CN117217785A - Pricing method and device for freight insurance service fee, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure relates to the technical field of artificial intelligence, in particular to a method, a device, electronic equipment and a storage medium for pricing freight insurance service fees. The specific implementation scheme is as follows: inputting first transaction related information of a merchant into a pre-trained first return rate prediction model to obtain a merchant return rate corresponding to the merchant; determining a first shipping charge insurance service fee according to the return rate of the merchant; inputting second transaction related information of the user into a pre-trained second return rate prediction model to obtain a user return rate corresponding to the user; and adjusting the first freight rate and the first freight rate service fee according to the user return rate to obtain the second freight rate and the first freight rate. And the balance of the return freight is ensured, the threshold of the return freight of the merchant is reduced, the coverage range of freight insurance is improved, and the after-sale experience of the electronic merchant is improved.
Description
Technical Field
The disclosure relates to the technical field of artificial intelligence, in particular to a method, a device, electronic equipment and a storage medium for pricing freight insurance service fees.
Background
In the e-commerce scene, due to online transaction, after the consumer returns goods, goods returning and goods changing actions usually occur, and freight risks can well solve the concern of returning goods of the consumer, so that the online shopping experience of the user is ensured. The freight insurance is freight insurance of a merchant buying bill, and the service fee is mainly used for deducting part of the money of the merchant as freight insurance service fee after the buyer places the bill.
The freight insurance service fee calculation mode in the prior art is single, the targeted intervention can not be carried out aiming at partial high-risk shops, high-risk users or sales promotion activities, the service fee is adjusted to be mostly in the routing lower flow, the adjustment period is long, and the technical problem that the balance of the return freight is not ensured can not be solved.
Disclosure of Invention
The disclosure provides a method, a device, electronic equipment and a storage medium for pricing freight insurance service fees.
According to an aspect of the present disclosure, there is provided a method of pricing a shipping fee risk service fee, comprising:
acquiring first transaction related information of a merchant, and inputting the first transaction related information into a pre-trained first return rate prediction model to obtain a merchant return rate corresponding to the merchant;
determining a first shipping cost insurance service fee according to the merchant return rate;
acquiring second transaction related information of a user, and inputting the second transaction related information into a pre-trained second return rate prediction model to obtain a user return rate corresponding to the user;
and adjusting the first freight rate and the first freight rate service fee according to the user return rate to obtain a second freight rate and first freight rate service fee.
According to another aspect of the present disclosure, there is provided a pricing device for a shipping fee risk service fee, comprising:
the first prediction module is configured to acquire first transaction related information of a merchant, input the first transaction related information into a pre-trained first return rate prediction model, and obtain a merchant return rate corresponding to the merchant;
a determining module configured to determine a first shipping cost insurance service fee according to the merchant return rate;
the second prediction module is configured to acquire second transaction related information of a user, input the second transaction related information into a pre-trained second return rate prediction model and acquire a user return rate corresponding to the user;
and the adjusting module is configured to adjust the first freight rate service fee according to the user return rate to obtain a second freight rate service fee.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the above claims.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method of any one of the above-mentioned technical solutions.
According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method of any of the above technical solutions.
The disclosure provides a pricing method, a pricing device, electronic equipment and a storage medium for freight insurance service fees, which are mainly used for guaranteeing balance of return freight and repair freight fees, reducing thresholds of return freight and repair freight fees of merchants, improving coverage of freight insurance and improving after-sales experience of electronic commerce.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram of steps of a method for pricing a shipping fee to be serviced in an embodiment of the present disclosure;
FIG. 2 is a flow diagram of a method of tariff pricing in an embodiment of the disclosure;
FIG. 3 is a functional block diagram of a freight rate insurance service rate pricing device in an embodiment of the present disclosure;
fig. 4 is a block diagram of an electronic device for implementing a shipping fee risk service fee pricing method according to embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The present disclosure provides a method for pricing a shipping insurance service fee, as shown in fig. 1, comprising:
step S101, acquiring first transaction related information of a merchant, and inputting the first transaction related information into a pre-trained first return rate prediction model to obtain a merchant return rate corresponding to the merchant;
step S102, determining a first freight rate insurance service fee according to the return rate of a merchant;
step S103, obtaining second transaction related information of the user, and inputting the second transaction related information into a pre-trained second return rate prediction model to obtain a user return rate corresponding to the user;
step S104, the first freight rate insurance service fee is adjusted according to the user return rate, and the second freight rate insurance service fee is obtained.
