CN117114759A - Commodity order generation method and device based on big data - Google Patents

Abstract

The application belongs to the technical field of big data analysis, and discloses a commodity order generation method and device based on big data, wherein the method comprises the following steps: receiving an order generation instruction, and acquiring commodity information and customer information corresponding to the order generation instruction; acquiring historical shopping data of a client and the type of the client according to the client information; carrying out big data analysis on the historical shopping data to obtain the consumption grade of the customer; determining full-reduction offers and discount offers corresponding to the clients according to the consumption level and the client types; calculating a transaction price according to the commodity information, the full-reduced offers and the discount offers; determining freight based on commodity information, a price for a transaction, a customer type, and customer information; an order is generated based on the cost of the transaction, the merchandise information, the customer information, and the freight rate. The application increases the purchasing desire of the clients through the incentive measures such as full reduction, discount and the like which are more in line with the demands and the preferences of the clients, and improves the shopping experience, satisfaction and loyalty of the clients.

Description

Commodity order generation method and device based on big data

Technical Field

The application relates to the technical field of big data analysis, in particular to a commodity order generation method and device based on big data.

Background

The existing manners of generating orders by on-line sales of industrial products are mainly divided into three modes: the first is that the customer directly places an order on a commodity page, and then generates a corresponding order according to the information of the commodity; the second is that the customer selects a plurality of commodities to make an order in the shopping cart, and generates an order according to the information and the price sum of the plurality of commodities, and the third is that the order is generated according to the quotation and the corresponding commodity information after the price is polled for the special commodity needing to be polled; however, when the three modes generate orders, different preference demands, shopping habits, return rates and the like of different clients are not considered, so that personalized customized products and services are not provided for the clients by sellers, the purchase experience of the clients is affected, and the loyalty degree, satisfaction degree and market competitiveness of the sellers of the clients are reduced.

Disclosure of Invention

The application provides a commodity order generation method and a commodity order generation device based on big data, which can customize different price offers and freight amounts according to different customers in a personalized way, and increase the purchasing desire of the customers and improve the shopping experience, satisfaction and loyalty of the customers by generating incentive measures such as full reduction, discount and the like which are more in line with the demands and the preference of the customers.

In a first aspect, an embodiment of the present application provides a method for generating a commodity order based on big data, where the method includes:

receiving an order generation instruction, and acquiring commodity information and customer information corresponding to the order generation instruction;

a historical data acquisition step: acquiring historical shopping data of a client and the type of the client according to the client information; the historical shopping data comprises accumulated consumption amount of the clients, historical consumption frequency, historical ordering time and shopping channel information;

carrying out big data analysis on the historical shopping data to obtain the consumption grade of the customer;

determining full-reduction offers and discount offers corresponding to the clients according to the consumption level and the client types;

calculating a transaction price according to the commodity information, the full-reduced offers and the discount offers;

determining freight based on commodity information, a price for a transaction, a customer type, and customer information;

an order is generated based on the cost of the transaction, the merchandise information, the customer information, and the freight rate.

Further, the order also comprises an order number; the order number is generated based on a preset template, customer information, and random characters.

Further, the method further comprises:

after acquiring commodity information, judging whether each preset filling option in the commodity information is empty or not;

If any preset filling option is empty, the order generation process is exited, and the incomplete commodity information is prompted.

Further, the method further comprises:

after acquiring commodity information, detecting whether the commodity information is matched in a material database;

if so, detecting whether the commodity information comprises the special commodity;

if the special commodity is included, detecting whether the client number in the client information exists in a special database;

if the special commodity exists or is not included, detecting whether the purchase quantity in the commodity information is smaller than the stock quantity;

and if the number is smaller than the stock number, executing the step of acquiring the historical data.

Further, the commodity information comprises commodity original total price, and the full-reduction preferential comprises a full-reduction threshold and corresponding full-reduction preferential amount;

the calculating the transaction price according to the commodity information, the full-reduction discount and the discount comprises the following steps:

judging whether the original total price of the commodity is larger than or equal to a full reduction threshold;

if yes, subtracting the full discount amount from the original total price of the commodity, and superposing the full discount amount with the discount to obtain a bargain price; otherwise, the original total price of the commodity is overlapped with the discount offer to obtain the bargain price.

Further, the method further comprises:

Acquiring a plurality of coupons corresponding to the client information;

determining a target coupon with the highest discount amount according to commodity information and the transaction price;

let the transaction price minus the offer of the targeted coupon.

Further, the method further comprises:

acquiring commodity cost and preset profit margin corresponding to commodity information;

calculating to obtain the lowest transaction amount according to commodity cost and preset profit margin;

after the transaction price is obtained, detecting whether the transaction price is smaller than the lowest transaction amount;

if yes, the order generation flow is exited, and the price abnormality is prompted.

