CN114140203A

CN114140203A - Data determination method and device, electronic equipment and storage medium

Info

Publication number: CN114140203A
Application number: CN202111450227.2A
Authority: CN
Inventors: 张丽娜; 王磊; 张伟微
Original assignee: Beijing Dajia Internet Information Technology Co Ltd
Current assignee: Beijing Dajia Internet Information Technology Co Ltd
Priority date: 2021-11-30
Filing date: 2021-11-30
Publication date: 2022-03-04

Abstract

The present disclosure relates to a data determination method, apparatus, electronic device and storage medium, including: acquiring service information of a resource to which data to be determined belongs and an adjustment coefficient of a belonging class; the adjusting coefficient is used for representing the proportion between the first resource data and the second resource data of the category; determining initial information of the data to be determined according to the service information; adjusting the initial information through an adjusting instruction corresponding to the adjusting coefficient to obtain target information of the data to be determined; and sending the service information, the adjustment coefficient and the target information to an auditing terminal so that the auditing terminal confirms the target information based on the service information and the adjustment coefficient. The target information obtained based on the method not only accords with the condition of the resource, but also improves the adaptation degree between the target information and the data to be determined.

Description

Data determination method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a data determining method and apparatus, an electronic device, and a storage medium.

Background

With the development of internet technology, in order to improve the consumption experience of users, many accounts can cooperate with third parties to open some services, for example, an online store cooperates with an insurance company to open a freight insurance service, so that the consumers can conveniently return goods. When the account opens the service, some data such as service fee and the like need to be accepted by the account and a third party at the same time, and a healthy cooperation state is created.

At present, most of methods for determining data are that a third party determines data according to the types of products sold, and because the same product sold by different accounts and the different products sold by the same account have different gross profit structures, the data determined by adopting the single mode are not matched with the conditions of the accounts, and the determined data are inaccurate.

Disclosure of Invention

The present disclosure provides a data determining method, apparatus, electronic device and storage medium, to at least solve the problem in the related art that the determined service cost is not matched with the condition of the account itself, and the determined service cost result is not accurate. The technical scheme of the disclosure is as follows:

according to a first aspect of the embodiments of the present disclosure, there is provided a data determining method, including:

acquiring service information of a resource to which data to be determined belongs and an adjustment coefficient of a belonging class; the adjusting coefficient is used for representing the proportion between the first resource data and the second resource data of the category;

determining initial information of the data to be determined according to the service information;

adjusting the initial information through an adjusting instruction corresponding to the adjusting coefficient to obtain target information of the data to be determined;

and sending the service information, the adjustment coefficient and the target information to an auditing terminal so that the auditing terminal confirms the target information based on the service information and the adjustment coefficient.

In an exemplary embodiment, the obtaining service information of a resource to which data to be determined belongs includes:

acquiring sample data size of the category in the resource to which the data to be determined belongs and sample data size meeting a preset condition in the sample data size;

and obtaining the probability which corresponds to the resource and accords with the preset condition based on the sample data size and the sample data size which accords with the preset condition, and using the probability as the service information of the resource.

In an exemplary embodiment, the obtaining the adjustment coefficient of the category to which the data to be determined belongs includes:

inquiring a pre-configured adjusting coefficient mapping relation table, and determining the adjusting coefficient of the category to which the data to be determined belongs from the adjusting coefficient mapping relation table; the adjustment coefficient mapping relation table stores a plurality of categories and adjustment coefficients corresponding to the categories.

In an exemplary embodiment, the determining initial information of the data to be determined according to the service information includes:

determining a target service information interval corresponding to the service information in a plurality of preset service information intervals;

and acquiring information corresponding to the target service information interval as initial information of the data to be determined.

In an exemplary embodiment, before determining a target service information interval corresponding to the service information in a plurality of preset service information intervals, the method further includes:

acquiring a comparison result of the service information and a preset information acquisition condition;

and when the comparison result shows that the service information meets the preset information acquisition condition, determining a target service information interval corresponding to the service information in a plurality of preset service information intervals.

In an exemplary embodiment, the adjusting the initial information by the adjusting instruction corresponding to the adjusting coefficient to obtain the target information of the data to be determined includes:

and responding to an adjusting instruction corresponding to the adjusting coefficient, and multiplying the initial information and the adjusting coefficient to obtain target information of the data to be determined.

According to a second aspect of the embodiments of the present disclosure, there is provided a data determination apparatus including:

the acquisition unit is configured to acquire service information of a resource to which the data to be determined belongs and an adjustment coefficient of the belonging class; the adjusting coefficient is used for representing the proportion between the first resource data and the second resource data of the category;

the determining unit is configured to determine initial information of the order to be quoted according to the business information;

the adjusting unit is configured to execute an adjusting instruction corresponding to the adjusting coefficient to adjust the initial information to obtain target information of the data to be determined;

a sending unit configured to send the service information, the adjustment coefficient, and the target information to an auditing terminal, so that the auditing terminal confirms the target information based on the service information and the adjustment coefficient.

In an exemplary embodiment, the obtaining unit is specifically configured to obtain a sample data size of the category in the resource to which the data to be determined belongs and a sample data size meeting a predetermined condition in the sample data size; and obtaining the probability which corresponds to the resource and accords with the preset condition based on the sample data size and the sample data size which accords with the preset condition, and using the probability as the service information of the resource.

