CN114565392A - Service strategy determination method and device for small and micro enterprise user and electronic equipment - Google Patents
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Abstract
The application relates to a method and a device for determining a service strategy of a small-micro enterprise user, electronic equipment and a computer readable medium. The method comprises the following steps: establishing a close user relationship network through an enterprise relationship network of small and micro enterprise users; generating enterprise relation characteristic data according to enterprise user nodes corresponding to the small and micro enterprise users in the enterprise relation network; generating close relationship characteristic data according to close user nodes corresponding to the small micro enterprise users in the close user relationship network; inputting the enterprise relationship characteristic data and the intimacy relationship characteristic data into a user risk analysis model to generate a risk score of the small and micro enterprise user; and determining a service strategy for the small micro enterprise user according to the risk score. According to the method and the system, the depth relation among the small and micro enterprise users can be fully excavated, convenient and fast internet information service is provided for the small and micro enterprise users, and the overall performance safety is improved under the condition of guaranteeing the information safety of the small and micro enterprise users.
Description
Technical Field
The application relates to the field of computer information processing, in particular to a method and a device for determining a service strategy of a small micro enterprise user, electronic equipment and a computer readable medium.
Background
A relationship network refers to a collection of social participants and their relationships. A relationship network is a collection of points (social participants) and links between points (relationships between participants). Enterprise dynamic relational networks (referred to simply as enterprise relational networks) are a variety of relational networks that extend upstream and downstream, and in other relevant directions, centered around an enterprise. It includes market chain relationships and market chain out-of-chain relationships. Market chain relationships include buyer-seller relationships, supplier relationships, and the like. Surrounding the market chain are various extrachain relationships, including relationships with related individuals, organizations, and associations, which expand the market chain to form a network of relationships. Market chains and market chain out-of-chain relationships imply yet another property relationship: knowledge and influence relationships, including corporate or learning unions between a corporation and customers, and associations.
Traditional enterprise networks only include user (business owner) to enterprise or enterprise to enterprise associations, such as users acting as corporate or high-master or stockholders, enterprise investment associations, supply chain upstream and downstream associations, and the like. The relational graph technology is based on a graph data structure, all data of an enterprise are connected in series according to dimensions to form an integral incidence relation graph, and a financial service institution can conveniently carry out risk analysis according to incidence relation and rules of a data main body. However, in the conventional enterprise relational network, the described relationship between the enterprise owner and the enterprise is emphasized, but the deep relationship between different enterprise owners is rarely concerned.
The above information disclosed in this background section is only for enhancement of understanding of the background of the application and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, the present application provides a method, an apparatus, an electronic device and a computer readable medium for determining a service policy of a small and micro enterprise user, which can fully mine a depth relationship between the small and micro enterprise users, thereby providing a convenient and fast internet information service for the small and micro enterprise user, and improving service security, data security, transaction security and overall performance security under the condition of ensuring the information security of the small and micro enterprise user.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned by practice of the application.
According to an aspect of the present application, a method for determining a service policy of a small micro enterprise user is provided, the method including: establishing a close user relationship network through an enterprise relationship network of small and micro enterprise users; at least one related enterprise of the close users in the close user relationship network is the same as that of the small micro enterprise user; generating enterprise relation characteristic data according to enterprise user nodes corresponding to the small and micro enterprise users in the enterprise relation network; generating close relationship characteristic data according to close user nodes corresponding to the small micro enterprise users in the close user relationship network; inputting the enterprise relationship characteristic data and the intimacy relationship characteristic data into a user risk analysis model to generate a risk score of the small and micro enterprise user; and determining a service strategy for the small micro enterprise user according to the risk score.
Optionally, constructing an affinity user relationship network through an enterprise relationship network of small and micro enterprise users includes: acquiring user data of a plurality of small and micro enterprise users, enterprise data of a plurality of small and micro enterprises, relations among the plurality of small and micro enterprises and relations among the small and micro enterprise users and the small and micro enterprises; using the small and micro enterprise users as enterprise user nodes; taking the small micro-enterprise as an enterprise node; taking the relationship between the small micro enterprises and the relationship between the small micro enterprise users and the small micro enterprises as enterprise edges; and generating the enterprise relational network through enterprise user nodes, enterprise nodes and enterprise edges.