Further, the historical shopping data also includes a return rate;

the determining of the freight based on the commodity information, the transaction price, the client type and the client information includes:

determining an initial freight rate according to the commodity information and the delivery address in the client information;

acquiring historical return data corresponding to commodity information and freight reduction and avoidance offers corresponding to client types;

obtaining the reduced freight according to the price and the freight reduced discount;

if the reduced freight rate is greater than the preset freight rate threshold value, predicting the lost freight rate according to the historical return data and the return rate;

and adding the lost freight and the reduced freight to obtain freight.

Further, the method further comprises:

Carrying out big data analysis according to the historical shopping data to obtain the consumption preference statistical condition of the client;

determining recommended commodity information of the client according to the consumption preference statistics and the consumption level;

and displaying the recommended commodity information in the generated order.

Further, the historical shopping data also comprises the exchange rate of the customer; the method further comprises the steps of:

acquiring historical goods changing data and quality complaint records corresponding to commodity information;

analyzing according to the historical goods change data, the quality complaint records and the goods change rate to obtain the predicted goods change quantity;

judging whether the difference between the inventory quantity and the purchase quantity corresponding to the commodity information is larger than the predicted change quantity or not;

if not, generating a goods regulating instruction according to the predicted goods changing quantity.

Further, the method further comprises:

matching the company main body of the customer according to the customer number of the customer information;

taking a company main body as a payment main body of the order;

after the order is generated, a payment process is performed based on the payment body.

Further, the method further comprises:

if a plurality of order generation instructions corresponding to the same commodity information are received at the same time, calculating the sum of the purchase quantity in the commodity information corresponding to the same commodity information of each order generation instruction;

Detecting whether the sum of the quantity is larger than the stock quantity of commodity information;

if yes, calculating the purchase weight of each customer according to the purchase quantity corresponding to each order generation instruction;

obtaining the corresponding distribution quantity of each customer according to the stock quantity and each purchase weight;

generating order inquiry information according to the distribution quantity;

and after receiving the order confirmation instruction, replacing the purchase quantity in the commodity information with the distribution quantity.

In a second aspect, an embodiment of the present application provides a commodity order generating apparatus based on big data, including:

the receiving module is used for receiving the order generation instruction and acquiring commodity information and customer information corresponding to the order generation instruction;

the acquisition module is used for acquiring historical shopping data of the client and the client type according to the client information; the historical shopping data comprises accumulated consumption amount of the clients, historical consumption frequency, historical ordering time and shopping channel information;

the analysis module is used for carrying out big data analysis on the historical shopping data to obtain the consumption grade of the customer;

the discount determining module is used for determining full discount and discount corresponding to the client according to the consumption grade and the client type;

the price calculation module is used for calculating the bargain price according to commodity information, full-reduction offers and discount offers;

The freight calculation module is used for determining freight based on commodity information, the price of the transaction, the type of the customer and the customer information;

and the generation module is used for generating orders based on the transaction price, commodity information, client information and freight.

In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to perform the steps of a commodity order generating method based on big data as in any of the above embodiments.

In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a big data based commodity order generating method of any of the embodiments described above.

In summary, compared with the prior art, the technical scheme provided by the embodiment of the application has the following beneficial effects:

according to the commodity order generation method based on the big data, the consumption grade of the customer is obtained through big data analysis on the historical shopping data of the customer; and determining full-reduction offers and discount offers corresponding to the clients according to the consumption level and the client types, determining the price and freight charge according to commodity information, the full-reduction offers and the discount offers, generating orders paid by the clients based on the customized price, freight charge and other information, customizing different price offers and freight charge amounts for different clients individually, increasing the purchasing desire of the clients by generating incentive measures such as full-reduction, discount and the like which are more in line with the requirements and preferences of the clients, and improving the shopping experience, satisfaction and loyalty of the clients.

Drawings

Fig. 1 is a flowchart of a commodity order generating method based on big data according to an exemplary embodiment of the present application.

FIG. 2 is a flow chart of order generation determination steps provided in an exemplary embodiment of the present application.

Fig. 3 is a block diagram of a commodity order generating apparatus according to an exemplary embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments.

All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Referring to fig. 1, an embodiment of the present application provides a commodity order generating method based on big data, including:

and receiving an order generation instruction, and acquiring commodity information and customer information corresponding to the order generation instruction.

The commodity information comprises commodity specification information, purchase quantity, SKU number, delivery mode, sales curve and the like; the customer information includes account information of the customer, customer number, shipping address, contract data with the seller, and the like.

In a specific implementation process, the order generation instruction can be received from a commodity detail page, wherein the commodity information only comprises information of one commodity; or may be received from a shopping cart interface, where the merchandise information may include information for a variety of merchandise.

A historical data acquisition step: acquiring historical shopping data of a client and the type of the client according to the client information; the historical shopping data includes the accumulated amount of consumption of the customer, the historical frequency of consumption, the historical time of order, and the shopping channel information.

The historical shopping data can further comprise a single consumption limit, an average consumption limit, a preferential order proportion and the like of the clients.

