatus for clustering items to be dispensed according to an embodiment of the present invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 6 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of the main steps of a method for clustering objects to be dispensed according to an embodiment of the present invention, as shown in fig. 1:
step S101 represents obtaining splitting information of an order, where the splitting information includes sub-orders split according to the order and items to be delivered included in the sub-orders. The basis of analyzing the relevance of the to-be-delivered articles is to split the to-be-delivered articles in the sub-orders, and the purpose of the step is to determine the data source and reduce the calculation range.
The obtained orders may have a certain time range, for example, historical orders in the last week or last 2 months are obtained, split orders are queried in the historical orders, splitting information of the split orders is obtained, the splitting information may include, in addition to sub-orders split according to the orders and articles to be dispensed included in the sub-orders, the number of the split orders, the order number of the sub-orders, the order generation time, and the number, name, number, and price of the articles to be dispensed included in the orders, and further, the splitting reason of the split orders may be determined, so as to select the orders that are split due to the fact that the orders are not in the same warehouse.
The obtained historical orders can be selected according to the brands of the articles to be delivered, the delivery warehouse and the classification range of the articles to be delivered, for example, according to the delivery warehouse, detailed warehouse order data of the top ten of the order splitting rate are extracted, and then split order data in the warehouses are analyzed; or according to the classification of the articles to be delivered, extracting order data of the article to be delivered in the ten-degree front order splitting rate, and analyzing splitting order data corresponding to all classes of the articles to be delivered.
The obtained split order data may be stored in a text file (e.g., in csv format). For example, as shown in table 1, orders with order numbers 1 to 5 are split into several sub-orders. Specifically, the order number 1 includes the items 1, 2, and 3 to be delivered, where the items 1 and 3 to be delivered belong to the same warehouse and are thus split into the sub-order 1-1, and the warehouse where the item 2 to be delivered is located is different from the warehouses where the items 1 and 3 to be delivered are located, and are thus split into the sub-order 1-2.
TABLE 1
Figure BDA0001975707790000061
Furthermore, order data can be stored by using a sparse matrix, and storage and calculation are convenient. For example, the order splitting information in table 1 is converted into the value in table 2, each row represents a sub-order, an element with a value of 0 in the row represents that the sub-order does not include the item to be delivered in the corresponding column, an element with a value of 0 represents that the sub-order includes the item to be delivered in the corresponding column, and the number of the item to be delivered is the value of the element. Specifically, for convenience of calculation and storage, the number of the items to be delivered may also be omitted, where 1 indicates that the corresponding items to be delivered are included in the sub-order, and 0 indicates that the corresponding items to be delivered are not included in the sub-order.
TABLE 2
Figure BDA0001975707790000071
After the split order is selected, the current rate of splitting orders is calculated, and a comparison basis is provided for the optimization of the subsequent rate of splitting orders.
Step S102 represents determining an association of the items to be delivered between the sub-orders according to the splitting information, and calculating an association degree for the association. The degree of association indicates that there is some relationship between the items to be dispensed in the relationship, and the relationship can be measured by the value of the degree of association. In the embodiment of the present invention, an association relationship is determined according to a relationship between the to-be-distributed items in each sub order in the split order, for example, a certain order is split into a sub order a and a sub order B, where the sub order a includes the to-be-distributed item 1 and the to-be-distributed item 2, and the sub order B includes the to-be-distributed item 3, the association relationship is an association between { the to-be-distributed item 1 and the to-be-distributed item 2} and { the to-be-distributed item 3}, and further the association relationship has a directivity, that is, { the to-be-distributed item 1 and the to-be-distributed item 2} are associated with { the to-be-distributed item 3} and { the to-be-distributed item 1 and the to-be-distributed item 2} are associated with each other. When calculating the association degree of the association relationship, it is preferable to calculate the two association relationships respectively, and further, when an order is divided into more than two sub-orders, the association relationship is more than two, and it is preferable to calculate the association degree of all the association relationships, so that the subsequent selection of the association degree is more accurate. Wherein the relevance comprises one or more of support, confidence and lift.