Optionally, the constructing an intimate user relationship network through an enterprise relationship network of small and micro enterprise users further includes: aggregating enterprise user nodes in the enterprise relational network to generate close user nodes; generating an intimate edge according to an enterprise edge corresponding to at least one enterprise user node contained in the intimate user node; and generating the close user relationship network through the close user nodes, the close edges and the enterprise nodes.
Optionally, aggregating the enterprise user nodes in the enterprise relational network to generate an intimate user node, including: determining relationship closeness among a plurality of enterprise user nodes in the enterprise relational network; and aggregating the plurality of enterprise user nodes according to the relationship closeness to generate a plurality of close user nodes.
Optionally, generating an affinity edge according to an enterprise edge corresponding to at least one enterprise user node included in the affinity user node includes: acquiring at least one enterprise edge corresponding to at least one enterprise user node contained in the close user node; aggregating the at least one business edge to generate the intimate edge.
Optionally, generating enterprise relationship characteristic data according to an enterprise user node corresponding to a small micro enterprise user in an enterprise relationship network includes: acquiring enterprise user nodes corresponding to small and micro enterprise users in the enterprise relational network; acquiring an enterprise edge corresponding to the enterprise user node; and sorting the data in the enterprise user nodes and the enterprise edges according to a first preset strategy to generate the enterprise relation characteristic data.
Optionally, generating affinity characteristic data according to the affinity user node corresponding to the small micro enterprise user in the affinity user relationship network includes: acquiring close user nodes corresponding to small and micro enterprise users in the close user relationship network; acquiring an intimate edge corresponding to the intimate user node; and sorting the data in the close user nodes and the close edges according to a second preset strategy to generate the close relationship characteristic data.
Optionally, inputting the enterprise relationship characteristic data and the affinity characteristic data into a user risk analysis model, and generating a risk score of the small enterprise user includes: inputting the enterprise relationship characteristic data and the intimacy relationship characteristic data into a user risk analysis model generated by training of a limit gradient lifting decision tree model; calculating and generating a plurality of risk labels and corresponding risk probabilities of the small micro enterprise users by a user risk analysis model; generating the risk score according to the plurality of risk labels and their corresponding risk probabilities.
Optionally, determining a service policy for the small micro enterprise user according to the risk score includes: determining service content for the small micro enterprise user according to the risk score; and/or determining resource limit for the small micro enterprise user according to the risk score; and/or determining a resource deadline for the small micro-enterprise user according to the risk score.
According to an aspect of the present application, a service policy determination apparatus for a small micro enterprise user is provided, the apparatus including: the network construction module is used for constructing an intimate user relationship network through an enterprise relationship network of small and micro enterprise users; at least one related enterprise of the close users in the close user relationship network is the same as that of the small micro enterprise user; the close data module is used for generating close relationship characteristic data according to the close user nodes corresponding to the small micro enterprise users in the close user relationship network; the risk analysis module is used for inputting the enterprise relationship characteristic data and the intimacy relationship characteristic data into a user risk analysis model to generate a risk score of the small and micro enterprise user; and the service strategy module is used for determining a service strategy for the small micro enterprise user according to the risk score.
According to an aspect of the present application, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the application, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.
According to the method, the device, the electronic equipment and the computer readable medium for determining the service strategy of the small and micro enterprise user, an intimate user relationship network is established through an enterprise relationship network of the small and micro enterprise user; generating enterprise relation characteristic data according to enterprise user nodes corresponding to the small and micro enterprise users in the enterprise relation network; generating close relationship characteristic data according to close user nodes corresponding to the small micro enterprise users in the close user relationship network; inputting the enterprise relationship characteristic data and the intimacy relationship characteristic data into a user risk analysis model to generate a risk score of the small and micro enterprise user; and determining a service strategy for the small and micro enterprise users according to the risk scores, and fully excavating the depth relation among the small and micro enterprise users, thereby providing convenient and quick internet information service for the small and micro enterprise users, and improving the service safety, data safety, transaction safety and overall performance safety under the condition of ensuring the information safety of the small and micro enterprise users.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The above and other objects, features and advantages of the present application will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are only some embodiments of the present application, and other drawings may be derived from those drawings by those skilled in the art without inventive effort.
Fig. 1 is a system block diagram illustrating a method and apparatus for determining a service policy of a small micro-enterprise user according to an exemplary embodiment.
