Data processing method and device, computer equipment, medium and product

Abstract

The invention discloses a data processing method and device, computer equipment, a medium and a product. The method comprises the steps of obtaining basic attribute information of N objects, at least one associated object of each object and resource transfer data of each object and each associated object in a specified service scene; calling a target evaluation model to score each object in the N objects and the basic attribute information of the at least one associated object to obtain a basic score result of each object and each associated object, and determining a comprehensive scoring result of each object according to the resource transfer data of each object and each associated object, the basic scoring result of each associated object and the basic scoring result of each object. And determining a target object according to the comprehensive scoring result of the N objects, wherein the target object is used for receiving activity information of the specified business scene. According to the invention, high-quality seed objects can be accurately determined, and business development can be effectively pulled through the seed objects.

Description

Technical Field

[0001] The present application relates to the field of Internet technology, and in particular to a data processing method, a data processing apparatus, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] There are many ways to attract new customers for the promotion of business scenarios such as loan business. The most common way is to adopt the fission-based approach, also referred to as MGM (member get member). MGM is to achieve linkage marketing to new customers through referrals from existing customers, thereby acquiring new customers.

[0003] A good MGM event can bring more "invited users", and the key to success is mainly to rely on existing customers (also known as seed customers). Therefore, how to accurately identify high-quality seed customers and drive business development has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The embodiments of the present application provide a data processing method, apparatus, computer equipment, medium, and product, which can accurately determine high-quality seed objects in a specified business scenario, thereby effectively driving business development in the specified business scenario through these seed objects.

[0005] On the one hand, an embodiment of the present application discloses a data processing method, the method comprising:

[0006] Obtain basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and resource transfer data between each object and each associated object, wherein the associated object is an object in the N objects, and N is a positive integer;

[0007] Calling a target evaluation model to perform scoring processing on basic attribute information of each of the N objects and basic attribute information of at least one associated object of each object to obtain a basic scoring result of each object and a basic scoring result of each associated object of each object, wherein the target evaluation model is obtained by training an initial evaluation model according to a model training sample, and the model training sample includes basic attribute information of multiple sample objects and a reference scoring result of each sample object;

[0008] Determine a comprehensive scoring result of each object according to the resource transfer data between each object and each associated object, the basic scoring result of each associated object of each object, and the basic scoring result of each object;

[0009] A target object under the specified business scenario is determined according to the comprehensive scoring results of the N objects, and the target object is used to receive activity information of the specified business scenario.

[0010] In one aspect, an embodiment of the present application discloses a data processing device, the device comprising:

[0011] an acquisition unit, configured to acquire basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and resource transfer data between each object and each associated object, wherein the associated object is an object in the N objects, and N is a positive integer;

[0012] a processing unit, configured to call a target evaluation model to perform scoring processing on basic attribute information of each of the N objects and basic attribute information of at least one associated object of each object, to obtain a basic scoring result of each object and a basic scoring result of each associated object of each object, wherein the target evaluation model is obtained by training an initial evaluation model according to a model training sample, wherein the model training sample includes basic attribute information of multiple sample objects and a reference scoring result of each sample object;

[0013] a determination unit, configured to determine a comprehensive scoring result of each object according to the resource transfer data of each object and each associated object, a basic scoring result of each associated object of each object, and a basic scoring result of each object;

[0014] The determination unit is further used to determine a target object under the specified business scenario according to the comprehensive scoring results of the N objects, and the target object is used to receive activity information of the specified business scenario.

[0015] On the one hand, an embodiment of the present application discloses a computer device, which includes a processor suitable for implementing one or more computer programs; and a computer storage medium, wherein the computer storage medium stores one or more computer programs, and the one or more computer programs are suitable for being loaded by the processor and executed with the above-mentioned data processing method.

[0016] In one aspect, the present application discloses a computer-readable storage medium storing one or more computer programs, wherein the one or more computer programs are suitable for being loaded by a processor and executing the above-mentioned data processing method.

[0017] The present application discloses a computer program product, which includes a computer program stored in a computer-readable storage medium. A processor of a computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the above-mentioned data processing method.

[0018] In an embodiment of the present application, for each of the N objects, the target evaluation model is called to score the basic attribute information of each object and the basic attribute information of at least one associated object of each object, and the basic scoring result of each object and each associated object of each object are obtained. Then, the comprehensive scoring result of each object is determined based on the resource transfer data of each object and each associated object, the basic scoring result of each associated object of each object, and the basic scoring result of each object; finally, the target object in the specified business scenario is determined based on the determined comprehensive scoring result of the N objects, and the activity information of the specified business scenario is received through the target object. In the present application, for any object among the N objects, not only its own qualifications are considered, but also the qualifications of the associated objects corresponding to the object are considered. Through such a comprehensive evaluation, the high-quality seed objects in the specified business scenario, i.e., the target objects, can be accurately determined, and then the requirements for attracting new customers in the specified business scenario can be achieved through these target objects, thereby effectively driving the business development of the specified business scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is a schematic diagram of a network architecture of a data processing system disclosed in an embodiment of the present application;

[0021] Figure 2 It is a flowchart of a data processing method disclosed in an embodiment of the present application;

[0022] Figure 3 It is a flowchart of another data processing method disclosed in an embodiment of the present application;

[0023] Figure 4 is a connection diagram of a first object and associated objects disclosed in an embodiment of the present application;

[0024] Figure 5 It is a schematic diagram of a flow chart of calculation of a comprehensive scoring result disclosed in an embodiment of the present application;

[0025] Figure 6 is a structural schematic diagram of a data processing device disclosed in an embodiment of the present application;