The support degree is the ratio of the number of orders of the to-be-distributed articles in the incidence relation to the total number of orders; the Support (Support) is defined as the Support of the association relation X to Y by setting the proportion of s% in the set W to simultaneously Support the sets X and Y. The support describes the probability that the intersection Z of the two sets X and Y occurs in the set W. Further, the support degree of the set X to the set Y is equal to the support degree of the set Y to the set X.
For example, in the history order, the total order number is N; the number of orders simultaneously including the article A to be distributed and the article B to be distributed is NABIn these orders, the article a to be delivered and the article B to be delivered are often split into two sub-orders, and when the association relationship between { article a to be delivered } and { article B to be delivered } is determined, the support degree S of the article a to be delivered to the article B to be delivered in the association relationship is determinedA-BThe calculation formula of (2) is as follows:
SA-B＝NAB/N
the confidence coefficient is the ratio of the order quantity of the to-be-dispensed articles in the incidence relation and the order quantity of the to-be-dispensed articles in at least one sub order in the incidence relation; the Confidence (Confidence) is defined as the Confidence of the association relation X to Y, when the support set X in the set W is given, the support set Y is also given at a ratio of c%. The confidence level is how high the probability that the set Y also appears simultaneously under the condition that the set X appears, and is described as the confidence level of the set X to the set Y.
For example, in the history order, the total order number is N; the order number including the article A to be delivered is NAThe order number of the items B to be delivered is NB(ii) a The number of orders simultaneously including the article A to be distributed and the article B to be distributed is NAB(wherein, N isAB＝NA∩NB) (ii) a In the orders, an article A to be delivered and an article B to be delivered are frequently split into two sub-orders; when the incidence relation between the { article A to be delivered } and the { article B to be delivered } is determined, the confidence coefficient C of the article A to be delivered to the article B to be delivered is determinedA-BThe calculation formula of (2) is as follows:
CA-B＝NAB/NA
when the association relation between the { article to be distributed B } and the { article to be distributed A } is determined, the confidence coefficient of the article to be distributed B to the article to be distributed A is CB-AThe calculation formula of (2) is as follows:
CB-A＝NAB/NB
the promotion degree is the ratio of the confidence degree of the to-be-delivered articles in the association relation to the support degree of the to-be-delivered articles. The Lift (Lift) is used to describe how much a thing promotes another thing if some relationship exists, and the promotion is mutual, i.e. the Lift of set X to set Y is equal to the Lift of set Y to set X.
For example, in the historical order, the article a to be delivered and the article B to be delivered are often split into two sub-orders, the total order number is N, and the order number including both the article a to be delivered and the article B to be delivered is NABThe order number of the article A to be delivered is NAThe order number of the article A to be delivered is NBIf the correlation between the item A to be distributed and the item B to be distributed is determined, the lifting degree L of the item A to be distributed to the item B to be distributed is determinedA-BThe calculation formula is as follows:
LA-B＝CA-B/SB＝NAB/NA/(NB/N)＝NAB·N/(NA·NB)
wherein SBIs the independent support of the article B to be delivered, SB＝NBand/N, i.e. the ratio of the number of orders comprising the item to be dispensed to the total number of orders.
When the incidence relation between the { article to be distributed B } and the { article to be distributed A } is determined, the lifting degree L of the article to be distributed A of the article to be distributed B is determinedB-AThe calculation formula is as follows:
LB-A＝CB-A/SA＝NAB/NB/(NA/N)＝NAB·N/(NA·NB)
it can be seen that LA-B＝LB-A. Further, if in the historical order, the total amount of orders is N; the article A to be delivered and the article B to be delivered are frequently split into the same sub-order, and the article C to be delivered and the article D to be delivered are split into the other sub-order; the number of orders simultaneously including the article A to be distributed and the article B to be distributed is NABThe order number of the article C to be delivered and the article D to be delivered is NCDThe orders of the articles A, B, C and D to be delivered are NABCD(NABCD＝NAB∩NCD) Determined as { article to be dispensed A, article to be dispensedWhen the article B is associated with the { article to be dispensed C, article to be dispensed D }, the degree of lift L of the article to be dispensed A and the article to be dispensed B in the association to the article to be dispensed C and the article to be dispensed DAB-CDThe calculation formula is as follows:
LAB-CD＝CAB-CD/SCD＝NABCD/NAB/(NCD/N)＝NABCD·N/(NAB·NCD)
for example, according to the order splitting information in table 2, if the total order number is N, and the to-be-delivered item 2 and the to-be-delivered item 3 are split into two sub-orders, it may be determined that the association is an association of { to-be-delivered item 2} with { to-be-delivered item 3}, and the order number including the to-be-delivered item 2 and the to-be-delivered item 3 is 1, the order number including the to-be-delivered item 2 is 2, the order number including the to-be-delivered item 3 is 2, and the support S for the to-be-delivered item 3 by the to-be-delivered item 2 is S 1-21/N, confidence C of the item to be dispensed 2 to the item to be dispensed 32-31/2, the degree of lift L of the article to be dispensed 2 to the article to be dispensed 32-3Is N/4, i.e. 1. multidot.N/(2. multidot.2).
