CN110675103A

CN110675103A - Goods distribution method, device, platform, computer equipment and storage medium

Info

Publication number: CN110675103A
Application number: CN201910772962.1A
Authority: CN
Inventors: 游海波; 毛小勇; 秦刚; 司孝波; 叶国华
Original assignee: Suning Cloud Computing Co Ltd
Current assignee: Suning Cloud Computing Co Ltd
Priority date: 2019-08-21
Filing date: 2019-08-21
Publication date: 2020-01-10

Abstract

The application relates to a goods distribution method, a goods distribution device, a goods distribution platform, computer equipment and a storage medium. The method comprises the following steps: reading attribute information of goods to be distributed from a database, wherein the goods to be distributed are goods with the browsing amount larger than a preset threshold value, and the browsing amount is determined according to the behavior track data of the goods browsed by a user counted by a big data platform; reading platform information of each platform for distributing goods to be distributed from a database; determining a goods distribution strategy according to the attribute information of goods to be distributed and the platform information of each platform; and sending the goods distribution strategy to a big data platform, calculating the goods distribution quantity of goods to be distributed by each platform according to the goods distribution strategy by the big data platform, and distributing the goods by each platform according to the received goods distribution quantity. According to the method, the goods distribution quantity is distributed to each platform, the goods quantity does not need to be distributed manually, and the problem that goods to be distributed are selected with low accuracy due to the fact that goods attribute information is not effectively judged by manual goods selection is avoided.

Description

Goods distribution method, device, platform, computer equipment and storage medium

Technical Field

The present application relates to the field of intelligent cargo distribution technologies, and in particular, to a cargo distribution method, apparatus, platform, computer device, and storage medium.

Background

With the development of internet technology, especially the rapid development of the e-commerce industry, goods sales platforms and sales channels are more and more, the stock sales management modes and platform marketing means of each sales platform are more and more diversified, and the types of goods operated by large-scale merchants reach the tens of millions of levels. Therefore, goods sales have not been satisfied with single platform sales, and merchants need to distribute numerous goods to multiple sales platforms for sales to improve overall sales performance. At present, the method is adopted to select goods to be distributed from a plurality of goods according to personal experience, which is called manual goods selection, then manually distribute the goods to be distributed to each sales platform for sales according to a certain proportion, and export goods inventory and goods sales related data from a plurality of systems to an EXCEL table to calculate the acquired quantity distributed to each sales platform, and then import the obtained quantity into a distribution system for goods distribution by the distribution system.

In the above goods distribution mode, the workload of manual goods selection is huge, the optimal goods cannot be accurately identified (generally, the goods with large browsing amount and high attention of users need to be sold in a goods distribution channel), and the attribute information of the goods is not effectively judged, so that the goods distribution cannot meet the current actual demand, faults exist among systems, the informatization integration level is low, and the information circulation is not smooth. When goods sales platforms are more and more, goods are more and more in types, and sales scales are larger and larger, manual distribution control of goods distribution amount of each platform and effective judgment on attribute information of goods are lacked, timeliness and rationality of goods distribution at each time are difficult to guarantee, and therefore the operational benefits of merchants to the goods are influenced.

Disclosure of Invention

In view of the above, it is necessary to provide a cargo distribution method, apparatus, platform, computer device and storage medium for automatically distributing the quantity of distributed cargo to each platform, without manually distributing the selected cargo and the quantity of distributed cargo, so as to improve the accuracy and timeliness of cargo distribution and distribution.

A method of distributing goods, the method comprising:

reading attribute information of goods to be distributed from a database, wherein the goods to be distributed are goods with the browsing amount larger than a preset threshold value, and the browsing amount is determined according to the behavior track data of the goods browsed by a user counted by a big data platform;

reading platform information of each platform for distributing goods to be distributed from a database;

determining a goods distribution strategy according to the attribute information of goods to be distributed and the platform information of each platform;

and sending the goods distribution strategy to a big data platform, calculating the goods distribution quantity of goods to be distributed by each platform according to the goods distribution strategy by the big data platform, and distributing the goods by each platform according to the received goods distribution quantity.

In one embodiment, determining a distribution strategy according to the attribute information of the goods to be distributed and the platform information of each platform includes:

extracting historical sales of goods to be distributed on each platform and actual goods sales in a warehouse through a big data platform according to the attribute information of the goods to be distributed and the platform information of each platform;

screening out target goods distribution rules from preset goods distribution rules according to historical sales volumes of all platforms and actual goods sales numbers in a warehouse;

and determining a goods distribution strategy according to the target goods distribution rule.

In one embodiment, the method for screening out the target goods sorting rule from the preset goods sorting rules according to the historical sales volume and the real goods saleable number in the warehouse of each platform comprises the following steps:

and screening out a target goods distribution rule from the preset goods distribution rules according to the historical sales volume and the actual goods sales number in the warehouse of each platform and the platform priority of each platform in the preset goods distribution rules.

extracting sales forecasts of goods to be distributed on each platform through a big data platform according to the attribute information of the goods to be distributed and the platform information of each platform;

screening a target goods sorting rule from preset goods sorting rules according to the sales forecast quantity of each platform;

In one embodiment, the step of screening out the target goods sorting rule from the preset goods sorting rules according to the sales forecast amount of each platform comprises the following steps:

and screening out a target goods sorting rule from the preset goods sorting rules according to the sales forecast of each platform and the platform priority of each platform in the preset goods sorting rules.

In one embodiment, the calculating, by the big data platform, the distribution quantity of the goods to be distributed by each platform according to the distribution strategy includes:

reading first target data matched with attribute information of goods to be distributed from a database by a big data platform according to a distribution strategy, and reading second target data matched with platform information of each platform; and calculating the distribution quantity of the goods to be distributed by each platform according to the first target data, the second target data and the distribution strategy.

A cargo distribution apparatus, the apparatus comprising:

the system comprises a first acquisition module, a second acquisition module and a data processing module, wherein the first acquisition module is used for reading attribute information of goods to be distributed from a database, the goods to be distributed are goods with browsing quantity larger than a preset threshold value, and the browsing quantity is determined according to behavior track data of goods browsed by a user counted by a big data platform;

the second acquisition module is used for reading platform information of each platform for distributing goods to be distributed from the database;

the determining module is used for determining a goods distribution strategy according to the attribute information of goods to be distributed and the platform information of each platform;

and the distribution module is used for sending the goods distribution strategy to the big data platform so as to calculate the goods distribution quantity of the goods to be distributed by each platform according to the goods distribution strategy by the big data platform, and each platform distributes the goods according to the received goods distribution quantity.