[0026] Figure 7 It is a structural schematic diagram of a computer device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] In order to more accurately determine the high-quality seed users in a specified business scenario, thereby driving the development of the business in the scenario, an embodiment of the present application provides a data processing method, which includes: ① Obtaining the basic attribute information of N objects in the specified business scenario, at least one associated object of each object, and the resource transfer data between each object and each associated object, the associated object is an object in the N objects, and N is a positive integer (i.e., an integer greater than or equal to 1). ② For any one of the N objects, calling the target evaluation model to score the basic attribute information of the object and the basic attribute information of at least one associated object of the object, and obtaining the basic scoring result of the object and the basic scoring result of each associated object of the object, wherein the target evaluation model is obtained by training the initial evaluation model according to the model training sample, and the model training sample includes the basic attribute information of multiple sample objects and the reference scoring result of each sample object. ③ Determine the comprehensive scoring result of the object according to the resource transfer data between the object and each associated object, the basic scoring result of each associated object of the object, and the basic scoring result of the object. ④ Determine the target object in the specified business scenario according to the comprehensive scoring result of each object of the N objects, and the target object is used to receive the activity information of the specified business scenario. Compared to directly scoring the attribute information of N objects, the present application scores each of the N objects based on the qualifications of the N objects themselves and the qualifications of the associated objects corresponding to the object, which can more accurately determine the score of each object, thereby accurately determining the high-quality target objects (i.e., seed objects) in the specified business scenario, and then using these target objects to achieve the new customer acquisition requirements in the specified business scenario, thereby effectively driving the business development of the specified business scenario.

[0029] In one possible implementation, the data processing method provided in this application can be implemented based on artificial intelligence (AI) technology, mainly involving machine learning (ML) technology in AI technology, and the target evaluation model is determined based on machine learning technology. Machine learning usually includes artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and learning by teaching.

[0030] In a possible implementation, the data processing method provided by the present application can also be implemented based on cloud technology. Specifically, it may involve one or more of cloud storage, cloud database, and big data in cloud technology. For example, the data required to execute the data processing method (such as basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and resource transfer data between each object and each associated object, etc.) is obtained from the cloud database.

[0031] In a possible implementation, the data processing method provided by the present application can also be implemented based on blockchain technology. For example, the data required to execute the data processing method can be stored in the form of blocks on the blockchain; the data generated by executing the data processing method (such as N comprehensive scoring results of N objects) can be stored in the form of blocks on the blockchain; in addition, the data processing device that executes the data processing method can be a node device in the blockchain network.

[0032] Among them, the relevant explanations of the above-mentioned artificial intelligence technology, cloud technology and blockchain technology are as follows:

[0033] 1. Artificial Intelligence

[0034] Artificial intelligence refers to the theory, method, technology and application system of simulating, extending and expanding human intelligence, perceiving the environment, acquiring knowledge and using knowledge to obtain the best results using digital computers or machines controlled by digital computers. AI technology is a comprehensive subject, and the fields involved are relatively wide. Among them, basic artificial intelligence technologies generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning. The data processing method provided in the embodiment of the present application mainly relates to machine learning (ML) technology in AI technology, which mainly uses machine learning methods to build task processing clusters.

[0035] 2. Cloud Technology

[0036] Cloud technology refers to a hosting technology that unifies hardware, software, network and other resources in a wide area network or local area network to achieve data computing, storage, processing and sharing. Cloud technology is a general term for network technology, information technology, integration technology, management platform technology, application technology, etc. based on the cloud computing business model application. It can form a resource pool, which is used on demand and is flexible and convenient. Cloud computing technology will become an important support. Cloud storage is a new concept extended and developed from the concept of cloud computing. Distributed cloud storage system (hereinafter referred to as storage system) refers to a storage system that uses cluster applications, grid technology and distributed storage file systems to bring together a large number of different types of storage devices (storage devices are also called storage nodes) in the network through application software or application interfaces to work together and provide data storage and business access functions to the outside world. In short, cloud database can be regarded as an electronic file cabinet - a place to store electronic files. Users can add, query, update, delete and other operations on the data in the files. The so-called "database" is a collection of data that is stored together in a certain way, can be shared with multiple users, has as little redundancy as possible, and is independent of the application.

[0037] 3. Blockchain

[0038] Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of the information (anti-counterfeiting) and generate the next block.

[0039] See also Figure 1 , is a schematic diagram of the architecture of a data processing system disclosed in an embodiment of the present application, and the data processing system may include at least a server 101, a terminal device 102, and a display interface 103. Among them, the server 101 is mainly used to obtain the basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and the resource transfer data between each object and each associated object, and the associated object is an object among the N objects. The acquired data is then analyzed and processed to obtain a comprehensive scoring result of the N objects, and the target object in the specified business scenario is determined based on the comprehensive scoring result of the N objects. The terminal device 102 is mainly used to receive activity information of the specified business scenario, and display the activity information in the display interface 103. For a specified business scenario, the display interface 103 can display the following. Figure 1 The text information (i.e., activity information) shown may include activity content and activity rewards. The activity content is "send coupons to friends", and the activity rewards may be customized as appropriate, such as Figure 1 There are two types shown: 1. If the invited friend submits a business application operation, the target object can get reward 1; 2. If the invited friend submits a business application operation and meets the admission conditions (i.e. the application is passed), the target object can get reward 2; reward 1 and reward 2 are different. In different business scenarios, the display interface can be customized, which is not limited here.

[0040] In one embodiment, the server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms, which are not limited in this embodiment of the present application. The terminal device 102 includes, but is not limited to: smart phones, tablet computers, smart wearable devices, smart voice interaction devices, smart home appliances, personal computers, vehicle terminals, smart cameras, virtual reality devices (such as AR (Augmented Reality) devices), etc., which are not limited in this application. Further, the server 101 and the terminal device 102 establish a direct communication connection through wired communication, or an indirect communication connection can be established through wireless communication, which is not limited in this embodiment of the present application.

[0041] In an embodiment of the present application, in combination with the data processing system, the data processing method may include: the server 101 obtains basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and resource transfer data between each object and each associated object from a database, where the associated object is an object among the N objects; then, for any one of the N objects, the target evaluation model is called to score the basic attribute information of the object and the basic attribute information of at least one associated object of the object, and obtain a basic scoring result of the object and a basic scoring result of each associated object of the object, wherein the target evaluation model is obtained by training an initial evaluation model based on a model training sample, and the model training sample includes basic attribute information of multiple sample objects and a reference scoring result of each sample object; then, a comprehensive scoring result of the object is determined based on the resource transfer data between the object and each associated object, the basic scoring result of each associated object of the object, and the basic scoring result of the object; finally, a target object in the specified business scenario is determined based on the comprehensive scoring result of each of the N objects, and the target object is used to receive activity information of the specified business scenario. In this process, for any object among the N objects, not only its own qualifications are considered, but also the qualifications of the associated objects corresponding to the object. Through such a comprehensive evaluation, better target objects can be determined, and then these target objects can be used to realize new customer requests in specified business scenarios, thereby driving the development of the business.