In an exemplary embodiment, the obtaining unit is further configured to perform querying a pre-configured adjustment coefficient mapping table, and determine, from the adjustment coefficient mapping table, an adjustment coefficient of a category to which the data to be determined belongs; the adjustment coefficient mapping relation table stores a plurality of categories and adjustment coefficients corresponding to the categories.

In an exemplary embodiment, the determining unit is specifically configured to perform determining a target service information interval corresponding to the service information in a plurality of preset service information intervals; and acquiring information corresponding to the target service information interval as initial information of the data to be determined.

In an exemplary embodiment, the apparatus further includes a comparing unit configured to perform a comparison result between the acquired service information and a preset information acquisition condition; and when the comparison result shows that the service information meets the preset information acquisition condition, determining a target service information interval corresponding to the service information in a plurality of preset service information intervals.

In an exemplary embodiment, the adjusting unit is specifically configured to perform, in response to an adjusting instruction corresponding to the adjusting coefficient, a multiplication process of the initial information and the adjusting coefficient, so as to obtain target information of the data to be determined.

According to a third aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the method of any one of the above.

According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium, in which instructions, when executed by a processor of an electronic device, enable the electronic device to perform the method of any one of the above.

According to a fifth aspect of embodiments of the present disclosure, there is provided a computer program product comprising instructions which, when executed by a processor of an electronic device, enable the electronic device to perform the method as defined in any one of the above.

The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:

according to the method, on one hand, the initial information of the data to be determined is determined based on the service information of the resource to which the data to be determined belongs so as to enable the determined initial information to be consistent with the service condition of the resource, on the other hand, the initial information is adjusted through the adjustment coefficient of the class to which the data to be determined belongs, so that the obtained target information is enabled to be consistent with the condition of the resource, and the adaptation degree of the target information and the data to be determined is improved besides the condition of the resource, and therefore the accuracy of the determined data can be improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure and are not to be construed as limiting the disclosure.

FIG. 1 is a diagram illustrating an application environment for a method of data determination, according to an exemplary embodiment.

Fig. 2 is a flow diagram illustrating a data determination method according to an example embodiment.

Fig. 3 is a flow chart diagram illustrating a data determination method according to another exemplary embodiment.

Fig. 4 is a diagram illustrating a return rate quote determination process according to an exemplary embodiment.

Fig. 5 is a logic diagram illustrating a return rate quote in accordance with an exemplary embodiment.

Fig. 6 is a block diagram illustrating a structure of a data determination apparatus according to an exemplary embodiment.

FIG. 7 is a block diagram illustrating an electronic device in accordance with an example embodiment.

Detailed Description

In order to make the technical solutions of the present disclosure better understood by those of ordinary skill in the art, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

It should be noted that the embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein.

The data determination method provided by the present disclosure may be applied to an application environment as shown in fig. 1, and includes: terminal 110 and terminal 120, terminal 120 may represent an audit terminal. Wherein the terminal 110 and the terminal 120 communicate through a network. The

terminals

110 and 120 may be, but are not limited to, various personal computers, notebook computers, smart phones, and tablet computers. In the application scenario of the present disclosure, after acquiring the service information of the resource to which the data to be determined belongs and the adjustment coefficient of the class to which the data to be determined belongs, the terminal 110 determines the initial information of the data to be determined according to the service information, and adjusts the initial information through the adjustment instruction corresponding to the adjustment coefficient to obtain the target information of the data to be determined; and sending the service information, the adjustment coefficient and the target information to the auditing terminal 120, so that the auditing terminal 120 confirms the target information based on the service information and the adjustment coefficient.

Fig. 2 is a flowchart illustrating a data determining method according to an exemplary embodiment, and as shown in fig. 2, the method is described as applied to the terminal 110 in fig. 1, and includes the following steps:

in step S210, acquiring service information of a resource to which the data to be determined belongs and an adjustment coefficient of a class to which the data to be determined belongs; the adjustment factor is used to characterize a ratio between the first resource data and the second resource data of the class.

The data to be determined may be an order to be quoted, the order to be quoted may represent an order for which the business cost needs to be paid, and the order to be quoted may be an order for a product sold in a certain account. For example, if the business cost is a freight risk, the order to be quoted may indicate that shipping has occurred, triggering the order for the return node.

Where the resource may represent an account or online store that sells the product in the order to be quoted.

The service information may represent information related to the determination of the service cost, i.e., the service information may be understood as a basis for determining the service cost. For example, if the service cost is freight risk, the service information may be return information of the product, such as a return rate.

Wherein, the category may represent a classification or a category of the product, the category may have a level, for example, a first level category, a second level category, a third level category, and the like, and the classification of the product is gradually refined from the first level category to the third level category. For example, for the product "women's thin coat", the primary category to which it belongs may be apparel, the secondary category may be women's dress, and the tertiary category may be women's autumn dress. The categories in the disclosure can represent three-level categories to accurately represent the categories to which the order to be evaluated belongs, thereby realizing the accuracy of the determined adjustment coefficient.

The adjustment coefficient may represent a ratio between first resource data and second resource data of business expenses of a category to which the order to be quoted belongs, where the first resource data may be cost of the business expenses of the category to which the order to be quoted belongs, and the second resource data may be revenue of the category to which the order to be quoted belongs. For example, the adjustment factor may represent a ratio between the cost of freight risk for the category to which the order to be quoted belongs and the revenue for the category to which the order to be quoted belongs.