Specifically, the first return rate prediction model is a model obtained by training historical transaction related information of a merchant based on a deep learning model as a training sample, and the second return rate prediction model is a model obtained by training based on a deep learning model by using historical transaction related information of a user as a training sample. In this embodiment, the first return rate prediction model is configured to predict a return rate of a dimension of a merchant according to historical transaction related information of the merchant, and determine a basic freight risk service fee according to the return rate of the merchant, for example, when the return rate of a store is predicted to be 20%, it is determined that the first freight risk service fee is 3 yuan. Further, the second goods return rate prediction model predicts the goods return rate of the user dimension according to the historical transaction related information of the user, for example, the goods return rate of the user for each month is 30% in the past 12 months, the goods return rate for the clothing class is 70%, if the current user purchases the clothing, the second goods return rate prediction model predicts the goods return rate of the current order, for example, the current order goods return rate is 65%, the system judges that the goods return risk of the user is higher, and 1 yuan can be added on the basis of the first freight risk service fee, so that the second freight risk service fee is 4 yuan.
Through the method, the service charge can be adjusted up in a targeted manner for high-risk merchants and high-risk users, and the service charge can also be adjusted down in a targeted manner for low-risk merchants and low-risk users, so that balance of return goods and freight repairing fees is ensured, after-sale experience of electronic commerce and online shopping experience of users are improved, and ecological virtuous circle of the electronic commerce is promoted.
As an optional implementation manner, after determining the first freight rate risk service fee according to the return rate of the merchant, the method further includes:
and acquiring an intervention strategy input manually, and adjusting the first freight insurance service fee according to the intervention strategy.
Illustratively, as shown in fig. 2, the manual intervention may also be performed on the freight risk service fee in this embodiment. Manual intervention mainly intervenes from three dimensions, namely, large-disc intervention, class intervention and shop intervention, wherein the large-disc intervention mainly refers to sales promotion activities in a specific period, the class intervention refers to sales promotion activities in certain classes, and the goods return rate of shops can be higher than usual, so that service fees are also required to be adjusted in a targeted way. The store intervention refers to the service fee intervention of a high-risk store with abnormal operation or a store with excellent credit, and the store dimension intervention mainly corrects a scene with a large difference between partial store risk rate estimation and actual return rate.
Thus, the service fee pricing party may determine a corresponding intervention policy based on the several dimensions described above, which may include in particular: floating proportional intervention, floating absolute intervention, absolute intervention. The floating proportion intervention refers to the current service charge price according to proportion intervention, for example, the current price is 1 yuan, the floating proportion intervention is 1 percent, and the post-intervention price is 1.01 yuan; the floating absolute intervention is to perform absolute intervention on the current service charge price, and if the current service charge is 1 yuan and the floating absolute intervention is 0.1 yuan, the price after the intervention is 1.1 yuan; absolute intervention, if the current service charge price is 1 yuan, directly using a fixed value of 1.2 yuan to replace the original 1 yuan, and the absolute dry prognosis service charge price is 1.2 yuan. The corresponding intervention module can be displayed on the operation interface, and a user can determine different intervention strategies through the intervention module, so that after-sale experience of the electronic commerce is ensured, and meanwhile, balance of freight insurance balance is ensured.
