CN114117245A - Product screening method and device based on big data - Google Patents
Product screening method and device based on big data Download PDFInfo
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Abstract
The application provides a product screening method and device based on big data, which comprises the steps of determining a first selected product result according to a selected product template and product information of a product library when receiving the selected product template pushed by a first sub front-end server and/or a second sub front-end server; pushing the first item selection result to the first sub front-end server and/or the second sub front-end server; when product information is added or updated, determining a second product selection result according to the added or updated product information and the product selection template; and pushing the second selection result to the second sub front-end server. By setting the first choice template containing the choice conditions and the weights of the choice conditions and carrying out calculation analysis on the choice conditions and the weights of the choice conditions, the target product information is automatically screened out from the product library of the background server, and the target product information is updated.
Description
Technical Field
The application relates to the technical field of electronic commerce, in particular to a product screening method and device based on big data.
Background
With the continuous development of scientific technology, the internet is developing faster and faster, and as one of the products of internet technology, e-commerce is in a rapid development stage and is receiving much attention. Electronic commerce meets the living needs of people by virtue of the advantages of convenience, high efficiency and the like, and gradually becomes an important field of national economic development. An online shopping mall (such as a B2C mall) is an important part of e-commerce, and provides a more efficient shopping mode for consumers and a wider business platform for enterprises than a conventional shopping mode.
The main docking modes of the existing B2C (Business-To-Customer) mall and the supply chain B2B (Business-To-Business) platform are as follows: the supply chain B2B platform previously defines part of the products to the white list, the supply chain B2B platform synchronizes the products with the B2C mall, and then the B2C mall selects the target product (i.e., the selected product) from the synchronized product list. Two major problems exist in the process, namely, the manual intervention is serious, and the product synchronization is not timely; and when a new product is supplied on the supply chain B2B platform, the selection process needs to be performed again manually.
Based on the above, the existing selection schemes have the following disadvantages:
1. the working experience of workers is seriously depended, and the workers can be tired of the workers under the condition that the variety and the quantity of products are constantly changed;
2. the uniform standard is difficult to achieve, and the product information lacks systematic and systematic historical sediment;
3. and the product information is not timely synchronized, and the selection is repeatedly operated by workers.
Disclosure of Invention
In view of the problems, the present application is proposed to provide a big data based product screening method and apparatus that overcomes or at least partially solves the problems, comprising:
a product screening method based on big data is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; wherein the product library comprises at least one product information; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the method involves a background server; the method comprises the following steps:
when an option template pushed by the first sub front-end server and/or the second sub front-end server is received, the background server determines a first option result according to the option template and the product information of the product library;
the background server pushes the first option result to the first sub front-end server and/or the second sub front-end server;
when product information is added or updated, the background server determines a second selection result according to the added or updated product information and the selection template;
and the background server pushes the second selection result to the second sub front-end server.
Optionally, wherein the choice template comprises a choice condition and a weight of the choice condition; when receiving the option template pushed by the first sub front-end server and/or the second sub front-end server, the background server determines a first option result according to the option template and the product information of the product library, including:
when an option template pushed by the first sub front-end server and/or the second sub front-end server is received, the background server determines related product information according to the option conditions and the product information of the product library;
the background server determines a comprehensive score of the related product information according to the related product information, the option conditions and the weights of the option conditions;
and the background server determines the first selection result according to the comprehensive score.
Optionally, wherein the option template comprises an option template; when the product information is added or updated, the step of determining a second option result according to the added or updated product information and the option template comprises the following steps:
when product information is added or updated, the background server determines a matching degree score of the added or updated product information and the option sub-template according to the added or updated product information, the option conditions and the weights of the option conditions;
and the background server determines the second selection result according to the matching degree score.
A product screening method based on big data is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; the method involves a front-end server; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the method comprises the following steps:
when an option condition and the weight of the option condition are obtained, the first sub front-end server and/or the second sub front-end server generate an option template according to the option condition and the weight of the option condition;
the first sub front-end server and/or the second sub front-end server pushes the selected product template to the background server;
when a first optional result sent by the background server is received, the first sub front-end server and/or the second sub front-end server updates target product information according to the first optional result and a first preset rule;
and when a second option result sent by the background server is received, the second sub front-end server updates the target product information according to the second option result and a second preset rule.
