WO2020253354A1

WO2020253354A1 - Genetic algorithm-based resource information recommendation method and apparatus, terminal, and medium

Info

Publication number: WO2020253354A1
Application number: PCT/CN2020/085851
Authority: WO
Inventors: 燕如
Original assignee: 深圳壹账通智能科技有限公司
Priority date: 2019-06-19
Filing date: 2020-04-21
Publication date: 2020-12-24
Also published as: CN110390047A

Abstract

A genetic algorithm-based resource information recommendation method and apparatus, a terminal, and a medium. The method comprises: obtaining data information of a user to be recommended (S10); determining, according to the data information, the resource type applicable to said user (S20); determining, according to the resource type, resource names corresponding to the resource type, and a fitness function corresponding to the resource type (S30); updating, according to the determined fitness function, a preset genetic algorithm model (S40); using the resource names as input parameters of the genetic algorithm model and running the genetic algorithm model (S50) to obtain the name of the resource to be recommended; finally, sending resource information corresponding to the name of said resource to said user (S60). The method can quickly determine the name of the resource applicable to the user, and has a good recommendation effect.

Description

Resource information recommendation method, device, terminal and medium based on genetic algorithm

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on June 19, 2019, the application number is 201910533742.3, and the invention title is "Resource information recommendation method, device, terminal and medium based on genetic algorithm", and its entire content Incorporated in this application by reference.

Technical field

This application relates to the technical field of artificial intelligence predictive analysis, and in particular to a method, device, terminal and medium for recommending resource information based on genetic algorithms.

Background technique

In the promotion of resource information, recommendations by recommenders are often used, and recommenders usually only recommend resource names to users based on experience values. However, the inventor found that due to the different personal circumstances of social workers and the wide variety of resources, each resource has different product terms and applicable scenarios, it is difficult for recommenders to analyze by themselves what is more suitable for users in a short time. Resource, resource recommendation accuracy is low.

The above content is only used to assist the understanding of the technical solution of this application, and does not mean that the above content is recognized as prior art.

Summary of the invention

The main purpose of this application is to provide a method, device, terminal and medium for recommending resource information based on genetic algorithm, which aims to solve the difficulty in determining the resource name suitable for users in a short time in the resource information recommendation process of the prior art and the accuracy of resource recommendation Technical problems of low sex.

In order to achieve the above objective, this application provides a method for recommending resource information based on genetic algorithm, which includes the following steps:

Obtain data information of users to be recommended;

According to the data information, determine the resource type applicable to the user to be recommended;

According to the resource type, determine each resource name corresponding to the resource type and the fitness function corresponding to the resource type;

Update the preset genetic algorithm model according to the determined fitness function;

Using the resource names as input parameters of the genetic algorithm model and running the genetic algorithm model to obtain the resource name to be recommended;

The resource information corresponding to the name of the resource to be recommended is sent to the user to be recommended.

Based on the above-mentioned purpose of the invention, this application also provides a resource information recommendation device based on genetic algorithm, including:

The obtaining module is used to obtain the data information of the user to be recommended;

The determining module is configured to determine the type of resource applicable to the user to be recommended according to the data information;

The selection module is configured to determine, according to the resource type, each resource name corresponding to the resource type and a fitness function corresponding to the resource type;

The update module is used to update the preset genetic algorithm model according to the determined fitness function;

A calculation module, configured to use the name of each resource as an input parameter of the genetic algorithm model and run the genetic algorithm model to obtain the name of the resource to be recommended;

The recommendation module is used to send the resource information corresponding to the name of the resource to be recommended to the user to be recommended.

Based on the above-mentioned object of the invention, the present application also provides a terminal, the terminal including: a memory, a processor, and a genetic algorithm-based resource information recommendation program stored on the memory and running on the processor, the The genetic algorithm-based resource information recommendation program is configured to implement the steps of the above-mentioned genetic algorithm-based resource information recommendation method.

Based on the above-mentioned purpose of the invention, this application also provides a storage medium on which a genetic algorithm-based resource information recommendation program is stored. When the genetic algorithm-based resource information recommendation program is executed by a processor, the above-mentioned The steps of the genetic algorithm resource information recommendation method.

The present invention obtains the data information of the user to be recommended, and then determines the resource type applicable to the user to be recommended according to the data information, and determines the name of each resource corresponding to the resource type according to the resource type and the corresponding resource type. According to the fitness function corresponding to the resource type, update the preset genetic algorithm model according to the determined fitness function, and then use the resource name as the input parameter of the genetic algorithm model and run the genetic algorithm model to obtain The resource name to be recommended, and finally the resource information corresponding to the resource name to be recommended is sent to the user to be recommended, the resource name suitable for the user can be quickly determined, and the recommendation effect is good.