As an alternative embodiment, the intervention module may obtain the merchant return rate currently predicted by the first return rate prediction model, compare the merchant return rate with the historical merchant return rate, calculate a difference between the two, and provide the service fee adjustment interval based on the difference between the two. For example, the first return rate prediction model predicts that the current return rate of the merchant is 30%, and the historical average return rate of the merchant is 40%, and because of the large difference, the intervention module can calculate that the corresponding service fee adjustment interval is 1% -3% of the floating height, and display the service fee adjustment interval on the operation interface so as to prompt the service fee pricing party to perform manual intervention.
As an optional implementation manner, step S101, obtaining first transaction related information of a merchant, inputting the first transaction related information into a pre-trained first return rate prediction model, and obtaining a merchant return rate corresponding to the merchant includes:
acquiring one or more information of store data, commodity data, store order data, store after-sales data and evaluation data of a merchant as first transaction related information;
extracting features of the first transaction related information to obtain first transaction related features;
and inputting the first transaction related characteristics into a first return rate prediction model to obtain the return rate of the merchant.
In particular, calculating the merchant return rate may be an offline calculation, updated once per calculation period. The extracting features of the first transaction related information to obtain the first transaction related features may specifically include at least one of the following: extracting features of commodity data to obtain at least one of commodity information, commodity price information, sales information and stock abundance information (sku abundance, namely a minimum product delivery unit, for example, a mobile phone product has three colors of red, black and white, and then a red mobile phone is a sku) and commodity heat information; extracting the characteristics of store data to obtain at least one of store sales information, store price information, store category information, store equity information (for example, whether the store has live equity or not, and the order in live activities has higher return risk relatively), and preferential information; extracting features of store order data to obtain at least one item of store order information and order period information; performing feature extraction on the after-sales data of the stores to obtain at least one item of store return information and store return period information; at least one of order grading, store grading and user emotion characteristics obtained by positive and negative emotion analysis based on user evaluation texts is obtained by feature extraction of evaluation data, in the embodiment, the user order grading and store grading are considered, emotion analysis is performed on the user evaluation texts based on NLP (Natural Language Processing ), emotion trends, attitudes and emotions in the texts are identified and extracted, comprehensive analysis is performed by combining the emotion of the user, satisfaction of the user on commodities and stores can be known more accurately, and accuracy of model prediction is improved.
As an optional implementation manner, step S103, obtaining second transaction related information of the user, inputting the second transaction related information into a pre-trained second return rate prediction model, and obtaining the user return rate corresponding to the user includes:
acquiring user portrait data and user behavior data of a user as second transaction related information;
extracting features of the second transaction related information to obtain second transaction related features;
and inputting the second transaction related features into a second return rate prediction model to obtain the user return rate.
Specifically, the user portrait data comprises at least one of user basic information, account risk information and consumption scene information; the user behavior data includes user order information (e.g., shopping cart, merchandise collection, time to order, etc.), user after-market information (e.g., returns, changes, etc.). Meanwhile, an online real-time feedback mechanism is established in the embodiment, user order data are updated in real time, and user return risks are predicted through offline data (user portrait data) and real-time data (user order data), so that service fees are automatically optimized, quick response of service fee update is guaranteed, user experience is guaranteed, and balance of balance is guaranteed.
As an optional implementation, step S102, determining the first freight rate risk service fee according to the merchant return rate includes:
and determining the first freight rate insurance service fee according to the mapping relation between the return rate of the merchant and the freight rate insurance service fee.
For example, a mapping table between the merchant return rate and the freight rate service fee may be preset, for example, when the merchant return rate is 10%, the corresponding freight rate service fee is 2 yuan, when the merchant return rate is 20%, the corresponding freight rate service fee is 2.2 yuan, and when the merchant return rate is 30%, the corresponding freight rate service fee is 2.4 yuan.
As an optional implementation manner, step S104, adjusting the first freight rate service fee according to the user return rate, and obtaining the second freight rate service fee includes:
determining a user risk level of the user according to the user return rate;
determining a corresponding service charge adjustment value according to the user risk level;
and calculating the second freight rate insurance service fee according to the first freight rate insurance service fee and the service fee adjustment value.
