CN115204677A

CN115204677A - Information determination method, device, equipment and computer readable storage medium

Info

Publication number: CN115204677A
Application number: CN202210832474.7A
Authority: CN
Inventors: 魏文婷; 邵培兴; 邱晓海; 王勇
Original assignee: CCB Finetech Co Ltd
Current assignee: CCB Finetech Co Ltd
Priority date: 2022-07-15
Filing date: 2022-07-15
Publication date: 2022-10-18

Abstract

The application discloses an information determination method, an information determination device, information determination equipment and a computer readable storage medium. Wherein, the method comprises the following steps: acquiring first attribute information of a first target product; determining a first branch corresponding to the first attribute information in the target decision tree model; the target decision tree model is determined according to the attribute information of the target product and the product element information associated with the attribute information; determining first product element information associated with the first attribute information in the first branch to obtain m pieces of first product element information, wherein the target decision tree model comprises the utilization rate of the product element information; and determining the first product element information with the utilization rate higher than the preset utilization rate as second product element information to obtain n pieces of second product element information. According to the information determining method, the time and labor for determining the information can be saved, and the efficiency and the accuracy of determining the information can be improved.

Description

Information determination method, device, equipment and computer readable storage medium

Technical Field

The present application belongs to the field of computer technologies, and in particular, to an information determining method, apparatus, device, and computer-readable storage medium.

Background

Generally, before a user purchases an insurance product and/or a financial product, it is necessary to show the user description information of the product, and when the user determines to purchase the product, it is necessary to show the user basic information to be filled in on a transaction form. However, there are differences in the information that needs to be presented to the user for different types of insurance products and/or financial products. Therefore, each time an insurance product and/or financial product is added, the information to be displayed is newly determined.

In the prior art, when an insurance product and/or a financial product is newly added, a worker needs to manually determine information to be displayed according to experience. However, the workload of manually determining information is large, which not only wastes time and labor, but also has low efficiency of information determination. In addition, the information required to be displayed is determined according to experience, and the accuracy rate of information determination is low.

Disclosure of Invention

The embodiment of the application provides an information determination method, an information determination device, information determination equipment, a computer readable storage medium and a computer program product, which can save the time and labor for information determination and improve the efficiency and accuracy of information determination.

In a first aspect, an embodiment of the present application provides an information determining method, where the method includes:

acquiring first attribute information of a first target product, wherein the first target product comprises an insurance product and/or a financing product;

determining a first branch corresponding to the first attribute information in a target decision tree model, wherein the first branch comprises the first attribute information and first product element information with an incidence relation; the target decision tree model is determined according to attribute information of a target product and product element information associated with the attribute information, and the target decision tree model also comprises the utilization rate of the product element information;

determining first product element information associated with the first attribute information in the first branch to obtain m pieces of first product element information, wherein m is a positive integer;

and determining the first product element information with the utilization rate higher than the preset utilization rate as second product element information to obtain n pieces of second product element information, wherein n is a positive integer and is not more than m.

In one possible implementation, before determining the first branch corresponding to the first attribute information in the objective decision tree model, the method further includes:

acquiring a plurality of first historical target products;

determining second attribute information, third product element information and an incidence relation between the second attribute information and the third product element information corresponding to the plurality of first historical target products;

determining the second attribute information and the third product element information which have the incidence relation as a branch to obtain a plurality of branches;

constructing a first decision tree model based on the plurality of branches;

and marking the utilization rate of the element information of the third product in the first decision tree model to obtain a target decision tree model.

In a possible implementation manner, before labeling, in the first decision tree model, the utilization rate of the third product element information to obtain a target decision tree model, the method further includes:

determining the number of times of occurrence of each third product element information and the total number of times of occurrence of the third product element information in the plurality of first historical target products;

and determining the utilization rate of each third product element information in the plurality of first historical target products based on the total occurrence times of the third product element information and the occurrence times of each third product element information.

In a possible implementation manner, the determining the second attribute information and the third product element information having an association relationship as one branch to obtain a plurality of branches includes:

constructing a knowledge graph according to second attribute information, third product element information and an incidence relation between the second attribute information and the third product element information corresponding to the plurality of first historical target products;

and determining the second attribute information and the third product element information which have the association relationship into one branch according to the knowledge graph to obtain a plurality of branches.