Description of the drawings

FIG. 1 is a schematic structural diagram of a terminal of a hardware operating environment involved in a solution of an embodiment of the present application;

2 is a schematic flowchart of a first embodiment of a method for recommending resource information based on genetic algorithms according to this application;

3 is a schematic flowchart of a second embodiment of a method for recommending resource information based on genetic algorithms according to this application;

4 is a schematic flowchart of a third embodiment of a method for recommending resource information based on genetic algorithms according to this application;

FIG. 5 is a schematic flowchart of a fourth embodiment of a method for recommending resource information based on genetic algorithms according to this application;

6 is a schematic flowchart of a fifth embodiment of a method for recommending resource information based on a genetic algorithm according to this application;

Fig. 7 is a structural block diagram of a first embodiment of a resource information recommendation device based on a genetic algorithm in this application.

Detailed ways

It should be understood that the specific embodiments described herein are only used to explain the application, but not to limit the application.

Referring to FIG. 1, FIG. 1 is a schematic diagram of a terminal structure of a hardware operating environment involved in a solution of an embodiment of the application.

As shown in FIG. 1, the terminal may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to implement connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input module such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the foregoing processor 1001.

Those skilled in the art can understand that the structure shown in FIG. 1 does not constitute a limitation on the terminal, and may include more or less components than shown in the figure, or combine some components, or arrange different components.

As shown in FIG. 1, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module, and a resource information recommendation program based on genetic algorithms.

In the terminal shown in FIG. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with users; the processor 1001 and the memory 1005 in the terminal of this application can be set in the terminal The terminal calls the genetic algorithm-based resource information recommendation program stored in the memory 1005 through the processor 1001, and executes the genetic algorithm-based resource information recommendation method provided in the embodiment of the present application.

The embodiment of the present application provides a method for recommending resource information based on a genetic algorithm. Referring to FIG. 2, FIG. 2 is a schematic flowchart of a first embodiment of the method for recommending resource information based on a genetic algorithm.

In this embodiment, the resource information recommendation method based on genetic algorithm includes the following steps:

Step S10: Obtain the data information of the user to be recommended; it should be understood that the subject of the method in this embodiment is the terminal, and the user to be recommended is the user whose resource name is recommended by the resource clerk. The data information of the user to be recommended can usually include The user’s age, company, address, children, etc.; it can also include family information, friends, whether to carry genetic diseases or other major diseases of the user to be recommended.

Step S20: Determine the resource type applicable to the user to be recommended according to the data information;

It should be understood that, in this embodiment, the resource types include return-type resources and consumption-type resources. In other embodiments, they may also be classified according to other rules. The resources can be insurance products. Taking insurance products as an example below, return resources are also called savings resources, that is, after being insured to survive for the agreed period, the insurance company has to return the premiums paid or the insurance use conversion value specified in the contract; Type insurance is a type of consumer insurance, that is, the user (the applicant) signs a contract with the insurance company (the insurer). If an insurance accident occurs within the agreed time, the insurance company will compensate or pay according to the originally agreed amount ; If no insurance accident occurs within the agreed time, the insurance company will not refund the premium paid.

In specific implementation, the data information includes the age, income, children, career status, and spending habits of the user to be recommended; the resource types include return-type resources and consumption-type resources; accordingly, the data information , The step of determining the resource type applicable to the user to be recommended includes the following steps: determining the resource type of the user to be recommended according to the age, income, children, career situation, and spending habits of the user to be recommended.

In this embodiment, according to the data information, if it is determined that the user to be recommended is someone who is still young, has a good income, and spends a lot of money, this type of user is usually suitable for buying back resources; if it is determined that the user to be recommended Users belong to people who are still young, have a growing career, and have low incomes. This type of user is suitable for purchase and consumption. Wherein, the data information may include the age, gender, career development and income, life habits, children, etc. of the user to be recommended.

Step S30: According to the resource type, determine each resource name corresponding to the resource type and the fitness function corresponding to the resource type; it should be understood that taking insurance products as an example, consumer resources usually include consumption Accident insurance, consumer medical insurance, consumer critical illness insurance, and consumer life insurance can be adjusted according to the specific products launched by the resource company; return-type resources usually include life insurance, pension, education funds, etc., which can be specifically based on Resource company's specific product launch adjustment.

In specific implementation, take the resource as an insurance product as an example. When the resource type is a consumer resource, the insurance products corresponding to the consumer resource include consumer accident insurance, consumer medical insurance, consumer critical illness insurance, and consumer life insurance When the resource type is a return-type resource, the insurance product insurance corresponding to the return-type resource includes life insurance, pension, education insurance and other products.