Illustratively, users are classified into three classes according to their return rates: low risk rate users, medium risk rate users, high risk rate users. The user risk rate is core logic for influencing the service fee, and for low risk rate users, the more the user orders, the service fee can be properly adjusted downwards; the medium risk rate user and the ordering condition basically do not influence the service charge change; the more high risk users place orders, the more service fees will be adjusted upwards appropriately.
The embodiment of the disclosure also provides a pricing device 300 for a freight rate insurance service fee, as shown in fig. 3, including:
the first prediction module 301 is configured to obtain first transaction related information of a merchant, and input the first transaction related information into a pre-trained first return rate prediction model to obtain a merchant return rate corresponding to the merchant;
a determination module 302 configured to determine a first shipping cost insurance service fee based on the merchant return rate;
the second prediction module 303 is configured to obtain second transaction related information of the user, input the second transaction related information into a pre-trained second return rate prediction model, and obtain a user return rate corresponding to the user;
the adjustment module 304 is configured to adjust the first freight rate and the first freight rate according to the user return rate, so as to obtain the second freight rate and the first freight rate.
Specifically, the first return rate prediction model is a model obtained by training historical transaction related information of a merchant based on a deep learning model as a training sample, and the second return rate prediction model is a model obtained by training based on a deep learning model by using historical transaction related information of a user as a training sample. In this embodiment, the first return rate prediction model is configured to predict a return rate of a dimension of a merchant according to historical transaction related information of the merchant, and determine a basic freight risk service fee according to the return rate of the merchant, for example, when the first prediction module 301 predicts that the return rate of a store is 20%, the determination module 302 determines that the first freight risk service fee is 3 yuan. Further, the second prediction module 303 predicts the user dimensional return rate according to the user's historical transaction related information through the second return rate prediction model, for example, the user returns 30% per month in the past 12 months, the commodity return rate for the clothing class is 70%, if the current user purchases the clothing, the current order return rate can be predicted through the second return rate prediction model, for example, 65% of the current order return rate, the system determines that the user return risk is higher, and the adjustment module 304 can add 1 element on the basis of the first freight risk service fee to obtain the second freight risk service fee as 4 elements.
Through the pricing device in this embodiment, can carry out the pertinence to adjust up the service charge to high risk merchant and high risk user, also can carry out the pertinence to adjust down the service charge to low risk merchant and low risk user to guarantee the balance of the expense of returning goods benefit freight charge, promote the after-sale experience of electricity merchant and user's online shopping experience, promote the ecological virtuous circle of electricity merchant.
As an alternative embodiment, as shown in fig. 2, the apparatus further includes:
the intervention module 201 is configured to acquire a manually input intervention policy after determining the first freight rate risk service fee according to the return rate of the merchant, and adjust the first freight rate risk service fee according to the intervention policy.
Illustratively, as shown in fig. 2, the manual intervention may also be performed on the freight risk service fee in this embodiment. Manual intervention mainly intervenes from three dimensions, namely, large-disc intervention, class intervention and shop intervention, wherein the large-disc intervention mainly refers to sales promotion activities in a specific period, the class intervention refers to sales promotion activities in certain classes, and the goods return rate of shops can be higher than usual, so that service fees are also required to be adjusted in a targeted way. The store intervention refers to the service fee intervention of a high-risk store with abnormal operation or a store with excellent credit, and the store dimension intervention mainly corrects a scene with a large difference between partial store risk rate estimation and actual return rate.
Thus, the service fee pricing party may determine a corresponding intervention policy based on the several dimensions described above, which may include in particular: floating proportional intervention, floating absolute intervention, absolute intervention. The floating proportion intervention refers to the current service charge price according to proportion intervention, for example, the current price is 1 yuan, the floating proportion intervention is 1 percent, and the post-intervention price is 1.01 yuan; the floating absolute intervention is to perform absolute intervention on the current service charge price, and if the current service charge is 1 yuan and the floating absolute intervention is 0.1 yuan, the price after the intervention is 1.1 yuan; absolute intervention, if the current service charge price is 1 yuan, directly using a fixed value of 1.2 yuan to replace the original 1 yuan, and the absolute dry prognosis service charge price is 1.2 yuan. The corresponding intervention module can be displayed on the operation interface, and a user can determine different intervention strategies through the intervention module, so that after-sale experience of the electronic commerce is ensured, and meanwhile, balance of freight insurance balance is ensured.
