CN112036730B

CN112036730B - Virtual asset data processing method and device and computer readable storage medium

Info

Publication number: CN112036730B
Application number: CN202010871703.7A
Authority: CN
Inventors: 袁畅; 任晨铭; 赵志殷; 陈启鹏; 张争宏; 陈彩萍; 晏宇辉; 刘叶; 梁军; 郭锐
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2020-08-26
Filing date: 2020-08-26
Publication date: 2024-06-28
Anticipated expiration: 2040-08-26
Also published as: CN112036730A

Abstract

The application discloses a virtual asset data processing method, a device and a computer readable storage medium, wherein the method comprises the following steps: acquiring asset risk levels of the concerned asset objects of the target user under at least two risk factors respectively; acquiring user operation behavior data of a target user aiming at an asset object; determining asset sequencing priority of the concerned asset object according to the asset risk level of the concerned asset object under each risk factor and the user operation behavior data; generating an asset object sequence formed by the asset objects of interest according to the sorting priority of the asset objects of interest; and generating asset risk prompt information aiming at the concerned asset object according to the asset object sequence, and outputting the asset risk prompt information. By adopting the method and the device, the information accuracy of the generated asset risk prompt information can be improved.

Description

Virtual asset data processing method and device and computer readable storage medium

Technical Field

The present application relates to the field of virtual asset data processing technologies, and in particular, to a virtual asset data processing method, device and computer readable storage medium.

Background

With the rise of the virtual asset market, more and more users participate in the business of virtual asset purchase, and for each virtual asset, the rise and fall of the asset value is changed at any time, so that the users need to pay attention to the rise and fall condition of each virtual asset at any time to ensure the interests of the users.

In the prior art, each virtual asset can be sequenced and output and displayed according to the risk condition of all the virtual assets, and the virtual asset with larger risk variation or larger risk degree can be output and displayed in front. By sequencing, outputting and displaying each virtual asset, the aim of prompting the user of the risk condition of each virtual asset can be fulfilled.

It can be seen that in the prior art, since all the virtual assets are displayed in a sorted output manner to prompt the user about the risk situation of each virtual asset, the user does not pay attention to most of the virtual assets in the whole virtual assets, and thus the risk situation of the virtual assets prompted to the user is inaccurate.

Disclosure of Invention

The application provides a virtual asset data processing method, a device and a computer readable storage medium, which can improve the information accuracy of generated asset risk prompt information.

In one aspect, the present application provides a method for processing virtual asset data, including:

acquiring asset risk levels of the concerned asset objects of the target user under at least two risk factors respectively;

acquiring user operation behavior data of a target user aiming at an asset object;

Determining asset sequencing priority of the concerned asset object according to the asset risk level of the concerned asset object under each risk factor and the user operation behavior data;

generating an asset object sequence formed by the asset objects of interest according to the sorting priority of the asset objects of interest;

and generating asset risk prompt information aiming at the concerned asset object according to the asset object sequence, and outputting the asset risk prompt information.

In one aspect, the present application provides a virtual asset data processing apparatus, comprising:

The level acquisition module is used for acquiring asset risk levels of the concerned asset objects of the target user under at least two risk factors respectively;

the behavior acquisition module is used for acquiring user operation behavior data of a target user aiming at the concerned asset object;

The priority acquisition module is used for determining the asset sequencing priority of the concerned asset object according to the asset risk level of the concerned asset object under each risk factor and the user operation behavior data;

the sequence generation module is used for generating an asset object sequence formed by the concerned asset objects according to the sorting priority of the concerned asset objects;

The information generation module is used for generating asset risk prompt information aiming at the concerned asset object according to the asset object sequence and outputting the asset risk prompt information.

Wherein, the grade acquisition module includes:

the industry determining unit is used for determining a target asset industry to which the concerned asset object belongs;

A factor obtaining unit, configured to obtain at least two risk factors associated with the target asset industry in a risk factor pool;

The parameter acquisition unit is used for acquiring risk level evaluation parameters of the concerned asset object under each risk factor respectively;

the level determining unit is used for determining the asset risk level of the concerned asset object under each risk factor according to the risk level evaluation parameters corresponding to each risk factor.

Wherein, the information generation module is used for:

generating an asset object list containing concerned asset objects according to the asset object sequence, taking the asset object list as asset risk prompt information, and outputting the asset risk prompt information;

The device further comprises:

The factor classification module is used for classifying the risk factors in the risk factor pool; one risk factor corresponds to one risk factor type;

the tag information generation module is used for generating risk tag prompt information of the concerned asset object aiming at each risk factor according to the asset risk level of the concerned asset object under each risk factor;

the response module is used for responding to the unfolding operation of the concerned asset objects in the asset object list, and classifying and outputting risk tag prompt information corresponding to each risk factor of the concerned asset objects according to the risk factor type of each risk factor.

Wherein the at least two risk factors include a risk factor f _n, n being a positive integer less than or equal to the total number of the at least two risk factors;

a rank determination unit comprising:

An associated object determination subunit configured to determine, from the aggregate set of asset objects, at least two asset objects under the asset industry associated with the risk factor f _n as at least two associated asset objects associated with the risk factor f _n; the at least two associated asset objects include an asset of interest object;

A parameter obtaining subunit, configured to obtain risk level evaluation parameters of the associated asset objects except the asset object of interest in the at least two associated asset objects under the risk factor f _n;

The ranking determining subunit is used for determining the asset object risk ranking of each associated asset object according to the numerical value of the risk level evaluation parameter corresponding to each associated asset object;

A ranking range obtaining subunit, configured to obtain a ranking range in which the asset object risk ranks of the asset object of interest are located in the asset object risk ranks of at least two associated asset objects;

and the grade determining subunit is used for determining the asset risk grade of the concerned asset object under the risk factor f _n according to the ranking range.

Wherein, parameter acquisition unit includes:

The asset acquisition subunit is used for acquiring enterprise receivable assets and enterprise total input assets of the enterprise to which the concerned asset object belongs when the risk factor f _n is the receivable asset proportion;

A duty ratio determining subunit, configured to determine an receivable asset duty ratio of the object of interest asset under the risk factor f _n according to the enterprise receivable asset and the enterprise total input asset;

And the grade parameter determining subunit is used for taking the receivable asset proportion of the object of interest under the risk factor f _n as a risk grade assessment parameter of the object of interest under the risk factor f _n.

Wherein the asset risk level of the subject asset under risk factor f _n includes an asset zero risk level, a first asset risk level, and a second asset risk level; the asset risk level of the concerned asset object indicated by the first asset risk level is greater than the asset risk level of the concerned asset object indicated by the asset zero risk level; the asset risk level of the asset object of interest indicated by the second asset risk level is greater than the asset risk level of the asset object of interest indicated by the first asset risk level;

A rank determination subunit comprising:

the first level determining subunit is configured to determine that the asset risk level of the focused asset object under the risk factor f _n is an asset zero risk level when the ranking range of the focused asset object is within the first ranking range to which the asset zero risk level belongs;

A second level determining subunit, configured to determine that the asset risk level of the focused asset object under the risk factor f _n is the first asset risk level when the ranking range of the focused asset object is in the second ranking range to which the first asset risk level belongs;

and the third level determining subunit is configured to determine that the asset risk level of the focused asset object under the risk factor f _n is the second asset risk level when the ranking range of the focused asset object is in the third ranking range to which the second asset risk level belongs.

The asset risk level of the concerned asset object under each risk factor is the current asset risk level of each risk factor of the concerned asset object under the current time; the user operation behavior data comprises object click quantity of a target user aiming at an object of interest asset; the asset of interest objects include asset of interest object z _i and asset of interest object z _j, i and j each being a positive integer less than or equal to the total number of objects of the asset of interest object;

A priority acquisition module comprising:

A history grade obtaining unit, configured to obtain a history asset risk grade of each risk factor of the object z _i of interest and the object z _j of interest at a history reference time;

A first change level determining unit configured to determine, as a first change risk level, an asset risk level that is different from a historical asset risk level of each risk factor of the asset of interest z _i at the current time, from a historical asset risk level of each risk factor of the asset of interest z _i at the historical reference time;

A second change level determining unit configured to determine, as a second change risk level, an asset risk level that is different from a historical asset risk level of each risk factor of the asset of interest z _j at the current time, from among the current asset risk levels of each risk factor of the asset of interest z _j at the historical reference time;

a first priority determining unit configured to determine that the asset-ordering priority of the asset-of-interest object z _i is smaller than the asset-ordering priority of the asset-of-interest object z _j when the number of first change risk levels is smaller than the change number threshold and the number of second change risk levels is greater than or equal to the change number threshold;

A second priority determining unit configured to determine that the asset-ordering priority of the asset-of-interest object z _i is smaller than the asset-ordering priority of the asset-of-interest object z _j when the number of second asset risk levels in the first variation risk levels is smaller than the number of second asset risk levels in the second variation risk levels;

a third priority determination unit configured to determine that the asset ordering priority of the attention asset object z _i is smaller than the asset ordering priority of the attention asset object z _j when the object click amount of the attention asset object z _i by the target user is smaller than the object click amount of the attention asset object z _j by the target user.

Wherein, the device further includes:

A first priority determining module configured to determine that the asset ordering priority of the asset under attention object z _i is less than the asset ordering priority of the asset under attention object z _j when the number of first asset risk levels in the current asset risk level of each risk factor of the asset under attention object z _i at the current time is less than the number of first asset risk levels in the current asset risk level of each risk factor of the asset under attention object z _j at the current time;

A second priority determination module for determining that the asset ordering priority of the asset of interest object z _i is less than the asset ordering priority of the asset of interest object z _j when the number of second asset risk levels in the current asset risk level of each risk factor of the asset of interest object z _i at the current time is less than the number of second asset risk levels in the current asset risk level of each risk factor of the asset of interest object z _j at the current time.

Wherein the number of objects of interest asset objects is at least two; an information generation module comprising:

The early warning object acquisition unit is used for acquiring the concerned asset object with the highest asset sequencing priority from at least two concerned asset objects in the asset object sequence according to the asset prompt quantity, and taking the concerned asset object with the highest asset sequencing priority as a risk early warning asset object; the number of the risk early warning asset objects is equal to the number of asset prompts;

The early warning message generation unit is used for generating a risk early warning pushing message aiming at the risk early warning asset object according to the current asset risk level of each risk factor of the risk early warning asset object at the current moment when the current asset risk level of each risk factor of the risk early warning asset object at the current moment is different from the historical asset risk level of each risk factor of the risk early warning asset object at the historical reference moment;

the prompt information determining unit is used for determining the risk early warning push message as asset risk prompt information.

Wherein, the device is also used for:

responding to the subscribing operation of the user terminal of the target user aiming at the risk early warning function, and subscribing the risk early warning function for the user terminal; and the user terminal is used for subscribing the risk early warning function and outputting the pushed asset risk prompt information.

Wherein, the priority acquisition module includes:

The input unit is used for inputting the asset risk level of the concerned asset object under each risk factor and the user operation behavior data into the sequencing model and generating risk characteristic data corresponding to the concerned asset object in the sequencing model;

And the output unit is used for outputting the asset sequencing priority of the concerned asset object in the sequencing model according to the risk characteristic data.

Wherein, the device further includes:

The consensus module is used for synchronizing the asset risk prompt information to the blockchain network and carrying out information consensus on the asset risk prompt information based on the blockchain network;

The adding module is used for adding the asset risk prompt information to the local block chain when the fact that the block chain network successfully consensus the asset risk prompt information is detected;

An information generating module for:

And acquiring asset risk prompt information from the local blockchain, and outputting the asset risk prompt information acquired from the local blockchain.

In one aspect the application provides a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the method of one aspect of the application.

An aspect of the present application provides a computer readable storage medium storing a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method of the above aspect.

According to one aspect of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the methods provided in the various alternatives of the above aspect and the like.

