CN110858336A - An article assembly method and device - Google Patents

Abstract

The invention discloses an article assembling method and device, and relates to the field of warehouse logistics. One embodiment of the method comprises: receiving an article assembly request, acquiring characteristic values of articles to be assembled, and determining total characteristic values of all the articles to be assembled; and when the total characteristic value exceeds a preset characteristic value threshold value, grouping all the articles according to the preset characteristic value threshold value and the characteristic values of the articles to obtain a corresponding article combination mode. This embodiment can use as few boxes as possible to accomplish the packing to can synthesize the price of considering article in the box, characteristics such as weight and final cost, with each packing scheme of effective evaluation, realize the optimum packing, and effectively reduce operation cost and risk.

Description

An article assembly method and device

technical field

The invention relates to the field of warehousing and logistics, in particular to an article assembly method and device.

Background technique

In the field of e-commerce, especially in cross-border e-commerce, users may purchase multiple items when placing an order. However, the customs stipulates that the total amount of items entering the customs cannot exceed a certain value (for example, 2,000 yuan), otherwise it cannot enter the customs. Therefore, if the total amount of the order placed by the user exceeds this value, multiple boxes must be used for packaging and transportation.

The existing packaging methods are mainly to pack items into boxes of suitable size according to their volume. But in the process of realizing the present invention, the inventor found that the prior art has at least the following problems:

The existing packaging methods focus on the speed of packaging, and do not use as few boxes as possible to save transportation costs while meeting the requirements of the customs; and they do not aim to pack multiple items into the same package in a high-security and low-cost way at the same time. A box solution, especially for cross-border e-commerce.

SUMMARY OF THE INVENTION

In view of this, the embodiments of the present invention provide an article assembly method and device, which can at least solve the problem that the prior art is aimed at cross-border e-commerce, and there is no solution to packing multiple articles into the same box in a high-security and low-cost manner. Program.

In order to achieve the above object, according to an aspect of the embodiments of the present invention, an article assembly method is provided, comprising: receiving an article assembly request, obtaining characteristic values of the articles to be assembled, and determining the total characteristic value of all the articles to be assembled; When the value exceeds the predetermined characteristic value threshold, according to the predetermined characteristic value threshold and the characteristic value of the article, all articles are grouped to obtain the corresponding article combination mode.

Optionally, the characteristic value includes price and volume;

When the total feature value exceeds the predetermined feature value threshold, all items are grouped according to the predetermined feature value threshold and the feature value of the item, including:

When the total price of all items exceeds the predetermined price threshold, sort the items according to the price, and sequentially extract the items whose sum of prices does not exceed the predetermined price threshold as a group; or

When the total volume of all items exceeds the predetermined volume threshold, sort the items according to the volume, and sequentially extract the items whose sum of volumes does not exceed the predetermined volume threshold to form a group; or

When the total price of all items exceeds the predetermined price threshold and the total volume exceeds the predetermined volume threshold, sort the items according to price and volume, and extract in order that the sum of the prices does not exceed the predetermined price threshold, and the sum of the volumes does not exceed the predetermined volume Threshold items are combined into a group.

Optionally, also include:

Move the first item in the obtained price sort to the end of the queue, get the moved sort, and sequentially extract items whose sum of prices does not exceed the predetermined price threshold to form a group; or

Move the first item in the resulting volume sort to the end of the queue to obtain the moved sort, and sequentially extract items whose sum of volumes does not exceed the predetermined volume threshold to form a group; or

Move the first item in the obtained price and volume sorting to the end of the queue, get the moved sorting, and sequentially extract the items whose sum of prices does not exceed the predetermined price threshold and the sum of volumes does not exceed the predetermined volume threshold and combine them into a group ;

Repeat the above process, move the first item in each obtained order to the end of the queue, and obtain the combination of items corresponding to each order, until the moved order is the same as the unmoved order.

Optionally, after obtaining the item combination mode corresponding to each sorting until the sorting after moving is the same as the sorting when not moving, it also includes: according to the characteristic value of the item and the predetermined similarity determination method, analyzing the sorting with each item. Sort the similarity between the corresponding item combinations; count the total similarity with all the corresponding item combinations in each sorting, extract the sorting with the smallest total similarity, and determine the item combination corresponding to the extracted sorting. Execute method for item combination.