FIG. 2 is a flow diagram illustrating a method for service policy determination for small micro enterprise users in accordance with an exemplary embodiment.
Fig. 3 is a schematic diagram illustrating a method for service policy determination for a small micro-enterprise user, according to another example embodiment.
Fig. 4 is a flow chart illustrating a method for service policy determination for small business users in accordance with another exemplary embodiment.
Fig. 5 is a schematic diagram illustrating a method for service policy determination for a small micro-enterprise user, according to another example embodiment.
Fig. 6 is a block diagram illustrating an apparatus for service policy determination for a small micro-enterprise user in accordance with an example embodiment.
FIG. 7 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 8 is a block diagram illustrating a computer-readable medium in accordance with an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the application.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the present concepts. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be appreciated by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present application and are, therefore, not intended to limit the scope of the present application.
Fig. 1 is a system block diagram illustrating a method and apparatus for determining a service policy of a small micro-enterprise user according to an exemplary embodiment.
As shown in fig. 1, the system architecture 10 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a financial services application, a shopping application, a web browser application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services, such as a background management server that supports financial services websites browsed by the user using the terminal apparatuses 101, 102, and 103. The backend management server may analyze and process the received enterprise host and enterprise related data, and feed back the processing result (e.g., internet service content) to the administrator of the financial services website and/or the terminal device 101, 102, 103.
The server 105 may also, for example, obtain user data for a plurality of small micro enterprise users, enterprise data for a plurality of small micro enterprises, relationships between small micro enterprise users and small micro enterprises; using small micro enterprise users as user nodes; the small micro-enterprise is used as an enterprise node; taking the relationship between the small micro enterprises and the relationship between the small micro enterprise users and the small micro enterprises as enterprise edges; and generating the enterprise relational network through the user nodes, the enterprise nodes and the enterprise edges.
The server 105 may be a single entity server, or may be composed of a plurality of servers, for example, it should be noted that the method for determining the service policy of the small enterprise user provided in the embodiment of the present application may be executed by the server 105, and accordingly, the device for determining the service policy of the small enterprise user may be disposed in the server 105.
FIG. 2 is a flow diagram illustrating a method for service policy determination for small business users in accordance with an exemplary embodiment. The method 20 for determining a service policy for a small business user includes at least steps S202 to S208.
As shown in fig. 2, in S202, an affinity user relationship network is constructed through an enterprise relationship network of small micro enterprise users; and at least one related enterprise of the close users in the close user relationship network is the same as that of the small micro enterprise user. The close users in the enterprise user relationship network may own the same enterprise as the small micro enterprise users, and the close users in the enterprise user relationship network may also have an association relationship with the enterprise owned by the small micro enterprise users, which may specifically include a superior-inferior enterprise relationship, or the enterprise owner of the corresponding enterprise may include other same small micro enterprise users.
The process of building an enterprise relational network is described in the corresponding example of fig. 3. In an enterprise relational network, an enterprise node and an enterprise user node are included.
In one embodiment, user data for a plurality of small micro enterprise users, enterprise data for a plurality of small micro enterprises, relationships between small micro enterprise users and small micro enterprises may be obtained; using the small and micro enterprise users as enterprise user nodes; taking the small micro-enterprise as an enterprise node; taking the relationship between the small micro enterprises and the relationship between the small micro enterprise users and the small micro enterprises as enterprise edges; and generating the enterprise relational network through enterprise user nodes, enterprise nodes and enterprise edges.
The process of establishing the intimate user relationship network is described in the corresponding examples of fig. 4 and 5. The close user relationship network comprises close user nodes and enterprise nodes.
In one embodiment, enterprise user nodes in the enterprise relational network may also be aggregated, for example, to generate intimate user nodes; generating an intimate edge according to an enterprise edge corresponding to at least one enterprise user node contained in the intimate user node; and generating the close user relationship network through the close user nodes, the close edges and the enterprise nodes.
In S204, enterprise relationship feature data is generated according to the enterprise user nodes corresponding to the small and micro enterprise users in the enterprise relationship network. For example, an enterprise user node corresponding to a small micro enterprise user in the enterprise relationship network is obtained; acquiring an enterprise edge corresponding to the enterprise user node; and sorting the data in the enterprise user nodes and the enterprise edges according to a first preset strategy to generate the enterprise relation characteristic data.