In one possible implementation, after determining the target decision tree model, the method further includes:

acquiring a plurality of second historical target products;

determining third attribute information, fourth product element information and an incidence relation between the third attribute information and the fourth product element information corresponding to the plurality of second historical target products;

updating the plurality of branches according to the third attribute information and the fourth product element information with the incidence relation to obtain a plurality of updated branches;

updating the target decision tree model based on the plurality of updated branches to obtain a second decision tree model;

the determining a first branch corresponding to the first attribute information in a target decision tree model comprises:

determining a second branch corresponding to the first attribute information in a second decision tree model;

the determining first product element information associated with the first attribute information in the first branch to obtain m pieces of first product element information includes:

and determining first product element information associated with the first attribute information in the second branch to obtain m pieces of first product element information.

In a possible implementation manner, the first product element information is a leaf node in the objective decision tree model, and the first attribute information is a tree node in the objective decision tree model.

In a second aspect, an embodiment of the present application provides an information determining apparatus, including:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring first attribute information of a first target product, and the first target product comprises an insurance product and/or a financing product;

a first determining module, configured to determine a first branch corresponding to the first attribute information in a target decision tree model, where the first branch includes the first attribute information and first product element information having an association relationship; the target decision tree model is determined according to attribute information of a target product and product element information associated with the attribute information, and the target decision tree model also comprises the utilization rate of the product element information;

a second determining module, configured to determine first product element information associated with the first attribute information in the first branch to obtain m pieces of first product element information, where the target decision tree model includes a usage rate of the product element information, and m is a positive integer;

and the third determining module is used for determining the first product element information with the utilization rate higher than the preset utilization rate as second product element information to obtain n pieces of second product element information, wherein n is a positive integer and is not more than m.

In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements the method of any one of the possible implementation methods of the first aspect described above.

In a fourth aspect, the present application provides a computer-readable storage medium, on which computer program instructions are stored, and the computer program instructions, when executed by a processor, implement the method in any one of the possible implementation methods of the first aspect.

In a fifth aspect, the present application provides a computer program product, where instructions of the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the method in any one of the possible implementation methods as described in the first aspect.

The information determination method, the information determination device, the information determination apparatus, the computer-readable storage medium, and the computer program product according to the embodiments of the application can automatically determine the first product element information of the first target product by determining, based on the first attribute information of the first target product, a first branch corresponding to the first attribute information in the target decision tree model, and determining the first product element information associated with the first attribute information in the first branch. Because the first attribute information and the first product element are in a many-to-many relationship, and the workload of information determination is large, compared with the artificial determination information, the information of the first product element is determined through the target decision tree, so that the time and labor for information determination can be saved, and the efficiency for information determination can be improved. In addition, the first product element information with the utilization rate higher than the preset utilization rate is determined to be the second product element information, so that the first product element information can be screened, the second product element information with the high utilization rate can be obtained, and the accuracy of information determination is improved. Therefore, by the embodiment of the application, the time and labor for information determination can be saved, and the efficiency and accuracy for information determination can be improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, 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 an information determination method provided in an embodiment of the present application;

fig. 2 is a schematic flowchart of another information determination method provided in an embodiment of the present application;

FIG. 3 is a diagram of a first decision tree model provided by an embodiment of the present application;

FIG. 4 is a schematic diagram of a knowledge-graph provided by an embodiment of the present application;

FIG. 5 is a schematic illustration of another knowledge-graph provided by an embodiment of the present application;

fig. 6 is a schematic flowchart of another information determination method provided in an embodiment of the present application;

fig. 7 is a schematic structural diagram of an information determination 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

Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative only and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising 8230; \8230;" comprises 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.

It should be noted that the knowledge graph is a structured semantic knowledge base for describing concepts and their interrelations in the physical world in symbolic form. The basic composition units are 'entity, relation and entity' triplets, and entities and related attribute value pairs thereof, and the entities are mutually connected through the relation to form a network knowledge structure.

In addition, the data acquisition, storage, use, processing and the like in the technical scheme of the application all conform to relevant regulations of national laws and regulations.

In order to solve the prior art problems, embodiments of the present application provide an information determining method, apparatus, device, computer-readable storage medium, and computer program product.

