CN114219564A

CN114219564A - Data resource processing method and device, electronic equipment and medium

Info

Publication number: CN114219564A
Application number: CN202111523953.2A
Authority: CN
Inventors: 严杨扬; 万晓辉
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2021-12-14
Filing date: 2021-12-14
Publication date: 2022-03-22

Abstract

The embodiment of the application relates to the technical field of computers, and discloses a data resource processing method, a data resource processing device, electronic equipment and a medium. The method comprises the following steps: acquiring order data sets generated in a preset time period and related to a plurality of target data resources; searching a target order data set in the order data set, and determining a first data resource group and associated data resources of the first data resource group based on the order data set and the target order data set; then determining each first data resource group and the association degree of the associated data resources; and selecting a target relevance degree from the relevance degrees and outputting relevance degree information. By adopting the method and the device, the condition that the electronic resources are not transferred in the transaction process can be considered when the association degree between the data resources is determined, and the association result is screened, so that the accuracy and the practicability of the association result are improved.

Description

Data resource processing method and device, electronic equipment and medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a data resource processing method and apparatus, an electronic device, and a medium.

Background

With the development of business digitization, more and more commodities are recorded in the form of electronic data in the processes of storage, sale and the like, and data resources are formed. In the traditional commodity sales field, in order to seek profit maximization, a merchant often adopts a shopping basket analysis algorithm and the like to analyze data resources of commodities, determine commodities with strong relevance, and then sell the commodities with strong relevance at the same time so as to improve the success rate of commodity sales. Most of application scenes of the existing shopping basket analysis algorithm are large-scale stores, and order data of the large-scale stores are usually generated based on successful trading; however, in some sales fields such as insurance, an order may be generated first and then payment is confirmed during the sales process, so that the generated order is not a successful order of a transaction, and if the data resources in the order data of a failed transaction are also used for analyzing the correlation between the data resources, a large error may easily occur in the analysis result, which is not favorable for the subsequent establishment of a sales policy. In addition, the existing shopping basket analysis algorithms directly output all the commodities with relevance after calculation, so that the marketing strategy is difficult to make in a large number of commodities with strong relevance. Therefore, the existing method does not consider the situation of transaction failure when determining the degree of association between data resources, and simultaneously outputs a large number of association results, resulting in poor accuracy and practicability of the association results.

Disclosure of Invention

The embodiment of the application provides a data resource processing method, a data resource processing device, an electronic device and a medium, which can realize that the condition of transaction failure is considered when determining the association degree between data resources, and the association result is screened, so that the accuracy and the practicability of the association result are improved.

In one aspect, an embodiment of the present application provides a data resource processing method, where the method includes:

acquiring order data sets generated in a preset time period and related to a plurality of target data resources;

searching a target order data set in the order data set, wherein the target order data set comprises one or more order data, and the one or more order data refer to: the client side does not transfer order data of the electronic resources;

determining at least one first data resource group and associated data resources of each first data resource group based on the order data set and the target order data set;

determining the association degree of each first data resource group and the associated data resources of each first data resource group to obtain a plurality of association degrees;

selecting a target relevance degree from the relevance degrees, and outputting relevance degree information, wherein the relevance degree information comprises the target relevance degree, a second data resource group used for determining the target relevance degree, and relevant data resources of the second data resource group.

In one embodiment, the determining at least one first set of data resources based on the order data set and the target order data set and associated data resources for each first set of data resources comprises: establishing a first tree model based on the order data set, and performing traversal processing on the first tree model to obtain each third data resource group in the order data set and associated data resources of each third data resource group; establishing a second tree model based on the target order data set, and performing traversal processing on the second tree model to obtain each fourth data resource group in the target order data set and associated data resources of each fourth data resource group; and determining the at least one first data resource group and the associated data resources of each first data resource group based on each third data resource group, the associated data resources of each third data resource group, each fourth data resource group and the associated data resources of each fourth data resource group.

In one embodiment, the degree of association includes a confidence level; the determining the association degree of each first data resource group and the associated data resource of each first data resource group includes: determining a first order quantity of order data of each first data resource group in the order data set, and a second order quantity of order data of associated data resources of each first data resource group in the order data set; determining a third order quantity of the target order data group, which comprises each first data resource group and the associated data resources of each first data resource group; and determining the confidence degree of each first data resource group and the associated data resource of each first data resource group based on the first order quantity, the second order quantity and the third order quantity.

In one embodiment, the degree of association comprises a degree of lift; the determining the association degree of each first data resource group and the associated data resource of each first data resource group includes: determining a first order quantity of order data of each first data resource group in the order data set, and a second order quantity of order data of associated data resources of each first data resource group in the order data set; determining a third order quantity of the target order data group containing the first data resource groups and the associated data resources of the first data resource groups, and a fourth order quantity of the target order data group containing the associated data resources of the first data resource groups; and determining the promotion degree of the associated data resources of each first data resource group and each first data resource group based on the first order quantity, the second order quantity, the third order quantity and the fourth order quantity.

In one embodiment, further comprising: determining the degree of confrontation of each third data resource group in the target order data set and the associated data resource of each third data resource group to obtain a plurality of degrees of confrontation; determining a target degree of confrontation in the plurality of degrees of confrontation, and outputting the degree of confrontation information, wherein the degree of confrontation information comprises the target degree of confrontation, a fifth data resource group used for determining the target degree of confrontation, and associated data resources of the fifth data resource group; determining a data resource marketing plan based on the relevancy information and the struggle information, the data resource marketing plan describing the associative marketing of the associated data resources of the second data resource group and the second data resource group, and the struggle marketing of the associated data resources of the fifth data resource group and the fifth data resource group.

In one embodiment, the determining a target relevance among the plurality of relevance comprises: determining the relevance degree of the plurality of relevance degrees which is greater than a preset relevance degree threshold value as the target relevance degree; or sequencing the plurality of relevance degrees to obtain the sequenced relevance degrees, and determining a target relevance degree in the sequenced relevance degrees, wherein the target relevance degree is greater than the relevance degrees except the target relevance degree in the plurality of relevance degrees.