A cargo distribution platform, the platform comprising:

the goods distribution management system is used for reading the attribute information of goods to be distributed from the database, the goods to be distributed are goods with the browsing amount larger than a preset threshold value, and the browsing amount is determined according to the user browsing goods behavior track data counted by the big data platform; reading platform information of each platform for distributing goods to be distributed from a database; determining a goods distribution strategy according to the attribute information of goods to be distributed and the platform information of each platform; sending the goods distribution strategy to a big data platform, calculating the goods distribution quantity of goods to be distributed by each platform according to the goods distribution strategy by the big data platform, and distributing the goods by each platform according to the received goods distribution quantity;

the big data platform is used for reading attribute data matched with the attribute information of the goods to be distributed from the database, reading platform data matched with the platform information of each platform, and calculating the distribution quantity of the goods to be distributed of each platform according to the attribute data, the platform data and the received distribution strategy;

and the goods distribution system is used for receiving the distribution quantity of the goods to be distributed of each platform sent by the big data platform and sending the distribution quantity of the goods to be distributed of each platform to the corresponding platform.

A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the steps of the method of any of the above embodiments being performed by the processor when the computer program is executed.

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 of any of the above embodiments.

According to the goods distribution method, the goods distribution device, the goods distribution platform, the computer equipment and the storage medium, the goods distribution management system reads the attribute information of goods to be distributed and the platform information of each platform for distributing the goods to be distributed from the database, and determines the distribution strategy according to the attribute information of the goods to be distributed and the platform information of each platform. The goods to be distributed are goods with the browsing amount larger than a preset threshold value, and the browsing amount is determined according to the behavior track data of the goods browsed by the user counted by the big data platform. Furthermore, the big data platform calculates the distribution quantity of the goods to be distributed of each platform according to the distribution strategy, and distributes the distribution quantity to each platform, so that each platform can distribute the goods according to the received distribution quantity of the goods to be distributed. Therefore, in the goods distribution method, the goods to be distributed are determined by the big data platform according to the goods browsing behavior track data of the user, the goods to be distributed do not need to be screened manually, the problem that due to the fact that the attribute information of the goods is not effectively judged in the manual goods selection process, the accuracy of the selected goods to be distributed is low is avoided, and the accuracy of goods distribution and distribution is improved. In addition, in the goods distribution method, the big data platform reads the attribute information of goods to be distributed and the platform information of each platform from the database to determine the distribution quantity, distributes the distribution quantity to each platform, realizes automatic distribution of the distribution quantity to each platform, and distributes goods according to the received distribution quantity by each platform, so that the timeliness of goods distribution and distribution is improved.

Drawings

FIG. 1 is a diagram of an application environment of a cargo distribution platform method in one embodiment;

FIG. 2 is a schematic flow chart diagram of a method for distributing goods in one embodiment;

FIG. 3 is a flowchart illustrating step S300 according to an embodiment;

FIG. 4 is a flowchart illustrating step S300 according to another embodiment;

FIG. 5 is a block diagram of a cargo distribution apparatus according to one embodiment;

FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

The goods distribution method provided by the application can be applied to a goods distribution platform shown in fig. 1. As shown in fig. 1, the cargo distribution platform includes a cargo distribution management system 100, a big data platform 200, and a cargo distribution system 300.

The goods distribution management system 100 is used for reading attribute information of goods to be distributed from the database, wherein the goods to be distributed are goods with browsing volume larger than a preset threshold value, and the browsing volume is determined according to the user browsing goods behavior track data counted by the big data platform; reading platform information of each platform for distributing goods to be distributed from a database; determining a goods distribution strategy according to the attribute information of goods to be distributed and the platform information of each platform; and sending the goods distribution strategy to a big data platform, calculating the goods distribution quantity of goods to be distributed by each platform according to the goods distribution strategy by the big data platform, and distributing the goods by each platform according to the received goods distribution quantity.

In one embodiment, the cargo distribution management system 100 reads the attribute information of the cargo to be distributed from the database after initialization. The attribute information of the goods to be distributed comprises the category information, brand information and characteristic information of the goods. The goods to be distributed are goods with the browsing amount larger than a preset threshold value, and the browsing amount is determined according to the behavior track data of the goods browsed by the user counted by the big data platform. In particular, the goods to be distributed may be commodities. The goods distribution management system 100 acquires master data information of the goods from the database. The master data information of the goods includes the category, brand, and goods themselves. The main data information of the commodity is generally derived from a commodity main data system of a database. The goods distribution management system 100 is also used to read platform information of each platform for distributing goods to be distributed from the database. The platform information of each platform is manually maintained in the database in advance. The platform information of each platform comprises a platform code, a platform name and a platform goods distribution time. A platform is an implementation object that distributes goods. The cargo distribution management system 100 may also pre-store preset distribution rules. The preset goods distribution rule comprises a goods distribution rule code, a goods distribution rule name, a platform priority, a goods distribution frequency and a goods distribution reference method. Finally, the cargo distribution management system 100 determines a distribution strategy according to the attribute information of the cargo to be distributed and the platform information of each platform, and sends the distribution strategy to the big data platform 200, so that the big data platform 200 calculates the distribution quantity of the cargo to be distributed of each platform according to the distribution strategy. Specifically, the cargo distribution management system 100 determines a distribution strategy according to preset distribution rules, attribute information of the cargo to be distributed, and platform information of each platform. The cargo distribution management system 100 maintains and queries a distribution strategy determined by preset distribution rules, attribute information of the cargo to be distributed and platform information of each platform, and controls the distribution quantity of the cargo to be distributed of each platform according to the distribution strategy so as to control each platform to distribute the cargo. Therefore, the automation of goods distribution is realized, the goods are not required to be distributed manually, and the efficiency of goods distribution and distribution is improved.

In addition, the cargo distribution management system 100 is also used for executing other implementation steps in a cargo distribution method provided by the present application. Reference will be made in detail to the following description of a method of distributing goods, which will not be described in detail for the time being.