A product screening method based on big data is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; wherein the product library comprises at least one product information; the method involves a front-end server and a back-end server; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the method comprises the following steps:
when an option condition and the weight of the option condition are obtained, the first sub front-end server and/or the second sub front-end server generate an option template according to the option condition and the weight of the option condition;
the first sub front-end server and/or the second sub front-end server pushes the selected product template to the background server;
when an option template pushed by the first sub front-end server and/or the second sub front-end server is received, the background server determines a first option result according to the option template and the product information of the product library;
the background server pushes the first option result to the first sub front-end server and/or the second sub front-end server;
when a first optional result sent by the background server is received, the first sub front-end server and/or the second sub front-end server updates target product information according to the first optional result and a first preset rule;
when product information is added or updated, the background server determines a second selection result according to the added or updated product information and the selection template;
the background server pushes the second option result to the second sub front-end server;
and when a second option result sent by the background server is received, the second sub front-end server updates the target product information according to the second option result and a second preset rule.
A product screening device based on big data is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; wherein the product library comprises at least one product information; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the device comprises:
the first determining module is used for determining a first optional result according to the optional template and the product information of the product library when the background server receives the optional template pushed by the first sub front-end server and/or the second sub front-end server;
the first pushing module is used for pushing the first selection result to the first sub front-end server and/or the second sub front-end server through the background server;
the second determining module is used for determining a second optional result according to the added or updated product information and the optional template when the product information is added or updated through the background server;
and the second pushing module is used for pushing the second selection result to the second sub front-end server through the background server.
A product screening device based on big data is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; the apparatus relates to a front-end server; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the device comprises:
the first generation module is used for generating an option template according to the option conditions and the weights of the option conditions when the option conditions and the weights of the option conditions are acquired by the first sub front-end server and/or the second sub front-end server;
the third pushing module is used for pushing the selected product template to the background server through the first sub front-end server and/or the second sub front-end server;
the first updating module is used for updating target product information according to a first preset rule according to a first optional result when the first optional result sent by the background server is received by the first sub front-end server and/or the second sub front-end server;
and the second updating module is used for updating the target product information according to a second preset rule according to a second optional result when the second optional result sent by the background server is received by the second sub front-end server.
A product screening system based on big data is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; wherein the product library comprises at least one product information; the system relates to a front-end server and a background server; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the system comprises:
the second generation module is used for generating an option template according to the option conditions and the weights of the option conditions when the option conditions and the weights of the option conditions are acquired by the first sub front-end server and/or the second sub front-end server;
the fourth pushing module is used for pushing the selected product template to the background server through the first sub front-end server and/or the second sub front-end server;
the third determining module is used for determining a first optional result according to the optional template and the product information of the product library when the background server receives the optional template pushed by the first sub-front-end server and/or the second sub-front-end server;
a fifth pushing module, configured to push the first selection result to the first sub front-end server and/or the second sub front-end server through the background server;
the third updating module is used for updating the target product information according to a first preset rule according to the first selection result when the first selection result sent by the background server is received by the first sub-front-end server and/or the second sub-front-end server;
the fourth determining module is used for determining a second optional result according to the added or updated product information and the optional template when the product information is added or updated through the background server;
the sixth pushing module is used for pushing the second selection result to the second sub front-end server through the background server;
and the fourth updating module is used for updating the target product information according to a second preset rule according to a second optional result when the second optional result sent by the background server is received by the second sub front-end server.
An electronic device comprising a processor, a memory and a computer program stored on the memory and being executable on the processor, the computer program, when executed by the processor, implementing the steps of the method as described above.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method as set forth above.