The application can acquire the asset risk level of the concerned asset object of the target user under at least two risk factors respectively; acquiring user operation behavior data of a target user aiming at an asset object; determining asset sequencing priority of the concerned asset object according to the asset risk level of the concerned asset object under each risk factor and the user operation behavior data; generating an asset object sequence formed by the asset objects of interest according to the sorting priority of the asset objects of interest; and generating asset risk prompt information aiming at the concerned asset object according to the asset object sequence, and outputting the asset risk prompt information. Therefore, the method provided by the application can generate the exclusive asset risk prompt information of the target user according to the concerned asset object of the target user and the user operation behavior data of the target user for the concerned asset object, so that the risk condition of the concerned asset object of the target user which is most concerned can be accurately prompted through the asset risk prompt information.

Drawings

In order to more clearly illustrate the application or the technical solutions of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained from them without inventive effort for a person skilled in the art.

Fig. 1 is a schematic structural diagram of a network architecture according to an embodiment of the present application;

FIG. 2 is a schematic illustration of a scenario for virtual asset data processing provided by the present application;

FIG. 3 is a flow chart of a method for processing virtual asset data provided by the application;

FIG. 4 is a schematic diagram showing the classification of risk factors according to the present application;

FIG. 5 is a schematic view of a scenario for acquiring an asset risk level provided by the present application;

FIGS. 6 a-6 c are schematic page views of a terminal page according to the present application;

FIG. 7 is a schematic diagram of a terminal page according to the present application;

FIG. 8 is a schematic view of a label display scenario provided by the present application;

FIG. 9 is a schematic diagram of a terminal page according to the present application;

FIG. 10 is a schematic diagram of a frame provided by the present application;

FIG. 11 is a schematic diagram of a virtual asset data processing device according to the present application;

fig. 12 is a schematic structural diagram of a computer device according to the present application.

Detailed Description

The following description of the embodiments of the present application will be made more apparent and fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the application are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Firstly, it should be noted that all data (such as related data of asset objects, user operation behavior data, etc.) collected by the present application are collected under the condition that the object (such as user, organization, or enterprise) to which the data belongs agrees and authorizes, and the collection, use, and processing of the related data are required to comply with related laws and regulations and standards of related countries and regions.

Referring to fig. 1, fig. 1 is a schematic structural diagram of a network architecture according to an embodiment of the present application. As shown in fig. 1, the network architecture may include a server 200 and a cluster of terminal devices, which may include one or more terminal devices, the number of which will not be limited here. As shown in fig. 1, the plurality of terminal devices may specifically include a terminal device 100a, a terminal device 101a, terminal devices 102a, …, and a terminal device 103a; as shown in fig. 1, the terminal device 100a, the terminal device 101a, the terminal devices 102a, …, and the terminal device 103a may be connected to the server 200 through a network, so that each terminal device may interact with the server 200 through the network connection.

The server 200 shown in fig. 1 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligence platforms, and the like. The terminal device may be: intelligent terminals such as smart phones, tablet computers, notebook computers, desktop computers, intelligent televisions and the like. A specific description of an embodiment of the present application will be made below taking communication between the terminal device 100a and the server 200 as an example.

Referring to fig. 2, fig. 2 is a schematic diagram of a scenario of virtual asset data processing according to the present application. As shown in FIG. 2, the asset objects of interest of user 100b may include zn asset objects of interest, which may include asset object of interest z1, asset objects of interest z2, … …, and asset object of interest zn, among others, as set forth in interest list 101 b. The interest list 101b may be a list of interests in a user account for the virtual asset held by the user 100b, and the interest list 101b may also be referred to as a favorites list of the user 100 b. The interest list 101b includes the asset objects added by the user 100b that want to pay attention in particular, and the user 100b may be added to the asset objects in the interest list 101b, which may be referred to as the interest asset objects of the target user 100b, and one asset object may be a stock. Thus, server 200 may be a background server of the user account for the virtual asset held by user 100 b.

The server 200 may calculate an asset risk level of each asset object of interest in the interest list 101b under a corresponding risk factor, see below. It should be noted that, the asset value of the concerned asset object may change at any time, and the change of the asset value of the concerned asset object represents the asset risk level under the corresponding risk factor. For example, the object of interest may be a stock, and thus, a change in the asset value of the object of interest may refer to a change in the price of the stock. Thus, it is understood that the risk factor is a condition or factor that causes stock prices of stocks to rise and fall. In other words, the change in the asset value of the asset-of-interest object is caused by the change in the asset-of-interest object under its risk factor, that is, the risk factor of the asset-of-interest object is the factor that causes the asset value of the asset-of-interest object to change.

First, the server 200 may detect the asset industries to which each asset object of interest respectively belongs. Because factors affecting the asset value of an asset object may be different for asset objects in different asset industries, asset objects in different asset industries may have different risk factors. For example, herein, the risk factors for the asset of interest z1 may include risk factor a1, risk factor a2, … …, and risk factor an; risk factors for the asset of interest object z2 may include risk factor b1, risk factors b2, … …, and risk factor bn; risk factors for the asset of interest zn may include risk factor c1, risk factors c2, … …, and risk factor cn.

Server 200 may then calculate an asset risk level for each asset object of interest under the corresponding risk factor. The manner of calculating the asset risk level of the object of interest may also be different for different risk factors, and a specific process of calculating the asset risk level of each object of interest under the corresponding risk factor may refer to step S101 described below. Here, as shown in block 102b, the server 200 may calculate an asset risk level for the asset of interest z1 under the risk factor a1, an asset risk level for the asset of interest z1 under the risk factor a2, … …, and an asset risk level for the asset of interest z1 under the risk factor an. The server 200 may calculate an asset risk level for the asset of interest z2 at risk factor b1, an asset risk level for the asset of interest z2 at risk factor b2, … …, and an asset risk level for the asset of interest z2 at risk factor bn. The server 200 may calculate the asset risk level for the asset of interest zn at risk factor c1, the asset risk level for the asset of interest zn at risk factor c2, … …, and the asset risk level for the asset of interest zn at risk factor cn.

Further, as indicated at block 104b, the server 200 may also obtain user operational performance data for each asset object of interest to the user 100b, which may refer to the amount of clicks the user 100b has on each asset object of interest.

The server 200 may rank each asset object of interest in the interest list 101b by the asset risk level of each asset object of interest under the corresponding risk factor calculated in the above-described block 102b and the user operation behavior data of the user 100b for each asset object of interest acquired in the above-described block 104 b. The principle of sorting is that the higher the asset risk level, the larger the asset risk level variation, and the higher the click number of the focused asset objects are arranged to be the front, wherein, the specific rule of sorting each focused asset object in the focused list 101b according to the asset risk level of each focused asset object under the corresponding risk factor and the user operation behavior data of the user 100b for each focused asset object can be seen from the following step S103-step S104.

After ordering each asset object of interest in interest list 101b, server 200 may obtain sequence 105b, where sequence 105b includes each asset object of interest in interest list 101 b. As shown in fig. 2, the asset of interest object 3, the asset of interest objects 1, … …, and the asset of interest object 10 are arranged in sequence 105 b.

The server 200 may then generate an asset object list based on the sequence 105b obtained as described above. The asset object list includes each of the asset objects of interest of the user 100b, and the ranking order of each of the asset objects of interest in the asset object list is the ranking order of each of the asset objects of interest in the sequence 105 b. And, the asset object list further includes asset risk related information of each concerned asset object, where the asset risk related information may refer to asset risk level related information of the concerned asset object under the corresponding risk factor.

Specifically, the server 200 may send the generated object list of the asset to the terminal device 100a held by the user 100b, where the user account of the user 100b for the virtual asset is logged in the terminal device 100 a. The terminal page 107b is a terminal page in the user account of the user 100b logged in to the terminal device 100a for the virtual asset, and after receiving the asset object list sent by the server 200, the terminal device may output the asset object list in the terminal page 107 b. As shown in terminal page 107b, the asset object list specifically includes the asset object of interest z3 in region 108b, asset objects of interest z1, … … in region 109b, and asset object of interest z10 in region 110 b.

Wherein, the number of the asset risk levels of the concerned asset object, which characterizes the risk of the concerned asset object under different risk factors, can be called the number of the risk items of the concerned asset object. For example, the asset risk level may include an asset zero risk level, an asset low risk level, an asset medium risk level, and an asset high risk level. The zero risk level of the asset characterizes that the concerned asset object has no risk under the corresponding risk factors and is safe; the asset low risk level, the risk level in the asset, and the asset high risk level characterize that the asset object of interest is risky under the corresponding risk factor and not secure. Thus, it will be appreciated that the number of risk items for an object of interest is the number of low risk levels of the asset, the risk levels in the asset, and the high risk levels of the asset that it has. For example, assuming that the asset risk level of the concerned asset object z1 under the risk factor a1 is the in-asset risk level, the asset risk level of the concerned asset object z1 under all the risk factors it has except the risk factor a1 is the asset zero risk level, the number of risk items of the concerned asset object z1 is 1.

Thus, as shown in FIG. 2, the number of risk items for the asset object z3 of interest, and the risk information associated with the asset object z3 of interest (e.g., under which risk factors the asset object z3 of interest is at risk) are also displayed in the region 108b of the terminal page 107 b. Also displayed in region 109b of terminal page 107b are the number of risk items for asset object z1 of interest, and risk information associated with asset object z1 of interest (e.g., under which risk factors asset object z1 of interest is at risk). Also displayed in region 110b of terminal page 107b are the number of risk items for asset of interest z10, and risk information associated with asset of interest z10 (e.g., under which risk factors asset of interest z10 is at risk).

Alternatively, the above-described process of obtaining an asset object list including each of the asset objects of interest may be performed by the terminal device 100a, or may be performed by the terminal device 100a and the server 200 together, in addition to the server 200. The execution subject for obtaining the asset object list is determined according to the actual application condition, and is not limited to this.

By adopting the method provided by the application, the exclusive asset object list of the user 100b can be generated through the concerned asset objects in the concerned list of the user 100 b. In addition, the asset object list is also generated by the user 100b aiming at the user operation behavior data of the concerned asset object, so that the generated asset object list exclusive to the user 100b can greatly meet the personalized requirement of the user 100 b. In other words, through the generated list of the asset objects exclusive to the user 100b, the user 100b can more quickly and intuitively check the risk condition of the asset objects which are more focused on by the user, and the user experience is improved.

Referring to fig. 3, fig. 3 is a flow chart of a virtual asset data processing method provided by the present application, and as shown in fig. 3, the method may include:

step S101, acquiring asset risk levels of the concerned asset objects of the target user under at least two risk factors respectively;

specifically, the execution body in the embodiment of the present application may be any terminal device. The target user may be any user that holds a user account for the virtual asset. A list of interests may be present in the user account of the target user, to which the target user may add an asset object, which is a virtual asset, which may be a security, for example, which may be a stock. The target user adds an asset object to the interest list indicating that the target user is more interested in the asset object added to the interest list. The target user may be added to the asset objects in its interest list, referred to as the target user's interest asset objects, which may be one or more (at least two).

The asset value of the asset object can be dynamically changed at any time, and the change of the asset value of the asset object represents the change of the asset risk level of the asset object under the risk factors of the asset object. For example, an asset object may be a stock, and a change in asset value of the asset object may refer to a change in the price of the stock. The stock price rises, indicating that the asset value of the stock is getting larger, and the stock price drops, indicating that the asset value of the stock is getting smaller. Thus, it is understood that a risk factor for a stock refers to a condition or factor that causes the stock price of the stock to rise and fall. In other words, the change in the asset value of an asset object is due to the change in the asset object under its risk factor, i.e., the factor that causes the asset value of the asset object of interest to change.

Thus, at least two risk factors that an asset object of interest has refer to factors that contribute to the variation in asset value of the asset object of interest. Because factors that affect an asset object's asset value to become higher or lower may be different for asset objects in different asset industries, asset objects in different asset industries may have different risk factors. Therefore, before determining the risk factor of the asset object of interest, the asset industry to which the asset object of interest belongs is first determined, and the asset industry to which the asset object of interest belongs may be referred to as a target asset industry.