In a specific implementation, after acquiring the data to be determined, the terminal 110 may determine the resource and the category to which the data to be determined belongs based on information carried by the data to be determined. Taking the to-be-quoted order as the to-be-quoted data as an example, for example, the affiliated account may be determined based on account information carried by the to-be-quoted order, and the affiliated category may be determined based on product name information carried by the to-be-quoted order. After determining the account and the category to which the order to be quoted belongs, the sample order in the recent time period of the account can be obtained from the database of the account to which the order to be quoted belongs, the service information of the account is calculated based on the information of the sample order, and the adjusting coefficient corresponding to the category to which the order to be quoted belongs can be inquired.

It is understood that, in one embodiment, the manner of obtaining the service information of the account to which the order to be quoted belongs may also be: the method comprises the steps of obtaining a sample order of an account in advance, calculating service information of the account based on the information of the sample order and storing the service information into a database, and then directly obtaining the service information of the account from the database after determining the account to which the order to be quoted belongs so as to improve the rate of quoting for determining the service cost of the order. In another embodiment, the service information of the account can be dynamically updated as the sample orders increase, so as to realize the real-time performance of the service information.

For example, in an application scenario, after an order to be quoted is obtained, if an account to which the determined order to be quoted belongs is set as an online store a, and the category to which the determined order to be quoted belongs is a third-level category B, the return information of the online store a can be obtained from a database of the online store a, and the adjustment coefficient corresponding to the third-level category B is queried, so that the quotation of the freight risk of the order to be quoted is determined based on the return information of the online store a and the adjustment coefficient corresponding to the third-level category B.

In step S220, according to the service information, initial information of the data to be determined is determined.

Wherein the initial information is determined based on the service information, and the initial information may include an initial reporting value. When the service information is the goods return rate and the service cost is the freight risk, the initial offer value and the service information are in a direct proportional relation, namely the higher the goods return rate is, the higher the claim settlement probability is, the higher the initial offer value is, otherwise, the goods return rate is reduced, and the corresponding initial offer value is also reduced.

In a specific implementation, since the service information is an information determination basis of the data to be determined, a mapping relationship between the service information and the initial information may be preset, so that after the terminal 110 obtains the service information of the resource to which the data to be determined belongs, the initial information matched with the service information of the resource to which the data to be determined belongs may be obtained based on the service information and the mapping relationship between the service information and the initial information, and is used as the initial information of the service cost of the data to be determined.

For example, in an application scenario, the data to be determined is an order to be quoted, the service information is a return rate, the information to be determined is a service cost, and the service cost is a freight risk, a mapping relationship between the return rate and an initial value can be preset, and after the return rate of an online store to which the order to be quoted belongs is obtained, the mapping relationship between the return rate and the initial value is queried to obtain the initial value of the freight risk of the order to be quoted.

In step S230, the initial information is adjusted through an adjustment instruction corresponding to the adjustment coefficient, so as to obtain target information of the data to be determined.

In a specific implementation, the terminal 110 may receive an adjustment instruction corresponding to the adjustment coefficient, perform multiplication processing on the initial information and the adjustment coefficient in response to the adjustment instruction, obtain adjusted information, and use the adjusted information as target information of the data to be determined.

For example, taking target price quote information for determining the service cost of an order to be quoted as an example, it can be understood that, since the categories of products are different, the selling prices are different, and the earnings are also different, and therefore, the determined service cost is not accurate by adopting the same price quote for the same service information, so that after the initial price quote information of the service cost of the order to be quoted determined based on the service information is obtained, the adjustment coefficient corresponding to the category to which the order to be quoted belongs is also adopted to adjust the initial price quote information, so as to ensure the degree of adaptation between the service cost and the categories, and the price quote for the service cost is more accurate.

In step S240, the service information, the adjustment coefficient, and the target information are sent to the auditing terminal, so that the auditing terminal confirms the target information based on the service information and the adjustment coefficient.

Specifically, in an application scenario, taking the determination of the target offer information of the to-be-offered order as an example, after obtaining the target offer information of the freight risk of the to-be-offered order, the terminal 110 may send the return information of the online shop to which the to-be-offered order belongs, the adjustment coefficient of the belonging category, and the target offer information to the audit terminal 120, so that the audit terminal 120 confirms the target offer information based on the return information and the adjustment coefficient.

In the data determination method, after the initial information of the data to be determined is determined according to the service information of the resource to which the data to be determined belongs, the initial information is adjusted through the adjustment coefficient of the class to which the data to be determined belongs.

In an exemplary embodiment, the obtaining of the service information of the resource to which the data to be determined belongs in step S210 may be implemented in the following manner: acquiring sample data size of the category in the resource to which the data to be determined belongs and sample data size meeting a preset condition in the sample data size; and obtaining the probability which corresponds to the resource and accords with the preset condition based on the sample data size and the sample data size which accords with the preset condition, and using the probability as the service information of the resource.

The sample data may be historical data of a resource to which the data to be determined belongs, and in an application scenario, the sample data may be a sample order representing a historical order of an account to which the order to be quoted belongs.

In an application scenario, the goods return data may be a goods return order and represent an order in which the goods return situation occurs in the historical order.

The probability of meeting the predetermined condition may be a return rate, which may represent a ratio between the number of products returned after sale for various reasons and the total number of products sold at the same time.

