CN114358865A - Commodity boxing recommendation method, device, equipment and medium based on big data calculation - Google Patents
Commodity boxing recommendation method, device, equipment and medium based on big data calculation Download PDFInfo
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Abstract
The invention discloses a commodity packing recommendation method, device, equipment and medium based on big data calculation, and belongs to the technical field of commodity packing. The method comprises the following steps: acquiring information of commodities to be boxed and information of a box body; determining the commodity with the largest size as a target commodity; determining a target box body, and obtaining boxing recommendation information of a target commodity; calculating the size of the residual space of the target box body based on the size of the target commodity, and updating the size of the available space inside the target box body; and determining the commodity to be selected with the largest commodity size from the rest commodities, updating the target commodity, returning to the execution of determining the target box body, and obtaining the boxing recommendation information of the target commodity until the boxing recommendation information of all commodities is obtained. The invention is based on big data calculation, can recommend a better commodity packing mode according to the size of each commodity to be packed and the available space of each box body, and improves the commodity packing recommendation efficiency on the premise of ensuring the accurate recommendation result.
Description
Technical Field
The invention relates to the technical field of commodity packing, in particular to a commodity packing recommendation method, device, equipment and medium based on big data calculation.
Background
In the related art, when a commodity is packed, a packing method based on commodity size superposition calculation or a packing method based on a genetic algorithm is often adopted.
However, the packing method based on the commodity size superposition calculation has single considered factor, and the obtained result has larger deviation; the packing method based on the genetic algorithm has the advantages that when the number of commodities exceeds a certain number, the calculation complexity is increased sharply, and the calculation time is long.
Disclosure of Invention
The invention mainly aims to provide a commodity packing recommendation method, a commodity packing recommendation device, commodity packing recommendation equipment and a commodity packing recommendation medium based on big data calculation, and aims to solve the technical problems that packing recommendation efficiency is low and recommendation results are not accurate enough in the prior art.
According to a first aspect of the invention, a commodity packing recommendation method based on big data calculation is provided, and the method comprises the following steps:
acquiring information of a plurality of commodities to be boxed and information of a plurality of box bodies;
determining the commodity with the largest size in the plurality of commodities to be boxed as a target commodity according to the information of the commodities to be boxed;
determining a target box body from the plurality of box bodies, and acquiring the packing recommendation information of the target commodity based on the target box body; the target box body is a box body which is matched with the target commodity in size and has the smallest available internal space;
calculating the size of the residual space of the target box body based on the size of the target commodity, and updating the size of the internal available space of the target box body;
determining the commodity to be selected with the largest commodity size from the rest commodities of the plurality of commodities to be boxed, updating the target commodity based on the commodity to be selected, returning to execute the step of determining the target box body from the plurality of box bodies, and obtaining the boxing recommendation information of the target commodity based on the target box body until the boxing recommendation information of all the commodities to be boxed is obtained.
Optionally, before the obtaining of the information of the plurality of commodities to be boxed and the information of the plurality of boxes, the method further includes:
acquiring box body size information of all available box bodies, commodity size information of all commodities to be boxed and a preset full box rate threshold value;
calculating the size of the internal available space of all the available boxes according to the box size information and the preset full-box-rate threshold;
and obtaining the information of the plurality of commodities to be boxed and the information of the plurality of box bodies according to the size of the internal available space and the commodity size information.
Optionally, the determining a target box from the plurality of boxes includes:
determining a target placing mode from at least one preset placing mode, and taking a box body with the smallest available space inside as a first box body to be selected;
judging whether the size of the available space in the first box to be selected is matched with the size of the target commodity or not based on the target placing mode;
if the size of the available space in the first box to be selected is not matched with the size of the target commodity, determining a second box to be selected with the smallest available space in the remaining boxes of the plurality of boxes, and updating the first box to be selected based on the second box to be selected;
and returning to execute the target placing mode, judging whether the size of the available space inside the first box to be selected is matched with the size of the target commodity or not until the size of the available space inside the first box to be selected is matched with the size of the target commodity, and determining the first box to be selected as the target box.
Optionally, the returning execution is performed, and whether the size of the available space inside the first box to be selected matches the size of the target commodity is determined based on the target placement mode until the size of the available space inside the first box to be selected matches the size of the target commodity, and after the first box to be selected is determined to be the target box, the method further includes:
if the sizes of the available internal spaces of the boxes are not matched with the size of the target commodity, determining a target placing mode from the rest placing modes in at least one preset placing mode, determining the box with the smallest available internal space as the first box to be selected, and returning to execute the target placing mode to judge whether the size of the available internal space of the first box to be selected is matched with the size of the target commodity.
