CN114676977A - Consumable material selection method and device - Google Patents
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Abstract
The invention discloses a method and a device for selecting consumables, and relates to the technical field of computers. One embodiment of the method comprises: acquiring characteristic data of multiple dimensions of target order data; inputting the feature data of multiple dimensions into a pre-trained consumable item selection model, and determining the target consumable items of the target order data, wherein the consumable item selection model is obtained by training according to the feature data of multiple dimensions of multiple historical order data and consumable item data corresponding to each historical order data. This embodiment has realized that the consumptive material is selected intellectuality, rationalization, has improved the efficiency that the consumptive material was selected, has reduced the extravagant cost of consumptive material.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a consumable material selection method and device.
Background
Consumables such as order consumables of an e-commerce system are used for containing articles in an order to realize logistics distribution. At present, the consumable materials are generally selected manually according to experience judgment or selected by a service system according to a single condition, but when the manual experience is insufficient or the condition is incomplete, the consumable materials are easily selected with poor rationality and low efficiency, and the waste cost of the consumable materials is increased.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for selecting consumables, which can determine a target consumable according to feature data of multiple dimensions of target order data and a consumable selection model trained in advance, so as to realize intellectualization and rationalization of consumable selection, improve consumable selection efficiency, and reduce consumable waste cost.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method of consumable selection, including:
acquiring characteristic data of multiple dimensions of target order data;
inputting the characteristic data of the multiple dimensions into a pre-trained consumable item selection model, and determining target consumable items corresponding to the target order data;
the consumable selection model is obtained by training according to the following modes: acquiring a plurality of historical order data and consumable data corresponding to each historical order data, wherein each historical order data comprises characteristic data of a plurality of dimensions; training according to the feature data of multiple dimensions of multiple historical order data and the consumable data to obtain the consumable selection model.
Optionally, training is performed according to feature data of multiple dimensions of multiple historical order data and the consumable data to obtain the consumable selection model, and the method includes:
Training according to feature data of each dimension of each historical order data in a plurality of historical order data and the consumable data to obtain a decision tree model corresponding to each dimension; and combining the decision tree models corresponding to all dimensions to obtain the consumable selection model.
Optionally, the step of inputting the feature data of multiple dimensions into a pre-trained consumable item selection model to determine a target consumable item corresponding to the target order data includes:
inputting the feature data of each dimension of the target order data into the consumable selection model, and determining a consumable selection result corresponding to the dimension of the target order data; and determining the target consumable according to consumable selection results corresponding to all dimensions of the target order data.
Optionally, inputting feature data of each dimension of the target order data into the consumable item selection model, and determining a consumable item selection result corresponding to the dimension of the target order data, including: matching the feature data of each dimension of the target order data with the feature data range of the corresponding dimension in the consumable item selection model, and taking a consumable item selection result corresponding to the matched feature data range as a consumable item selection result corresponding to the dimension of the target order data;
Determining the target consumable according to consumable selection results corresponding to all dimensions of the target order data, wherein the method comprises the following steps: and determining the selection probability of each consumable corresponding to the target order data according to the consumable selection result corresponding to each dimension of the target order data, and selecting one consumable meeting the preset probability from the consumables as the target consumable according to the selection probability.
Optionally, matching the feature data of each dimension of the target order data with the feature data range of the corresponding dimension in the consumable item selection model includes:
if the target order data only comprises one article, matching the feature data of the article in each dimension of the target order data with the feature data range of the corresponding dimension in the consumable item selection model;
if the target order data comprises a plurality of articles, determining the sum of the feature data of the articles in each dimension of the target order data, and matching the sum of the feature data with the feature data range of the corresponding dimension in the consumable item selection model.
Optionally, before determining the target consumable item according to the consumable item selection result corresponding to each dimension of the target order data, the method further includes: determining that the feature data of each dimension of the target order data are matched with the feature data range of the corresponding dimension in the consumable item selection model; otherwise, all the articles in the target order data are grouped, the grouped consumable part corresponding to each group is determined, and each grouped consumable part is used as the target consumable part.
Optionally, grouping all items in the target order data includes:
when the number of all the articles is less than or equal to 2, taking each article in all the articles as a group;
and under the condition that the number of all the articles is larger than 2, determining an evaluation index value for grouping all the articles according to a plurality of combination strategies, screening a target combination strategy from the plurality of combination strategies according to the evaluation index value, and grouping all the articles in the target order data according to the target combination strategy.