The application has the following advantages:
in the embodiment of the application, when an option template pushed by the first sub front-end server and/or the second sub front-end server is received, a first option result is determined according to the option template and product information of the product library; pushing the first item selection result to the first sub front-end server and/or the second sub front-end server; when product information is added or updated, determining a second product selection result according to the added or updated product information and the product selection template; and pushing the second selection result to the second sub front-end server. And automatically screening target product information from a product library of a background server by setting a first choice template containing the choice conditions and the weights of the choice conditions and carrying out calculation analysis on the choice conditions and the weights of the choice conditions, so as to realize the update of the target product information.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings needed to be used in the description of the present application will be briefly introduced below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.
FIG. 1 is a flow chart of one step of a big data based product screening method according to an embodiment of the present application;
FIG. 2 is a flow chart illustrating another step of a big data based product screening method according to an embodiment of the present application;
FIG. 3 is a block diagram of a big data based product screening apparatus according to an embodiment of the present application;
fig. 4 is another block diagram of a big data-based product screening apparatus according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description. It is to be understood that the embodiments described are only a few embodiments of the present application and 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 application.
It should be noted that, in any embodiment of the present application, the backend server may be a supply chain B2B platform, and the front-end server may be a B2C mall.
Among them, supply chain B2B platform: the method supports accessing various external product suppliers, shields various differences of products of each supplier from technical and business levels, and provides the products to each signed B2C mall in a uniform and standard mode which is more consistent with product sales habits, so that each signed B2C mall selects products from a supply chain B2B platform for sale.
B2C mall: one mode of electronic commerce is the commercial retail mode that is directed towards the sale of products and services by consumers. The B2C mall referred to in the present application mainly refers to an e-commerce system that delivers goods from the B2B platform of the supply chain and completes the goods delivery by the B2B platform of the supply chain.
It should be noted that the option defined in the present application refers to that the B2C mall selects a product set meeting retail requirements of the mall from a product library of the B2B platform of the supply chain according to its own target user group. At present, similar selection requirements mainly have the following scenes: B2C marts desire to enrich their products through other channels; B2C shopping mall expects to be able to quickly set up products for sale after accessing the platform of the supply chain B2B; after the B2C mall accesses the supply chain B2B platform, the product information and the state can be expected to be accurately synchronized in real time; the B2C shopping mall, after accessing the platform of the supply chain B2B, expects to automatically update or put on shelf the products through preset conditions, and so on, and the final purpose is to conveniently, quickly and accurately obtain the target product set.
At present, the existing selection method aiming at the scenes comprises the following steps: the selection of the products by staff of the supply chain B2B platform is based on past experience according to the selection requirements provided by the B2C mall, for example: if the B2C mart is mainly used for selling clothes, articles such as clothes, shoes, hats and the like are defined for the B2C mart, and if certain articles are excluded or other sporadic articles are added, the articles need to be manually selected one by one, and once the articles are in a problem, the two parties need to communicate and select the articles repeatedly. If the technical scheme of new products, sold out products and off-shelf products on the B2B platform of the existing supply chain mainly takes B2C mall inquiry as a main part, timely and on-demand product information synchronization cannot be achieved.
Referring to fig. 1, a big data based product screening method provided by an embodiment of the present application is shown; the method is applied to a scene that a front-end server automatically acquires target product information in a product library of a background server; wherein the product library comprises at least one product information; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the method involves a background server;
the method comprises the following steps:
s110, when an option template pushed by the first sub front-end server and/or the second sub front-end server is received, the background server determines a first option result according to the option template and product information of the product library;
s120, the background server pushes the first option result to the first sub front-end server and/or the second sub front-end server;
s130, when the product information is added or updated, the background server determines a second product selection result according to the added or updated product information and the product selection template;
and S140, the background server pushes the second option result to the second sub front-end server.
In the embodiment of the application, when an option template pushed by the first sub front-end server and/or the second sub front-end server is received, a first option result is determined according to the option template and product information of the product library; pushing the first item selection result to the first sub front-end server and/or the second sub front-end server; when product information is added or updated, determining a second product selection result according to the added or updated product information and the product selection template; and pushing the second selection result to the second sub front-end server. And automatically screening target product information from a product library of a background server by setting a first choice template containing the choice conditions and the weights of the choice conditions and carrying out calculation analysis on the choice conditions and the weights of the choice conditions, so as to realize the update of the target product information.
