WO2017167054A1 - 抢购平台商品上线方法、装置及抢购系统 - Google Patents

抢购平台商品上线方法、装置及抢购系统 Download PDF

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Publication number
WO2017167054A1
WO2017167054A1 PCT/CN2017/077301 CN2017077301W WO2017167054A1 WO 2017167054 A1 WO2017167054 A1 WO 2017167054A1 CN 2017077301 W CN2017077301 W CN 2017077301W WO 2017167054 A1 WO2017167054 A1 WO 2017167054A1
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commodity
online
goods
platform
time period
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PCT/CN2017/077301
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English (en)
French (fr)
Inventor
叶舟
王瑜
张亚楠
胡旻
庞飞凤
吴龙景
Original Assignee
阿里巴巴集团控股有限公司
叶舟
王瑜
张亚楠
胡旻
庞飞凤
吴龙景
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Publication of WO2017167054A1 publication Critical patent/WO2017167054A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • G06Q30/0239—Online discounts or incentives
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0607—Regulated
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0623—Item investigation
    • G06Q30/0625—Directed, with specific intent or strategy

Definitions

  • the invention relates to the field of electronic commerce, in particular to a method, a device and a snapping system for rushing to buy platform products.
  • E-commerce is a business activity centered on information exchange technology and commodity exchange. It usually refers to a wide range of commercial and trade activities around the world. In the open Internet environment, based on the browser/server application method, buyers and sellers do not face all kinds of business activities, realizing consumers' online shopping and merchants. A new type of business operation model for online transactions and online electronic payments, as well as various business activities, trading activities, financial activities and related integrated service activities.
  • Online shopping is a specific way of e-commerce. In today's era where network popularity is widespread, online shopping has become one of the most important and most popular shopping methods for people. In order to satisfy the user's “buy” mentality of low price and affordable, there are more and more online shopping channels with the theme of “buying”, which we call the “buy” system.
  • the object of the present invention is to provide a method, a device and a snapping system for rushing to buy platform products.
  • System realize the automatic online and update of goods, avoid manual intervention, and improve the operational efficiency of the panning system.
  • the present invention provides a method for predicting the volume of a commodity, comprising:
  • the future volume of the commodity is predicted based on the volume prediction model.
  • the foregoing method may further have the following feature: the volume prediction model obtained based on the historical operation data includes:
  • the volume prediction model is obtained by training the feature set.
  • the above method may further have the following feature: the predicting the future volume of the commodity according to the volume prediction model includes:
  • the volume of the commodity in the future set time period is predicted based on the volume prediction model.
  • the method for predicting the volume of goods in the embodiment of the present invention can predict the future transaction volume of the commodity based on the historical operation data of the commodity, thereby providing a basis for determining the order of the merchandise on the basis of the transaction volume, thereby providing a basis for realizing automatic online launch and update of the commodity. , avoiding manual intervention and helping to increase the automation of the platform.
  • the present invention also provides a commodity volume prediction device, comprising:
  • a first obtaining module configured to acquire historical operation data of the commodity
  • a model obtaining module configured to obtain a volume prediction model based on the historical operation data
  • the volume prediction module is configured to predict the future volume of the commodity according to the volume prediction model.
  • the model obtaining module includes:
  • a building unit configured to construct a feature set of the historical operational data
  • a training unit configured to obtain the volume prediction model by using the feature set constructed by the building unit.
  • the foregoing apparatus may further have the following feature, and the predicting the future transaction volume of the commodity according to the volume prediction model includes:
  • the first prediction unit is configured to predict the volume of the commodity in the future set time period according to the volume prediction model.
  • the commodity volume prediction device can predict the future transaction volume of the commodity based on the historical operation data of the commodity, thereby providing a basis for determining the order of the commodity online based on the transaction volume, thereby providing a basis for realizing automatic online launch and update of the commodity. , avoiding manual intervention and helping to increase the automation of the platform.
  • the present invention also proposes an evaluation method for rushing to purchase a product online time period, including:
  • the online time period of the commodity to be evaluated is determined according to the statistical result.
  • the foregoing method may further have the following feature: determining, according to the statistical result, the online time period of the item to be evaluated, including:
  • the previously set number of time periods in which the total turnover amount is the largest is determined as the online time period of the commodity to be evaluated.
  • the above method may further have the following feature, the set time interval is one hour.
  • the method for evaluating the online time period of the snapped goods is analyzed by the historical transaction amount data of the commodity, and the time periods with the highest turnover of the commodity are analyzed, and the time periods are used as the preferred online time of the commodity, so that each type can be optimized.
  • the time when the goods are put on the line will help to increase the turnover of the goods, thereby increasing the economic benefits of the platform.
  • the present invention also provides an evaluation device for rushing for an online time period of an item, comprising:
  • a time division dividing module configured to divide the daily panning time into multiple time segments according to the set time interval
  • the transaction statistics module is used to count the items to be evaluated in various time periods during the set historical period. Total turnover;
  • the online time period determining module is configured to determine an online time period of the item to be evaluated according to the statistical result of the transaction statistics module.
  • the online time period determining module includes:
  • the first determining unit is configured to determine a preset number of time periods in which the total transaction amount is the largest as an online time period of the commodity to be evaluated.
  • the above device may further have the following feature, the set time interval is one hour.
  • the device for rushing for the online time of the commodity is analyzed, and the historical transaction amount data of the commodity is used to analyze the time periods in which the transaction volume of the commodity is the highest, and these time segments are used as the preferred online time of the commodity, so that each type can be optimized.
  • the time when the goods are put on the line will help to increase the turnover of the goods, thereby increasing the economic benefits of the platform.
  • the present invention also provides a method for evaluating the fatigue of a commodity, including:
  • the fatigue of the commodity is calculated based on the transaction explosive force, the category weighted score, and the number of times of the line.
  • the above method may further have the following feature, the transaction explosive force is equal to the product of the predicted volume of the commodity and the unit price of the commodity.
  • the above method may further have the following feature, the category weighting score is equal to the percentage of the turnover of the commodity in all commodities of its category in the same period of last year.
  • the method for evaluating the fatigue degree of the snapped-up product in the embodiment of the present invention makes it easier for the products in the panning platform to go online, thereby increasing the frequency of product update of the snapping platform, and giving the user a more intense “robbing” purchase feeling, thereby improving the user.
  • the present invention also provides an apparatus for evaluating the fatigue of a commodity, comprising:
  • a second obtaining module configured to obtain a transaction explosive force and a category weighted score of the commodity
  • the number statistics module is configured to count the number of times the product is online for consecutive days
  • the fatigue calculation module is configured to calculate the fatigue degree of the commodity according to the transaction explosive force, the category weighted score, and the number of times of going online.
  • the above apparatus may further have the following feature, the transaction explosive force is equal to a product of the predicted volume of the commodity and the unit price of the commodity.
  • the above apparatus may further have the following feature, the category weighting score is equal to the percentage of the turnover of the commodity in all the commodities of the category in the same period of last year.
  • the device for estimating the fatigue degree of the product is purchased, so that the goods that are over the line in the snapping platform are less likely to go online, thereby increasing the frequency of product update of the snapping platform, and giving the user a more intense “robbing” purchase feeling, thereby improving the user.
  • the present invention also provides an optimized delivery method for snapping up platform products, including:
  • the goods are placed on the snapping platform in descending order of fatigue.
  • the optimized delivery method of the rushing platform product is put into the rushing platform according to the order of fatigue descending, so that the goods on the rushing platform are not easy to go online, thereby improving the product update frequency of the rushing platform, and giving the user a frequency.
  • the present invention also provides an optimized delivery device for snapping up platform products, including:
  • a determination module for determining an item that is online at this time period
  • a third obtaining module configured to acquire fatigue of the commodity determined by the determining module
  • a delivery module configured to determine the determining module in descending order of fatigue in the current time period
  • the products are placed on the snapping platform.
  • the optimized delivery device for the snapped-up platform merchandise is put into the snapping platform in descending order of fatigue, so that the goods that are on the line in the snapping platform are less likely to go online, thereby increasing the frequency of product update of the snapping platform to the user.
  • the present invention also provides a method for rushing to buy a platform product, including:
  • the above method may further have the following feature: the offline triggering condition is that the current time is the ending time of the online time period of the snapped goods.
  • the above method may further have the following feature: the offline triggering condition is that the online duration of the snapped goods reaches a set duration.
  • the above method may further have the following feature, the offline trigger condition is that the snapped goods are sold out.
  • the online shopping method for snapping up the platform of the embodiment of the invention realizes automatic on-line and update of the commodity, improves the operation efficiency of the shopping system, and saves labor costs and reduces operating costs because manual intervention is not required.
  • the present invention also provides a device for rushing to purchase platform merchandise, including:
  • a monitoring module for monitoring a downline trigger condition of the snapped goods
  • a offline module configured to: when the monitoring module detects the offline triggering condition, disconnect the corresponding snapped goods
  • a sending module configured to send the offline information of the snapped goods to a server
  • a receiving and going online module configured to receive the recommended item sent by the server, and buy the flat The recommended item is on the stage.
  • the device may further have the following feature: the offline trigger condition is that the current time is the end time of the online time period of the snapped goods.
  • the above device may further have the following feature: the offline trigger condition is that the online duration of the snapped goods reaches a set duration.
  • the above device may further have the following feature: the offline trigger condition is that the snapped item has been sold out.
  • the online launching device of the snapping platform of the embodiment of the invention realizes the automatic online loading and updating of the commodity, improves the operating efficiency of the shopping system, and saves labor costs and reduces operating costs because manual intervention is not required.
  • the present invention also proposes a method for rushing to purchase commodity categories, including:
  • the method for rushing for commodity category planning can obtain the weighted score of the category reflecting the seasonality of the commodity according to the historical transaction amount data of the commodity, thereby providing a basis for optimizing the commodity delivery, and contributing to improving the economic benefit of the snapping platform.
  • the present invention also proposes a device for rushing to purchase commodity categories, including:
  • the transaction amount acquisition module is configured to obtain the transaction amount of the target commodity in the historical period, record the first transaction amount, and obtain the total transaction amount of all the commodities of the target commodity category in the same period of the history, and record the second transaction. amount;
  • a calculation module configured to calculate a category weighted score of the target commodity according to the first turnover amount and the second turnover amount.
  • the snapped-up commodity category planning device can obtain a category weighted score reflecting the seasonality of the commodity according to the historical transaction amount data of the commodity, thereby providing a basis for optimizing the commodity delivery. It will help to improve the economic benefits of the panning platform.
  • the present invention also provides a snapping system, comprising the commodity volume prediction device according to any of the preceding claims.
  • the rushing system of the embodiment of the present invention includes a commodity volume prediction device, which can predict the future transaction volume of the commodity based on the historical operation data of the commodity, thereby providing a basis for determining the order of the commodity-based online order, and further realizing the automatic launch of the commodity. And the update provides the basis for avoiding manual intervention and helping to increase the automation of the platform.