Wherein risk factors that contribute to changes in asset value of asset objects under each asset industry may be referred to as risk factors associated with each asset industry, respectively. The risk factors associated with each asset industry can be set according to the actual application scenario.

Thus, the terminal device may obtain a plurality of risk factors associated with the target asset industry, and the terminal device may obtain asset risk levels for the asset object of interest under the plurality of risk factors, respectively. The risk factor pool comprises risk factors associated with all asset industries, and the terminal equipment can acquire the risk factors associated with the target asset industries from the risk factor pool. It will be appreciated that the risk factors associated with the target asset industry to which the asset object of interest belongs may be referred to as risk factors corresponding to the asset object of interest, and that different asset objects of interest may correspond to different risk factors. Taking an asset object of interest as an example, how to obtain the asset risk level of the asset object of interest under the corresponding risk factor is described below:

The terminal device may obtain risk level assessment parameters for the asset object of interest under each of its corresponding risk factors. The risk factors corresponding to the concerned asset objects can have a plurality of risk factors in the risk factor pool, and the calculation modes of the risk level evaluation parameters of the concerned asset objects under different risk factors are different.

Referring to fig. 4, fig. 4 is a schematic diagram illustrating classification of risk factors according to the present application. Because the asset object may be a stock, the risk factors may include several of the risk factors listed in the risk factor pool 102c of FIG. 4. The risk factors in the risk factor pool 102c may be classified, and each type of risk factor may include a plurality of risk factors. Here, the risk factors in the risk factor pool 102c may be classified into a risk factor of the "management risk" type 100c, a risk factor of the "corporate financial risk" type 103c, a risk factor of the "stock market risk" type 104c, and a risk factor of the "block specific risk" type 105c, which are 5 types (dimensions) in total.

As shown in fig. 4, the risk factors of the "management risk" type 100c include a risk factor of "stock right mortgage ratio is too high", a risk factor of "stock or asset freezing", a risk factor of "high-rise or large-stockholder deduction", a risk factor of "high-rise frequent variation", a risk factor of "supervision inquiry or offending penalty", a risk factor of "litigation arbitration", a risk factor of "investment or mergers failure", a risk factor of "major security accident", a risk factor of "major product defect", a risk factor of "business pause", and a risk factor of "high-rise or employee crime.

The risk factors of the "corporate financial risk" type 103c include a risk factor "performance deficiency or unexpected", a risk factor "insufficient cash flow", a risk factor "accounts receivable duty ratio too high", a risk factor "financial lever too high", a risk factor "reputation duty ratio too high", a risk factor "abnormal audit opinion", a risk factor "z value too low", a risk factor "financial falsification", a risk factor "significant asset reduction value", and a risk factor "liability violation. Wherein the z value refers to a risk level evaluation parameter of the asset object calculated by a z index analysis method. The stock z index analysis method is also called as a z score method, and is an analysis method for predicting the financial crisis of a company through a variable mode, the lower the z value is, the more likely an enterprise is to break, and whether the enterprise has the sign of the financial crisis can be found by calculating the z value of a certain enterprise for a plurality of years. Generally, when the z value is greater than 2.675, the financial condition of the enterprise is indicated to be good, and the possibility of cracking is small; when the z value is less than 1.81, indicating that the enterprise is latent to crack the crisis; when the z value is between 1.81 and 2.675, it is called a "grey zone", indicating that the financial situation of the enterprise is extremely unstable.

The risk factors of the "stock market risk" type 104c include a risk factor "ST stock (stock with investment risk) or return risk", a risk factor "stop and return license", a risk factor "limit share placing forbid", a risk factor "north funds largely flow out", a risk factor "main force largely flow out", a risk factor "overestimate", a risk factor "history fluctuation rate", a risk factor "maximum withdrawal rate", a risk factor "20 day average traffic", a risk factor "stock price drop break 20 day average line", and a risk factor "technical index dead line".

The risk factors of the type 105c of the "plate specific risk" include the risk factors of the industry plate, namely the risk factors of the industry in case of a first-class and the risk factors of the industry in case of a second-class, and the risk factors of the topic plate, namely the risk factors of the focus topic plate, and the risk factors of the concept plate, namely the risk factors of the focus concept plate.

Accordingly, the risk factors corresponding to the asset object of interest may include a plurality of risk factors in the risk factor pool 102c, that is, a plurality of risk factors associated with the target asset industry to which the asset object of interest belongs. Therefore, according to different risk factors corresponding to the concerned asset object, the risk level evaluation parameters of the concerned asset object under each corresponding risk factor can be calculated. The classifying of risk factors in the risk factor pool can be further performed in step S105 described below.

For example, if the risk factor corresponding to the object of interest includes the risk factor f _n, when the risk factor f _n is the risk factor "accounts receivable ratio" (may be referred to as an accounts receivable ratio), the terminal device may obtain an enterprise accounts receivable (may be referred to as an enterprise accounts receivable) of the enterprise to which the object of interest belongs, and an enterprise total input asset. The terminal equipment can divide the accounts receivable of the enterprise by the total input assets of the enterprise to obtain the value of the accounts receivable duty ratio of the concerned asset object under the risk factor accounts receivable duty ratio, wherein the value of the accounts receivable duty ratio is the risk grade evaluation parameter of the concerned asset object under the risk factor accounts receivable duty ratio.

For another example, when the risk factor corresponding to the concerned asset object is "z-value too low", the terminal device may calculate the z-value of the concerned asset object, and use the z-value as the risk level evaluation parameter of the concerned asset object under the risk factor "z-value too low". The calculation principle of the z value is 0.0656x1+0.0326x2+0.01x3+0.0672x4, x1 is equal to the operation funds of the enterprise to which the concerned asset object belongs divided by the total sum of assets multiplied by 100, x2 is equal to the remaining benefits of the enterprise to which the concerned asset object belongs divided by the total sum of assets multiplied by 100, x3 is equal to the profit before tax TTM (latest twelve month market rate) of the enterprise to which the concerned asset object belongs divided by the total sum of assets multiplied by 100, and x4 is equal to the enterprise account value of the enterprise to which the concerned asset object belongs divided by the liability account value multiplied by 100.

After the risk level evaluation parameters of the concerned asset object under each risk factor corresponding to the concerned asset object are calculated, the asset risk level of the concerned asset object under each risk factor can be obtained according to the numerical value of the risk level evaluation parameters. Since the principle of acquiring the risk level of the asset object under each risk factor corresponding to the asset object is the same according to the risk level evaluation parameter of the asset object under each risk factor corresponding to the asset object, assuming that the risk factor corresponding to the asset object includes the risk factor f _n, the following description will be given taking the asset risk level of the asset object under the risk factor f _n as an example, referring to the following:

if the total set of asset objects includes asset objects in all asset industries, the terminal device may obtain all asset objects in the asset industries associated with the risk factor f _n in the total set of asset objects. The terminal device may refer to the acquired asset object as a plurality of associated asset objects associated with the risk factor f _n, in other words, the plurality of associated asset objects are all asset objects in the total set of asset objects, and the corresponding risk factor includes the risk factor f _n. It is to be appreciated that the plurality of associated asset objects includes an asset object of interest.

The terminal device may acquire the same principle as the risk level evaluation parameter of the asset object under the risk factor f _n, and acquire the risk level evaluation parameter of the associated asset object under the risk factor f _n. The terminal equipment can obtain the asset object risk ranking of each associated asset object according to the numerical value of the risk level evaluation parameter corresponding to each associated asset object. For example, the terminal device may rank according to the magnitude of the risk level evaluation parameter corresponding to each associated asset object, from small to large (or from large to small in some scenarios), so as to obtain an asset object risk ranking of each associated asset object, where the risk under the risk factor f _n is greater for the associated asset object with the later asset object risk ranking.

The end device may obtain a ranking range in which the asset object risk ranking of the asset object of interest is in the asset object risk rankings of all associated asset objects. The terminal device may obtain an asset risk level of the object of interest under the risk factor f _n according to a ranking range in which the asset object risk ranks of the object of interest are in the asset object risk ranks of all associated asset objects, where one risk factor corresponds to one asset risk level, see below.

The asset risk level may include, among other things, an asset zero risk level, an asset low risk level, an asset medium risk level, and an asset high risk level. The zero risk level of the asset characterizes that the concerned asset object has no risk under the corresponding risk factors and is safe; the asset low risk level, the risk level in the asset, and the asset high risk level characterize that the asset object of interest is risky under the corresponding risk factor and not secure. It will be appreciated that the risk level of the asset object of interest, as characterized by the risk level in the asset, is greater than the risk level of the asset object of interest, as characterized by the low risk level in the asset, and the risk level of the asset object of interest, as characterized by the high risk level in the asset, is greater than the risk level of the asset object of interest, as characterized by the risk level in the asset.

For example, the asset risk levels may include a first asset risk level and a second asset risk level, the second asset risk level indicating an asset risk level of the asset object of interest that is greater than the asset risk level of the asset object of interest indicated by the first asset risk level. The first asset risk level may be any one of the above-mentioned asset zero risk level, asset low risk level, and asset medium risk level, and the second asset risk level may be any one of the above-mentioned asset low risk level, asset medium risk level, and asset high risk level.

The rank range to which the first asset risk level belongs may be referred to as a first rank range. The rank range to which the second asset risk level belongs is referred to as the second rank range. When the above-described ranking range of the focused asset object is in the first ranking range to which the first asset risk level belongs, it may be determined that the asset risk level of the focused asset object is the first asset risk level. When the ranking range of the focused asset object is in the second ranking range to which the second asset risk level belongs, the asset risk level corresponding to the focused asset may be determined to be the second asset risk level.

For example, if risk factor f _n is the risk factor "accounts receivable duty cycle", the ranking range may be divided into a ranking range [0%, 95%), a ranking range [95%, 99%) and a ranking range [99%,100% ]. Therefore, if the asset object risk ranks of the concerned asset object are within the ranking range [0%, 95%) of the asset object risk ranks of all the associated asset objects, the asset risk level of the concerned asset object under the risk factor f _n may be the asset zero risk level. If the risk ranks of the asset objects of the concerned asset object are in the ranking range of 95% and 99% of the risk ranks of the asset objects of all the associated asset objects, the risk ranks of the concerned asset object under the risk factor f _n may be the low risk ranks of the asset. If the risk ranks of the asset objects of the concerned asset object are in the ranking range of 99% and 100% of the risk ranks of the asset objects of all the related asset objects, the risk level of the concerned asset object under the risk factor f _n can be the risk level in the asset. When the total input assets of the enterprises to which the concerned asset objects belong are negative or zero, the asset risk level of the concerned asset objects under the risk factor f _n can be directly determined to be the asset high risk level.

Generally, the asset risk level of the asset object of interest under the risk factor f _n is obtained as described above, based on the asset object risk ranks of the asset object of interest, and the ranking scope that is in the asset object risk ranks of all associated asset objects. But for some special risk factors, the risk factors are obtained according to the parameter intervals to which the risk level evaluation parameters of the object of interest under the risk factors belong, see below.

For example, when the risk factor f _n is the risk factor "z-value is too low", then the parameter interval may include the parameter interval [1.23,2.9], the parameter interval (2.9, +inf), and the parameter interval (-inf, 1.23), +inf represents an upper bound, which may be understood as positive infinity, -inf represents a lower bound, which may be understood as negative infinity. When the risk level assessment parameter (z value) for the asset-of-interest object at risk factor f _n is in the parameter interval (2.9, +inf), then the asset risk level for the asset-of-interest object at risk factor f _n is considered to be an asset zero risk level. When the risk level assessment parameter (z value) for the asset-of-interest subject under risk factor f _n is in parameter interval [1.23,2.9], then the asset risk level for the asset-of-interest subject under risk factor f _n is considered to be an asset low risk level. When the risk level assessment parameter (z value) for the asset-of-interest object at risk factor f _n is in the parameter interval (-inf, 1.23), then the asset risk level for the asset-of-interest object at risk factor f _n is considered to be an asset high risk level.