According to still another aspect of an embodiment of the present invention, there is provided an apparatus for consumable selection, including:
the acquisition module is used for acquiring characteristic data of multiple dimensions of target order data;
the determining module is used for inputting the characteristic data of the multiple dimensions into a pre-trained consumable material selecting model and determining target consumable materials corresponding to the target order data;
the consumable selection model is obtained by training according to the following modes: acquiring a plurality of historical order data and consumable data corresponding to each historical order data, wherein each historical order data comprises characteristic data of a plurality of dimensions; training according to the feature data of multiple dimensions of multiple historical order data and the consumable data to obtain the consumable selection model.
According to another aspect of an embodiment of the present invention, there is provided an electronic device including:
one or more processors;
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 processors are enabled to implement the method for selecting consumables provided by the invention.
According to a further aspect of an embodiment of the present invention, there is provided a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the method of consumable selection provided by the present invention.
One embodiment of the above invention has the following advantages or benefits: the method comprises the steps that a consumable material selection model is obtained according to feature data of multiple dimensions of multiple historical order data and consumable material data corresponding to each historical order data in a training mode, when target consumable materials corresponding to the target order data are determined, the feature data of the multiple dimensions of the target order data are obtained and input into the consumable material selection model, and therefore the target consumable materials corresponding to the target order data are determined. According to the method provided by the embodiment of the invention, the target consumable material is selected according to the characteristic data of multiple dimensions by adopting the consumable material selection model, so that the consumable material selection is more intelligent and reasonable, the consumable material waste cost is reduced, and the operation efficiency of the consumable material selection is improved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of a main flow of a method for consumable material selection according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a main flow of another method for consumable material selection according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the main flow of a method of grouping all items in a target order according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a flowchart of a method for consumable material selection according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the main modules of an apparatus for consumable selection according to an embodiment of the present invention;
FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 7 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow of a method for selecting consumables according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S101: acquiring characteristic data of multiple dimensions of target order data;
step S102: and inputting the characteristic data of multiple dimensions into a pre-trained consumable item selection model, and determining the target consumable items corresponding to the target order data.
In the embodiment of the invention, the consumable is used for packaging logistics distributed articles, such as order consumables for packaging articles in an e-commerce system, such as packaging bags, packaging boxes and the like. Aiming at the articles in the order data, if the consumable materials are selected too much, the waste of the consumable materials is easily caused, and therefore, the reasonable selection of the consumable materials is favorable for reducing the waste cost of the consumable materials.
In the embodiment of the invention, the pre-trained consumable item selection model is obtained by training according to the following modes: the method comprises the steps of firstly obtaining a plurality of historical order data and consumable data corresponding to each historical order data, wherein each historical order data comprises characteristic data of multiple dimensions of the historical order data, and then training according to the characteristic data of the multiple dimensions of the historical order data and the consumable data corresponding to each historical order data to obtain a consumable selection model.
Optionally, according to a large amount of historical order data and consumable data corresponding to the historical order data, consumables are selected from the consumable data to select a plurality of more reasonable historical order data and consumable data corresponding to each historical order data to serve as a training data set, and training of the consumable selection model is carried out, so that the obtained consumable selection model is more accurate and reasonable when consumable selection is carried out.
In the embodiment of the present invention, the feature data of multiple dimensions may be one or more of dimensions of length, width, height, weight, category, and the like of items in the historical order data or the target order data, and when multiple items are included in the historical order data or the target order data, the feature data of each dimension may be the sum of the feature data of the dimension of the multiple items; the consumable selection model is obtained by training the feature data of multiple dimensions of multiple historical order data, so that the target consumables of the target order data can be determined according to the feature data of the multiple dimensions, the generalization capability of the model is enhanced, and the consumables can be selected more accurately and reasonably.
Optionally, the consumable data includes a consumable identifier for identifying consumables of different sizes and/or types, which may be different consumables such as big bag, medium bag, small bag, etc. according to the size of the consumable, or different consumables such as freezer, according to the type of the consumable.
In the embodiment of the present invention, training is performed according to feature data of multiple dimensions of multiple historical order data and consumable data to obtain a consumable selection model, which may include: and training the random forest model by adopting the characteristic data of multiple dimensions of multiple historical order data and consumable data corresponding to each historical order data, and determining each parameter in the random forest model so as to obtain a consumable selection model.