First, a method for determining information provided in the embodiment of the present application is described below.

Fig. 1 shows a schematic flow chart of an information determination method provided in an embodiment of the present application. As shown in fig. 1, the information determining method provided in the embodiment of the present application includes the following steps:

s110, acquiring first attribute information of a first target product, wherein the first target product comprises an insurance product and/or a financing product;

s120, determining a first branch corresponding to the first attribute information in the target decision tree model, wherein the first branch comprises the first attribute information and first product element information which have an incidence relation; the target decision tree model is determined according to the attribute information of the target product and the product element information associated with the attribute information, and the target decision tree model also comprises the utilization rate of the product element information;

s130, determining first product element information associated with the first attribute information in the first branch to obtain m pieces of first product element information, wherein m is a positive integer;

s140, determining the first product element information with the utilization rate higher than the preset utilization rate as second product element information to obtain n pieces of second product element information, wherein n is a positive integer and is not more than m.

The information determining method of the embodiment of the application can automatically determine the first product element information of the first target product by determining the first branch corresponding to the first attribute information in the target decision tree model and determining the first product element information associated with the first attribute information in the first branch based on the first attribute information of the first target product. Because the first attribute information and the first product element are in a many-to-many relationship, and the workload of information determination is large, compared with the manual determination information, the information of the first product element is determined through the target decision tree, so that the time and labor for information determination can be saved, and the efficiency of information determination can be improved. In addition, the first product element information with the utilization rate higher than the preset utilization rate is determined to be the second product element information, so that the first product element information can be screened, the second product element information with the high utilization rate can be obtained, and the accuracy of information determination is improved. Therefore, by the embodiment of the application, the time and labor for information determination can be saved, and the efficiency and accuracy for information determination can be improved.

Specific implementations of the above steps are described below.

In some embodiments, in S110, the first target product may be an insurance product or a financial product. The first attribute information may be characteristic information of the first target product. For example, in the case that the first target product is an insurance product, the first attribute information may be information such as a type, a payment method, a category of dangerous goods, an insurance subject, and an insurance age requirement of an insurance company. Wherein, the type of the insurance company can be life insurance, financial insurance, etc.; the payment mode can be term payment, wharfstage payment and the like; the dangerous species product category can be dividend insurance, medical insurance, personal insurance, credit insurance, group insurance and the like; the insuring objects can be public and private; the guarantee age requirement can be below 60, unlimited and the like.

As an example, by identifying a first target product, a plurality of first attribute information of the first target product may be obtained.

In some embodiments, in S120, the product element information may be description information of the target product, or may be display information on a transaction form of the target product. In the case where the target product is an insurance product, the display information on the transaction form may be, for example, title information to be filled in on an insurance policy. The title information may be user information such as name, sex, occupation, age, etc. of the applicant, or product information such as insurance fee, insurance time limit, and insurance company name, etc.

In addition, the first branch may be one branch in the objective decision tree model, and a plurality of branches may be included in the objective decision tree model. The first branch may include first attribute information and first product element information having an association relationship. There may be one or more first attribute information in the first branch, and there may be one first product element information in the first branch. That is, one piece of first product factor information may correspond to one piece of first attribute information, or may correspond to a plurality of pieces of first attribute information.

As an example, after the first attribute information is acquired, whether a branch corresponding to the first attribute information exists among a plurality of branches of the target decision tree model may be retrieved. If a branch corresponding to the first attribute information exists in the target decision tree model, the branch may be determined to be a first branch. The number of the first branches may be one or more, and is not limited herein.

In some embodiments, in S130, since the first branch may include the first attribute information and the first product element information having an association relationship, and there may be one first product element information corresponding to the first branch, after the first branch is determined, the product element information corresponding to the first branch may be determined as the first product element information.

In addition, one first attribute information may correspond to a plurality of first branches, and one first attribute information may correspond to a plurality of first product factor information.

Based on this, the target decision tree model may be used to determine the first product element information based on the one first attribute information or the plurality of first attribute information. That is, the first product element information may be determined by one piece of first attribute information or may be determined by a plurality of pieces of first attribute information in common.

Based on this, in some embodiments, the first product element information may be leaf nodes in the objective decision tree model, and the first attribute information may be tree nodes in the objective decision tree model.