The calculation steps can also be carried out by means of an Apriori machine learning algorithm (the Apriori algorithm is a frequent item set algorithm for mining association, the core idea is that a frequent item set is mined through two stages of candidate set generation and downward closed detection of plot, the algorithm is widely applied to various fields of commerce, network security and the like), an association degree calculation model of the association is established, in the process of training the model, numerical values of support degree, confidence degree and/or promotion degree can be initialized, and order data with lower support degree, confidence degree and/or promotion degree are screened out.
An example code is:
grocery_rules<-apriori(data＝Groceries,parameter＝list(support＝,confidence＝,minlen＝))
step S103 represents determining the to-be-delivered items to be clustered according to the association degree. Since the association relation is determined according to the split order, after the association degree of the association relation is obtained through calculation, the items to be distributed in the sub-orders can be measured according to the value of the association degree, the higher the value of the association degree is, the stronger association relation exists among the items to be distributed among the sub-orders, the items of the items to be distributed with the high association degree are selected for clustering, the order splitting operation caused by the fact that the items to be distributed are not in the same warehouse can be reduced, and the order splitting rate is reduced. Furthermore, it is desirable to measure the association degree of the to-be-distributed articles by integrating three association degrees of support degree, confidence degree and promotion degree. It should be noted that the clustering of the to-be-dispensed articles in the present invention refers to not only logically clustering the to-be-dispensed articles, but also physically clustering the to-be-dispensed articles, which may be referred to as binning operation in the field of logistics.
Further, the incidence relations can be sorted according to the incidence degrees from high to low; and selecting the articles to be dispensed in the incidence relation with the incidence degree larger than or equal to the first threshold value. After the association degree is calculated, the association degree can be summarized into a table, as shown in table 3, the calculation result of the association degree of the association relation of a certain time is shown, the support degree of the sub order { article to be delivered 1, article to be delivered 2} to the sub order { article to be delivered 4} is 0.0069, the confidence degree is 0.40, and the promotion degree is 2.8, and the above numerical values indicate that in all orders, when a customer purchases the article to be delivered 1 and the article to be delivered 2, the customer purchases the article to be delivered 4 at the same time with a probability of 0.69%; there is a 40% probability that the customers who purchased the article to be dispensed 1 and the article to be dispensed 2 also purchased the article to be dispensed 4 at the same time, so the article to be dispensed 1 and the article to be dispensed 2 have a great lifting effect on the article to be dispensed 4. According to the sorting result of the promotion degree, if the first threshold is set to be 1, the correlation relationship larger than the first threshold is { to-be-delivered article 1} to { to-be-delivered article 3} and { to-be-delivered article 1, to-be-delivered article 2} to { to-be-delivered article 4}, so that it can be determined that the to-be-delivered article 1 and the to-be-delivered article 3 need to be clustered, and the to-be-delivered article 1, the to-be-delivered article 2 and the to-be-delivered article 4 are clustered, so that the order splitting operation is reduced, and the order splitting rate is reduced.
TABLE 3
Figure BDA0001975707790000111
As shown in fig. 2, the method for determining the to-be-dispensed articles to be clustered according to the relevance further includes:
s201, sorting the incidence relations according to the incidence degrees from high to low; wherein, the incidence relations can be sorted according to the confidence degrees and the promotion degrees respectively.