In one embodiment, the plurality of target data resources refers to M target data resources, each of the first data resource groups includes x target data resources, x is greater than 0 and less than or equal to M, and x is a positive integer; the determining the association degree of each first data resource group and the associated data resource of each first data resource group includes: and determining y associated data resources of the x target data resources and the association degree of the x target data resources and each associated data resource in the y associated data resources based on the order data set and the target order data set to obtain a plurality of association degrees, wherein x is greater than 0 and less than M, y is greater than 0 and less than or equal to M-x, and x and y are integers.

In another aspect, an embodiment of the present application provides a data resource processing apparatus, where the data resource processing apparatus includes:

an acquisition unit configured to acquire an order data set regarding a plurality of target data resources generated within a preset time period;

a processing unit, configured to search a target order data set in the order data set, where the target order data set includes one or more order data, and the one or more order data refer to: the client side does not transfer order data of the electronic resources;

the processing unit is further configured to determine at least one first data resource group and associated data resources of each first data resource group based on the order data set and the target order data set;

the processing unit is further configured to determine a degree of association between each first data resource group and an associated data resource of each first data resource group, so as to obtain a plurality of degrees of association;

and the output unit is used for selecting a target relevance degree from the relevance degrees and outputting relevance degree information, wherein the relevance degree information comprises the target relevance degree, a second data resource group used for determining the target relevance degree and relevant data resources of the second data resource group.

In another aspect, an embodiment of the present application provides an electronic device, including a processor, a storage device, and a communication interface, where the processor, the storage device, and the communication interface are connected to each other, where the storage device is used to store a computer program that supports a terminal to execute the foregoing method, the computer program includes program instructions, and the processor is configured to call the program instructions to perform the following steps: acquiring order data sets generated in a preset time period and related to a plurality of target data resources; searching a target order data set in the order data set, wherein the target order data set comprises one or more order data, and the one or more order data refer to: the client side does not transfer order data of the electronic resources; determining at least one first data resource group and associated data resources of each first data resource group based on the order data set and the target order data set; determining the association degree of each first data resource group and the associated data resources of each first data resource group to obtain a plurality of association degrees; selecting a target relevance degree from the relevance degrees, and outputting relevance degree information, wherein the relevance degree information comprises the target relevance degree, a second data resource group used for determining the target relevance degree, and relevant data resources of the second data resource group.

In still another aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored, the computer program including program instructions, which, when executed by a processor, cause the processor to execute the above data resource processing method.

In the embodiment of the application, order data sets which are generated in a preset time period and are related to a plurality of target data resources are obtained; searching a target order data set in the order data set, wherein the target order data set comprises one or more order data, and the order data refers to the order data of the electronic resources which are not transferred by the client; determining at least one first data resource group and associated data resources of each first data resource group based on the order data set and the target order data set; then determining the association degree of each first data resource group and the associated data resources of each first data resource group to obtain a plurality of association degrees; and finally, determining target relevance among the multiple relevance, and outputting relevance information, wherein the relevance information comprises the target relevance, a second data resource group for determining the target relevance and relevant data resources of the second data resource group. According to the method and the device, the target order data group is searched in the order data set, so that the order data of electronic resources which are not transferred by all the clients in all the order data, namely the order data which are failed in transaction or unfinished, can be searched; then, at least one first data resource group, the associated data resources of each first data resource group and the association degree of the associated data resources of each first data resource group and each first data resource group are determined through the order data set and the target order data set, so that the condition of transaction failure can be considered when the association degree between the data resources is determined, and the accuracy of the association result is improved; and finally, selecting the target relevance degree from the relevance degrees, outputting the relevance degree information, and realizing targeted screening of the relevance degrees, thereby improving the practicability of the relevance result and being beneficial to subsequent accurate formulation of a marketing strategy.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flowchart of a data resource processing method according to an embodiment of the present application;

fig. 2 is a setting interface diagram of a preset scene provided in the embodiment of the present application;

fig. 3 is a schematic diagram of a process for determining a first data resource group according to an embodiment of the present application;

FIG. 4 is a schematic flow chart of another data resource processing method provided in the embodiments of the present application;

FIG. 5 is a diagram of a first tree model provided by an embodiment of the present application;

FIG. 6 is a diagram of a second tree model provided by an embodiment of the present application;

fig. 7 is a schematic structural diagram of a data resource processing apparatus according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

With the development of business digitization, more and more commodities are recorded in the form of electronic data in the processes of storage, sale and the like, and data resources related to the commodities are formed; for example, in a supermarket, the data resource may refer to each commodity, and in insurance sales, the data resource may refer to each insurance type. In the traditional commodity sales field, in order to seek profit maximization, a merchant often adopts an algorithm for calculating the association degree, such as a shopping basket analysis algorithm, to analyze the association degree between commodities in the commodity sales process, so as to determine commodities with strong association, and then the commodities with strong association are sold at the same time, so as to improve the success rate of commodity sales. For example, if there is a high probability that an order containing beer contains diapers, the beer and the diapers belong to strongly related products, so that a merchant can choose to place a shelf for selling the diapers beside a shelf for selling beer, thereby increasing the purchase rate of the diapers.

However, most of the application scenarios of the existing shopping basket analysis algorithms are large-scale stores, and order data of the large-scale stores are usually generated based on successful transactions; in some sales fields such as insurance and medical treatment, orders are generated firstly and then payment is confirmed in the sales process, so that the generated orders are not all orders with successful transaction, and if data resources in order data with failed transaction are also used for analyzing the relevance between the data resources, a large error is easily caused in an analysis result, and the subsequent sales strategy formulation is not facilitated. Meanwhile, although the existing shopping basket analysis algorithm can find out the commodities with strong relevance, if too many commodities with strong relevance exist, a merchant still has difficulty in making a sales strategy in a large number of commodities with strong relevance. Moreover, finding strongly associated commodities such as beer and diapers in an order seems simple, and the real problem is heavy; as the volume of orders grows, the data is typically on the order of millions or even tens of millions, and the number of items in each order can be hundreds or thousands, easily resulting in geometric multiplication of the final overall search space.