Optionally, when the total feature value exceeds the predetermined feature value threshold, all items are grouped according to the predetermined feature value threshold and the feature value of the item, including:

Classify items of the same price into one category, and extract items from each category to combine, and the sum of the prices of the extracted items does not exceed a predetermined price threshold and the sum of the volumes does not exceed a predetermined box volume; or

Items of the same volume are classified into one category, and items from each category are extracted and combined, and the sum of the prices of the extracted items does not exceed a predetermined price threshold and the sum of the volumes does not exceed a predetermined packing volume.

To achieve the above object, according to another aspect of the embodiments of the present invention, an article assembling device is provided, comprising:

The feature value acquisition module is used to receive the item assembly request, obtain the feature value of the item to be assembled, and determine the total feature value of all the items to be assembled;

The article assembly module is used for grouping all articles according to the predetermined characteristic value threshold and the characteristic value of the article when the total characteristic value exceeds the predetermined characteristic value threshold to obtain the corresponding article combination mode.

Optionally, feature values include price and volume;

Item assembly module for:

When the total price of all items exceeds the predetermined price threshold, sort the items according to the price, and sequentially extract the items whose sum of prices does not exceed the predetermined price threshold as a group; or

When the total volume of all items exceeds the predetermined volume threshold, sort the items according to the volume, and sequentially extract the items whose sum of volumes does not exceed the predetermined volume threshold to form a group; or

When the total price of all items exceeds the predetermined price threshold and the total volume exceeds the predetermined volume threshold, sort the items according to price and volume, and extract in order that the sum of the prices does not exceed the predetermined price threshold, and the sum of the volumes does not exceed the predetermined volume Threshold items are combined into a group.

Optionally, the article assembly module is also used for:

Move the first item in the obtained price sort to the end of the queue, get the moved sort, and sequentially extract items whose sum of prices does not exceed the predetermined price threshold to form a group; or

Move the first item in the resulting volume sort to the end of the queue to obtain the moved sort, and sequentially extract items whose sum of volumes does not exceed the predetermined volume threshold to form a group; or

Move the first item in the obtained price and volume sorting to the end of the queue, get the moved sorting, and sequentially extract the items whose sum of prices does not exceed the predetermined price threshold and the sum of volumes does not exceed the predetermined volume threshold and combine them into a group ;

Repeat the above process, move the first item in each obtained order to the end of the queue, and obtain the combination of items corresponding to each order, until the moved order is the same as the unmoved order.

Optionally, the article assembly module is further configured to: analyze the similarity between the article combination methods corresponding to each order according to the characteristic value of the article and the predetermined similarity determination method; The total similarity of all item combination modes is extracted, and the sorting with the smallest total similarity is extracted, and the item combination mode corresponding to the extracted sorting is determined as the item combination execution mode.

Optionally, the article assembly module is used for:

Classify items of the same price into one category, and extract items from each category to combine, and the sum of the prices of the extracted items does not exceed a predetermined price threshold and the sum of the volumes does not exceed a predetermined box volume; or

Items of the same volume are classified into one category, and items from each category are extracted and combined, and the sum of the prices of the extracted items does not exceed a predetermined price threshold and the sum of the volumes does not exceed a predetermined packing volume.

To achieve the above object, according to yet another aspect of the embodiments of the present invention, an electronic device for assembling articles is provided.

An electronic device according to an embodiment of the present invention includes: one or more processors; and a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more programs cause the One or more processors implement any of the above-described methods of assembling articles.

To achieve the above object, according to yet another aspect of the embodiments of the present invention, a computer-readable medium is provided, on which a computer program is stored, and when the program is executed by a processor, any one of the above-mentioned methods for assembling an article is implemented.

According to the solution provided by the present invention, one embodiment of the above-mentioned invention has the following advantages or beneficial effects: using a "split" method to pack a plurality of items into a box to reduce the number of boxes; and considering weight and cost , price and other factors, choose the most favorable packaging plan to reduce transportation costs and transportation risks.

Further effects of the above non-conventional alternatives will be described below in conjunction with specific embodiments.

Description of drawings

The accompanying drawings are used for better understanding of the present invention and do not constitute an improper limitation of the present invention. in:

FIG. 1 is a schematic diagram of the main flow of an article assembling method according to an embodiment of the present invention;

Fig. 2 is a kind of article splitting process and packing process provided by the embodiment of the present invention;

Fig. 3 is another kind of article splitting process and packing process provided by the embodiment of the present invention;

4 is a schematic diagram of main modules of an article assembling device according to an embodiment of the present invention;

5 is an exemplary system architecture diagram to which an embodiment of the present invention may be applied;

FIG. 6 is a schematic structural diagram of a computer system suitable for implementing a mobile device or a server according to an embodiment of the present invention.