S202, calculating the order splitting rate of the order after the objects to be distributed in the M incidence relations before sorting are selected for clustering, wherein the order splitting rate is the ratio of the number of the split orders to the total number of the orders; the first M incidence relations of the confidence degree ranking and the promotion degree ranking can be selected respectively, and the initial value of M can be 1 or selected according to experience.
S203, determining whether the order splitting rate is greater than a second threshold value; wherein the second threshold may be a preset maximum list splitting rate threshold.
S204, if the order splitting rate is greater than the second threshold, increasing the number of M until the order splitting rate after the items to be distributed in the M incidence relations before the sorting are selected for clustering is not greater than the second threshold, and determining the items to be distributed in the M incidence relations as the items to be distributed which need to be clustered.
It should be noted that, in the steps S201 to S204, when the to-be-delivered items determined to need clustering are removed from the split order data of the historical order, the order splitting rate of the historical order is calculated in a trial manner, so as to achieve the purpose of determining the to-be-delivered items to need clustering and further optimizing the order splitting rate.
For example, after sorting according to the association relationship, all the articles to be delivered in the association relationship of the top 10 of the sorting are selected to perform clustering operation, that is, after the split orders of the articles to be delivered are removed from the historical orders, the order splitting rate of the historical orders is calculated, if the order splitting rate is still greater than the second threshold value, all the articles to be delivered in the association relationship of the top 11 of the sorting are calculated to perform clustering operation, the order splitting rate of the historical orders is calculated, and if the order splitting rate is not greater than the second threshold value, all the articles to be delivered in the association relationship of the top 11 of the sorting are determined to be the articles to be delivered, which need to be clustered.
Further, after determining that the N items to be delivered are the categories of the items to be delivered that need to be clustered, the method may further include: and determining the quantity of the to-be-delivered articles to be clustered according to the predicted sales volume of the to-be-delivered articles. After determining that the items to be delivered need to be clustered, the future sales of the items to be delivered can be predicted, so that the quantity of the items to be delivered need to be clustered is determined according to the predicted sales.
For example, after determining that the to-be-delivered items 1 and the to-be-delivered items 2 are to-be-delivered items that need to be clustered, the sales of the to-be-delivered items 1 and the to-be-delivered items 2 in a future month may be further predicted according to the historical sales of the to-be-delivered items 1 and the to-be-delivered items 2, and assuming that the predicted sales of the to-be-delivered items 1 is 100 and the predicted sales of the to-be-delivered items 2 is 200, it may be further determined that 100 to-be-delivered items 1 and 200 to-be-delivered items 2 need to be clustered. As shown in fig. 3, step S301 represents determining the to-be-delivered goods that need to be clustered according to the association degree, step S302 represents querying the inventory quantity of the to-be-delivered goods in each warehouse, and if the location of the shipping warehouse is not considered, in order to balance the inventory between the warehouses and ensure that the quantity of the to-be-delivered goods that are determined to need to be clustered between each warehouse is the same, step S303 is executed to allocate the to-be-delivered goods that need to be clustered between the warehouses so that the quantity of the to-be-delivered goods that are determined to need to be clustered between each warehouse is the same.
Fig. 4 is a schematic diagram of a main part of an apparatus 400 for clustering articles to be dispensed according to an embodiment of the present invention, as shown in fig. 4:
the obtaining module 401 is configured to obtain splitting information of an order, where the splitting information includes sub-orders split according to the order and items to be delivered included in the sub-orders. The purpose is to determine the data source and narrow the calculation range.
The order obtained by the obtaining module 401 may have a certain time range, for example, historical orders in the last week or last 2 months are obtained, split orders are queried in the historical orders, splitting information of the split orders is obtained, the splitting information may include, in addition to sub-orders split according to the order and items to be dispensed included in the sub-orders, the number of the split sub-orders, the order number of the split order, the order generation time of the sub-orders, and information of the number, name, number, and price of the items to be dispensed included in the order, and further, a splitting reason of the split order may be determined, so as to select an order that is split if the order is not in the same warehouse.