Wherein the category represents a category to which the data to be determined belongs.

In a specific implementation, the data volume of the data to be determined, which belongs to the category of the data to be determined, may be obtained from a database of the resource to which the data to be determined belongs, sample data meeting a predetermined condition may be screened from the sample data volume based on data information of each sample data, to obtain a sample data volume meeting the predetermined condition, a ratio between the sample data volume meeting the predetermined condition and the sample data volume may be calculated as a probability that the category in the resource to which the data to be determined corresponds meets the predetermined condition, and the probability meeting the predetermined condition may be used as the service information of the resource to which the data to be determined belongs.

In an application scenario, taking an order to be quoted as data to be determined, and obtaining service information of an account to which the order to be quoted belongs as an example, since the service information may be affected by the account itself and a category, in order to improve a confidence of the determined service information, the embodiment determines the service information of the account based on a sample order quantity belonging to the category to which the order to be quoted belongs in the account to which the order to be quoted belongs and a sample order quantity meeting a predetermined condition in the sample order quantity. More specifically, the sample order quantity with the service and the category belonging to the order to be quoted is obtained from the database of the account belonging to the order to be quoted, the sample orders with successful refunds are screened from the sample order quantity based on the order information of each sample order, the refund order quantity is obtained, the ratio between the refund order quantity and the sample quantity is calculated and is used as the refund rate corresponding to the category in the account belonging to the order to be quoted, and the refund rate is used as the service information of the account belonging to the order to be quoted.

For example, if the account to which the to-be-quoted order belongs is an online store a, and the category to which the to-be-quoted order belongs is a third-level category B, the return information of the online store a can be obtained through the following steps: the method comprises the steps of firstly obtaining a sample order quantity of historical orders which are communicated with freight risks and belong to a third-level category B in an online store A, then screening out sample orders which are successfully returned and refunded from the sample order quantity to obtain a returned and refunded order quantity, calculating a ratio of the returned and refunded order quantity to the sample quantity to serve as a return rate corresponding to the third-level category B in the online store A, and taking the return rate as return information of the online store A.

In this embodiment, the service information of the resource is determined by the sample data size of the resource to which the data to be determined belongs, which belongs to the category to which the data to be determined belongs, and the sample data size of the sample data size, which meets the predetermined condition, so that the determined service information, the data to be determined, and the resource have higher adaptability, and thus the confidence of the determined service information can be improved.

Further, in an exemplary embodiment, after acquiring the sample data size belonging to the category in the resource to which the data to be determined belongs, the sample data size may be compared with a threshold, and when the sample data size exceeds the threshold, the probability that the category in the resource corresponds to the predetermined condition is calculated, and the probability is used as the service information of the resource; when the sample data size does not exceed the threshold value, acquiring second sample data sizes of a plurality of classes in the resource; if the second sample data size exceeds a second threshold value, acquiring a second data size meeting the preset condition in the second sample data size; and obtaining the probability which corresponds to the plurality of categories in the resource and accords with the preset condition based on the second sample data size and the second sample data size which accords with the preset condition, and using the probability as the service information of the resource.

The plurality of categories may represent categories associated with the data to be determined, and include categories to which the data to be determined belongs, specifically, the plurality of categories may select a similar category or a previous category to the category to which the data to be determined belongs, for example, if the category to which the data to be determined belongs is autumn clothing for women, the selected plurality of categories may be similar categories such as autumn clothing for men, summer clothing for women, and the like, or may be a previous category of autumn clothing for women, such as women's clothing, and the like.

In a specific implementation, when the sample data size of the class in the resource to which the data to be determined belongs does not exceed the threshold, in order to avoid the problem that the error of the calculated service information is large due to too small sample data size, second sample data size of multiple classes in the resource to which the data to be determined belongs may be obtained, whether the second sample data size exceeds the second threshold is judged, if yes, sample data meeting a predetermined condition is screened out from the second sample data size based on the data information of each sample data, a second sample data size meeting the predetermined condition is obtained, a ratio between the second sample data size meeting the predetermined condition and the second sample size is calculated, the ratio is used as the probability that the multiple classes in the resource to which the data to be determined belong meet the predetermined condition, and the return rate is used as the service information of the resource to which the data to be determined belongs.

For example, still taking the account to which the order to be quoted belongs as the online store a and the category to which the order belongs is the third-level category B as an example, when the first sample order quantity does not exceed the first threshold value, the return information of the online store a can be obtained through the following steps: the method comprises the steps of firstly obtaining a second sample order quantity of historical orders which are opened in the online shop A and have freight risks and belong to a plurality of preset categories (such as B, C, D and the like), then screening sample orders which are successfully returned and refunded from the second sample order quantity to obtain a second returned and refunded order quantity, calculating a ratio of the second returned and refunded order quantity to the second sample quantity to serve as a return rate corresponding to the categories which are related to the to-be-quoted orders in the online shop A, and taking the return rate as return information of the online shop A.

In this embodiment, when the first sample data volume does not exceed the first threshold, the service information of the resource is determined based on the second sample data volumes of the multiple categories which have services in the resource to which the data to be determined belongs and are associated with the category to which the data to be determined belongs and the second sample data volumes which meet the predetermined condition, so that the problem that the determined service information has a large error due to the fact that the resource to which the data to be determined belongs has services and the first sample data volumes belonging to the categories are too small can be avoided, and the confidence of the determined service information is improved.