Detailed ways

Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, which include various details of the embodiments of the present invention to facilitate understanding and should be considered as exemplary only. 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 from the following description for clarity and conciseness.

In traditional e-commerce, the buyers and sellers of transactions generally belong to the same country, that is, domestic merchants and sellers sell online to domestic buyers; while cross-border e-commerce is a transaction between buyers and sellers in different countries or customs areas, from the business model. From a simple point of view, there are more international logistics, entry and exit customs clearance, international settlement and other services.

It should be noted that, because cross-border travel is from abroad to home, a package may contain items of different users, which will be unpacked and mailed to users after arriving at home, but there is no such situation in China. Therefore, the present invention mainly considers cross-border electricity. The items involved are all cross-border items.

Referring to FIG. 1, it shows a main flowchart of an article assembling method provided by an embodiment of the present invention, including the following steps:

S101: Receive an item assembly request, obtain characteristic values of the items to be assembled, and determine the total characteristic values of all the items to be assembled;

S102: When the total feature value exceeds the predetermined feature value threshold, perform grouping processing on all the items according to the predetermined feature value threshold and the feature values of the items to obtain a corresponding item combination mode.

In the above embodiment, for step S101, the customs has restrictions on the price of transit items, and packing has a volume limit on the items. Therefore, the present invention mainly considers two factors of item price and volume (ie length, width and height).

For step S102, it is set that the total amount of items in each cross-border box cannot exceed ap yuan, and the present invention takes 2,000 yuan as an example to illustrate (the price here can also be in other units, such as US dollars, and the present invention takes RMB as an example to illustrate ); the volume of the box used by the e-commerce business, and its length, width, and height are represented by [boxwidth, boxpepth, boxhigh], and the present invention is described by taking [2, 2, 3] as an example.

For the cross-border goods goods purchased by the user, the array [price, width, depth, hight] is used to represent the price and volume, and it is assumed that they are all positive integers. It should be noted that the user here is not limited to one user, because the items purchased by multiple users can be transported together, and then unpacked and distributed after entering the customs.

If the user buys n items at a time, it can be represented by the following matrix:

If the total price of all items is less than or equal to the price set by the customs, and the total volume is less than or equal to the packing volume, then only one box is required for packing. For example, a user purchases 4 items as:

However, if the total volume of all items exceeds the volume of the box, and/or the total price exceeds the total price set by the customs, it is necessary to "split" the items, that is, multiple boxes need to be assembled. For example, the 4 items purchased by the user are:

或者

or

or

Two boxes are required, for item g4, it needs to be boxed separately, and the other three items are boxed together.

In extreme cases, each piece needs to be individually packaged, such as:

For over-limited cross-border items, you can sort by their volume and price, specifically, sort by item price first, and then sort items with the same price based on item volume.

It should be noted that the sorting here is a necessary process to be able to find out which items can be placed in the same box as soon as possible. Sorting can be descending or ascending.

In life, extreme items are usually dealt with separately. The so-called extremes are items that are too large or too small, which can be dealt with first or last, so that you don’t need to consider all the combinations of items together, as long as you can effectively use the box space, use Use as few boxes as possible. E.g:

1) Before packing, there is a filtering mechanism to filter out items whose shape exceeds the packing specification and pack them separately; for example, items with a volume of [5,5,1];

2) For items with a particularly small volume, they can be placed in the final processing; for example, items with a volume of [1, 0.5, 0.5] can be placed at the end.

For the "split" process of items, the "binary tree" method can be used. In a binary tree, each node has at most two subtrees, a "left subtree" and a "right subtree". Corresponding to the present invention, that is, the items in each box can be split into two boxes at most, and this operation is performed continuously until all the items can be put into the boxes.

See Figure 2, which demonstrates the "split" process and packaging results of items, where "Spliting" means the splitting process, "Create New Box" means creating a new box, and the items in the box are output later; in addition, "PRI" means The price of the item; "WDH" means the length, width and height of the item; "VOL" means the volume of the item.