  • the present invention also provides a snapping system, comprising the apparatus for evaluating the online time period for snapping up goods as described in any of the preceding claims.
  • the rushing system of the embodiment of the present invention includes an evaluation device for rushing for the online time of the commodity, and analyzing the time period of the highest turnover of the commodity through the historical transaction amount data of the commodity, and using these time segments as the preferred online time of the commodity, such that It can optimize the online time of each product, which helps to increase the turnover of goods, and thus improve the economic benefits of the platform.
  • the present invention also provides a snapping system comprising the apparatus for evaluating the fatigue of a commercially available product according to any of the preceding claims.
  • the rushing system of the embodiment of the present invention includes an evaluation device for rushing to purchase product fatigue, so that the products on the rushing platform that are over the line are less likely to go online, thereby increasing the frequency of product update of the rushing platform, and giving the user a more intense “robbing” purchase feeling. In order to increase the user's willingness to purchase, thereby improving the economic benefits of the panning platform.
  • the present invention also proposes a snapping system, including the aforementioned optimized placing device for snapping up platform products.
  • the panning system of the embodiment of the present invention includes an optimized placing device for snapping up platform products, and the products are put into the snapping platform according to the order of fatigue descending order, so that the goods in the panning platform are not easy to go online, thereby improving the product update of the snapping platform.
  • the frequency gives the user a stronger sense of “grab” purchase, thereby increasing the user's willingness to purchase and thus improving the economic benefits of the panning platform.
  • the present invention also provides a snapping system, comprising the snapping platform commodity launching device according to any of the preceding claims.
  • the snapping system of the embodiment of the invention includes the online launching device for snapping up the platform, realizing the automatic online launching and updating of the commodity, improving the operating efficiency of the shopping system, and saving labor costs and reducing operating costs because manual intervention is not required.
  • the present invention also proposes a snapping system, including the aforementioned snapped goods category planning device.
  • the panning system of the embodiment of the present invention includes a panning commodity category planning device, which can obtain a class weighting score reflecting the seasonality of the product according to the historical transaction amount data of the commodity, thereby providing a basis for optimizing the commodity delivery, and contributing to improving the economy of the panning platform. benefit.
  • Figure 1 is a schematic diagram of the panning scenario of the snapping platform.
  • FIG. 2 is a structural diagram of a panning system according to an embodiment of the present invention.
  • FIG. 3 is a flowchart of a method for predicting the volume of a commodity in the first embodiment of the present invention.
  • FIG. 4 is a structural block diagram of a commodity volume prediction apparatus according to an embodiment of the present invention.
  • FIG. 5 is a flowchart of a method for evaluating a time period for rushing for an item to be purchased in the second embodiment of the present invention.
  • FIG. 6 is a structural block diagram of an apparatus for evaluating an online time period for snapping up an item according to an embodiment of the present invention.
  • FIG. 7 is a flowchart of a method for procuring a commodity category in the third embodiment of the present invention.
  • FIG. 8 is a structural block diagram of a device for rushing for commodity category planning according to an embodiment of the present invention.
  • FIG. 9 is a flowchart of a method for evaluating the fatigue degree of a snapped product according to Embodiment 4 of the present invention.
  • Fig. 10 is a block diagram showing the structure of an apparatus for evaluating the fatigue of a product in the embodiment of the present invention.
  • FIG. 11 is a flowchart of an optimized delivery method for snapping up platform products according to Embodiment 5 of the present invention.
  • FIG. 12 is a structural block diagram of an apparatus for optimizing the placement of a platform product in an embodiment of the present invention.
  • FIG. 13 is a flowchart of a method for rushing to buy a platform product in the sixth embodiment of the present invention.
  • FIG. 14 is a structural block diagram of a device for downloading a commodity on a platform according to an embodiment of the present invention.
  • FIG. 15 is a structural block diagram of a panning system according to an embodiment of the present invention.
  • Figure 1 is a schematic diagram of the panning scenario of the snapping platform. As shown in Figure 1, it is assumed that there are 3 booths in the snapping platform. In the first period, the products on the 3 booths on the snapping platform are Commodity 1, Commodity 2 and Commodity 3, respectively, and in the second period, on the buying platform. The items on the three booths were replaced with the goods 4, the goods 5, and the goods 6. At this time, the goods 1, the goods 2, and the goods 3 have been taken offline. It can be seen that as time goes by, the goods on the snapping platform also change. By updating the merchandise on the platform, you can give the user a "buy" feeling.
  • FIG. 2 is a structural diagram of a panning system according to an embodiment of the present invention.
  • the online time period prediction, the volume evaluation, and the category planning may be separately performed, based on the volume evaluation and the category.
  • fatigue evaluation can be carried out.
  • the delivery optimization can be carried out.
  • the products can be put on the buying platform.
  • FIG. 3 is a flowchart of a method for predicting the volume of a commodity in the first embodiment of the present invention.
  • the commodity volume prediction method may include the following steps:
  • Step S301 acquiring historical operation data of the commodity
  • the historical operation data of the commodity reflects the sales of the commodity in the past period of time. For example, it can be used to know the sales situation of the commodity of a certain category at the best time to determine the best online time of the commodity. .
  • the category of goods refers to the specific types of goods, such as fruits, flowers, women's wear, men's wear, toys, and so on.
  • the item can be uniquely identified by the item ID or the item code.
  • Step S302 obtaining a volume prediction model based on historical operation data
  • the volume prediction model obtained based on the historical operation data may include the following sub-steps: constructing a feature set of the historical operation data; and using the feature set to train the volume prediction model.
  • a feature set may be constructed first, and the feature set may include short-term traffic of the commodity, long-term traffic, sales volume, and the ability of the store to sell the inventory, the complaint rate, the refund rate, etc.; and then predict the t+ using the feature of the time t in the historical data.
  • the volume prediction model can adopt a nonlinear regression model (such as the Gradient Boost Regression Tree model), which can cross the features to achieve better prediction results; finally, based on the volume prediction model Use today's characteristics to predict the sales volume of the product tomorrow, and then multiply the sales volume by the price to get the predicted turnover of the product. In this article, the forecasted turnover is called the explosive power score.
  • Step S303 predicting the future volume of the commodity according to the volume prediction model.
  • predicting the future transaction volume of the commodity according to the volume prediction model may include: predicting the volume of the commodity in the future set time period according to the volume prediction model. For example, according to the volume prediction model, the volume of today's goods in the period from 11:00 to 12:00 is predicted to be equal to the volume of the goods in the period from 11:00 to 12:00 yesterday.
  • the method for predicting the volume of goods in the embodiment of the present invention can predict the future transaction volume of the commodity based on the historical operation data of the commodity, thereby providing a basis for determining the order of the merchandise on the basis of the transaction volume, thereby providing a basis for realizing automatic online launch and update of the commodity. , avoiding manual intervention and helping to increase the automation of the platform.
  • the embodiment of the present invention further provides an embodiment of the commodity volume prediction device.
  • FIG. 4 is a structural block diagram of a commodity volume prediction apparatus according to an embodiment of the present invention.
  • the commodity volume prediction apparatus 400 may include a first acquisition module 410, a model acquisition module 420, and a volume prediction module 430.
  • the first obtaining module 410, the model obtaining module 420, and the volume prediction module 430 may be sequentially connected.
  • the first obtaining module 410 is configured to acquire historical operation data of the commodity.
  • the model acquisition module 420 is configured to obtain a volume prediction model based on historical operational data.
  • the volume prediction module 430 is configured to predict the future volume of the commodity based on the volume prediction model.
  • the model obtaining module 420 may include a building unit and a training unit.
  • the building unit is configured to construct a feature set of the historical operational data.
  • the training unit is configured to obtain the volume prediction model by using a feature set constructed by the building unit.
  • the volume prediction module 430 may include a first prediction unit.
  • the first prediction unit is configured to predict the volume of the commodity in the future set time period according to the volume prediction model.
  • the commodity volume prediction device in the embodiment can execute the foregoing commodity volume prediction method, the portion not described in detail in the embodiment can refer to the related description of the foregoing commodity volume prediction method embodiment.
  • the commodity volume prediction device can predict the future transaction volume of the commodity based on the historical operation data of the commodity, thereby providing a basis for determining the order of the commodity online based on the transaction volume, thereby providing a basis for realizing automatic online launch and update of the commodity. , avoiding manual intervention and helping to increase the automation of the platform.
  • FIG. 5 is a flowchart of a method for evaluating a time period for rushing for an item to be purchased in the second embodiment of the present invention. As shown in FIG. 5, in this embodiment, the method for evaluating the online time period for snapping up an item may include the following steps:
  • Step S501 dividing the daily panning time into multiple time segments according to the set time interval
  • the set time interval can be one hour.
  • the set time interval can also be set to two hours, half an hour, and so on.
  • Step S502 the total transaction of the goods to be evaluated in each time period in the set historical period is counted. amount
  • the set history period can be the past year, the past six months, the past three months and so on. In general, we can take the historical period as the past year.
  • the total turnover of the time period from 10:00 to 11:00 in the past year is equal to the sum of the turnovers of the time period from 10:00 to 11:00 in each day of the past year.
  • the total turnover of other time periods is the same.
  • Step S503 determining an online time period of the item to be evaluated according to the statistical result.
  • determining, according to the statistical result, the online time period of the item to be evaluated may include: determining a previously set number of time periods with a maximum total transaction amount as an online time period of the commodity to be evaluated.
  • the number of settings may be one, two or three, and the like.
  • the first two time periods in which the total turnover is the largest are determined as the online time period of the item to be evaluated.
  • the online time period of the commodity a is 10:00-11:00, 11:00-12:00, and 15:00- 16:00; If the first two time periods in which the total turnover is the largest are determined as the online time period of the commodity a, the online time period of the commodity a is 10:00-11:00 and 11:00-12:00; If one time period in which the total transaction amount is the largest is determined as the online time period of the commodity a, the online time period of the commodity a is 10:00-11:00.
  • these three time periods can be regarded as the preferred online time period of the commodity a. Not only does this better cover the time period when the item is online, but it also makes the item's online time more flexible.
  • the method for evaluating the online time period of the snapped goods is analyzed by the historical transaction amount data of the commodity, and the time periods with the highest turnover of the commodity are analyzed, and the time periods are used as the preferred online time of the commodity, so that each type can be optimized.
  • the time when the goods are put on the line will help to increase the turnover of the goods, thereby increasing the economic benefits of the platform.
  • the embodiment of the present invention further provides an embodiment of an evaluation device for rushing the online time of the product.
  • FIG. 6 is a structural block diagram of an apparatus for evaluating an online time period for snapping up an item according to an embodiment of the present invention.
  • the evaluation apparatus 600 for rushing the commodity online time period may include a time division dividing module 610 , a transaction statistics module 620 , and an online time period determining module 630 .