Optionally, after returning to execute the determining whether the size of the available space inside the first box to be selected matches the size of the target commodity based on the target placement mode, the method further includes:
if the size of the available space in the first box to be selected is not matched with the size of the target commodity according to all the preset placing modes, determining a second box to be selected with the smallest available space in the rest boxes of the boxes, and updating the first box to be selected based on the second box to be selected;
and returning to execute the target placing mode, judging whether the size of the available space inside the first box to be selected is matched with the size of the target commodity or not until the size of the available space inside the first box to be selected is matched with the size of the target commodity, and determining the first box to be selected as the target box.
Optionally, the obtaining of the package recommendation information of the target product based on the target box body includes:
acquiring any target commodity information, target box information corresponding to the target commodity and target placing mode information corresponding to the target commodity;
and obtaining the packing recommendation information according to the target commodity information, the target box body information and the target placing mode information.
Optionally, the method further includes, after determining a commodity to be selected with the largest commodity size from the remaining commodities in the plurality of commodities to be boxed, updating the target commodity based on the commodity to be selected, and returning to execute determining a target box from the boxes and obtaining boxing recommendation information of the target commodity until boxing recommendation information of all commodities to be boxed is obtained, where:
generating and outputting a packing recommendation result according to the packing recommendation information of all the commodities to be packed; the boxing recommendation result comprises the number and the size of the required boxes, the preset full boxing rate threshold value and the boxing recommendation information.
According to a second aspect of the present invention, there is provided a commodity packing recommendation device based on big data calculation, including:
the information acquisition module is used for acquiring information of a plurality of commodities to be boxed and information of a plurality of boxes;
the target commodity determining module is used for determining the commodity with the largest size in the plurality of commodities to be boxed as the target commodity according to the information of the plurality of commodities to be boxed;
the target box body determining module is used for determining a target box body from the box bodies and obtaining the packing recommendation information of the target commodity based on the target box body;
the data updating module is used for calculating the size of the residual space of the target box body based on the size of the target commodity and updating the size of the internal available space of the target box body;
and the circulation execution module is used for determining a commodity to be selected with the largest commodity size from the rest commodities in the plurality of commodities to be boxed, updating the target commodity based on the commodity to be selected, returning to execute the determination of the target box body from the plurality of box bodies, and obtaining the boxing recommendation information of the target commodity based on the target box body until the boxing recommendation information of all the commodities to be boxed is obtained.
According to a third aspect of the present invention, there is provided a commodity packing recommendation device based on big data calculation, comprising: a memory, a processor and a big-data-computation-based item binning recommendation program stored on the memory and executable on the processor, the big-data-computation-based item binning recommendation program when executed by the processor implementing the various steps described in any of the possible implementations of the first aspect.
According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a big-data-calculation-based item binning recommendation program that, when executed by a processor, implements the various steps set forth in any one of the possible implementations of the first aspect.
The embodiment of the invention provides a commodity packing recommendation method, a device, equipment and a medium based on big data calculation, wherein a plurality of commodity information to be packed and a plurality of box body information are obtained through commodity packing recommendation equipment based on big data calculation; determining the commodity with the largest size in the plurality of commodities to be boxed as a target commodity according to the information of the commodities to be boxed; determining a target box body from the plurality of box bodies, and acquiring the packing recommendation information of the target commodity based on the target box body; calculating the size of the residual space of the target box body based on the size of the target commodity, and updating the size of the internal available space of the target box body; determining the commodity to be selected with the largest commodity size from the rest commodities of the plurality of commodities to be boxed, updating the target commodity based on the commodity to be selected, returning to execute the step of determining the target box body from the plurality of box bodies, and obtaining the boxing recommendation information of the target commodity based on the target box body until the boxing recommendation information of all the commodities to be boxed is obtained.