In the embodiment of the present invention, training is performed according to feature data of multiple dimensions of multiple historical order data and consumable data to obtain a consumable selection model, which may include: training according to feature data of each dimension of each historical order data in the plurality of historical order data and corresponding consumable data to obtain a decision tree model corresponding to each dimension; and combining the decision tree models corresponding to all dimensions to obtain a consumable selection model.
Optionally, the decision tree model may be trained by using feature data of each of a length dimension, a width dimension, a height dimension, a category dimension, and a category dimension of the plurality of historical order data, and consumable data corresponding to each of the historical order data to obtain the decision tree model corresponding to each of the dimensions, and further obtain each of the decision tree models corresponding to each of the dimensions, where each of the decision tree models is a branch of the random forest model, and the random forest model, that is, the consumable selection model, may be obtained by combining the decision tree models. Alternatively, combining the decision tree models may be implemented using an integration algorithm, such as a bagging integration algorithm, an XGboost algorithm, and an Adaboost algorithm (e.g., Adaboost.
And after the pre-trained consumable material selection model is obtained, determining the target consumable material corresponding to the target order data by using the pre-trained consumable material selection model. Firstly, acquiring characteristic data of multiple dimensions of target order data, wherein the multiple dimensions of the target order data are one or more of the multiple dimensions of historical order data; and then inputting the characteristic data of multiple dimensions of the target order data into a pre-trained consumable selection model, determining a consumable selection result corresponding to each dimension of the target order data, and determining the target consumables corresponding to the target order data according to the consumable selection results corresponding to each dimension of the target order data.
According to an implementation manner of the embodiment of the present invention, as shown in fig. 2, determining the target consumable corresponding to the target order data includes:
step S201: acquiring characteristic data of multiple dimensions of target order data;
step S202: inputting the feature data of each dimension of the target order data into a consumable item selection model, matching the feature data of each dimension of the target order data with the feature data range of the corresponding dimension in the consumable item selection model, and taking a consumable item selection result corresponding to the matched feature data range as a consumable item selection result corresponding to each dimension of the target order data;
Step S203: determining the selection probability of each consumable corresponding to the target order data according to the consumable selection result corresponding to each dimension of the target order data;
step S204: and selecting one of the consumables which meets the preset probability as a target consumable according to the selection probability.
In the embodiment of the present invention, the consumable selection result corresponding to each dimension of the target order data is a consumable selection result corresponding to the feature range of the corresponding dimension in the consumable selection model, and may be selected as the selection probability of the target order data for each consumable in each dimension, for example, the feature data range of the weight dimension matched to the consumable selection model is less than or equal to 5kg according to the feature data of the weight dimension of the target order data being 5kg, and the selection probabilities of the feature data ranges for consumables 1, consumable 2, consumable 3, and consumable 4 are 80%, 15%, 5%, and 0%, respectively, which is the selection probability of the weight dimension of the target order data for each consumable. Then, for any consumable part, calculating an average value of the selection probability of each dimension of the target order data to the consumable part, and taking the average value of the selection probability of the target order data to each consumable part as the selection probability of each consumable part corresponding to the target order data. Then, an average value of the selection probabilities in the consumables is calculated, and one of the consumables, in which the average value of the selection probabilities meets a preset probability, or one of the consumables, in which the average value of the selection probabilities is the largest, can be used as a target consumable corresponding to the target order data.
In the embodiment of the present invention, matching the feature data of each dimension of the target order data with the feature data range of the corresponding dimension in the consumable item selection model may include:
determining whether the target order data includes only one item or a plurality of items;
if the target order data only comprises one article, matching the feature data of the article in each dimension of the target order data with the feature data range of the corresponding dimension in the consumable item selection model;
and if the target order data comprises a plurality of articles, determining the sum of the characteristic data of the articles in each dimension of the target order data, and matching the sum of the characteristic data with the characteristic data range of the corresponding dimension in the consumable item selection model.