  • the time division dividing module 610, the transaction statistics module 620, and the online time period determining module 630 may be sequentially connected.
  • the time segmentation module 610 is configured to divide the daily panning time into multiple time segments according to the set time interval.
  • the transaction statistics module 620 is configured to count the total turnover of the goods to be evaluated in each time period in the set historical period.
  • the online time period determining module 630 is configured to determine an online time period of the item to be evaluated according to the statistical result of the transaction statistics module 620.
  • the online time period determining module 630 may include a first determining unit.
  • the first determining unit is configured to determine a pre-set number of time periods in which the total turnover amount is the maximum as an online time period of the commodity to be evaluated.
  • the set time interval can be one hour.
  • the evaluation device for the online time period of the snapped goods in the embodiment can perform the foregoing method for evaluating the online time period of the snapped goods. Therefore, the portion not described in detail in the embodiment can be implemented by referring to the evaluation method for the online time period of the snapped goods. A description of the example.
  • the device for rushing for the online time of the commodity is analyzed, and the historical transaction amount data of the commodity is used to analyze the time periods in which the transaction volume of the commodity is the highest, and these time segments are used as the preferred online time of the commodity, so that each type can be optimized.
  • the time of launch of the product which helps to improve the quotient
  • the turnover of the products will further increase the economic benefits of the platform.
  • FIG. 7 is a flowchart of a method for procuring a commodity category in the third embodiment of the present invention. As shown in FIG. 7, in this embodiment, the method for rushing for commodity category planning may include the following steps:
  • Step S701 obtaining the transaction amount of the target commodity in the historical period, recording the first transaction amount, and obtaining the total transaction amount of all the commodities of the category belonging to the target commodity in the same period, and recording the second transaction amount;
  • Step S702 calculating a category weighted score of the target commodity according to the first turnover amount and the second turnover amount.
  • the category weighted score may be equal to the ratio of the first turnover amount to the second turnover amount, or the category weighted score may be equal to the transaction of the target commodity in the same period of history as the total commodity of the category of the target commodity in the same period of the history. The percentage of the total.
  • Apple's turnover in October last year was 10 million, and the total turnover of fruit in October last year was 1 billion.
  • the category weighted score can be used to evaluate the seasonality of the item. By classifying the weighted points, you can automatically filter out the hot items in the current season, thus providing a basis for optimizing the delivery of goods to improve the economic benefits of the platform.
  • the method for rushing for commodity category planning can obtain the weighted score of the category reflecting the seasonality of the commodity according to the historical transaction amount data of the commodity, thereby providing a basis for optimizing the commodity delivery, and contributing to improving the economic benefit of the snapping platform.
  • the embodiment of the present invention further provides an embodiment for rushing to purchase a commodity category planning device.
  • FIG. 8 is a structural block diagram of a device for rushing for commodity category planning according to an embodiment of the present invention.
  • the snapped goods category planning apparatus 800 may include a turnover acquiring module 810 and a calculating module 820 .
  • the transaction amount obtaining module 810 is configured to obtain the transaction amount of the target commodity in the historical period, record the first transaction amount, and obtain all the commodities of the category of the target commodity in the history. The total amount of transactions during the same period is recorded as the second turnover.
  • the calculation module 820 is configured to calculate a category weighted score of the target commodity according to the first turnover amount and the second turnover amount.
  • the rushing commodity category planning apparatus in the embodiment can perform the foregoing rushing commodity category planning method, the part not described in detail in the embodiment may refer to the related description of the foregoing rushing commodity category planning method embodiment.
  • the snapped goods category planning device can obtain the category weighted scores reflecting the seasonality of the commodities according to the historical transaction amount data of the commodities, thereby providing a basis for optimizing the commodity delivery, and contributing to improving the economic benefit of the snapping platform.
  • FIG. 9 is a flowchart of a method for evaluating the fatigue degree of a snapped product according to Embodiment 4 of the present invention. As shown in FIG. 9, in this embodiment, the method for evaluating the fatigue of the purchased product may include the following steps:
  • Step S901 obtaining a transaction explosive force and a category weighted score of the commodity
  • the explosive power of the transaction may be equal to the product of the predicted volume of the commodity and the unit price of the commodity.
  • the volume of the commodity can be predicted according to the foregoing method for predicting the volume of the commodity volume of the present invention. For the principle of the prediction, refer to the related description of the first embodiment, and details are not described herein again.
  • the category weighted score can be equal to the percentage of the turnover of the goods in all the commodities in their category in the same period last year.
  • Step S902 counting the number of times the product is online for a consecutive day
  • step S903 the fatigue degree of the commodity is calculated according to the explosive power of the transaction, the weighted score of the category, and the number of times of the online submission.
  • the fatigue of the goods can be calculated using the following formula:
  • Commodity fatigue transaction explosive power score * (1 + category weighted score) / consecutive a-day online number, where the symbol "*" indicates multiplication and "/" indicates division.
  • the method for evaluating the fatigue degree of the snapped-up product in the embodiment of the present invention makes it easier for the products in the panning platform to go online, thereby increasing the frequency of product update of the snapping platform, and giving the user a more intense “robbing” purchase feeling, thereby improving the user.
  • the embodiment of the present invention further provides an embodiment of an apparatus for evaluating the fatigue of the product.
  • Fig. 10 is a block diagram showing the structure of an apparatus for evaluating the fatigue of a product in the embodiment of the present invention.
  • the device for estimating the fatigue of the product may include a second acquisition module 1010, a frequency statistics module 1020, and a fatigue calculation module 1030.
  • the second obtaining module 1010 is configured to obtain a transaction explosive force and a category weighted score of the commodity.
  • the number of statistics module 1020 is used to count the number of times the item is online for a consecutive day.
  • the fatigue degree calculation module 1030 is configured to calculate the fatigue degree of the commodity according to the transaction explosive force, the category weighted score, and the number of times of the line.
  • the explosive power of the transaction may be equal to the product of the predicted volume of the commodity and the unit price of the commodity.
  • the category weighted score can be equal to the percentage of the turnover of the goods in all the commodities in their category in the same period last year.
  • the device for estimating the fatigue degree of the purchased product in the embodiment can perform the foregoing method for evaluating the fatigue of the product, the portion not described in detail in the embodiment can be referred to the related embodiment of the method for evaluating the fatigue of the purchased product. Description.
  • the device for estimating the fatigue degree of the product is purchased, so that the goods that are over the line in the snapping platform are less likely to go online, thereby increasing the frequency of product update of the snapping platform, and giving the user a more intense “robbing” purchase feeling, thereby improving the user.
  • FIG. 11 is a flowchart of an optimized delivery method for snapping up platform products according to Embodiment 5 of the present invention. As shown in FIG. 11 , in this embodiment, the optimized delivery method of the snapped platform product may include the following steps:
  • Step S1101 determining the goods on the line in the current time period
  • the commodity that is online in the current time period can be determined by the evaluation method of the online time period of the snapped goods in the foregoing embodiment (the second embodiment) of the present invention.
  • the time period in which the total turnover amount is the largest may be determined as the online time period of the commodity to be evaluated.
  • all the items with the largest turnover in this time period are the items that are online in the current time period determined in step S1101.
  • Step S1102 obtaining fatigue of the commodity
  • the fatigue degree of the product can be obtained by the evaluation method of the product fatigue degree in the foregoing embodiment (the fourth embodiment) of the present invention. I will not repeat them here.
  • step S1103 the goods are delivered to the snapping platform in descending order of fatigue in this time period.
  • the optimized delivery method of the rushing platform product is put into the rushing platform according to the order of fatigue descending, so that the goods on the rushing platform are not easy to go online, thereby improving the product update frequency of the rushing platform, and giving the user a frequency.
  • the embodiment of the present invention further provides an embodiment of an optimized device for rushing to purchase platform products.
  • FIG. 12 is a structural block diagram of an apparatus for optimizing the placement of a platform product in an embodiment of the present invention.
  • the optimized delivery device for snapping platform products may include a determining module 1210, a third obtaining module 1220, and a placing module 1230.
  • the determining module 1210, the third obtaining module 1220, and the placing module 1230 may be sequentially connected.
  • the determining module 1210 is configured to determine an item that is online at the time period.
  • the third obtaining module 1220 is configured to acquire the fatigue degree of the commodity determined by the determining module 1210.
  • the delivery module 1230 is configured to deliver the products determined by the determining module 1210 to the snapping level in the descending order of fatigue in the current time period. station.
  • the optimized delivery device of the rushing platform product in the embodiment can perform the foregoing method for optimizing the smashing platform product. Therefore, the part not described in detail in this embodiment may refer to the embodiment of the optimized delivery method for the rushing platform product. Description.
  • the optimized delivery device for the snapped-up platform merchandise is put into the snapping platform in descending order of fatigue, so that the goods that are on the line in the snapping platform are less likely to go online, thereby increasing the frequency of product update of the snapping platform to the user.
  • FIG. 13 is a flowchart of a method for rushing to buy a platform product in the sixth embodiment of the present invention. As shown in FIG. 13, in this embodiment, the method for rushing the platform product online may include the following steps:
  • Step S1301 monitoring a downlink trigger condition of the snapped goods
  • the offline triggering condition may be: the current time is the ending time of the online time period of the snapped goods. For example, suppose the online time period for snapping goods is 11:00-12:00, then the time at 12:00 is the end time of the time period for snapping up the merchandise. When this time arrives, it will trigger the snapping of the merchandise offline.
  • the offline triggering condition may be: the online duration of the snapped goods reaches the set duration. For example, it is still assumed that the online time period for snapping goods is 11:00-12:00, but the online time for rushing goods is up to 10 minutes. Assume that the online time of the snapped goods is 11:20, and the current time is 11:30. Although the current time is not the end time of the online time of the snapped goods, the online time of the snapped goods has reached the prescribed time (10 minutes). It will also trigger the snapping of goods offline.
  • the downline triggering condition may be: the snapped goods are sold out.
  • the online time period for snapping goods is 11:00-12:00, and the online time for rushing goods is up to 10 minutes.
  • the ordering time of the snapped goods is 11:20, and the current time is 11:25.
  • the current time is not the end time of the online time of the snapped goods, and the online duration of the snapped goods does not reach the specified time (10 minutes), The snapped goods have been sold out, so it will still trigger the snapping of goods offline.
  • Step S1302 in the case that the offline trigger condition is monitored, the corresponding snapped goods are taken offline;
  • Step S1303 Send the offline information of the snapped goods to the server;
  • the server After receiving the offline information of the snapped goods, the server sends the recommended products to be launched to the platform according to the list of products arranged in the order of the online order.
  • Step S1304 receiving the recommended item sent by the server, and launching the recommended item on the snapping platform.
  • the recommended item may be the item currently ranked first in the item list of the current time period, and the item list may be a list of items arranged in descending order of fatigue.