The invention determines the target commodity to be boxed and the target box body corresponding to the target commodity to be boxed according to the size information of the plurality of commodities to be boxed and the plurality of available box bodies, obtains the boxing recommendation information based on the target commodity and the target box body corresponding to the target commodity, updates the available space information of the corresponding box body in real time according to the size of the commodity and the box body recommended to be boxed, and finally obtains the boxing recommendation information of all commodities by circulating the operation, thereby ensuring that all commodities can be reasonably boxed. The method is different from the situation that the boxing recommendation efficiency is low and the recommendation result is not accurate enough in the prior art, based on big data calculation, a better commodity boxing mode can be recommended according to the size of each commodity to be boxed and the available space of each box body, and the commodity boxing recommendation efficiency is improved on the premise of ensuring the accuracy of the recommendation result.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a big data computing-based commodity packing recommendation device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a commodity packing recommendation method based on big data calculation according to the present invention;
FIG. 3 is a flowchart illustrating a process before step S201 in FIG. 2 according to the present invention;
FIG. 4 is a flowchart illustrating an embodiment of the step S203 in FIG. 2 according to the present invention;
FIG. 5 is a flowchart illustrating an embodiment of the step S203 in FIG. 2 according to the present invention;
FIG. 6 is a flowchart illustrating an embodiment of the step S203 in FIG. 2 according to the present invention;
fig. 7 is a functional module schematic diagram of a commodity packing recommendation device based on big data calculation according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows: acquiring information of commodities to be boxed and information of a box body; determining the commodity with the largest size as a target commodity; determining a target box body, and obtaining boxing recommendation information of a target commodity; calculating the size of the residual space of the target box body based on the size of the target commodity, and updating the size of the available space inside the target box body; and determining the commodity to be selected with the largest commodity size from the rest commodities, updating the target commodity, returning to the execution of determining the target box body, and obtaining the boxing recommendation information of the target commodity until the boxing recommendation information of all commodities is obtained.
In the prior art, the packing method based on the commodity size superposition calculation has single considered factor and larger deviation of the obtained result; the packing method based on the genetic algorithm has the advantages that when the number of commodities exceeds a certain number, the calculation complexity is increased sharply, and the calculation time is long.
The invention provides a solution, which is used for commodity packing recommendation equipment based on big data calculation, and is characterized in that target commodities to be packed and target boxes corresponding to the target commodities are determined according to size information of a plurality of commodities to be packed and a plurality of available boxes, packing recommendation information is obtained according to the target commodities and the target boxes corresponding to the target commodities, available space information of the corresponding boxes can be updated in real time according to the sizes of the commodities and the boxes which are recommended to be packed, and finally, the operation is circulated to obtain the packing recommendation information of all the commodities, so that all the commodities can be reasonably packed. The method is different from the situation that the boxing recommendation efficiency is low and the recommendation result is not accurate enough in the prior art, based on big data calculation, a better commodity boxing mode can be recommended according to the size of each commodity to be boxed and the available space of each box body, and the commodity boxing recommendation efficiency is improved on the premise of ensuring the accuracy of the recommendation result.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Where "first" and "second" are used in the description and claims of embodiments of the invention to distinguish between similar elements and not necessarily for describing a particular sequential or chronological order, it is to be understood that such data may be interchanged where appropriate so that embodiments described herein may be implemented in other sequences than those illustrated or described herein.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a big data computing-based commodity packing recommendation device of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the big data calculation-based item packing recommendation apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 does not constitute a limitation of the big data computing-based item packing recommendation device, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, the memory 1005 as a storage medium may include an operating system, an information acquisition module, an information processing module, a circular execution module, and a commodity packing recommendation program based on big data calculation, wherein the information processing module may be further detailed as a target commodity determination module, a target box determination module, and a data update module.
In the big data calculation-based goods packing recommendation device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the commodity packing recommendation device based on big data calculation may be disposed in the commodity packing recommendation device based on big data calculation, and the commodity packing recommendation device based on big data calculation calls the commodity packing recommendation program based on big data calculation stored in the memory 1005 through the processor 1001 and executes the commodity packing recommendation method based on big data calculation according to the embodiment of the present invention.
Based on the above hardware structure but not limited to the above hardware structure, the present invention provides a first embodiment of a commodity packing recommendation method based on big data calculation. Referring to fig. 2, fig. 2 is a schematic flowchart of a first embodiment of a commodity packing recommendation method based on big data calculation according to the present invention.