In the embodiment of the invention, if the target order data only comprises one article, directly matching the feature data of each dimension of the article with the feature data range of the corresponding dimension in the consumable item selection model to determine the selection probability of the target order data for each consumable item, and taking the consumable item with the maximum selection probability as the target consumable item corresponding to the target order data; if the target order data includes multiple items, such as skuA (5kg, 100|50|30, hardware); skuB (6kg, 100|50| 50; hardware); skuC (6kg, 100|50| 50; hardware); the plurality of articles can be taken as a whole article, the feature data of each dimension of the whole article is the sum of the feature data of each dimension of the plurality of articles, namely the feature data of each dimension of the whole article is respectively 17kg, 300|150|130 and hardware, then the feature data of each dimension of the whole article (namely the sum of the feature data of each dimension of the plurality of articles) is matched with the feature data range of the corresponding dimension in the consumable item selection model, and the consumable item selection result corresponding to the matched feature data range is taken as the consumable item selection result corresponding to each dimension of the whole article, namely the selection probability of the target order data to each consumable item in the dimension; and then determining the selection probability of the whole article to each consumable part under multiple dimensions according to the consumable part selection result corresponding to each dimension of the whole article, and then determining one of the consumable parts with the maximum selection probability as a target consumable part.
Optionally, when the categories of the plurality of items in the target order data are different, determining a selection probability of each consumable corresponding to the target order data according to a plurality of dimensions other than the categories, so as to determine one of the consumables with the highest selection probability as the target consumable; the plurality of articles may be grouped according to categories, the articles of different categories are grouped differently, and then for each group, the grouped consumable part corresponding to each group is determined according to the feature data of a plurality of dimensions including the category, and each grouped consumable part is used as the target consumable part.
Before determining the target consumable according to the consumable selection result corresponding to each dimension of the target order data, the method further comprises the following steps: determining that the feature data of each dimension of the target order data are matched with the feature data range of the corresponding dimension in the consumable item selection model; otherwise, all the articles in the target order data are grouped, the grouped consumable part corresponding to each group is determined, and each grouped consumable part is used as the target consumable part. If the feature data of at least one dimension in the target order data is not matched with the feature data range of the corresponding dimension in the consumable item selection model, that is, if the feature data range of the corresponding dimension matched with the feature data of the corresponding dimension in the consumable item selection model of at least one dimension is 0 (that is, not present), all the articles in the target order data need to be grouped, the grouped consumable items corresponding to each group are determined, and each grouped consumable item is used as the target consumable item of the target order data. Alternatively, the grouping may be grouping using one or more combining strategies.
For example, training according to feature data of each dimension of five dimensions of weight, length, width, height and type in a plurality of historical order data and consumable material data corresponding to each historical order data to obtain a decision tree model corresponding to each dimension, then combining the decision tree models corresponding to each dimension to obtain consumable material selection models, as shown in tables 1-5, selecting probabilities of each consumable material for different feature data ranges of each dimension of the consumable material selection models respectively, when selecting target consumable materials of the target order data, firstly obtaining feature data of five dimensions of weight, length, width, height and type in the target order data, wherein the feature data are 5kg, 80cm, 50cm, 30cm and hardware respectively, matching the feature data of each dimension in the target order data with the feature data range of the corresponding dimension in the consumable material selection models to obtain consumable material selection results corresponding to each dimension of the target order data, that is, the selection probability of the feature data range of the corresponding dimension to each consumable part, as shown in table 6, then the selection probability of each consumable part corresponding to the target order data is calculated according to the consumable part selection model, that is, the average value of the selection probability of each consumable part in each dimension is calculated, and the consumable part with the largest average value of the selection probability, that is, consumable part 2 (middle part), is used as the target consumable part of the target order data.
TABLE 1
TABLE 2
TABLE 3
TABLE 4
TABLE 5
TABLE 6
In the embodiment of the present invention, as shown in fig. 3, all items in the target order data are grouped, including:
step S301: judging whether the number of all articles is more than 2; if not, executing step S302, if yes, executing step S303;
step S302: treating each of all items as a group;
step S303: determining an evaluation index value for grouping all the articles according to a plurality of combination strategies;
step S304: screening a target combination strategy from a plurality of combination strategies according to the evaluation index value;
step S305: all items in the target order data are grouped according to the target combination strategy.
In the embodiment of the present invention, when all the items in the target order data are grouped, if there are only two items in the target order data, each item is regarded as one group, that is, all the items are divided into two groups. If there is only one item in the target order data, the item may be treated as a group. If all items in the target order data are more than 2, all items are grouped according to a plurality of combination strategies. For example, if all items in the target order data are three items, item 1, item 2, and item 3, respectively, then the three items may be grouped according to three combination strategies, the first combination strategy: item 1 and item 2 are in one group and item 3 is in the other group; the second combination strategy: item 1 and item 3 are in one group and item 2 is in the other group; the third combination strategy: item 2 and item 3 are in one group, and item 1 is in the other group; then, aiming at each combination strategy, the grouping consumable part corresponding to each group in the combination strategy is determined, and further, each grouping consumable part corresponding to each combination strategy is determined.