[0042] In one implementation, the data processing method can be applied to new user acquisition activities, which can include acquiring new users for bank loan services, acquiring new users after various applications such as supermarkets are launched, and so on. For example, acquiring new users for bank loan services specifically refers to inviting more users to apply for bank loan operations through target objects; acquiring new users for supermarkets specifically refers to inviting more users to shop online through the application through target objects after a supermarket application is launched. Based on the data processing method of the embodiment of the present application, new users can be acquired through old users in specific scenarios.

[0043] It should be noted that in this application, the relevant data involved in the data processing process, such as the basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and the resource transfer data between each object and each associated object, etc. When the above embodiments of this application are applied to specific products or technologies, user permission or consent must be obtained, and the relevant data collection, use and processing process must comply with relevant laws, regulations and standards of the country and region, comply with the principles of legality, legitimacy and necessity, and do not involve the acquisition of data types prohibited or restricted by laws and regulations. In some optional embodiments, the relevant data involved in the embodiments of this application are obtained after the object is authorized separately. In addition, when obtaining the separate authorization of the object, the purpose of the relevant data involved is indicated to the object.

[0044] The data processing method proposed in the embodiment of the present application is introduced in detail below.

[0045] See also Figure 2 , is a flow chart of a data processing method disclosed in an embodiment of the present application. The data processing method can be executed by a computer device, and the computer device can be Figure 1 The data processing method of the server 101 shown may specifically include but is not limited to the following steps:

[0046] S201: Obtain basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and resource transfer data between each object and each associated object, where the associated object is an object among the N objects, and N is a positive integer.

[0047] The designated business scenario is relatively broad. In this application, the designated business scenario may refer to the business scenario of a designated bank or the business scenario of a designated application. For any designated business scenario, basic attribute information of N objects under the designated business scenario, at least one associated object of each object, and resource transfer data between each object and each associated object may be obtained from the database.

[0048] In a possible implementation, N objects and basic attribute information of the N objects in a specified business scenario are first obtained. For different specified business scenarios, the objects are different. For example, for banking business, each of the N objects is an object that has opened an account in the corresponding bank (i.e., has a bank account number of the bank). These N objects include objects that have used the account and objects that have not used the account; for application business, each of the N objects is an object that has registered the application. These N objects include objects that have used the application and objects that have not used the application. More specifically, for example, for an application that can place an order, the N objects include objects that have used the application to place an order and objects that have an account with the application but have not placed an order. The object here can refer to a user. The basic attribute information of each object includes the object's gender, age, industry, position, asset status, etc.

[0049] For any one of the N objects (referred to as the second object), the object among the N objects that has a resource transfer operation with the second object is taken as an associated object of the second object. In other words, the associated object is also an object among the N objects. In the business scenario of a bank, the resource transfer operation includes a transfer behavior, that is, user A transfers money to user B's bank account through A's bank account. For any second object, the corresponding associated object may or may not be a friend of the second object. This application does not limit this. It is only necessary to determine that there is a resource transfer operation between the second object and the associated object. Then, the resource transfer data of the second object and each associated object is obtained from the resource transfer database. Take each of the N objects as the second object, and repeat the above operation to determine at least one associated object of each object and the resource transfer data of each object and each associated object.

[0050] In a possible implementation, the resource transfer data includes one or more of the resource transfer times, resource transfer quantities, and resource transfer time. The resource transfer times and resource transfer time can reflect the frequency of resource transfers between the object and the associated objects, and the resource transfer quantity can include the resource transfer quantity from any object to the associated object and the resource transfer quantity from the associated object to the object. The resource transfer quantity can be used to roughly evaluate the asset status of the object and the associated objects.

[0051] Among them, it should be noted that the collection and processing of relevant data in this application should be strictly in accordance with the requirements of relevant national laws and regulations when applied in examples, and the informed consent or separate consent of the personal information subject should be obtained, and subsequent data use and processing should be carried out within the scope of authorization of laws and regulations and the personal information subject.

[0052] S202: Call the target evaluation model to score the basic attribute information of each object in the N objects and the basic attribute information of at least one associated object of each object to obtain the basic scoring result of each object and the basic scoring result of each associated object of each object. The target evaluation model is obtained by training the initial evaluation model according to the model training sample. The model training sample includes the basic attribute information of multiple sample objects and the reference scoring result of each sample object.

[0053] For any one of the N objects, the target evaluation model can be called to score the basic attribute information of the object to obtain the basic score result of the object; at the same time, the target evaluation model can be called to score the basic attribute information of at least one associated object of the object to obtain the basic score result of each associated object of the object. The higher the basic score result, the better the quality of the object in the specified business scenario.

[0054] Among them, the target evaluation model is determined according to the basic attribute information of the object, and the target evaluation model can specifically refer to a binary classification model. The basic attribute information of the object is processed according to the corresponding model training method to obtain the corresponding target evaluation model. The model training method includes but is not limited to logistic regression, KNN (k-nearestneighbor, nearest neighbor algorithm, a basic classification and regression method, and also a common method in supervised learning methods), decision tree, GBDT (Gradient Boosting Decision Tree, gradient boosting decision tree, an iterative decision tree algorithm) and neural network algorithms. For example, the steps of determining the target evaluation model using the neural network method include: obtaining model training samples, the model training samples include basic attribute information of multiple sample objects and reference evaluation results of each sample object; calling the initial evaluation model to predict the basic attribute information of multiple sample objects to obtain the predicted evaluation results of each sample object; finally, adjusting the network parameters of the initial evaluation model according to the reference evaluation label of each sample object and the predicted evaluation results of each sample object to obtain the target evaluation model.

[0055] S203: Determine a comprehensive scoring result of each object according to the resource transfer data between each object and each associated object, the basic scoring result of each associated object of each object, and the basic scoring result of each object.

