Methods, devices, equipment, and media for generating digital coupons based on big data

Abstract

The present application discloses a method, device, equipment and medium for generating digital coupons based on big data, wherein the method includes: parsing the commodity selection range in the preferential policy generation request; querying all commodity orders that fall into the commodity selection range on the e-commerce platform; obtaining a preferential policy source data set based on at least the commodity SKU code, the original price of the commodity, the commodity order price, the commodity preferential type, the commodity order time and the buyer identification code in the queried commodity order; inputting the preferential policy source data set into a random forest classification model to output the classification result to obtain several preferential policy sub-data sets; generating digital coupons based on the situation of the digital coupons used by the commodity orders corresponding to the preferential policy sub-data sets. The present application is beneficial in that it provides a method, device, equipment and medium for generating digital coupons based on big data that intelligently generates preferential policies based on existing commodity order data.

Description

Technical Field

[0001] The present application relates to the field of computers, and more specifically, to a method, device, equipment, and medium for generating digital coupons based on big data. Background Art

[0002] Digital coupons are the digital form of coupons, which are generally issued by e-commerce platforms and used to deduct discounts between cooperating stores and consumers. Among them, digital discount coupons and digital vouchers are the main forms. Digital coupons are different from ordinary paper coupons. Their production and dissemination costs are low, and the dissemination effect can be accurately quantified. In the prior art, the issuance of digital coupons cannot generate corresponding preferential strategies based on the existing product order data of the e-commerce platform, and has little significance for the sales volume of merchants. Summary of the invention

[0003] The content of this application is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this application is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0004] Some embodiments of the present application propose a method, device, equipment and medium for generating digital coupons based on big data to solve the technical problems mentioned in the above background technology section.

[0005] As a first aspect of the present application, some embodiments of the present application provide a digital coupon generation method based on big data, including: in response to a discount policy generation request sent by a merchant terminal, parsing the product selection range in the discount policy generation request; based on the product selection range, querying all product orders on the e-commerce platform that fall within the product selection range; obtaining a discount policy source data set based on at least the product SKU code, product original price, product order price, product discount type, product order time and buyer identification code in the queried product order; inputting the discount policy source data set into a random forest classification model so that the random forest classification model outputs a classification result for classifying the discount policy source data set to obtain several discount policy sub-data sets; generating digital coupons based on the digital coupons used for the product orders corresponding to the discount policy sub-data sets.

[0006] Furthermore, generating digital coupons based on the digital coupons used for the commodity orders corresponding to the preferential policy sub-dataset includes: querying the digital coupons used for all commodity orders corresponding to a preferential policy sub-dataset; judging and selecting a group of preferential policy sub-datasets with the highest proportion of digital coupon use as the preferential policy selected data set.

[0007] Furthermore, generating digital coupons according to the digital coupons used for the commodity orders corresponding to the preferential policy sub-dataset also includes: performing clustering operations on the digital coupons used for all commodity orders corresponding to the preferential policy selected data set to obtain a plurality of digital coupon cluster sets.

[0008] Furthermore, generating digital coupons based on the digital coupons used for the commodity orders corresponding to the discount policy sub-dataset also includes: calculating the average discount effectiveness parameter of each digital coupon cluster set; and selecting one of several digital coupon cluster sets as the discount policy selected cluster based on the average discount effectiveness parameter.

[0009] Furthermore, generating digital coupons based on the digital coupons used for the commodity orders corresponding to the preferential policy sub-dataset also includes: selecting a number of digital coupons in the preferential policy selection cluster according to preset sorting conditions and generating preferential policy feature data based on the selected digital coupons; inputting the preferential policy feature data into a preferential policy generation model so that the preferential policy generation model outputs an alternative preferential policy and a corresponding confidence level; determining whether the confidence level of the alternative preferential policy is greater than or equal to a confidence threshold, and if so, feeding back the alternative preferential policy to the merchant terminal, and if not, feeding back the digital coupon corresponding to the preferential policy feature data to the merchant terminal.

[0010] Furthermore, generating a digital coupon based on the digital coupon used for the commodity order corresponding to the discount policy sub-data set also includes: generating a digital coupon in response to the merchant terminal's operation feedback instruction on the digital coupon corresponding to the alternative discount policy or discount policy feature data.

[0011] Furthermore, the clustering operation performed on the digital coupons used in all the commodity orders corresponding to the preferential policy selected data set to obtain a number of digital coupon cluster sets is a K-means clustering operation.