Specifically, the client type is confirmed according to account information in the client information, and the client type comprises a common client, a member client and a key cooperative client; wherein, the common customer is a customer only having a registered account number and not having a member service opened/not purchased; the member clients are clients of the exclusive member service of opening/purchasing, and the important cooperative clients are clients who make relevant cooperative contracts with sellers.

And carrying out big data analysis on the historical shopping data to obtain the consumption grade of the customer.

And determining full-reduction offers and discount offers corresponding to the clients according to the consumption level and the client types.

Specifically, the full-down offers and the discount offers corresponding to different types of customers at the same consumption level are different:

the full-reduction coupons shown in the above table are only one embodiment of the present application, i.e., the higher the consumption level, the higher the consumer's consumption capacity, and therefore the higher the full-reduction threshold setting in the full-reduction coupons, to promote multiple consumption by the consumer with high consumption capacity. In the specific implementation process, the full reduction offers or discount offers can be designed according to specific sales conditions of sellers and commodity prices.

For example, for customers whose historical consumption frequency reaches a certain threshold, or for some type of commodity in commodity information, the current market competition is more intense, or the number of commodity stocks corresponding to commodity information exceeds a certain threshold for a long time, the full reduction threshold can be further reduced, the discount preference is further improved, more customers are attracted to purchase, so that sales and loyalty of the customers can be improved, and meanwhile, stock pressure and stock cost of sellers can be reduced.

And calculating the transaction price according to the commodity information, the full-reduction discount and the discount.

Specifically, after full reduction, discount offers are superimposed with the price after full reduction of the original price, thereby obtaining the price for the exchange.

The freight rate is determined based on the commodity information, the price of the deal, the type of customer, and the customer information.

An order is generated based on the cost of the transaction, the merchandise information, the customer information, and the freight rate.

Further, for the commodity needing to be inquired, after an order generation instruction is provided by a customer, a supervisor approves quotation and freight, after the approval is passed, the quotation is put into commodity information to transfer the order generation flow, the flow is the same as the flow, the freight is not required to be calculated again, and the freight is determined in the quotation approval process; the plurality of micro-services form an order system, and data are called in parallel.

According to the commodity order generation method based on the big data, the big data analysis is carried out on the historical shopping data of the clients, so that the consumption grade of the clients is obtained; and determining full-reduction offers and discount offers corresponding to the clients according to the consumption level and the client types, determining the price and freight charge according to commodity information, the full-reduction offers and the discount offers, generating orders paid by the clients based on the customized price, freight charge and other information, customizing different price offers and freight charge amounts for different clients, generating incentive measures such as full-reduction and discount which are more in line with the needs and preferences of the clients, increasing the purchasing desire of the clients, and improving the shopping experience, satisfaction and loyalty of the clients.

In some embodiments, the order further includes an order number.

The order number is generated based on a preset template, customer information, and random characters.

Wherein, preset template is: time + client id + random character.

In some embodiments, the method further comprises:

after the commodity information is acquired, whether each preset filling option in the commodity information is empty or not is judged.

If any preset filling option is empty, the order generation process is exited, and the incomplete commodity information is prompted.

Specifically, judging whether the specification information in the commodity information selects/fills in the preset necessary filling options; for example, if the customer does not select the color, when receiving the order generation instruction, generating prompt information of incomplete commodity information and displaying the prompt information to the customer so as to remind the customer to recheck the specification of the selected commodity until ensuring that the specification information of each commodity selects the preset necessary option, and then executing the historical data acquisition step.

In the specific implementation process, the client information can be detected, and whether the client information comprises the invoice information, the delivery address and other necessary filling parameters or not is detected.

The embodiment ensures accurate generation of orders by detecting the preset necessary filling options.

Referring to fig. 2, in some embodiments, the method further comprises:

after acquiring commodity information, detecting whether the commodity information is matched in a material database.

If so, detecting whether the commodity information comprises the special commodity.

If the special commodity is included, whether the client number in the client information exists in the special database is detected.

If the special commodity is present or not, detecting whether the purchase quantity in the commodity information is smaller than the stock quantity.

Specifically, if the commodity information includes the purchase quantity of a plurality of commodities, the purchase quantity and the corresponding stock quantity are checked one by one.

And if the number is smaller than the stock number, executing the step of acquiring the historical data.

The special commodity is only provided for the cooperative clients by the sellers, the clients can purchase the commodity after signing corresponding contracts, and the client numbers of the clients signing special commodity purchase contracts with the sellers are stored in the special database. When the commodity information corresponding to the order generation instruction comprises a special commodity, whether the client has the authority to purchase the special commodity or not can be determined by comparing the client numbers.

Specifically, whether the commodity exists or not is detected according to specification information and SKU number in commodity information; if so, detecting the purchasing rights of the special commodity and the client, and finally detecting whether the stock quantity meets the requirement of the client.