1) At the initial moment, assuming that all items are placed in a box, if the total volume and total price of the current items do not meet either the box volume or the cross-border specified price, it needs to be "split";

2) Sort the items in descending order by volume and price, and set the ID for the item according to the sorting order; for example, set the ID of the item that is ranked first as 1;

3) According to the obtained sorting, extract the items in order and combine them. For example, the ID1 item is packed in a separate box, and the remaining items are assembled into one box, which requires a total of two boxes.

See Figure 3, which shows another splitting process and packaging process, which is slightly more complicated than the example in Figure 2. Although the volume of each item is smaller than the box volume, the price of each item is equal to the cross-border price set value , four boxes are required for packing at this time.

It should be noted that the box logos are unique and can also be customized, just for the purpose of distinguishing, and they are used internally and not visible to users. The box logo is the same, and the logo will not be repeated. When a box is split into two, it can be generated by subtracting one from the left and adding one to the right. For example, the original 1000 becomes left 999 and right 1001.

In addition to grouping items in a binary tree manner, there are other grouping methods:

Assume that there are 4 items with a price of 1000 yuan, 4 items with a price of 900 yuan, 3 items with a price of 100 yuan and 1 item with a price of 10 yuan, and it is assumed that the volume of a single item is smaller than the packing volume :

1) Arrange the items in descending order based on the item price and volume to get the set {1000, 1000, 1000, 1000, 900, 900, 900, 900, 100, 100, 100, 10}, and the sum of the extracted prices in order does not exceed 2000 yuan, and the sum of the volumes does not exceed the packing volume [ 2,2,3] items are combined, for example, 2*(1000+1000), 900+900, 2*900+2*100, 100+10;

2) Arrange the items in ascending order based on the item price and volume to get the set {10, 100, 100, 100, 900, 900, 900, 900, 1000, 1000, 1000, 1000}, and then sequentially extract the items whose sum of price and volume does not exceed the limit, for example, 10+ 3*100+900, 900+900, 900+1000, 1000+1000, 1000;

In view of the idea that large items are usually processed first, and then small items are processed later, the present invention mainly adopts a descending order method to reduce the number of times of traversing the items, and find the items whose volume or price exceeds the limit as soon as possible.

3) Group the items according to the item price, start from the group with the highest price, and select an item from each group to combine in sequence, and the total price and total volume of the combined items do not exceed the limit, for example, 3*(1000+ 900+100), 1000+900+10; this method can balance the price ratio of items in each box.

In addition, you can also choose the volume of the items in each group, for example, 1000 (largest) + 900 (smallest) + 100 (largest), or 1000 (smallest) + 900 (largest) + 100 ( The smallest volume), or 1000 (the largest volume) + 900 (the smallest volume) + 100 (the smallest volume), in order to balance the volume ratio of the items in each box, there will be no situation where one box is too crowded and the other is too loose.

In addition to the above methods, some items can also be extracted from each group for combined packaging. For example, two items of 1,000 yuan can be extracted and combined into a group for packaging.

4) Items are grouped according to their volume, starting from the group with the largest volume, extracting an item from each group in order to combine, and the combined item price and volume do not exceed the limit;

However, in this method, it is impossible to determine that the prices of the items in each group are relatively balanced, and there may be a situation where all the valuables are in a group. Therefore, methods 3) and 4) above are mainly selected for method 3).

In addition, there are also cases where the total price of all items does not exceed the limit, but the total volume exceeds the limit; or the total volume does not exceed the limit, but the total price exceeds the limit. At this time, only the characteristics of the limit (price or volume) can be considered. The factors that do not exceed the limit can be randomly assigned, for example:

1) The 4 items are worth 900, 900, 100 and 100 yuan respectively; but the volumes are [2,3,2], [1,2,1], [2,1,1] and [1,1,2 ], the volume [2,3,3] is the largest and exceeds the limit after sorting, so the items with the volume [2,3,3] worth 900 yuan can be packed separately, and the rest of the items can be packed in one box;

2) The values of the 4 items are 1000, 1000, 100, 100, respectively, and the volumes are [1,2,1], [2,1,1], [1,1,2] and [1,1,2] , after sorting by price, you can assemble items with a value of 1,000 yuan into a box, and the remaining items into a box; or assemble 1,000+100 items in a distributed manner, a total of two boxes.