[0056] In a possible implementation, the associated scoring result of each object is first determined based on the resource transfer data between each object and each associated object and the basic scoring result of each associated object of each object. Specifically, the method may include: first determining the weight value of each associated object of each object based on the resource transfer data between each object and each associated object, where the resource transfer data includes one or more of the number of resource transfers, the amount of resource transfers, and the time of resource transfer; and then performing weighted summation processing on the basic scoring result of each associated object of each object based on the weight value of each associated object to obtain the associated scoring result of each object.

[0057] The resource transfer data is different, and the weight value of each associated object of each object is determined in a different way. An arbitrary object among the N objects (referred to as the first object) is taken as an example to explain it.

[0058] In a possible implementation, when the resource transfer data is one of the resource transfer times, resource transfer quantity, and resource transfer time, the weight value of each associated object of the first object is determined as follows:

[0059] When the resource transfer data is the number of resource transfers, determining the weight value of each associated object of the first object according to the resource transfer data of the first object and each associated object specifically includes: determining the total number of resource transfers according to the number of resource transfers between the first object and each associated object; determining the weight value of each associated object of the first object according to the total number of resource transfers and the number of resource transfers between the first object and each associated object. In one implementation, the ratio between the number of resource transfers between the first object and each associated object and the total number of resource transfers can be calculated, and the ratio is used as the weight value of each associated object of the first object.

[0060] When the resource transfer data is the resource transfer quantity, determining the weight value of each associated object of the first object according to the resource transfer data of the first object and each associated object specifically includes: determining the total resource transfer quantity according to the resource transfer quantity of the first object and each associated object, and determining the weight value of each associated object of the first object according to the total resource transfer quantity and the resource transfer quantity of the first object and each associated object. In one implementation, the ratio between the resource transfer quantity of the first object and each associated object and the total resource transfer quantity can be calculated, and the ratio is used as the weight value of each associated object of the first object.

[0061] When the resource transfer data is the resource transfer time, the weight value of each associated object of the first object is determined according to the resource transfer data of the first object and each associated object, specifically including: obtaining the current time, calculating the time difference between the resource transfer time of the first object and each associated object and the current time, and determining the weight value of each associated object of the first object according to the time difference. There is an inverse correlation between the time difference and the weight value of each associated object, for example, the weight value of the associated object whose resource transfer time is close to the current time is set to a larger value, and the weight value of the associated object whose resource transfer time is far from the current time is set to a smaller value.

[0062] In another implementation, when the resource transfer data is two of the resource transfer times, resource transfer quantity, and resource transfer time, a weight value is first determined based on the above description, and then the two weight value data corresponding to each associated object are processed to obtain the weight value of each associated object. This processing can be to add and average the two weight values, or to multiply the two weight values. For example, when the resource transfer data is the resource transfer times and resource transfer quantity, the first weight value of the associated object is first determined based on the resource transfer times, and then the second weight value of the associated object is determined based on the resource transfer quantity, and then the first weight value and the second weight value are multiplied or added to obtain the average to obtain the weight value of each associated object.

[0063] In another implementation, when the resource transfer data includes the number of resource transfers, the amount of resource transfers, and the resource transfer time, a weight value is first determined based on the above description, and then the three weight value data corresponding to each associated object are processed to obtain the weight value of each associated object. Similarly, this processing can be to add and average the three weight values, or to multiply the three weight values. For example, first determine the first weight value of the associated object based on the number of resource transfers, then determine the second weight value of the associated object based on the amount of resource transfers, and then determine the third weight value of the associated object based on the resource transfer time. Finally, multiply or add the first weight value, the second weight value, and the third weight data to obtain the weight value of each associated object.

[0064] Further, the comprehensive scoring result of each object is determined according to the associated scoring result and the basic scoring result of each object. Specifically, the expected object type under the specified business scenario is first obtained. The expected object type is to indicate the object type of the target object to be obtained. The expected object type includes a first object type and a second object type. The object of the first object type is an object that submits a business application operation for the specified business scenario and meets the access conditions, and the object of the second object type is an object that does not submit a business application operation for the specified business scenario. For any specified business scenario, the objects corresponding to the first object type and the second object type can be understood as objects that have accounts under the business scenario. The difference is that one type of object is an object that has an account under the business scenario, has submitted a business application operation and meets the access conditions. The weight value of each object is determined according to the expected object type; for different types of objects, the distribution of weight values ​​is different. In the embodiment of the present application, the weight value of the object corresponding to the first object type is less than the weight value of the object corresponding to the second object type. If the expected object type is the first object type, it means that the focus is on attracting new objects. Correspondingly, the weight value of the object will be set lower, for example, set to 0.3. If the expected object type is the second object type, it means that the focus is on attracting new objects, and at the same time, it is hoped that the target object will also maintain its liking for the business. Correspondingly, the weight value of the object will be set to a more balanced value, such as 0.5. Finally, the comprehensive score result of each object in the N objects is determined based on the weight value of each object, the associated scoring result, and the basic scoring result of each object. In a possible implementation method, taking the first object as an example, the product of the weight value of the first object and the basic scoring result of the first object can be calculated first, and then the product of the associated scoring result and the weight value corresponding to the associated scoring result can be calculated, and the two products can be added to obtain the comprehensive scoring result of the first object. The sum of the weight value corresponding to the associated scoring result and the weight value of the first object is 1.

[0065] S204: Determine a target object in a specified business scenario according to the comprehensive scoring results of the N objects, where the target object is used to receive activity information of the specified business scenario.

[0066] In this application, the main purpose is to determine the target object from N objects for promoting activities in a specified business scenario. The target object can be determined in the following two ways:

[0067] The first method: sort the comprehensive scoring results of N objects in order from high to low to obtain the sorting results; then use the objects corresponding to the preset number of comprehensive scoring results that are ranked top in the sorting results as the target objects in the specified business scenario. In some specified business scenarios, a certain number of customers are required to promote an activity. Therefore, high-quality customers can be determined for activity promotion based on this method. Optionally, after determining a preset number of comprehensive scoring results, it is possible to further determine whether the preset number of comprehensive scoring results is greater than or equal to a set scoring threshold, and eliminate the objects corresponding to the comprehensive scoring results that are less than the set scoring threshold.