The embodiment further ensures the accuracy of order generation through multiple detection of commodity information, inventory quantity and the like.

In some embodiments, the merchandise information includes an original total price of the merchandise, and the full offer includes a full offer threshold and a corresponding full offer amount; the calculating the transaction price according to the commodity information, the full-reduction discount and the discount comprises the following steps:

judging whether the original total price of the commodity is larger than or equal to a full reduction threshold.

If yes, subtracting the full discount amount from the original total price of the commodity, and superposing the full discount amount with the discount to obtain a bargain price; otherwise, the original total price of the commodity is overlapped with the discount offer to obtain the bargain price.

Specifically, whether the original total price of the commodity still meets the full reduction threshold after the discount coupon is superimposed can be judged, and if the original total price of the commodity still meets the full reduction threshold after the discount coupon is superimposed, the original total price of the commodity is discounted before full reduction; if not, then the discount is followed by the full subtraction.

In some embodiments, the method further comprises:

and acquiring a plurality of coupons corresponding to the client information.

And determining the target coupon with the highest discount amount according to the commodity information and the transaction price.

Let the transaction price minus the offer of the targeted coupon.

Specifically, after a plurality of coupons corresponding to the customer information are acquired, determining the applicable commodity range (general, specified brand, certain type of SKU, specified SKU), the use time limit and the use price threshold of each coupon; and determining available coupons to be used according to commodity information, the transaction price, the applicable commodity range of each coupon, the use time limit and the use price threshold, taking the coupon with the highest discount amount in the coupons to be used as a target coupon, and subtracting the discount amount of the target coupon from the transaction price after full subtraction and discount to obtain the final transaction price.

The embodiment improves the attraction of commodities through full reduction, discount and coupon multi-coupon superposition, and can attract more other clients to purchase while prompting the clients to place orders quickly.

In some embodiments, the method further comprises:

and acquiring commodity cost and preset profit margin corresponding to commodity information.

And calculating to obtain the lowest transaction amount according to the commodity cost and the preset profit margin.

After the transaction price is obtained, it is detected whether the transaction price is less than the minimum transaction amount.

If yes, the order generation flow is exited, and the price abnormality is prompted.

Specifically, for commodities requiring guarantee of profit and profit, the minimum transaction amount can be guaranteed by setting preset profit margin, and if the transaction price after full reduction and discount preference is lower than the minimum transaction amount, the price is displayed abnormally and the ordering is impossible.

The embodiment avoids the vulnerability of some clients to "pull out" through coupons by setting the minimum transaction amount, ensuring that sellers can obtain sustainable profitability.

In some embodiments, the historical shopping data further includes a return rate.

The determining of the freight based on the commodity information, the transaction price, the client type and the client information includes:

An initial shipping cost is determined based on the shipping address in the merchandise information and the customer information.

And acquiring historical return data corresponding to commodity information and freight reduction and avoidance benefits corresponding to the client type.

And obtaining the reduced freight according to the price and the freight reducing and releasing discount.

Specifically, the application can set different freight reduction offers for different customer types, such as a common customer to pay a full 299 freight-free fee; the member clients have a full price of 75 and a freight rate of less than or equal to 15, the freight rate is completely free, the full price of 75 and the freight rate of more than 15, and the freight rate is halved; important collaboration client freight complete exemption, and so on.

If the reduced freight rate is greater than the preset freight rate threshold, predicting the lost freight rate according to the historical return data and the return rate.

Specifically, a big data analysis algorithm is used for analyzing common goods returning reasons, time distribution of goods returning occurrence, goods returning proportion and the like according to historical goods returning data, goods returning probability of goods in an order is obtained, the goods returning probability of a customer is weighted with the goods returning rate of the customer, the predicted goods returning rate of the order is obtained, and the predicted goods returning rate, the purchase quantity and the freight rate of single goods are multiplied to obtain loss freight rate.

And adding the lost freight and the reduced freight to obtain freight.

If the reduced freight rate is less than or equal to the preset freight rate threshold value, the lost freight rate is 0, and the reduced freight rate is the freight rate.

In particular, for industrial sellers, it is possible that a lot of industrial equipment is transported to a remote address in foreign or domestic by a special way, so the express freight is usually a very high number for the industrial seller, and sometimes even exceeds the price of the commodity itself; if the reduced freight rate exceeds the preset freight rate threshold value, the order is returned later and the seller undertakes the time for returning the freight, so that the risk of losing the cost is high for the seller.

The embodiment sets the lost freight based on the preset freight threshold, and can avoid the possibility of subsequent return loss while continuously maintaining the good service of the seller for bearing the return freight.

In some embodiments, the method further comprises:

and analyzing big data according to the historical shopping data to obtain the consumption preference statistical condition of the client.

And determining the recommended commodity information of the client according to the consumption preference statistics and the consumption grade.

And displaying the recommended commodity information in the generated order.