For the combined analysis of items in the above embodiments, multiple items can be packed into the same box, so as to effectively reduce the number of boxes. However, using less boxes may not be optimal. Not only should you use as few boxes as possible, but also consider the relevant operating costs and risks. Because various problems will inevitably occur during transportation, high-priced, low-volume items need to be scattered into different boxes for assembly.

According to the process shown in Figure 1, after analyzing how many boxes are needed and obtaining the packaging plan, the items in the order can be sorted out and the packaging plan can be regenerated.

For example, the sorting result of the original item is {g1,g2,g3...gn}, and a packing scheme is obtained; then the item g1 is moved to the end, namely {g2,g3...,gn,g1}, and a packing scheme is obtained again, repeating the The process stops when the original nth item moves to the first one, that is:

This is mainly carried out for methods 1) and 2) in "Other grouping methods". In the assembly method, adjacent items will only be split if they exceed either the total price or the total volume. Also taking the above example as an example, all descriptions are in descending order:

1) Sort {1000, 1000, 1000, 1000, 900, 900, 900, 900, 100, 100, 100, 10} for the first time, and the combined result is 2*(1000+1000), 900+900, 2*900+2*100, 100+10;

2) Move the item ranked first to the end of the queue, the sorting result at this time is {1000, 1000, 1000, 900, 900, 900, 900, 100, 100, 100, 10, 1000}, and the combined result is 1000+1000, 1000+900, 900+900, 900 +100*3+10, 1000;

3) Move the item ranked first to the end of the queue again, the sorting result is {1000, 1000, 900, 900, 900, 900, 100, 100, 100, 10, 1000, 1000}, and the combined result is 1000+1000, 900+900, 2*900+ 2*100, 100+10+1000, 1000;

...

12) When the last item in the original sorting is moved to the head of the queue, the sorting result is {10, 1000, 1000, 1000, 1000, 900, 900, 900, 900, 100, 100, 100}, and the combined result is 10+1000, 1000+1000, 1000+900 , 900+900, 900+100*3.

It should be noted that, in the above method, the last item in the queue can also be moved to the head of the queue to determine various combination methods. 12) above are the same.

It can be seen from the above that for different sorting results, the obtained packing schemes are different. Sorting is to quickly find solutions, and placing items in sequence at the end of the queue is to avoid missing the best solution.

For the packaging schemes obtained by different sorting, the optimal one needs to be selected. The present invention determines by evaluating the price of each packaging method. Specifically, the similarity between each box in each scheme can be calculated, and then the obtained similarity values are accumulated to obtain the evaluation value of the scheme.

There are many methods for calculating similarity, including Euclidean distance, Manhattan distance, Minkowski distance, cosine similarity, Jaccard correlation coefficient, Pearson correlation coefficient, etc. The present invention adopts the Manhattan distance calculation method because its Computational efficiency is high.

Assuming that a scheme pl uses m boxes, if only the item price factor is considered, the Manhattan distance d between the i-th and j-th boxes (i, j≤m) is:

d _{i,j =|pi -p j |}

个曼哈顿距离，累加所有曼哈顿距离得到该方案pl的评估值：There are a total of

A Manhattan distance is accumulated, and all Manhattan distances are accumulated to obtain the evaluation value of the scheme pl:

When the total price of the items in the m boxes is as close as possible. For example, if two chests are needed, the same item can be placed in either chest, but the only difference may be the total price of the two chests. Therefore, all valuables will not be placed in the same box, and if they are lost, the loss will be greater.

Finally, the one with the smallest distance among all the schemes is selected: p=minpl _k , as the final package execution scheme.

In addition to price, box weight and final cost are also important for packing. Among them, the packing weight can be determined by the weight of the items carried in the box and the weight of the box, and the cost here refers to the logistics cost, which is provided by the logistics company.

Considering the weight, price, and cost (weight, price, cost) simultaneously, the Manhattan distance d between the ith and jth bins (i, j≤m) is changed to:

d _{i,j =} |pi -p _j |+|w _i -w _j |+|c _i -c _j

The above price, weight, and cost can all be regarded as features. Further, it can also be extended to other features, such as whether it is fragile, whether it is urgent, etc. The resulting feature set can be (a ₁ , a ₂ ..., at _t ), the similarity between the boxes is:

The method provided by the embodiment of the present invention can use as few boxes as possible to complete the packaging, and can comprehensively consider the price, weight and final cost of the items in the box, so as to effectively evaluate each packaging scheme, achieve optimal packaging, and effectively Reduce operating costs and risks.