As an alternative embodiment, the first prediction module 301 includes:
a first acquisition unit configured to acquire, as first transaction-related information, one or more of store data, commodity data, store order data, store after-sales data, and evaluation data of a merchant;
the first feature extraction unit is configured to perform feature extraction on the first transaction related information to obtain first transaction related features;
the first prediction unit is configured to input the first transaction related features into a first return rate prediction model to obtain a merchant return rate.
Specifically, the first prediction module 301 calculates the merchant return rate may be calculated offline, updated once per calculation period. The feature extraction of the first transaction related information by the first feature extraction unit to obtain the first transaction related feature may specifically include at least one of the following: extracting features of commodity data to obtain at least one of commodity information, commodity price information, sales information and stock abundance information (sku abundance, namely a minimum product delivery unit, for example, a mobile phone product has three colors of red, black and white, and then a red mobile phone is a sku) and commodity heat information; extracting the characteristics of store data to obtain at least one of store sales information, store price information, store category information, store equity information (for example, whether the store has live equity or not, and the order in live activities has higher return risk relatively), and preferential information; extracting features of store order data to obtain at least one item of store order information and order period information; performing feature extraction on the after-sales data of the stores to obtain at least one item of store return information and store return period information; at least one of order grading, store grading and user emotion characteristics obtained by positive and negative emotion analysis based on user evaluation texts is obtained by feature extraction of evaluation data, in the embodiment, the user order grading and store grading are considered, emotion analysis is performed on the user evaluation texts based on an NLP algorithm, emotion trends, attitudes and emotions in the texts are identified and extracted, comprehensive analysis is performed by combining the emotion of the user, satisfaction degree of the user on commodities and stores can be known more accurately, and accuracy of model prediction is improved.
As an alternative embodiment, the second prediction module 303 includes:
a second acquisition unit configured to acquire user portrait data and user behavior data of a user as second transaction-related information;
the second feature extraction unit is configured to perform feature extraction on the second transaction related information to obtain second transaction related features;
the second prediction unit is configured to input the second transaction related features into a second return rate prediction model to obtain the user return rate.
Specifically, the user portrait data comprises at least one of user basic information, account risk information and consumption scene information; the user behavior data includes user order information (e.g., shopping cart, merchandise collection, time to order, etc.), user after-market information (e.g., returns, changes, etc.). Meanwhile, an online real-time feedback mechanism is established in the embodiment, user order data are updated in real time, and user return risks are predicted through offline data (user portrait data) and real-time data (user order data), so that service fees are automatically optimized, quick response of service fee update is guaranteed, user experience is guaranteed, and balance of balance is guaranteed.
As an alternative embodiment, determining module 302 determines the first shipping cost benefit based on the merchant return rate includes:
and determining the first freight rate insurance service fee according to the mapping relation between the return rate of the merchant and the freight rate insurance service fee.
For example, a mapping table between the merchant return rate and the freight rate service fee may be preset, for example, when the merchant return rate is 10%, the corresponding freight rate service fee is 2 yuan, when the merchant return rate is 20%, the corresponding freight rate service fee is 2.2 yuan, and when the merchant return rate is 30%, the corresponding freight rate service fee is 2.4 yuan.
As an optional implementation manner, the adjusting module 304 adjusts the first freight rate service fee according to the user return rate, and obtaining the second freight rate service fee includes:
determining a user risk level of the user according to the user return rate;
determining a corresponding service charge adjustment value according to the user risk level;
and calculating the second freight rate insurance service fee according to the first freight rate insurance service fee and the service fee adjustment value.