In this embodiment, the method includes:
step S201, obtaining information of a plurality of commodities to be boxed and a plurality of box bodies;
in the present embodiment, the execution subject is a commodity packing recommendation device based on big data calculation. When goods to be boxed need to be boxed, if the obtained recommendation result is prone to have deviation or the recommendation speed is slow according to a traditional boxing recommendation method, for example, for a boxing mode based on goods size superposition calculation, in this mode, only simply carrying out boxing recommendation according to whether the sum of the sizes of the goods reaches the volume of the box, specifically, for a box with the volume of A and a group of goods to be boxed with the volumes of a, b and c, the size relationship between a and A is judged firstly, if a is smaller than A, the goods are loaded into the box, then the size relationship between a + b and A is judged, if a + b is smaller than A, b is also loaded into the box, otherwise, the size relationship between a + c and A is calculated, and the like in sequence. However, if the size and volume relationship is simply calculated, the recommended result is inevitably deviated, because the irregular shape of part of the commodities and different placing modes of the commodities affect the loading of subsequent commodities, and if a plurality of small commodities are loaded into a large box at the beginning, even if a plurality of small boxes are left, some large commodities cannot be loaded. In addition, for the boxing method based on the genetic algorithm, when the number of commodities exceeds a certain number, the calculation complexity is increased sharply, the calculation time is long, and the application scene of online order real-time millisecond-level response cannot be met.
Therefore, in order to obtain a better boxing method more quickly and accurately, the commodity boxing recommendation device based on big data calculation comprehensively considers factors such as commodity size, box body size, full box rate, placement mode and placement sequence, and recommends a better boxing method. The equipment acquires information of a plurality of commodities to be boxed and a plurality of box bodies, wherein the commodity information to be boxed comprises information of names, quantity, sizes and the like of the commodities to be boxed, and the box body information comprises information of specifications of available box bodies, sizes of available internal spaces and the like.
Step S202, determining the commodity with the largest size in the plurality of commodities to be boxed as a target commodity according to the information of the commodities to be boxed;
as mentioned above, if a large number of small items are initially loaded into a large box, even if a large number of small boxes remain, some of the large items may not be loaded. To avoid this, the packing method of the large goods is determined. Therefore, after the information of the commodities to be boxed is obtained, the commodities with the largest size are determined as target commodities, and then the boxing recommendation information of the commodities is obtained through the subsequent steps.
Step S203, determining a target box body from the box bodies, and acquiring the packing recommendation information of the target commodity based on the target box body;
after the target commodity to be boxed is determined, a corresponding target box body which is recommended to be loaded into the target commodity is determined according to the size of the target commodity, specifically, in order to avoid the situation that the large commodity cannot be loaded into the small box, the size of the available internal space is matched with the size of the target commodity, and the box body with the smallest available internal space is used as the target box body, so that the space resources of each box body can be fully utilized.
After the target box body corresponding to the target commodity is determined, the packing recommendation information of the target commodity can be generated and recorded, namely, the target commodity is recommended to be packed into which box body.
In an embodiment, referring to fig. 4, fig. 4 is a flowchart illustrating an embodiment of the step S203 in fig. 2 according to the present invention, where the determining a target box from a plurality of boxes includes:
step A10, determining a target placing mode from at least one preset placing mode, and taking a box body with the smallest available space inside as a first box body to be selected;
as described above, in order to avoid the situation that the large commodity cannot be loaded into the small box, after the target commodity is determined, the boxes are judged in the order from small to large, and the target box with the size of the available internal space matching the size of the target commodity and the smallest available internal space is screened out. In addition, for a commodity, a plurality of placing modes are provided, such as vertical placing, horizontal placing and the like, and particularly, for some commodities with larger sizes, the commodity can be placed in the box body along the diagonal line of the box body in an inclined mode; therefore, in order to facilitate subsequent comparison, a target placing mode is determined from a plurality of preset placing modes.
Step A20, based on the target placing mode, judging whether the size of the available space in the first box to be selected is matched with the size of the target commodity;
after the size of the target commodity, the size of the available space in the first box to be selected and the target placing mode are obtained, whether the target commodity can be placed in the first box to be selected can be judged according to the size information and the placing mode. Specifically, for a product with a narrow width and a long length, such as a selfie stick, it is likely that the product cannot be placed in a vertical box, but is easy to be placed in a horizontal box, so that different placement modes must be considered on the basis of size information to obtain more accurate box recommendation information and box recommendation results.