The user information of the small and micro enterprise can include basic information authorized by the user, such as business account information, terminal equipment identification information of the enterprise owner, region information of the enterprise owner, and the like; the user information of the small and micro enterprise may further include behavior information, which may be, for example, page operation data of the enterprise owner, service access duration of the enterprise owner, service access frequency of the enterprise owner, and the like, and specific content of the user information of the small and micro enterprise may be determined according to an actual application scenario, which is not limited herein.
More specifically, the business owner information of the current business owner can be obtained based on the mode that the business owner authorizes to obtain the information actively uploaded by the user. The remote information may be business owner data for the business owner at other trading platforms or other business sectors. The user information of the small micro-enterprise may further include operation information of the corresponding enterprise, which may be, for example, registration information, enterprise default information, enterprise tax payment information, and the like.
In a specific embodiment, based on the relationship between the enterprise user nodes and the enterprise edges in the enterprise relational network, the information of the company is used for deriving variables and carrying out aggregation to generate a first preset strategy, and the related information in the enterprise user nodes is sorted according to the first preset strategy to derive the enterprise relational feature data of the small and micro enterprise users.
More specifically, the business relationship characteristic data may be, for example: the number of enterprises in the state of the individual equity investment organization, the average amount of funds registered by the individual equity investors, the minimum amount of funds registered by the individual equity investors, the total amount of registered funds subjected to the individual duty supervision, the accumulated value of registered funds, the acceptance and payment proportion of the individual equity investment, the total amount of registered funds subjected to the individual equity investment, the quantity of the individual duty supervision, the total amount of registered funds of the individual equity investors, the state number of the individual duty supervision organization, the quantity of the type of the individual duty supervision and the acceptance and payment proportion of the individual equity investment are more than 70 percent.
In S206, affinity feature data is generated according to the corresponding affinity user node of the small micro enterprise user in the affinity user network. For example, obtaining the corresponding close user node of the small micro enterprise user in the close user relationship network; acquiring an intimate edge corresponding to the intimate user node; and sorting the data in the close user nodes and the close edges according to a second preset strategy to generate the close relationship characteristic data.
In a specific embodiment, edges included in a plurality of enterprise user nodes in the close user nodes may be aggregated based on the relationship between the close user nodes and the enterprise edges in the close user relationship network, and the related information in the close user nodes is sorted according to a second preset policy to derive close relationship characteristic data of the small and micro enterprise users.
More specifically, the affinity feature data may be, for example: the method comprises the steps that a spouse user node performs individual account of registered fund average amount, the whole close user node common company performs individual account of registered fund minimum amount, the whole close user node common enterprise number, the whole close user node individual equity investment acceptance proportion accumulation, the spouse user node individual equity investment mechanism state enterprise number ratio, the whole close user node individual equity investment mechanism state enterprise number, the whole close user node individual account of registered fund average amount, the whole close user node individual account of registered fund minimum amount, the whole close user node individual account of registered fund total amount, the whole close user node individual account of supervision high registered fund total amount, the whole close user node registered fund accumulated value and the whole close user node individual equity investment acceptance proportion accumulation.
In S208, the enterprise relationship feature data and the affinity feature data are input into a user risk analysis model, so as to generate a risk score of the small enterprise user. The enterprise relationship feature data and the intimacy relationship feature data can be input into a user risk analysis model generated by extreme gradient boosting decision tree model training, for example; calculating and generating a plurality of risk labels and corresponding risk probabilities of the small micro enterprise users by a user risk analysis model; generating the risk score according to the plurality of risk labels and their corresponding risk probabilities.
Specifically, data of enterprise user nodes in a plurality of enterprise relationship networks and a plurality of close user relationship networks are obtained, different labels are distributed to the data, and a training sample set is generated by arranging the labels. Constructing an initial model aiming at a training sample set, inputting the training sample set into the extreme gradient boosting decision tree model, to obtain a predicted tag, comparing the predicted tag with a corresponding real tag, judging whether the predicted tag is consistent with the real tag, counting the number of the predicted tags consistent with the real tag, and calculating the ratio of the number of the predicted labels consistent with the real labels to the number of all the predicted labels, if the ratio is larger than or equal to a preset ratio, the extreme gradient boosting decision tree model converges to obtain a trained user risk analysis model, if the proportion is less than the preset proportion value, and adjusting parameters in the extreme gradient boost decision tree model, and predicting the prediction label of each object again through the adjusted extreme gradient boost decision tree model until the ratio is greater than or equal to the preset ratio.