On the basis, the embodiment of the application also provides a data resource processing method, and on the first hand, by determining a target order data group of order data of electronic resources which are not transferred by a client in an order data set and subsequent processing, the situation of transaction failure can be considered when determining the association degree between data resources, so that the accuracy of the association result is improved; in the second aspect, the relevance degree can be screened in a targeted manner by determining the target relevance degree in the relevance degrees and outputting the relevance degree information, so that the practicability of the relevance result is improved, and the subsequent accurate formulation of a marketing strategy is facilitated; in the third aspect, data can be efficiently traversed in a mode of establishing a tree model, and the time complexity of the algorithm is reduced.

It should be noted that, in the embodiment of the present application, the data resource processing scheme mentioned in the embodiment of the present application is introduced by taking a related scenario of insurance sales as an example, and does not limit the embodiment of the present application.

Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a data resource processing method according to an embodiment of the present disclosure; the data resource processing scheme shown in fig. 1 may be performed by the electronic device, and includes, but is not limited to, steps S101 to S105, wherein:

s101, acquiring order data sets generated in a preset time period and related to a plurality of target data resources.

In the embodiments of the present application, the target data resource refers to a digital form capable of representing a certain resource. In particular, the target data resource may be a commodity type, such as oil, rice, salt, sugar, etc., as well as clothing, accessories, shoes, etc.; or may be of the insurance type, such as automobile insurance, accident insurance, travel insurance, etc. The order data set refers to an order data set composed of a plurality of orders containing one or more target data resources.

Illustratively, the target data resources include child medical insurance, middle-aged and elderly integrated medical insurance, critical illness insurance, filial piety insurance and workplace integrated medical insurance, the order was taken for the last 2 days, for a total of 7 orders, and then the order data set may be as shown in table 1:

TABLE 1

Wherein, the client in the order 4 originally purchases 2 parts of child medical insurance and 3 parts of filial piety insurance, and the order 4 is labeled; that is, the insurance type purchased in order 4 is labeled as 1, and the insurance type not purchased is labeled as 0; after all 7 orders are labeled, the order data set shown in table 1 is obtained.

In one possible implementation, the obtaining condition for obtaining the order data set generated in the preset time period and related to the plurality of target data resources may be at least one of: the method comprises the steps of presetting a time condition, a scene condition and a trigger condition. The preset time condition may be the acquired time frequency or a preset fixed time. Specifically, the preset time condition may be acquired every month, and then the acquisition may be performed at any time or a fixed time every month, such as six or 10 to 18 numbers each month; the preset time condition may also be a preset time, such as setting acquisition No. 11/10 in 2021.

The preset scene condition may be a scene during acquisition, specifically, the scene during acquisition may include time-related scenes such as holidays and seasons, and may also include work-related scenes such as meetings and reports, which is not limited herein. For example, the order data set associated with a commodity may be obtained 3 days before a business department of a company makes a meeting with the commodity. Illustratively, before a labor segment comes, company A wants to increase the sales volume of travel insurance, and therefore needs to know that there is a strong correlation between those travel insurance, thereby facilitating the formulation of a sales strategy for travel insurance. Therefore, as shown in fig. 2, the manager of company a can set the scenario to "labor savings" in the setting interface 201 of the preset scenario; preferably, the target data resource can also be set as various traveling-related insurance, such as 'domestic short-term traveling insurance', 'group domestic traveling insurance', 'domestic self-service traveling insurance', 'Hongkong and Macau traveling insurance', 'domestic self-driving traveling insurance' and 'domestic plateau traveling insurance', etc.; the manager may also click on "+" to choose to add more target data resources. And the setting can be completed by clicking the 'completion' button.

The preset trigger condition may be when the acquisition will of the user is detected. For example, when it is detected that the user clicks a button on the terminal device to "get" the order data set, the order data set may be started to be taken.

S102, searching a target order data set in the order data set.

In an embodiment of the present application, the target order data set includes one or more order data, and the one or more order data refer to: the client does not transfer the order data of the electronic resource. The electronic resource may be money that circulates in a data format, such as electronic cash, bitcoin, and puppy coin, or money that is paid in an electronic format by payment software, and is not limited herein.

In the embodiment of the present application, the order data of the electronic resource not transferred by the client refers to data of an order for which payment is not completed at the client, that is, data of an order for which a transaction fails. Preferably, the order data in the target order data set may also refer to order data that has been generated for the first preset time period but the client has not transferred the electronic resource. For example, some enterprises may purchase a large amount of insurance for their employees in a unified manner, but due to fund turnover of the enterprises and the like, order payment needs to be completed after three months of purchase or when the funds are abundant, and this type of order does not belong to an order that has failed in transaction, so it may be preset that the order data in the target order data set is the order data of electronic resources that are not transferred by the client after the order is generated for 3 months. Preferably, the order data requiring the delayed payment may also be screened from the order data of the electronic resource which is not transferred by the client, which is not limited herein.

S103, determining at least one first data resource group and associated data resources of each first data resource group based on the order data set and the target order data set.

In this embodiment of the application, the determining at least one first data resource group based on the order data set and the target order data set, and the manner of associating data resources of each first data resource group may be: 1) establishing a first tree model based on the order data set, and performing traversal processing on the first tree model to obtain each third data resource group in the order data set and associated data resources of each third data resource group; 2) establishing a second tree model based on the target order data set, and performing traversal processing on the second tree model to obtain each fourth data resource group in the target order data set and associated data resources of each fourth data resource group; 3) and determining at least one first data resource group and the associated data resources of each first data resource group based on each third data resource group, the associated data resources of each third data resource group, each fourth data resource group and the associated data resources of each fourth data resource group. Preferably, the Tree model may be a Frequent Pattern Tree (frequency Pattern Tree) in the FP-Growth algorithm, or may be a Tree model such as a decision Tree that can determine the association degree between items, and is not limited herein.