Step A30, if the size of the internal available space of the first box to be selected is not matched with the size of the target commodity, determining a second box to be selected with the smallest internal available space from the rest boxes of the plurality of boxes, and updating the first box to be selected based on the second box to be selected;
and A40, returning to execute the target placing mode, judging whether the size of the available space inside the first box to be selected is matched with the size of the target commodity or not until the size of the available space inside the first box to be selected is matched with the size of the target commodity, and determining the first box to be selected as the target box.
If the target commodity cannot be placed in the first box to be selected according to the placement mode, similarly, in order to avoid a situation that a large commodity cannot be placed in a small box, a second box to be selected with the smallest available internal space is determined from the remaining boxes, then the second box to be selected is determined again as the first box to be selected based on the second box to be selected, and then the previous steps a20 and a30 are executed again, and the above steps are executed in a circulating manner until the size of the available internal space of the first box to be selected matches the size of the target commodity, and the first box to be selected at this time is taken as the target box, and the specific mode is consistent with the above, and will not be described again.
In an embodiment, referring to fig. 5, fig. 5 is a flowchart illustrating an embodiment of the step S203 in fig. 2 according to the present invention, where the determining a target box from a plurality of boxes includes:
step A50, if the sizes of the available internal spaces of the boxes are not matched with the size of the target commodity, determining a target placing mode from the rest placing modes in at least one preset placing mode, determining the box with the smallest available internal space as the first box to be selected, and returning to execute the step A, wherein the step A determines whether the size of the available internal space of the first box to be selected is matched with the size of the target commodity or not based on the target placing mode;
if all the boxes cannot contain the target commodity according to the currently determined target placing mode, the placing mode of the commodity needs to be adjusted in order to smoothly pack the target commodity. As described above, for a product having a narrow width and a long length such as a selfie stick, it is highly likely that it will not be put down if it is vertically packed, but it is easily put in if it is laid down. Then, after the target placement mode is determined again, the step a10 and the step a20 are executed again, and the specific mode is the same as the above, and is not described herein again.
Step A60, if the size of the available space inside the first box to be selected is not matched with the size of the target commodity according to all the preset placing modes, determining a second box to be selected with the smallest available space inside the remaining boxes of the plurality of boxes, and updating the first box to be selected based on the second box to be selected;
step a70, returning to execute the target placing mode, determining whether the size of the available space inside the first box to be selected matches the size of the target commodity, until the size of the available space inside the first box to be selected matches the size of the target commodity, and determining the first box to be selected as the target box.
If the target commodity cannot be placed into the first box body to be selected according to all the preset placing modes, a larger available box body needs to be replaced to continue subsequent judgment. Therefore, the steps a30 and a40 are performed in the same manner as described above, and are not described herein again.
In an embodiment, referring to fig. 6, fig. 6 is a flowchart illustrating an embodiment of the step S203 in fig. 2 according to the present invention, where the obtaining of the package recommendation information of the target product based on the target box includes:
step B10, acquiring any target commodity information, target box information corresponding to the target commodity and target placing mode information corresponding to the target commodity;
and acquiring box body information recommended to be put in and corresponding placing modes corresponding to the commodities, wherein the box body information and the placing modes jointly form packing recommendation information of any commodity.
And step B20, obtaining the packing recommendation information according to the target commodity information, the target box body information and the target placing mode information.
And obtaining the packing recommendation information according to the obtained target commodity information, the corresponding target box body information and the target placing mode information. In addition, in order to obtain more accurate packing recommendation information, it is also described that the overall packing sequence is from large to small commodities and from small to large boxes, so that the packing sequence of the commodities can be added to the packing recommendation information, and further, since the commodities loaded first occupy a part of the space, the commodities loaded later can only be placed in the remaining space of the boxes, and after the packing recommendation information of one commodity is obtained each time, the available space size information of the boxes is updated (see step S204 below, which is not described herein again), so that specific positions for packing the commodities can be added to the packing recommendation information.
Step S204, calculating the size of the residual space of the target box body based on the size of the target commodity, and updating the size of the internal available space of the target box body;
after the target box body corresponding to the target commodity is determined and the relevant boxing recommendation information is recorded, because the corresponding space of the target box body is occupied by the target commodity, subsequent recommendation can not be performed according to original data, the size of the residual space of the target box body needs to be recalculated, the size of the available space in the target box body is updated, and the subsequent boxed commodities can be boxed according to the updated size of the box body, so that the boxing recommendation can be performed on the subsequent commodities according to the updated data of the box body, and the accuracy of a recommendation result is improved.