Referring to FIG. 4 , a schematic diagram of main modules of an article assembling device 400 provided by an embodiment of the present invention is shown, including:

The feature value acquisition module 401 is configured to receive an item assembly request, obtain the feature value of the item to be assembled, and determine the total feature value of all the items to be assembled;

The article assembling module 402 is configured to perform grouping processing on all articles according to the predetermined characteristic value threshold and the characteristic value of the article when the total characteristic value exceeds the predetermined characteristic value threshold to obtain the corresponding article combination mode.

In the implementation device of the present invention, the characteristic value includes price and volume;

Item assembly module 402 for:

When the total price of all items exceeds the predetermined price threshold, sort the items according to the price, and sequentially extract the items whose sum of prices does not exceed the predetermined price threshold as a group; or

When the total volume of all items exceeds the predetermined volume threshold, sort the items according to the volume, and sequentially extract the items whose sum of volumes does not exceed the predetermined volume threshold to form a group; or

When the total price of all items exceeds the predetermined price threshold and the total volume exceeds the predetermined volume threshold, sort the items according to price and volume, and extract in order that the sum of the prices does not exceed the predetermined price threshold, and the sum of the volumes does not exceed the predetermined volume Threshold items are combined into a group.

In the implementation device of the present invention, the article assembly module 402 is also used for:

Move the first item in the obtained price sort to the end of the queue, get the moved sort, and sequentially extract items whose sum of prices does not exceed the predetermined price threshold to form a group; or

Move the first item in the resulting volume sort to the end of the queue to obtain the moved sort, and sequentially extract items whose sum of volumes does not exceed the predetermined volume threshold to form a group; or

Move the first item in the obtained price and volume sorting to the end of the queue, get the moved sorting, and sequentially extract the items whose sum of prices does not exceed the predetermined price threshold and the sum of volumes does not exceed the predetermined volume threshold and combine them into a group ;

Repeat the above process, move the first item in each obtained order to the end of the queue, and obtain the combination of items corresponding to each order, until the moved order is the same as the unmoved order.

In the implementation device of the present invention, the article assembly module 402 is also used for:

According to the eigenvalues of the items and the predetermined similarity determination method, analyze the similarity between the item combinations corresponding to each order; count the total similarity with all the item combinations corresponding to each order, and extract the minimum total similarity , and determine the item combination mode corresponding to the extracted sequence as the item combination execution mode.

In the implementation device of the present invention, the article assembly module 402 is used for:

Classify items of the same price into one category, and extract items from each category to combine, and the sum of the prices of the extracted items does not exceed a predetermined price threshold and the sum of the volumes does not exceed a predetermined box volume; or

Items of the same volume are classified into one category, and items from each category are extracted and combined, and the sum of the prices of the extracted items does not exceed a predetermined price threshold and the sum of the volumes does not exceed a predetermined packing volume.

In addition, the specific implementation content of the article assembling device described in the embodiments of the present invention has been described in detail in the article assembling method described above, so the repeated content will not be described here.

The device provided by the embodiment of the present invention can use as few boxes as possible to complete packaging, and can comprehensively consider the price, weight and final cost of the items in the box, so as to effectively evaluate each packaging scheme, achieve optimal packaging, and effectively Reduce operating costs and risks.

FIG. 5 illustrates an exemplary system architecture 500 of an article assembly method or article assembly apparatus to which embodiments of the present invention may be applied.

As shown in Figure 5, the system architecture 500 may include end devices 501, 502, 503, a network 504, and a server 505 (just an example). The network 504 is a medium used to provide a communication link 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, among others.

The user can use the terminal devices 501, 502, 503 to interact with the server 505 through the network 504 to receive or send messages and the like. Various communication client applications may be installed on the terminal devices 501 , 502 and 503 , such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only examples).

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 computers, desktop computers, and the like.

The server 505 may be a server that provides various services, for example, a background management server that provides support for shopping websites browsed by the terminal devices 501 , 502 , and 503 (just an example). The background management server can analyze and process the received product information query request and other data, and feed back the processing results (such as target push information, product information—just an example) to the terminal device.

It should be noted that the article assembling method provided by the embodiment of the present invention is generally performed by the server 505 , and accordingly, the article assembling device is generally arranged in the server 505 .

It should be understood that the numbers of terminal devices, networks and servers in FIG. 5 are only illustrative. There can be any number of terminal devices, networks and servers according to implementation needs.