[0012] As a second aspect of the present application, some embodiments of the present application provide a digital coupon generation device based on big data, including: a parsing module, used to respond to a preferential policy generation request sent by a merchant terminal, and parse the product selection range in the preferential policy generation request; a query module, used to query all product orders that fall into the product selection range on the e-commerce platform according to the product selection range; an acquisition module, used to obtain a preferential policy source data set based on at least the product SKU code, original product price, product order price, product discount type, product order time and buyer identification code in the queried product order; an input module, used to input the preferential policy source data set into a random forest classification model so that the random forest classification model outputs a classification result for classifying the preferential policy source data set to obtain several preferential policy sub-data sets; a generation module, used to generate digital coupons according to the digital coupons used for the product orders corresponding to the preferential policy sub-data sets.

[0013] As the third aspect of the present application, some embodiments of the present application provide an electronic device, comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0014] As a fourth aspect of the present application, some embodiments of the present application provide a computer-readable medium on which a computer program is stored, wherein when the program is executed by a processor, the method described in any implementation of the above-mentioned first aspect is implemented.

[0015] The beneficial effect of the present application is that it provides a method, device, equipment and medium for generating digital coupons based on big data, which intelligently generates preferential strategies based on existing commodity order data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The illustrative embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application.

[0017] In addition, throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the components and elements are not necessarily drawn to scale.

[0018] In the attached picture:

[0019] Figure 1 This is an application scenario diagram of a digital coupon generation method based on big data according to an embodiment of the present application;

[0020] Figure 2 This is a schematic diagram of the main steps of a method for generating digital coupons based on big data according to an embodiment of the present application;

[0021] Figure 3 It is a schematic diagram of a part of specific steps of step S4 in the method for generating digital coupons based on big data according to an embodiment of the present application;

[0022] Figure 4 It is a schematic diagram of a part of specific steps of step S5 in the method for generating digital coupons based on big data according to an embodiment of the present application;

[0023] Figure 5 It is a schematic diagram of a part of specific steps of step S35 in the method for generating digital coupons based on big data according to an embodiment of the present application;

[0024] Figure 6 It is a schematic diagram of a part of specific steps of step S45 in the method for generating digital coupons based on big data according to an embodiment of the present application;

[0025] Figure 7 is a schematic block diagram of modules of a digital coupon generating device based on big data according to an embodiment of the present application;

[0026] Figure 8 It is a structural schematic diagram of an electronic device according to an embodiment of the present application.

[0027] Meaning of the reference numerals in the figures:

[0028] 101. Merchant terminal; 102. Buyer terminal; 103. Server;

[0029] 800, electronic device; 801, processing device; 802, ROM; 803, RAM; 804, bus; 805, I / O interface; 806, input device; 807, output device; 808, storage device; 809, communication device. DETAILED DESCRIPTION

[0030] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0031] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0032] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0033] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0034] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0035] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0036] refer to Figure 1 As shown, Figure 1 A specific application scenario of the present application is shown. In this application scenario, a two-way communication connection is established between the merchant terminal and the server, and the server generates a digital coupon based on a request generated by the merchant terminal's preferential policy; a two-way communication connection is established between the buyer terminal and the server, and the server sends the generated digital coupon to the buyer terminal.

[0037] Specifically, the merchant terminal and the buyer terminal of the present application can be constructed as an electronic device such as a smart phone, a tablet computer or a notebook computer.

[0038] like Figure 2 As shown, the digital coupon generation method based on big data of the present application mainly includes the following steps:

[0039] S1: In response to a preferential policy generation request sent by a merchant terminal, a commodity selection range in the preferential policy generation request is parsed.

[0040] Specifically, the server generates a request based on the merchant's preferential policy for a certain product, automatically determines the product selection range, where the product selection range can be the product category to which the product belongs on the e-commerce platform, and then obtains all product orders on the e-commerce platform that fall within the product selection range to obtain a large amount of related historical order data that can be used for analysis.

[0041] S2: According to the selected product range, query all product orders on the e-commerce platform that fall within the selected product range.

[0042] S3: Obtain a discount policy source data set based on at least the product SKU code, the product original price, the product order price, the product discount type, the product order time, and the buyer identification code in the queried product order.

[0043] S4: Input the preferential policy source data set into a random forest classification model so that the random forest classification model outputs a classification result for classifying the preferential policy source data set to obtain a plurality of preferential policy sub-data sets.