Further, in an exemplary embodiment, after obtaining the second sample data size of the plurality of classes having services in the resource, the method further includes: if the second sample data size does not exceed the second threshold, acquiring a third sample data size of the class with the service in the plurality of resources; when the third sample data size is larger than a third threshold value, acquiring a third sample data size which meets a preset condition in the third sample data size; obtaining the probability which corresponds to the category in the plurality of resources and accords with the preset condition based on the third sample data size and the third sample data size which accords with the preset condition, and using the probability as the service information of the resources; when the third sample data size does not exceed the third threshold, acquiring fourth sample data sizes of a plurality of classes with services in the plurality of resources and a fourth sample data size which meets a preset condition in the fourth sample data sizes; and obtaining the probability of meeting the preset conditions corresponding to a plurality of categories in the plurality of resources as the service information of the resources based on the fourth sample data size and the fourth sample data size meeting the preset conditions.

Wherein the category represents the category to which the data to be determined belongs.

The plurality of resources may represent an online store selling products of categories to which the data to be determined belongs, and the plurality of resources include resources to which the data to be determined belongs.

It should be noted that the process of calculating the return rate in this embodiment is similar to the method in the previous embodiment, and the details of this embodiment are not described herein.

By the method of the embodiment, the plurality of threshold values are set to be respectively compared with the sample data size under different conditions, and when the comparison result does not meet the conditions, the number of categories or the number of resources is enlarged to increase the obtained sample size, so that the problem that the determined service information is inaccurate due to too small sample size is avoided.

In an exemplary embodiment, the obtaining of the adjustment coefficient corresponding to the category to which the data to be determined belongs in step S210 may be implemented by: inquiring a pre-configured adjusting coefficient mapping relation table, and determining the adjusting coefficient of the category to which the data to be determined belongs from the adjusting coefficient mapping relation table; the adjustment coefficient mapping relation table stores a plurality of categories and adjustment coefficients corresponding to the categories.

In the specific implementation, the first resource data and the second resource data of the service costs of a plurality of categories can be obtained in advance, the ratio of the first resource data and the second resource data of the service costs of each category is calculated respectively to obtain the adjustment coefficients corresponding to each category, so that a mapping relation table between each category and the corresponding adjustment coefficients is established, when a certain category is not configured in the adjustment coefficient mapping relation table, the adjustment coefficient corresponding to the category is defaulted to be 1, that is, when the adjustment coefficient corresponding to the category to which the data to be determined belongs is not inquired in the adjustment coefficient mapping relation table, the adjustment coefficient corresponding to the category to which the data to be determined belongs is set to be 1. The first resource data may be cost of business cost of a category to which the data to be determined belongs, and the second resource data may be profit of the category to which the data to be determined belongs.

For example, for category B, if the cost of the freight risk for the product belonging to category B is obtained is B₁The yield of a product belonging to category B is B₂If the adjustment coefficient corresponding to category B is B₁/B₂Class B and adjustment factor B₁/B₂Storing the data into an adjustment coefficient mapping relation table, and subsequently inquiring the adjustment coefficient mapping relation table if the category to which the order to be quoted belongs is B, so as to obtain the adjustment coefficient B corresponding to the category to which the order to be quoted belongs₁/B₂。

In an embodiment, after determining the category to which the data to be determined belongs, the first resource data and the second resource data of the service cost of the category to which the data to be determined belongs may also be obtained in real time, and the ratio of the first resource data and the second resource data is calculated to obtain the adjustment coefficient corresponding to the category to which the data to be determined belongs, so as to implement the real-time performance of the adjustment coefficient.

In this embodiment, the adjustment coefficient mapping table is preconfigured with the first resource data and the second resource data of the service cost of each category, so that when the category to which the data to be determined belongs is determined, the adjustment coefficient mapping table can be queried, the adjustment coefficient corresponding to the category to which the data to be determined belongs is determined, and the efficiency of obtaining the adjustment coefficient is improved.

In an exemplary embodiment, the determining initial information of the data to be determined according to the service information in step S220 includes: determining a target service information interval corresponding to the service information in a plurality of preset service information intervals; and acquiring information corresponding to the target service information interval as initial information of the data to be determined.

Specifically, the present embodiment is described by taking the business information as the return rate as an example, a plurality of return rate sections may be preset, each return rate section has corresponding information, after the return rate of the online shop to which the data to be determined belongs is obtained, the return rate is compared with each return rate section, a target return rate section corresponding to the return rate is determined from the plurality of return rate sections, and the information corresponding to the target return rate section is used as the initial information of the return risk of the data to be determined.

In the embodiment, the initial information is determined according to the service information by setting the information corresponding to each of the plurality of service information intervals, and the method fully considers the incidence relation between the service information and the service cost, so that the accuracy of the determined initial information can be improved.

In an exemplary embodiment, before step S220, the method further includes: acquiring a comparison result of the service information and a preset information acquisition condition; and when the comparison result is that the service information meets the preset information acquisition condition, determining a target service information interval corresponding to the service information in a plurality of preset service information intervals.

For example, in an application scenario, if the information is a freight risk, the preset freight risk offer condition is that the return rate is smaller than a preset return rate threshold.