The big data platform 200 is configured to read attribute data matched with the attribute information of the goods to be distributed from the database, read platform data matched with the platform information of each platform, and calculate the distribution quantity of the goods to be distributed of each platform according to the attribute data, the platform data, and the received distribution strategy. Specifically, the behavior trace of the goods browsed by the user through the e-commerce sales platform is recorded in the big data platform 200, so that the big data platform 200 stores the behavior trace into the corresponding database. The user behavior track data reaches hundred million levels of data volume, so a big data platform 200 is adopted to be connected with a goods browsing behavior track data source of the user, data of a period of history is extracted, distributed parallel batch processing is carried out on the data in a memory, fast statistical analysis is carried out on goods browsing volume, and goods with browsing volume ranked n are automatically screened out and initialized to the goods distribution management system 100. Platform information participating in distribution of goods to be distributed is manually maintained in a database in advance, historical sales order data, sales forecast quantity and actual sales quantity of each sales platform are also stored in the database, the historical sales quantity data, the sales forecast quantity and the actual sales quantity of each sales platform can be rapidly extracted by adopting the big data platform 200, and a target distribution result is rapidly calculated in an internal memory by combining a distribution strategy. The big data platform 200 is used to provide data corresponding to various parameters in the goods distribution management system 100. For example, attribute data matched with attribute information of goods to be distributed, platform data matched with platform information of each platform, and the like, thereby providing data support for the goods distribution system 300.

The goods distribution system 300 is configured to receive the distribution quantity of the goods to be distributed of each platform sent by the big data platform 200, and send the distribution quantity of the goods to be distributed of each platform to the corresponding platform. In particular, the goods distribution system 300 includes a goods inventory management system. The goods inventory management system is used for receiving data output by the big data platform 200 and distribution information which is output by the goods distribution management system 100 and distributes goods to each platform, and a goods distribution result is generated. And the goods inventory management system executes the goods distribution result and issues the goods to each platform. The goods inventory management system synchronously pushes the goods distribution result to a fourth-level page of the third-party E-commerce sales platform for display through an Application Programming Interface (API) of the third-party E-commerce sales platform, so that the goods are distributed.

The goods distribution management system 100 includes one or more servers, the big data platform 200 includes one or more data computing devices, and the goods distribution system 300 includes one or more servers. That is, the cargo distribution management system 100 and the cargo distribution system 300 may be implemented by separate servers or a server cluster composed of a plurality of servers. The big data platform 200 is typically implemented with a database cluster.

In one embodiment, as shown in fig. 2, a method for distributing goods is provided, which is illustrated by applying the method to the goods distribution management system 100 in fig. 1, and includes the following steps:

s100, reading attribute information of goods to be distributed from a database, wherein the goods to be distributed are goods with the browsing amount larger than a preset threshold value, and the browsing amount is determined according to the behavior track data of the goods browsed by a user counted by a big data platform.

In the present embodiment, the goods to be distributed include various attribute information. The plurality of attribute information includes a category of the goods, a brand of the goods, and a property of the goods. The information level can be set by various attribute information of the goods to be distributed. If the category of the goods is the first grade, the brand of the goods is the second grade, and the goods characteristic of the goods is the third grade. The cargo distribution management system 100 distributes the cargo according to the information level of the attribute information of the cargo to be distributed. In particular, the goods to be distributed may be commodities. The attribute information of the commodity is commodity master data. The commodity master data includes categories, brands, commodities themselves, and the like. The range of the categories is the widest, such as mother and infant categories and mobile phone categories. The second category of brands is, say, the king brand, the hua brand. The range of the product is the minimum, for example, Huawei/HUAWEI P30 bright black 8GB +64 GB.

And determining the goods to be distributed by the big data platform according to the behavior track data of the goods browsed by the user. And when the browsing amount of the goods browsed by the user is larger than the preset threshold value, the goods are the goods to be distributed. Further, the attribute information of the goods to be distributed is stored in the database by the big data platform. Specifically, the behavior track of the user browsing the goods is analyzed and counted by adopting the big data platform 200, the goods with large browsing amount are the goods to be distributed, and the attribute information of the goods to be distributed is stored in the database.

And S200, reading platform information of each platform for distributing goods to be distributed from the database.

In the present embodiment, the goods distribution management system 100 distributes goods to be distributed to a plurality of platforms, each for distributing the goods to be distributed. Each platform is identified with platform information. The cargo distribution management system 100 can acquire platform information of each platform for distributing the cargo to be distributed. The platform information is manually maintained in advance and stored in a database. Specifically, when the goods to be distributed are commodities, each platform refers to a sales platform participating in distribution, and generally refers to an online sales platform, such as an easy-to-purchase platform, a skatecat platform, a current platform, and the like. The platform information also includes the time of the platform's shipment, i.e., the time point at which the shipment is performed. Further, the platform information may also include a platform code, a platform name, and the like.

S300, determining a goods distribution strategy according to the attribute information of the goods to be distributed and the platform information of each platform.

In this embodiment, the cargo distribution management system 100 determines a distribution strategy of the cargo to be distributed according to the attribute information of the cargo to be distributed and the platform information of each platform. Specifically, the attribute information of the goods to be distributed includes category information of the goods. The cargo distribution management system 100 determines a distribution strategy of the cargo to be distributed according to the category information of the cargo and the platform information of each platform.

In one embodiment, the cargo distribution management system 100 stores preset distribution rules in advance. The cargo distribution management system 100 determines a distribution strategy of the cargo to be distributed according to preset distribution rules, the attribute information of the cargo to be distributed and the platform information of each platform.

Specifically, the preset goods distribution rule comprises a rule code, a code name, a platform priority, a goods distribution frequency and a goods distribution reference method. Wherein, the platform priority refers to the distribution of which platform is guaranteed preferentially. The distribution frequency refers to a time interval between two distribution of goods performed, for example, 24 hours 1, 12 hours 1, 2 hours 1. The reference method for goods distribution includes calculating the amount of goods distributed according to the historical sales amount of goods in a period of time, calculating the amount of goods based on the sales forecast amount of goods in a period of time, and the like. The period of time may be 7 days, 30 days, etc. in days, and the number of each platform shipment is conventionally calculated based on historical sales. When a certain goods, such as a commodity, has a sales promotion activity for a certain sales platform in a future period of time, the calculation based on the historical sales amount is inaccurate, and the calculation based on the sales platform predicted sales amount is performed.

The distribution rules are explained by an example: for example, if there are two platforms, the easy purchase is the owned platform, and the kitten is the non-owned platform. The purchase is preferred, the goods are distributed based on the average sales volume of 30 days in history, and the distribution frequency is 24 hours and 1 time. If the actual goods sales number of the goods is larger than or equal to the sum of the daily average sales of the goods easy to purchase and the daily average sales of the skatecat history, the reserved goods number of the skatecat is equal to the daily average sales of the skatecat history, and the left goods are reserved stocks easy to purchase. If the sold number of the goods is less than the sum of the daily average sale of the easy-to-buy history and the daily average sale of the skynohot history, the number of the skynohot goods is equal to the sold number of the goods minus the daily average sale of the easy-to-buy history, the reserved goods number of the skynohot here is not less than 0, and the reserved goods number of the skynohot minus the real goods number is the reserved stock of the easy-to-buy. At this time, the piece of sorting rule may be exemplified by performing the calculation every 24 hours.