The historical order acquired by the acquisition module 401 may also be selected according to the brand of the article to be delivered, the shipping warehouse, and the classification range of the article to be delivered, for example, according to the shipping warehouse, the acquisition module 401 extracts the detailed data of the warehouse order in the top ten of the order splitting rate, and analyzes the split order data in these warehouses; or according to the classification of the articles to be delivered, extracting order data of the article to be delivered in the ten-degree front order splitting rate, and analyzing splitting order data corresponding to all classes of the articles to be delivered.
The split order data obtained by the obtaining module 401 may be stored in a text file (e.g., csv format). For example, as shown in table 1, orders with order numbers 1 to 5 are split into several sub-orders. Specifically, the order number 1 includes the items 1, 2, and 3 to be delivered, where the items 1 and 3 to be delivered belong to the same warehouse and are thus split into the sub-order 1-1, and the warehouse where the item 2 to be delivered is located is different from the warehouses where the items 1 and 3 to be delivered are located, and are thus split into the sub-order 1-2.
Further, the obtaining module 401 may also use a sparse matrix to store order data, which is convenient for storage and calculation. For example, the order splitting information in table 1 is converted into the value in table 2, each row represents a sub-order, an element with a value of 0 in the row represents that the sub-order does not include the item to be delivered in the corresponding column, an element with a value of 0 represents that the sub-order includes the item to be delivered in the corresponding column, and the number of the item to be delivered is the value of the element. Specifically, for convenience of calculation and storage, the number of the items to be delivered may also be omitted, where 1 indicates that the corresponding items to be delivered are included in the sub-order, and 0 indicates that the corresponding items to be delivered are not included in the sub-order.
A calculating module 402, configured to determine an association relationship between the sub-orders and the items to be delivered according to the splitting information of the order, and calculate an association degree of the association relationship; the degree of association indicates that there is some relationship between the items to be dispensed in the relationship, and the relationship can be measured by the value of the degree of association. In the embodiment of the present invention, an association relationship is determined according to a relationship between the to-be-distributed items in each sub order in the split order, for example, a certain order is split into a sub order a and a sub order B, where the sub order a includes the to-be-distributed item 1 and the to-be-distributed item 2, and the sub order B includes the to-be-distributed item 3, the association relationship is an association between { the to-be-distributed item 1 and the to-be-distributed item 2} and { the to-be-distributed item 3}, and further the association relationship has a directivity, that is, { the to-be-distributed item 1 and the to-be-distributed item 2} are associated with { the to-be-distributed item 3} and { the to-be-distributed item 1 and the to-be-distributed item 2} are associated with each other. When calculating the association degree of the association relationship, it is preferable to calculate the two association relationships respectively, and further, when an order is divided into more than two sub-orders, the association relationship is more than two, and it is preferable to calculate the association degree of all the association relationships, so that the subsequent selection of the association degree is more accurate. Wherein the relevance comprises one or more of support, confidence and lift.
The support degree is the ratio of the number of orders of the to-be-distributed articles in the incidence relation to the total number of orders;
the confidence coefficient is the ratio of the order quantity of the to-be-dispensed articles in the incidence relation and the order quantity of the to-be-dispensed articles in at least one sub order in the incidence relation;
the promotion degree is the ratio of the confidence degree and the support degree of the articles to be delivered in the association relation.
The calculation module 402 may further include an Apriori machine learning algorithm (Apriori algorithm is a frequent item set algorithm for mining association, and the core idea thereof is to mine a frequent item set through two stages of candidate set generation and downward closed detection of plot, and the algorithm is already widely applied to various fields of commerce, network security, and the like), establish an association degree calculation model for association, and in the process of training the model, initialize the values of support degree, confidence degree, and/or promotion degree, and screen out order data with lower support degree, confidence degree, and/or promotion degree.