In the specific implementation, since the obtained target information is influenced by the service information, the information determination may be performed only after the service information satisfies a certain condition, so that after the service information is obtained, the service information may be compared with a preset information obtaining condition, that is, whether the service information is smaller than a preset information threshold value is determined, when the service information conforms to the preset information obtaining condition, that is, the service information is smaller than the preset information threshold value, the step of determining the target service information section corresponding to the service information in the preset multiple service information sections is performed, otherwise, when the service information does not conform to the preset information obtaining condition, that is, the service information is greater than or equal to the preset information threshold value, the target information determination is not required for the data to be determined.

For example, in an application scenario, if the target information is freight insurance and the service information is a return rate, after obtaining the return rate of the account to which the order to be quoted belongs, the return rate may be compared with a return rate threshold, when the return rate is less than the return rate threshold, the step of determining the initial quote information of the freight insurance of the order to be quoted is performed according to the return rate, and when the return rate is greater than or equal to the return rate threshold, because the return rate is higher, the opening of the freight insurance service for the account will be more lost, so that the freight insurance service for the category to which the order to be quoted belongs may not be opened.

In this embodiment, by comparing the service information with the preset information acquisition condition and determining whether to execute the step of determining the target information to be determined according to the comparison result, the waste of computing resources caused by executing the determining step without determining the target information can be avoided.

In an exemplary embodiment, the step S230 may be specifically implemented by: and responding to an adjusting instruction corresponding to the adjusting coefficient, and multiplying the initial information and the adjusting coefficient to obtain target information of the data to be determined.

For example, taking a scenario of determining the freight risk of the online shop order as an example, after determining the initial reporting value of the freight risk of the order to be quoted and the adjustment coefficient corresponding to the category to which the order to be quoted belongs, in response to the adjustment instruction corresponding to the adjustment coefficient, calculating a product between the initial reporting value and the adjustment coefficient, and taking the obtained product as the target quotation information of the freight risk of the order to be quoted.

In this embodiment, in response to the adjustment instruction corresponding to the adjustment coefficient, the product between the initial information and the adjustment coefficient is calculated as the target information of the data to be determined, so that the initial information is corrected based on the adjustment coefficient corresponding to the category to which the data to be determined belongs, and the degree of adaptation between the determined target information and the data to be determined is improved.

Referring to fig. 3, a flow chart of a method of data determination is shown according to another exemplary embodiment, which in this embodiment includes the steps of:

step S310, determining the resources and categories to which the data to be determined belong;

step S320, obtaining the service information of the resource to which the data to be determined belongs based on the sample data size in the resource to which the data to be determined belongs and the sample data size which accords with the preset condition in the sample data size;

step S330, inquiring a pre-configured adjusting coefficient mapping relation table, and determining the adjusting coefficient of the category to which the data to be determined belongs from the adjusting coefficient mapping relation table;

step S340, obtaining a comparison result of the service information and a preset information obtaining condition;

step S350, when the comparison result is that the service information accords with the preset information acquisition condition, determining a target service information interval corresponding to the service information in a plurality of preset service information intervals;

step S360, acquiring information corresponding to the target service information interval as initial information of the data to be determined;

step S370, in response to the adjustment instruction corresponding to the adjustment coefficient, multiplying the initial information by the adjustment coefficient to obtain target information of the data to be determined;

and step S380, sending the service information, the adjustment coefficient and the target information to an auditing terminal so that the auditing terminal confirms the target information based on the service information and the adjustment coefficient.

In the data determining method provided by this embodiment, after the initial information of the data to be determined is determined according to the service information of the resource to which the data to be determined belongs, the initial information is adjusted by the adjustment coefficient of the class to which the data to be determined belongs.

In order to clarify the data determining method provided by the embodiments of the present disclosure more clearly, the data determining method is described below with a specific embodiment; referring to fig. 4, a schematic diagram of a process for determining a return rate offer in an application example includes the following steps:

(1) determining the affiliated online shop and the affiliated third-level category of the order to be quoted;

(2) and after determining the online store to which the order to be quoted belongs, acquiring the return rate of the online store. Specifically, when calculating the return rate, first obtaining a sample volume of the third-level category in the online store, determining whether the sample volume exceeds 100, if so, pricing according to the return rate of the third-level category in the online store, specifically, the return rate of the third-level category of the online store is the successful return bill volume/sample volume of the third-level category of the online store from T-45 days to T-15 days. If not, acquiring sample volumes of all categories of the online shop opening freight insurance, judging whether the sample volumes exceed 100, if so, pricing according to the goods return rate of the online shop, and specifically, the goods return rate of the online shop is the successful goods return bill volume/sample volume of the online shop from T-45 days to T-15 days. If not, acquiring a third-level category which is the main category of the main operation of the merchant, namely the sample volume of the main operation category in the opening transportation risk category, judging whether the sample volume exceeds 100, if so, pricing according to the goods return rate of the third-level category, and specifically, the goods return rate of the third-level category is the successful goods return single volume/sample volume of the third-level category from T-45 days to T-15 days. If not, acquiring sample quantities of all categories of the opened freight insurance, and calculating a large-tray goods return rate, namely calculating the goods return rate of all categories of the opened freight insurance, wherein the large-tray goods return rate is the successful goods return bill quantity/sample quantity of all categories of the opened freight insurance from T-45 days to T-15 days.