  • the product list not only indicates which categories of goods are online at what time period, but also indicates the order of the items that are online at the same time. Therefore, according to the product list, the server can automatically recommend the corresponding products on the line in the corresponding time period. .
  • This method realizes the automatic online and update of “rushed-to-buy” products, without manual intervention, so it can greatly improve the operational efficiency of the “rush-to-buy” system, and at the same time greatly improve the update frequency of “rushed-to-buy” products, giving users more intense The feeling of "buying".
  • the preset duration is 10 minutes
  • the preferred online time period of 100 items is the time period 10:00-11:00
  • the product numbers are 1 to 100 respectively
  • the order of the goods list of 00-11:00 is also from 1 to 100, and the top 10 items of goods 1 to 10 are first put on the line, each occupying one booth.
  • the goods 1 to 10 are automatically offline.
  • the top unsold items in the product list are items 11 to 20, and items 11 to 20 are automatically online at 10:10.
  • the goods 1 are sold out, and the goods are sold. 1Automatic offline, because the products 2 to 10 are not sold out and the online time has not reached 10 minutes, the products 2 to 10 are still in the online state. At this time, the top unsold items in the product list are the goods 11, so the goods 11 Automatically go online at the booth of the original product 1.
  • the online shopping method for snapping up the platform of the embodiment of the invention realizes automatic on-line and update of the commodity, improves the operation efficiency of the shopping system, and saves labor costs and reduces operating costs because manual intervention is not required.
  • the merchandise update frequency is fast, and the user can be more strongly “snapped”.
  • the merchandise is put on the line according to the preferred online time period of the merchandise, so that the transaction amount of the merchandise can be greatly improved, and high operational benefits are obtained.
  • the embodiment of the present invention further provides an embodiment for rushing the platform for merchandising.
  • FIG. 14 is a structural block diagram of a device for downloading a commodity on a platform according to an embodiment of the present invention.
  • the snap-on platform commodity online device 1400 may include a monitoring module 1410 , a downlink module 1420 , a sending module 1430 , and a receiving and going online module 1440 .
  • the monitoring module 1410, the offline module 1420, the transmitting module 1430, and the receiving and going online module 1440 can be sequentially connected.
  • the monitoring module 1410 is configured to monitor a downlink trigger condition of the snapped goods.
  • the offline module 1420 is configured to take the corresponding snapped goods offline when the monitoring module 1410 monitors the offline triggering condition.
  • the sending module 1430 is configured to send the offline information of the snapped goods to the server.
  • the receiving and going online module 1440 is configured to receive the recommended item sent by the server, and launch the recommended item on the snapping platform.
  • the offline triggering condition may be: the current time is the ending time of the online time period of the snapped goods.
  • the offline triggering condition may also be: the online duration of the snapped goods reaches the set duration.
  • the downline triggering condition may also be: the snapped goods are sold out.
  • the rushing platform product on-line device in the embodiment can perform the foregoing rushing platform product on-line method
  • the part not described in detail in this embodiment may refer to the related description of the foregoing method for rushing the platform for merchandising.
  • the online launching device of the snapping platform of the embodiment of the invention realizes the automatic online loading and updating of the commodity, improves the operating efficiency of the shopping system, and saves labor costs and reduces operating costs because manual intervention is not required.
  • the merchandise is automatically online and updated, so the merchandise update frequency is fast, and the user can feel a stronger "buy" feeling.
  • the merchandising device of the snapping platform of the embodiment of the present invention arranges the merchandise to go online according to the preferred online time period of the merchandise, thereby greatly increasing the turnover of the merchandise and achieving high operational benefits.
  • the embodiment of the present invention further provides a rushing system, which may include the commodity volume prediction device in the foregoing embodiment of the present invention, an evaluation device for rushing the commodity online time period, snapping up the commodity category planning device, and rushing for product fatigue. Any one or more or all of the evaluation device, the optimized placement device for snapping up the platform product, and the snap-on platform product launching device.
  • the snapping system 1500 shown in FIG. FIG. 15 is a structural block diagram of a panning system according to an embodiment of the present invention.