Hereinafter, the big-data-based product screening method in the present exemplary embodiment will be further described.
As stated in step S110, when receiving the option template pushed by the first sub front-end server and/or the second sub front-end server, the backend server determines a first option result according to the option template and the product information of the product library.
The selection template includes selection conditions and weights of the selection conditions.
In an embodiment of the present application, a specific process of "when receiving the option template pushed by the first sub-front-end server and/or the second sub-front-end server, the backend server determines a first option result according to the option template and the product information of the product library" in step S110 may be further described with reference to the following description.
It should be noted that the option template includes merchant information and option conditions; the merchant information is key information left when a merchant signs a contract, and the merchant does not need to input the key information again; the selection conditions comprise conditions such as product types, product price ranges, product delivery places, product brands and/or product keywords and the like; wherein the product category is a category of products, such as: if the B2C mall is dominated by the sale of apparel, the product categories include clothing, shoes, hats, and the like. The merchant can quickly and accurately complete the product selection work through the upper server only by arranging the product selection template meeting the requirements of the merchant in the lower server.
It should be noted that the first option result includes information of at least one target product screened by the background server.
It should be noted that the option template includes a historical option template and a current newly added option template; the first selection result may be generated for the historical selection template and/or the current newly added selection template.
When receiving an option template pushed by the first sub front-end server and/or the second sub front-end server, the background server determines related product information according to the option condition and the product information of the product library;
the background server determines a comprehensive score of the related product information according to the related product information, the option conditions and the weights of the option conditions;
and the background server determines the first option result according to the comprehensive score as described in the following steps.
As an example, the backend server determines product information of the product category in the product information of the product library according to the product category in the selection condition; then the background server determines the comprehensive score of the product information of the product category according to the product information of the product category, the selection condition and the weight of the selection condition; and finally, the background server selects the product information of the product categories with the comprehensive scores higher than a certain value or sorted into the first items as the first selection result.
In a specific embodiment, when the product category in the selection condition is assumed to be clothes, the backend server determines product information of all different clothes which can be searched in the product information of the product library according to the clothes; then the background server determines the comprehensive scores of the product information of various clothes according to the product information of the clothes, the selection conditions and the weights of the selection conditions; and finally, the background server selects the product information of the clothes with the comprehensive score higher than a certain value or sorted into the first items as the first selection result.
As stated in step S130, when product information is added or updated, the backend server determines a second option result according to the added or updated product information and the option template.
In an embodiment of the present application, a specific process of "when product information is added or updated, the backend server determines a second option result according to the added or updated product information and the option template" in step S130 may be further described with reference to the following description.
It should be noted that the option template includes an option template.
When product information is added or updated, the background server determines a matching degree score of the added or updated product information and the option sub-template according to the added or updated product information, the option condition and the weight of the option condition;
and the background server determines the second option result according to the matching degree score as described in the following steps.
It should be noted that the first preset rule and the second preset rule include updating the product information in a manual screening or automatic screening manner.
It should be noted that the second option result includes matching items of the added or updated product information and the option template, where the matching degree score is higher than a certain value or is ranked as the first few items, that is, better matching items of the added or updated product information and the option template.
As an example, when product information is added or updated, the backend server traverses all the option sub-templates according to the added or updated product information, the option conditions and the weights of the option conditions, and determines matching degree scores of the added or updated product information and each option sub-template; and then the background server takes the matching items of the added or updated product information and the selected product sub-template which are ranked into the first items or higher than a certain value according to the matching degree score as the second selected product result, so that the second lower server updates the added or updated product information to a display area corresponding to the selected product sub-template.
Referring to fig. 2, a big data based product screening method provided by an embodiment of the present application is shown; the method is applied to a scene that a front-end server automatically acquires target product information in a product library of a background server; the method involves a front-end server; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template;
the method comprises the following steps:
s210, when an item selection condition and the weight of the item selection condition are obtained, the first sub front-end server and/or the second sub front-end server generate an item selection template according to the item selection condition and the weight of the item selection condition;
s220, the first sub front-end server and/or the second sub front-end server push the selected product template to the background server;
s230, when a first optional result sent by the background server is received, the first sub front-end server and/or the second sub front-end server updates target product information according to the first optional result and a first preset rule;
and S240, when a second option result sent by the background server is received, the second sub front-end server updates the target product information according to the second option result and a second preset rule.