And a clustering module 403, configured to determine, according to the association degree, items to be delivered that need to be clustered. Since the association relationship is determined according to the split order, after the calculation module 402 calculates the association degree of the association relationship, the clustering module 403 may measure the items to be delivered in the sub-orders according to the value of the association degree, and the higher the value of the association degree is, it is indicated that the items to be delivered between the sub-orders have a strong association relationship, and the clustering module 403 selects the categories of the items to be delivered with high association degree for clustering, so that the order splitting operation caused by the fact that the items to be delivered are not in the same warehouse can be reduced, and the order splitting rate is reduced. Further, the clustering module 403 preferably integrates the support degree, the confidence degree, and the promotion degree to measure the association degree of the to-be-distributed items.
The clustering module 403 is further configured to sort the association relations according to the association degrees from high to low; and selecting the articles to be dispensed in the incidence relation with the incidence degree larger than or equal to the first threshold value.
The clustering module 403 is further configured to sort the association relations according to the association degrees from high to low; calculating the order splitting rate of the order after the objects to be distributed in the M incidence relations before the sorting are selected for clustering, wherein the order splitting rate is the ratio of the number of the split orders to the total number of the orders; determining whether the order splitting rate is greater than a second threshold; if the order splitting rate is greater than a second threshold value, increasing the number of M progressively until the order splitting rate after the items to be distributed in the M incidence relations before the sorting are selected for clustering is not greater than the second threshold value, and determining the items to be distributed in the M incidence relations as the items to be distributed which need to be clustered.
The clustering module 403 is further configured to determine the number of the to-be-delivered items to be clustered according to the predicted sales volume of the to-be-delivered items.
Fig. 5 shows an exemplary system architecture 500 of a method of clustering items to be dispensed or an apparatus for clustering items to be dispensed to which embodiments of the present invention may be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like.
The terminal devices 501, 502, 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 505 may be a server providing various services, such as a background management server providing support for users using the terminal devices 501, 502, 503. The background management server may analyze and perform other processing on the received data such as the information query request, and feed back a processing result (for example, order splitting information) to the terminal device.
It should be noted that the method for clustering the to-be-distributed items provided in the embodiment of the present invention is generally executed by the server 505, and accordingly, an apparatus for clustering the to-be-distributed items is generally disposed in the server 505.
It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
FIG. 6 is a block diagram illustrating a computer system 600 suitable for implementing a terminal device according to an embodiment of the invention. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, the processes described in the above step diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, the disclosed embodiments of the invention include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the step diagrams. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable media shown in the present invention include computer readable signal media or computer readable storage media, or any combination of the two. A computer readable storage medium includes, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, semiconductor system, apparatus, or device, or any combination of the foregoing. Computer-readable storage media specifically include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any combination of the foregoing. In the present invention, a computer readable storage medium includes any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device; a computer readable signal medium includes a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave, which may take many forms, including, but not limited to, electromagnetic signals, optical signals, or any combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF (radio frequency), etc., or any combination of the preceding.
The block diagrams or step diagrams in the figures, which illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention, may each represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or step diagrams, and combinations of blocks in the block diagrams or step diagrams, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules or units described in the embodiments of the present invention may be implemented by software, or may be implemented by hardware. The described modules or units may also be provided in a processor, and may be described as: a processor includes an acquisition module, a computation module, and a clustering module. The names of these modules or units do not in some cases constitute a limitation on the modules or units themselves, and for example, the acquiring module may also be described as a "module for acquiring splitting information of an order".
On the other hand, the embodiment of the present invention also provides a computer-readable medium, which may be included in the apparatus described in the above embodiment; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: acquiring splitting information of an order, wherein the splitting information comprises sub orders split according to the order and articles to be delivered included in the sub orders; determining the incidence relation of the articles to be delivered among the sub orders according to the splitting information of the orders, and calculating the incidence degree of the incidence relation; and determining the articles to be delivered which need to be clustered according to the association degree.
According to the technical scheme of the embodiment of the invention, the articles to be distributed which need to be clustered can be determined according to the splitting information of the historical orders, so that the order splitting rate is reduced, the order distribution cost is saved, the task operation is saved, the timeliness control on order distribution is improved, and the customer experience is optimized.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (12)

1. A method of clustering items to be dispatched, comprising:
acquiring splitting information of an order, wherein the splitting information comprises sub orders split according to the order and articles to be delivered included in the sub orders;
determining the incidence relation of the articles to be delivered among the sub orders according to the splitting information of the orders, and calculating the incidence degree of the incidence relation;
and determining the articles to be delivered which need to be clustered according to the association degree.