[0068] The second method: directly compare the comprehensive scoring results of N objects with the set scoring threshold; select the objects with a comprehensive scoring result greater than the set scoring threshold from the comprehensive scoring results of N objects as the target objects in the specified business scenario. In this case, you only need to directly filter out the objects that meet the conditions without sorting. This method can improve the speed of determining the target objects to a certain extent.

[0069] In a possible implementation, after determining the target object in the specified business scenario, obtain activity information of the specified business scenario, the activity information includes incentive information; send the activity information to the terminal device corresponding to the target object, so that the terminal device outputs the activity information (such as Figure 1 In the display interface 103 in the example, if the associated object of the target object submits a business application operation for the specified business scenario and meets the access conditions, it can be understood that the target object invites the corresponding associated object to submit a business application operation for the specified business scenario and meets the access conditions, then the corresponding reward is issued to the target object according to the incentive information. Optionally, if the associated object of the target object submits a business application operation for the specified business scenario, the corresponding reward can also be issued to the target object according to the incentive information. Since the conditions met by the associated objects are different, the rewards are also different. By encouraging the target object to invite new users in this way, the probability of successful invitation can be increased, thereby driving business development in the specified business scenario.

[0070] In an embodiment of the present application, for each of the N objects, the target evaluation model is called to score the basic attribute information of each object and the basic attribute information of at least one associated object of each object, and the basic scoring result of each object and each associated object of each object are obtained. Then, the comprehensive scoring result of each object is determined based on the resource transfer data of each object and each associated object, the basic scoring result of each associated object of each object, and the basic scoring result of each object; finally, the target object in the specified business scenario is determined based on the determined comprehensive scoring result of the N objects, and the activity information of the specified business scenario is received through the target object. In the present application, for any object among the N objects, not only its own qualifications are considered, but also the qualifications of the associated objects corresponding to the object are considered. Through such a comprehensive evaluation, high-quality seed objects in the specified business scenario can be accurately determined, and then the requirements for attracting new customers in the specified business scenario can be achieved through these target objects, thereby effectively driving the business development of the specified business scenario.

[0071] See also Figure 3 , is a flow chart of another data processing method disclosed in an embodiment of the present application. The data processing method describes how to determine a comprehensive scoring result of an object. Taking the first object as an example, the first object is any one of the N objects. The data processing method includes but is not limited to the following steps:

[0072] S301: Obtain basic attribute information of a first object, n associated objects of the first object, and resource transfer data between the first object and each associated object, where n is an integer greater than or equal to 1.

[0073] In a possible implementation, the first object has n associated objects. Accordingly, when the basic attribute information of the first object is obtained, the basic attribute information of the n associated objects of the first object is obtained at the same time, including [basic attribute information of associated object 1, basic attribute information of associated object 2, ..., basic attribute information of associated object n]. In addition, the resource transfer data of the first object and each associated object is also obtained at the same time, such as the resource transfer data between the first object and associated object 1, the resource transfer data between the first object and associated object 2, etc., which are not listed one by one here.

[0074] S302: calling the target evaluation model to score the basic attribute information of the first object and the basic attribute information of the n associated objects, and obtaining a basic scoring result of the first object and a basic scoring result of each of the n associated objects.

[0075] Specifically, the basic score result of each of the n associated objects can be represented by score 1, score 2, score 3, ..., score n, and the basic score result of the first object can be represented by score self express.

[0076] S303: Determine the association scoring result of the first object according to the resource transfer data between the first object and each associated object and the basic scoring result of each associated object in the n associated objects.

[0077] See also Figure 4 , is a connection diagram of a first object and associated objects disclosed in an embodiment of the present application, such as Figure 4 As shown, it includes resource transfer data between the first object A and three associated objects (associated object 1, associated object 2, and associated object 3). Taking the resource transfer data as the number of resource transfers as an example, the number of resource transfers are num1, num2, and num3 respectively. According to the target evaluation model, the basic scoring results of the three associated objects are determined as score1, score2, and score3 respectively. Then, the number of resource transfers is used as the weight, and the above three basic scoring results are weighted averaged to obtain the associated scoring result of the first object A. Specifically, the associated scoring result score of the first object A is friend The calculation formula is shown in formula (1):

[0078]

[0079] Among them, num_tot is the total number of resource transfers, num tot =num1+num2+num3.

[0080] S304: Determine a comprehensive scoring result of the first object according to the associated scoring result of the first object, the basic scoring result of the first object, and the weight value of the first object.

[0081] exist Figure 2 In the illustrated embodiment, it has been described in detail that the weight value of the first object is related to the expected object type in the specified business scenario. The weight value of the first object can be set to r. Then, according to the correlation score result of the first object A, fianl The calculation formula is shown in formula (2):

[0082] score fianl =score self ×r+score friend ×(1-r) (2)

[0083] Figure 3The flowchart of the following illustrates how the comprehensive scoring structure of an object is determined, based on Figure 3 Steps S301-S304 can determine the comprehensive score result of each object in the N objects, and then determine the target object based on the N comprehensive score results. Figure 5 As shown, it is a schematic diagram of the flow chart of calculating a comprehensive scoring result in an embodiment of the present application, from Figure 5 It can be seen that for any object A, the process of determining the comprehensive scoring result may include: first calling the target evaluation model to determine the basic scoring results of object A and each associated object of object A (a total of n associated objects), then determining the associated scoring result of object A based on the basic scoring results of the associated objects, and finally determining the comprehensive scoring result of object A based on the associated scoring results of object A and the basic scoring result of object A.

[0084] The method of the embodiment of the present application is described in detail above. In order to facilitate better implementation of the above scheme of the embodiment of the present application, the device of the embodiment of the present application is provided below accordingly.