Specifically, after analyzing all the order data in the order data set, obtaining the associated data resources of a part of the third data resource groups in the associated data resources of each third data resource group may be because the third data resource group and the associated data resources of the third data resource group account for too large in the order in which the transaction fails, so that the part of the data resource groups and the associated data resources thereof are finally determined to be the third data resource group and the associated data resources of the third data resource group. Therefore, it is possible to avoid the above situation by screening out the associated data resources of the third data resource group from the associated data resources that are the same as the associated data resources of the fourth data resource group.

For example, referring to fig. 3, 3 third data resource groups 301 are obtained after processing the order data set, and are respectively an risky species a, a risky species B, and a risky species C, where the associated data resources of the risky species a are the risky species C and the risky species E, the associated data resources of the risky species B are the risky species D, and the associated data resources of the risky species C are the risky species D and the risky species F; then, 3 fourth data resource groups 302 are obtained by processing the target order data groups, which are respectively an risky species a, a risky species B and a risky species E, wherein the associated data resources of the risky species a are the risky species C, the associated data resources of the risky species B are the risky species D, and the associated data resources of the risky species E are the risky species F. And screening out the associated data resources of the fourth data resource group and the fourth data resource group from the associated data resources of the third data resource group and the third data resource group to obtain a final first data resource group 303, wherein the number of the first data resource groups is two, and the first data resource groups are respectively a risk type A and a risk type C, the associated data resources of the risk type A are a risk type E, and the associated data resources of the risk type C are a risk type D and a risk type F.

S104, determining the association degree of each first data resource group and the associated data resources of each first data resource group to obtain a plurality of association degrees.

In this embodiment, the association degree may include a confidence degree indicating a probability of occurrence of the associated data resource of the first data resource group in the order data including the first data resource group, and the association degree between the first data resource group and the associated data resource of the first data resource group may be visually evaluated by the confidence degree.

Then, the way of determining the association degree of each first data resource group with the associated data resource of each first data resource group may be: determining a first order quantity of order data of each first data resource group in the order data set, and a second order quantity of order data of associated data resources of each first data resource group in the order data set; determining a third order quantity of the target order data group containing each first data resource group and the associated data resources of each first data resource group; determining a confidence level of each first data resource group and the associated data resource of each first data resource group based on the first order quantity, the second order quantity and the third order quantity.

Specifically, the first order quantity is a quantity of order data in the order data set that contains the first data resource group, the second order quantity is a quantity of order data in the order data set that contains the associated data resources of both the first data resource group and the first data resource group, and the third order quantity is a quantity of order data in the target order data set that contains the associated data resources of both the first data resource group and the first data resource group.

Illustratively, the first data resource group includes a risk type a, the associated data resources of the first data resource group are a risk type B, and the order data set has order data of 1000 orders, where 800 orders in the 1000 orders contain the risk type a, and then the number of the first orders is 800; 400 orders in the 1000 orders simultaneously contain the dangerous seed A and the dangerous seed B, and then the quantity of the second order is 400; the target order data set has order data for 100 orders, wherein 50 of the 100 orders contain both risk type a and risk type B, and the third order quantity is 50. The confidence may be calculated as:

wherein i represents a first set of data resources; j represents an associated data resource of the first set of data resources; confidence (ij) represents a confidence of the first set of data resources with respect to the set of associated data resources of the first set of data resources; order _ content _ num_all(ij) indicating the number of order data containing both the first data resource group and the associated data resource of the first data resource group in the order data set, that is, the second order number; order _ content _ num_fail(ij) indicating the number of order data containing both the first data resource group and the associated data resource of the first data resource group in the target order data group, that is, the third order number; order _ content _ num_all(i) The quantity of order data that contains the first set of data resources in the order data set, i.e., the first order quantity, is represented. Finally, the confidence between the risk species A and the risk species B is 0.4375 according to the calculation formula of the confidence.

Preferably, in addition to calculating the confidence level by the order number, the confidence level of the associated data resources of the first data resource group and the first data resource group can be calculated by the support level of the order data containing the first data resource group in the order data set, the support level of the order data containing the associated data resources of the first data resource group and the first data resource group at the same time in the order data set, and the support level of the order data containing the associated data resources of the first data resource group and the first data resource group at the target order data set, wherein the support level refers to the probability of occurrence of a certain data resource in all order data.

In one possible implementation manner, the association degree may include a promotion degree, where the promotion degree is used to indicate a possibility that the associated data resource of the first data resource group appears after the first data resource group appears and the support degree of the associated data resource of the first data resource group is known.

Then, the way of determining the association degree of each first data resource group with the associated data resource of each first data resource group may be: determining a first order quantity of order data of each first data resource group in the order data set, and a second order quantity of order data of associated data resources of each first data resource group in the order data set; determining a third order quantity of associated data resources of each first data resource group and each target order data group, and a fourth order quantity of associated data resources of each first data resource group in the target order data group; and determining the promotion degree of each first data resource group and the associated data resources of each first data resource group based on the first order quantity, the second order quantity, the third order quantity and the fourth order quantity.

Illustratively, the first data resource group includes a risk type a, the associated data resources of the first data resource group are a risk type B, and the order data set has order data of 1000 orders, where 800 orders in the 1000 orders contain the risk type a, and then the number of the first orders is 800; 400 orders in the 1000 orders simultaneously contain the dangerous seed A and the dangerous seed B, and then the quantity of the second order is 400; the object order data group comprises order data of 100 orders, wherein 50 orders in the 100 orders simultaneously contain the risk seeds A and the risk seeds B, and then the quantity of the third order is 50; 20 of the 100 orders contain hazard B, then the fourth order quantity is 20. The calculation formula of the lifting degree can be as follows:

wherein i represents a first set of data resources; j represents an associated data resource of the first set of data resources; lift (ij) represents the promotion degree of the first data resource group with respect to the associated data resource group of the first data resource group; order _ content _ num_all(ij) indicating the number of order data containing both the first data resource group and the associated data resource of the first data resource group in the order data set, that is, the second order number; order _ content _ num_fail(ij) indicating the number of order data containing both the first data resource group and the associated data resource of the first data resource group in the target order data group, that is, the third order number; order _ content _ num_all(i) Representing a quantity of order data containing a first set of data resources in an order data set, i.e. a first order quantity; support (j) represents the probability that the order data for the associated data resource of the first set of data resources is contained in the target order data set, i.e., the ratio of the quantity of the fourth order to the quantity of all order data in the target order data set. Finally, the lifting degree between the dangerous seed A and the dangerous seed B is 2.1875 through a calculation formula of the lifting degree.