After all the articles are grouped according to the plurality of combination strategies, the evaluation index values of the plurality of combination strategies for grouping all the articles are determined, and optionally, the evaluation index values can be obtained by performing weighted summation on the index values corresponding to the grouped consumables in each combination strategy and the preset weight of each grouped consumable.
Optionally, the evaluation index value may be a consumable cost corresponding to the target order data, for example, a consumable cost corresponding to the target order data under each combination policy may be calculated by determining a cost of each grouped consumable and a preset weight (for example, 1) of each grouped consumable through each combination policy, then, a combination policy with the lowest consumable cost is used as a target combination policy, all items in the target order data are grouped according to the target combination policy, and the grouped consumable corresponding to each group of the target combination policy is used as a target consumable.
In another optional implementation, the index value of each grouped consumable part, that is, each consumable part, may also be set according to business requirements, for example, a priority score, so that the evaluation index value may be obtained by performing weighted summation on the priority score of each grouped consumable part determined by each combination policy and the preset weight of each grouped consumable part.
The priority level value can also be determined according to the inventory of each grouped consumable part, and then the evaluation index value of each combination strategy is determined. For example, the order of the priority scores from high to low is set according to the order of the stock quantity from high to low, that is, when the stock quantity of a certain group consumable is lower than a set threshold value, and the stock quantities of other group consumables exceed the set threshold value, the priority scores of the other group consumables of which the stock quantities exceed the set threshold value are set to be higher than the stock quantities of the group consumables of which the stock quantities are lower than the set threshold value, so that the evaluation index values are calculated according to the priority scores of the group consumables and the preset weight of each group consumable in each group strategy, and the combination strategy with the highest evaluation index value is screened out as the target combination strategy, so as to group all the articles in the target order data according to the target combination strategy.
In an implementation manner of the embodiment of the present invention, when all the items in the target order data are grouped according to the plurality of combination policies, when at least one group in a certain combination policy does not match to a corresponding grouping consumable part, the at least one group may be further grouped, and each grouping consumable part after the further grouping may be determined. For example, in the first combination strategy, if the group corresponding to the item 3 can be matched with the group consumable, and the groups corresponding to the items 1 and 2 cannot be matched with the group consumable, the groups of the items 1 and 2 are further grouped, that is, the items 1 and 2 are respectively regarded as one group, and then the group consumables corresponding to the items 1 and 2 are respectively determined.
Fig. 4 is a schematic flow chart of a method for selecting a consumable part according to an embodiment of the present invention, where the method for selecting a consumable part includes the following steps:
step S401: acquiring characteristic data of multiple dimensions of target order data;
step S402: inputting the feature data of multiple dimensions into a pre-trained consumable selection model, and matching the feature data of each dimension of the target order data with the feature data range of the corresponding dimension in the consumable selection model, wherein the feature data of each dimension is the sum of the feature data of the dimension of all articles in the target order data;
step S403: judging whether the feature data of each dimension is matched with the feature data range of the corresponding dimension in the consumable item selection model or not; if yes, go to step S404; if not, go to step S406;
step S404: taking a consumable selection result corresponding to the feature data range of the corresponding dimension in the consumable selection model as a consumable selection result corresponding to the corresponding dimension in the target order data;
step S405: obtaining the selection probability of each consumable corresponding to the target order data according to the consumable selection result corresponding to each dimension in the target order data, and taking the consumable with the highest selection probability as the target consumable corresponding to the target order data;
Step S406: judging whether the number of all articles in the target order data is greater than 2, if not, executing a step S407; if yes, go to step S408;
step S407: taking each item in the target order data as a group; then, step S410 is executed;
step S408: grouping all articles in the target order data according to a plurality of combination strategies, and determining an evaluation index value (consumable cost) corresponding to each combination strategy;
step S409: taking the combination strategy with the lowest evaluation index value as a target combination strategy, and grouping all articles in the target order data according to the target combination strategy;
step S410: acquiring characteristic data of multiple dimensions of each group;
step S411: inputting the characteristics of multiple dimensions of each group into a pre-trained consumable material selection model, and matching the characteristic data of each dimension of the target order data with the characteristic data range of the corresponding dimension in the consumable material selection model, wherein the characteristic data of each dimension is the sum of the characteristic data of the dimension of all articles in each group;
step S412: taking the consumable selection result corresponding to the feature data range of the corresponding dimension in the consumable selection model as the consumable selection result corresponding to the corresponding dimension of each group;
Step S413: obtaining the selection probability of each consumable corresponding to each group according to the consumable selection result corresponding to each dimensionality of each group;
step S414: and taking the grouped consumable items with the highest selection probability as the grouped consumable items corresponding to each group, and taking the grouped consumable items as target consumable items.