  • the snapping system 1500 can include snapping up the platform merchandise launching device 1400. Since the snapping system includes snapping up the platform product launching device, the panning system can realize automatic online launching and updating of the product, improve the operating efficiency of the shopping system, and save labor costs and reduce operating costs because manual intervention is not required. .
  • the goods are automatically online and updated, so the snap-up system product update frequency is fast, and can give the user a stronger "buy” feeling.
  • the merchandise is put on the line according to the preferred online time period of the commodity, so the snapping system can also greatly increase the turnover of the commodity and achieve higher operational benefits.

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Abstract

一种抢购平台商品上线方法、装置及抢购系统。其中，抢购平台商品上线方法包括：监测抢购商品的下线触发条件（S1301）；在监测到所述下线触发条件的情况下，将对应抢购商品下线（S1302）；将所述抢购商品的下线信息发送给服务器（S1303）；接收所述服务器发送的推荐商品，并在抢购平台上上线所述推荐商品（S1304）。所述方法实现了商品的自动上线及更新，商品更新频率快，提高了购物系统的运营效率。

Description

抢购平台商品上线方法、装置及抢购系统 技术领域
本发明涉及电子商务领域，尤其涉及一种抢购平台商品上线方法、装置及抢购系统。
背景技术
电子商务是以信息网络技术为手段，以商品交换为中心的商务活动。它通常是指在全球各地广泛的商业贸易活动中，在因特网开放的网络环境下，基于浏览器/服务器应用方式，买卖双方不谋面地进行各种商贸活动，实现消费者的网上购物、商户之间的网上交易和在线电子支付以及各种商务活动、交易活动、金融活动和相关的综合服务活动的一种新型的商业运营模式。
网络购物是电子商务的一种具体方式。在网络普及已十分广泛的当今时代，网络购物已经成为人们最重要和最常用的购物方式之一。为了满足用户喜欢低价实惠的“抢购”心理，目前出现了越来越多以“抢购”为主题的网络购物频道，我们称之为“抢购”系统。
这些“抢购”系统在一定程度上满足了用户的“抢购”需求。但是，这些“抢购”系统还存在以下问题：一是商品的更新频率很低，大多在小时级别，并且商品品类单一，无法进一步激发用户“抢购”的感觉；二是这些“抢购”系统中，商品的更新大多依赖人工运营的方式，不仅效率低下，而且由于过多的人工费用导致成本较高。而随着“抢购”系统涉及商品数的不断增多，通过人工方式已经无法对“抢购”商品的运营进行有效干预。
发明内容
本发明的目的在于提供一种抢购平台商品上线方法、装置及抢购系 统，实现商品的自动上线及更新，避免人工干预，提高抢购系统的运营效率。
为实现上述目的，本发明提出了一种商品成交量预测方法，包括：
获取商品的历史运营数据；
基于所述历史运营数据得到成交量预测模型；
根据所述成交量预测模型预测商品的未来成交量。
进一步地，上述方法还可具有以下特点，所述基于所述历史运营数据得到成交量预测模型包括：
构建所述历史运营数据的特征集；
利用所述特征集训练得到所述成交量预测模型。
进一步地，上述方法还可具有以下特点，所述根据所述成交量预测模型预测商品的未来成交量包括：
根据所述成交量预测模型预测商品在未来的设定时间段的成交量。
本发明实施例的商品成交量预测方法，能够根据商品的历史运营数据预测商品的未来成交量，从而为基于成交量的商品上线顺序的确定提供依据，进而为实现商品的自动上线及更新提供基础，避免了人工干预，有助于提高平台的自动化程度。
为实现上述目的，本发明还提出了一种商品成交量预测装置，包括：
第一获取模块，用于获取商品的历史运营数据；
模型取得模块，用于基于所述历史运营数据得到成交量预测模型；
成交量预测模块，用于根据所述成交量预测模型预测商品的未来成交量。
进一步地，上述装置还可具有以下特点，所述模型取得模块包括：
构建单元，用于构建所述历史运营数据的特征集；
训练单元，用于利用所述构建单元构建的特征集训练得到所述成交量预测模型。
进一步地，上述装置还可具有以下特点，所述根据所述成交量预测模型预测商品的未来成交量包括：
第一预测单元，用于根据所述成交量预测模型预测商品在未来的设定时间段的成交量。
本发明实施例的商品成交量预测装置，能够根据商品的历史运营数据预测商品的未来成交量，从而为基于成交量的商品上线顺序的确定提供依据，进而为实现商品的自动上线及更新提供基础，避免了人工干预，有助于提高平台的自动化程度。
为实现上述目的，本发明还提出了一种抢购商品上线时间段的评价方法，包括：
按照设定时间间隔将每天的抢购时间划分为多个时间段；
统计待评价商品在设定历史时期内各个时间段的总成交额；
根据统计结果确定所述待评价商品的上线时间段。
进一步地，上述方法还可具有以下特点，所述根据统计结果确定所述待评价商品的上线时间段，包括：
将总成交额最大的前设定数目个时间段确定为所述待评价商品的上线时间段。
进一步地，上述方法还可具有以下特点，所述设定时间间隔为一小时。
本发明实施例的抢购商品上线时间段的评价方法，通过商品的历史成交额数据分析出商品的成交额最高的几个时间段，将这些时间段作为商品的优选上线时间，这样能够优化每种商品的上线时间，从而有助于提高商品的成交额，进而提高抢购平台的经济效益。
为实现上述目的，本发明还提出了一种抢购商品上线时间段的评价装置，，包括：
时段划分模块，用于按照设定时间间隔将每天的抢购时间划分为多个时间段；
成交统计模块，用于统计待评价商品在设定历史时期内各个时间段的 总成交额；
上线时段确定模块，用于根据所述成交统计模块的统计结果确定所述待评价商品的上线时间段。
进一步地，上述装置还可具有以下特点，所述上线时段确定模块包括：
第一确定单元，用于将总成交额最大的前设定数目个时间段确定为所述待评价商品的上线时间段。
进一步地，上述装置还可具有以下特点，所述设定时间间隔为一小时。
本发明实施例的抢购商品上线时间段的评价装置，通过商品的历史成交额数据分析出商品的成交额最高的几个时间段，将这些时间段作为商品的优选上线时间，这样能够优化每种商品的上线时间，从而有助于提高商品的成交额，进而提高抢购平台的经济效益。
为实现上述目的，本发明还提出了一种抢购商品疲劳度的评价方法，包括：
获取商品的成交爆发力和品类加权分；
统计所述商品连续a天的上线次数；
根据所述成交爆发力、品类加权分和上线次数，计算所述商品的疲劳度。
进一步地，上述方法还可具有以下特点，所述成交爆发力等于预测的所述商品的成交量与所述商品的单价的乘积。
进一步地，上述方法还可具有以下特点，所述品类加权分等于所述商品去年同期在其所属类目全部商品中的成交额百分比。
本发明实施例的抢购商品疲劳度的评价方法，使得抢购平台中上过线的商品更不容易上线，从而提高抢购平台的商品更新频率，给用户更加强烈的“抢”购感觉，从而提高用户的购买意愿，进而提高抢购平台的经济效益。
为实现上述目的，本发明还提出了一种抢购商品疲劳度的评价装置，包括：
第二获取模块，用于获取商品的成交爆发力和品类加权分；
次数统计模块，用于统计所述商品连续a天的上线次数；
疲劳度计算模块，用于根据所述成交爆发力、品类加权分和上线次数，计算所述商品的疲劳度。
进一步地，上述装置还可具有以下特点，所述成交爆发力等于预测的所述商品的成交量与所述商品的单价的乘积。
进一步地，上述装置还可具有以下特点，所述品类加权分等于所述商品去年同期在其所属类目全部商品中的成交额百分比。
本发明实施例的抢购商品疲劳度的评价装置，使得抢购平台中上过线的商品更不容易上线，从而提高抢购平台的商品更新频率，给用户更加强烈的“抢”购感觉，从而提高用户的购买意愿，进而提高抢购平台的经济效益。
为实现上述目的，本发明还提出了一种抢购平台商品的优化投放方法，包括：
确定本时间段上线的商品；
获取所述商品的疲劳度；
在本时间段按照疲劳度降序顺序将所述商品投放到抢购平台。
本发明实施例的抢购平台商品的优化投放方法，按照疲劳度降序顺序将商品投放到抢购平台，使得抢购平台中上过线的商品更不容易上线，从而提高抢购平台的商品更新频率，给用户更加强烈的“抢”购感觉，从而提高用户的购买意愿，进而提高抢购平台的经济效益。
为实现上述目的，本发明还提出了一种抢购平台商品的优化投放装置，包括：
确定模块，用于确定本时间段上线的商品；
第三获取模块，用于获取所述确定模块确定的商品的疲劳度；
投放模块，用于在本时间段按照疲劳度降序顺序将所述确定模块确定 的商品投放到抢购平台。
本发明实施例的抢购平台商品的优化投放装置，按照疲劳度降序顺序将商品投放到抢购平台，使得抢购平台中上过线的商品更不容易上线，从而提高抢购平台的商品更新频率，给用户更加强烈的“抢”购感觉，从而提高用户的购买意愿，进而提高抢购平台的经济效益。
为实现上述目的，本发明还提出了一种抢购平台商品上线方法，包括：
监测抢购商品的下线触发条件；
在监测到所述下线触发条件的情况下，将对应抢购商品下线；
将所述抢购商品的下线信息发送给服务器；
接收所述服务器发送的推荐商品，并在抢购平台上上线所述推荐商品。
进一步地，上述方法还可具有以下特点，所述下线触发条件为当前时刻为所述抢购商品所在上线时间段的结束时刻。
进一步地，上述方法还可具有以下特点，所述下线触发条件为所述抢购商品的在线时长达到设定时长。
进一步地，上述方法还可具有以下特点，所述下线触发条件为所述抢购商品已售完。
本发明实施例的抢购平台商品上线方法，实现了商品的自动上线及更新，提高了购物系统的运营效率，而且由于不需要人工干预，还节省了人工费用，降低了运营成本。
为实现上述目的，本发明还提出了一种抢购平台商品上线装置，包括：
监测模块，用于监测抢购商品的下线触发条件；
下线模块，用于在所述监测模块监测到所述下线触发条件的情况下，将对应抢购商品下线；
发送模块，用于将所述抢购商品的下线信息发送给服务器；
接收及上线模块，用于接收所述服务器发送的推荐商品，并在抢购平 台上上线所述推荐商品。
进一步地，上述装置还可具有以下特点，所述下线触发条件为当前时刻为所述抢购商品所在上线时间段的结束时刻。
进一步地，上述装置还可具有以下特点，所述下线触发条件为所述抢购商品的在线时长达到设定时长。
进一步地，上述装置还可具有以下特点，所述下线触发条件为所述抢购商品已售完。
本发明实施例的抢购平台商品上线装置，实现了商品的自动上线及更新，提高了购物系统的运营效率，而且由于不需要人工干预，还节省了人工费用，降低了运营成本。
为实现上述目的，本发明还提出了一种抢购商品品类规划方法，包括：
获取目标商品在历史同期的成交额，记为第一成交额，以及获取所述目标商品所属类目的全部商品在所述历史同期的成交总额，记为第二成交额；
根据所述第一成交额和所述第二成交额计算所述目标商品的品类加权分。
本发明实施例的抢购商品品类规划方法，能够根据商品的历史成交额数据获得反映商品季节性的品类加权分，从而为优化商品投放提供了基础，有助于提高抢购平台的经济效益。
为实现上述目的，本发明还提出了一种抢购商品品类规划装置，包括：
成交额获取模块，用于获取目标商品在历史同期的成交额，记为第一成交额，以及获取所述目标商品所属类目的全部商品在所述历史同期的成交总额，记为第二成交额；
计算模块，用于根据所述第一成交额和所述第二成交额计算所述目标商品的品类加权分。
本发明实施例的抢购商品品类规划装置，能够根据商品的历史成交额数据获得反映商品季节性的品类加权分，从而为优化商品投放提供了基 础，有助于提高抢购平台的经济效益。
为实现上述目的，本发明还提出了一种抢购系统，包括前述任一项所述的商品成交量预测装置。
本发明实施例的抢购系统中包括商品成交量预测装置，能够根据商品的历史运营数据预测商品的未来成交量，从而为基于成交量的商品上线顺序的确定提供依据，进而为实现商品的自动上线及更新提供基础，避免了人工干预，有助于提高平台的自动化程度。