Genetic Algorithm (Genetic Algorithm) is a computational model that simulates the biological evolution process of natural selection and genetic mechanism of Darwin's biological evolution theory, and is a method of searching for the optimal solution by simulating the natural evolution process. The genetic algorithm starts from a population that represents the possible potential solution set of the problem, and a population is composed of a certain number of individuals coded by genes. Each individual is actually an entity with chromosome characteristics. The fitness function is the guarantee for solving the optimal solution or the suboptimal solution. According to the resource type, the fitness function corresponding to the resource type is determined.

Step S40: Update the preset genetic algorithm model according to the determined fitness function; it should be understood that different resource types correspond to different fitness functions, and correspondingly, different resource types correspond to different genetic algorithm models.

Step S50: Use the name of each resource as an input parameter of the genetic algorithm model and run the genetic algorithm model to obtain the name of the resource to be recommended;

It should be understood that the solving steps of the genetic algorithm are as follows: 1) Initialize the population; 2) Calculate the fitness value of each individual in the population; 3) Select according to a certain rule determined by the individual fitness value to enter the next generation Individual; 4) Crossover operation according to probability Pc; 5) Mutation operation according to probability Pc; 6) If a certain stopping condition is not met, then switch to no switch 2), otherwise go to the next step; 7) Output the fitness value in the population The optimal chromosome is regarded as the satisfactory solution or optimal solution of the problem.

In specific implementation, the determined resource names are used as the input parameters of the genetic algorithm model and the genetic algorithm model is run to obtain the names of the resources to be recommended. The names of the resources are coded. Conventional binary codes can be used, or The corresponding number can be formulated for each resource name; the initial population can be randomly generated initial chromosomes based on the determined resource name and the premium of the resource name; then genetic operations (selection operations, crossover operations, mutation operations) are performed, and the optimal solution is finally obtained , The optimal resource plan.

Step S60: Send the resource information corresponding to the name of the resource to be recommended to the user to be recommended.

It should be understood that, in this embodiment, the resource information corresponding to the resource name to be recommended is sent to the user to be recommended. In other embodiments, the resource information corresponding to the resource name to be recommended may also be Send to the person who implements the recommended resource, such as a salesperson.

This application obtains the data information of the user to be recommended, and then determines the resource type applicable to the user to be recommended according to the data information, and determines the name of each resource corresponding to the resource type according to the resource type, and the name of each resource corresponding to the resource type. According to the fitness function corresponding to the resource type, update the preset genetic algorithm model according to the determined fitness function, and then use the resource name as the input parameter of the genetic algorithm model and run the genetic algorithm model to obtain The resource name to be recommended, and finally the resource information corresponding to the resource name to be recommended is sent to the user to be recommended, the resource name suitable for the user can be quickly determined, and the recommendation effect is good.

Referring to FIG. 3, FIG. 3 is a schematic flowchart of a second embodiment of a method for recommending resource information based on a genetic algorithm according to this application. Based on the above-mentioned first embodiment, the data information includes information about the user to be recommended and related persons; in this embodiment, the step S20 includes:

Step S201: Using the entities in the data information as nodes and the relationship between the entities as side lengths, the knowledge graph of the user to be recommended is established; it should be understood that the knowledge graph (Knowledge Graph) is to combine all different types of Information (Heterogeneous Information) is a relational network connected together. In this embodiment, the data source of the knowledge graph may be the data information, and may also include data published and captured on the Internet. Each node represents an "entity" that exists in the real world, and each edge is a "relationship" between an entity and an entity.

It should be noted that the people related to the user to be recommended may be relatives, friends, etc.; and the information of the user to be recommended and the related personnel may include age, gender, children, work, medical history, etc.

Step S202: Determine the resource type applicable to the user to be recommended according to the knowledge graph.

In specific implementation, the type of resources applicable to the user to be recommended is determined according to the knowledge graph. Since consumer resources consider spending money to buy a guarantee, the more common one is consumer critical illness insurance, especially for families Users with genetic diseases and serious illnesses are more likely to judge whether the user is more suitable for consumer critical illness insurance by estimating the possibility of the user's serious illness. The possibility of a user’s critical illness can be estimated by calculating the relationship between the entity suffering from a serious illness in the knowledge graph and the user entity (usually by calculating the distance between the user entity to be recommended and the entity carrying the critical illness mark in the knowledge graph. Determine the association relationship between the entity with a serious illness and the user entity) as a critical illness index (critical illness hit rate). When the critical illness index reaches a preset threshold, it is determined that the user to be recommended is suitable for consumption resources. In other embodiments, the calculation rules can also be set according to the characteristics of the specific resource type product. For example, it can also be used to calculate the fixed asset entity (real estate, etc.) and the education of children of the user to be recommended through the knowledge graph, and comprehensively consider the burden of the user to be recommended. The user’s economic pressure and economic conditions determine whether the user to be recommended is suitable for return-type resources.