Illustratively, users are classified into three classes according to their return rates: low risk rate users, medium risk rate users, high risk rate users. The user risk rate is core logic for influencing the service fee, and for low risk rate users, the more the user orders, the service fee can be properly adjusted downwards; the medium risk rate user and the ordering condition basically do not influence the service charge change; the more high risk users place orders, the more service fees will be adjusted upwards appropriately.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 4 illustrates a schematic block diagram of an example electronic device 400 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 4, the apparatus 400 includes a computing unit 401 that can perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In RAM403, various programs and data required for the operation of device 400 may also be stored. The computing unit 401, ROM 402, and RAM403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Various components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, etc.; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408, such as a magnetic disk, optical disk, etc.; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 401 may be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 401 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning objective function algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as the method of pricing a freight premium service fee. For example, in some embodiments, the method of pricing a freight rate insurance service fee may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 400 via the ROM 402 and/or the communication unit 409. When the computer program is loaded into RAM403 and executed by computing unit 401, one or more steps of the freight rate insurance service fee pricing method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the method of pricing the freight rate insurance service fee in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (20)
1. A method of pricing a freight rate insurance service fee, comprising:
acquiring first transaction related information of a merchant, and inputting the first transaction related information into a pre-trained first return rate prediction model to obtain a merchant return rate corresponding to the merchant;
determining a first shipping cost insurance service fee according to the merchant return rate;
acquiring second transaction related information of a user, and inputting the second transaction related information into a pre-trained second return rate prediction model to obtain a user return rate corresponding to the user;
and adjusting the first freight rate and the first freight rate service fee according to the user return rate to obtain a second freight rate and first freight rate service fee.
2. The method of claim 1, wherein the determining a first shipping cost risk service fee based on the merchant return rate further comprises:
and acquiring an intervention strategy input manually, and adjusting the first freight insurance service fee according to the intervention strategy.
3. The method of claim 1 or 2, wherein the intervention strategy comprises at least one of: floating proportion intervention; absolute intervention of floating; absolute intervention.
4. The method according to any one of claims 1-3, wherein the obtaining the first transaction-related information of the merchant, inputting the first transaction-related information into a pre-trained first return rate prediction model, and obtaining the merchant return rate corresponding to the merchant includes:
acquiring one or more information of store data, commodity data, store order data, store after-sales data and evaluation data of the merchant as the first transaction related information;
extracting features of the first transaction related information to obtain first transaction related features;
and inputting the first transaction related features into the first return rate prediction model to obtain the merchant return rate.
5. The method of claim 4, wherein the feature extraction of the first transaction-related information to obtain a first transaction-related feature comprises at least one of:
extracting features of the commodity data to obtain at least one item of commodity information, commodity price information, sales information, stock quantity richness information and commodity heat information;
performing feature extraction on the store data to obtain at least one of store sales information, store price information, store category information, store equity information and preferential information;
extracting features of the store order data to obtain at least one item of store order information and order period information;
performing feature extraction on the after-store data to obtain at least one item of store return information and store return period information;
and carrying out feature extraction on the evaluation data to obtain at least one of order scores, store scores and user emotion features obtained by carrying out positive and negative emotion analysis based on user evaluation texts.
6. The method according to any one of claims 1-5, wherein the obtaining second transaction-related information of the user, inputting the second transaction-related information into a pre-trained second return rate prediction model, and obtaining a user return rate corresponding to the user includes:
acquiring user portrait data and user behavior data of the user as the second transaction related information;
extracting features of the second transaction related information to obtain second transaction related features;
and inputting the second transaction related features into the second return rate prediction model to obtain the user return rate.
7. The method of claim 6, wherein the feature extraction of the second transaction-related information to obtain a second transaction-related feature comprises:
extracting features of the user portrait data to obtain at least one of user basic information, account risk information and consumption scene information;
and extracting the characteristics of the user behavior data to obtain user order information and after-sales information.