[0085] See also Figure 6 , Figure 6 It is a structural diagram of a data processing device provided in an embodiment of the present application. The data processing device can be set in the computer device provided in an embodiment of the present application. Figure 6 The data processing device shown may be a computer program (including program code) running in a computer device, and the data processing device may be used to execute Figure 2 , Figure 3 as well as Figure 5 Some or all of the steps in the method embodiment shown. Figure 6 , the data processing device may include the following units:

[0086] An acquisition unit 601 is used to acquire basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and resource transfer data between each object and each associated object, wherein the associated object is an object in the N objects, and N is an integer greater than 1;

[0087] A processing unit 602 is used to call a target evaluation model to perform scoring processing on basic attribute information of each object in the N objects and basic attribute information of at least one associated object of each object, to obtain a basic scoring result of each object and a basic scoring result of each associated object of each object, wherein the target evaluation model is obtained by training an initial evaluation model according to a model training sample, and the model training sample includes basic attribute information of multiple sample objects and a reference scoring result of each sample object;

[0088] A determination unit 603 is configured to determine a comprehensive scoring result of each object according to the resource transfer data of each object and each associated object, the basic scoring result of each associated object of each object, and the basic scoring result of each object;

[0089] The determining unit 603 is further configured to determine a target object in the specified business scenario according to the comprehensive scoring results of the N objects, and the target object is configured to receive activity information of the specified business scenario.

[0090] In a possible implementation, when the acquisition unit 601 acquires basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and resource transfer data between each object and each associated object, it is specifically configured to perform the following steps:

[0091] Obtain N objects in a specified business scenario and basic attribute information of the N objects;

[0092] For each of the N objects, taking an object among the N objects that has a resource transfer operation with the object as an associated object of the object;

[0093] The resource transfer data of each object and each associated object is obtained from a resource transfer database.

[0094] In a possible implementation, when the determination unit 603 determines the comprehensive scoring result of each object according to the resource transfer data of each object and each associated object, the basic scoring result of each associated object of each object, and the basic scoring result of each object, it is specifically configured to perform the following steps:

[0095] Determine the association scoring result of each object according to the resource transfer data between each object and each associated object and the basic scoring result of each associated object of each object;

[0096] The comprehensive scoring result of each object is determined according to the associated scoring result and the basic scoring result of each object.

[0097] In a possible implementation, when the determining unit 603 determines the associated scoring result of each object according to the resource transfer data between each object and each associated object and the basic scoring result of each associated object of each object, it is specifically configured to perform the following steps:

[0098] Determine a weight value of each associated object of each object according to resource transfer data between each object and each associated object, wherein the resource transfer data includes one or more of the resource transfer times, the resource transfer quantity and the resource transfer time;

[0099] The basic scoring result of each associated object of each object is weighted and summed according to the weight value of each associated object to obtain the associated scoring result of each object.

[0100] In a possible implementation, the resource transfer data includes the number of resource transfers, and when the determining unit 603 determines the weight value of each associated object of each object according to the resource transfer data of each object and each associated object, it is specifically configured to perform the following steps:

[0101] Determine the total number of resource transfers according to the number of resource transfers between each object and each associated object;

[0102] The weight value of each associated object of each object is determined according to the total number of resource transfers and the number of resource transfers between each object and each associated object.

[0103] In a possible implementation, when the determination unit 603 determines the comprehensive scoring result of each object according to the associated scoring result and the basic scoring result of each object, it is specifically configured to perform the following steps:

[0104] Acquire an expected object type under the specified business scenario, where the expected object type includes a first object type and a second object type, where an object of the first object type is an object that has submitted a business application operation for the specified business scenario and meets an access condition, and an object of the second object type is an object that has not submitted a business application operation for the specified business scenario;

[0105] Determining a weight value of each object according to the expected object type;

[0106] The comprehensive scoring result of each object is determined according to the weight value of each object, the associated scoring result and the basic scoring result of each object.

[0107] In one implementation, when the determination unit 603 determines the target object in the specified business scenario according to the comprehensive scoring results of the N objects, it is specifically configured to perform the following steps:

[0108] Sorting the comprehensive scoring results of the N objects in descending order to obtain a sorting result;

[0109] The objects corresponding to the preset number of comprehensive scoring results ranked first in the sorting results are used as the target objects in the specified business scenario.

[0110] In one implementation, the acquisition unit 601 is further configured to acquire activity information of the specified business scenario, wherein the activity information includes incentive information;

[0111] Processing unit 602 is also used to send the activity information to the terminal device corresponding to the target object so that the terminal device outputs the activity information; if the associated object of the target object submits a business application operation for the specified business scenario and meets the access conditions, the corresponding reward is issued to the target object according to the incentive information.

[0112] According to another embodiment of the present application, Figure 6 The various units in the data processing device shown can be respectively or all combined into one or several other units to constitute, or some of the units (some) can also be split into multiple smaller units in function to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In practical applications, the function of a unit can also be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of the present application, the data processing device may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0113] According to another embodiment of the present application, the program can be executed by running on a general computing device such as a computer including a central processing unit (CPU), a random access memory medium (RAM), a read-only memory medium (ROM), and other processing elements and storage elements. Figure 2 , Figure 3 or Figure 5 A computer program (including program code) for each step involved in part or all of the method shown in the figure is used to construct a computer program (including program code) for each step involved in part or all of the method shown in the figure. Figure 6 The data processing device shown in the embodiment of the present application is implemented by the data processing device. The computer program can be recorded on a computer-readable storage medium, for example, and loaded into the above-mentioned computing device through the computer-readable storage medium and run therein.

[0114] In an embodiment of the present application, for each of the N objects, the target evaluation model is called to score the basic attribute information of each object and the basic attribute information of at least one associated object of each object, and the basic scoring result of each object and each associated object of each object are obtained. Then, the comprehensive scoring result of each object is determined based on the resource transfer data of each object and each associated object, the basic scoring result of each associated object of each object, and the basic scoring result of each object; finally, the target object in the specified business scenario is determined based on the determined comprehensive scoring result of the N objects, and the activity information of the specified business scenario is received through the target object. In the present application, for any object among the N objects, not only its own qualifications are considered, but also the qualifications of the associated objects corresponding to the object are considered. Through such a comprehensive evaluation, high-quality seed objects in the specified business scenario can be accurately determined, and then the requirements for attracting new customers in the specified business scenario can be achieved through these target objects, thereby effectively driving the business development of the specified business scenario.