Referring to fig. 5, fig. 5 is a schematic view of a scenario for acquiring an asset risk level according to the present application. As shown in fig. 5, first the terminal device may determine the target asset industry 101k to which the asset object of interest 100k belongs. The terminal device may then obtain a plurality of risk factors associated with the target asset industry 101k (here including risk factor f1, risk factors f2, … …, and risk factor fn in block 102 k) as a plurality of risk factors corresponding to the asset object of interest 100 k.

The principle of calculating the asset risk level of the asset object of interest 100k under each of its corresponding risk factors is the same, and a process of calculating the asset risk level of the asset object of interest 100k under the risk factor fn is described here as an example. The terminal device may obtain all asset objects under the asset industry (here including asset industry 1, asset industry 2, … …, and asset industry m) associated with the risk factor fn as associated asset objects 104k. The asset objects to which asset industry 1 belongs include asset objects in asset object set 1, the asset objects to which asset industry 2 belongs include asset objects in asset object set 2, … …, and the asset objects to which asset industry m belongs include asset objects in asset object set m. Thus, the associated asset object 104k includes the asset object in asset object set 1, the asset object in asset object set 2, … …, and the asset object in asset object set m.

The end device may then derive a rank 105k of the associated asset objects (i.e., derive an asset object risk rank for each associated asset object) based on the risk level evaluation parameters for each associated asset object. Then, the terminal device can obtain a ranking range 106k of the asset object risk ranks of the concerned asset object in the ranking 105k, and then the terminal device can obtain the asset risk grade 107k of the concerned asset object 100k under the risk factor fn according to the ranking range 106 k.

Step S102, obtaining user operation behavior data of a target user aiming at an object of interest asset;

specifically, the terminal device may further obtain user operation behavior data of the target user for the object of interest, where the user operation behavior data may be a click rate of the target user for the object of interest. For example, the focused asset object may include asset object 1 and asset object 2, and the user operation behavior data of the target user for the focused asset object is the click rate of the target user for asset object 1 (for example, the click rate is 10), and the click rate of the target user for asset object 2 (for example, the click rate is 20).

Step S103, determining the asset sequencing priority of the concerned asset object according to the asset risk level of the concerned asset object under each risk factor and the user operation behavior data;

Specifically, the terminal device may determine the asset sorting priority of each asset object of interest according to the asset risk level of the asset object of interest under each risk factor and the user operation behavior data. The rules for determining asset prioritization for each asset object of interest may include the following 3 step logic rules, specifically including the following 1 st step logic rule, the 2 nd step logic rule, and the 3 rd step logic rule. Firstly, consider the logic rule of the 1 st step, then, based on the 1 st logic rule, consider the logic rule of the 2 nd step and the logic rule of the 3 rd step, please see the following:

Logic rules of step 1:

assuming that the asset risk level of the concerned asset object under the corresponding risk factor acquired in the above step S101 is the current asset risk level of each risk factor of the concerned asset object under the current time. In other words, the asset risk level of the object of interest under the corresponding risk factor acquired in step S101 is the asset risk level of the object of interest under the corresponding risk factor acquired at the latest time.

The terminal device may also obtain a historical asset risk level of the asset object of interest under the corresponding risk factor at the historical reference moment. The historical reference time may be the time of day preceding the current time described above, for example, the historical reference time may be the last time of day preceding the current time. In other words, the current time may refer to the latest time of day, and the historical reference time may be the last time of yesterday. The terminal device may determine whether the current asset risk level of the risk factor of the asset object of interest at the current time instant is changed compared with the historical asset risk level of each risk factor of the asset object of interest at the historical reference time instant by comparing whether the current asset risk level of each risk factor of the asset object of interest at the current time instant is the same as the historical asset risk level of each risk factor of the asset object of interest at the historical reference time instant. Thus, the asset prioritization priority of asset objects of interest that are subject to change (i.e., the current asset risk level is different from the historical asset risk level) may be greater than the asset prioritization priority of asset objects of interest that are not subject to change (i.e., the current asset risk level is the same as the historical asset risk level).

For example, assuming that the asset of interest objects includes asset of interest object z _i and asset of interest object z _j, the current asset risk level of asset of interest object z _i, which is different from the historical asset risk level, may be referred to as a first varying risk level. The current asset risk level of the asset of interest object z _j that is different from the historical asset risk level may be referred to as a second varying risk level.

More, the change number threshold may be 1, and when the number of first change risk levels is less than the change number threshold, it indicates that the number of first change risk levels is 0, that is, the current asset risk level of the concerned asset object z _i under each corresponding risk factor is the same as the historical asset risk level of the concerned asset object z _i under each corresponding risk factor, and no change occurs. And when the number of the second variation risk levels is greater than or equal to the variation number threshold, it indicates that the number of the second variation risk levels is at least 1, that is, the current asset risk level of the concerned asset object z _j under each corresponding risk factor is different from at least one of the historical asset risk levels of the concerned asset object z _i under each corresponding risk factor, and the variation occurs. Accordingly, the asset ordering priority of the asset of interest object z _i is greater than the asset ordering priority of the asset of interest object z _j.

Logic rules of step 2:

The logic rule of step 2 is mainly for the object of interest whose current asset risk level changes compared with the historical asset risk level, that is, the object of interest whose number of the change risk levels (for example, the number of the second change risk levels) is greater than or equal to the change number threshold.

The asset risk level of the object of interest under the risk factor may include an asset zero risk level, an asset low risk level, an asset risk level, and an asset high risk level. The terminal device may count the number of newly added high risk items for each asset object of interest. The high risk item may refer to a risk factor item whose asset risk level of the concerned asset object is an asset high risk level, and the newly added high risk item refers to a risk factor item whose current asset risk level is different from a historical asset risk level and whose current asset risk level is an asset high risk level. In other words, the newly added high risk item refers to a risk factor item that becomes an asset high risk level at the present moment, instead of the asset high risk level at the risk factor at the historic reference moment. Thus, the asset ordering priority of the asset objects of interest with a greater number of newly added high risk items may be greater than the asset ordering priority of the asset objects of interest with a lesser number of newly added high risk items.

If the number of newly added high risk items of different interest asset objects is the same, the terminal device may count the click rate (user operation behavior data, which may be referred to as object click rate) of the target user for the interest asset object. The more clicks the object of interest has, the greater priority than the object of interest has. For example, when the object click amount of the target user for the attention asset object z _i is less than the object click amount of the target user for the attention asset object z _j, the asset ordering priority of the attention asset object z _i is less than the asset ordering priority of the attention asset object z _j.

If the target user clicks on different interest asset objects in the same amount, the terminal device may count the total number of high risk items of the interest asset objects. The total number of high risk items for the asset object of interest is also referred to as the number of high risk levels for the asset in the current asset risk level for each risk factor for the asset object of interest at the current time. Thus, the asset ordering priority of the asset objects of interest having a greater total number of high risk items may be greater than the asset ordering priority of the asset objects of interest having a lesser total number of high risk items.

If the total number of high risk items of different interest asset objects is the same, the terminal device may count the total number of risk items in the interest asset object. The total number of risk items in the asset of interest is also referred to as the number of risk levels in the asset in the current asset risk level for each risk factor at the current time of the asset of interest. Thus, an asset-ordering priority of an asset-object of interest having a greater total number of risk items may be greater than an asset-ordering priority of an asset-object of interest having a lesser total number of risk items.

If the total number of risk items in different interest asset objects is the same, the terminal device may count the total number of low risk items for the interest asset object. The total number of low risk items for the asset object of interest is also referred to as the number of low risk levels for the asset in the current asset risk level for each risk factor for the asset object of interest at the current time. Thus, the asset ordering priority of the asset objects of interest having a greater total number of low risk items may be greater than the asset ordering priority of the asset objects of interest having a lesser total number of low risk items.

If the second asset risk level is an asset high risk level and the number of second asset risk levels (i.e., the total number of high risk items) in the first variant risk level is less than the number of second asset risk levels (i.e., the total number of high risk items) in the second variant risk level, then the asset ordering priority of the subject asset z _i is less than the asset ordering priority of the subject asset z _j.

If the number of first asset risk levels in the current asset risk level of each risk factor at the current time is smaller than the number of first asset risk levels in the current asset risk level of each risk factor at the current time for the asset object of interest z _j, the asset ordering priority of the asset object of interest z _i may be smaller than the asset ordering priority of the asset object of interest z _j.

If the number of second asset risk levels in the current asset risk level of each risk factor at the current time is smaller than the number of second asset risk levels in the current asset risk level of each risk factor at the current time for the asset object of interest z _j, the asset ordering priority of the asset object of interest z _i may be smaller than the asset ordering priority of the asset object of interest z _j.

Logic rules of step 3:

The logic rule of step 3 is mainly directed to the object of interest whose current asset risk level is unchanged from the historical asset risk level, that is, the object of interest whose number of the variation risk levels (for example, the number of the first variation risk levels) is smaller than the variation number threshold.

First, the terminal device may count the click amount (user operation behavior data) of the target user for each asset object of interest for which the asset risk level does not change. The more clicks that have an asset object of interest has an asset prioritization priority that is greater than the asset prioritization priority of an asset object of interest that has a fewer clicks.

Optionally, the terminal device may also input the asset risk level of the asset object of interest under each risk factor, and user operation behavior data (e.g., click-through amount) of the target user for each asset object of interest into the ranking model. The logic rules described above are written in the sorting model in advance, and the logic rules are rules how to determine the asset sorting priority of each concerned asset object according to the asset risk level of each concerned asset object under each risk factor and the user operation behavior data of the target user for each concerned asset object.

Firstly, the risk feature data of each concerned asset object can be obtained through the sorting model, and the risk feature data can comprise feature information such as the number of click times of a target user on the concerned asset object, the number of high risk items of the concerned asset object (namely, the risk factor items of which the asset risk level is the asset high risk level), the number of medium risk items (namely, the risk factor items of which the asset risk level is the risk level in the asset), the number of low risk items (namely, the risk factor items of which the asset risk level is the asset low risk level), whether the current asset risk level of the concerned asset object changes compared with the historical asset risk level or not.

Furthermore, the sorting model can obtain the asset sorting priority of each concerned asset object according to the obtained risk characteristic data of each concerned asset object and the written logic rule (the specific logic rule refers to the logic rules of the 3 steps).

Thus, through the above-described process, the asset ordering priority of each asset object of interest may be obtained.

Step S104, generating an asset object sequence formed by the concerned asset objects according to the sorting priority of the concerned asset objects;

Specifically, the terminal device may sort all the objects of interest according to the order of the priority of the asset sorting of each object of interest from large to small, so as to obtain an asset object sequence containing all the objects of interest. In other words, in the sequence of asset objects, the asset objects of interest having a greater asset ordering priority are ranked behind the asset objects of interest having a lesser asset ordering priority.

Alternatively, the process of obtaining the asset object sequence (i.e., the process described in step S101-step S104) may be performed by a server, which may be a background server of the user account for the virtual asset held by the target user. After obtaining the asset object sequence, the server may send the obtained asset object sequence to the terminal device.

Step S105, generating asset risk prompt information aiming at the concerned asset object according to the asset object sequence, and outputting the asset risk prompt information;

Specifically, the terminal device may generate asset risk prompt information for the asset object of interest according to the obtained asset object sequence. For example, the terminal device may generate an asset object list from the asset object sequence. The asset object list comprises all the concerned asset objects, and the ranking order of the concerned asset objects in the asset object list is the ranking order of the concerned asset objects in the asset object sequence. In addition, risk related information (e.g., asset risk level related information) for the asset under each risk factor may also be included in the asset object list. The terminal device can take the asset object list as asset risk prompt information aiming at the concerned asset object, and output the asset risk prompt information in the terminal page.