In some embodiments, in S140, the second product element information may be information obtained after the screening of the first product element information. In the case where the usage rate of the first product factor information is higher than a preset usage rate, the first product factor information may be determined as the second product factor information. The number of the second product element information may be less than or equal to the number of the first product element information.

In order to determine second product element information corresponding to a first target product based on a target decision tree model and first attribute information, as another implementation manner of the present application, the present application further provides another implementation manner of an information determination method, and specifically refer to the following embodiments.

Referring to fig. 2, before S120 shown in the foregoing embodiment, the information determining method provided in the embodiment of the present application may further include the following steps:

s210, obtaining a plurality of first historical target products;

s220, determining second attribute information, third product element information and an incidence relation between the second attribute information and the third product element information corresponding to a plurality of first historical target products;

s230, determining the second attribute information and the third product element information which have the incidence relation as a branch to obtain a plurality of branches;

s240, constructing a first decision tree model based on the multiple branches;

and S250, marking the utilization rate of the third product element information in the first decision tree model to obtain a target decision tree model.

According to the embodiment of the application, the second attribute information and the third product element information which have the incidence relation are determined to be one branch, and the first decision tree model is constructed based on a plurality of branches, so that the first decision tree model for determining the product element information according to the attribute information can be obtained. By marking the utilization rate of the product element information in the first decision tree model, the first decision tree model can be improved, and the target decision tree model can be obtained. Therefore, through the embodiment of the application, the second product element information corresponding to the first target product can be determined based on the target decision tree model and the first attribute information.

Specific implementations of the above steps are described below.

In some embodiments, in S210, the first historical target product may be an insurance product or a financial product.

In some embodiments, in S220, the second attribute information may be characteristic information of the first historical target product. The third product element information may be description information of the first historical target product, or may be display information on a transaction form of the first historical target product. One piece of second attribute information may correspond to a plurality of pieces of third product element information, and one piece of third product element information may also correspond to a plurality of pieces of second attribute information. That is, there may be a many-to-many relationship between the second attribute information and the third product element information.

In the present embodiment, the association relationship is not limited to a direct association relationship between the second attribute information and the third product element information, and may be an indirect association relationship. And the third product element information can also have an association relationship. Based on this, if a and B are the second attribute information, C is the third product element information, a direct association relationship exists between a and B, and a direct association relationship exists between B and C, an indirect association relationship may exist between a and C.

As an example, by identifying a first historical target product, a plurality of second attribute information of the first historical target product may be obtained. For example, in the case where the first history target product is an insurance product, the second attribute information may be information of a group insurance, a public insurance, a personal insurance, a private insurance, and the like.

As another example, a plurality of third product factor information for the first historical target product may be obtained by identifying a transaction form for the first target product. For example, in the case where the first history target product is an insurance product, the third product element information may be information such as a name, an applicant name, a company name, and an application account number.

As yet another example, by identifying the first historical target product, an association between the second attribute information and the third product factor information may be determined. For example, in the case that the first history target product is an insurance product, on one hand, the name of the person may be included in the company name, the dangerous species product of the name may be personal insurance, the insurance object of the personal insurance may be personal contraband, and the like. On the other hand, the dangerous species product of the company name can be a group risk, and the insurance application object of the group risk can be a public and the like.

In some embodiments, a plurality of second attribute information and a third product element information may determine a branch in S230. The second attribute information corresponding to one branch may have a direct association relationship with the third product element information, or may have an indirect association relationship with the third product element information.

As an example, in the case where the first history target product is an insurance product, it can be known from the above-described association relationship that: the dangerous seed product of the name can be personal insurance, and the insurable object of the personal insurance can be contraband. Since the name can be the third product element information and the personal insurance and the contraband can be the second attribute information, the name, the personal insurance and the contraband can be determined as a branch through the association relationship.

In some embodiments, in S240, the first decision tree model may include a plurality of branches. The first decision tree model may be used to determine third product element information based on the one second attribute information or the plurality of second attribute information. That is, the third product element information may be determined by one piece of second attribute information or may be determined by a plurality of pieces of second attribute information in common.

As an example, based on the above-mentioned association relationship, a schematic diagram of the first decision tree model may be as shown in fig. 3. In fig. 3, the name may be determined by life insurance and privacy.