The consumable selection method provided by the embodiment of the invention is characterized in that a consumable selection model is obtained by training the characteristic data of multiple dimensions of multiple historical order data and the consumable data corresponding to each historical order data, the consumable selection result of each dimension of target order data is determined by inputting the characteristic data of multiple dimensions of the target order data into the consumable selection model, and the target consumable corresponding to the target order data is determined according to the consumable selection result of each dimension. When all the articles in the target order data cannot be matched with corresponding consumables as a whole article, all the articles in the target order data can be grouped, each grouped consumable is determined, and each grouped consumable is used as a target consumable. And grouping can also be carried out according to a plurality of combination strategies during grouping, a target combination strategy is screened out according to the evaluation index value, and grouping is carried out according to the target combination strategy so as to determine the target consumable item. The method provided by the embodiment of the invention realizes intellectualization and rationalization of consumable material selection, improves the consumable material selection efficiency, reduces the consumable material waste cost, and simultaneously improves the user experience due to reasonable consumable materials.
As shown in FIG. 5, another aspect of the present invention provides an apparatus 500 for consumable selection, comprising:
an obtaining module 501, configured to obtain feature data of multiple dimensions of target order data;
the determining module 502 is used for inputting the feature data of multiple dimensions into the pre-trained consumable item selecting model and determining the target consumable items corresponding to the target order data;
the model training module 503 is configured to obtain a plurality of historical order data and consumable data corresponding to each historical order data, where each historical order data includes feature data of multiple dimensions; training is carried out according to the feature data of multiple dimensions of the multiple historical order data and the consumable data to obtain a consumable selection model.
In this embodiment of the present invention, the model training module 503 is further configured to: training according to feature data and consumable data of each dimension of each historical order data in the plurality of historical order data to obtain a decision tree model corresponding to each dimension; and combining the decision tree models corresponding to all dimensions to obtain a consumable selection model.
In this embodiment of the present invention, the determining module 502 is further configured to: inputting the characteristic data of each dimension of the target order data into a consumable material selection model, and determining a consumable material selection result corresponding to each dimension of the target order data; and determining target consumables according to consumable selection results corresponding to all dimensions of the target order data.
In this embodiment of the present invention, the determining module 502 is further configured to: matching the feature data of each dimension of the target order data with the feature data range of the corresponding dimension in the consumable item selection model, and taking the consumable item selection result corresponding to the feature data range obtained by matching as the consumable item selection result corresponding to the dimension of the target order data; and is further configured to: and determining the selection probability of each consumable corresponding to the target order data according to the consumable selection result corresponding to each dimension of the target order data, and selecting one consumable meeting the preset probability from the consumables as the target consumable according to the selection probability.
In this embodiment of the present invention, the determining module 502 is further configured to: if the target order data only comprises one article, matching the feature data of the article in each dimension of the target order data with the feature data range of the corresponding dimension in the consumable item selection model; and if the target order data comprises a plurality of articles, determining the sum of the characteristic data of the articles in each dimension of the target order data, and matching the sum of the characteristic data with the characteristic data range of the corresponding dimension in the consumable item selection model.
In this embodiment of the present invention, the determining module 502 is further configured to: before determining target consumables according to consumable selection results corresponding to all dimensions of the target order data, determining that the feature data of each dimension of the target order data are matched with the feature data range of the corresponding dimension in a consumable selection model; otherwise, all the articles in the target order data are grouped, the grouped consumable part corresponding to each group is determined, and each grouped consumable part is used as the target consumable part.