为实现上述目的，本发明还提出了一种抢购系统，包括前述任一项所述的抢购商品上线时间段的评价装置。
本发明实施例的抢购系统中包括抢购商品上线时间段的评价装置，通过商品的历史成交额数据分析出商品的成交额最高的几个时间段，将这些时间段作为商品的优选上线时间，这样能够优化每种商品的上线时间，从而有助于提高商品的成交额，进而提高抢购平台的经济效益。
为实现上述目的，本发明还提出了一种抢购系统，包括前述任一项所述的抢购商品疲劳度的评价装置。
本发明实施例的抢购系统中包括抢购商品疲劳度的评价装置，使得抢购平台中上过线的商品更不容易上线，从而提高抢购平台的商品更新频率，给用户更加强烈的“抢”购感觉，从而提高用户的购买意愿，进而提高抢购平台的经济效益。
为实现上述目的，本发明还提出了一种抢购系统，包括前述的抢购平台商品的优化投放装置。
本发明实施例的抢购系统中包括抢购平台商品的优化投放装置，按照疲劳度降序顺序将商品投放到抢购平台，使得抢购平台中上过线的商品更不容易上线，从而提高抢购平台的商品更新频率，给用户更加强烈的“抢”购感觉，从而提高用户的购买意愿，进而提高抢购平台的经济效益。
为实现上述目的，本发明还提出了一种抢购系统，包括前述任一项所述的抢购平台商品上线装置。
本发明实施例的抢购系统中包括抢购平台商品上线装置，实现了商品的自动上线及更新，提高了购物系统的运营效率，而且由于不需要人工干预，还节省了人工费用，降低了运营成本。
为实现上述目的，本发明还提出了一种抢购系统，包括前述的抢购商品品类规划装置。
本发明实施例的抢购系统中包括抢购商品品类规划装置，能够根据商品的历史成交额数据获得反映商品季节性的品类加权分，从而为优化商品投放提供了基础，有助于提高抢购平台的经济效益。
附图说明
图1为抢购平台的抢购场景示意图。
图2为本发明实施例中抢购系统的架构图。
图3为本发明实施例一中商品成交量预测方法的流程图。
图4为本发明实施例中商品成交量预测装置的结构框图。
图5为本发明实施例二中抢购商品上线时间段的评价方法的流程图。
图6为本发明实施例中抢购商品上线时间段的评价装置的结构框图。
图7为本发明实施例三中抢购商品品类规划方法的流程图。
图8为本发明实施例中抢购商品品类规划装置的结构框图。
图9为本发明实施例四中抢购商品疲劳度的评价方法的流程图。
图10为本发明实施例中抢购商品疲劳度的评价装置的结构框图。
图11为本发明实施例五中抢购平台商品的优化投放方法的流程图。
图12为本发明实施例中抢购平台商品的优化投放装置的结构框图。
图13为本发明实施例六中抢购平台商品上线方法的流程图。
图14为本发明实施例中抢购平台商品上线装置的结构框图。
图15为本发明实施例中抢购系统的一种结构框图。
具体实施方式
以下结合附图对本发明的原理和特征进行描述，所举实施例只用于解释本发明，并非用于限定本发明的范围。对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，根据本发明精神所获得的所有实施例，都属于本发明的保护范围。
图1为抢购平台的抢购场景示意图。如图1所示，假设抢购平台共有3个展位，在第一时间段，抢购平台上3个展位上的商品分别为商品1、商品2和商品3，而到了第二时间段，抢购平台上3个展位上的商品换成了商品4、商品5和商品6，此时商品1、商品2和商品3已经被下线。可见，随着时间的改变，抢购平台上的商品也发生变化。通过更新抢购平台上的商品，可以给用户“抢购”的感觉。
图2为本发明实施例中抢购系统的架构图。如图2所示，本实施例中，基于商品数据(可以包括商品基础数据和商品的历史运营数据等)，可以分别进行上线时间段预测、成交量评价和品类规划，基于成交量评价和品类规划的结果，可以进行疲劳度评价，基于疲劳度评价和上线时间段预测的结果，可以进行投放优化，在投放优化的基础上，可以将商品上线在抢购平台上。
实施例一
图3为本发明实施例一中商品成交量预测方法的流程图。如图3所示，本实施例中，商品成交量预测方法可以包括如下步骤：
步骤S301，获取商品的历史运营数据；
商品的历史运营数据反映了商品在过去一段时间内的销售情况，比如可以通过统计获知某一类目的商品在哪一个时间段的销售情况最好，以便确定该类目商品的最佳上线时间。其中，商品的类目是指商品的具体种类，比如水果、鲜花、女装、男装、玩具等。商品可以用商品ID或商品编码来唯一标识。
步骤S302，基于历史运营数据得到成交量预测模型；
在本发明实施例中，基于历史运营数据得到成交量预测模型可以包括如下子步骤：构建历史运营数据的特征集；利用该特征集训练得到成交量预测模型。
在具体应用中，可以首先构建特征集，特征集可以包括商品短期流量、长期流量、销量以及店铺销售库存的能力、投诉率、退款率等；然后用历史数据中时间t的特征预测t+1的销量(即成交量)，成交量预测模型可以采用非线性回归方式的模型(例如Gradient Boost Regression Tree模型)，这样能够将特征交叉起来从而达到更好的预测效果；最后根据成交量预测模型，用今天的特征预测商品明天的销量，销量再乘以价格就得到了商品的预测成交额，本文中，将该预测成交额称为成交爆发力分值。
步骤S303，根据成交量预测模型预测商品的未来成交量。
例如，可以根据成交量预测模型用商品在去年同期的成交量来预测商品今年的成交量，也根据成交量预测模型用商品昨天的成交量来预测商品今天的成交量。
在本发明实施例中，根据成交量预测模型预测商品的未来成交量可以包括：根据成交量预测模型预测商品在未来的设定时间段的成交量。例如，根据成交量预测模型，预测今天商品在11：00至12:00这个时间段的成交量等于商品在昨天的11：00至12:00的时间段的成交量。
本发明实施例的商品成交量预测方法，能够根据商品的历史运营数据预测商品的未来成交量，从而为基于成交量的商品上线顺序的确定提供依据，进而为实现商品的自动上线及更新提供基础，避免了人工干预，有助于提高平台的自动化程度。
为了实现上述商品成交量预测方法实施例中各步骤及方法，本发明实施例还提供了商品成交量预测装置实施例。
图4为本发明实施例中商品成交量预测装置的结构框图。如图4所示，本实施例中，商品成交量预测装置400可以包括第一获取模块410、模型取得模块420和成交量预测模块430。包括第一获取模块410、模型取得模块420和成交量预测模块430可以顺次相连。
其中，第一获取模块410用于获取商品的历史运营数据。模型取得模块420用于基于历史运营数据得到成交量预测模型。成交量预测模块430用于根据成交量预测模型预测商品的未来成交量。
在本发明实施例中，模型取得模块420可以包括构建单元和训练单元。构建单元用于构建所述历史运营数据的特征集。训练单元用于利用构建单元构建的特征集训练得到所述成交量预测模型。
在本发明实施例中，成交量预测模块430可以包括第一预测单元。第一预测单元用于根据成交量预测模型预测商品在未来的设定时间段的成交量。
由于本实施例中的商品成交量预测装置能够执行前述的商品成交量预测方法，因此本实施例未详细描述的部分，可参考对前述商品成交量预测方法实施例的相关说明。
本发明实施例的商品成交量预测装置，能够根据商品的历史运营数据预测商品的未来成交量，从而为基于成交量的商品上线顺序的确定提供依据，进而为实现商品的自动上线及更新提供基础，避免了人工干预，有助于提高平台的自动化程度。
实施例二
图5为本发明实施例二中抢购商品上线时间段的评价方法的流程图。如图5所示，本实施例中，抢购商品上线时间段的评价方法可以包括如下步骤：
步骤S501，按照设定时间间隔将每天的抢购时间划分为多个时间段；
其中，每天的抢购时间一般为24小时，如果系统需要每天关闭一段时间用于维护等，每天的抢购时间等于24小时减去关闭时间。例如，如果系统在每天的0点至7点关闭，则每天的抢购时间等于24-7＝17小时。
其中，设定时间间隔可以为一小时。设定时间间隔也可以设置为两小时、半小时等等。
步骤S502，统计待评价商品在设定历史时期内各个时间段的总成交 额；
其中，设定历史时期可以是过去一年，过去半年，过去三个月等等。一般情况下，我们可以取设定历史时期为过去一年。
如果设定时间间隔为一小时，那么在过去一年内时间段10：00-11:00的总成交额等于过去一年内每一天中时间段10：00-11:00的成交额之和。其他时间段的总成交额依此类推。
步骤S503，根据统计结果确定待评价商品的上线时间段。
在本发明实施例中，根据统计结果确定所述待评价商品的上线时间段可以包括：将总成交额最大的前设定数目个时间段确定为待评价商品的上线时间段。
其中，设定数目可以是一个、两个或三个等。例如，将总成交额最大的前两个时间段确定为待评价商品的上线时间段。
这里对如何根据统计结果确定商品上线时间段举例说明。以商品a为例。假设将24小时划分为24个时间段，每一小时为一个时间段，设定历史时期为过去一个月。
首先统计商品a在过去一个月内分别在24个时间段的成交额；
计算成交额最高的3个时间段，假设这三个时间段为10：00-11:00(成交额为15万)、11：00-12:00(成交额为10万)、15：00-16:00(成交额为9万)；
如果将将总成交额最大的前3个时间段确定为商品a的上线时间段，则商品a的上线时间段为10：00-11:00、11：00-12:00和15：00-16:00；如果将将总成交额最大的前2个时间段确定为商品a的上线时间段，则商品a的上线时间段为10：00-11:00、11：00-12:00；如果将将总成交额最大的1个时间段确定为商品a的上线时间段，则商品a的上线时间段为10：00-11:00。
10：00-11:00这个时间段的成交额远远大于排在第二位的时间段11：00-12:00的成交额，因此可以选时间段10：00-11:00作为商品a的优选 上线时间段。
如果商品a成交额最高的3个时间段的成交额差距很小，可以将这3个时间段都作为商品a的优选上线时间段。这样做不仅能够更好地覆盖商品上线的时间段，而且能够使商品的上线时间更灵活。
本发明实施例的抢购商品上线时间段的评价方法，通过商品的历史成交额数据分析出商品的成交额最高的几个时间段，将这些时间段作为商品的优选上线时间，这样能够优化每种商品的上线时间，从而有助于提高商品的成交额，进而提高抢购平台的经济效益。
为了实现上述抢购商品上线时间段的评价方法实施例中各步骤及方法，本发明实施例还提供了抢购商品上线时间段的评价装置实施例。
图6为本发明实施例中抢购商品上线时间段的评价装置的结构框图。如图6所示，本实施例中，抢购商品上线时间段的评价装置600可以包括时段划分模块610、成交统计模块620和上线时段确定模块630。时段划分模块610、成交统计模块620和上线时段确定模块630可以顺次相连。
其中，时段划分模块610用于按照设定时间间隔将每天的抢购时间划分为多个时间段。成交统计模块620用于统计待评价商品在设定历史时期内各个时间段的总成交额。上线时段确定模块630用于根据成交统计模块620的统计结果确定待评价商品的上线时间段。
在本发明实施例中，上线时段确定模块630可以包括第一确定单元。第一确定单元用于将总成交额最大的前设定数目个时间段确定为待评价商品的上线时间段。
其中，设定时间间隔可以为一小时。
由于本实施例中的抢购商品上线时间段的评价装置能够执行前述的抢购商品上线时间段的评价方法，因此本实施例未详细描述的部分，可参考对前述抢购商品上线时间段的评价方法实施例的相关说明。
本发明实施例的抢购商品上线时间段的评价装置，通过商品的历史成交额数据分析出商品的成交额最高的几个时间段，将这些时间段作为商品的优选上线时间，这样能够优化每种商品的上线时间，从而有助于提高商 品的成交额，进而提高抢购平台的经济效益。
实施例三
图7为本发明实施例三中抢购商品品类规划方法的流程图。如图7所示，本实施例中，抢购商品品类规划方法可以包括如下步骤：
步骤S701，获取目标商品在历史同期的成交额，记为第一成交额，以及获取目标商品所属类目的全部商品在该历史同期的成交总额，记为第二成交额；
步骤S702，根据第一成交额和第二成交额计算目标商品的品类加权分。
其中，品类加权分可以等于第一成交额与第二成交额的比值，或者说，品类加权分可以等于目标商品在历史同期的成交额占目标商品所属类目的全部商品在该历史同期的成交总额的百分比。
比如，苹果在去年10月份的成交额为1000万，水果在去年10月份的成交总额为10亿，则苹果的品类加权分等于1000万/10亿＝1％＝0.1。
品类加权分可以用于对商品的季节性进行评价。通过品类加权分，可以自动筛选出当前季节的热卖商品，从而为优化商品投放提供基础，以提高平台的经济效益。