The historical shopping data are analyzed in big data, wherein the analysis of the historical shopping data comprises analysis of data such as purchase history, browsing behaviors and geographic positions of clients, and the consumption preference statistical conditions comprise information such as age, gender, preference and purchase preference of the clients.

Specifically, among the commodities satisfying the customer consumption preference statistics, a number of recommended commodities conforming to the customer consumption level are randomly called up, and these recommended commodity information is displayed on the generated order page.

Further, cross-selling and recommending can also be performed by considering the relevance between different products. For example, if a customer purchases a camera, it may be recommended an adapted lens or other related accessory.

The embodiment indirectly improves sales of other commodities by analyzing customer preferences and recommending relevant commodities to customers.

In some embodiments, the historical shopping data further includes a customer's rate of change; the method further comprises the steps of:

and acquiring historical goods change data and quality complaint records corresponding to the commodity information.

And analyzing according to the historical goods change data, the quality complaint records and the goods change rate to obtain the predicted goods change quantity.

And judging whether the difference value between the inventory quantity and the purchase quantity corresponding to the commodity information is larger than the predicted change quantity.

If not, generating a goods regulating instruction according to the predicted goods changing quantity.

Further, considering the condition that the commodity is sold in the logistics transportation process, the sales condition of the corresponding commodity can be obtained according to the commodity information, and the expected delivery time is obtained according to the delivery address of the customer information; predicting the predicted sales quantity of the corresponding commodity from the current moment to the predicted delivery time through big data analysis; and then subtracting the purchase quantity from the stock quantity at the current moment and subtracting the predicted sales quantity, and if the obtained difference is smaller than the predicted change quantity, generating a goods regulating instruction.

The embodiment takes the change demand of the following clients into consideration in advance when generating orders, makes the goods adjustment in time, optimizes inventory management, logistics distribution and production plan, and is beneficial to providing better after-sales service for the clients.

In some embodiments, the method further comprises:

the company main body of the customer is matched according to the customer number of the customer information.

The company main body is taken as a payment main body of the order.

After the order is generated, a payment process is performed based on the payment body.

Where the corporate entity includes a particular sub-company, branch office, or legal entity. There is a relationship or binding between a client number and the company's principal. This relationship may take different forms depending on the specific business scenario and system design.

Typically, the customer number is a unique identification assigned to each customer internally by the seller. It is used to distinguish between different customers and to establish an association between a customer and a company. The relationship between the client number and the company may be a one-to-one relationship or may be associated with a plurality of companies or organizations. This may occur in the case of a cross-sub-company or branch, where one customer shares the same customer number between different business units. Thus, unified management of client information and data sharing across companies can be realized.

In particular, during the payment process, involving settlement and checkout of money, the correct matching company entity can ensure that the payment amount is correctly attributed and flows into the corresponding bank account or financial department. And different corporate principals may have different tax requirements and tax liabilities, by matching corporate principals, it may be ensured that relevant tax laws and regulations are complied with during payment and that the required tax information is accurately recorded. Correct matching of corporate principals may also aid in financial verification and reporting. The payment amount and order information may be compared to the financial records of the company's principal and used to generate accurate financial statements and performance indicators.

In the specific implementation process, after the payment flow is finished, if the order generation instruction is received from the shopping cart interface, subtracting the number of corresponding commodities in the customer shopping cart according to commodity information, and deleting the used coupon from the customer account information.

In some embodiments, the method further comprises:

if a plurality of order generation instructions corresponding to the same commodity information are received at the same time, calculating the sum of the purchase quantity in the commodity information corresponding to the same commodity information of each order generation instruction.

It is detected whether the sum of the amounts is larger than the stock amount of the commodity information.

If yes, the purchase weight of each customer is calculated according to the purchase quantity corresponding to each order generation instruction.

And obtaining the distribution quantity corresponding to each customer according to the inventory quantity and each purchase weight.

And generating order inquiry information according to the distribution quantity.

And after receiving the order confirmation instruction, replacing the purchase quantity in the commodity information with the distribution quantity.

The same commodity information is that a certain type of same commodity is included in the commodity information, and a plurality of clients simultaneously request to purchase the same type of commodity when receiving order generation instructions corresponding to the same commodity information, wherein the sum of the purchase quantity of the plurality of requests is larger than the stock quantity, and priority can be set for the clients through client types to meet important clients preferentially; the method of the above embodiment may also be used to assign goods by calculating weights.

In the implementation process, for a plurality of clients who make simultaneous orders for similar commodities, the sum of the purchase numbers of the clients is calculated, and the purchase number of each client is compared with the total purchase number to obtain the purchase number weight. For example, if 100 clients a are to be purchased, 200 clients B are to be purchased, and if there are only 250 clients in the total inventory, the purchase amount weight of the clients a is 100/300=0.33, and the purchase amount weight of the clients B is 200/300=0.67.