[0115] Based on the above method and device embodiments, the present application embodiment provides a computer device. Figure 7 , Figure 7 is a schematic diagram of a computer device provided in an embodiment of the present application. The computer device may be Figure 1 Server 101 is shown. Figure 7 The computer device shown includes at least a processor 701, an input interface 702, an output interface 703, and a computer-readable storage medium 704. The processor 701, the input interface 702, the output interface 703, and the computer-readable storage medium 704 may be connected via a bus or other means.

[0116] The computer-readable storage medium 704 may be stored in a memory of a computer device. The computer-readable storage medium 704 is used to store a computer program. The computer program includes computer instructions. The processor 701 is used to execute the program instructions stored in the computer-readable storage medium 704. The processor 701 (or CPU (Central Processing Unit)) is the computing core and control core of the computer device, which is suitable for implementing one or more computer instructions, and is specifically suitable for loading and executing one or more computer instructions to implement a corresponding method flow or a corresponding function.

[0117] The embodiment of the present application also provides a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the computer device. In addition, one or more computer instructions suitable for being loaded and executed by the processor are also stored in the storage space. These computer instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk storage; optionally, it can also be at least one computer-readable storage medium located away from the aforementioned processor.

[0118] In some embodiments, the processor 701 may load and execute one or more computer instructions stored in the computer-readable storage medium 704 to implement the above-mentioned Figure 2 , Figure 3 as well as Figure 5 In a specific implementation, the computer instructions in the computer-readable storage medium 704 are loaded by the processor 701 and execute the following steps:

[0119] Obtain basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and resource transfer data between each object and each associated object, wherein the associated object is an object in the N objects, and N is an integer greater than 1;

[0120] Calling a target evaluation model to perform scoring processing on basic attribute information of each of the N objects and basic attribute information of at least one associated object of each object to obtain a basic scoring result of each object and a basic scoring result of each associated object of each object, wherein the target evaluation model is obtained by training an initial evaluation model according to a model training sample, and the model training sample includes basic attribute information of multiple sample objects and a reference scoring result of each sample object;

[0121] Determine a comprehensive scoring result of each object according to the resource transfer data between each object and each associated object, the basic scoring result of each associated object of each object, and the basic scoring result of each object;

[0122] A target object under the specified business scenario is determined according to the comprehensive scoring results of the N objects, and the target object is used to receive activity information of the specified business scenario.

[0123] In a possible implementation, when the computer instructions in the computer-readable storage medium 704 are loaded and executed by the processor 701 to obtain basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and resource transfer data between each object and each associated object, the computer instructions are specifically used to perform the following steps:

[0124] Obtain N objects in a specified business scenario and basic attribute information of the N objects;

[0125] For each of the N objects, taking an object among the N objects that has a resource transfer operation with the object as an associated object of the object;

[0126] The resource transfer data of each object and each associated object is obtained from a resource transfer database.

[0127] In a possible implementation, when the computer instructions in the computer-readable storage medium 704 are loaded and executed by the processor 701 to determine the comprehensive scoring result of each object according to the resource transfer data of each object and each associated object, the basic scoring result of each associated object of each object, and the basic scoring result of each object, the computer instructions are specifically used to perform the following steps:

[0128] Determine the association scoring result of each object according to the resource transfer data between each object and each associated object and the basic scoring result of each associated object of each object;

[0129] The comprehensive scoring result of each object is determined according to the associated scoring result and the basic scoring result of each object.

[0130] In a possible implementation, when the computer instructions in the computer-readable storage medium 704 are loaded and executed by the processor 701 to determine the association scoring result of each object according to the resource transfer data of each object and each associated object and the basic scoring result of each associated object of each object, the computer instructions are specifically used to perform the following steps:

[0131] Determine a weight value of each associated object of each object according to resource transfer data between each object and each associated object, wherein the resource transfer data includes one or more of the resource transfer times, the resource transfer quantity and the resource transfer time;

[0132] The basic scoring result of each associated object of each object is weighted and summed according to the weight value of each associated object to obtain the associated scoring result of each object.

[0133] In a possible implementation, the resource transfer data includes the number of resource transfers. When the computer instructions in the computer-readable storage medium 704 are loaded and executed by the processor 701 to determine the weight value of each associated object of each object according to the resource transfer data of each object and each associated object, the computer instructions are specifically used to perform the following steps:

[0134] Determine the total number of resource transfers according to the number of resource transfers between each object and each associated object;

[0135] The weight value of each associated object of each object is determined according to the total number of resource transfers and the number of resource transfers between each object and each associated object.

[0136] In a possible implementation, when the computer instructions in the computer-readable storage medium 704 are loaded and executed by the processor 701 to determine the comprehensive scoring result of each object according to the associated scoring result and the basic scoring result of each object, the computer instructions are specifically used to perform the following steps:

[0137] Acquire an expected object type under the specified business scenario, where the expected object type includes a first object type and a second object type, where an object of the first object type is an object that has submitted a business application operation for the specified business scenario and meets an access condition, and an object of the second object type is an object that has not submitted a business application operation for the specified business scenario;

[0138] Determining a weight value of each object according to the expected object type;

[0139] The comprehensive scoring result of each object is determined according to the weight value of each object, the associated scoring result and the basic scoring result of each object.

[0140] In a possible implementation, when the computer instructions in the computer-readable storage medium 704 are loaded and executed by the processor 701 to determine the target object in the specified business scenario according to the comprehensive scoring results of the N objects, the computer instructions are specifically used to perform the following steps:

[0141] Sorting the comprehensive scoring results of the N objects in descending order to obtain a sorting result;

[0142] The objects corresponding to the preset number of comprehensive scoring results ranked at the top in the sorting results are used as the target objects in the specified business scenario.

[0143] In one implementation, the computer instructions in the computer readable storage medium 704 are further used by the processor 701 to load and execute the following steps:

[0144] Acquire activity information of the specified business scenario, wherein the activity information includes incentive information;

[0145] Sending the activity information to a terminal device corresponding to the target object, so that the terminal device outputs the activity information;

[0146] If an associated object of the target object submits a business application operation for the specified business scenario and meets the access conditions, a corresponding reward is issued to the target object according to the incentive information.