Referring to fig. 6 a-6 c, fig. 6 a-6 c are schematic page diagrams of a terminal page according to the present application. As shown in fig. 6a, in the terminal page 101e of the terminal device 100e, there is displayed a result after risk scanning of all the concerned asset objects of the target user, that is, a result of a market mine sweeping, that is, whether there is a risk of scanning the asset objects. In the area 102e of the terminal page 101e, there is a result description after risk scanning is performed on the concerned asset object, including "currently detecting 100 selected stocks of your own, wherein 56 risks exist, the risk stock accounts for 56%, and the risk is lower than the market risk degree, and attention is paid to avoiding the risk" to be avoided ". The interesting asset object may be a stock, and the optional stock (i.e., optional stock) is a stock selected by the target user, in other words, the optional stock is the interesting asset object of the target user. Thus, the result in region 102e illustrates that 100 asset objects of interest to the target user are detected, 56 of the 100 asset objects of interest are risky (if the asset risk level of the asset object of interest under the risk factor is not the asset zero risk level described above, it is illustrated that the asset object of interest is at risk), and the asset object of interest at risk accounts for 56% of all asset objects of interest.

In the area 103e of the terminal page 101e, it is also shown that the proportion of stock items with risks in all the options is 56%, and the proportion of stock items without risks is 44%. In the area 104e of the terminal page 104e, there is also displayed a comprehensive ranking of 100 self-selected stocks for the target user, that is, the above-mentioned asset object list containing all the asset objects of interest.

As shown in fig. 6b, the page displayed by the terminal page 101f in fig. 6b is the same page as the page displayed by the terminal page 101e in fig. 6a, and the terminal page 101f can be displayed by sliding up the terminal page 101e in fig. 6 a. It is understood that the page content displayed in the terminal page 101f is the content below the page content displayed in the terminal page 101 e.

Specific content of the asset object list (i.e., comprehensive ranking) is displayed in the terminal page 101f, and the ranking order of each of the attention asset objects displayed in the asset object list is the ranking order of each of the attention asset objects in the asset object sequence. As shown in fig. 6b, a prompt message "17 self-selected stock risk items change yesterday" is displayed in the area 102f, which indicates that, of the 100 above-mentioned interesting asset objects, there are 17 interesting asset objects whose risk factors at the current time instant have a different current asset risk level than the corresponding historical asset risk level of the risk factors at the historical reference time instant.

The asset object list displayed on the terminal page 101f includes an object of interest (i.e., stock G1) displayed in the area 103f, an object of interest (i.e., stock G2) displayed in the area 104f, an object of interest (i.e., stock G3) displayed in the area 105f, and the like.

Where risk information associated with stock G1 is also displayed in region 103f, the risk information including 7 of the risk items of stock G1, indicating that the asset risk level of stock G1 at the corresponding 7 risk factors is not an asset zero risk level, but any one of an asset low risk level, an asset medium risk level, and an asset high risk level. And, among the 7 risk items, 1 high risk item, 5 stroke risk items, and 1 low risk item are included.

Also displayed in region 103f is risk tag hint information for stock G1, which is generated based on the asset risk level of stock G1 under the corresponding risk factor. For example, the risk tag hint information for stock G1 herein may include "latest performance deficit", "insufficient cash flow", "excessive historical volatility", and "overestimated". The risk tag prompt message "latest performance deficit" may be generated when the asset risk level of the stock G1 under the risk factor "latest performance deficit" is not the asset zero risk level. The side of the risk tag prompt information 'latest performance deficit' is also marked with a 'new' word, which indicates that the current asset risk level of the stock G1 under the risk factor 'latest performance deficit' is changed compared with the historical asset risk level, and the risk tag prompt information 'latest performance deficit' is generated newly at the current moment instead of the historical reference moment.

It will be appreciated that the asset risk level of stock G1 at the risk factor "latest performance deficit" is either an asset zero risk level, or the asset risk level of stock G1 at the risk factor "latest performance deficit" is any one of the asset risk levels of the asset low risk level, the asset risk level in the asset, and the asset high risk level, and that stock G1 is at risk at the risk factor "latest performance deficit" when the asset risk level of stock G1 at the risk factor "latest performance deficit" is any one of the asset risk level of the asset low risk level, the asset risk level in the asset, and the asset high risk level.

Thus, similarly, the risk tag hint information "insufficient cash flow" may be generated when the asset risk level of stock G1 under the risk factor "insufficient cash flow" is not an asset zero risk level. The risk tag hint information "history volatility too high" may be generated when the asset risk level of stock G1 under the risk factor "history volatility too high" is not an asset zero risk level. The risk tag hint information "overestimated" may be generated when the asset risk level of stock G1 under the risk factor "overestimated" is not an asset zero risk level.

Similarly, there is also displayed in region 104f risk information associated with stock G2, including 5 risk items of stock G2, indicating that the asset risk level of stock G2 at the corresponding 5 risk factors is not an asset zero risk level, but is any one of an asset low risk level, an asset medium risk level, and an asset high risk level. And, 1 high risk item, 2 medium risk items, and 2 low risk items are included in the 5 risk items.

Also displayed in region 104f is a risk tag hint for stock G2, which is generated based on the asset risk level of stock G2 under the corresponding risk factor. For example, the risk tag hint information for stock G2 herein may include "high management frequent changes", "reputation duty is slightly higher", "z value is too low", and "financial leverage is slightly higher". The risk tag prompt message "high-tube frequent change" may be generated when the asset risk level of the stock G2 under the risk factor "high-tube frequent change" is not the asset zero risk level. In addition, a new word is marked beside the risk tag prompt information 'high management frequent change', which indicates that the current asset risk level of the stock G2 under the risk factor 'high management frequent change' is changed compared with the historical asset risk level, and the risk tag prompt information 'high management frequent change' is generated newly at the current moment instead of the historical reference moment.

Thus, similarly, the risk tag hint information "reputation ratio is slightly higher" may be generated when the asset risk level of stock G2 at the risk factor "reputation ratio is too high" is an asset low risk level. Alternatively, if the risk level of the asset of the stock G2 under the risk factor "reputation ratio is too high" is the risk level in the asset, the generated risk tag hint information may also be "reputation ratio is relatively high". If the asset risk level of stock G2 under the risk factor "too high a reputation ratio" is an asset high risk level, the generated risk tag hint information may also be "too high a reputation ratio".

Further, the risk tag hint information "z-value too low" may be generated when the asset risk level of stock G2 at the risk factor "z-value too low" is not an asset zero risk level. The risk tag hint information "financial level is slightly higher" may be generated when the asset risk level of stock G2 under the risk factor "financial level is too high" is an asset low risk level. Alternatively, if the risk level of the asset of stock G2 under the risk factor "financial level too high" is the risk level in the asset, the generated risk tag hint information may also be "financial level high". If the asset risk level of stock G2 under the risk factor "financial leverage is an asset high risk level, the generated risk tag hint information may also be" financial leverage is too high ". In other words, the terminal device may generate different risk tag prompt information according to different asset risk levels of the object of interest under the risk factor.

Similarly, there is also displayed in region 105f risk information associated with stock G3, including 4 of the risk items of stock G3, indicating that the asset risk level of stock G3 at the corresponding 4 risk factors is not an asset zero risk level, but is any one of an asset low risk level, an asset medium risk level, and an asset high risk level. And, 1 high risk item, 1 medium risk item, and 2 low risk items are included in the 4 risk items.

Also displayed in region 105f is risk tag hint information for stock G3, which is generated based on the asset risk level of stock G3 under the corresponding risk factor. For example, the risk tag hint information for stock G3 herein may include "maximum withdrawal rate is too high", "valuation is too high", "z value is slightly low", and "transaction liveness is very low". The risk tag prompt message "the maximum withdrawal rate is too large" may be generated when the asset risk level of the stock G3 under the risk factor "the maximum withdrawal rate is too large" is not the zero risk level of the asset. In addition, a new word is marked beside the risk tag prompt message 'maximum withdrawal rate is too large', which indicates that the current asset risk level of the stock G3 under the risk factor 'maximum withdrawal rate is changed compared with the historical asset risk level, and the risk tag prompt message' maximum withdrawal rate is not generated at the historical reference moment but is newly generated at the current moment.

Thus, similarly, the risk tag hint information "overestimated" may be generated when the asset risk level of stock G3 under the risk factor "overestimated" is not an asset zero risk level. The risk tag hint information "z-value is slightly lower" may be generated when the asset risk level of stock G3 at the risk factor "z-value is too low" is an asset low risk level. The risk tag hint information "very low activity in a deal" may be generated when the asset risk level of stock G3 under the risk factor "20 day average deal" is an asset high risk level. Alternatively, if the asset risk level of stock G3 under the risk factor "20 day average volume" is an asset low risk level, the generated risk tag prompt information can also be 'slightly lower in transaction liveness'. If the risk level of the asset of the stock G3 under the risk factor "20-day equal volume of the transaction" is the risk level in the asset, the generated risk tag prompt information may also be "the transaction activity is low".

Further, when the target user presses (or other user operates) the control 106f in the terminal page 101f for a long time, the terminal device 100e may also display from the terminal page 101f to the terminal page 101g. A function block 102g is displayed in the terminal page 101g. The function box 102g includes a function field "total risk item from high to low" and a function field "high risk item from high to low".

The terminal device 100e may also output and display each of the asset objects of interest in the ranking order of the number of the total risk items that belong to, in response to the clicking operation of the target user on the function bar "total risk items from high to low". Wherein the number of total risk items of one concerned asset object is equal to the number that the asset risk level of the concerned asset object under the corresponding risk factor is not the zero risk level of the asset. In other words, the number of total risk items for an asset object of interest is equal to the number of asset risk levels for the asset object of interest at the corresponding risk factors being the low risk level of the asset, the risk level in the asset, and the high risk level of the asset.

For example, the risk factors corresponding to the concerned asset object z1 include a risk factor 1, a seal 2 and a risk factor 3, the asset risk level of the concerned asset object z1 under the risk factor 1 is an asset zero risk level, the asset risk level of the concerned asset object z1 under the risk factor 2 is an in-asset risk level, the asset risk level of the concerned asset object z1 under the risk factor 3 is an asset high risk level, and the number of total risk items of the concerned asset object z1 is 2 because the asset risk levels of the concerned asset object z1 under the risk factor 2 and the risk factor 3 are not asset zero risk levels.

The terminal equipment can respond to the clicking operation of the target user aiming at the function bar 'high risk item from high to low', and output and display each concerned asset object according to the ranking sequence of the number of the high risk levels of the belonging assets from high to low. The high risk item refers to a risk factor item with an asset risk level being an asset high risk level. For example, the risk factors corresponding to the concerned asset object z1 include a risk factor 1, a seal 2 and a risk factor 3, the asset risk level of the concerned asset object z1 under the risk factor 1 is the risk level in the asset, the asset risk level of the concerned asset object z1 under the risk factor 2 is the asset high risk level, and the number of high risk items of the concerned asset object z1 is 2.

The terminal device may further respond to the expansion operation (may be a clicking operation) of the target user on the focused asset object in the asset object list, and display an asset detail page of the focused asset object clicked by the target user, where risk tag prompt information of the focused asset object may be displayed. Wherein all risk factors in the risk factor pool may be classified, one risk factor belongs to one risk factor type, for example, the risk factors in the risk factor pool 102c are classified as risk factors of the "management risk" type 100c, the risk factors of the "corporate financial risk" type 103c, the risk factors of the "stock market risk" type 104c, and the risk factors of the "plate-specific risk" type 105c in the above step S101, and a total of 5 types (dimensions) of risk factors are included. Therefore, the risk tag prompt information in the asset detail page can be classified and output according to the risk factor type of the risk factor to which the risk tag prompt information belongs, so that a target user can more intuitively check which risk exists under each risk factor (the risk can be seen through the risk tag prompt information).

Referring to fig. 7, fig. 7 is a schematic page diagram of a terminal page according to the present application. As shown in fig. 7, the terminal page 101j, the terminal page 102j, and the terminal page 103j are terminal pages of the terminal device 100j, and the terminal page 101j, the terminal page 102j, and the terminal page 103j are asset detail pages of the asset object of interest (here, stock G10) clicked by the target user in the asset object list. In the area 104j of the terminal page 101j, there are displayed rise and fall curves of the stock G10, and in the area 105j, there are displayed 9 total risk items of the stock G10, which means that the asset risk level of the stock G10 under the corresponding 9 risk factors is not the zero risk level of the asset.