2. The method of claim 1, wherein the degree of association comprises one or more of a support degree, a confidence degree, and a boost degree;
the support degree is the ratio of the number of orders of the to-be-distributed articles in the incidence relation to the total number of orders;
the confidence coefficient is the ratio of the order quantity of the to-be-dispensed articles in the incidence relation and the order quantity of the to-be-dispensed articles in at least one sub order in the incidence relation;
the promotion degree is the ratio of the confidence degree and the support degree of the articles to be delivered in the association relation.
3. The method according to claim 1 or 2, wherein the method for determining the items to be dispensed which need to be clustered according to the relevance comprises:
sorting the incidence relations according to the incidence degrees from high to low;
and selecting the articles to be dispensed in the incidence relation with the incidence degree larger than or equal to the first threshold value.
4. The method according to claim 1 or 2, wherein the method for determining the items to be dispensed which need to be clustered according to the relevance comprises:
sorting the incidence relations according to the incidence degrees from high to low;
calculating the order splitting rate of the order after the objects to be distributed in the M incidence relations before the sorting are selected for clustering, wherein the order splitting rate is the ratio of the number of the split orders to the total number of the orders;
determining whether the order splitting rate is greater than a second threshold;
if the order splitting rate is greater than a second threshold value, increasing the number of M progressively until the order splitting rate after the items to be distributed in the M incidence relations before the sorting are selected for clustering is not greater than the second threshold value, and determining the items to be distributed in the M incidence relations as the items to be distributed which need to be clustered.
5. The method according to claim 4, wherein after determining that the items to be delivered in the M association relations are the categories of the items to be delivered which need to be clustered, the method further comprises:
and determining the quantity of the to-be-delivered articles to be clustered according to the predicted sales volume of the to-be-delivered articles.
6. An apparatus for clustering items to be delivered, comprising:
the acquisition module is used for acquiring splitting information of an order, wherein the splitting information comprises sub-orders split according to the order and the articles to be delivered contained in the sub-orders;
the calculation module is used for determining the incidence relation of the articles to be distributed among the sub orders according to the splitting information of the orders and calculating the incidence degree of the incidence relation;
and the clustering module is used for determining the to-be-delivered articles to be clustered according to the association degree.
7. The apparatus of claim 6, wherein the degree of association comprises one or more of a support degree, a confidence degree, and a boost degree;
the support degree is the ratio of the number of orders of the to-be-distributed articles in the incidence relation to the total number of orders;
the confidence coefficient is the ratio of the order quantity of the to-be-dispensed articles in the incidence relation and the order quantity of the to-be-dispensed articles in at least one sub order in the incidence relation;
the promotion degree is the ratio of the confidence degree and the support degree of the articles to be delivered in the association relation.
8. The apparatus according to claim 6 or 7, wherein the clustering module is further configured to sort the association relations according to the association degrees from high to low; and selecting the articles to be dispensed in the incidence relation with the incidence degree larger than or equal to the first threshold value.
9. The apparatus according to claim 6 or 7, wherein the clustering module is further configured to sort the association relations according to the association degrees from high to low;
calculating the order splitting rate of the order after the objects to be distributed in the M incidence relations before the sorting are selected for clustering, wherein the order splitting rate is the ratio of the number of the split orders to the total number of the orders;
determining whether the order splitting rate is greater than a second threshold;
if the order splitting rate is greater than a second threshold value, increasing the number of M progressively until the order splitting rate after the items to be distributed in the M incidence relations before the sorting are selected for clustering is not greater than the second threshold value, and determining the items to be distributed in the M incidence relations as the items to be distributed which need to be clustered.
10. The apparatus of claim 9, wherein the clustering module is further configured to determine the number of the items to be delivered that need to be clustered based on the predicted sales of the items to be delivered.
11. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
12. A computer-readable medium, on which a computer program is stored, which, when being executed by one or more processors, carries out the method according to any one of claims 1-5.
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