Referring to FIG. 6 below, it shows a schematic structural diagram of a computer system 600 suitable for implementing a terminal device according to an embodiment of the present invention. The terminal device shown in FIG. 6 is only an example, and should not impose any limitations on the functions and scope of use of the embodiments of the present invention.

As shown in FIG. 6, a computer system 600 includes a central processing unit (CPU) 601, which can be loaded into a random access memory (RAM) 603 according to a program stored in a read only memory (ROM) 602 or a program from a storage section 608 Instead, various appropriate actions and processes are performed. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601 , the ROM 602 , and the RAM 603 are connected to each other through 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 section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc. ; and a communication section 609 including a network interface card such as a LAN card, a modem, and the like. The communication section 609 performs communication processing via a network such as the Internet. A drive 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, etc., is mounted on the drive 610 as needed so that a computer program read therefrom is installed into the storage section 608 as needed.

In particular, the processes described above with reference to the flowcharts may be implemented as computer software programs in accordance with the disclosed embodiments of the present invention. For example, embodiments disclosed herein include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the method illustrated in the flowchart. In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 609 and/or installed from the removable medium 611 . When the computer program is executed by the central processing unit (CPU) 601, the above-described functions defined in the system of the present invention are performed.

It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), fiber optics, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In the present invention, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device . Program code embodied on a computer readable medium may be transmitted using any suitable medium including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logical functions for implementing the specified functions executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the blocks 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 is also noted that each block of the block diagrams or flowchart illustrations, and combinations of blocks in the block diagrams or flowchart illustrations, can be implemented in special purpose hardware-based systems that perform the specified functions or operations, or can be implemented using A combination of dedicated hardware and computer instructions is implemented.

The modules involved in the embodiments of the present invention may be implemented in a software manner, and may also be implemented in a hardware manner. The described modules can also be set in the processor, for example, it can be described as: a processor includes a feature value acquisition module, an item assembly module, and an acquisition module. Among them, the names of these modules do not constitute a limitation of the module itself under certain circumstances. For example, the feature value acquisition module can also be described as "the feature value of a single item and the total feature value acquisition module of all items".

As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or may exist alone without being assembled into the device. The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by a device, the device includes:

Receive an item assembly request, obtain the characteristic value of the item to be assembled, and determine the total characteristic value of all the items to be assembled;

When the total eigenvalue exceeds the predetermined eigenvalue threshold, all items are grouped according to the predetermined eigenvalue threshold and the eigenvalues of the items to obtain the corresponding item combination mode.

According to the technical solutions of the embodiments of the present invention, the packaging can be completed using as few boxes as possible, and the price, weight, and final cost of the items in the boxes can be comprehensively considered, so as to effectively evaluate each packaging solution, achieve optimal packaging, and effectively Reduce operating costs and risks.

The above-mentioned specific embodiments do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (12)