Finally, the cargo distribution management system 100 determines a distribution strategy of the cargo to be distributed according to preset distribution rules, the attribute information of the cargo to be distributed and the platform information of each platform. Specifically, the attribute information of the goods to be distributed includes category information of the goods. The cargo distribution management system 100 determines a distribution strategy of the cargo to be distributed according to preset distribution rules, the category information of the cargo and the platform information of each platform.

In one embodiment, as shown in fig. 3, step S300 includes the following steps:

and S310, extracting the historical sales volume of the goods to be distributed on each platform and the actual sales number of the goods in the warehouse through the big data platform according to the attribute information of the goods to be distributed and the platform information of each platform.

S320, screening out target goods sorting rules from preset goods sorting rules according to historical sales of all platforms and actual goods sales in the warehouse.

S330, determining a goods distribution strategy according to the target goods distribution rule.

After the cargo distribution management system 100 distributes the distribution quantity of the cargo to be distributed to each platform, each platform performs distribution sales processing on the cargo to be distributed according to the received distribution quantity. In this embodiment, the cargo distribution management system 100 extracts the historical sales volume and the actual sales volume of the cargo to be distributed on each platform through the big data platform 200 according to the attribute information of the cargo to be distributed and the platform information of each platform. The historical sales amount may be sales amount of goods to be distributed on each platform within a preset time period from the current time point. And the in-store actual selling number may be an actual selling number of the goods to be distributed in the current each platform in the in-store state. Further, target goods sorting rules are screened out from preset goods sorting rules according to historical sales and actual goods sales in the warehouse of each platform. The preset goods distribution rules comprise a plurality of goods distribution rules, and each goods distribution rule corresponds to the historical sales volume of different goods to be distributed on different platforms. And screening a target goods distribution rule from the multiple goods distribution rules according to the attribute information of each goods to be distributed and the historical sales volume and the actual goods sales number in the warehouse determined by the platform information of each platform, and determining a goods distribution strategy according to the target goods distribution rule.

Therefore, the historical sales volume and the in-warehouse actual goods selling number of the goods to be distributed on each platform are determined according to the attribute information of the goods to be distributed and the platform information of each platform, the target distribution rule is screened out from a plurality of distribution rules of preset distribution rules according to the historical sales volume and the in-warehouse actual goods selling number, the distribution strategy is determined according to the target distribution rule, the target distribution rule determined by the method is more consistent with the distribution rule of the goods to be distributed, the distribution strategy determined according to the target distribution rule is more matched with the distribution mode of the goods to be distributed, and finally the goods distribution management system 100 distributes the goods according to the distribution strategy, so that the accurate distribution of the goods to be distributed can be improved.

In one embodiment, step S320 includes: and screening out a target goods distribution rule from the preset goods distribution rules according to the historical sales volume and the actual goods sales number in the warehouse of each platform and the platform priority of each platform in the preset goods distribution rules.

Specifically, platform priorities are carried out on each platform in the preset goods distribution rule. That is, when the historical sales volume and the actual sales number of each platform in the warehouse meet the same conditions, the target distribution rule can be screened out according to the platform priority of each platform in the preset distribution rule. The following are exemplified:

the goods to be distributed are commodities. When the actual goods saleable number of the goods can meet the sum of the historical daily average sales of each platform, the reserved goods distribution number of each platform is the historical daily average sales of the platform. At the moment, the target goods distribution rule reserves the goods distribution number for each platform, namely the historical daily average sales of the platform. When the actual commodity sales number of the commodity cannot meet the sum of the historical daily average sales of each platform, the platform priority of the platforms is distinguished, and the reserved commodity distribution number of each platform commodity is calculated according to the platform priority. And calculating the reserved goods distribution number of each platform commodity according to the platform priority, namely the target goods distribution rule. The target goods distribution rule is as follows:

when the platform priority of the platform is the same priority: the non-self platform commodity reserve number of the commodity is equal to the real commodity saleable number multiplied by the sum of the non-self platform historical daily average sales of the commodity/the historical daily average sales of each platform; the commodity reserve number of the own platform of the commodity is equal to the actual commodity saleable number multiplied by the sum of the historical daily average sales of the own platform of the commodity/the historical daily average sales of each platform.

The platform priority of the platform is that the self platform is prior: the non-self platform commodity reserve number of the commodity is the real commodity saleable number-self platform historical daily average sales. The distribution significance is achieved when the number of the reserved commodities of the non-owned platform is larger than 0, and when the number of the reserved commodities of the non-owned platform is smaller than or equal to 0, the number of the reserved commodities of the owned platform is the actual saleable number. When the commodity reservation number of the non-owned platform is larger than 0, the commodity reservation number of the owned platform is the historical daily average sale of the owned platform.

The platform priority of the platform is that the platform is not self-owned: and (4) the non-self platform commodity reservation number of the commodity is MIN. Wherein, MIN is the actual sold number of the historical daily average sale of the non-owned platform. At this time, the surplus behind the non-owned platform which meets the requirement of the commodity preferentially is the commodity reserved number of the owned platform. When the number of the non-owned platforms of the commodity is more than 1, the calculation is continued. And (4) sorting the goods according to the non-self platform goods reservation number of the goods, calculating the goods reservation number of each platform by differentiating the platform priority, and randomly selecting a platform A to be sorted according to the sequence.

Same priority: the non-A platform commodity reservation number is the sum of the non-self platform commodity reservation number multiplied by the non-A platform historical daily average sales and/or the non-self platform historical daily average sales, and the A platform commodity reservation number is the sum of the non-self platform commodity reservation number multiplied by the A platform historical daily average sales/the non-self platform historical daily average sales.

The A platform has priority, and the commodity reservation number of the non-A platform is equal to the commodity reservation number of the non-self platform, namely the historical date of the A platform is even sold. The non-A platform commodity reservation number is greater than 0, so that distribution significance is achieved, when the non-A platform commodity reservation number is less than or equal to 0, the A platform commodity reservation number is the non-A platform commodity reservation number, and when the non-A platform commodity reservation number is greater than 0, the A platform commodity reservation number is the A platform historical daily average.