Based on this, in order to improve the efficiency of constructing the first decision tree model, in some embodiments, S230 may specifically include:

constructing a knowledge graph according to second attribute information, third product element information and an incidence relation between the second attribute information and the third product element information corresponding to a plurality of first historical target products;

Here, the second attribute information and the third product element information may be entities in a knowledge graph, and the association relationship may be a relationship in the knowledge graph. Based on this, in the case where the first historical target product is an insurance product, a schematic diagram of the knowledge-graph may be as shown in fig. 4. The insurance product element (entity) can be third product element information, the insurance product (entity) and the insurance object (entity) can be second attribute information, and the relationship can be an association relationship.

As one example, a knowledge graph may be stored in a graph database. Based on the method, the multiple branches can be conveniently and quickly determined through the knowledge graph in the graph database, and the first decision tree model is constructed according to the multiple branches.

As another example, if in the above-described embodiment, the second product element information corresponding to the first target product has been obtained, the knowledge graph may be updated based on the correspondence between the first target product and the second product element. In the case where the first target product is a supplemental medical insurance a, the updated knowledge-graph may be as shown in fig. 5. The supplementary medical insurance A can be an insurance product, and the insurance product (entity) can be a first target product.

In this way, by determining a plurality of branches from the knowledge graph and constructing the first decision tree model based on the plurality of branches, the efficiency of constructing the first decision tree model can be improved.

In some embodiments, the third product element may have a corresponding usage rate at S250. And marking the utilization rate corresponding to the third product element to the corresponding position of the first decision tree model to obtain the target decision tree model.

Based on this, in order to determine the target decision tree model based on the usage of the third product element and the first decision tree model, in some embodiments, before S250, the method may further include:

determining the number of times of occurrence of each third product element information and the total number of times of occurrence of the third product element information in a plurality of first historical target products;

and determining the utilization rate of each third product element information in the plurality of first historical target products based on the total number of times of the third product element information and the number of times of each third product element information.

Here, there may be a plurality of third product factor information in the plurality of first history target products, and each product factor information may appear a plurality of times. Therefore, by sequentially calculating the frequency of occurrence of each third product element information based on the total number of occurrences of the third product element information and the number of occurrences of each third product element information, the usage rate of each third product element information can be determined. For example, the usage rate of names may be 100%, and the usage rate of professions may be 30%.

In this way, by determining the usage rate of each of the third product element information in the plurality of first historical target products, the target decision tree model can be determined based on the usage rates of the third product elements and the first decision tree model.

In addition, other steps of the method in the embodiment of the present application can be referred to the related description of the embodiment shown in fig. 1, and are not described herein again.

Based on this, in order to continuously improve the accuracy of the objective decision tree model, in some embodiments, after S250, the method may further include:

acquiring a plurality of second historical target products;

determining third attribute information, fourth product element information and an incidence relation between the third attribute information and the fourth product element information corresponding to a plurality of second historical target products;

based on this, S120 may specifically include:

determining a second branch in the second decision tree model corresponding to the first attribute information;

based on this, S130 may specifically include:

Here, the second historical target product may be a historical target product obtained after the target decision tree is obtained. The generation time of the second target product may be later than the generation time of the first target product.

As an example, the knowledge graph may be updated according to third attribute information, fourth product element information, and an association relationship between the third attribute information and the fourth product element information corresponding to a plurality of second history target products. After obtaining the updated knowledge-graph, the plurality of branches may be updated based on the updated knowledge-graph, and the target decision tree model may be updated based on the updated plurality of branches, resulting in a second decision tree model.

For example, if the product element information that no occupation exists in the personal insurance product is obtained based on the second historical target product, the association relation related to the occupation in the knowledge graph can be deleted, and the occupation and association relation related to the occupation in the branch can be deleted, so as to obtain the second decision tree model.

In this way, the target decision tree model is updated according to the third attribute information, the fourth product element information and the incidence relation between the third attribute information and the fourth product element information corresponding to the plurality of second historical target products to obtain the second decision tree model, the target decision tree model can be continuously updated according to actual conditions, and the accuracy of the target decision tree model is continuously improved.

In order to better describe the whole scheme, specific examples are given based on the above embodiments.

For example, a flow chart of the information determination method shown in fig. 6 is shown.