In an embodiment of the application, when an option condition and a weight of the option condition are obtained, the first sub front-end server and/or the second sub front-end server generate an option template according to the option condition and the weight of the option condition; the first sub front-end server and/or the second sub front-end server pushes the selected product template to the background server; when a first optional result sent by the background server is received, the first sub front-end server and/or the second sub front-end server updates target product information according to the first optional result and a first preset rule; and when a second option result sent by the background server is received, the second sub front-end server updates the target product information according to the second option result and a second preset rule. And automatically screening target product information in a product library of a background server by setting a choice template containing the choice conditions and the weights of the choice conditions and carrying out calculation analysis on the choice conditions and the weights of the choice conditions.
It should be noted that the option template includes merchant information and option conditions; the merchant information is key information left when a merchant signs a contract, and the merchant does not need to input the key information again; the selection conditions comprise conditions such as product types, product price ranges, product delivery places, product brands and/or product keywords and the like; wherein the product category is a category of products, such as: if the B2C mall is dominated by the sale of apparel, the product categories include clothing, shoes, hats, and the like.
Hereinafter, the big-data-based product screening method in the present exemplary embodiment will be further described.
As described in step S210, when an item condition and a weight of the item condition are obtained, the first sub front end server and/or the second sub front end server generates an item template according to the item condition and the weight of the item condition.
In an embodiment of the present application, a specific process of "when acquiring the option condition and the weight of the option condition, the first sub front-end server and/or the second sub front-end server generates the option template according to the option condition and the weight of the option condition" in step S210 may be further described with reference to the following description.
Acquiring the option conditions and the weight of the option conditions input into a setting page of an option template by a merchant;
and generating a selection template according to the selection condition and the weight of the selection condition.
It should be noted that each item of the option condition has a corresponding weight value, and is specifically set according to the needs of the merchant.
In step S220, the first sub front-end server and/or the second sub front-end server pushes the option template to the backend server.
In an embodiment of the present application, a specific process of "the first sub-front-end server and/or the second sub-front-end server pushes the option template to the backend server" in step S220 may be further described with reference to the following description.
It should be noted that the option template is pushed to the background server by calling a template synchronization interface of the background server.
In step S230, when receiving the first option result sent by the background server, the first sub front-end server and/or the second sub front-end server updates the target product information according to the first option result and a first preset rule.
In an embodiment of the present application, a specific process of "when receiving the first option result sent by the background server, the first sub front-end server and/or the second sub front-end server updates the target product information according to the first option result and the first preset rule" in step S230 may be further described with reference to the following description.
It should be noted that the first preset rule includes updating the product information in a manual screening or automatic screening manner.
It should be noted that the first option result includes information of at least one target product screened by the background server.
As an example, the front-end server is provided with a result query interface; the merchant can update the product information in a manual screening or automatic screening mode; when the manual selection mode is set, the merchant inquires the first selection result through the result inquiry interface, so that the needed product information can be manually selected on the basis of the first selection result, and then the needed product information is updated to the display area of the front-end server; when the mode is set as the automatic screening mode, the target product information in the first selection result is directly updated after the first selection result is obtained.
In an embodiment of the present application, the front-end server includes a first sub front-end server and a second sub front-end server; wherein the second sub front end server comprises the option template; the big data based product screening method further comprises the following steps: the background server is used for generating a second optional result according to the added or updated product information, the optional condition and the weight of the optional condition when the product information is added or updated, and the background server is also used for pushing the second optional result to the second sub front-end server;
the second sub front-end server receives the second selection result and updates target product information according to the second selection result and a second preset rule; wherein the second selection result comprises at least one target product information.
It should be noted that the second preset rule includes updating the product information in a manual selection or automatic screening manner.
It should be noted that the second option result includes matching items of the added or updated product information and the option template, where the matching degree score is higher than a certain value or is ranked as the first few items, that is, better matching items of the added or updated product information and the option template.