In this embodiment of the present invention, the determining module 502 is further configured to: when the number of all the articles is less than or equal to 2, taking each article in all the articles as a group; and under the condition that the number of all the articles is larger than 2, determining an evaluation index value for grouping all the articles according to the plurality of combination strategies, screening a target combination strategy from the plurality of combination strategies according to the evaluation index value, and grouping all the articles in the target order data according to the target combination strategy.
The consumable material selecting device provided by the embodiment of the invention can be accessed into a business system (such as a warehouse operation system), when order data is generated, the characteristic data of multiple dimensions of the order data is obtained, and then reasonable consumable materials corresponding to the order data can be obtained through the consumable material selecting device. Optionally, the consumable selection device can serve as a middleware odd number to provide consumable selection service; interface technology such as issuing an interface through a protocol (such as http, rpc and the like) can also be adopted to provide consumable item selection service; the consumable selection service can also be provided in a component mode, and is introduced by a business system in the form of java jar packages. The consumable selection device is introduced into the business system, so that the consumable selected by each order data can be more efficiently and reasonably determined, the working efficiency is improved, and the defects of unreasonable consumable selection and low efficiency caused by manual selection and single condition selection are overcome.
Another aspect of an embodiment of the present invention provides an electronic device, including: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by one or more processors, the one or more processors realize the method for selecting the consumable part in the embodiment of the invention.
Yet another aspect of the embodiments of the present invention provides a computer-readable medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for consumable selection according to the embodiments of the present invention.
Fig. 6 illustrates an exemplary system architecture 600 of a method or apparatus for consumable selection to which embodiments of the present invention may be applied.
As shown in fig. 6, the system architecture 600 may include terminal devices 601, 602, 603, a network 604, and a server 605. The network 604 serves as a medium for providing communication links between the terminal devices 601, 602, 603 and the server 605. Network 604 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 601, 602, 603 to interact with the server 605 via the network 604 to receive or send messages or the like. The terminal devices 601, 602, 603 may have installed thereon various communication client applications, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 605 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 601, 602, 603. The backend management server may analyze and perform other processing on the received data such as the consumable item selection request, and feed back a processing result (for example, a consumable item selection result — only an example) to the terminal device.
It should be noted that the method for selecting consumables provided by the embodiment of the present invention is generally executed by the server 605, and accordingly, the apparatus for selecting consumables is generally disposed in the server 605.
It should be understood that the number of terminal devices, networks, and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the use range of the embodiment of the present invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the system 700 are also stored. The CPU 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including components such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, according to embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 701.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, 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, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an acquisition module, a determination module, and a model training module. Where the names of these modules do not in some cases constitute a limitation on the modules themselves, for example, the acquisition module may also be described as "acquiring characteristic data for multiple dimensions of target order data".
As another aspect, the present invention also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not assembled into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: acquiring characteristic data of multiple dimensions of target order data; inputting the characteristic data of multiple dimensions into a pre-trained consumable item selection model, and determining target consumable items corresponding to target order data; the consumable selection model is obtained by training according to the following modes: acquiring a plurality of historical order data and consumable data corresponding to each historical order data, wherein each historical order data comprises characteristic data of a plurality of dimensions; training is carried out according to the feature data of multiple dimensions of the multiple historical order data and the consumable data to obtain a consumable selection model.
According to the technical scheme of the embodiment of the invention, a consumable material selection model is obtained by training the characteristic data of multiple dimensions of multiple historical order data and the consumable material data corresponding to each historical order data, the consumable material selection result of each dimension of the target order data is determined by inputting the characteristic data of multiple dimensions of the target order data into the consumable material selection model, and the target consumable material corresponding to the target order data is determined according to the consumable material selection result of each dimension. When all the articles in the target order data cannot be matched with corresponding consumables as a whole article, all the articles in the target order data can be grouped, each grouped consumable is determined, and each grouped consumable is used as a target consumable. And grouping can also be carried out according to a plurality of combination strategies during grouping, a target combination strategy is screened out according to the evaluation index value, and grouping is carried out according to the target combination strategy so as to determine the target consumable item. The method provided by the embodiment of the invention realizes intellectualization and rationalization of consumable material selection, improves the consumable material selection efficiency, reduces the consumable material waste cost, and simultaneously improves the user experience due to reasonable consumable materials.