As shown in fig. 6, with the first history target product, the second attribute information, the third product element information, and the association relationship between the second attribute information and the third product element information corresponding thereto can be determined. Based on the incidence relation, a knowledge graph can be constructed, and based on the knowledge graph, a target decision tree model can be constructed. After the target decision tree model is obtained, the first attribute information corresponding to the first target product may be input to the target decision tree model, so as to obtain second product element information for configuring the first target product.

Therefore, the second product element information used for configuring the first target product is obtained by inputting the first attribute information corresponding to the first target product into the target decision tree model, the second product element information can be automatically determined, the information determination time and labor can be further saved, and the information determination efficiency and accuracy are improved.

Based on the information determination method provided by the above embodiment, correspondingly, the application also provides a specific implementation manner of the information determination device. Please see the examples below.

As shown in fig. 7, an information determination apparatus 700 provided in the embodiment of the present application includes the following modules:

a first obtaining module 710, configured to obtain first attribute information of a first target product, where the first target product includes an insurance product and/or a financial product;

a first determining module 720, configured to determine a first branch corresponding to the first attribute information in the target decision tree model, where the first branch includes the first attribute information and the first product element information having an association relationship; the target decision tree model is determined according to the attribute information of the target product and the product element information associated with the attribute information, and the target decision tree model also comprises the utilization rate of the product element information;

a second determining module 730, configured to determine first product element information associated with the first attribute information in the first branch to obtain m pieces of first product element information, where the target decision tree model includes a usage rate of the product element information, and m is a positive integer;

the third determining module 740 is configured to determine the first product element information with the usage rate higher than the preset usage rate as second product element information to obtain n pieces of second product element information, where n is a positive integer and n is not greater than m.

The information determination apparatus 700 will be described in detail below, specifically as follows:

in some of these embodiments, the information determining apparatus 700 may further include:

the second obtaining module is used for obtaining a plurality of first historical target products before a first branch corresponding to the first attribute information is determined in the target decision tree model;

the fourth determining module is used for determining second attribute information, third product element information and an incidence relation between the second attribute information and the third product element information corresponding to the plurality of first historical target products;

the fifth determining module is used for determining the second attribute information and the third product element information which have the incidence relation into one branch to obtain a plurality of branches;

a building module for building a first decision tree model according to the plurality of branches;

and the marking module is used for marking the utilization rate of the element information of the third product in the first decision tree model to obtain a target decision tree model.

a sixth determining module, configured to mark, in the first decision tree model, a usage rate of the third product element information, and determine, in a plurality of first historical target products, a number of times that each third product element information appears and a total number of times that the third product element information appears before obtaining the target decision tree model;

and the seventh determining module is used for determining the utilization rate of each third product element information in the plurality of first historical target products based on the total number of times of appearance of the third product element information and the number of times of appearance of each third product element information.

In some embodiments, the fifth determining module may specifically include:

the first construction submodule is used for constructing a knowledge graph according to second attribute information, third product element information and an incidence relation between the second attribute information and the third product element information corresponding to a plurality of first historical target products;

and the first determining submodule is used for determining the second attribute information and the third product element information which have the association relationship into one branch according to the knowledge graph to obtain a plurality of branches.

the third acquisition module is used for acquiring a plurality of second historical target products;

the eighth determining module is used for determining third attribute information, fourth product element information and an incidence relation between the third attribute information and the fourth product element information, which correspond to a plurality of second historical target products;

the first updating module is used for updating the plurality of branches according to the third attribute information and the fourth product element information with the incidence relation to obtain a plurality of updated branches;

a second updating module, configured to update the target decision tree model based on the plurality of updated branches to obtain a second decision tree model;

based on this, the first determining module 720 may specifically include:

a second determining submodule, configured to determine a second branch corresponding to the first attribute information in a second decision tree model;

based on this, the second determining module 730 may specifically include:

and the third determining submodule is used for determining the first product element information associated with the first attribute information in the second branch to obtain m pieces of first product element information.

In some embodiments, the first product element information may be leaf nodes in the objective decision tree model, and the first attribute information may be tree nodes in the objective decision tree model.