Preferably, the support (j) may also represent a probability that the order data of the associated data resource of the first data resource group is contained in the order data set, that is, a ratio of the order quantity of the order data of the associated data resource of the first data resource group to the quantity of all order data in the order data set.

In a possible implementation manner, the association degree may further include both the confidence degree and the promotion degree, which is not limited herein. Preferably, the lifting degree may also be calculated by the confidence, wherein the formula for calculating the lifting degree by the confidence may be:

and S105, selecting a target relevance from the relevance, and outputting relevance information, wherein the relevance information comprises the target relevance, a second data resource group for determining the target relevance and relevant data resources of the second data resource group.

In this embodiment of the present application, the manner of determining the target relevance degree in the multiple relevance degrees may be: and determining the association degree which is greater than a preset association degree threshold value in the plurality of association degrees as the target association degree. For example, if the preset association threshold is 0.7, the association degree greater than 0.7 may be the target association degree. Preferably, the manner of determining the target relevance degree in the plurality of relevance degrees may also be: and sequencing the plurality of relevance degrees to obtain the sequenced relevance degrees, and determining a target relevance degree in the sequenced relevance degrees, wherein the target relevance degree is greater than the relevance degrees except the target relevance degree in the plurality of relevance degrees. Specifically, the sorting manner may be descending or ascending, or may be other manners, which is not limited herein. Preferably, the manner of determining the target relevance among the sorted relevance may also be to set a percentage or set the first few names. Illustratively, 10 relevance degrees are sorted in descending order, the top 3 relevance degrees of the sorting are taken as the target relevance degrees, or the relevance degrees of the top 40% of the sorting are taken as the target relevance degrees.

In a possible implementation manner, when the association degree includes both the confidence degree and the promotion degree, the target association degree may be determined among the multiple association degrees by setting different weights for the confidence degree and the promotion degree. For example, if the lift is 2.1875 and the confidence is 0.4375, wherein the weight of the lift is 0.3 and the weight of the confidence is 0.7, then the total relevance is 0.9625. Preferably, the target association degree may also be determined in other manners, which are not limited herein.

In a possible implementation manner, the plurality of target data resources in step S101 refer to M target data resources, each first data resource group includes x target data resources, x is greater than 0 and less than or equal to M, and x is a positive integer. Then, determining the association degree of the respective first data resource group with the associated data resource of the respective first data resource group may be: and determining y associated data resources of the x target data resources and the association degree of the x target data resources and each associated data resource in the y associated data resources based on the order data set and the target order data set to obtain a plurality of association degrees, wherein x is more than 0 and less than M, y is more than 0 and less than or equal to M-x, and x and y are integers.

In particular, one first set of data resources may include one or more target data resources, and each first set of data resources may have one or more associated data resources. Exemplarily, the first set of data resources M₁Including a seed A, a first set of data resources M₂Including a risk seed A and a risk seed C, a first data resource group M₃Comprises a dangerous seed B, a dangerous seed C and a dangerous seed G; wherein, the first data resource group M₁2 associated data resources, namely a dangerous seed B and a dangerous seed F; first group of data resources M ₃1 associated data resource is a dangerous seed E; first group of data resources M₃There are 1 associated data resources, which are D-risky. Then, a first set of data resources M can be obtained₁The degree of association between the dangerous species A in the set and the dangerous species B of the associated data resources; first group of data resources M₁The degree of association between the dangerous species A and the associated data resource dangerous species F; first group of data resources M₂The dangerous species A and the dangerous species C in the data resource are related to the dangerous species E of the related data resource; first group of data resources M₃The dangerous species B, the dangerous species C and the dangerous species G in the data resource are associated with the associated dangerous species D of the associated data resource.

Referring to fig. 4, fig. 4 is a schematic flowchart illustrating a data resource processing method according to an embodiment of the present application; the data resource processing scheme shown in fig. 4 may be performed by the electronic device, and includes, but is not limited to, steps S401 to S410, wherein:

s401, acquiring order data sets generated in a preset time period and related to a plurality of target data resources.

S402, searching a target order data set in the order data set. Wherein the target order data set includes one or more order data, the one or more order data referring to: the client does not transfer the order data of the electronic resource.

It should be noted that, for the specific implementation process of steps S401 to S402, reference may be made to the related description of the specific implementation process shown in steps S101 to S102 in the embodiment shown in fig. 1, and details are not repeated herein.

And S403, establishing a second tree model based on the target order data set, and performing traversal processing on the second tree model to obtain each fourth data resource group in the target order data set and associated data resources of each fourth data resource group.

S404, establishing a first tree model based on the order data set, and performing traversal processing on the first tree model to obtain each third data resource group in the order data set and associated data resources of each third data resource group.

S405, determining at least one first data resource group and the associated data resources of each first data resource group based on each third data resource group, the associated data resources of each third data resource group, each fourth data resource group and the associated data resources of each fourth data resource group.