The above-described embodiments should not be construed as limiting the scope of the 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 modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for consumable selection, comprising:
acquiring characteristic data of multiple dimensions of target order data;
inputting the feature data of the multiple dimensions into a pre-trained consumable item selection model, and determining target consumable items corresponding to the target order data;
the consumable selection model is obtained by training according to the following modes: acquiring a plurality of historical order data and consumable data corresponding to each historical order data, wherein each historical order data comprises characteristic data of a plurality of dimensions; training according to the feature data of multiple dimensions of multiple historical order data and the consumable data to obtain the consumable selection model.
2. The method of claim 1, wherein training according to the consumable data and the feature data of multiple dimensions of multiple historical order data to obtain the consumable selection model comprises:
Training according to feature data of each dimension of each historical order data in a plurality of historical order data and the consumable data to obtain a decision tree model corresponding to each dimension; and combining the decision tree models corresponding to all dimensions to obtain the consumable selection model.
3. The method of claim 1, wherein inputting the feature data of the plurality of dimensions into a pre-trained consumable selection model, and determining a target consumable corresponding to the target order data comprises:
inputting the characteristic data of each dimension of the target order data into the consumable selection model, and determining a consumable selection result corresponding to each dimension of the target order data; and determining the target consumable according to consumable selection results corresponding to all dimensions of the target order data.
4. The method of claim 3, wherein inputting the characteristic data for each dimension of the target order data into the consumable selection model, determining consumable selection results corresponding to each dimension of the target order data, comprises: matching the feature data of each dimension of the target order data with the feature data range of the corresponding dimension in the consumable item selection model, and taking a consumable item selection result corresponding to the matched feature data range as a consumable item selection result corresponding to the dimension of the target order data;
Determining the target consumable according to consumable selection results corresponding to all dimensions of the target order data, wherein the method comprises the following steps: and determining the selection probability of each consumable corresponding to the target order data according to the consumable selection result corresponding to each dimension of the target order data, and selecting one consumable meeting the preset probability from the consumables as the target consumable according to the selection probability.
5. The method of claim 4, wherein matching the characteristic data for each dimension of the target order data to a range of characteristic data for a corresponding dimension in the consumable selection model comprises:
if the target order data only comprises one article, matching the feature data of the article in each dimension of the target order data with the feature data range of the corresponding dimension in the consumable item selection model;
if the target order data comprise a plurality of articles, determining the sum of the feature data of the articles in each dimension of the target order data, and matching the sum of the feature data with the feature data range of the corresponding dimension in the consumable material selection model.
6. The method according to claim 3, wherein before determining the target consumable according to the consumable selection result corresponding to each dimension of the target order data, further comprising: determining that the feature data of each dimension of the target order data are matched with the feature data range of the corresponding dimension in the consumable item selection model; otherwise, all the articles in the target order data are grouped, the grouped consumable corresponding to each group is determined, and each grouped consumable is used as the target consumable.
7. The method of claim 6, wherein grouping all items in the target order data comprises:
under the condition that the number of all the articles is less than or equal to 2, taking each article in all the articles as a group;
and under the condition that the number of all the articles is larger than 2, determining an evaluation index value for grouping all the articles according to a plurality of combination strategies, screening a target combination strategy from the plurality of combination strategies according to the evaluation index value, and grouping all the articles in the target order data according to the target combination strategy.
8. An apparatus for consumable selection, comprising:
the acquisition module is used for acquiring characteristic data of multiple dimensions of the target order data;
the determining module is used for inputting the characteristic data of the multiple dimensions into a pre-trained consumable item selecting model and determining a target consumable item corresponding to the target order data;
the consumable selection model is obtained by training according to the following modes: acquiring a plurality of historical order data and consumable data corresponding to each historical order data, wherein each historical order data comprises characteristic data of a plurality of dimensions; training according to the feature data of multiple dimensions of multiple historical order data and the consumable data to obtain the consumable selection model.
9. An electronic device, comprising:
one or more processors;
a storage device to store 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 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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| US20190347607A1 (en) * | 2018-05-09 | 2019-11-14 | Beijing Jingdong Shangke Information Technology Co., Ltd. | System and method of determining item storage strategy |
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| US20190347607A1 (en) * | 2018-05-09 | 2019-11-14 | Beijing Jingdong Shangke Information Technology Co., Ltd. | System and method of determining item storage strategy |
| CN113762835A (en) * | 2020-08-24 | 2021-12-07 | 北京京东振世信息技术有限公司 | Method and device for processing order data |
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