Step S205, determining a commodity to be selected with the largest commodity size from the remaining commodities of the commodities to be boxed, updating the target commodity based on the commodity to be selected, returning to execute the determination of a target box body from the boxes, and obtaining the boxing recommendation information of the target commodity based on the target box body until the boxing recommendation information of all the commodities to be boxed is obtained;
after the boxing recommendation information of the target commodity is obtained and the corresponding box size data is updated, the next commodity can be subjected to boxing recommendation. Specifically, the commodity to be selected with the largest commodity size is determined from the remaining commodities of all the commodities to be boxed, then the commodity to be selected is determined as the target commodity, and then the steps S203 and S204 are executed again, and the above steps are executed in a circulating manner until the boxing recommendation information of all the commodities to be boxed is obtained, and the specific manner is consistent with the above and is not described herein again.
And S206, generating and outputting a packing recommendation result according to the packing recommendation information of all the commodities to be packed.
After the boxing recommendation information of all the commodities to be boxed is obtained, a boxing recommendation result can be generated and output according to all the information, and related workers can perform manual boxing or control a machine to finish boxing according to the boxing recommendation result. The boxing recommendation result comprises related information such as the number and the size of the needed boxes, a full boxing rate threshold value, boxing recommendation information and the like.
In the embodiment, according to the information such as the commodity size, the box body size, the full box rate, the placing mode and the placing sequence, all aspects of factors are comprehensively considered, compared with the traditional mode, a better boxing mode can be recommended, the boxing recommendation result can be quickly obtained when the number of ordered commodities is large through information processing based on big data calculation, and the commodity boxing recommendation efficiency is greatly improved on the premise that the recommendation result is accurate.
Further, as an embodiment, referring to fig. 3, fig. 3 is a flowchart illustrating a process before the step of S201 in fig. 2 of the present invention, before the obtaining of the information of the plurality of commodities to be boxed and the information of the plurality of boxes, the method further includes:
step S301, acquiring box size information of all available boxes, commodity size information of all commodities to be boxed and a preset full-box-rate threshold;
in order to facilitate subsequent acquisition of relevant information of each commodity to be boxed and each box body, firstly, original data and parameters such as the size of each commodity, the size of each box body, the full box rate and the like are acquired so as to facilitate subsequent relevant processing. Therefore, the box size information of all available boxes, the commodity size information of all commodities to be boxed and the preset full-box rate threshold are obtained firstly. Specifically, the box size information of all available boxes can be used for calling box data and states from a background, screening out vacant available boxes and obtaining the size and specification information of the vacant available boxes; the commodity size information of all commodities to be boxed can call corresponding order information from a background, and the size information of each commodity is obtained from the order information; in addition, a preset full-box-rate threshold value can be set, because certain gaps are often left among the commodities for safety and avoiding factors such as collision and damage among the commodities, and each box body cannot be completely filled, the preset full-box-rate threshold value can be set according to actual requirements.
Step S302, calculating the internal available space size of all available boxes according to the box size information and the preset full box rate threshold;
as described above, in practical applications, a full-box-rate threshold value is required to be set for safety and other factors, so that when subsequently judging whether a commodity can be loaded into a box, the judgment cannot be made according to the original size of the box, and for convenience of subsequent processing, the available space and size of the box are required instead of the original space and size of the box, so that before subsequent processing, the size of the available space inside each available box is calculated according to the obtained available box size information and the preset full-box-rate threshold value.
And step S303, obtaining the information of the commodities to be boxed and the information of the boxes according to the size of the internal available space and the commodity size information.
After the size of the internal available space of each available box body and the size information of each commodity to be boxed are obtained, the information of the commodity to be boxed and the information of the box body can be obtained according to the information. Specifically, the commodities to be boxed can be sorted from large to small according to the sizes of the commodities to be boxed to generate a commodity list to be boxed, and the commodities can be sorted from small to large according to the available space in the available box body to generate a box body list, so that the list containing a plurality of commodity information to be boxed and a plurality of box body information can be obtained, and the target commodities can be determined from large to small and the target box body can be determined from small to large in the following process conveniently.
In the embodiment, before formal processing, the commodities to be boxed are sorted according to the size specification and the available boxes are sorted according to the available space, so that the target commodities and the target boxes can be determined from large to small in the follow-up process conveniently, and the commodities and the boxes do not need to be compared one by one when boxing is recommended, so that the efficiency of the follow-up processing is greatly improved.