The non-A platform is preferred, the commodity reservation number of the non-A platform is MIN (the historical daily average sales of the non-A platform, the commodity reservation number of the non-A platform is not the commodity reservation number of the self-owned platform), and the surplus after the non-A platform is preferentially met is the commodity reservation number of the A platform.

When there are more than 1 non-A platforms, the above calculation is repeated until the last non-A platform to be distributed.

Based on the above embodiment, a specific implementation of the embodiment is given below:

the goods to be distributed are commodities. The following easy-to-purchase and non-easy-to-purchase are short for two platforms. And when the actual commodity sales number of the commodity is more than or equal to the sum of the historical daily average sales of all the platforms, the reserved number of the non-easily purchased commodity is equal to the historical daily average sales of the non-easily purchased commodity. The target goods distribution rule is that the reserved number of the non-easily purchased commodities is equal to the daily average sales of the non-easily purchased commodities. When the actual merchantable number of the commodity is less than the sum of the historical daily average sales of each platform, the platform is prioritized to calculate in the case of insufficient goods (the target distribution rule refers to the following description):

1. same priority among platform priorities:

the reserved number of the non-easily bought commodities is the ratio of the historical daily average sales of the non-easily bought commodities divided by the sum of the historical daily average sales of each platform multiplied by the actual commodity sales number, namely the commodity is distributed according to the historical daily average sales ratio of the platforms, and the reserved number of the easily bought commodities is equal to the actual commodity sales number minus the reserved number of the non-easily bought commodities.

2. The platform priority is easy to purchase and has priority:

the reserved number of the non-purchase commodities is equal to the sold number of the real commodities minus the daily average sale of the purchase history. Note that the reserved number of non-purchasable items is greater than 0 before they can be distributed.

And when the reservation number of the non-easily purchased commodities is more than 0, the reservation number of the easily purchased commodities is equal to the daily average sales of the easily purchased histories.

When the reserved number of the non-purchasable items is equal to 0, the reserved number of the purchasable items is equal to the available number of the real items.

3. The priority of the platform is that the platform is not easy to purchase:

the average daily sales of the non-easy-to-buy history may be larger than the current actual quantity, and if the average daily sales of the non-easy-to-buy history is directly taken, the actual quantity is not enough to be distributed, so the reserved quantity of the non-easy-to-buy commodities is a small value of the average daily sales of the non-easy-to-buy history and the actual quantity, and the reserved quantity of the easy-to-buy commodities is equal to the actual quantity minus the reserved quantity of the non-easy-to-buy commodities.

In one embodiment, as shown in fig. 4, step S300 includes the following steps:

and S340, extracting the sales forecast of the goods to be distributed on each platform through the big data platform according to the attribute information of the goods to be distributed and the platform information of each platform.

And S350, screening out a target goods sorting rule from preset goods sorting rules according to the sales forecast of each platform.

And S360, determining a goods distribution strategy according to the target goods distribution rule.

After the cargo distribution management system 100 distributes the distribution quantity of the to-be-distributed cargo to each platform, each platform performs distribution sale processing on the to-be-distributed cargo according to the received distribution quantity. In this embodiment, the goods distribution management system 100 extracts the sales forecast amount of the goods to be distributed on each platform through the big data platform according to the attribute information of the goods to be distributed and the platform information of each platform. The sales forecast amount may be a sales forecast amount assigned to each platform, which is set in advance by the goods distribution management system 100. Further, target goods sorting rules are screened out from preset goods sorting rules according to the sales forecast of each platform. The preset goods distribution rule comprises a plurality of goods distribution rules, and each goods distribution rule corresponds to different sales forecasts of goods to be distributed on different platforms. And screening a target goods sorting rule from the multiple goods sorting rules according to the attribute information of each goods to be sorted and the sales forecast determined by the platform information of each platform, and determining a goods sorting strategy according to the target goods sorting rule.

Therefore, the sales forecast of the goods to be distributed on each platform is determined according to the attribute information of the goods to be distributed and the platform information of each platform, the target goods distribution rule is screened out from a plurality of goods distribution rules of the preset goods distribution rule according to the sales forecast, the goods distribution strategy is determined according to the target goods distribution rule, the target goods distribution rule determined by the method is more consistent with the goods distribution rule of the goods to be distributed, the goods distribution strategy determined according to the target goods distribution rule is more matched with the distribution mode of the goods to be distributed, and finally the goods distribution management system 100 distributes the goods according to the goods distribution strategy, so that the accurate distribution of the goods can be improved.

In one embodiment, step S350 includes: and screening out a target goods sorting rule from the preset goods sorting rules according to the sales forecast of each platform and the platform priority of each platform in the preset goods sorting rules.

Specifically, platform priorities are carried out on each platform in the preset goods distribution rule. That is, when the sales forecast of each platform meets the same condition, the target distribution rule can be screened out according to the platform priority of each platform in the preset distribution rule. The following are exemplified:

the goods to be distributed are commodities. And when the actual sold quantity of the commodity can meet the sum of the average daily sales forecast of each platform, reserving the branch quantity of each platform, namely the average daily sales forecast. The target goods distribution rule reserves the goods distribution number for each platform, namely the daily average sales forecast. And when the actual merchantable number of the commodity cannot meet the sum of the daily average sales forecast of each platform, the priority of the platform is distinguished to calculate the reserved goods distribution number of each platform.

The priority of the platform is the same priority: the non-self platform commodity reserve number of the commodity is the sum of the actual commodity saleable number multiplied by the non-self platform daily average sales forecast/each platform daily average sales forecast. The commodity reserve number of the own platform is the sum of the actual commodity sales number multiplied by the daily average sales forecast of the own platform/the daily average sales forecast of each platform.

The priority of the platform is that the own platform is prior: the non-self platform commodity reserve number of the commodity is the real commodity saleable number-self platform daily average sales forecast. The commodity allocation significance is achieved only when the commodity reservation number of the non-owned platform is larger than 0, when the commodity reservation number of the non-owned platform is smaller than or equal to 0, the commodity reservation number of the owned platform of the commodity is the real commodity saleable number, and when the commodity reservation number of the non-owned platform is larger than 0, the commodity reservation number of the owned platform is the daily average sales prediction of the owned platform.

The priority of the platform is that the platform is not owned: the non-self commodity reservation number of the commodity is MIN. MIN is the predicted actual merchantable number of non-owned average sales. And the surplus after the non-owned platform is preferentially met is the commodity reserved number of the owned platform.