Also shown in region 105j are 6 risk factor items for stock G10 with no risk temporarily indicating that the asset risk level for stock G10 under the corresponding 6 risk factors is an asset zero risk level. Also shown in region 105j is 1 item for a risk factor item for stock G10 low risk, indicating that the asset risk level for stock G10 at the corresponding 1 risk factor is the asset low risk level. Also shown in region 105j are 4 risk factor items for the risk in stock G10, indicating that the risk level of the asset for stock G10 at the corresponding 4 risk factors is the risk level in the asset. Also shown in region 105j are 4 risk factor items for high risk of stock G10, indicating that the asset risk level for stock G10 at the corresponding 4 risk factors is an asset high risk level.

The terminal device 100j may respond to the sliding operation of the target user on the terminal page 101j, and display the page content in the area 106j of the terminal page 102j to the terminal page 102j, which is the content below the page content in the terminal page 101 j. The risk tag prompt information of the stock G10 is displayed in the area 106j of the terminal page 102j in a classified manner, and includes risk tag prompt information "high-management ultra-large-scale debt" under the management risk factor type, risk tag prompt information "non-debt", "z value is too low", "asset is negative", and "cash flow is slightly low" under the company financial risk factor type, and risk tag prompt information "overestimate" and "transaction liveness" under the stock market risk factor type.

The terminal device 100j may respond to the sliding operation of the target user on the terminal page 102j, and display the page content in the terminal page 103j is the content below the page content in the terminal page 101j to the terminal page 103 j. Specific information of the stock G10 under each risk factor type is displayed in the terminal page 103j, and may include high management information (such as a high management name and a job title) of a company to which the stock G10 belongs, and may also include information such as a company performance, a market value, a reputation, and a net asset.

Referring to fig. 8, fig. 8 is a schematic view of a label display scene according to the present application. As shown in fig. 8, the region 100d belongs to the same property region as the region 106j in fig. 7 described above. Because the corresponding risk tag prompt information can be generated according to the asset risk level of the concerned asset object under the corresponding risk factor, in the area 100d, the risk tag prompt information corresponding to each risk factor can be classified and output according to the risk factor type of each risk factor.

Here, the risk factor type may include a type of management, a type of corporate finance, a type of stock quotation, and a type specific to a block. Therefore, risk tag hint information (here, risk tag hint information including tag 1, tag 2, tag 3, tag 4, and the like) corresponding to the risk factors belonging to the type of management may be displayed in the area 101 d; risk tag hints corresponding to risk factors belonging to the type of corporate finance (here including risk tag hints such as tag 5, tag 6, tag 7, tag 8, and tag 9) may be displayed in region 102 d; risk tag hints corresponding to risk factors belonging to the type of stock quote (here, risk tag hints including tag 12, tag 13, tag 14, and tag 15) may be displayed in region 104 d; risk tag cues (here including risk tag cues such as tag 16 and tag 17) corresponding to risk factors belonging to the type unique to the tile may be displayed in region 105 d. Therefore, the risk tag prompt information corresponding to the risk factors of different types can be classified and output by displaying the risk tag prompt information corresponding to the risk factors of different types in different areas.

Optionally, the terminal device may further obtain, according to the number of asset hints, a plurality of asset objects of interest with highest asset sorting priority from the asset object sequence, as risk early warning asset objects. The number of the risk early warning asset objects is equal to the number of the asset prompts, the number of the asset prompts can be set according to the actual application situation, for example, the number of the asset prompts can be 3.

When the current asset risk level (at the current moment) of the risk early-warning asset object is changed compared with the historical asset risk level (at the historical reference moment), the terminal device can generate a risk early-warning push message according to the current asset risk level of the risk early-warning asset object under the corresponding risk factor. Optionally, the precondition for generating the risk early warning push message may be set according to an actual application scenario, for example, the precondition may also be set that when the current asset risk level (at the current time) of the risk early warning asset object changes compared with the historical asset risk level (at the historical reference time), and the current asset risk level of the risk early warning asset object is the asset high risk level or the risk level in the asset, the risk early warning push message may also be generated according to the current asset risk level of the risk early warning asset object under the corresponding risk factor.

The terminal equipment can take the generated risk early warning push message as asset risk prompt information aiming at the concerned asset object, and the terminal equipment can output and display the risk early warning push message in a terminal page.

More, the terminal device (which can be understood as a user terminal of the target user) can also subscribe the target user to the risk early warning function, and subscribe the risk early warning function for the user account of the virtual asset held by the target user. After subscribing the risk early warning function, the terminal equipment can push the generated risk early warning push message in the user account for the virtual asset held by the target user, and output and display the risk early warning push message in the terminal page to which the user account for the virtual asset held by the target user belongs. Because the user account number for the virtual asset held by the target user is logged in the user terminal of the target user, the terminal page to which the user account number for the virtual asset held by the target user belongs is the terminal page in the user terminal of the target user. In other words, after the user account number of the target user for the virtual asset subscribes to the risk early warning function, the user terminal of the target user can automatically output and display the risk early warning push message so as to remind the target user that the concerned asset object of the target user has risk.

It can be understood that if the user account for the virtual asset held by the target user is not subscribed to the risk early warning function, the user terminal of the target user may not output and display the risk early warning push message.

Referring to fig. 9, fig. 9 is a schematic page diagram of a terminal page according to the present application. In the terminal page 101h of the terminal device 100h, a risk early warning push message 102h for the asset object of interest of the target user is displayed. The risk early warning push message 102h includes that the time for generating the risk early warning push message is 3 months, 6 days, 20 points and 30 minutes. The risk early warning push message 102h also includes the name of the risk early warning asset object (i.e., the security name), and the code of the risk early warning asset object (i.e., the security code). The risk early warning push message also comprises prompting information generated according to the asset risk level of the risk early warning asset object under the risk factor, wherein the newly increased risk item comprises high management and maintenance, excessively high reputation ratio, excessively high z value and the like, and the data come from public information of the whole market for reference. The prompt information is generated according to the fact that the current asset risk level of the risk early warning asset object is changed compared with the historical asset risk level, and the current asset risk level is a risk factor (for example, a related risk factor of 'high Guan Jianchi', a risk factor of 'high reputation ratio', a risk factor of 'high z value', and the like in the prompt information) of the asset high risk level.

More, the terminal device may be a blockchain node in the blockchain network, and after the terminal device obtains the asset risk prompt information, the terminal device may synchronize the asset risk prompt information to the blockchain network, so that a consensus node in the blockchain network may perform consensus on the asset risk prompt information. When the terminal equipment detects that the consensus node in the blockchain network commonly passes through the asset risk prompt information, the terminal equipment can add the asset risk prompt information into a local blockchain (namely a local ledger). After the asset risk prompt information is added into the local blockchain, the asset risk prompt information can be ensured not to be tampered, and the safety of the asset risk prompt information is ensured.

Subsequently, the terminal equipment can acquire the asset risk prompt information from the local blockchain, and the terminal equipment can output and display the asset risk prompt information acquired from the local blockchain in a terminal page.

Referring to fig. 10, fig. 10 is a schematic diagram of a frame according to the present application. Firstly, in a logic processing layer and a cache layer, data such as an asset risk level of an object of interest under each risk factor can be obtained through calculation of the logic processing layer, and list data (for example, the above-mentioned asset object list) can be obtained through data such as the asset risk level of the object of interest under each risk factor. The data obtained by the logic processing layer may then be cached in the caching layer. Thus, individual stock data for each asset object of interest (i.e., data for the asset object of interest itself, such as data in the asset details page of the asset object of interest), as well as an asset object list of all asset objects of interest, may be cached in the caching layer. As shown in fig. 10, the caching layer may cache data such as an asset risk level of an object of interest under each type of risk factors (including a type of management, a type of finance of a company, a type of stock quotation, and a type specific to a plate), may cache data such as a list of the object of interest (e.g., the above-mentioned object list of the asset), and may cache data such as an asset risk level of an object of interest under risk factors (including "funds", "quotation", "evaluation criteria", and "risk factors related to a rating of an organization") that need to be invoked through other call interfaces (other mine sweeping interfaces).

The logic processing layer is used for calculating and obtaining data such as asset risk level of the concerned asset object under each risk factor and the original data of the list data, and the data can be obtained from the data storage layer. And the raw data obtained by the logic processing layer can be given to the logic processing layer by the data processing layer. The data processing layer obtains the data required by the logic processing layer from a large amount of bottom layer data in the data storage layer, and performs preprocessing (such as data format processing) on the data. As shown in fig. 10, the large amount of underlying data in the data store layer may include an overview table, a tag map table, a tag table (which may be a table of risk tag hint information), day data (day data for each asset object), quarter data (quarter data for each asset object), and year data (year data for each asset object) of all the underlying data. The tag mapping table may include a mapping relationship between an asset risk level and a corresponding identifier, for example, a mapping relationship between an asset risk level of "0", an asset risk level of "1", an asset risk level of "2", an asset risk level of "3", and an asset risk level of "3", where the tag mapping table includes a mapping relationship between an identifier "0" and an asset risk level of "0", a mapping relationship between an identifier "1" and an asset risk level of "low", a mapping relationship between an identifier "2" and an asset risk level of "3", and an asset risk level of "high".

Next, at the access layer, data of the asset object of interest under different risk factors (including data of asset risk levels of the asset object of interest under various risk factors, etc.) may be acquired in the cache layer through different minesweeping interfaces (here including minesweeping interface 1, minesweeping interfaces 2, … …, and minesweeping interface n). The synchronization of the background data (data cached in the cache layer) to the terminal equipment at the front end can be realized through each mine sweeping interface in the access layer.

By the method provided by the application, the risk prompt information (such as the asset object list and the risk early warning push message) specific to the target user can be generated, and personalized risk prompt to the target user can be realized through the risk asset prompt information. And the risk early warning pushing message is actively pushed to the target user, so that the target user can be timely reminded of the risk condition of the asset object concerned.

Referring to fig. 11, fig. 11 is a schematic structural diagram of a virtual asset data processing device according to the present application. The virtual asset data processing apparatus may be a computer program (comprising program code) running in a computer device, for example, the virtual asset data processing apparatus is an application software, which may be used to perform the respective steps in the methods provided by embodiments of the application. As shown in fig. 11, the virtual asset data processing apparatus 1 may include: a rank acquisition module 101, a behavior acquisition module 102, a priority acquisition module 103, a sequence generation module 104, and an information generation module 105;

a level obtaining module 101, configured to obtain asset risk levels of the focused asset objects of the target user under at least two risk factors, respectively;

A behavior acquisition module 102, configured to acquire user operation behavior data of a target user for an asset object of interest;

a priority obtaining module 103, configured to determine an asset ordering priority of the object of interest according to the asset risk level of the object of interest under each risk factor and the user operation behavior data;

a sequence generation module 104, configured to generate an asset object sequence composed of the asset objects of interest according to the sorting priority of the asset objects of interest;

The information generating module 105 is configured to generate asset risk prompt information for the object of interest according to the asset object sequence, and output the asset risk prompt information.

The specific functional implementation manners of the level obtaining module 101, the behavior obtaining module 102, the priority obtaining module 103, the sequence generating module 104, and the information generating module 105 refer to step S101-step S105 in the embodiment corresponding to fig. 3, and are not described herein.

Wherein, the grade acquisition module 101 includes: an industry determination unit 1011, a factor acquisition unit 1012, a parameter acquisition unit 1013, and a rank determination unit 1014;

an industry determination unit 1011 for determining a target asset industry to which the asset object of interest belongs;

A factor acquisition unit 1012 for acquiring at least two risk factors associated with the target asset industry in a risk factor pool;

A parameter acquiring unit 1013 configured to acquire risk level evaluation parameters of the asset object of interest under each risk factor, respectively;

The level determining unit 1014 is configured to determine an asset risk level of the object of interest under each risk factor according to the risk level evaluation parameter corresponding to each risk factor.