For example, the order data set may be as shown in table 2, wherein the target data resources include risk types a to H, and a total of 10 orders generated in the last 5 days are acquired:

TABLE 2

	Dangerous seed A	Dangerous seed B	Dangerous seed C	Dangerous species D	Dangerous species E	Dangerous species F	Dangerous seed G	Dangerous species H	Status of state
										Order
1	0	0	1	1	0	0	1	0	Application insurance
										Order 2	0	0	0	1	0	0	0	1	Application insurance
Order
	3	0	0	1	1	0	0	1	0	Application insurance
Order
	4	1	0	0	0	0	0	0	1	Is not applied for protection
Order 5											0	0	1	1	0	0	1	0	Application insurance
	Order 6	0	0	1	0	1	0	0	0	Is not applied for protection
Order
	7	0	0	1	0	1	0	0	0	Is not applied for protection
Order 8											0	0	1	1	0	0	1	0	Application insurance
	Order 9	0	0	1	0	1	0	0	0	Is not applied for protection
Order 10											0	0	1	0	0	0	0	1	Is not applied for protection

A target order data set with a status of "Unguaranteed" (i.e., electronic resource not transferred) may be determined from the order data set as shown in Table 2, as shown in Table 3:

TABLE 3

	Dangerous seed A	Dangerous seed B	Dangerous seed C	Dangerous species D	Dangerous species E	Dangerous species F	Dangerous seed G	Dangerous species H	Status of state
										Order
4	1	0	0	0	0	0	0	1	Is not applied for protection
										Order 6	0	0	1	0	1	0	0	0	Is not applied for protection
Order
	7	0	0	1	0	1	0	0	0	Is not applied for protection
Order 9											0	0	1	0	1	0	0	0	Is not applied for protection
	Order 10	0	0	1	0	1	0	0	1	Is not applied for protection

The first tree model built based on the order data set may be as shown in the frequent pattern tree 501 in FIG. 5; in addition, if the condition mode base is set to be 1, traversing the frequent mode tree 501 to obtain third data resource groups of { C } and { C, D }, respectively, wherein associated data resources of the third data resource group of { C } include { E } and { D }; the associated data resource of the third data resource group { C, D } has { G }.

Then, a second tree model built based on the target order data set may be as shown in the frequent pattern tree 601 in fig. 6, and further, if the condition pattern base is set to be 1, then, traversing the frequent pattern tree 601 may obtain that the fourth data resource group is { C } and the associated data resource of the fourth data resource group { C } is { E }.

Finally, based on the third data resource group and its associated data resources, and the fourth data resource group and its associated data resources, it can be determined that the final first data resource group is { C } and { C, D }, where the associated data resource of the first data resource group { C } is { D }, and the associated data resource of the first data resource group { C, D } is { G }.

It should be noted that, for the specific implementation process of steps S403 to S405, reference may be made to the related description of the specific implementation process of step S103 in the embodiment shown in fig. 1, which is not described herein again.

S406, determining the association degree of each first data resource group and the associated data resource of each first data resource group to obtain a plurality of association degrees.

S407, a target degree of association is selected from the plurality of degrees of association, and degree of association information is output. The relevancy information comprises target relevancy, a second data resource group used for determining the target relevancy and the relevancy data resources of the second data resource group.

It should be noted that, for the specific implementation process of steps S406 to S407, reference may be made to the related description of the specific implementation process shown in steps S104 to S105 in the embodiment shown in fig. 1, and details are not repeated herein.

Meanwhile, after step S403, the method further includes:

s408, determining the degree of opposition of each fourth data resource group in the target order data set and the associated data resource of each fourth data resource group to obtain a plurality of degrees of opposition.

S409, a target degree of confrontation is determined among the plurality of degrees of confrontation, and the confrontation information is output. The degree-of-confrontation information comprises target degree of confrontation, a fifth data resource group used for determining the target degree of confrontation and associated data resources of the fifth data resource group.

In an embodiment of the present application, the degree of confrontation may be a degree of confidence and/or a degree of promotion between the respective fourth data resource group and the associated data resource of the respective fourth data resource group. Because the order data in the target order data group is the order data that fails to be traded or is not completed, the greater the confidence and/or the promotion of the data resource group and the associated data resource in the order data, the greater the probability that the order trade fails or cannot be completed when the data resource group and the associated data resource in the order data occur simultaneously.

In a possible implementation manner, the manner of determining each first data resource group and the associated data resource of each first data resource group may also be to determine a target degree of resistance among the plurality of degrees of resistance, and then filter a fifth data resource group and its associated data resource for determining the target degree of resistance among the third data resource group and its associated data resource. Preferably, the determining manner may also be other manners, which are not limited herein.

It should be noted that, for the specific implementation processes of determining the degree of opposition and determining the target degree of opposition in steps S408 to S409, reference may be made to the description of the specific implementation processes of determining the degree of association and determining the target degree of association shown in steps S104 to S105 in the embodiment shown in fig. 1, which is not repeated herein.

And S410, determining a data resource marketing scheme based on the relevancy information and the confrontation degree information.

In the embodiment of the application, the data resource marketing scheme is used for describing the associated marketing of the second data resource group and the associated data resources of the second data resource group, and the countermeasure marketing of the associated data resources of the fifth data resource group and the fifth data resource group.

Specifically, it can be known through the fifth data resource group, the associated data resources of the fifth data resource group, and the degree of opposition of the two, which target data resources appear in the same order at the same time and easily cause failure of order transaction, so that when a data resource marketing scheme is established, simultaneous marketing of the associated data resources of the fifth data resource group and the fifth data resource group can be avoided. Similarly, when the data resource marketing scheme is customized, because the degree of association between the second data resource group and the associated data resources of the second data resource group is large, in order to increase sales volume, the associated data resources of the second data resource group and the associated data resources of the second data resource group can be associated and marketed simultaneously, so that the sales of the associated data resources of the second data resource group can be promoted by the second data resource group.

Illustratively, the risk C obtained through the analysis of FIG. 5 and FIG. 6 can promote the sale of the risk D, the risk C and the risk D can promote the sale of the risk G, and the risk C and the risk E simultaneously appear and easily cause the failure of the order transaction, so that the risk C, the risk D and the risk G can be arranged together for marketing when a marketing scheme about various types of risk is formulated; furthermore, countermarketing of the dangerous species C and the dangerous species E is also required, thereby avoiding simultaneous marketing of the dangerous species C and the dangerous species E.