[0044] S5: Generate a digital coupon according to the digital coupon used for the product order corresponding to the discount policy sub-dataset.

[0045] By adopting such a solution, the sales volume of merchants can be promoted by intelligently generating preferential strategies based on the orders of goods that are in the same product selection range as the goods for which coupons are to be generated and based on the data of these orders of goods.

[0046] As a specific plan, refer to Figure 3 As shown, the above step S4 specifically includes the following steps:

[0047] S41: randomly divide 80% of the samples in the preferential policy source data set D into a training data set F and 20% of the samples into a testing data set G according to uniform distribution.

[0048] S42: Train a random forest model on the training data set F to generate an initial random forest model containing m trees. During training, the value of m can be 100. When training the model, a CART classification and regression tree is used as an algorithm for generating a decision tree.

[0049] S43: Input the test data set G into the initial random forest model, and calculate the similarity and accuracy of the classification results of each decision tree.

[0050] As a specific solution, the similarity in step S43 is determined by Kappa statistics. The accuracy is measured based on the variance between the predicted classification of the sample and the sample label classification of the decision tree. After normalization, the accuracy of the jth decision tree for all samples in the test data set G is obtained.

[0051] S44: Calculate the comprehensive performance index of each decision tree by combining classification accuracy and similarity.

[0052] S45: The first s decision trees with higher comprehensive indicators are taken to form an improved random forest, which is used to classify the preferential policy source data set.

[0053] As a specific plan, refer to Figure 4 As shown, the above step S5 specifically includes the following steps:

[0054] S51: Query the digital coupons used by all product orders corresponding to a discount policy sub-dataset.

[0055] S52: Determine and select a group of preferential policy sub-datasets with the highest proportion of digital coupon usage as the preferential policy selected data set.

[0056] S53: performing clustering operations on the digital coupons used in all the commodity orders corresponding to the preferential policy selected data set to obtain a plurality of digital coupon cluster sets.

[0057] As a preferred solution, the clustering operation in step S53 is a K-means clustering operation.

[0058] S54: Calculate the average discount effectiveness parameter of each digital coupon cluster set.

[0059] S55: Select one of several digital coupon cluster sets as a preferential policy selected cluster according to the average preferential effectiveness parameter.

[0060] S56: Selecting a number of digital coupons in the preferential policy selection cluster according to a preset sorting condition and generating preferential policy feature data according to the selected digital coupons.

[0061] As a specific solution, the preset sorting condition in step S56 may be the number of digital coupons used in the preferential policy selected cluster or the sum of preferential values ​​of all digital coupons in the preferential policy selected cluster.

[0062] S57: Inputting the preferential policy feature data into a preferential policy generation model so that the preferential policy generation model outputs an alternative preferential policy and a corresponding confidence level.

[0063] Specifically, the preferential strategy generation model in step S57 adopts a CNN model, wherein the prediction model is constructed by collecting digital coupon data from hot-selling products in historical orders of the e-commerce platform to form corresponding training set data, thereby training the preferential strategy generation model so that it can output an alternative preferential strategy and corresponding confidence level based on the preferential strategy feature data.

[0064] S58: Determine whether the confidence of the candidate preferential policy is greater than or equal to the confidence threshold, if so, feed back the candidate preferential policy to the merchant terminal, if not, feed back the digital coupon corresponding to the preferential policy feature data to the merchant terminal.

[0065] S59: In response to the merchant terminal's operation feedback instruction on the digital coupon corresponding to the candidate preferential policy or preferential policy characteristic data, a digital coupon is generated.

[0066] As a specific plan, refer to Figure 5As shown, the above step S53 specifically includes the following steps:

[0067] S531: The digital coupons used by all the commodity orders corresponding to the selected data set are obtained according to the discount policy to obtain the first discount parameter of the digital coupons.

[0068] S532: The digital coupons used by all the commodity orders corresponding to the selected data set are obtained according to the discount policy to obtain the second discount parameter of the digital coupons.

[0069] S533: The digital coupons used by all the commodity orders corresponding to the selected data set are obtained according to the discount policy to obtain the third discount parameter of the digital coupons.

[0070] S534: The first discount parameter, the second discount parameter, and the third discount parameter are used as three coordinate dimensions of a discount feature coordinate system to obtain coordinate values ​​of digital coupons used for all product orders corresponding to the discount strategy selected data set.