When the number of the non-owned platforms of the commodities is more than 1, the calculation is continued downwards, the commodity reserves of the platforms are calculated according to the priorities by sorting the commodities of the non-owned platforms of the commodities, and a platform A to be sorted is randomly selected in sequence.

And the non-A platform commodity reservation number is the sum of the non-self platform commodity reservation number multiplied by the non-A platform daily average sales forecast and/or the non-self platform daily average sales forecast, and the A platform commodity reservation number is the sum of the non-self platform commodity reservation number multiplied by the A platform daily average sales forecast/the non-self platform daily average sales forecast.

The A platform has priority, the non-A platform commodity reservation number is not the self platform commodity reservation number-A platform daily average sales forecast, the non-A platform commodity reservation number is larger than 0 and has distribution significance, when the non-A platform commodity reservation number is smaller than or equal to 0, the A platform commodity reservation number is the non-self platform commodity reservation number, and when the non-A platform commodity reservation number is larger than 0, the A platform commodity reservation number is the A platform daily average sales forecast.

The non-A platform has priority, the commodity reservation number of the non-A platform is MIN (the daily average sales prediction of the non-A platform is not the commodity reservation number of the self-owned platform), and the surplus behind the priority meeting the non-A platform is the commodity reservation number of the A platform.

the goods to be distributed are commodities. The following easy-to-purchase and non-easy-to-purchase are short for two platforms. And when the actual commodity sales number of the commodity is more than or equal to the sum of the daily average sales predictions of all the platforms, the non-purchase commodity reservation number is equal to the non-purchase daily average sales prediction. The target goods distribution rule is that the reserved number of the non-easily purchased commodities is equal to the daily average sales forecast of the non-easily purchased commodities. When the actual merchantable number of the commodity is less than the sum of the daily average sales forecasts of each platform, the platform is prioritized to calculate under the condition that the commodity is insufficient (the target distribution rule refers to the following description):

1. the platform priorities are the same:

the reserved number of the non-easy-to-buy commodities is the ratio of the non-easy-to-buy daily average sales forecast divided by the sum of the platform daily average sales forecasts multiplied by the real commodity saleable number, namely the reserved number is distributed according to the platform daily average sales forecast ratio. The reserved number of the easy-to-buy commodities is equal to the sold number minus the reserved number of the non-easy-to-buy commodities.

2. The platform priority is the easy purchase priority:

the reserved number of the non-easy-to-buy commodities is equal to the sold number minus the daily average sales prediction of the easy-to-buy commodities, and the reserved number of the non-easy-to-buy commodities is distributed only when the reserved number is larger than 0.

When the number of non-purchasable reservations is greater than 0, the number of reservations for purchasable items is equal to the easy-to-purchase daily average sales forecast.

When the number of non-purchasable reservations is equal to 0, the number of reservations of the purchasable items is equal to the real merchantable number of the items.

3. The platform priority is non-purchase priority:

the non-easy-to-buy daily average sales forecast is possibly larger than the actual sales of the current commodity, and if the actual sales forecast is directly taken, the actual sales are not distributed enough, so that the reserved number of the non-easy-to-buy commodities is a small value in the non-easy-to-buy daily average sales forecast and the actual sales, and the reserved number of the easy-to-buy commodities is equal to the actual sales minus the non-easy-to-buy reserved number.

In a specific embodiment, in any specific implementation manner of screening out a target goods sorting rule from preset goods sorting rules according to sales forecasts or historical sales of each platform and platform priorities of each platform in the preset goods sorting rules, when the number of non-easy-to-buy platforms is more than 1, the calculated number of non-easy-to-buy reservations can refer to the following manner:

1. the platform priorities are the same:

the non-A platform commodity reserve number is the proportion of the non-A platform historical daily average sales divided by the sum of the non-easy-to-purchase historical daily average sales multiplied by the non-easy-to-purchase reserve number, namely the non-A platform commodity reserve number is distributed according to the platform historical daily average sales ratio, and the A platform commodity reserve number is equal to the non-easy-to-purchase commodity reserve number minus the non-A platform commodity reserve number.

2. The platform priority is A platform priority:

the non-A platform commodity reservation number is equal to the non-easy-to-purchase commodity reservation number minus the historical daily average sales of the A platform, and the non-A platform commodity reservation number is distributed only when the non-A platform commodity reservation number is larger than 0.

And when the commodity reservation number of the non-A platform is greater than 0, the commodity reservation number of the A platform is equal to the historical daily average sales of the A platform.

When the non-A platform commodity reservation number is equal to 0, the A platform commodity reservation number is equal to the non-easy-to-buy reservation number.

3. The platform priority is non-A platform priority:

the historical daily average sales of the non-A platform is possibly larger than the non-A platform commodity reserve number, if the historical daily average sales of the non-A platform is directly taken, the non-A platform commodity reserve number is not enough to be distributed, so the non-A platform commodity reserve number is a small value of the non-A platform historical daily average sales and the non-A platform commodity reserve number, and the A platform commodity reserve number is equal to the non-A platform commodity reserve number minus the non-A platform commodity reserve number.

S400, sending the goods distribution strategy to a big data platform, calculating the goods distribution quantity of goods to be distributed by each platform according to the goods distribution strategy by the big data platform, and distributing the goods by each platform according to the received goods distribution quantity.

In the present embodiment, the goods distribution management system 100 transmits the distribution policy to the big data platform 200. When the big data platform 200 receives the goods distribution strategy, the goods distribution quantity of the goods to be distributed by each platform is calculated according to the goods distribution strategy, and the goods distribution quantity of the goods to be distributed is distributed to the goods distribution system 300, so that the goods distribution system distributes the goods distribution quantity of each platform. And each platform distributes goods according to the received goods distribution quantity. The goods distribution strategy is to screen out a target goods distribution rule from preset goods distribution rules according to the attribute information of goods to be distributed and the platform information of each platform, and generate a goods distribution strategy according to the target goods distribution rule. Specifically, the goods distribution strategy selects a corresponding goods distribution rule for a certain goods, and the goods are distributed to a corresponding platform according to the goods distribution rule. For example, the goods to be distributed are commodities. The commodity A needs to be distributed, and the target platform for distributing the commodity A is a self platform which is easy to purchase and a non-self platform Tianmao. The goods distribution rule is based on 30-day calendar history daily average sales, the goods are easily purchased and preferentially distributed once every 24 hours, and the number of sold goods is 1000. 600 are sold on the easy-to-purchase platform in the last 30 days, 500 are sold on the daily cat platform in the last 30 days, the number of commodities distributed to the daily cat platform is 400, and the remaining 600 are distributed to the own platform for easy purchase. And recalculating the actual merchantable number of the commodity A and the daily average sales refreshing change of the calendar history of each platform 30 every 24 hours.