In S210, a service policy is determined for the small micro-enterprise user according to the risk score. Determining service content for the small micro enterprise user based on the risk score; determining resource limits for the small micro-enterprise users according to the risk scores; a resource deadline may be determined for the small micro enterprise user based on the risk score.
In this application, resources refer to any available material, information, time, information resources including computing resources and various types of data resources. The data resources include various private data in various domains. Thus, the present application can be applied to the distribution of various resources including physical goods, water, electricity, and meaningful data, essentially. However, for convenience, the resource allocation is described as being implemented by taking financial data resources as an example, but those skilled in the art will understand that the present application can also be used for allocating other resources.
In the application, close user nodes are aggregated into a whole, and the integral relational network characteristics are calculated. The method can serve the resource service of small and micro enterprises, and provides a new user characteristic dimension for the resource service scene of an individual user.
According to the method for determining the service strategy of the small and micro enterprise user, an intimate user relationship network is constructed through an enterprise relationship network of the small and micro enterprise user; generating enterprise relation characteristic data according to enterprise user nodes corresponding to the small and micro enterprise users in the enterprise relation network; generating close relationship characteristic data according to close user nodes corresponding to the small micro enterprise users in the close user relationship network; inputting the enterprise relationship characteristic data and the intimacy relationship characteristic data into a user risk analysis model to generate a risk score of the small and micro enterprise user; and determining a service strategy for the small and micro enterprise users according to the risk scores, and fully excavating the depth relation among the small and micro enterprise users, thereby providing convenient and quick internet information service for the small and micro enterprise users, and improving the service safety, data safety, transaction safety and overall performance safety under the condition of ensuring the information safety of the small and micro enterprise users.
It should be clearly understood that this application describes how to make and use particular examples, but the principles of this application are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Fig. 3 is a schematic diagram illustrating a method for service policy determination for a small micro-enterprise user, according to another example embodiment.
In one embodiment, further comprising: acquiring user data of a plurality of small and micro enterprise users, enterprise data of a plurality of small and micro enterprises, relations among the plurality of small and micro enterprises and relations among the small and micro enterprise users and the small and micro enterprises; using the small and micro enterprise users as enterprise user nodes; taking the small micro-enterprise as an enterprise node; taking the relationship between the small micro enterprises and the relationship between the small micro enterprise users and the small micro enterprises as enterprise edges; and generating the enterprise relational network through enterprise user nodes, enterprise nodes and enterprise edges.
In one embodiment, the relationship between businesses, business owners, and businesses may also be, for example, undirected edges in the enterprise relational network; and determining the weight of the undirected edge according to the relationship between enterprises and the closeness of the relationship between enterprise owners and the enterprises.
More specifically, a relational network may be constructed according to enterprise and enterprise owner behavior/communication data, and the relationships and attributes between nodes in the enterprise network, such as degrees of the nodes, weight of the edges, and the like, may be described. And establishing an enterprise relationship network through legally-compliant enterprise master related data, wherein the degree of a node can be defined as the number of directly-connected adjacent nodes of the current node, and the weight of an edge between two points can be defined as the frequency of contact between the two nodes and the like. The degree and the weight of the edge can be flexibly defined according to the actual application scene.
In one embodiment, the enterprise information submitted and searched by the small and micro enterprise user during application can be utilized, and the enterprise relationship network comprises information of the position, the investment proportion, the establishment time and the like after the data connection, the fusion and the screening are carried out through a plurality of credit source authentication parts.
Fig. 4 is a flow chart illustrating a method for service policy determination for small business users in accordance with another exemplary embodiment. The flow 40 shown in fig. 4 is a detailed description of "generating an intimate user relationship network".
As shown in fig. 4, in S402, the enterprise user nodes in the enterprise relational network are aggregated to generate close user nodes. The relationship closeness among a plurality of enterprise user nodes in the enterprise relational network can be calculated, for example; aggregating the plurality of enterprise user nodes based on the closeness of relationship to generate a plurality of intimate user nodes.