As an example, the merchant may set a manual filtering or automatic filtering manner to update the product information; when the manual option mode is set, the second sub front-end server is provided with a sub-result query interface, and the merchant queries the second option result through the sub-result query interface, so that the better matching items of the added or updated product information and the option sub-template can be manually adjusted on the basis of the second option result, and then the added or updated product information is updated to a display area corresponding to the option sub-template; and when the mode is set as an automatic screening mode, directly updating the added or updated product information to a display area corresponding to the selected product sub-template after the second selected product result is obtained.
According to the product screening method based on the big data, the method is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; wherein the product library comprises at least one product information; the method involves a front-end server and a back-end server; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the method comprises the following steps:
when an option condition and the weight of the option condition are obtained, the first sub front-end server and/or the second sub front-end server generate an option template according to the option condition and the weight of the option condition;
the first sub front-end server and/or the second sub front-end server pushes the selected product template to the background server;
when an option template pushed by the first sub front-end server and/or the second sub front-end server is received, the background server determines a first option result according to the option template and the product information of the product library;
the background server pushes the first option result to the first sub front-end server and/or the second sub front-end server;
when a first optional result sent by the background server is received, the first sub front-end server and/or the second sub front-end server updates target product information according to the first optional result and a first preset rule;
when product information is added or updated, the background server determines a second selection result according to the added or updated product information and the selection template;
the background server pushes the second option result to the second sub front-end server;
and when a second option result sent by the background server is received, the second sub front-end server updates the target product information according to the second option result and a second preset rule.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
Referring to fig. 3, a big data based product screening apparatus according to an embodiment of the present application is shown; the device is applied to a scene that a front-end server automatically acquires target product information in a product library of a background server; wherein the product library comprises at least one product information; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template;
the method specifically comprises the following steps:
a first determining module 310, configured to determine, by the backend server, a first option result according to the option template and the product information of the product library when receiving the option template pushed by the first sub front-end server and/or the second sub front-end server;
a first pushing module 320, configured to push the first selection result to the first sub front-end server and/or the second sub front-end server through the background server;
a second determining module 330, configured to determine, by the backend server, a second option result according to the product information added or updated and the option template when the product information is added or updated;
the second pushing module 340 is configured to push the second selection result to the second sub front-end server through the background server.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
Referring to fig. 4, a big data based product screening apparatus provided by an embodiment of the present application is shown; the device is applied to a scene that a front-end server automatically acquires target product information in a product library of a background server; the apparatus relates to a front-end server; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template;
the method specifically comprises the following steps:
a first generating module 410, configured to generate an option template according to an option condition and a weight of the option condition when the option condition and the weight of the option condition are obtained by the first sub front-end server and/or the second sub front-end server;
a third pushing module 420, configured to push the selected template to the background server through the first sub front-end server and/or the second sub front-end server;
a first updating module 430, configured to update target product information according to a first preset rule according to a first option result when the first sub front-end server and/or the second sub front-end server receives the first option result sent by the background server;
the second updating module 440 is configured to update the target product information according to a second preset rule according to a second option result sent by the background server when the second sub-front-end server receives the second option result sent by the background server.
According to the product screening system based on the big data, the system is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; wherein the product library comprises at least one product information; the system relates to a front-end server and a background server; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the system comprises:
the second generation module is used for generating an option template according to the option conditions and the weights of the option conditions when the option conditions and the weights of the option conditions are acquired by the first sub front-end server and/or the second sub front-end server;
the fourth pushing module is used for pushing the selected product template to the background server through the first sub front-end server and/or the second sub front-end server;
the third determining module is used for determining a first optional result according to the optional template and the product information of the product library when the background server receives the optional template pushed by the first sub-front-end server and/or the second sub-front-end server;
a fifth pushing module, configured to push the first selection result to the first sub front-end server and/or the second sub front-end server through the background server;
the third updating module is used for updating the target product information according to a first preset rule according to the first selection result when the first selection result sent by the background server is received by the first sub-front-end server and/or the second sub-front-end server;
the fourth determining module is used for determining a second optional result according to the added or updated product information and the optional template when the product information is added or updated through the background server;
the sixth pushing module is used for pushing the second selection result to the second sub front-end server through the background server;
and the fourth updating module is used for updating the target product information according to a second preset rule according to a second optional result when the second optional result sent by the background server is received by the second sub front-end server.