In the embodiment of the application, each fourth data resource group and associated data resources thereof in the target order data group are obtained by establishing a second tree model based on the target order data group and performing traversal processing, and each third data resource group and associated data resources thereof in the order data group are obtained by establishing a first tree model based on the order data set and performing traversal processing. Then, based on the data resource group and the associated data resources thereof, determining at least one first data resource group and the associated data resources thereof, and determining the obtained association degree of the first data resource group and the associated data resources thereof, thereby outputting association degree information containing the target association degree; based on the determined degree of confrontation of each fourth data resource group and the associated data resources in the target order data group, outputting confrontation degree information containing the target confrontation degree; and finally, determining a data resource marketing scheme based on the relevancy information and the confrontation degree information. According to the method and the device, the target order data group containing the electronic resources which are not transferred is screened out from the order data set, so that the data resource group and the associated data resources thereof under the condition of considering transaction failure can be determined through the order data set and the target order data group; meanwhile, a tree model is constructed based on the order data set and the target order data set, so that efficient traversal of data can be realized, and the time complexity of an algorithm is reduced; and finally, a digital resource marketing scheme is formulated based on the output association degree information and the output confrontation degree information, which data resources can be associated and crossed in marketing, and which data resources need to be prevented from being simultaneously marketed, so that the sales volume of the data resources is favorably improved.

The embodiment of the present application further provides a computer storage medium, in which program instructions are stored, and when the program instructions are executed, the computer storage medium is used for implementing the corresponding method described in the above embodiment.

Referring to fig. 7 again, fig. 7 is a schematic structural diagram of a data resource processing apparatus according to an embodiment of the present application.

In one implementation of the apparatus of the embodiment of the application, the apparatus includes the following structure.

An acquiring unit 701 configured to acquire an order data set regarding a plurality of target data resources generated within a preset time period;

a processing unit 702 configured to find a target order data set in the order data set, the target order data set comprising one or more order data, the one or more order data being: the client side does not transfer order data of the electronic resources;

a processing unit 702, further configured to determine at least one first data resource group based on the order data set and the target order data set, and associated data resources of each first data resource group;

an output unit 703 is configured to select a target relevance degree from the multiple relevance degrees, and output relevance degree information, where the relevance degree information includes the target relevance degree, a second data resource group used for determining the target relevance degree, and relevant data resources of the second data resource group.

In one embodiment, the processing unit 702 is further configured to: establishing a first tree model based on the order data set, and performing traversal processing on the first tree model to obtain each third data resource group in the order data set and associated data resources of each third data resource group;

establishing a second tree model based on the target order data set, and performing traversal processing on the second tree model to obtain each fourth data resource group in the target order data set and associated data resources of each fourth data resource group;

and determining at least one first data resource group and the associated data resources of each first data resource group based on each third data resource group, the associated data resources of each third data resource group, each fourth data resource group and the associated data resources of each fourth data resource group.

In one embodiment, the association includes a confidence level, and the processing unit 702 is further configured to: determining a first order quantity of order data of each first data resource group in the order data set, and a second order quantity of order data of associated data resources of each first data resource group in the order data set;

determining a third order quantity of the target order data group containing each first data resource group and the associated data resources of each first data resource group;

determining a confidence level of each first data resource group and the associated data resource of each first data resource group based on the first order quantity, the second order quantity and the third order quantity.

In one embodiment, the association degree includes a degree of promotion, and the processing unit 702 is further configured to: determining the association degree of each first data resource group and the associated data resource of each first data resource group, wherein the determining comprises the following steps:

determining a first order quantity of order data of each first data resource group in the order data set, and a second order quantity of order data of associated data resources of each first data resource group in the order data set;

determining a third order quantity of the target order data group containing each first data resource group and the associated data resources of each first data resource group, and a fourth order quantity of the target order data group containing the associated data resources of each first data resource group;

and determining the promotion degree of each first data resource group and the associated data resources of each first data resource group based on the first order quantity, the second order quantity, the third order quantity and the fourth order quantity.

In one embodiment, the processing unit 702 is further configured to: determining the degree of confrontation of each fourth data resource group in the target order data set and the associated data resources of each fourth data resource group to obtain a plurality of degrees of confrontation;

an output unit 703, configured to determine a target degree of confrontation among the plurality of degrees of confrontation, and output the degree of confrontation information, where the degree of confrontation information includes the target degree of confrontation, a fifth data resource group used for determining the target degree of confrontation, and associated data resources of the fifth data resource group;

and determining a data resource marketing scheme based on the association degree information and the countermeasure information, wherein the data resource marketing scheme is used for describing association marketing of the second data resource group and associated data resources of the second data resource group and countermeasure marketing of the associated data resources of the fifth data resource group and the fifth data resource group.

In one embodiment, the processing unit 702 is further configured to: determining the relevance degree which is greater than a preset relevance degree threshold value in the multiple relevance degrees as a target relevance degree;

or sequencing the multiple relevance degrees to obtain the sequenced relevance degrees, and determining a target relevance degree in the sequenced relevance degrees, wherein the target relevance degree is greater than the relevance degrees except the target relevance degree in the multiple relevance degrees.

In one embodiment, the plurality of target data resources refers to M target data resources, each first data resource group includes x target data resources, 0< x ≦ M, x being a positive integer. The processing unit 702 is further configured to: and determining y associated data resources of the x target data resources and the association degree of the x target data resources and each associated data resource in the y associated data resources based on the order data set and the target order data set to obtain a plurality of association degrees, wherein x is 0< M, y is 0< M-x, and x and y are integers.

Referring to fig. 8 again, fig. 8 is a schematic structural diagram of an electronic device provided in the embodiment of the present application, where the electronic device in the embodiment of the present application includes a power supply module and the like, and includes a processor 801, a storage device 802, and a communication interface 803. Data can be exchanged between the processor 801, the storage device 802 and the communication interface 803, and a corresponding data resource processing scheme is implemented by the processor 801.