In an embodiment, the step S400 of "calculating, by the big data platform, the distribution quantity of the goods to be distributed by each platform according to the distribution policy" includes: and reading first target data matched with the attribute information of the goods to be distributed from the database by the big data platform according to a distribution strategy, reading second target data matched with the platform information of each platform, and calculating the distribution quantity of the goods to be distributed by each platform according to the first target data, the second target data and the distribution strategy.

Specifically, in the cargo distribution management system 100, the distribution policy determined according to the attribute information of the cargo to be distributed and the platform information of each platform includes the related parameters of the attribute information of the cargo to be distributed and the related parameters of the platform information of each platform. The big data platform 200 can calculate the distribution quantity of the goods to be distributed of each platform by reading the first target data matched with the attribute information of the goods to be distributed from the database, reading the second target data matched with the platform information of each platform, and inputting the first target data and the second target data into corresponding parameters in the distribution strategy. And finally distributing the distribution quantity of the goods to be distributed to each platform. Therefore, the cargo distribution management system 100 can distribute the distribution quantity of the cargo to be distributed to each platform more accurately, so that each platform can distribute the cargo more accurately according to the received distribution quantity.

In addition, the cargo distribution management system 100 may output the distribution policy, read the distribution amount from the big data platform 200 by the cargo distribution system 300, and distribute the distribution policy to each platform according to the distribution amount of the to-be-distributed cargo.

In the cargo distribution method, the cargo distribution management system 100 reads the attribute information of the cargo to be distributed and the platform information of each platform for distributing the cargo to be distributed from the database, and determines the distribution strategy according to the attribute information of the cargo to be distributed and the platform information of each platform. The goods to be distributed are goods with the browsing amount larger than a preset threshold value, and the browsing amount is determined according to the behavior track data of the goods browsed by the user counted by the big data platform. Furthermore, the big data platform calculates the distribution quantity of the goods to be distributed of each platform according to the distribution strategy, and distributes the distribution quantity to each platform, so that each platform can distribute the goods according to the received distribution quantity of the goods to be distributed. Therefore, in the goods distribution method, the goods to be distributed are determined by the big data platform 200 according to the behavior track data of the goods browsed by the user, the goods to be distributed do not need to be screened manually, the problem that due to the fact that the attribute information of the goods is not effectively judged in the manual goods selection process, the accuracy of the selected goods to be distributed is low is avoided, and the accuracy of goods distribution and distribution is improved. In addition, in the goods distribution method, the big data platform reads the attribute information of goods to be distributed and the platform information of each platform from the database to determine the distribution quantity, distributes the distribution quantity to each platform, realizes automatic distribution of the distribution quantity to each platform, and distributes goods according to the received distribution quantity by each platform, so that the timeliness of goods distribution and distribution is improved.

It should be understood that, although the steps in the flowchart are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps of the figures may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least a portion of the sub-steps or stages of other steps.

In one embodiment, as shown in fig. 5, there is provided a goods distribution apparatus comprising a first obtaining module 10, a second obtaining module 20, a determining module 30 and a distributing module 40, wherein:

the first acquisition module 10 is configured to read attribute information of goods to be distributed from a database, where the goods to be distributed are goods whose browsing amount is greater than a preset threshold, and the browsing amount is determined according to the user browsing goods behavior trajectory data counted by the big data platform;

the second obtaining module 20 is configured to read platform information of each platform for distributing goods to be distributed from the database;

the determining module 30 is configured to determine a cargo distribution strategy according to the attribute information of the cargo to be distributed and the platform information of each platform;

the distribution module 40 is configured to send the distribution strategy to the big data platform, so that the big data platform calculates, according to the distribution strategy, the distribution quantity of the goods to be distributed by each platform, and each platform distributes the goods according to the received distribution quantity.

In one embodiment, the determining module 30 may include (not shown in fig. 5):

the first determining unit is used for extracting the historical sales volume of the goods to be distributed on each platform and the actual goods available quantity in the warehouse through the big data platform according to the attribute information of the goods to be distributed and the platform information of each platform;

the first screening unit is used for screening target goods sorting rules from preset goods sorting rules according to historical sales volumes of all platforms and actual goods sold in a warehouse;

and the second determining unit is used for determining a goods distribution strategy according to the target goods distribution rule.

In one embodiment, the first screening unit may include:

and the first screening subunit is used for screening the target goods sorting rule from the preset goods sorting rules according to the historical sales volume and the actual goods saleable number in the warehouse of each platform and the platform priority of each platform in the preset goods sorting rules.

In one embodiment, the determining module 30 may include (not shown in fig. 5):

the third determining unit is used for extracting the sales forecast of the goods to be distributed on each platform through the big data platform according to the attribute information of the goods to be distributed and the platform information of each platform;

the second screening unit is used for screening out a target goods sorting rule from preset goods sorting rules according to the sales forecast of each platform;

and the fourth determining unit is used for determining the goods distribution strategy according to the target goods distribution rule.

In one embodiment, the second screening unit may include:

and the second screening subunit is used for screening the target goods distribution rule from the preset goods distribution rules according to the sales forecast of each platform and the platform priority of each platform in the preset goods distribution rules.

In one embodiment, the distribution module 40 may include (not shown in fig. 5):

and the calculating unit is used for reading first target data matched with the attribute information of the goods to be distributed from the database by the big data platform according to the distribution strategy, reading second target data matched with the platform information of each platform, and calculating the distribution quantity of the goods to be distributed by each platform according to the first target data, the second target data and the distribution strategy.

For specific limitations of the goods distribution device, reference may be made to the above limitations of the goods distribution method, which are not described herein again.

The various modules in the above-described cargo distribution apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing goods related data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of distributing goods as described in any of the above embodiments.

Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:

reading attribute information of goods to be distributed from a database, wherein the goods to be distributed are goods with the browsing amount larger than a preset threshold value, and the browsing amount is determined according to the behavior track data of the goods browsed by a user counted by a big data platform; reading platform information of each platform for distributing goods to be distributed from a database; determining a goods distribution strategy according to the attribute information of goods to be distributed and the platform information of each platform; and sending the goods distribution strategy to a big data platform, calculating the goods distribution quantity of goods to be distributed by each platform according to the goods distribution strategy by the big data platform, and distributing the goods by each platform according to the received goods distribution quantity.