Based on the same inventive concept, an embodiment of the present invention further provides a commodity packing recommendation device based on big data calculation, as shown in fig. 7, including:
the information acquisition module is used for acquiring information of a plurality of commodities to be boxed and information of a plurality of boxes;
the target commodity determining module is used for determining the commodity with the largest size in the plurality of commodities to be boxed as the target commodity according to the information of the plurality of commodities to be boxed;
the target box body determining module is used for determining a target box body from the box bodies and obtaining the packing recommendation information of the target commodity based on the target box body;
the data updating module is used for calculating the size of the residual space of the target box body based on the size of the target commodity and updating the size of the internal available space of the target box body;
and the circulation execution module is used for determining a commodity to be selected with the largest commodity size from the rest commodities in the plurality of commodities to be boxed, updating the target commodity based on the commodity to be selected, returning to execute the determination of the target box body from the plurality of box bodies, and obtaining the boxing recommendation information of the target commodity based on the target box body until the boxing recommendation information of all the commodities to be boxed is obtained.
As an alternative embodiment, the big data calculation-based commodity packing recommendation device may further include:
and the result output module generates and outputs a packing recommendation result according to the packing recommendation information of all the commodities to be packed.
It should be noted that, since the steps executed by the apparatus of this embodiment are the same as the steps of the foregoing method embodiment, the specific implementation and the achievable technical effects thereof can refer to the foregoing embodiment, and are not described herein again.
Furthermore, in an embodiment, the present application further provides a computer storage medium having a computer program stored thereon, where the computer program is executed by a processor to implement the steps of the method in the foregoing method embodiments.
In some embodiments, the computer-readable storage medium may be memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash, magnetic surface memory, optical disk, or CD-ROM; or may be various devices including one or any combination of the above memories. The computer may be a variety of computing devices including intelligent terminals and servers.
In some embodiments, executable instructions may be written in any form of programming language (including compiled or interpreted languages), in the form of programs, software modules, scripts or code, and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
By way of example, executable instructions may correspond, but do not necessarily have to correspond, to files in a file system, may be stored in a portion of a file that holds other programs or data, e.g., in one or more scripts in a hypertext Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code).
By way of example, executable instructions may be deployed to be executed on one computing device or on multiple computing devices at one site or distributed across multiple sites and interconnected by a communication network.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A commodity packing recommendation method based on big data calculation is characterized by comprising the following steps:
acquiring information of a plurality of commodities to be boxed and information of a plurality of box bodies;
determining the commodity with the largest size in the plurality of commodities to be boxed as a target commodity according to the information of the commodities to be boxed;
determining a target box body from the plurality of box bodies, and acquiring the packing recommendation information of the target commodity based on the target box body; the target box body is a box body which is matched with the target commodity in size and has the smallest available internal space;
calculating the size of the residual space of the target box body based on the size of the target commodity, and updating the size of the internal available space of the target box body;
determining the commodity to be selected with the largest commodity size from the rest commodities of the plurality of commodities to be boxed, updating the target commodity based on the commodity to be selected, returning to execute the step of determining the target box body from the plurality of box bodies, and obtaining the boxing recommendation information of the target commodity based on the target box body until the boxing recommendation information of all the commodities to be boxed is obtained.
2. The method of claim 1, wherein prior to obtaining the plurality of information on the items to be boxed and the plurality of information on the boxes, the method further comprises:
acquiring box body size information of all available box bodies, commodity size information of all commodities to be boxed and a preset full box rate threshold value;
calculating the size of the internal available space of all the available boxes according to the box size information and the preset full-box-rate threshold;
and obtaining the information of the plurality of commodities to be boxed and the information of the plurality of box bodies according to the size of the internal available space and the commodity size information.
3. The method of claim 1, wherein said identifying a target bin from a plurality of said bins comprises:
determining a target placing mode from at least one preset placing mode, and taking a box body with the smallest available space inside as a first box body to be selected;
judging whether the size of the available space in the first box to be selected is matched with the size of the target commodity or not based on the target placing mode;
if the size of the available space in the first box to be selected is not matched with the size of the target commodity, determining a second box to be selected with the smallest available space in the remaining boxes of the plurality of boxes, and updating the first box to be selected based on the second box to be selected;
and returning to execute the target placing mode, judging whether the size of the available space inside the first box to be selected is matched with the size of the target commodity or not until the size of the available space inside the first box to be selected is matched with the size of the target commodity, and determining the first box to be selected as the target box.