The current inventory is then proportionally distributed to the individual customers using the purchase quantity weights. In this embodiment, if the current inventory is only 250, the inventory amount to be obtained by the customer a is 250×0.33=82.5, and the inventory amount to be obtained by the customer B is 250×0.67=167.5 according to the purchase amount weight.

The result is required to be rounded downwards during actual allocation, so that the allocation quantity is ensured to be an integer; while presenting the customer with a message of inventory shortage and displaying the actual number of purchases available. For example, customer a is presented with an inventory shortage and advised that there are only 82 available purchases, asking if the customer would like to purchase in 82 amounts. And meanwhile, the internal logistics information is called, and the predicted time for goods dispatching and replenishment is provided for customers. If a subsequent restock is planned, the customer is informed of the predicted restock time and possible inventory.

The embodiment realizes reasonable distribution when the inventory quantity is insufficient, ensures that each customer can obtain the wanted commodity, and provides possibility for the subsequent secondary ordering purchase of the customer.

Referring to fig. 3, another embodiment of the present application provides a commodity order generating apparatus based on big data, which includes:

the receiving module 101 is configured to receive an order generation instruction, and obtain commodity information and customer information corresponding to the order generation instruction.

An acquisition module 102, configured to acquire historical shopping data of a customer and a customer type according to customer information; the historical shopping data includes the accumulated amount of consumption of the customer, the historical frequency of consumption, the historical time of order, and the shopping channel information.

And the analysis module 103 is used for carrying out big data analysis on the historical shopping data to obtain the consumption grade of the customer.

And the offer determination module 104 is used for determining full-reduced offers and discount offers corresponding to the clients according to the consumption level and the client types.

The price calculating module 105 is used for calculating the bargain price according to commodity information, full-reduction offers and discount offers.

The freight calculation module 106 is used for determining freight based on commodity information, the price of the transaction, the type of the customer and the customer information.

A generation module 107 for generating an order based on the price of the deal, the commodity information, the customer information, and the freight.

According to the commodity order generating device based on the big data, the consumption grade of the customer is obtained through big data analysis on the historical shopping data of the customer; and determining full-reduction offers and discount offers corresponding to the clients according to the consumption level and the client types, determining the price and freight charge according to commodity information, the full-reduction offers and the discount offers, generating orders paid by the clients based on the customized price, freight charge and other information, customizing different price offers and freight charge amounts for different clients, generating incentive measures such as full-reduction and discount which are more in line with the needs and preferences of the clients, increasing the purchasing desire of the clients, and improving the shopping experience, satisfaction and loyalty of the clients.

In some embodiments, the apparatus further comprises:

the necessary filling option judging module is used for judging whether each preset necessary filling option in the commodity information is empty or not after the commodity information is acquired; and when any preset filling option is empty, exiting the order generation flow and prompting that the commodity information is incomplete.

In some embodiments, the apparatus further comprises:

and the material matching module is used for detecting whether the commodity information is matched in the material database after the commodity information is acquired.

And the special supply judging module is used for detecting whether the commodity information comprises special supply commodities or not when the commodity information is matched.

And the client number module is used for detecting whether the client number in the client information exists in the special supply database when the special supply commodity is included.

The inventory judging module is used for detecting whether the purchase quantity in the commodity information is smaller than the inventory quantity when the client number does not exist in the special supply database or the commodity information does not contain special supply commodities; and executes the acquisition module 102 when the inventory count is less.

In some embodiments, the merchandise information includes an original total price of the merchandise, and the full offer includes a full offer threshold and a corresponding full offer amount; the price calculating module 105 includes:

The full-reduction judging unit is used for judging whether the original total price of the commodity is larger than or equal to a full-reduction threshold;

the system comprises a commodity original total price calculation unit, a discount calculation unit and a commodity price calculation unit, wherein the commodity original total price is subtracted by the full discount amount when the commodity original total price is larger than or equal to a full discount threshold, and the commodity original total price is overlapped with the discount to obtain a commodity price; and when the original total price of the commodity is smaller than the full reduction threshold, superposing the original total price of the commodity and discount offers to obtain the price for the transaction.

In some embodiments, the apparatus further comprises:

and the coupon acquisition module is used for acquiring a plurality of coupons corresponding to the client information.

And the target coupon determining module is used for determining the target coupon with the highest discount amount according to the commodity information and the transaction price.

And the deal price updating module is used for subtracting the discount amount of the target coupon from the deal price.

In some embodiments, the apparatus further comprises:

the cost acquisition module is used for acquiring commodity cost corresponding to commodity information and preset profit margin.

And the minimum amount calculation module is used for calculating and obtaining the minimum transaction amount according to the commodity cost and the preset profit margin.

And the transaction price detection module is used for detecting whether the transaction price is smaller than the lowest transaction amount after the transaction price is obtained.

And the price abnormality prompting module is used for exiting the order generation flow and prompting the price abnormality when the price abnormality prompting module is smaller than the lowest transaction amount.