The storage 802 may include volatile memory (volatile memory), such as random-access memory (RAM); the storage device 802 may also include a non-volatile memory (non-volatile memory), such as a flash memory (flash memory), a solid-state drive (SSD), or the like; the storage means 802 may also comprise a combination of memories of the kind described above.

The processor 801 may be a Central Processing Unit (CPU) 801. The processor 801 may also be a combination of a CPU and a GPU. In the electronic device, a plurality of CPUs and GPUs may be included as necessary to perform corresponding data processing. In one embodiment, the storage 802 is used to store program instructions. The processor 801 may invoke program instructions to implement the various methods as described above in the embodiments of the present application.

In a first possible embodiment, the processor 801 of the electronic device calls program instructions stored in the storage device 802 for acquiring order data sets generated in a preset time period for a plurality of target data resources; searching a target order data set in the order data set, wherein the target order data set comprises one or more order data, and the one or more order data refer to: the client side does not transfer order data of the electronic resources; determining at least one first data resource group and associated data resources of each first data resource group based on the order data set and the target order data set; determining the association degree of each first data resource group and the associated data resources of each first data resource group to obtain a plurality of association degrees; and selecting a target relevance from the multiple relevance, and outputting relevance information, wherein the relevance information comprises the target relevance, a second data resource group for determining the target relevance and relevant data resources of the second data resource group.

In one embodiment, the processor 801 is further configured to: establishing a first tree model based on the order data set, and performing traversal processing on the first tree model to obtain each third data resource group in the order data set and associated data resources of each third data resource group;

In one embodiment, the association includes a confidence level, and the processor 801 is further configured to:

In one embodiment, the association includes a degree of lift, and the processor 801 is further configured to:

In one embodiment, the processor 801 is further configured to: determining the degree of confrontation of each fourth data resource group in the target order data set and the associated data resources of each fourth data resource group to obtain a plurality of degrees of confrontation;

determining a target degree of confrontation in the plurality of degrees of confrontation, and outputting the degree of confrontation information, wherein the degree of confrontation information comprises the target degree of confrontation, a fifth data resource group used for determining the target degree of confrontation, and associated data resources of the fifth data resource group;

In one embodiment, the processor 801 is further configured to: determining the relevance degree which is greater than a preset relevance degree threshold value in the multiple relevance degrees as a target relevance degree;

In one embodiment, the plurality of target data resources refers to M target data resources, each first data resource group includes x target data resources, 0< x ≦ M, x being a positive integer, and the processor 801 is further configured to:

and determining y associated data resources of the x target data resources and the association degree of the x target data resources and each associated data resource in the y associated data resources based on the order data set and the target order data set to obtain a plurality of association degrees, wherein x is 0< M, y is 0< M-x, and x and y are integers.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like. The computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.

While the invention has been described with reference to a number of embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. A method for processing data resources, comprising:

2. The method of claim 1, wherein determining at least one first set of data resources based on the order data set and the target order data set and associated data resources for each first set of data resources comprises:

establishing a first tree model based on the order data set, and performing traversal processing on the first tree model to obtain each third data resource group in the order data set and associated data resources of each third data resource group;

and determining the at least one first data resource group and the associated data resources of each first data resource group based on each third data resource group, the associated data resources of each third data resource group, each fourth data resource group and the associated data resources of each fourth data resource group.

3. The method of claim 2, wherein the degree of association comprises a confidence level;

the determining the association degree of each first data resource group and the associated data resource of each first data resource group includes:

determining a third order quantity of the target order data group, which comprises each first data resource group and the associated data resources of each first data resource group;

and determining the confidence degree of each first data resource group and the associated data resource of each first data resource group based on the first order quantity, the second order quantity and the third order quantity.

4. The method of claim 3, wherein the degree of association comprises a degree of lift;

determining a third order quantity of the target order data group containing the first data resource groups and the associated data resources of the first data resource groups, and a fourth order quantity of the target order data group containing the associated data resources of the first data resource groups;

and determining the promotion degree of the associated data resources of each first data resource group and each first data resource group based on the first order quantity, the second order quantity, the third order quantity and the fourth order quantity.

5. The method of claim 2, further comprising:

determining the degree of confrontation of each fourth data resource group in the target order data set and the associated data resource of each fourth data resource group to obtain a plurality of degrees of confrontation;

determining a data resource marketing plan based on the relevancy information and the struggle information, the data resource marketing plan describing the associative marketing of the associated data resources of the second data resource group and the second data resource group, and the struggle marketing of the associated data resources of the fifth data resource group and the fifth data resource group.

6. The method of claim 1, wherein determining a target relevance among the plurality of relevance comprises:

determining the relevance degree of the plurality of relevance degrees which is greater than a preset relevance degree threshold value as the target relevance degree;

or sequencing the plurality of relevance degrees to obtain the sequenced relevance degrees, and determining a target relevance degree in the sequenced relevance degrees, wherein the target relevance degree is greater than the relevance degrees except the target relevance degree in the plurality of relevance degrees.

7. The method according to any one of claims 1 to 6, wherein the plurality of target data resources refers to M target data resources, each of the first data resource groups includes x target data resources, 0< x ≦ M, x being a positive integer;

and determining y associated data resources of the x target data resources and the association degree of the x target data resources and each associated data resource in the y associated data resources based on the order data set and the target order data set to obtain a plurality of association degrees, wherein x is greater than 0 and less than M, y is greater than 0 and less than or equal to M-x, and x and y are integers.

8. A data resource processing apparatus, characterized in that the apparatus comprises:

9. An electronic device, comprising a processor, a storage device and a communication interface, the processor, the storage device and the communication interface being interconnected, wherein the storage device is configured to store computer program instructions, and the processor is configured to execute the program instructions to implement the data resource processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored therein, which when executed by a processor, are configured to perform the data resource processing method of any one of claims 1-7.