Referring to FIG. 4, FIG. 4 is a schematic flowchart of a third embodiment of a method for recommending resource information based on a genetic algorithm in this application.

Based on the above second embodiment, the data information includes information about whether the user to be recommended and their related personnel carry genetic diseases; in this embodiment, the step S202 includes:

Step S2021: Calculate the side length between the entity carrying the genetic disease in the knowledge graph and the user to be recommended, and determine the degree of association between the user to be recommended and the entity carrying the genetic disease; it should be understood that the knowledge graph is calculated The side length between the entity carrying the genetic disease and the user to be recommended (usually the degree of association between the two entities is expressed by side length), that is, the degree of association between the entity carrying the genetic disease and the user to be recommended is calculated, In this way, the probability of the user to be recommended is judged, especially for some serious diseases. In other embodiments, the degree of association between the entity carrying a major disease in the knowledge graph and the user to be recommended may also be calculated.

Step S2022: Use the correlation degree calculation result as the genetic disease hit rate of the user to be recommended;

It should be understood that the degree of association between the entity carrying the genetic disease in the knowledge graph and the user to be recommended is taken as the probability of the user to be recommended suffering from the genetic disease, and the probability of the user to be recommended suffering from the genetic disease is taken as the Recommend users' genetic disease hit rate.

Step S2023: Determine the resource type applicable to the user to be recommended according to the genetic disease hit rate. In specific implementation, when the genetic disease hit rate reaches the preset threshold, it is determined that the user to be recommended is more likely to suffer from the genetic disease. If the genetic disease is a serious disease, the user to be recommended is more suitable for consumer resources.

Referring to FIG. 5, FIG. 5 is a schematic flowchart of a fourth embodiment of a method for recommending resource information based on a genetic algorithm in this application. Based on the foregoing first embodiment, the resource type is a consumer resource; in this embodiment, before step S10, it further includes:

Step S01: Establish a genetic algorithm model, where the fitness function of the genetic algorithm model is:

M _i is the expected conversion value of resource use (in the case of an insurance product, it can be the insurance amount), T _i is the effective period of resource use (in the case of an insurance product, it can be the insurance period), and N _i is the conversion value of the resource acquisition (take Example insurance products, insurance costs may be), _X-i is the number of resources, i = 1,2, ......, n , where i desired using the weight conversion value of a predetermined amount of resources for the resource, R _m, R _t is the weight of the effective period of resource use, R _i is the weight of the risk protection, R _mf is the weight of the conversion value obtained by the resource, and I(x) is the actual conversion value of the resource use (taking insurance products as an example, it can be compensation Quota).

It should be understood that the parameters in the fitness function of the genetic algorithm model are usually determined according to the resource name corresponding to the resource type. For example, consumption-type resources have consumption-type accident insurance, and consumption-type accident insurance corresponds to resource usage expectations The conversion value, the effective period of the resource use, the conversion value of the resource acquisition, etc. may be set according to the specific provisions of the resource company for the specific resource name.

Referring to FIG. 6, FIG. 6 is a schematic flowchart of a fifth embodiment of a method for recommending resource information based on a genetic algorithm according to this application. Based on the foregoing first embodiment, the resource type is a return type resource; in this embodiment, before step S10, it further includes:

Step S01': Establish a genetic algorithm model, wherein the fitness function of the genetic algorithm model is:

Among them, _M'i is the expected conversion value of resource usage (in the case of an insurance product, it can be the insurance amount), T _i 'is the effective period of resource usage (in the case of an insurance product, it can be the insurance period), and N _i ' is the resource Get the conversion value (take insurance product as an example, it can be insurance cost), X _i 'is the number of resources, i = 1, 2, ..., n, where n is the preset number of resources, R _m ' is the expected use of resources The weight of the conversion value, R _t 'is the weight of the effective period of resource use, R _i ' is the weight of the risk protection, R _mf 'is the weight of the resource to obtain the conversion value, I'(x) is the actual conversion of the resource use Value, Rs' is the probability of resource return.