(3) Inquiring the initial quotation A of the freight insurance of the order to be quoted according to the return rate;

(4) after determining the category of the order to be quoted, inquiring an adjusting coefficient mapping relation table configured based on the category dimension to obtain an adjusting coefficient X of the category to which the order to be quoted belongs; specifically, a plurality of three-level categories and adjustment coefficient mapping relationship tables corresponding to the categories may be configured through the category configuration background shown in fig. 5.

In the schematic diagram shown in fig. 5, the quotation center needs to maintain two sets of quotations, one set is an online store, the other set is an insurance company, and each set of quotation consists of two parts: return rate quotes and category coefficients; the return rate quotation consists of a return rate interval and quotation, and the meaning is that the higher the return rate is, the higher the claim settlement probability is, and the higher the insurable amount is. The category coefficient can be used for adjusting the profit rate of each industry, so that the ratio of the freight insurance premium/the profit among each industry is in a certain range, the meaning is that the gross profit structures of different categories are different in the same return rate interval, and the quotation is finely adjusted according to the categories in order to lead more online stores to open the freight insurance. The implementation of the category coefficient is: and (4) configuring category coefficients and effective time (two sets of the system-the department side and the merchant side) corresponding to the three-level categories in a category configuration background according to the month dimension.

For insurance company side quotes:

the insurance company only senses the whole quotation (the return rate and the quotation), and does not sense the category and the preferential quotation, etc.; and calculating the return rate quotation and the category coefficient in the system to obtain the quotation obtained by calculation, upwards taking the adjacent interval, and reversely calculating to obtain the return rate interval and the final quotation.

For merchant-side offers:

the online shop can only pay attention to the price quote of the online shop in the period, and the calculation logic is the return rate price quote multiplied by the category coefficient.

By the method of maintaining two sets of quoted prices, value distribution can be better carried out, more freight insurance opening and order covering are created by using price discrimination, and quoted prices are more flexibly adjusted; externally, all interfaces of the insurance companies have a uniform format, so that the maintenance is convenient, the final quotation calculation is logically online, and the strategy adjustment and the problem finding are easy; in the interior, the price quoted can be heightened by adjusting the return rate quoted price and the category coefficient, and the profit part can be used for making personalized price quoted (second stage) so as to attract more online stores.

(5) And calculating the product A X% of the initial price A and the category coefficient X% as the target price of the order to be quoted.

For example, a new category scene:

if the online store initially has no insurable categories and later has new insurable categories added, the second tier is not met and the third tier is followed.

If the online store has the insurable category 1 at the beginning, the online store is added with the insurable category 2 later, which accords with the second layer

If the online store has an insurable category at the beginning and the operation configuration is newly added with the category later, the online store has historical data of the category and therefore goes to the second layer.

Extreme scenarios:

if the third-level category, the second-level category and the first-level category of the commodity are not on line for more than 45 days, taking the commodity to the industry from the top; and if the industry is on line for less than 45 days, performing alarm processing.

And if the goods return rate of the multi-bin side or a configuration table of the goods return rate and the insurance price cannot be inquired, or X%, performing alarm processing.

In addition, if the new industry category is added, the return rate of the category commodity cannot be calculated within 45 days on line, and further the quoted price cannot be obtained, namely in order to avoid the situation that the new category is operated, the situation that the time does not exceed 45 days is generated, a background capable of inquiring the category on line is provided (if the background cannot be provided in the early stage, the situation that the three-level category and the upper level thereof and the industry category do not exceed 45 days are confirmed by BI help and running number during the new category addition).

The reason for 45 days is as follows: taking the validity period of the policy as 30 days as an example, the policy needs to be sold after the validity period of the policy is started; the effective period of the application is 45 days, the application and the claim are required to be settled in the period, otherwise, the claim cannot be settled. The insurance company completes the complete calculation of the settlement odds and the actuarial of the quotation within a complete 45-day period.

As shown in table 1 below, four types of judgment logic nodes are included for further explanation of each judgment logic node in fig. 5.

According to the method provided by the embodiment, for the merchant, the freight cost of the merchant is determined by the return rate of the merchant, the merchant with the high return rate cannot raise the overall price, for the insurance company, the malicious bidding condition of the insurance company can be avoided, and for the user, the trouble caused by the return of the freight can be reduced. By the method, the premium can be greatly reduced, great power is brought to merchants to open freight insurance services, the claim settlement rate of insurance companies can gradually approach to a profit state from an unhealthy state, and a healthy state with stable profit rate is maintained, so that the freight insurance coverage rate of the merchants is increased, and a healthy shopping environment is provided for consumers.

It should be understood that although the various steps in the flow charts of fig. 2-5 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-5 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.

It is understood that the same/similar parts between the embodiments of the method described above in this specification can be referred to each other, and each embodiment focuses on the differences from the other embodiments, and it is sufficient that the relevant points are referred to the descriptions of the other method embodiments.

Fig. 6 is a block diagram illustrating a structure of a data determination apparatus according to an exemplary embodiment. Referring to fig. 6, the apparatus includes: an obtaining unit 610, a determining unit 620, an adjusting unit 630, and a transmitting unit 640, wherein,

the acquiring unit 610 is configured to perform acquiring service information of a resource to which data to be determined belongs and an adjustment coefficient of a belonging class; the adjusting coefficient is used for representing the proportion between the first resource data and the second resource data of the category;

a determining unit 620 configured to perform determining initial information of an order to be quoted according to the business information;

an adjusting unit 630 configured to execute an adjusting instruction corresponding to the adjustment coefficient to adjust the initial information to obtain target information of the data to be determined;

and a sending unit 640 configured to perform sending of the traffic information, the adjustment coefficient, and the target information to the auditing terminal, so that the auditing terminal confirms the target information based on the traffic information and the adjustment coefficient.