The information determining apparatus of the embodiment of the application is capable of automatically determining the first product element information of the first target product by determining the first branch corresponding to the first attribute information in the target decision tree model based on the first attribute information of the first target product and determining the first product element information associated with the first attribute information in the first branch. Because the first attribute information and the first product element are in a many-to-many relationship, and the workload of information determination is large, compared with the artificial determination information, the information of the first product element is determined through the target decision tree, so that the time and labor for information determination can be saved, and the efficiency for information determination can be improved. In addition, the first product element information with the utilization rate higher than the preset utilization rate is determined as the second product element information, so that the first product element information can be screened, the second product element information with the higher utilization rate can be obtained, and the accuracy of information determination is improved. Therefore, by the embodiment of the application, the time and labor for information determination can be saved, and the efficiency and accuracy for information determination can be improved.

Based on the information determination method provided by the above embodiment, the embodiment of the present application further provides a specific implementation manner of the electronic device. Fig. 8 shows a schematic diagram of an electronic device 800 provided in an embodiment of the present application.

The electronic device 800 may include a processor 810 and a memory 820 that stores computer program instructions.

In particular, the processor 810 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.

Memory 820 may include mass storage for data or instructions. By way of example, and not limitation, memory 820 may include a Hard Disk Drive (HDD), a floppy Disk Drive, flash memory, an optical Disk, a magneto-optical Disk, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 820 may include removable or non-removable (or fixed) media, where appropriate. Memory 820 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 820 is a non-volatile solid-state memory.

The memory may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors), it is operable to perform operations described with reference to the methods according to an aspect of the application.

The processor 810 implements any of the information determination methods in the above embodiments by reading and executing computer program instructions stored in the memory 820.

In one example, electronic device 800 may also include a communication interface 830 and a bus 840. As shown in fig. 8, the processor 810, the memory 820 and the communication interface 830 are connected via a bus 840 to complete communication therebetween.

The communication interface 830 is mainly used for implementing communication between various modules, apparatuses, units and/or devices in this embodiment.

The bus 840 includes hardware, software, or both to couple the components of the electronic device to each other. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hypertransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. Bus 840 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.

Illustratively, the electronic device 800 may be a mobile phone, a tablet computer, a notebook computer, a palm top computer, a vehicle-mounted electronic device, an ultra-mobile personal computer (UMPC), a netbook or a Personal Digital Assistant (PDA), and the like.

The electronic device may execute the information determination method in the embodiment of the present application, thereby implementing the information determination method and apparatus described in conjunction with fig. 1 to 7.

In addition, in combination with the information determination method in the foregoing embodiments, the embodiments of the present application may be implemented by providing a computer storage medium. The computer storage medium having computer program instructions stored thereon; the computer program instructions, when executed by a processor, implement any of the information determination methods in the above embodiments.

It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.

The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.

It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.

Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.

As described above, only the specific embodiments of the present application are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.

Claims

1. An information determination method, comprising:

2. The method of claim 1, wherein prior to determining the first branch corresponding to the first attribute information in the target decision tree model, the method further comprises:

acquiring a plurality of first historical target products;

determining the second attribute information and the third product element information with the incidence relation as a branch to obtain a plurality of branches;

constructing a first decision tree model based on the plurality of branches;

3. The method of claim 2, wherein before labeling the usage of the third product element information in the first decision tree model to obtain a target decision tree model, the method further comprises:

4. The method according to claim 2, wherein the determining the second attribute information and the third product element information having the association relationship as one branch, resulting in a plurality of branches, comprises:

5. The method of claim 2, wherein after determining the target decision tree model, the method further comprises:

acquiring a plurality of second historical target products;

the determining a first branch corresponding to the first attribute information in a target decision tree model includes:

6. The method of claim 1, wherein the first product element information is a leaf node in the objective decision tree model, and the first attribute information is a tree node in the objective decision tree model.

7. An information determination apparatus, characterized in that the apparatus comprises:

a first determining module, configured to determine a first branch corresponding to the first attribute information in a target decision tree model, where the first branch includes the first attribute information and first product element information that have an association relationship; the target decision tree model is determined according to attribute information of a target product and product element information associated with the attribute information, and the target decision tree model also comprises the utilization rate of the product element information;

8. An electronic device, characterized in that the electronic device comprises: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements the information determination method of any of claims 1-6.

9. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the information determination method according to any one of claims 1-6.

10. A computer program product, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the information determination method of any one of claims 1-6.