It should be understood that the various parameters in the fitness function of the genetic algorithm model are usually determined according to the resource name corresponding to the resource type. For example, taking resources as an insurance product as an example, the return type insurance includes education fund insurance, while education fund The insurance amount, insurance period, insurance cost, etc. corresponding to the insurance may be set according to the specific regulations of the insurance company for specific insurance products. The following methods can be used to determine the resource return probability in this embodiment:

The data information includes the eating habits and current health index of the user to be recommended; accordingly, the resource name corresponding to the resource type and the fitness function corresponding to the resource type are determined according to the resource type After the steps, the method further includes the following steps:

Determine the lifespan of the user to be recommended according to the degree of association between the entity carrying the family genetic disease in the knowledge graph and the user to be recommended, the eating habits of the user to be recommended, current health index, and a preset lifespan prediction model Predicted value; it should be understood that the degree of association between the entity carrying family genetic diseases in the knowledge map and the user to be recommended, the eating habits of the user to be recommended, the current health index, and the preset life expectancy Model, before the step of determining the life prediction value of the user to be recommended, it further includes: establishing a life prediction model; wherein, the life prediction model may be:

Where X represents the degree of association between the entity carrying the family genetic disease and the user to be recommended, and Y represents the eating habit value of the user to be recommended (in this embodiment, vegetarian, Y is 1, carnivores, Y is 0.5), Z represents the current health index, λ1, λ2, and λ3 are the weights of X, Y, and Z respectively. According to the life prediction value, the resource return probability of each resource name is determined. In addition, an embodiment of the present application also proposes a storage medium that stores a genetic algorithm-based resource information recommendation program, and when the genetic algorithm-based resource information recommendation program is executed by a processor, the above-mentioned The steps of the method of resource information recommendation based on genetic algorithm.

Referring to Fig. 7, Fig. 7 is a structural block diagram of a first embodiment of a resource information recommendation device based on a genetic algorithm in this application.

As shown in FIG. 7, the genetic algorithm-based resource information recommendation device proposed in the embodiment of the present application includes: an acquisition module 701, configured to acquire data information of users to be recommended;

It should be understood that the user to be recommended is the user whose resource clerk recommends the resource name. The data information of the user to be recommended can usually include the age, company, address, children, etc. of the user to be recommended; it can also include family information, Friends and other information.

The determining module 702 is configured to determine the resource type applicable to the user to be recommended according to the data information;

The selection module 703 is configured to determine each resource name corresponding to the resource type and the fitness function corresponding to the resource type according to the resource type;

It should be understood that, taking insurance products as an example, consumer resources usually include consumer accident insurance, consumer medical insurance, consumer critical illness insurance, and consumer life insurance, which can be adjusted according to the specific product launches of the resource company; return resources It usually includes life insurance, pension, education fund, etc., which can be adjusted according to the specific products launched by the resource company.

In specific implementation, take the resource as an insurance product as an example. When the resource type is a consumer resource, the insurance products corresponding to the consumer resource include consumer accident insurance, consumer medical insurance, consumer critical illness insurance, and consumer life insurance When the resource type is a return-type resource, the insurance product insurance corresponding to the return-type insurance includes life insurance, pension, education insurance and other products.

Genetic Algorithm (Genetic Algorithm) is a computational model that simulates the biological evolution process of natural selection and genetic mechanism of Darwin's biological evolution theory, and is a method of searching for the optimal solution by simulating the natural evolution process. The genetic algorithm starts from a population that represents the possible potential solution set of the problem, and a population is composed of a certain number of individuals coded by genes. Each individual is actually an entity with chromosome characteristics. The fitness function is the guarantee for solving the optimal or suboptimal solution. According to the resource type, the fitness function corresponding to the resource type is determined.

The update module 704 updates the preset genetic algorithm model according to the determined fitness function; it should be understood that different resource types correspond to different fitness functions, and correspondingly, different resource types correspond to different genetic algorithm models. The calculation module 705 is configured to use the name of each resource as an input parameter of the genetic algorithm model and run the genetic algorithm model to obtain the name of the resource to be recommended; the genetic operation obtains the optimal solution, which is used as the optimal resource recommendation plan.

In specific implementation, the determined resource names are used as the input parameters of the genetic algorithm model and the genetic algorithm model is run to obtain the resource names to be recommended; the resource names can be coded using conventional binary codes or The corresponding number can be formulated for each resource name; the initial population can be randomly generated initial chromosomes based on the determined resource name and the premium of the resource name; then genetic operations (selection operations, crossover operations, mutation operations) are performed, and the optimal solution is finally obtained , The optimal resource name scheme.

The recommendation module 706 sends the resource information corresponding to the name of the resource to be recommended to the user to be recommended.