In some embodiments, the historical shopping data further includes a return rate; the freight calculation module 106 includes:

and the initial freight determining unit is used for determining the initial freight according to the commodity information and the delivery address in the client information.

And the deduction acquisition unit is used for acquiring historical return data corresponding to the commodity information and freight deduction preferential corresponding to the client type.

And the reduction freight calculation unit is used for obtaining the reduction freight according to the cost and the freight reduction discount.

And the loss prediction unit is used for predicting the loss freight according to the historical return data and the return rate when the reduced freight is larger than a preset freight threshold.

And the freight calculation unit is used for adding the lost freight and the reduced freight to obtain freight.

In some embodiments, the apparatus further comprises:

and the preference statistics module is used for analyzing big data according to the historical shopping data to obtain the consumption preference statistics condition of the client.

And the recommended commodity determining module is used for determining the recommended commodity information of the client according to the consumption preference statistics and the consumption grade.

And the display module is used for displaying the recommended commodity information in the generated order.

In some embodiments, the historical shopping data further includes a customer's rate of change; the apparatus further comprises:

and the goods change data acquisition module is used for acquiring historical goods change data and quality complaint records corresponding to the commodity information.

And the prediction module is used for analyzing according to the historical goods change data, the quality complaint records and the goods change rate to obtain the predicted goods change quantity.

The difference judging module is used for judging whether the difference between the inventory quantity and the purchase quantity corresponding to the commodity information is larger than the predicted change quantity or not; and generating a goods regulating instruction according to the predicted goods changing quantity when the predicted goods changing quantity is smaller than or equal to the predicted goods changing quantity.

In some embodiments, the apparatus further comprises:

and the client number matching module is used for matching the company main body of the client according to the client number of the client information.

And the payment body determining module is used for taking the company body as the payment body of the order.

And the payment module is used for executing a payment flow based on the payment main body after the order is generated.

In some embodiments, the apparatus further comprises:

and the sum calculating module is used for calculating the sum of the purchase quantity in the commodity information corresponding to each order generating instruction when receiving a plurality of order generating instructions corresponding to the same commodity information at the same time.

And the sum detection module is used for detecting whether the sum of the quantity is larger than the stock quantity of the commodity information.

And the weight calculation module is used for calculating the purchase weight of each customer according to the purchase quantity corresponding to each order generation instruction when the sum of the quantity is larger than the inventory quantity of the commodity information.

And the distribution calculation module is used for obtaining the distribution quantity corresponding to each customer according to the inventory quantity and each purchase weight.

And the inquiry module is used for generating order inquiry information according to the distribution quantity.

And the replacement module is used for replacing the purchase quantity in the commodity information with the distribution quantity after receiving the order confirmation instruction.

The specific limitation of the commodity order generating apparatus based on big data provided in this embodiment may refer to the above embodiment of the commodity order generating method based on big data, which is not described herein. Each of the modules in the commodity order generating apparatus based on big data may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

Embodiments of the present application provide a computer device that may include a processor, memory, network interface, and 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 includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, causes the processor to perform the steps of a big data based commodity order generating method according to any of the embodiments described above.

The working process, working details and technical effects of the computer device provided in this embodiment may be referred to the above embodiments of a commodity order generating method based on big data, which are not described herein.

An embodiment of the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a big data based commodity order generating method according to any of the embodiments described above. The computer readable storage medium refers to a carrier for storing data, and may include, but is not limited to, a floppy disk, an optical disk, a hard disk, a flash Memory, and/or a Memory Stick (Memory Stick), etc., where the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. The working process, working details and technical effects of the computer readable storage medium provided in this embodiment may be referred to the above embodiments of a commodity order generating method based on big data, and will not be described herein.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.

Claims (15)

1. A commodity order generation method based on big data, the method comprising:

receiving an order generation instruction, and acquiring commodity information and customer information corresponding to the order generation instruction;

a historical data acquisition step: acquiring historical shopping data of a client and a client type according to the client information; the historical shopping data comprises accumulated consumption amount, historical consumption frequency, historical ordering time and shopping channel information of the clients;

Carrying out big data analysis on the historical shopping data to obtain the consumption grade of the client;

determining full-reduction offers and discount offers corresponding to the clients according to the consumption level and the client type;

calculating a transaction price according to the commodity information, the full-reduction coupon and the discount coupon;

determining a shipping cost based on the commodity information, the cost of goods, the customer type, and the customer information;

an order is generated based on the cost of the deal, the merchandise information, the customer information, and the freight rate.

2. The big data based commodity order generating method according to claim 1, wherein said order further comprises an order number; the order number is generated according to a preset template, the client information and random characters.

3. The big data based commodity order generating method according to claim 1, further comprising:

after acquiring the commodity information, judging whether each preset filling option in the commodity information is empty or not;

if any one of the preset necessary filling options is empty, the order generation flow is exited, and the commodity information is prompted to be incomplete.