The specific functional implementation manner of the industry determining unit 1011, the factor acquiring unit 1012, the parameter acquiring unit 1013, and the level determining unit 1014 is referred to step S101 in the corresponding embodiment of fig. 3, and will not be described herein.

Wherein, the information generation module 105 is used for:

the above device 1 further comprises: a factor classification module 106, a tag information generation module 107, and a response module 108;

A factor classification module 106, configured to classify risk factors in the risk factor pool; one risk factor corresponds to one risk factor type;

a tag information generating module 107, configured to generate risk tag prompt information of the asset object of interest for each risk factor according to an asset risk level of the asset object of interest under each risk factor;

The response module 108 is configured to respond to the unfolding operation for the focused asset object in the asset object list, and classify and output risk tag prompt information corresponding to each risk factor for the focused asset object according to the risk factor type of each risk factor.

The specific function implementation manner of the factor classification module 106, the tag information generation module 107, and the response module 108 is please refer to step S105 in the embodiment corresponding to fig. 3, and a detailed description is omitted herein.

The rank determination unit 1014 includes: an associated object determination subunit 10141, a parameter acquisition subunit 10142, a rank determination subunit 10143, a rank range acquisition subunit 10144, and a rank determination subunit 10145;

An associated object determination subunit 10141 configured to determine at least two asset objects in the asset industry associated with the risk factor f _n from the aggregate set of asset objects as at least two associated asset objects associated with the risk factor f _n; the at least two associated asset objects include an asset of interest object;

A parameter obtaining subunit 10142, configured to obtain risk level evaluation parameters of the associated asset objects except the asset object of interest in the at least two associated asset objects under the risk factor f _n;

A ranking determining subunit 10143, configured to determine an asset object risk ranking of each associated asset object according to the value of the risk level evaluation parameter corresponding to each associated asset object;

a ranking range obtaining subunit 10144, configured to obtain a ranking range in which the asset object risk ranks of the asset object of interest are located in the asset object risk ranks of at least two associated asset objects;

A rank determination subunit 10145 is configured to determine, according to the ranking range, an asset risk rank of the asset object of interest under the risk factor f _n.

The specific functional implementation manner of the association object determining subunit 10141, the parameter acquiring subunit 10142, the ranking determining subunit 10143, the ranking range acquiring subunit 10144, and the ranking determining subunit 10145 is referred to step S101 in the embodiment corresponding to fig. 3, and will not be described herein.

Wherein the parameter acquisition unit 1013 includes: an asset acquisition subunit 10131, a duty determination subunit 10132, and a rank parameter determination subunit 10133;

An asset acquisition subunit 10131, configured to acquire an enterprise receivable asset and an enterprise total input asset of an enterprise to which the object of interest asset belongs when the risk factor f _n is a receivable asset duty;

A duty determination subunit 10132, configured to determine an receivable asset duty of the object of interest asset under the risk factor f _n according to the enterprise receivable asset and the enterprise total input asset;

The level parameter determination subunit 10133 is configured to take the accounts receivable of the object of interest under the risk factor f _n as a risk level assessment parameter of the object of interest under the risk factor f _n.

The specific functional implementation manner of the asset acquisition subunit 10131, the duty ratio determining subunit 10132, and the level parameter determining subunit 10133 refer to step S101 in the embodiment corresponding to fig. 3, and will not be described herein.

Wherein the asset risk level of the subject asset under risk factor f _n includes a first asset risk level and a second asset risk level; the asset risk level of the asset object of interest indicated by the second asset risk level is greater than the asset risk level of the asset object of interest indicated by the first asset risk level;

The level determination subunit 10145 includes: a first level determination subunit 101451 and a second level determination subunit 101452;

a first level determining subunit 101451, configured to determine that the asset risk level of the focused asset object under the risk factor f _n is the first asset risk level when the ranking range of the focused asset object is in the second ranking range to which the first asset risk level belongs;

the second level determining subunit 101452 is configured to determine that the asset risk level of the target asset under the risk factor f _n is the second asset risk level when the ranking range of the target asset is within the third ranking range to which the second asset risk level belongs.

The specific function implementation manner of the first level determining subunit 101451 and the second level determining subunit 101452 is referred to step S101 in the embodiment corresponding to fig. 3, and will not be described herein.

The priority acquisition module 103 includes: a history level acquisition unit 1031, a first change level determination unit 1032, a second change level determination unit 1033, a first priority determination unit 1034, a second priority determination unit 1035, and a third priority determination unit 1036;

A history level acquiring unit 1031 for acquiring a history asset risk level of each risk factor of the asset object of interest z _i and the asset object of interest z _j at the history reference time, respectively;

A first change level determining unit 1032 configured to determine, as a first change risk level, an asset risk level that is different from a historical asset risk level of each risk factor of the asset of interest z _i at the current time, from a historical asset risk level of each risk factor of the asset of interest z _i at the historical reference time;

A second change level determining unit 1033 configured to determine, as a second change risk level, an asset risk level that is different from a historical asset risk level of each risk factor of the asset of interest z _j at the current time, from among the current asset risk levels of each risk factor of the asset of interest z _j at the historical reference time;

A first priority determination unit 1034 for determining that the asset-ordering priority of the asset-of-interest object z _i is less than the asset-ordering priority of the asset-of-interest object z _j when the number of first variation risk levels is less than the variation number threshold and the number of second variation risk levels is greater than or equal to the variation number threshold;

a second priority determining unit 1035 for determining that the asset-ordering priority of the asset-of-interest object z _i is less than the asset-ordering priority of the asset-of-interest object z _j when the number of second asset risk levels in the first varying risk levels is less than the number of second asset risk levels in the second varying risk levels;

The third priority determining unit 1036 is configured to determine that the asset ordering priority of the focused asset object z _i is smaller than the asset ordering priority of the focused asset object z _j when the object click amount of the focused asset object z _i by the target user is smaller than the object click amount of the focused asset object z _j by the target user.

The specific functional implementation manner of the history level acquisition unit 1031, the first change level determination unit 1032, the second change level determination unit 1033, the first priority determination unit 1034, the second priority determination unit 1035, and the third priority determination unit 1036 is referred to step S103 in the embodiment corresponding to fig. 3, and will not be described herein.

Wherein the device 1 further comprises: a first priority determination module 109 and a second priority determination module 110;

A first priority determining module 109 configured to determine that the asset ordering priority of the asset under attention object z _i is less than the asset ordering priority of the asset under attention object z _j when the number of first asset risk levels in the current asset risk level of each risk factor under consideration object z _i at the current time is less than the number of first asset risk levels in the current asset risk level of each risk factor of the asset under attention object z _j at the current time;

A second priority determination module 110 for determining that the asset ordering priority of the asset under attention object z _i is less than the asset ordering priority of the asset under attention object z _j when the number of second asset risk levels in the current asset risk levels of each risk factor of the asset under attention object z _i at the current time is less than the number of second asset risk levels in the current asset risk levels of each risk factor of the asset under attention object z _j at the current time.

The specific function implementation manner of the first priority determining module 109 and the second priority determining module 110 is please refer to step S103 in the corresponding embodiment of fig. 3, and a detailed description is omitted herein.

Wherein the number of objects of interest asset objects is at least two; the information generation module 105 includes: an early warning object acquisition unit 1051, an early warning message generation unit 1052, and a prompt information determination unit 1053;

An early warning object obtaining unit 1051, configured to obtain, from at least two attention asset objects in the asset object sequence, an attention asset object with the highest asset sequencing priority as a risk early warning asset object according to the number of asset prompts; the number of the risk early warning asset objects is equal to the number of asset prompts;

An early warning message generating unit 1052, configured to generate, when the current asset risk level of each risk factor of the risk early warning asset object at the current time is different from the historical asset risk level of each risk factor of the risk early warning asset object at the historical reference time, a risk early warning push message for the risk early warning asset object according to the current asset risk level of each risk factor of the risk early warning asset object at the current time;

The prompt information determining unit 1053 is configured to determine the risk early warning push message as asset risk prompt information.

The specific function implementation manner of the early warning object acquiring unit 1051, the early warning message generating unit 1052 and the prompt message determining unit 1053 is referred to step S105 in the embodiment corresponding to fig. 3, and will not be described herein.

Wherein the device 1 is further configured to:

Wherein, the priority obtaining module 103 includes: an input unit 1037 and an output unit 1038;

An input unit 1037, configured to input the asset risk level of the asset object of interest under each risk factor and the user operation behavior data, into a ranking model, and generate risk feature data corresponding to the asset object of interest in the ranking model;

An output unit 1038 for outputting asset ordering priorities of the asset objects of interest in the ordering model based on the risk characteristic data.

The specific functional implementation manner of the input unit 1037 and the output unit 1038 is please refer to step S103 in the embodiment corresponding to fig. 3, and a detailed description is omitted herein.

Wherein the device 1 further comprises: a consensus module 111 and an addition module 112;

The consensus module 111 is configured to synchronize the asset risk prompt information to a blockchain network, and perform information consensus on the asset risk prompt information based on the blockchain network;

An adding module 112, configured to add the asset risk prompt information to the local blockchain when it is detected that the blockchain network successfully consensus the asset risk prompt information;

An information generation module 105 for:

In the specific function implementation manner of the consensus module 111 and the adding module 112, please refer to step S105 in the corresponding embodiment of fig. 3, and a detailed description is omitted herein.

The application can acquire the asset risk level of the concerned asset object of the target user under at least two risk factors respectively; acquiring user operation behavior data of a target user aiming at an asset object; determining asset sequencing priority of the concerned asset object according to the asset risk level of the concerned asset object under each risk factor and the user operation behavior data; generating an asset object sequence formed by the asset objects of interest according to the sorting priority of the asset objects of interest; and generating asset risk prompt information aiming at the concerned asset object according to the asset object sequence, and outputting the asset risk prompt information. Therefore, the device provided by the application can generate the exclusive asset risk prompt information of the target user according to the concerned asset object of the target user and the user operation behavior data of the target user for the concerned asset object, so that the risk condition of the concerned asset object of the target user which is most concerned can be accurately prompted through the asset risk prompt information.

Referring to fig. 12, fig. 12 is a schematic structural diagram of a computer device according to the present application. As shown in fig. 12, the computer device 1000 may include: processor 1001, network interface 1004, and memory 1005, in addition, computer device 1000 may further comprise: a user interface 1003, and at least one communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display (Display), a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface, among others. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 1005 may also optionally be at least one storage device located remotely from the processor 1001. As shown in fig. 12, an operating system, a network communication module, a user interface module, and a device control application program may be included in the memory 1005, which is one type of computer storage medium.

In the computer device 1000 shown in fig. 12, the network interface 1004 may provide network communication functions; while user interface 1003 is primarily used as an interface for providing input to a user; and the processor 1001 may be configured to invoke the device control application stored in the memory 1005 to implement the description of the virtual asset data processing method in the embodiment corresponding to fig. 3. It should be understood that the computer device 1000 described in the present application may also perform the description of the virtual asset data processing apparatus 1 in the embodiment corresponding to fig. 11, which is not described herein. In addition, the description of the beneficial effects of the same method is omitted.

Furthermore, it should be noted here that: the present application further provides a computer readable storage medium, in which the aforementioned computer program executed by the virtual asset data processing apparatus 1 is stored, and the computer program includes program instructions, when executed by a processor, can execute the description of the virtual asset data processing method in the corresponding embodiment of fig. 3, and therefore, the description will not be repeated here. In addition, the description of the beneficial effects of the same method is omitted. For technical details not disclosed in the embodiments of the computer storage medium according to the present application, please refer to the description of the method embodiments of the present application. As an example, program instructions may be deployed to be executed on one computing device or on multiple computing devices at one site or on multiple computing devices distributed across multiple sites and interconnected by a communication network, where the multiple computing devices distributed across multiple sites and interconnected by a communication network may constitute a blockchain system.

Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of computer programs, which may be stored on a computer-readable storage medium, and which, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random-access Memory (Random Access Memory, RAM), or the like.