More specifically, the relationship closeness among a plurality of enterprise user nodes in the enterprise relational network can be determined according to the user information in the enterprise user nodes, and the relationship closeness can be recorded through an address book, a contact record, a user side interactive use record, the frequency of mutual attention on a network platform and the like. And calculating the closeness among a plurality of users according to the data, and aggregating the users with close connection relation to generate an intimate user node.
In S404, an affinity edge is generated according to an enterprise edge corresponding to at least one enterprise user node included in the affinity user nodes. At least one enterprise edge corresponding to at least one user node contained in the close user node is obtained; aggregating the at least one business edge to generate the intimate edge.
The edges of multiple enterprise user nodes in the close user nodes are also aggregated, and more particularly, the weights of the enterprise edges can be updated according to the aggregation relationship to generate the weights of the close edges.
In S406, the close user relationship network is generated by the close user node, the close edge, and the enterprise node. As shown in fig. 5, based on the enterprise relational network, a close user relational network is generated by merging user nodes.
The feature data of the close user nodes can be generated according to the close user relationship network, the feature data of the close user nodes can be used as supplementary user portrait features of each user node, in an actual application scene, a small micro enterprise user A and a small micro enterprise user B belong to a family point C together, and therefore portrait features of the close user nodes C are contained in the features of the small micro enterprise user A and the small micro enterprise user B.
In practical application, when a service strategy is provided for the small enterprise user A and the small enterprise user B, the image characteristics related to the node C can be supplemented on the basis of the original user image. As can be appreciated: the habitual features or financial risks of members in a family, people with the same experience or people with the same circle of interaction are related and synchronous, for example, when the family members all have enterprise-related information, the income level and overdue risk of the family members are identical. Therefore, it is considered that the small micro enterprise user a and the small micro enterprise user B included in the close user node C have the same consistency on the related images of the related enterprise, and the images of the customer a and the customer B are viewed as a whole, so that the user characteristics can be further analyzed.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the methods provided herein. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the method according to exemplary embodiments of the present application, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Fig. 6 is a block diagram illustrating a service policy determination apparatus for a small micro-enterprise user, according to another example embodiment. As shown in fig. 6, the service policy determination device 60 for a small micro-enterprise user includes: a network construction module 602, an enterprise data module 604, a close data module 606, a risk analysis module 608, and a service policy module 610.
The network construction module 602 is configured to construct an intimate user relationship network through an enterprise relationship network of a small-sized enterprise user; and the close users in the close user relationship network are the same as at least one associated enterprise of the small micro enterprise users.
The enterprise data module 604 is configured to construct an affinity network through an enterprise network; generating enterprise relation characteristic data according to enterprise user nodes corresponding to the small and micro enterprise users in the enterprise relation network; the enterprise data module 602 is further configured to obtain an enterprise user node corresponding to a small micro enterprise user in the enterprise relationship network; acquiring an enterprise side corresponding to the user node; and sorting the data in the enterprise user nodes and the enterprise edges according to a first preset strategy to generate the enterprise relation characteristic data.
The close data module 606 is configured to generate close relationship feature data according to close user nodes corresponding to the small micro enterprise users in the close user relationship network; the close data module 604 is further configured to obtain a close user node corresponding to the small micro enterprise user in the close user relationship network; acquiring an intimate edge corresponding to the intimate user node; and sorting the data in the close user nodes and the close edges according to a second preset strategy to generate the close relationship characteristic data.
The risk analysis module 608 is configured to input the enterprise relationship feature data and the close relationship feature data into a user risk analysis model, and generate a risk score of the small and micro enterprise user; the risk analysis module 608 is further configured to input the enterprise relationship feature data and the affinity feature data into a user risk analysis model generated by training a limit gradient boosting decision tree model; calculating and generating a plurality of risk labels and corresponding risk probabilities of the small micro enterprise users by a user risk analysis model; generating the risk score according to the plurality of risk labels and their corresponding risk probabilities.
The service policy module 610 is configured to determine a service policy for the small micro-enterprise user according to the risk score. The service policy module 610 may determine service content for the small micro enterprise user based on the risk score; the service policy module 610 may determine resource limits for the small micro enterprise user based on the risk score; the service policy module 610 may determine a resource deadline for the small micro enterprise user based on the risk score.