In an exemplary embodiment, the obtaining unit 610 is specifically configured to perform obtaining a sample data size of a category in a resource to which data to be determined belongs and a sample data size that meets a predetermined condition in the sample data size; and obtaining the probability which corresponds to the resource and accords with the preset condition based on the sample data size and the sample data size which accords with the preset condition, and using the probability as the service information of the resource.

In an exemplary embodiment, the obtaining unit 610 is further configured to perform querying a pre-configured adjustment coefficient mapping table, and determine, from the adjustment coefficient mapping table, an adjustment coefficient of a category to which data to be determined belongs; the adjustment coefficient mapping relation table stores a plurality of categories and adjustment coefficients corresponding to the categories.

In an exemplary embodiment, the determining unit 620 is specifically configured to perform determining a target service information interval corresponding to the service information in a plurality of preset service information intervals; and acquiring information corresponding to the target service information interval as initial information of the data to be determined.

In an exemplary embodiment, the apparatus further includes a comparing unit configured to perform a comparison result between the acquired service information and a preset information acquisition condition; and when the comparison result is that the service information meets the preset information acquisition condition, determining a target service information interval corresponding to the service information in a plurality of preset service information intervals.

In an exemplary embodiment, the adjusting unit 630 is specifically configured to perform a multiplication process on the initial information and the adjustment coefficient in response to an adjusting instruction corresponding to the adjustment coefficient, so as to obtain target information of the data to be determined.

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

Fig. 7 is a block diagram illustrating an electronic device 700 for implementing a data determination method according to an example embodiment. For example, the electronic device 700 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a gaming console, a tablet device, a medical device, an exercise device, a personal digital assistant, and so forth.

Referring to fig. 7, electronic device 700 may include one or more of the following components: processing component 702, memory 704, power component 706, multimedia component 708, audio component 710, input/output (I/O) interface 712, sensor component 714, and communication component 716.

The processing component 702 generally controls overall operation of the electronic device 700, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 702 may include one or more processors 720 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 702 may include one or more modules that facilitate interaction between the processing component 702 and other components. For example, the processing component 702 may include a multimedia module to facilitate interaction between the multimedia component 708 and the processing component 702.

The memory 704 is configured to store various types of data to support operations at the electronic device 700. Examples of such data include instructions for any application or method operating on the electronic device 700, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 704 may be implemented by any type or combination of volatile or non-volatile storage devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, optical disk, or graphene memory.

The power supply component 706 provides power to the various components of the electronic device 700. The power components 706 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the electronic device 700.

The multimedia component 708 includes a screen providing an output interface between the electronic device 700 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 708 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 700 is in an operation mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 710 is configured to output and/or input audio signals. For example, the audio component 710 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 700 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in the memory 704 or transmitted via the communication component 716. In some embodiments, audio component 710 also includes a speaker for outputting audio signals.

The I/O interface 712 provides an interface between the processing component 702 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 714 includes one or more sensors for providing various aspects of status assessment for the electronic device 700. For example, the sensor assembly 714 may detect an open/closed state of the electronic device 700, the relative positioning of components, such as a display and keypad of the electronic device 700, the sensor assembly 714 may also detect a change in the position of the electronic device 700 or components of the electronic device 700, the presence or absence of user contact with the electronic device 700, the orientation or acceleration/deceleration of the device 700, and a change in the temperature of the electronic device 700. The sensor assembly 714 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 714 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 714 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 716 is configured to facilitate wired or wireless communication between the electronic device 700 and other devices. The electronic device 700 may access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G, or 5G), or a combination thereof. In an exemplary embodiment, the communication component 716 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 716 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a computer-readable storage medium comprising instructions, such as the memory 704 comprising instructions, executable by the processor 720 of the electronic device 700 to perform the above-described method is also provided. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

In an exemplary embodiment, a computer program product is also provided that includes instructions executable by the processor 720 of the electronic device 700 to perform the above-described method.

It should be noted that the descriptions of the above-mentioned apparatus, the electronic device, the computer-readable storage medium, the computer program product, and the like according to the method embodiments may also include other embodiments, and specific implementations may refer to the descriptions of the related method embodiments, which are not described in detail herein.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for determining data, comprising:

2. The method according to claim 1, wherein the obtaining the service information of the resource to which the data to be determined belongs includes:

3. The method according to claim 1, wherein the obtaining of the adjustment coefficient of the category to which the data to be determined belongs comprises:

4. The method of claim 1, wherein the determining initial information of the data to be determined according to the service information comprises:

5. The method of claim 4, further comprising, before determining a target service information interval corresponding to the service information among a plurality of preset service information intervals, the following steps:

6. The method according to claim 1, wherein the adjusting the initial information by an adjusting instruction corresponding to the adjusting coefficient to obtain the target information of the data to be determined comprises:

7. A data determination apparatus, comprising:

8. An electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the data determination method of any one of claims 1 to 6.

9. A computer-readable storage medium, wherein instructions in the computer-readable storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the data determination method of any of claims 1 to 6.

10. A computer program product comprising instructions which, when executed by a processor of an electronic device, enable the electronic device to perform the data determination method of any one of claims 1 to 6.