本发明实施例的抢购商品品类规划方法，能够根据商品的历史成交额数据获得反映商品季节性的品类加权分，从而为优化商品投放提供了基础，有助于提高抢购平台的经济效益。
为了实现上述抢购商品品类规划方法实施例中各步骤及方法，本发明实施例还提供了抢购商品品类规划装置实施例。
图8为本发明实施例中抢购商品品类规划装置的结构框图。如图8所示，本实施例中，抢购商品品类规划装置800可以包括成交额获取模块810和计算模块820。
其中，成交额获取模块810用于获取目标商品在历史同期的成交额，记为第一成交额，以及获取所述目标商品所属类目的全部商品在所述历史 同期的成交总额，记为第二成交额。计算模块820用于根据第一成交额和第二成交额计算目标商品的品类加权分。
由于本实施例中的抢购商品品类规划装置能够执行前述的抢购商品品类规划方法，因此本实施例未详细描述的部分，可参考对前述抢购商品品类规划方法实施例的相关说明。
本发明实施例的抢购商品品类规划装置，能够根据商品的历史成交额数据获得反映商品季节性的品类加权分，从而为优化商品投放提供了基础，有助于提高抢购平台的经济效益。
实施例四
图9为本发明实施例四中抢购商品疲劳度的评价方法的流程图。如图9所示，本实施例中，抢购商品疲劳度的评价方法可以包括如下步骤：
步骤S901，获取商品的成交爆发力和品类加权分；
其中，成交爆发力可以等于预测的商品的成交量与该商品的单价的乘积。其中，商品的成交量可以根据本发明前述的商品成交量预测方法实施例来进行预测，预测的原理参见实施例一的相关说明，此处不再赘述。
其中，品类加权分可以等于商品去年同期在其所属类目全部商品中的成交额百分比。有关品类加权分的获得可以参考本发明前述的抢购商品品类规划方法实施例的说明，此处不再赘述。
步骤S902，统计商品连续a天的上线次数；
步骤S903，根据成交爆发力、品类加权分和上线次数，计算商品的疲劳度。
其中，商品的疲劳度可以使用如下的公式计算：
商品的疲劳度＝成交爆发力分值*(1+品类加权分值)/连续a天上线次数，其中，符号“*”表示乘运算，“/”表示除以运算。
这样，可以使得抢购平台中上过线的商品更不容易上线，从而提高抢购平台的商品更新频率，给用户更加强烈的“抢”购感觉，从而提高用户的购买意愿，进而提高抢购平台的经济效益。
本发明实施例的抢购商品疲劳度的评价方法，使得抢购平台中上过线的商品更不容易上线，从而提高抢购平台的商品更新频率，给用户更加强烈的“抢”购感觉，从而提高用户的购买意愿，进而提高抢购平台的经济效益。
为了实现上述抢购商品疲劳度的评价方法实施例中各步骤及方法，本发明实施例还提供了抢购商品疲劳度的评价装置实施例。
图10为本发明实施例中抢购商品疲劳度的评价装置的结构框图。如图10所示，本实施例中，抢购商品疲劳度的评价装置1000可以包括第二获取模块1010、次数统计模块1020和疲劳度计算模块1030。
其中，第二获取模块1010用于获取商品的成交爆发力和品类加权分。次数统计模块1020用于统计商品连续a天的上线次数。疲劳度计算模块1030用于根据成交爆发力、品类加权分和上线次数，计算商品的疲劳度。
其中，成交爆发力可以等于预测的商品的成交量与商品的单价的乘积。
其中，品类加权分可以等于商品去年同期在其所属类目全部商品中的成交额百分比。
由于本实施例中的抢购商品疲劳度的评价装置能够执行前述的抢购商品疲劳度的评价方法，因此本实施例未详细描述的部分，可参考对前述抢购商品疲劳度的评价方法实施例的相关说明。
本发明实施例的抢购商品疲劳度的评价装置，使得抢购平台中上过线的商品更不容易上线，从而提高抢购平台的商品更新频率，给用户更加强烈的“抢”购感觉，从而提高用户的购买意愿，进而提高抢购平台的经济效益。
实施例五
图11为本发明实施例五中抢购平台商品的优化投放方法的流程图。如图11所示，本实施例中，抢购平台商品的优化投放方法可以包括如下步骤：
步骤S1101，确定本时间段上线的商品；
其中，本时间段上线的商品可以通过本发明前述实施例(实施例二)中的抢购商品上线时间段的评价方法来确定。例如，可以将总成交额最大的时间段确定为待评价商品的上线时间段。这样，在本时间段成交额最大的所有商品就是步骤S1101确定的本时间段上线的商品。
步骤S1102，获取商品的疲劳度；
其中，可以通过本发明前述实施例(实施例四)中的抢购商品疲劳度的评价方法来获取商品的疲劳度。此处不再赘述。
步骤S1103，在本时间段按照疲劳度降序顺序将商品投放到抢购平台。
根据前述实施例中对疲劳度的说明可知，商品连续a天上线的次数越多，疲劳度越小，那么按照疲劳度降序顺序排列的话商品的上线排位就越靠后。因此，按照疲劳度降序顺序将商品投放到抢购平台，使得抢购平台中上过线的商品更不容易上线，从而提高抢购平台的商品更新频率，给用户更加强烈的“抢”购感觉，从而提高用户的购买意愿，进而提高抢购平台的经济效益。
本发明实施例的抢购平台商品的优化投放方法，按照疲劳度降序顺序将商品投放到抢购平台，使得抢购平台中上过线的商品更不容易上线，从而提高抢购平台的商品更新频率，给用户更加强烈的“抢”购感觉，从而提高用户的购买意愿，进而提高抢购平台的经济效益。
为了实现上述抢购平台商品的优化投放方法实施例中各步骤及方法，本发明实施例还提供了抢购平台商品的优化投放装置实施例。
图12为本发明实施例中抢购平台商品的优化投放装置的结构框图。如图12所示，本实施例中，抢购平台商品的优化投放装置可以包括确定模块1210、第三获取模块1220和投放模块1230。确定模块1210、第三获取模块1220和投放模块1230可以顺次相连。
其中，确定模块1210用于确定本时间段上线的商品。第三获取模块1220用于获取确定模块1210确定的商品的疲劳度。投放模块1230用于在本时间段按照疲劳度降序顺序将确定模块1210确定的商品投放到抢购平 台。
由于本实施例中的抢购平台商品的优化投放装置能够执行前述的抢购平台商品的优化投放方法，因此本实施例未详细描述的部分，可参考对前述抢购平台商品的优化投放方法实施例的相关说明。
本发明实施例的抢购平台商品的优化投放装置，按照疲劳度降序顺序将商品投放到抢购平台，使得抢购平台中上过线的商品更不容易上线，从而提高抢购平台的商品更新频率，给用户更加强烈的“抢”购感觉，从而提高用户的购买意愿，进而提高抢购平台的经济效益。
实施例六
图13为本发明实施例六中抢购平台商品上线方法的流程图。如图13所示，本实施例中，抢购平台商品上线方法可以包括如下步骤：
步骤S1301，监测抢购商品的下线触发条件；
其中，下线触发条件可以为：当前时刻为抢购商品所在上线时间段的结束时刻。例如，假设抢购商品的上线时间段为11：00-12:00，那么12:00这个时刻就是抢购商品上线时间段的结束时刻，这个时刻一到，就会触发抢购商品下线。
其中，下线触发条件可以为：抢购商品的在线时长达到设定时长。例如，仍然假设抢购商品的上线时间段为11：00-12:00，但是规定抢购商品的在线时长最长为10分钟。假设抢购商品的上线时刻为11：20，当前时刻为11：30，虽然当前时刻不是抢购商品上线时间段的结束时刻，但是由于抢购商品的在线时长已经达到了规定的时长(10分钟)，因此也会触发抢购商品下线。
其中，下线触发条件可以为：抢购商品已售完。例如，仍然假设抢购商品的上线时间段为11：00-12:00，而且规定抢购商品的在线时长最长为10分钟。假设抢购商品的上线时刻为11：20，当前时刻为11：25，虽然当前时刻不是抢购商品上线时间段的结束时刻，而且抢购商品的在线时长也没有达到规定的时长(10分钟)，但是由于抢购商品已售完，所以仍然会触发抢购商品下线。
在设置多个下线触发条件的情况下，只要满足其中一个下线触发条件，就会触发相应的抢购商品自动下线。
这些下线触发条件的设置，使得抢购商品能够自动下线，不需要人工操作，因此大大提高了平台的自动化程度，节约了人力资源和成本。
步骤S1302，在监测到下线触发条件的情况下，将对应抢购商品下线；
步骤S1303，将抢购商品的下线信息发送给服务器；
服务器接收到抢购商品的下线信息后，会根据按照上线顺序排列好的商品列表向平台发送待上线的推荐商品。
步骤S1304，接收该服务器发送的推荐商品，并在抢购平台上上线该推荐商品。
其中，推荐商品可以是本时间段的商品列表中当前排位在最前面的商品，商品列表可以是按照疲劳度降序排列的商品列表。
商品列表不仅表明了在什么时间段上线哪些类目的商品，还指出了在同一时间段上线的各个商品的上线顺序，因此，根据商品列表，服务器可以在对应的时间段自动推荐上线相应的商品。这种方式实现了“抢购”商品的自动上线及更新，不需要人工干预，因此可以大大提高“抢购”系统的运营效率，同时还可以大大提高“抢购”商品的更新频率，给用户更加强烈的“抢购”的感觉。
下面通过一个示例说明根据本发明实施例中的抢购平台商品上线方法，商品的上线场景。
假设抢购系统的展位有10个，预设时长为10分钟，有100个商品的优选上线时间段为时间段10：00-11:00，商品编号分别1至100，同时，该时间段10：00-11:00的商品列表的排列顺序也是由1至100，商品1至10这10个排在最前面的商品首先上线，各自占据1个展位。假设在10：10这个时刻，商品1至10这10个商品都没有售完，那么由于商品1至10这10个商品的上线时间已经达到10分钟，因此商品1至10自动下线，此时商品列表中排位最前的未上线商品为商品11至20，商品11至20在10：10这个时刻自动上线。假设在10：05这个时刻，商品1售完，商品 1自动下线，由于商品2至10没有售完并且上线时间已经未达到10分钟，所以商品2至10仍然处于上线状态，此时商品列表中排位最前的未上线商品为商品11，因此商品11在原商品1的展位上自动上线。
本发明实施例的抢购平台商品上线方法，实现了商品的自动上线及更新，提高了购物系统的运营效率，而且由于不需要人工干预，还节省了人工费用，降低了运营成本。同时，由于本发明实施例的抢购平台商品上线方法中，商品是自动上线和更新的，因此商品更新频率快，能够给用户更强烈的“抢购”感觉。再者，本发明实施例的抢购平台商品上线方法，按照商品的优选上线时间段安排商品上线，因此能够大幅度提高商品的成交额，取得较高的运营效益。
为了实现上述抢购平台商品上线方法实施例中各步骤及方法，本发明实施例还提供了抢购平台商品上线装置实施例。
图14为本发明实施例中抢购平台商品上线装置的结构框图。如图14所示，本实施例中，抢购平台商品上线装置1400可以包括监测模块1410、下线模块1420、发送模块1430和接收及上线模块1440。监测模块1410、下线模块1420、发送模块1430和接收及上线模块1440可以顺次相连。
其中，监测模块1410用于监测抢购商品的下线触发条件。下线模块1420用于在监测模块1410监测到下线触发条件的情况下，将对应抢购商品下线。发送模块1430用于将抢购商品的下线信息发送给服务器。接收及上线模块1440用于接收服务器发送的推荐商品，并在抢购平台上上线所述推荐商品。
其中，下线触发条件可以为：当前时刻为抢购商品所在上线时间段的结束时刻。
其中，下线触发条件还可以为：抢购商品的在线时长达到设定时长。
其中，下线触发条件还可以为：抢购商品已售完。
由于本实施例中的抢购平台商品上线装置能够执行前述的抢购平台商品上线方法，因此本实施例未详细描述的部分，可参考对前述抢购平台商品上线方法实施例的相关说明。
本发明实施例的抢购平台商品上线装置，实现了商品的自动上线及更新，提高了购物系统的运营效率，而且由于不需要人工干预，还节省了人工费用，降低了运营成本。同时，由于本发明实施例的抢购平台商品上线装置中，商品是自动上线和更新的，因此商品更新频率快，能够给用户更强烈的“抢购”感觉。再者，本发明实施例的抢购平台商品上线装置，按照商品的优选上线时间段安排商品上线，因此能够大幅度提高商品的成交额，取得较高的运营效益。
本发明实施例还提出了一种抢购系统，该抢购系统可以包括本发明前述实施例中的商品成交量预测装置、抢购商品上线时间段的评价装置、抢购商品品类规划装置、抢购商品疲劳度的评价装置、抢购平台商品的优化投放装置和抢购平台商品上线装置中的任意一种或多种或全部装置。
例如图15所示的抢购系统1500。图15为本发明实施例中抢购系统的一种结构框图。如图15所示，抢购系统1500可以包括抢购平台商品上线装置1400。由于该抢购系统包括抢购平台商品上线装置，因此，该抢购系统能够实现商品的自动上线及更新，提高了购物系统的运营效率，而且由于不需要人工干预，还节省了人工费用，降低了运营成本。同时，由于抢购平台商品上线装置中，商品是自动上线和更新的，因此该抢购系统商品更新频率快，能够给用户更强烈的“抢购”感觉。再者，由于抢购平台商品上线装置，按照商品的优选上线时间段安排商品上线，因此该抢购系统还能够大幅度提高商品的成交额，取得较高的运营效益。