Wherein, the network construction module 602 may include an enterprise relationship unit and a family relationship unit;
the enterprise relation unit is used for acquiring user data of a plurality of small and micro enterprise users, enterprise data of a plurality of small and micro enterprises, relations among the plurality of small and micro enterprises and relations among the small and micro enterprise users and the small and micro enterprises; the small and micro enterprise users are used as enterprise user nodes; taking the small micro-enterprise as an enterprise node; taking the relationship between the small micro enterprises and the relationship between the small micro enterprise users and the small micro enterprises as enterprise edges; and generating the enterprise relational network through enterprise user nodes, enterprise nodes and enterprise edges.
The family relation unit is used for aggregating the enterprise user nodes in the enterprise relation network to generate close user nodes; generating an intimate edge according to an enterprise edge corresponding to at least one enterprise user node contained in the intimate user node; and generating the close user relationship network through the close user nodes, the close edges and the enterprise nodes.
According to the service strategy determination device of the small and micro enterprise user, an intimate user relationship network is established through an enterprise relationship network; generating enterprise relation characteristic data according to enterprise user nodes corresponding to the small and micro enterprise users in the enterprise relation network; generating close relationship characteristic data according to close user nodes corresponding to the small micro enterprise users in the close user relationship network; inputting the enterprise relationship characteristic data and the intimacy relationship characteristic data into a user risk analysis model to generate a risk score of the small and micro enterprise user; and determining a service strategy for the small and micro enterprise users according to the risk score, wherein the depth relation among the small and micro enterprise users can be fully excavated, so that convenient and rapid Internet information service is provided for the small and micro enterprise users, and under the condition of ensuring the information safety of the small and micro enterprise users, the service safety, the data safety, the transaction safety and the overall performance safety are improved.
FIG. 7 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 700 according to this embodiment of the present application is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 7, electronic device 700 is in the form of a general purpose computing device. The components of the electronic device 700 may include, but are not limited to: at least one processing unit 710, at least one memory unit 720, a bus 730 that connects the various system components (including the memory unit 720 and the processing unit 710), a display unit 740, and the like.
Wherein the storage unit stores program code that can be executed by the processing unit 710 to cause the processing unit 710 to perform the steps according to various exemplary embodiments of the present application. For example, the processing unit 710 may perform the steps as shown in fig. 2, fig. 4.
The memory unit 720 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)7201 and/or a cache memory unit 7202, and may further include a read only memory unit (ROM) 7203.
The memory unit 720 can also include programs/utilities 7204 having a set (at least one) of program modules 7205, such program modules 7205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may comprise an implementation of a network environment.
The electronic device 700 may also communicate with one or more external devices 700' (e.g., keyboard, pointing device, bluetooth device, etc.), such that a user can communicate with devices with which the electronic device 700 interacts, and/or any devices (e.g., router, modem, etc.) with which the electronic device 700 can communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 750. Also, the electronic device 700 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 760. The network adapter 760 may communicate with other modules of the electronic device 700 via the bus 730. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAI D systems, tape drives, and data backup storage systems, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, as shown in fig. 8, the technical solution according to the embodiment of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present application.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: establishing a close user relationship network through an enterprise relationship network of small and micro enterprise users; at least one related enterprise of the close users in the close user relationship network is the same as that of the small micro enterprise user; generating enterprise relation characteristic data according to enterprise user nodes corresponding to the small and micro enterprise users in the enterprise relation network; generating close relationship characteristic data according to close user nodes corresponding to the small micro enterprise users in the close user relationship network; inputting the enterprise relationship characteristic data and the intimacy relationship characteristic data into a user risk analysis model to generate a risk score of the small and micro enterprise user; and determining a service strategy for the small micro enterprise user according to the risk score.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiment of the present application.
Exemplary embodiments of the present application are specifically illustrated and described above. It is to be understood that the application is not limited to the details of construction, arrangement, or method of implementation described herein; on the contrary, the intention is to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (12)
1. A method for determining a service strategy of a small micro enterprise user is characterized by comprising the following steps:
establishing a close user relationship network through an enterprise relationship network of small and micro enterprise users; at least one related enterprise of the close users in the close user relationship network is the same as that of the small micro enterprise user;
generating enterprise relation characteristic data according to enterprise user nodes corresponding to the small and micro enterprise users in the enterprise relation network;
generating close relationship characteristic data according to close user nodes corresponding to the small micro enterprise users in the close user relationship network;
inputting the enterprise relationship characteristic data and the intimacy relationship characteristic data into a user risk analysis model to generate a risk score of the small and micro enterprise user;
and determining a service strategy for the small micro enterprise user according to the risk score.