4. The big data based commodity order generating method according to claim 1, further comprising:

After acquiring the commodity information, detecting whether the commodity information is matched in a material database;

if yes, detecting whether the commodity information comprises a special commodity;

if the special commodity is included, detecting whether the client number in the client information exists in a special database;

if the special commodity exists or is not included, detecting whether the purchase quantity in the commodity information is smaller than the stock quantity;

and if the number of the historical data is smaller than the inventory number, executing the historical data acquisition step.

5. The big data based commodity order generating method according to claim 1, wherein said commodity information includes a commodity original total price, and said full-down offer includes a full-down threshold and a corresponding full-down offer amount;

the calculating the transaction price according to the commodity information, the full-reduced offer and the discount offer comprises the following steps:

judging whether the original total price of the commodity is larger than or equal to the full reduction threshold;

if yes, subtracting the full discount amount from the original total price of the commodity, and then superposing the full discount amount with the discount to obtain the bargain price; and if not, superposing the original total price of the commodity with the discount offer to obtain the bargain price.

6. The big data based commodity order generating method according to claim 5, further comprising:

acquiring a plurality of coupons corresponding to the client information;

determining a target coupon with the highest discount amount according to the commodity information and the transaction price;

subtracting the discount amount of the target coupon from the transaction price.

7. The big data based commodity order generating method according to claim 1, further comprising:

acquiring commodity cost and preset profit margin corresponding to the commodity information;

calculating to obtain the lowest transaction amount according to the commodity cost and the preset profit margin;

after the transaction price is obtained, detecting whether the transaction price is smaller than the minimum transaction amount;

if yes, the order generation flow is exited, and the price abnormality is prompted.

8. The big data based commodity order generating method according to claim 1, wherein said historical shopping data further comprises a return rate;

the determining a shipping cost based on the commodity information, the price for the deal, the customer type, and the customer information includes:

determining an initial freight rate according to the commodity information and the delivery address in the client information;

Acquiring historical return data corresponding to the commodity information and freight reduction and avoidance offers corresponding to the client type;

obtaining the reduced freight according to the cost and the freight reduced discount;

if the reduced freight rate is greater than a preset freight rate threshold, predicting lost freight rate according to the historical return data and the return rate;

and adding the lost freight and the reduced freight to obtain the freight.

9. The big data based commodity order generating method according to claim 6, further comprising:

carrying out big data analysis according to the historical shopping data to obtain the consumption preference statistical condition of the client;

determining recommended commodity information of the client according to the consumption preference statistics and the consumption grade;

and displaying the recommended commodity information in the generated order.

10. The big data based commodity order generating method according to claim 1, wherein said historical shopping data further comprises a rate of change of said customer; the method further comprises the steps of:

acquiring historical goods changing data and quality complaint records corresponding to the commodity information;

analyzing according to the historical goods change data, the quality complaint records and the goods change rate to obtain predicted goods change quantity;

Judging whether the difference value between the inventory quantity and the purchase quantity corresponding to the commodity information is larger than the predicted change quantity or not;

if not, generating a goods regulating instruction according to the predicted goods changing quantity.

11. The big data based commodity order generating method according to claim 1, further comprising:

matching the company main body of the client according to the client number of the client information;

taking the company main body as a payment main body of the order;

after generating the order, executing a payment flow based on the payment body.

12. The big data based commodity order generating method according to claim 1, further comprising:

if a plurality of order generation instructions corresponding to the same commodity information are received at the same time, calculating the sum of the purchase quantity in the commodity information corresponding to the same order generation instructions;

detecting whether the sum of the numbers is greater than an inventory number of the commodity information;

if yes, calculating the purchase weight of each customer according to the purchase quantity corresponding to each order generation instruction;

obtaining the distribution quantity corresponding to each client according to the stock quantity and each purchase weight;

Generating order inquiry information according to the distribution quantity;

and after receiving the order confirmation instruction, replacing the purchase quantity in the commodity information with the distribution quantity.

13. A commodity order generating apparatus based on big data, the apparatus comprising:

the receiving module is used for receiving an order generation instruction and acquiring commodity information and customer information corresponding to the order generation instruction;

the acquisition module is used for acquiring historical shopping data of the client and the client type according to the client information; the historical shopping data comprises accumulated consumption amount, historical consumption frequency, historical ordering time and shopping channel information of the clients;

the analysis module is used for carrying out big data analysis on the historical shopping data to obtain the consumption grade of the client;

the discount determining module is used for determining full discount and discount corresponding to the client according to the consumption grade and the client type;

the price calculation module is used for calculating a transaction price according to the commodity information, the full-reduction discount and the discount;

a freight calculation module for determining freight based on the commodity information, the price for the transaction, the customer type, and the customer information;

And the generation module is used for generating an order based on the transaction price, the commodity information, the client information and the freight.

14. 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 processor implements the steps of the big data based commodity order generating method according to any of claims 1 to 12 when the computer program is executed.

15. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the big data based commodity order generating method according to any one of claims 1 to 12.