以上所述仅为本发明的较佳实施例，并不用以限制本发明，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (36)

  1. 一种商品成交量预测方法，其特征在于，包括：
    获取商品的历史运营数据；
    基于所述历史运营数据得到成交量预测模型；
    根据所述成交量预测模型预测商品的未来成交量。
  2. 根据权利要求1所述的商品成交量预测方法，其特征在于，所述基于所述历史运营数据得到成交量预测模型包括：
    构建所述历史运营数据的特征集；
    利用所述特征集训练得到所述成交量预测模型。
  3. 根据权利要求1所述的商品成交量预测方法，其特征在于，所述根据所述成交量预测模型预测商品的未来成交量包括：
    根据所述成交量预测模型预测商品在未来的设定时间段的成交量。
  4. 一种商品成交量预测装置，其特征在于，包括：
    第一获取模块，用于获取商品的历史运营数据；
    模型取得模块，用于基于所述历史运营数据得到成交量预测模型；
    成交量预测模块，用于根据所述成交量预测模型预测商品的未来成交量。
  5. 根据权利要求4所述的商品成交量预测装置，其特征在于，所述模型取得模块包括：
    构建单元，用于构建所述历史运营数据的特征集；
    训练单元，用于利用所述构建单元构建的特征集训练得到所述成交量预测模型。
  6. 根据权利要求4所述的商品成交量预测装置，其特征在于，所述根据所述成交量预测模型预测商品的未来成交量包括：
    第一预测单元，用于根据所述成交量预测模型预测商品在未来的设定时间段的成交量。
  7. 一种抢购商品上线时间段的评价方法，其特征在于，包括：
    按照设定时间间隔将每天的抢购时间划分为多个时间段；
    统计待评价商品在设定历史时期内各个时间段的总成交额；
    根据统计结果确定所述待评价商品的上线时间段。
  8. 根据权利要求7所述的抢购商品上线时间段的评价方法，其特征在于，所述根据统计结果确定所述待评价商品的上线时间段，包括：
    将总成交额最大的前设定数目个时间段确定为所述待评价商品的上线时间段。
  9. 根据权利要求7所述的抢购商品上线时间段的评价方法，其特征在于，所述设定时间间隔为一小时。
  10. 一种抢购商品上线时间段的评价装置，其特征在于，包括：
    时段划分模块，用于按照设定时间间隔将每天的抢购时间划分为多个时间段；
    成交统计模块，用于统计待评价商品在设定历史时期内各个时间段的总成交额；
    上线时段确定模块，用于根据所述成交统计模块的统计结果确定所述待评价商品的上线时间段。
  11. 根据权利要求10所述的抢购商品上线时间段的评价装置，其特征在于，所述上线时段确定模块包括：
    第一确定单元，用于将总成交额最大的前设定数目个时间段确定为所述待评价商品的上线时间段。
  12. 根据权利要求10所述的抢购商品上线时间段的评价方法，其特征在于，所述设定时间间隔为一小时。
  13. 一种抢购商品疲劳度的评价方法，其特征在于，包括：
    获取商品的成交爆发力和品类加权分；
    统计所述商品连续a天的上线次数；
    根据所述成交爆发力、品类加权分和上线次数，计算所述商品的疲劳 度。
  14. 根据权利要求13所述的抢购商品疲劳度的评价方法，其特征在于，所述成交爆发力等于预测的所述商品的成交量与所述商品的单价的乘积。
  15. 根据权利要求13所述的抢购商品疲劳度的评价方法，其特征在于，所述品类加权分等于所述商品去年同期在其所属类目全部商品中的成交额百分比。
  16. 一种抢购商品疲劳度的评价装置，其特征在于，包括：
    第二获取模块，用于获取商品的成交爆发力和品类加权分；
    次数统计模块，用于统计所述商品连续a天的上线次数；
    疲劳度计算模块，用于根据所述成交爆发力、品类加权分和上线次数，计算所述商品的疲劳度。
  17. 根据权利要求16所述的抢购商品疲劳度的评价装置，其特征在于，所述成交爆发力等于预测的所述商品的成交量与所述商品的单价的乘积。
  18. 根据权利要求16所述的抢购商品疲劳度的评价装置，其特征在于，所述品类加权分等于所述商品去年同期在其所属类目全部商品中的成交额百分比。
  19. 一种抢购平台商品的优化投放方法，其特征在于，包括：
    确定本时间段上线的商品；
    获取所述商品的疲劳度；
    在本时间段按照疲劳度降序顺序将所述商品投放到抢购平台。
  20. 一种抢购平台商品的优化投放装置，其特征在于，包括：
    确定模块，用于确定本时间段上线的商品；
    第三获取模块，用于获取所述确定模块确定的商品的疲劳度；
    投放模块，用于在本时间段按照疲劳度降序顺序将所述确定模块确定的商品投放到抢购平台。
  21. 一种抢购平台商品上线方法，其特征在于，包括：
    监测抢购商品的下线触发条件；
    在监测到所述下线触发条件的情况下，将对应抢购商品下线；
    将所述抢购商品的下线信息发送给服务器；
    接收所述服务器发送的推荐商品，并在抢购平台上上线所述推荐商品。
  22. 根据权利要求21所述的抢购平台商品上线方法，其特征在于，所述下线触发条件为当前时刻为所述抢购商品所在上线时间段的结束时刻。
  23. 根据权利要求21所述的抢购平台商品上线方法，其特征在于，所述下线触发条件为所述抢购商品的在线时长达到设定时长。
  24. 根据权利要求21所述的抢购平台商品上线方法，其特征在于，所述下线触发条件为所述抢购商品已售完。
  25. 一种抢购平台商品上线装置，其特征在于，包括：
    监测模块，用于监测抢购商品的下线触发条件；
    下线模块，用于在所述监测模块监测到所述下线触发条件的情况下，将对应抢购商品下线；
    发送模块，用于将所述抢购商品的下线信息发送给服务器；
    接收及上线模块，用于接收所述服务器发送的推荐商品，并在抢购平台上上线所述推荐商品。
  26. 根据权利要求25所述的抢购平台商品上线装置，其特征在于，所述下线触发条件为当前时刻为所述抢购商品所在上线时间段的结束时刻。
  27. 根据权利要求25所述的抢购平台商品上线装置，其特征在于，所述下线触发条件为所述抢购商品的在线时长达到设定时长。
  28. 根据权利要求25所述的抢购平台商品上线装置，其特征在于，所述下线触发条件为所述抢购商品已售完。
  29. 一种抢购商品品类规划方法，其特征在于，包括：
    获取目标商品在历史同期的成交额，记为第一成交额，以及获取所述目标商品所属类目的全部商品在所述历史同期的成交总额，记为第二成交额；
    根据所述第一成交额和所述第二成交额计算所述目标商品的品类加权分。
  30. 一种抢购商品品类规划装置，其特征在于，包括：
    成交额获取模块，用于获取目标商品在历史同期的成交额，记为第一成交额，以及获取所述目标商品所属类目的全部商品在所述历史同期的成交总额，记为第二成交额；
    计算模块，用于根据所述第一成交额和所述第二成交额计算所述目标商品的品类加权分。
  31. 一种抢购系统，其特征在于，包括权利要求4至6任一项所述的商品成交量预测装置。
  32. 一种抢购系统，其特征在于，包括权利要求10至12任一项所述的抢购商品上线时间段的评价装置。
  33. 一种抢购系统，其特征在于，包括权利要求16至18任一项所述的抢购商品疲劳度的评价装置。
  34. 一种抢购系统，其特征在于，包括权利要求20所述的抢购平台商品的优化投放装置。
  35. 一种抢购系统，其特征在于，包括权利要求25至28任一项所述的抢购平台商品上线装置。
  36. 一种抢购系统，其特征在于，包括权利要求30所述的抢购商品品类规划装置。
PCT/CN2017/077301 2016-03-29 2017-03-20 抢购平台商品上线方法、装置及抢购系统 WO2017167054A1 (zh)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111275479A (zh) * 2020-01-07 2020-06-12 北京爱笔科技有限公司 一种人流量预测方法、装置及系统
CN115115416A (zh) * 2022-07-21 2022-09-27 湖南大学 一种商品销量预测方法
CN116402457A (zh) * 2023-03-27 2023-07-07 深圳市丰宜科技有限公司 一种运营管理系统及其管理方法

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113656681B (zh) * 2021-07-08 2023-08-11 北京奇艺世纪科技有限公司 一种对象评价方法、装置、设备及存储介质
CN114580903A (zh) * 2022-03-04 2022-06-03 长沙图灵教育科技有限公司 一种电商仿真实训平台

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1996297A (zh) * 2006-11-08 2007-07-11 胡增宝 利用检索系统发布供求信息并按市场占有率排序的方法
CN102024224A (zh) * 2009-09-11 2011-04-20 阿里巴巴集团控股有限公司 实现商品最优时间上架和/或下架的电子商务系统及方法
JP2012027757A (ja) * 2010-07-26 2012-02-09 Dainippon Printing Co Ltd 商品推薦装置、商品推薦システム、商品推薦方法、及びプログラム
CN103177371A (zh) * 2011-12-21 2013-06-26 阿里巴巴集团控股有限公司 一种信息展示方法和装置
CN103632289A (zh) * 2013-10-17 2014-03-12 常熟市华安电子工程有限公司 一种商品自动上下架工具
CN104820938A (zh) * 2015-05-15 2015-08-05 南京大学 一种季节性、周期性商品的最优订购周期预测方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102968670B (zh) * 2012-10-23 2016-08-17 北京京东世纪贸易有限公司 预测数据的方法和装置
CN104517224B (zh) * 2014-12-22 2017-09-29 浙江工业大学 一种网络热销商品的预测方法及系统
CN104915734B (zh) * 2015-06-25 2017-03-22 深圳市腾讯计算机系统有限公司 基于时间序列的商品热度预测方法和系统

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1996297A (zh) * 2006-11-08 2007-07-11 胡增宝 利用检索系统发布供求信息并按市场占有率排序的方法
CN102024224A (zh) * 2009-09-11 2011-04-20 阿里巴巴集团控股有限公司 实现商品最优时间上架和/或下架的电子商务系统及方法
JP2012027757A (ja) * 2010-07-26 2012-02-09 Dainippon Printing Co Ltd 商品推薦装置、商品推薦システム、商品推薦方法、及びプログラム
CN103177371A (zh) * 2011-12-21 2013-06-26 阿里巴巴集团控股有限公司 一种信息展示方法和装置
CN103632289A (zh) * 2013-10-17 2014-03-12 常熟市华安电子工程有限公司 一种商品自动上下架工具
CN104820938A (zh) * 2015-05-15 2015-08-05 南京大学 一种季节性、周期性商品的最优订购周期预测方法

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111275479A (zh) * 2020-01-07 2020-06-12 北京爱笔科技有限公司 一种人流量预测方法、装置及系统
CN111275479B (zh) * 2020-01-07 2023-11-10 北京爱笔科技有限公司 一种人流量预测方法、装置及系统
CN115115416A (zh) * 2022-07-21 2022-09-27 湖南大学 一种商品销量预测方法
CN116402457A (zh) * 2023-03-27 2023-07-07 深圳市丰宜科技有限公司 一种运营管理系统及其管理方法

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