Referring to fig. 5, a computer device of a big data based product screening method according to the present application is shown, which may specifically include the following:
the computer device 12 described above is embodied in the form of a general purpose computing device, and the components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a memory 28, and a bus 18 that couples various system components including the memory 28 and the processing unit 16.
The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory 30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (commonly referred to as "hard drives"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42, with the program modules 42 configured to carry out the functions of embodiments of the application.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules 42, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described herein.
The processing unit 16 executes programs stored in the memory 28 to execute various functional applications and data processing, for example, to implement a big data based product screening method provided in the embodiment of the present application.
That is, the processing unit 16 implements, when executing the program,: when an option template pushed by the first sub front-end server and/or the second sub front-end server is received, the background server determines a first option result according to the option template and the product information of the product library; the background server pushes the first option result to the first sub front-end server and/or the second sub front-end server; when product information is added or updated, the background server determines a second selection result according to the added or updated product information and the selection template; and the background server pushes the second selection result to the second sub front-end server.
In an embodiment of the present application, a computer-readable storage medium is further provided, on which a computer program is stored, which when executed by a processor, implements a big data based product screening method as provided in all embodiments of the present application.
That is, the program when executed by the processor implements: when an option template pushed by the first sub front-end server and/or the second sub front-end server is received, the background server determines a first option result according to the option template and the product information of the product library; the background server pushes the first option result to the first sub front-end server and/or the second sub front-end server; when product information is added or updated, the background server determines a second selection result according to the added or updated product information and the selection template; and the background server pushes the second selection result to the second sub front-end server.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. 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 (a non-exhaustive list) of the computer readable storage medium would include the following: 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 context of this document, 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.
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 any of a variety of 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.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the operator's computer, partly on the operator's computer, as a stand-alone software package, partly on the operator's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the operator's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The method and the device for screening products based on big data provided by the application are introduced in detail, specific examples are applied in the method to explain the principle and the implementation mode of the application, and the description of the embodiments is only used for helping to understand the method and the core idea of the application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A product screening method based on big data is characterized in that the method is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; wherein the product library comprises at least one product information; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the method involves a background server; the method comprises the following steps:
when an option template pushed by the first sub front-end server and/or the second sub front-end server is received, the background server determines a first option result according to the option template and the product information of the product library;
the background server pushes the first option result to the first sub front-end server and/or the second sub front-end server;
when product information is added or updated, the background server determines a second selection result according to the added or updated product information and the selection template;
and the background server pushes the second selection result to the second sub front-end server.
2. The method of claim 1, wherein the option template comprises option conditions and weights for option conditions; when receiving the option template pushed by the first sub front-end server and/or the second sub front-end server, the background server determines a first option result according to the option template and the product information of the product library, including:
when an option template pushed by the first sub front-end server and/or the second sub front-end server is received, the background server determines related product information according to the option conditions and the product information of the product library;
the background server determines a comprehensive score of the related product information according to the related product information, the option conditions and the weights of the option conditions;
and the background server determines the first selection result according to the comprehensive score.
3. The method of claim 2, wherein the option template comprises an option template; when the product information is added or updated, the step of determining a second option result by the background server according to the added or updated product information and the option template comprises the following steps:
when product information is added or updated, the background server determines a matching degree score of the added or updated product information and the option sub-template according to the added or updated product information, the option conditions and the weights of the option conditions;
and the background server determines the second selection result according to the matching degree score.
4. A product screening method based on big data is characterized in that the method is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; the method involves a front-end server; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the method comprises the following steps:
when an option condition and the weight of the option condition are obtained, the first sub front-end server and/or the second sub front-end server generate an option template according to the option condition and the weight of the option condition;
the first sub front-end server and/or the second sub front-end server pushes the selected product template to the background server;
when a first optional result sent by the background server is received, the first sub front-end server and/or the second sub front-end server updates target product information according to the first optional result and a first preset rule;
and when a second option result sent by the background server is received, the second sub front-end server updates the target product information according to the second option result and a second preset rule.