1. A method for assembling an article, comprising: Receive an item assembly request, obtain the characteristic value of the item to be assembled, and determine the total characteristic value of all the items to be assembled; When the total eigenvalue exceeds the predetermined eigenvalue threshold, all items are grouped according to the predetermined eigenvalue threshold and the eigenvalues of the items to obtain a corresponding item combination mode. 2. The method according to claim 1, wherein the characteristic value comprises price and volume; When the total feature value exceeds the predetermined feature value threshold, grouping processing is performed on all items according to the predetermined feature value threshold and the feature value of the item, including: When the total price of all items exceeds the predetermined price threshold, sort the items according to the price, and sequentially extract the items whose sum of prices does not exceed the predetermined price threshold to form a group; or When the total volume of all the items exceeds the predetermined volume threshold, sort the items according to the volume, and sequentially extract the items whose sum of volumes does not exceed the predetermined volume threshold to form a group; or When the total price of all items exceeds the predetermined price threshold and the total volume exceeds the predetermined volume threshold, the items are sorted according to price and volume, and the sum of prices is extracted in order that does not exceed the predetermined price threshold, and the volume Items whose sum does not exceed the predetermined volume threshold are combined into a group. 3. The method of claim 2, further comprising: Move the first item in the obtained price sort to the end of the queue to obtain the moved sort, and sequentially extract items whose sum of prices does not exceed the predetermined price threshold to form a group; or Move the first item in the obtained volume sort to the end of the queue to obtain the moved sort, and sequentially extract items whose sum of volumes does not exceed the predetermined volume threshold to form a group; or Move the first item in the obtained price and volume sorting to the end of the queue, obtain the moved sorting, and sequentially extract the items whose price sum does not exceed the predetermined price threshold and whose volume sum does not exceed the predetermined volume threshold combined into a group; Repeat the above process, move the first item in each obtained order to the end of the queue, and obtain the combination of items corresponding to each order, until the moved order is the same as the unmoved order. 4. The method according to claim 3, characterized in that, after obtaining the combination of items corresponding to each order, until the order after moving is the same as the order when not moving, the method further comprises: According to the characteristic value of the item and the predetermined similarity determination method, analyze the similarity between the item combination modes corresponding to each order; The total similarity of all item combinations corresponding to each ranking is counted, the ranking with the smallest total similarity is extracted, and the item combination corresponding to the extracted ranking is determined as the item combination execution mode. 5. The method according to claim 2, wherein when the total feature value exceeds a predetermined feature value threshold, all items are grouped according to the predetermined feature value threshold and the feature values of the items, include: Classify items of the same price into one class, and extract items from each class for combination, and the sum of the prices of the extracted items does not exceed said predetermined price threshold and the sum of volumes does not exceed said predetermined packing volume; or Items of the same volume are grouped into one class, and items from each class are extracted and combined, and the sum of the prices of the extracted items does not exceed the predetermined price threshold and the sum of the volumes does not exceed the predetermined boxed volume. 6. An article assembling device, characterized in that, comprising: The feature value acquisition module is used to receive the item assembly request, obtain the feature value of the item to be assembled, and determine the total feature value of all the items to be assembled; The article assembling module is used for grouping all articles according to the predetermined characteristic value threshold and the characteristic value of the article when the total characteristic value exceeds the predetermined characteristic value threshold to obtain the corresponding article combination mode. 7. The device according to claim 6, wherein the characteristic value comprises price and volume; The article assembly module is used for: When the total price of all items exceeds the predetermined price threshold, sort the items according to the price, and sequentially extract the items whose sum of prices does not exceed the predetermined price threshold to form a group; or When the total volume of all the items exceeds the predetermined volume threshold, sort the items according to the volume, and sequentially extract the items whose sum of volumes does not exceed the predetermined volume threshold to form a group; or When the total price of all items exceeds the predetermined price threshold and the total volume exceeds the predetermined volume threshold, the items are sorted according to price and volume, and the sum of prices is extracted in order that does not exceed the predetermined price threshold, and the volume Items whose sum does not exceed the predetermined volume threshold are combined into a group. 8. The device according to claim 7, wherein the article assembly module is further used for: Move the first item in the obtained price sort to the end of the queue to obtain the moved sort, and sequentially extract items whose sum of prices does not exceed the predetermined price threshold to form a group; or Move the first item in the obtained volume sort to the end of the queue to obtain the moved sort, and sequentially extract items whose sum of volumes does not exceed the predetermined volume threshold to form a group; or Move the first item in the obtained price and volume sorting to the end of the queue, obtain the moved sorting, and sequentially extract the items whose price sum does not exceed the predetermined price threshold and whose volume sum does not exceed the predetermined volume threshold combined into a group; Repeat the above process, move the first item in each obtained order to the end of the queue, and obtain the combination of items corresponding to each order, until the moved order is the same as the unmoved order. 9. The device according to claim 8, wherein the article assembly module is further used for: According to the characteristic value of the item and the predetermined similarity determination method, analyze the similarity between the item combination modes corresponding to each order; The total similarity of all item combinations corresponding to each ranking is counted, the ranking with the smallest total similarity is extracted, and the item combination corresponding to the extracted ranking is determined as the item combination execution mode. 10. The device according to claim 7, wherein the article assembly module is used for: Classify items of the same price into one class, and extract items from each class for combination, and the sum of the prices of the extracted items does not exceed said predetermined price threshold and the sum of volumes does not exceed said predetermined packing volume; or Items of the same volume are grouped into one class, and items from each class are extracted and combined, and the sum of the prices of the extracted items does not exceed the predetermined price threshold and the sum of the volumes does not exceed the predetermined boxed volume. 11. An electronic device, characterized in that, comprising: one or more processors; storage means for storing one or more programs, The 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, characterized in that, when the program is executed by a processor, the method according to any one of claims 1-5 is implemented.

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