2. The method of claim 1, wherein building a close user relationship network through an enterprise relationship network of small micro enterprise users comprises:
acquiring user data of a plurality of small micro-enterprise users, enterprise data of a plurality of small micro-enterprises, relationships among the small micro-enterprises and relationships between the small micro-enterprise users and the small micro-enterprises;
using the small and micro enterprise users as enterprise user nodes;
taking the small micro-enterprise as an enterprise node;
taking the relationship between the small micro enterprises and the relationship between the small micro enterprise users and the small micro enterprises as enterprise edges;
and generating the enterprise relational network through enterprise user nodes, enterprise nodes and enterprise edges.
3. The method of claim 2, wherein constructing an affinity network through an enterprise network of small micro enterprise users further comprises:
aggregating enterprise user nodes in the enterprise relational network to generate close user nodes;
generating an intimate edge according to an enterprise edge corresponding to at least one enterprise user node contained in the intimate user node;
and generating the close user relationship network through the close user nodes, the close edges and the enterprise nodes.
4. The method of claim 3, wherein aggregating enterprise user nodes in the enterprise relational network to generate close user nodes comprises:
determining relationship closeness among a plurality of enterprise user nodes in the enterprise relational network;
and aggregating the plurality of enterprise user nodes according to the relationship closeness to generate a plurality of close user nodes.
5. The method of claim 3, wherein generating the affinity edge based on an enterprise edge corresponding to at least one enterprise user node included in the affinity user nodes comprises:
acquiring at least one enterprise edge corresponding to at least one enterprise user node contained in the close user node;
aggregating the at least one business edge to generate the intimate edge.
6. The method of claim 1, wherein generating business relationship characteristic data from the corresponding business user nodes of the small business users in the business relationship network comprises:
acquiring enterprise user nodes corresponding to small and micro enterprise users in the enterprise relational network;
acquiring an enterprise edge corresponding to the enterprise user node;
and sorting the data in the enterprise user nodes and the enterprise edges according to a first preset strategy to generate the enterprise relationship characteristic data.
7. The method of claim 1, wherein generating affinity feature data from affinity user nodes corresponding to the small micro enterprise users in an affinity user relationship network comprises:
acquiring close user nodes corresponding to small and micro enterprise users in the close user relationship network;
acquiring an intimate edge corresponding to the intimate user node;
and sorting the data in the close user nodes and the close edges according to a second preset strategy to generate the close relationship characteristic data.
8. The method of claim 1, wherein entering the business relationship characteristic data and the affinity characteristic data into a user risk analysis model to generate a risk score for the small business user comprises:
inputting the enterprise relation feature data and the intimacy relation feature data into a user risk analysis model generated by training of a limit gradient lifting decision tree model;
calculating and generating a plurality of risk labels and corresponding risk probabilities of the small micro enterprise users by a user risk analysis model;
generating the risk score according to the plurality of risk labels and their corresponding risk probabilities.
9. The method of claim 1, wherein determining a service policy for the small micro enterprise user based on the risk score comprises:
determining service content for the small micro enterprise user according to the risk score; and/or
Determining resource limit for the small micro enterprise user according to the risk score; and/or
And determining a resource period for the small micro-enterprise user according to the risk score.
10. A service policy determination apparatus for a small micro enterprise user, comprising:
the network construction module is used for constructing an intimate user relationship network through an enterprise relationship network of small and micro enterprise users; at least one related enterprise of the close users in the close user relationship network is the same as that of the small micro enterprise user;
the enterprise data module is used for generating enterprise relation characteristic data according to enterprise user nodes corresponding to small and micro enterprise users in an enterprise relation network;
the close data module is used for generating close relationship characteristic data according to the close user nodes corresponding to the small micro enterprise users in the close user relationship network;
the risk analysis module is used for inputting the enterprise relationship characteristic data and the intimacy relationship characteristic data into a user risk analysis model to generate a risk score of the small and micro enterprise user;
and the service strategy module is used for determining a service strategy for the small and micro enterprise user according to the risk score.
11. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-9.
12. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-9.
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