In one embodiment, the processor executes the computer program to determine a cargo distribution strategy according to the attribute information of the cargo to be distributed and the platform information of each platform, and further performs the following steps:

extracting historical sales of goods to be distributed on each platform and actual goods sales in a warehouse through a big data platform according to the attribute information of the goods to be distributed and the platform information of each platform; screening out target goods distribution rules from preset goods distribution rules according to historical sales volumes of all platforms and actual goods sales numbers in a warehouse; and determining a goods distribution strategy according to the target goods distribution rule.

In one embodiment, the processor executes a computer program to realize the following steps when screening out target goods sorting rules from preset goods sorting rules according to historical sales volume at each platform and actual sales number in a warehouse:

extracting sales forecasts of goods to be distributed on each platform through a big data platform according to the attribute information of the goods to be distributed and the platform information of each platform; screening a target goods sorting rule from preset goods sorting rules according to the sales forecast quantity of each platform; and determining a goods distribution strategy according to the target goods distribution rule.

In one embodiment, the processor executes a computer program to realize the following steps when screening out a target goods sorting rule from preset goods sorting rules according to the sales forecast quantity of each platform:

In one embodiment, the processor executes the computer program to realize the following steps when the big data platform calculates the distribution quantity of the goods to be distributed by each platform according to the distribution strategy:

and reading first target data matched with the attribute information of the goods to be distributed from the database by the big data platform according to a distribution strategy, reading second target data matched with the platform information of each platform, and calculating the distribution quantity of the goods to be distributed by each platform according to the first target data, the second target data and the distribution strategy.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, carries out a method of distributing goods as in any of the above embodiments.

In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:

In one embodiment, the computer program is executed by a processor to determine a distribution strategy according to the attribute information of the goods to be distributed and the platform information of each platform, and further includes the following steps:

In one embodiment, the computer program is executed by a processor to realize the following steps when screening out target goods sorting rules from preset goods sorting rules according to historical sales amount at each platform and actual sales number at a stock:

In one embodiment, the computer program is executed by the processor to realize the following steps when the target goods sorting rule is screened out from the preset goods sorting rules according to the sales forecast quantity of each platform:

In one embodiment, the computer program is executed by a processor to realize the following steps when the big data platform calculates the distribution quantity of the distributed goods to be distributed of each platform according to the distribution strategy:

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. A method of distributing goods, the method comprising:

reading attribute information of goods to be distributed from a database, wherein the goods to be distributed are goods with browsing quantity larger than a preset threshold value, and the browsing quantity is determined according to behavior track data of goods browsed by a user counted by a big data platform;

reading platform information of each platform for distributing the goods to be distributed from the database;

determining a goods distribution strategy according to the attribute information of the goods to be distributed and the platform information of each platform;

and sending the goods distribution strategy to the big data platform, so that the big data platform calculates the goods distribution quantity of the goods to be distributed by each platform according to the goods distribution strategy, and each platform distributes the goods according to the received goods distribution quantity.

2. The method according to claim 1, wherein the determining a distribution strategy according to the attribute information of the goods to be distributed and the platform information of each platform comprises:

extracting historical sales and actual goods sales in the warehouse of the goods to be distributed on each platform through the big data platform according to the attribute information of the goods to be distributed and the platform information of each platform;

screening out a target goods sorting rule from preset goods sorting rules according to the historical sales volume of each platform and the actual goods sales number in the warehouse;

and determining the goods distribution strategy according to the target goods distribution rule.

3. The method according to claim 2, wherein the step of screening out target sorting rules from preset sorting rules according to the historical sales volume at each platform and the actual sales number in the warehouse comprises the steps of:

and screening target goods sorting rules from the preset goods sorting rules according to the historical sales volume of each platform, the actual goods sold in the stock and the platform priority of each platform in the preset goods sorting rules.

4. The method according to claim 1, wherein the determining a distribution strategy according to the attribute information of the goods to be distributed and the platform information of each platform comprises:

extracting the sales forecast of the goods to be distributed on each platform through the big data platform according to the attribute information of the goods to be distributed and the platform information of each platform;

screening out a target goods sorting rule from preset goods sorting rules according to the sales forecast quantity of each platform;

5. The method of claim 4, wherein the step of screening target sorting rules from preset sorting rules according to the sales forecast of each platform comprises:

6. The method of claim 1, wherein said calculating, by said big data platform and according to said distribution strategy, a distribution quantity of said goods to be distributed by said each platform comprises:

and reading first target data matched with the attribute information of the goods to be distributed from the database by the big data platform according to the distribution strategy, reading second target data matched with the platform information of each platform, and calculating the distribution quantity of the goods to be distributed by each platform according to the first target data, the second target data and the distribution strategy.

7. A cargo dispensing apparatus, the apparatus comprising:

the system comprises a first acquisition module, a second acquisition module and a display module, wherein the first acquisition module is used for reading attribute information of goods to be distributed from a database, the goods to be distributed are goods with browsing quantity larger than a preset threshold value, and the browsing quantity is determined according to behavior track data of goods browsed by a user counted by a big data platform;

the second acquisition module is used for reading platform information of each platform for distributing the goods to be distributed from the database;

the determining module is used for determining a goods distribution strategy according to the attribute information of the goods to be distributed and the platform information of each platform;

the distribution module is used for sending the goods distribution strategy to the big data platform so that the big data platform calculates the goods distribution quantity of the goods to be distributed by each platform according to the goods distribution strategy, and the platforms distribute the goods according to the received goods distribution quantity.

8. A cargo distribution platform, the platform comprising:

the goods distribution management system is used for reading attribute information of goods to be distributed from the database, wherein the goods to be distributed are goods with browsing quantity larger than a preset threshold value, and the browsing quantity is determined according to the behavior track data of the goods browsed by the user counted by the big data platform; reading platform information of each platform for distributing the goods to be distributed from the database; determining a goods distribution strategy according to the attribute information of the goods to be distributed and the platform information of each platform; sending the goods distribution strategy to the big data platform, so that the big data platform calculates the goods distribution quantity of the goods to be distributed by each platform according to the goods distribution strategy, and each platform distributes the goods according to the received goods distribution quantity;

and the goods distribution system is used for receiving the distribution quantity of the goods to be distributed, sent by the big data platform, of each platform and sending the distribution quantity of the goods to be distributed, sent by each platform, to the corresponding platform.

9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 6 are implemented when the computer program is executed by the processor.

10. 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 of any one of claims 1 to 6.