[0071] In this way, the preferential parameters corresponding to the digital coupons used in all the commodity orders corresponding to the preferential policy selected data set are reflected in the three-dimensional feature coordinate system, and the clustering set of digital coupons with similar preferential features can be intuitively obtained.

[0072] The first preferential parameter is a parameter linearly associated with the commodity price corresponding to the digital coupon, the second preferential parameter is a parameter linearly associated with the preferential value of the digital coupon, and the third preferential parameter is a parameter linearly associated with the validity period of the digital coupon. In this way, the coordinate value distribution of the digital coupons used in all commodity orders corresponding to the preferential strategy selected data set in the preferential feature coordinate system will not be too discrete, thereby improving the clustering effect.

[0073] As a specific plan, refer to Figure 6 As shown, the above step S54 specifically includes the following steps:

[0074] S541: Calculate the discount average value P of all digital coupons in each digital coupon cluster set.

[0075] Specifically, the discount average Pa = ∑Pi / N1, where Pi is the discount price corresponding to the digital coupon, i is the number corresponding to the coupon in the digital coupon cluster set, and i = N + , N1 is the number of orders corresponding to the digital coupon cluster set. Of course, the discount average value P here can be various means in statistics.

[0076] S541: Calculate the total number of orders N2 associated with all digital coupons in each digital coupon cluster set.

[0077] S543: Combining the average discount value Pa and the total number of orders N2, obtain an average discount effectiveness parameter E, where the average discount effectiveness parameter E = N2 / Pa.

[0078] Considering that a product order may use multiple coupons, if we simply use the average value of the total discount price of all digital coupons in the digital coupon cluster set relative to the total number of digital coupons, it cannot accurately reflect the actual effect of the digital coupons in the digital coupon cluster set. By adopting the above scheme, the average discount effectiveness parameter is associated with each product order, so that the actual effect of the digital coupons in the digital coupon cluster set can be well reflected.

[0079] like Figure 7 As shown, the digital coupon generation device based on big data of the present application includes: a parsing module, which is used to respond to a preferential policy generation request sent by a merchant terminal and parse the product selection range in the preferential policy generation request; a query module, which is used to query all product orders that fall into the product selection range on the e-commerce platform according to the product selection range; an acquisition module, which is used to obtain a preferential policy source data set based on at least the product SKU code, the original price of the product, the product order price, the product discount type, the product order time and the buyer identification code in the queried product order; an input module, which is used to input the preferential policy source data set into a random forest classification model so that the random forest classification model outputs a classification result for classifying the preferential policy source data set to obtain a number of preferential policy sub-data sets; a generation module, which is used to generate digital coupons according to the situation of digital coupons used for the product orders corresponding to the preferential policy sub-data sets.

[0080] like Figure 8 As shown, the electronic device 800 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage device 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 are also stored. The processing device 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0081] Typically, the following devices may be connected to the I / O interface 805: input devices 806 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 807 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 808 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 809. The communication device 809 may allow the electronic device 800 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 8 The electronic device 800 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 8 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0082] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 809, or installed from the storage device 808, or installed from the ROM 802. When the computer program is executed by the processing device 801, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0083] It should be noted that the computer-readable medium described above in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0084] In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device, or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0085] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperTextTransferProtocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an adhoc peer-to-peer network), as well as any currently known or future developed network.

[0086] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: responds to a preferential policy generation request sent by a merchant terminal, parses the commodity selection range in the preferential policy generation request; queries all commodity orders on the e-commerce platform that fall within the commodity selection range according to the commodity selection range; obtains a preferential policy source data set according to at least the commodity SKU code, commodity original price, commodity order price, commodity preferential type, commodity order time and buyer identification code in the queried commodity order; inputs the preferential policy source data set into a random forest classification model so that the random forest classification model outputs a classification result of the preferential policy source data set to obtain a number of preferential policy sub-data sets; generates digital coupons according to the situation of digital coupons used in the commodity orders corresponding to the preferential policy sub-data sets.

[0087] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0088] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, which contains one or more executable instructions for implementing a specified logical function.

[0089] It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.