5. A product screening method based on big data is characterized in that the method is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; wherein the product library comprises at least one product information; the method involves a front-end server and a back-end server; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the method comprises the following steps:
when an option condition and the weight of the option condition are obtained, the first sub front-end server and/or the second sub front-end server generate an option template according to the option condition and the weight of the option condition;
the first sub front-end server and/or the second sub front-end server pushes the selected product template to the background server;
when an option template pushed by the first sub front-end server and/or the second sub front-end server is received, the background server determines a first option result according to the option template and the product information of the product library;
the background server pushes the first option result to the first sub front-end server and/or the second sub front-end server;
when a first optional result sent by the background server is received, the first sub front-end server and/or the second sub front-end server updates target product information according to the first optional result and a first preset rule;
when product information is added or updated, the background server determines a second selection result according to the added or updated product information and the selection template;
the background server pushes the second option result to the second sub front-end server;
and when a second option result sent by the background server is received, the second sub front-end server updates the target product information according to the second option result and a second preset rule.
6. The product screening device based on the big data is characterized in that the device is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; wherein the product library comprises at least one product information; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the device comprises:
the first determining module is used for determining a first optional result according to the optional template and the product information of the product library when the background server receives the optional template pushed by the first sub front-end server and/or the second sub front-end server;
the first pushing module is used for pushing the first selection result to the first sub front-end server and/or the second sub front-end server through the background server;
the second determining module is used for determining a second optional result according to the added or updated product information and the optional template when the product information is added or updated through the background server;
and the second pushing module is used for pushing the second selection result to the second sub front-end server through the background server.
7. The product screening device based on the big data is characterized in that the device is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; the apparatus relates to a front-end server; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the device comprises:
the first generation module is used for generating an option template according to the option conditions and the weights of the option conditions when the option conditions and the weights of the option conditions are acquired by the first sub front-end server and/or the second sub front-end server;
the third pushing module is used for pushing the selected product template to the background server through the first sub front-end server and/or the second sub front-end server;
the first updating module is used for updating target product information according to a first preset rule according to a first optional result when the first optional result sent by the background server is received by the first sub front-end server and/or the second sub front-end server;
and the second updating module is used for updating the target product information according to a second preset rule according to a second optional result when the second optional result sent by the background server is received by the second sub front-end server.
8. A big data-based product screening system is characterized in that the system is applied to a scene that a front-end server automatically obtains target product information in a product library of a background server; wherein the product library comprises at least one product information; the system relates to a front-end server and a background server; the front-end server comprises a first sub front-end server and a second sub front-end server; the second sub front-end server comprises a choice template; the system comprises:
the second generation module is used for generating an option template according to the option conditions and the weights of the option conditions when the option conditions and the weights of the option conditions are acquired by the first sub front-end server and/or the second sub front-end server;
the fourth pushing module is used for pushing the selected product template to the background server through the first sub front-end server and/or the second sub front-end server;
the third determining module is used for determining a first optional result according to the optional template and the product information of the product library when the background server receives the optional template pushed by the first sub-front-end server and/or the second sub-front-end server;
a fifth pushing module, configured to push the first selection result to the first sub front-end server and/or the second sub front-end server through the background server;
the third updating module is used for updating the target product information according to a first preset rule according to the first selection result when the first selection result sent by the background server is received by the first sub-front-end server and/or the second sub-front-end server;
the fourth determining module is used for determining a second optional result according to the added or updated product information and the optional template when the product information is added or updated through the background server;
the sixth pushing module is used for pushing the second selection result to the second sub front-end server through the background server;
and the fourth updating module is used for updating the target product information according to a second preset rule according to a second optional result when the second optional result sent by the background server is received by the second sub front-end server.
9. An electronic device comprising a processor, a memory, and a computer program stored on the memory and capable of running on the processor, the computer program, when executed by the processor, implementing the method of any of claims 1 to 3.
10. A computer-readable storage 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 to 3.
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