The present application of the present invention obtains data information of the user to be recommended, and then determines the resource type applicable to the user to be recommended according to the data information, and determines the resource name corresponding to the resource type according to the resource type, and According to the fitness function corresponding to the resource type, update the preset genetic algorithm model according to the determined fitness function, and then use the resource name as the input parameter of the genetic algorithm model and run the genetic algorithm model , Obtain the name of the resource to be recommended, and finally send the resource information corresponding to the resource name to be recommended to the user to be recommended, which can quickly determine the resource name suitable for the user and the recommendation effect is good.

For other embodiments or specific implementations of the device for recommending resource information based on the genetic algorithm of the present invention, reference may be made to the foregoing method embodiments, which will not be repeated here.

It should be noted that in this article, the terms "include", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, It also includes other elements that are not explicitly listed, or elements inherent to the process, method, article, or system. If there are no more restrictions, the element defined by the sentence "including a..." does not exclude the existence of other identical elements in the process, method, article or system that includes the element.

The sequence numbers of the foregoing embodiments of the present invention are only for description, and do not represent the superiority of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the method of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better.的实施方式。 Based on this understanding, the technical solution of this application essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as read-only memory/random access The memory, magnetic disk, and optical disk) includes several instructions to make a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present application. Wherein, the storage medium may be a non-volatile readable storage medium or a readable storage medium

The above are only the preferred embodiments of the present invention, and do not limit the scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the description and drawings of the present invention, or directly or indirectly applied to other related technical fields , The same reason is included in the scope of patent protection of the present invention. The method includes: S10, obtaining data information of the user to be recommended; S20, determining the resource type applicable to the user to be recommended according to the data information; S30, determining the resource type corresponding to the resource type according to the resource type The name of each resource and the fitness function corresponding to the resource type; S40, update the preset genetic algorithm model according to the determined fitness function; S50, use the name of each resource as an input parameter of the genetic algorithm model And run the genetic algorithm model to obtain the name of the resource to be recommended; S60. Send the resource information corresponding to the name of the resource to be recommended to the user to be recommended.

Claims

A method for recommending resource information based on genetic algorithm, which includes the following steps:

Obtain data information of users to be recommended;

According to the data information, determine the resource type applicable to the user to be recommended;

According to the resource type, determine each resource name corresponding to the resource type and the fitness function corresponding to the resource type;

Update the preset genetic algorithm model according to the determined fitness function;

Using the resource names as input parameters of the genetic algorithm model and running the genetic algorithm model to obtain the resource name to be recommended;

The resource information corresponding to the name of the resource to be recommended is sent to the user to be recommended.
8. The method for recommending resource information based on genetic algorithm according to claim 1, wherein the data information includes information about the user to be recommended and related personnel;

Correspondingly, the step of determining the resource type applicable to the user to be recommended according to the data information includes the following steps:

Taking the entities in the data information as nodes and the relationship between the entities as side lengths, establishing the knowledge graph of the users to be recommended;

According to the knowledge graph, the resource type applicable to the user to be recommended is determined.
The method for recommending resource information based on genetic algorithm according to claim 2, wherein the data information includes information about whether the user to be recommended and the related personnel carry genetic disease;

Correspondingly, the step of determining the resource type applicable to the user to be recommended according to the knowledge graph includes the following steps:

Determine the degree of association between the user to be recommended and the entity carrying the genetic disease according to the side length of the entity carrying the genetic disease in the knowledge graph and the user to be recommended;

The calculation result of the degree of association is used as the genetic disease hit rate of the user to be recommended;

According to the genetic disease hit rate, the resource type applicable to the user to be recommended is determined.
The method for recommending resource information based on genetic algorithm according to any one of claims 1 to 3, wherein the resource type is a consumer resource;

Correspondingly, before the step of obtaining data information of the user to be recommended, the following steps are further included:

Establish a genetic algorithm model, where the fitness function of the genetic algorithm model is:

M i using a resource desired conversion value, T i is valid for use of resources, N i to obtain the conversion value to the resource, X i acquires the number of resources, i = 1,2, ......, n , where i is a preset resource Number, R m is the weight of the expected conversion value of resource use, R t is the weight of the effective period of resource use, R i is the weight of risk protection, R mf is the weight of the resource to obtain the conversion value, I(x) is The resource uses the actual conversion value.
The method for recommending resource information based on genetic algorithm according to any one of claims 1 to 3, wherein the resource type is a return type resource;

Correspondingly, before the step of obtaining data information of the user to be recommended, the following steps are further included:

Establish a genetic algorithm model, where the fitness function of the genetic algorithm model is:

Among them, M'i is the expected conversion value of resource usage, T i 'is the effective period of the resource usage, N i ' is the conversion value obtained by the resource, X i 'is the number of resources, i=1, 2, ..., n, where i Is the preset number of resources, R m 'is the weight of the expected conversion value of resource use, R t ' is the weight of the effective period of resource use, R i 'is the weight of risk protection, R mf ' is the conversion value of the resource acquisition I'(x) is the actual conversion value of resource usage, and Rs' is the probability of resource return.
5. The method for recommending resource information based on genetic algorithm according to claim 5, wherein the data information includes the eating habits and current health index of the user to be recommended;

Correspondingly, after the step of determining each resource name corresponding to the resource type and the fitness function corresponding to the resource type according to the resource type, the method further includes the following steps:

Determine the lifespan of the user to be recommended according to the degree of association between the entity carrying the family genetic disease in the knowledge graph and the user to be recommended, the eating habits of the user to be recommended, current health index, and a preset lifespan prediction model Predictive value;

According to the life prediction value, the resource return probability of each resource name is determined.
2. The method for recommending resource information based on genetic algorithm according to claim 1, wherein the data information includes the age, income, children, career status, and spending habits of the user to be recommended;

The resource types include return type resources and consumption type resources;

Correspondingly, the step of determining the resource type applicable to the user to be recommended according to the data information includes the following steps:

According to the age, income, children, career situation, and spending habits of the user to be recommended, the resource type of the user to be recommended is determined, and the determination method is:

If the to-be-recommended user is a person who is still young, has a large income, and spends a lot of money, then the to-be-recommended user is suitable for return-type resources;

If the to-be-recommended user belongs to a group of young people with a growing career and low income, the to-be-recommended user is suitable for consumption-type resources.
A resource information recommendation device based on genetic algorithm, which includes:

The obtaining module is used to obtain the data information of the user to be recommended;

The determining module is configured to determine the resource type applicable to the user to be recommended according to the data information;

The selection module is configured to determine, according to the resource type, each resource name corresponding to the resource type and a fitness function corresponding to the resource type;

The update module is used to update the preset genetic algorithm model according to the determined fitness function;

A calculation module, configured to use the name of each resource as an input parameter of the genetic algorithm model and run the genetic algorithm model to obtain the name of the resource to be recommended;

The recommendation module is used to send the resource information corresponding to the name of the resource to be recommended to the user to be recommended.
A terminal, wherein the terminal includes: a memory, a processor, and a genetic algorithm-based resource information recommendation program stored on the memory and running on the processor, and the genetic algorithm-based resource information recommendation program The program is configured to implement the following steps:

Obtain data information of users to be recommended;

According to the data information, determine the resource type applicable to the user to be recommended;

According to the resource type, determine each resource name corresponding to the resource type and the fitness function corresponding to the resource type;

Update the preset genetic algorithm model according to the determined fitness function;

Using the resource names as input parameters of the genetic algorithm model and running the genetic algorithm model to obtain the resource name to be recommended;

The resource information corresponding to the name of the resource to be recommended is sent to the user to be recommended.
The terminal according to claim 9, wherein the data information includes information about the user to be recommended and related personnel;

Correspondingly, the step of determining the resource type applicable to the user to be recommended according to the data information includes the following steps:

Taking the entities in the data information as nodes and the relationship between the entities as side lengths, establishing the knowledge graph of the users to be recommended;

According to the knowledge graph, the resource type applicable to the user to be recommended is determined.
The terminal according to claim 10, wherein the data information includes information about whether the user to be recommended and related persons carry genetic diseases;

Correspondingly, the step of determining the resource type applicable to the user to be recommended according to the knowledge graph includes the following steps:

Determine the degree of association between the user to be recommended and the entity carrying the genetic disease according to the side length of the entity carrying the genetic disease in the knowledge graph and the user to be recommended;

The calculation result of the degree of association is used as the genetic disease hit rate of the user to be recommended;

According to the genetic disease hit rate, the resource type applicable to the user to be recommended is determined.
The terminal according to any one of claims 9 to 11, wherein the resource type is a consumer resource;

Correspondingly, before the step of obtaining data information of the user to be recommended, the following steps are further included:

Establish a genetic algorithm model, where the fitness function of the genetic algorithm model is:

M i using a resource desired conversion value, T i is valid for use of resources, N i to obtain the conversion value to the resource, X i acquires the number of resources, i = 1,2, ......, n , where i is a preset resource Number, R m is the weight of the expected conversion value of resource use, R t is the weight of the effective period of resource use, R i is the weight of risk protection, R mf is the weight of the resource to obtain the conversion value, I(x) is The resource uses the actual conversion value.
The terminal according to any one of claims 9-11, wherein the resource type is a return type resource;

Correspondingly, before the step of obtaining data information of the user to be recommended, the following steps are further included:

Establish a genetic algorithm model, where the fitness function of the genetic algorithm model is:

Among them, M'i is the expected conversion value of resource usage, T i 'is the effective period of the resource usage, N i ' is the conversion value obtained by the resource, X i 'is the number of resources, i=1, 2, ..., n, where i Is the preset number of resources, R m 'is the weight of the expected conversion value of resource use, R t ' is the weight of the effective period of resource use, R i 'is the weight of risk protection, R mf ' is the conversion value of the resource acquisition I'(x) is the actual conversion value of resource usage, and Rs' is the probability of resource return.
The terminal according to claim 13, wherein the data information includes the eating habits and current health index of the user to be recommended;

Correspondingly, after the step of determining each resource name corresponding to the resource type and the fitness function corresponding to the resource type according to the resource type, the method further includes the following steps:

Determine the lifespan of the user to be recommended according to the degree of association between the entity carrying the family genetic disease in the knowledge graph and the user to be recommended, the eating habits of the user to be recommended, current health index, and a preset lifespan prediction model Predictive value;

According to the life prediction value, the resource return probability of each resource name is determined.
The terminal of claim 9, wherein the data information includes the age, income, children, career status, and spending habits of the user to be recommended;

The resource types include return type resources and consumption type resources;

Correspondingly, the step of determining the resource type applicable to the user to be recommended according to the data information includes the following steps:

According to the age, income, children, career situation, and spending habits of the user to be recommended, the resource type of the user to be recommended is determined, and the determination method is:

If the to-be-recommended user is a person who is still young, has a large income, and spends a lot of money, then the to-be-recommended user is suitable for return-type resources;

If the to-be-recommended user belongs to a group of young people with a growing career and low income, the to-be-recommended user is suitable for consumption-type resources.
A storage medium, wherein a genetic algorithm-based resource information recommendation program is stored on the storage medium, and the following steps are implemented when the genetic algorithm-based resource information recommendation program is executed by a processor:

Obtain data information of users to be recommended;

According to the data information, determine the resource type applicable to the user to be recommended;

According to the resource type, determine each resource name corresponding to the resource type and the fitness function corresponding to the resource type;

Update the preset genetic algorithm model according to the determined fitness function;

Using the resource names as input parameters of the genetic algorithm model and running the genetic algorithm model to obtain the resource name to be recommended;

The resource information corresponding to the name of the resource to be recommended is sent to the user to be recommended.
15. The storage medium of claim 16, wherein the data information includes information about the user to be recommended and related personnel;

Correspondingly, the step of determining the resource type applicable to the user to be recommended according to the data information includes the following steps:

Taking the entities in the data information as nodes and the relationship between the entities as side lengths, establishing the knowledge graph of the users to be recommended;

According to the knowledge graph, the resource type applicable to the user to be recommended is determined.
17. The storage medium of claim 17, wherein the data information includes information about whether the user to be recommended and the related personnel carry genetic disease;

Correspondingly, the step of determining the resource type applicable to the user to be recommended according to the knowledge graph includes the following steps:

Determine the degree of association between the user to be recommended and the entity carrying the genetic disease according to the side length of the entity carrying the genetic disease in the knowledge graph and the user to be recommended;

The calculation result of the degree of association is used as the genetic disease hit rate of the user to be recommended;

According to the genetic disease hit rate, the resource type applicable to the user to be recommended is determined.
The storage medium according to any one of claims 16 to 18, wherein the resource type is a consumer resource;

Correspondingly, before the step of obtaining data information of the user to be recommended, the following steps are further included:

Establish a genetic algorithm model, where the fitness function of the genetic algorithm model is:

M i using a resource desired conversion value, T i is valid for use of resources, N i to obtain the conversion value to the resource, X i acquires the number of resources, i = 1,2, ......, n , where i is a preset resource Number, R m is the weight of the expected conversion value of resource use, R t is the weight of the effective period of resource use, R i is the weight of risk protection, R mf is the weight of the resource to obtain the conversion value, I(x) is The resource uses the actual conversion value.
18. The storage medium according to any one of claims 16-18, wherein the resource type is a return type resource;

Correspondingly, before the step of obtaining data information of the user to be recommended, the following steps are further included:

Establish a genetic algorithm model, where the fitness function of the genetic algorithm model is:

Among them, M'i is the expected conversion value of resource usage, T i 'is the effective period of the resource usage, N i ' is the conversion value obtained by the resource, X i 'is the number of resources, i=1, 2, ..., n, where i Is the preset number of resources, R m 'is the weight of the expected conversion value of resource use, R t ' is the weight of the effective period of resource use, R i 'is the weight of risk protection, R mf ' is the conversion value of the resource acquisition I'(x) is the actual conversion value of resource usage, and Rs' is the probability of resource return.