The foregoing disclosure is illustrative of the present application and is not to be construed as limiting the scope of the application, which is defined by the appended claims.

Claims

1. A method of virtual asset data processing, comprising:

Acquiring asset risk levels of the concerned asset objects of the target user under at least two risk factors respectively; the number of the concerned asset objects is one or more, and one or more of the concerned asset objects are asset objects for which the target user performs the closing operation;

acquiring user operation behavior data of the target user aiming at the concerned asset object; the user operational behavior data includes a click rate of the target user for each of the asset objects of interest;

Determining asset sequencing priority of the concerned asset object according to the asset risk level of the concerned asset object under each risk factor and the user operation behavior data; one of the asset objects of interest has an asset ordering priority;

generating an asset object sequence formed by the concerned asset objects according to the sorting priority of the concerned asset objects;

generating asset risk prompt information aiming at the concerned asset object according to the asset object sequence, and outputting the asset risk prompt information;

Wherein the asset-of-interest objects include asset-of-interest object z _i and asset-of-interest object z _j, each of i and j being a positive integer less than or equal to the total number of objects of the asset-of-interest object;

If the asset risk level of the asset of interest object z _i changes and the asset risk level of the asset of interest object z _j does not change, the asset ordering priority of the asset of interest object z _i is greater than the asset ordering priority of the asset of interest object z _j;

if the asset risk levels of the asset of interest object z _i and the asset of interest object z _j both change and the number of newly increased risk items of the asset of interest object z _i is greater than the number of newly increased risk items of the asset of interest object z _j, then the asset ordering priority of the asset of interest object z _i is greater than the asset ordering priority of the asset of interest object z _j;

If the asset risk levels of both the asset of interest object z _i and the asset of interest object z _j change, and the number of newly increased risk items of the asset of interest object z _i is equal to the number of newly increased risk items of the asset of interest object z _j, and the target user's click rate on the asset of interest object z _i is greater than the target user's click rate on the asset of interest object z _j, then the asset ordering priority of the asset of interest object z _i is greater than the asset ordering priority of the asset of interest object z _j;

If the asset risk levels of the asset of interest object z _i and the asset of interest object z _j both change, and the number of newly added risk items of the asset of interest object z _i is equal to the number of newly added risk items of the asset of interest object z _j, and the click-through amount of the target user on the asset of interest object z _i is equal to the click-through amount of the target user on the asset of interest object z _j, the total number of high risk items of the asset of interest object z _i is greater than the total number of high risk items of the asset of interest object z _j, the asset ordering priority of the asset of interest object z _i is greater than the asset ordering priority of the asset of interest object z _j;

If the asset risk levels of both the asset of interest object z _i and the asset of interest object z _j do not change, responsive to the target user's click rate on the asset of interest object z _i being greater than the target user's click rate on the asset of interest object z _j, the asset ordering priority of the asset of interest object z _i is greater than the asset ordering priority of the asset of interest object z _j;

if neither the asset risk level of the asset of interest object z _i nor the asset of interest object z _j changes and the target user's click-through amount for the asset of interest object z _i is equal to the target user's click-through amount for the asset of interest object z _j, in response to the asset of interest object z _i having a total number of high risk items greater than the asset of interest object z _j, the asset ordering priority of the asset of interest object z _i is greater than the asset ordering priority of the asset of interest object z _j.

2. The method of claim 1, wherein the acquiring asset risk levels of the asset object of interest of the target user under at least two risk factors, respectively, comprises:

determining a target asset industry to which the asset object of interest belongs;

acquiring the at least two risk factors associated with the target asset industry in a risk factor pool;

acquiring risk level evaluation parameters of the concerned asset object under each risk factor respectively;

And determining the asset risk level of the concerned asset object under each risk factor according to the risk level evaluation parameters corresponding to each risk factor.

3. The method of claim 2, wherein generating asset risk cue information for the asset object of interest from the sequence of asset objects, outputting the asset risk cue information, comprises:

generating an asset object list containing the concerned asset object according to the asset object sequence, taking the asset object list as the asset risk prompt information, and outputting the asset risk prompt information;

The method further comprises the steps of:

Classifying risk factors in the risk factor pool; one risk factor corresponds to one risk factor type;

generating risk tag prompt information of the concerned asset object aiming at each risk factor according to the asset risk level of the concerned asset object under each risk factor;

Responding to the unfolding operation of the concerned asset object in the asset object list, and classifying and outputting risk tag prompt information corresponding to each risk factor of the concerned asset object according to the risk factor type of each risk factor.

4. The method of claim 2, wherein the at least two risk factors include a risk factor f _n, n being a positive integer less than or equal to the total number of the at least two risk factors;

The determining, according to the risk level evaluation parameters corresponding to each risk factor, the asset risk level of the concerned asset object under each risk factor includes:

Determining at least two asset objects in the asset industry associated with the risk factor f _n from a total set of asset objects as at least two associated asset objects associated with the risk factor f _n; the at least two associated asset objects include the asset object of interest;

Acquiring risk level evaluation parameters of associated asset objects except the concerned asset object in the at least two associated asset objects under the risk factor f _n;

Determining the asset object risk ranking of each associated asset object according to the numerical value of the risk level evaluation parameter corresponding to each associated asset object;

Acquiring a ranking range of the asset object risk ranks of the concerned asset objects in the asset object risk ranks of the at least two associated asset objects;

And determining the asset risk level of the concerned asset object under the risk factor f _n according to the ranking range.

5. The method of claim 4, wherein the obtaining risk level assessment parameters for the asset object of interest at each risk factor, respectively, comprises:

When the risk factor f _n is the receivable asset proportion, acquiring the enterprise receivable asset and the enterprise total input asset of the enterprise to which the concerned asset object belongs;

determining an receivable asset duty ratio of the concerned asset object under the risk factor f _n according to the enterprise receivable asset and the enterprise total input asset;

Taking the receivable asset proportion of the concerned asset object under the risk factor f _n as a risk level assessment parameter of the concerned asset object under the risk factor f _n.

6. The method of claim 4, wherein the asset risk level of the asset-of-interest object under the risk factor f _n comprises a first asset risk level and a second asset risk level; the asset risk level of the asset object of interest indicated by the second asset risk level is greater than the asset risk level of the asset object of interest indicated by the first asset risk level;

the determining, according to the ranking range, an asset risk level of the asset object of interest under the risk factor f _n includes:

When the ranking range of the concerned asset object is in a first ranking range to which the first asset risk level belongs, determining that the asset risk level of the concerned asset object under the risk factor f _n is the first asset risk level;

And when the ranking range of the concerned asset object is in a second ranking range to which the second asset risk level belongs, determining that the asset risk level of the concerned asset object under the risk factor f _n is the second asset risk level.

7. The method of claim 6, wherein the asset risk level of the asset object of interest at each of the risk factors is a current asset risk level of the asset object of interest at the current time instance for each of the risk factors; the user operational behavior data includes object click amounts of the target user for the asset object of interest;

the determining the asset sequencing priority of the concerned asset object according to the asset risk level of the concerned asset object under each risk factor and the user operation behavior data comprises the following steps:

Acquiring historical asset risk levels of each risk factor of the concerned asset object z _i and the concerned asset object z _j at historical reference moments respectively;

Determining an asset risk level different from a historical asset risk level of each risk factor of the asset of interest object z _i at the current time point from a historical asset risk level of each risk factor of the asset of interest object z _i at the historical reference time point as a first change risk level;

Determining an asset risk level different from a historical asset risk level of each risk factor of the asset of interest object z _j at the current time point from a historical asset risk level of each risk factor of the asset of interest object z _j at the historical reference time point as a second change risk level;

Determining that the asset-ordering priority of the asset-of-interest object z _i is less than the asset-ordering priority of the asset-of-interest object z _j when the number of first varying risk levels is less than a varying number threshold and the number of second varying risk levels is greater than or equal to the varying number threshold;

Determining that the asset-ordering priority of the asset-of-interest object z _i is less than the asset-ordering priority of the asset-of-interest object z _j when the number of the second asset risk levels in the first varying risk levels is less than the number of the second asset risk levels in the second varying risk levels;

When the object click amount of the target user for the concerned asset object z _i is less than the object click amount of the target user for the concerned asset object z _j, determining that the asset ordering priority of the concerned asset object z _i is less than the asset ordering priority of the concerned asset object z _j.

8. The method of claim 7, wherein the method further comprises:

Determining that the asset ordering priority of the asset of interest object z _i is less than the asset ordering priority of the asset of interest object z _j when the number of the first asset risk levels in the current asset risk level of each risk factor of the asset of interest object z _i at the current time is less than the number of the first asset risk levels in the current asset risk level of each risk factor of the asset of interest object z _j at the current time;

And determining that the asset ordering priority of the asset of interest object z _i is less than the asset ordering priority of the asset of interest object z _j when the number of the second asset risk levels in the current asset risk level of each risk factor of the asset of interest object z _i at the current time is less than the number of the second asset risk levels in the current asset risk level of each risk factor of the asset of interest object z _j at the current time.

9. The method of claim 7, wherein the number of objects of interest asset objects is at least two; the generating asset risk prompt information for the concerned asset object according to the asset object sequence comprises the following steps:

according to the number of the asset prompts, acquiring the concerned asset object with the highest asset sequencing priority from at least two concerned asset objects of the asset object sequence as a risk early warning asset object; the number of the risk early warning asset objects is equal to the number of the asset prompts;

When the current asset risk level of each risk factor of the risk early-warning asset object at the current time is different from the historical asset risk level of each risk factor of the risk early-warning asset object at the historical reference time, generating a risk early-warning pushing message for the risk early-warning asset object according to the current asset risk level of each risk factor of the risk early-warning asset object at the current time;

And determining the risk early warning push message as the asset risk prompt message.

10. The method according to claim 9, wherein the method further comprises:

Responding to the subscription operation of the user terminal of the target user for the risk early warning function, and subscribing the risk early warning function for the user terminal; and the user terminal subscribed to the risk early warning function is used for outputting the pushed asset risk prompt information.

11. The method of claim 1, wherein said determining asset prioritization of said asset-of-interest object based on said asset risk level of said asset-of-interest object under each risk factor, respectively, and said user operational behavior data, comprises:

Inputting the asset risk level of the concerned asset object under each risk factor and the user operation behavior data into a sequencing model, and generating risk characteristic data corresponding to the concerned asset object in the sequencing model;

and outputting asset sequencing priority of the concerned asset object in the sequencing model according to the risk characteristic data.

12. The method according to claim 1, wherein the method further comprises:

synchronizing the asset risk prompt information to a blockchain network, and carrying out information consensus on the asset risk prompt information based on the blockchain network;

When the fact that the blockchain network successfully consensus the asset risk prompt information is detected, adding the asset risk prompt information to a local blockchain;

The outputting the asset risk prompt information comprises:

and acquiring the asset risk prompt information from the local blockchain, and outputting the asset risk prompt information acquired from the local blockchain.

13. A virtual asset data processing apparatus, comprising:

The level acquisition module is used for acquiring asset risk levels of the concerned asset objects of the target user under at least two risk factors respectively; the number of the concerned asset objects is one or more, and one or more of the concerned asset objects are asset objects for which the target user performs the closing operation;

The behavior acquisition module is used for acquiring user operation behavior data of the target user aiming at the concerned asset object; the user operational behavior data includes a click rate of the target user for each of the asset objects of interest;

the priority acquisition module is used for determining the asset sequencing priority of the concerned asset object according to the asset risk level of the concerned asset object under each risk factor and the user operation behavior data; one of the asset objects of interest has an asset ordering priority;

The sequence generation module is used for generating an asset object sequence formed by the attention asset objects according to the sorting priority of the attention asset objects;

The information generation module is used for generating asset risk prompt information aiming at the concerned asset object according to the asset object sequence and outputting the asset risk prompt information;

14. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method of any of claims 1-12.

15. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, perform the method of any of claims 1-12.

16. A computer program product comprising computer instructions which, when executed by a processor, implement the method of any one of claims 1 to 12.