4. The method of claim 3, wherein the returning execution determines whether the size of the internal available space of the first box to be selected matches the size of the target product based on the target placement mode until the size of the internal available space of the first box to be selected matches the size of the target product, and after the first box to be selected is determined to be the target box, the method further comprises:
if the sizes of the available internal spaces of the boxes are not matched with the size of the target commodity, determining a target placing mode from the rest placing modes in at least one preset placing mode, determining the box with the smallest available internal space as the first box to be selected, and returning to execute the target placing mode to judge whether the size of the available internal space of the first box to be selected is matched with the size of the target commodity.
5. The method of claim 4, wherein after the returning execution of the determining whether the size of the available internal space of the first box to be selected matches the size of the target product based on the target placement mode, the method further comprises:
if the size of the available space in the first box to be selected is not matched with the size of the target commodity according to all the preset placing modes, determining a second box to be selected with the smallest available space in the rest boxes of the boxes, and updating the first box to be selected based on the second box to be selected;
and returning to execute the target placing mode, judging whether the size of the available space inside the first box to be selected is matched with the size of the target commodity or not until the size of the available space inside the first box to be selected is matched with the size of the target commodity, and determining the first box to be selected as the target box.
6. The method of claim 1, wherein the obtaining of the target good's bin recommendation information based on the target box comprises:
acquiring any target commodity information, target box information corresponding to the target commodity and target placing mode information corresponding to the target commodity;
and obtaining the packing recommendation information according to the target commodity information, the target box body information and the target placing mode information.
7. The method as claimed in claim 1, wherein the step of determining the selected commodity with the largest commodity size from the remaining commodities in the plurality of commodities to be boxed, updating the target commodity based on the selected commodity, and returning to perform the steps of determining the target box from the plurality of boxes and obtaining the boxing recommendation information of the target commodity until after obtaining the boxing recommendation information of all commodities to be boxed further comprises:
generating and outputting a packing recommendation result according to the packing recommendation information of all the commodities to be packed; the boxing recommendation result comprises the number and the size of the required boxes, the preset full boxing rate threshold value and the boxing recommendation information.
8. An apparatus for recommending boxing of an article based on big data calculation, the apparatus comprising:
the information acquisition module is used for acquiring information of a plurality of commodities to be boxed and information of a plurality of boxes;
the target commodity determining module is used for determining the commodity with the largest size in the plurality of commodities to be boxed as the target commodity according to the information of the plurality of commodities to be boxed;
the target box body determining module is used for determining a target box body from the box bodies and obtaining the packing recommendation information of the target commodity based on the target box body;
the data updating module is used for calculating the size of the residual space of the target box body based on the size of the target commodity and updating the size of the internal available space of the target box body;
and the circulation execution module is used for determining a commodity to be selected with the largest commodity size from the rest commodities in the plurality of commodities to be boxed, updating the target commodity based on the commodity to be selected, returning to execute the determination of the target box body from the plurality of box bodies, and obtaining the boxing recommendation information of the target commodity based on the target box body until the boxing recommendation information of all the commodities to be boxed is obtained.
9. A big-data-calculation-based item packing recommendation apparatus comprising a memory, a processor and a big-data-calculation-based item packing recommendation program stored on the memory and executable on the processor, wherein the big-data-calculation-based item packing recommendation program, when executed by the processor, implements the steps of the big-data-calculation-based item packing recommendation method according to any one of claims 1 to 7.
10. A computer-readable storage medium, wherein a big-data-computation-based item packing recommendation program is stored on the computer-readable storage medium, and when executed by a processor, the big-data-computation-based item packing recommendation program implements the steps of the big-data-computation-based item packing recommendation method according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116228093A (en) * | 2022-12-26 | 2023-06-06 | 上海通天晓信息技术有限公司 | Boxing method and device and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116228093A (en) * | 2022-12-26 | 2023-06-06 | 上海通天晓信息技术有限公司 | Boxing method and device and storage medium |
| CN116228093B (en) * | 2022-12-26 | 2024-04-12 | 上海通天晓信息技术有限公司 | Boxing method and device and storage medium |
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