8. The method of any of claims 1-7, wherein the determining a first shipping cost insurance service fee from the merchant return rate comprises:
and determining the first freight rate insurance service fee according to the mapping relation between the merchant return rate and the freight rate insurance service fee.
9. The method of any of claims 1-8, wherein said adjusting the first freight rate at the user return rate to obtain a second freight rate at the first freight rate comprises:
determining a user risk level of the user according to the user return rate;
determining a corresponding service charge adjustment value according to the user risk level;
and calculating the second freight rate insurance service fee according to the first freight rate insurance service fee and the service fee adjustment value.
10. A pricing device for shipping fee insurance service fees, comprising:
the first prediction module is configured to acquire first transaction related information of a merchant, input the first transaction related information into a pre-trained first return rate prediction model, and obtain a merchant return rate corresponding to the merchant;
a determining module configured to determine a first shipping cost insurance service fee according to the merchant return rate;
the second prediction module is configured to acquire second transaction related information of a user, input the second transaction related information into a pre-trained second return rate prediction model and acquire a user return rate corresponding to the user;
and the adjusting module is configured to adjust the first freight rate service fee according to the user return rate to obtain a second freight rate service fee.
11. The apparatus of claim 10, further comprising:
and the intervention module is configured to acquire an intervention strategy input manually after the first freight rate dangerous service fee is determined according to the merchant return rate, and adjust the first freight rate dangerous service fee according to the intervention strategy.
12. The apparatus of claim 10 or 11, wherein the intervention policy comprises at least one of: floating proportion intervention; absolute intervention of floating; absolute intervention.
13. The apparatus of any of claims 10-12, wherein the first prediction module comprises:
a first acquisition unit configured to acquire, as the first transaction-related information, one or more of store data, commodity data, store order data, store after-sales data, and evaluation data of the merchant;
the first feature extraction unit is configured to perform feature extraction on the first transaction related information to obtain first transaction related features;
and the first prediction unit is configured to input the first transaction related characteristic into the first return rate prediction model to obtain the merchant return rate.
14. The apparatus of claim 13, wherein the first feature extraction unit performs feature extraction on the first transaction-related information to obtain a first transaction-related feature comprises at least one of:
extracting features of the commodity data to obtain at least one item of commodity information, commodity price information, sales information, stock quantity richness information and commodity heat information;
performing feature extraction on the store data to obtain at least one of store sales information, store price information, store category information, store equity information and preferential information;
extracting features of the store order data to obtain at least one item of store order information and order period information;
performing feature extraction on the after-store data to obtain at least one item of store return information and store return period information;
and carrying out feature extraction on the evaluation data to obtain at least one of order scores, store scores and user emotion features obtained by carrying out positive and negative emotion analysis based on user evaluation texts.
15. The apparatus of any of claims 10-14, wherein the second prediction module comprises:
a second acquisition unit configured to acquire user portrait data and user behavior data of the user as the second transaction-related information;
the second feature extraction unit is configured to perform feature extraction on the second transaction related information to obtain second transaction related features;
and the second prediction unit is configured to input the second transaction related features into the second return rate prediction model to obtain the user return rate.
16. The apparatus of any of claims 10-15, wherein the determining module to determine a first shipping charge risk service fee from the merchant return rate comprises:
and determining the first freight rate insurance service fee according to the mapping relation between the merchant return rate and the freight rate insurance service fee.
17. The apparatus of any of claims 10-16, wherein the means for adjusting the first freight rate service fee according to the user return rate comprises:
determining a user risk level of the user according to the user return rate;
determining a corresponding service charge adjustment value according to the user risk level;
and calculating the second freight rate insurance service fee according to the first freight rate insurance service fee and the service fee adjustment value.
18. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.
19. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-9.
20. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any of claims 1-9.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311049834.7A CN117217785A (en) | 2023-08-18 | 2023-08-18 | Pricing method and device for freight insurance service fee, electronic equipment and storage medium |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311049834.7A CN117217785A (en) | 2023-08-18 | 2023-08-18 | Pricing method and device for freight insurance service fee, electronic equipment and storage medium |
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