[0147] In an embodiment of the present application, for each of the N objects, the target evaluation model is called to score the basic attribute information of each object and the basic attribute information of at least one associated object of each object, and the basic scoring result of each object and each associated object of each object are obtained. Then, the comprehensive scoring result of each object is determined based on the resource transfer data of each object and each associated object, the basic scoring result of each associated object of each object, and the basic scoring result of each object; finally, the target object in the specified business scenario is determined based on the determined comprehensive scoring result of the N objects, and the activity information of the specified business scenario is received through the target object. In the present application, for any object among the N objects, not only its own qualifications are considered, but also the qualifications of the associated objects corresponding to the object are considered. Through such a comprehensive evaluation, high-quality seed objects in the specified business scenario can be accurately determined, and then the requirements for attracting new customers in the specified business scenario can be achieved through these target objects, thereby effectively driving the business development of the specified business scenario.

[0148] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the data processing method provided in the above-mentioned various optional modes.

[0149] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, some steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0150] In the several embodiments provided in this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, and the division of the modules described above is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0151] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A data processing method, characterized in that: The method comprises: Obtain basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and resource transfer data between each object and each associated object, wherein the associated object is an object in the N objects, and N is a positive integer; Calling a target evaluation model to perform scoring processing on basic attribute information of each of the N objects and basic attribute information of at least one associated object of each object to obtain a basic scoring result of each object and a basic scoring result of each associated object of each object, wherein the target evaluation model is obtained by training an initial evaluation model according to a model training sample, and the model training sample includes basic attribute information of multiple sample objects and a reference scoring result of each sample object; Determine a comprehensive scoring result of each object according to the resource transfer data between each object and each associated object, the basic scoring result of each associated object of each object, and the basic scoring result of each object; A target object under the specified business scenario is determined according to the comprehensive scoring results of the N objects, and the target object is used to receive activity information of the specified business scenario.

2. The method according to claim 1, characterized in that The obtaining of basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and resource transfer data between each object and each associated object includes: Obtain N objects in a specified business scenario and basic attribute information of the N objects; For each of the N objects, taking an object among the N objects that has a resource transfer operation with the object as an associated object of the object; The resource transfer data of each object and each associated object is obtained from a resource transfer database.

3. The method according to claim 1 or 2, characterized in that: The step of determining the comprehensive scoring result of each object according to the resource transfer data between each object and each associated object, the basic scoring result of each associated object of each object, and the basic scoring result of each object includes: Determine the association scoring result of each object according to the resource transfer data between each object and each associated object and the basic scoring result of each associated object of each object; The comprehensive scoring result of each object is determined according to the associated scoring result and the basic scoring result of each object.

4. The method according to claim 3, characterized in that The determining the association scoring result of each object according to the resource transfer data between each object and each associated object and the basic scoring result of each associated object of each object includes: Determine a weight value of each associated object of each object according to resource transfer data between each object and each associated object, wherein the resource transfer data includes one or more of the resource transfer times, the resource transfer quantity and the resource transfer time; The basic scoring result of each associated object of each object is weighted and summed according to the weight value of each associated object to obtain the associated scoring result of each object.

5. The method according to claim 4, characterized in that The resource transfer data includes the number of resource transfers, and determining the weight value of each associated object of each object according to the resource transfer data of each object and each associated object includes: Determine the total number of resource transfers according to the number of resource transfers between each object and each associated object; The weight value of each associated object of each object is determined according to the total number of resource transfers and the number of resource transfers between each object and each associated object.

6. The method according to claim 3, characterized in that Determining the comprehensive scoring result of each object according to the associated scoring result and the basic scoring result of each object includes: Acquire an expected object type under the specified business scenario, where the expected object type includes a first object type and a second object type, where an object of the first object type is an object that has submitted a business application operation for the specified business scenario and meets an access condition, and an object of the second object type is an object that has not submitted a business application operation for the specified business scenario; Determining a weight value of each object according to the expected object type; The comprehensive scoring result of each object is determined according to the weight value of each object, the associated scoring result and the basic scoring result of each object.

7. The method according to claim 1 or 2, characterized in that: The determining the target object in the specified business scenario according to the comprehensive scoring results of the N objects includes: Sorting the comprehensive scoring results of the N objects in descending order to obtain a sorting result; The objects corresponding to the preset number of comprehensive scoring results ranked at the top in the sorting results are used as the target objects in the specified business scenario.

8. The method according to claim 1 or 2, characterized in that: The method further comprises: Acquire activity information of the specified business scenario, wherein the activity information includes incentive information; Sending the activity information to a terminal device corresponding to the target object, so that the terminal device outputs the activity information; If an associated object of the target object submits a business application operation for the specified business scenario and meets the access conditions, a corresponding reward is issued to the target object according to the incentive information.

9. A data processing device, characterized in that: The device comprises: an acquisition unit, configured to acquire basic attribute information of N objects in a specified business scenario, at least one associated object of each object, and resource transfer data between each object and each associated object, wherein the associated object is an object in the N objects, and N is a positive integer; a processing unit, configured to call a target evaluation model to perform scoring processing on basic attribute information of each of the N objects and basic attribute information of at least one associated object of each object, to obtain a basic scoring result of each object and a basic scoring result of each associated object of each object, wherein the target evaluation model is obtained by training an initial evaluation model according to a model training sample, wherein the model training sample includes basic attribute information of multiple sample objects and a reference scoring result of each sample object; a determination unit, configured to determine a comprehensive scoring result of each object according to the resource transfer data of each object and each associated object, a basic scoring result of each associated object of each object, and a basic scoring result of each object; The determination unit is further used to determine the target object in the specified business scenario according to the comprehensive scoring results of the N objects, and the target object is used to receive the activity information of the specified business scenario.

10. A computer device, characterized in that: The computer device comprises: a processor adapted to implement one or more computer programs; and, A computer storage medium storing one or more computer programs, wherein the one or more computer programs are suitable for being loaded by the processor and executing the data processing method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more computer programs, and the one or more computer programs are suitable for being loaded by a processor and executing the data processing method according to any one of claims 1 to 8.

12. A computer program product, characterized in that The computer program product includes a computer program, which is stored in a computer-readable storage medium. A processor of a computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the data processing method according to any one of claims 1 to 8.

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