[0090] For example, two boxes shown in succession may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of boxes in the block diagram and / or flow chart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0091] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0092] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for generating digital coupons based on big data, comprising: In response to a preferential policy generation request sent by a merchant terminal, parsing a commodity selection range in the preferential policy generation request; According to the selected product range, query all product orders on the e-commerce platform that fall within the selected product range; Obtaining a preferential policy source data set based on at least the product SKU code, the original price of the product, the order price of the product, the type of preferential product, the order time of the product, and the buyer identification code in the queried product order; Inputting the preferential policy source data set into a random forest classification model so that the random forest classification model outputs a classification result of classifying the preferential policy source data set to obtain a plurality of preferential policy sub-data sets; Generate a digital coupon according to the digital coupon used for the commodity order corresponding to the discount policy sub-dataset; The generating of the digital coupon according to the digital coupon used for the commodity order corresponding to the discount policy sub-dataset includes: Querying the digital coupons used by all the commodity orders corresponding to the discount policy sub-dataset; Determine and select a group of the preferential policy sub-datasets with the highest proportion of digital coupon usage as the preferential policy selected data set; The step of generating a digital coupon according to the digital coupon used for the commodity order corresponding to the discount policy sub-dataset also includes: Performing clustering operations on the digital coupons used in all the commodity orders corresponding to the preferential policy selected data set to obtain a plurality of digital coupon cluster sets; The step of generating a digital coupon according to the digital coupon used for the commodity order corresponding to the discount policy sub-dataset also includes: Calculating an average discount effectiveness parameter for each of the digital coupon cluster sets; One of the plurality of digital coupon cluster sets is selected as a preferential policy selected cluster according to the average preferential effectiveness parameter.

2. The method for generating digital coupons based on big data according to claim 1, wherein: The step of generating a digital coupon according to the digital coupon used for the commodity order corresponding to the discount policy sub-dataset also includes: Selecting a number of the digital coupons in the preferential policy selection cluster according to a preset sorting condition and generating preferential policy feature data according to the selected digital coupons; Inputting the preferential policy feature data into a preferential policy generation model so that the preferential policy generation model outputs an alternative preferential policy and a corresponding confidence level; It is determined whether the confidence of the candidate preferential policy is greater than or equal to a confidence threshold, if so, the candidate preferential policy is fed back to the merchant terminal, if not, the digital coupon corresponding to the preferential policy characteristic data is fed back to the merchant terminal.

3. The method for generating digital coupons based on big data according to claim 2, wherein: The step of generating a digital coupon according to the digital coupon used for the commodity order corresponding to the discount policy sub-dataset also includes: In response to the merchant terminal's operation feedback instruction on the digital coupon corresponding to the candidate discount policy or the discount policy characteristic data, the digital coupon is generated.

4. The method for generating digital coupons based on big data according to claim 3, wherein: The digital coupons used in all the commodity orders corresponding to the preferential policy selected data set are clustered to obtain a number of digital coupons, and the clustering operation in the cluster set is a K-means clustering operation.

5. A digital coupon generation device based on big data, comprising: A parsing module, configured to respond to a preferential policy generation request sent by a merchant terminal and parse a commodity selection range in the preferential policy generation request; A query module, used to query all commodity orders on the e-commerce platform that fall within the commodity selection range according to the commodity selection range; An acquisition module, used to acquire a preferential policy source data set according to at least a product SKU code, a product original price, a product order price, a product preferential type, a product order time and a buyer identification code in the queried product order; An input module, used for inputting the preferential policy source data set into a random forest classification model so that the random forest classification model outputs a classification result of classifying the preferential policy source data set to obtain a plurality of preferential policy sub-data sets; A generating module, configured to generate a digital coupon according to the digital coupon used by the commodity order corresponding to the preferential policy sub-dataset; The generating of the digital coupon according to the digital coupon used for the commodity order corresponding to the discount policy sub-dataset includes: Querying the digital coupons used by all the commodity orders corresponding to the discount policy sub-dataset; Determine and select a group of the preferential policy sub-datasets with the highest proportion of digital coupon usage as the preferential policy selected data set; The step of generating a digital coupon according to the digital coupon used for the commodity order corresponding to the discount policy sub-dataset also includes: Performing clustering operations on the digital coupons used in all the commodity orders corresponding to the preferential policy selected data set to obtain a plurality of digital coupon cluster sets; The step of generating a digital coupon according to the digital coupon used for the commodity order corresponding to the discount policy sub-dataset also includes: Calculating an average discount effectiveness parameter for each of the digital coupon cluster sets; One of the plurality of digital coupon cluster sets is selected as a preferential policy selected cluster according to the average preferential effectiveness parameter.

6. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the processors implement the method according to any one of claims 1 to 4.

7. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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