CN111984840B - Online asset security display locking method and device - Google Patents
Online asset security display locking method and device Download PDFInfo
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Abstract
The invention provides a locking method and a device for online asset security display, wherein the method comprises the following steps: acquiring customer set data and determining a plurality of customer subsets; analyzing each client subset, acquiring the active locking proportion in the corresponding asset locking setting condition information, calculating the probability of active locking of old clients which are not actively locked after receiving the locking setting push message, and sending the locking setting message to the old clients which are not set according to the probability; training a machine learning model according to the customer set data and the corresponding asset locking setting condition information; acquiring new client information, inputting a trained machine learning model, determining a new client set locking probability estimated value, and sending a locking setting message to the new client; acquiring an identity verification mode in a client history transaction of an old client, and locking an online asset by combining locking setting messages pushed to the old client and a new client; wherein after locking of the online property, authentication is required when viewing the online property.
Description
Technical Field
The invention relates to the technical field of computer information processing, in particular to a method and a device for locking online asset security display.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
In the current online banking application, the number information of the assets of the customers is displayed, and some applications are made into symbol display, which is similar to the display of a sign (asterisk), and the numbers can be displayed by clicking related icons such as an eye-like icon.
With the increasing content of applications on the banking line, the frequency of application is higher and higher, and particularly the probability of application under the condition that others participate together is higher and higher.
The current method for protecting the condition of the number of assets and the like by online banking application is generally replaced by symbols such as an asterisk and the like, and specific information such as numerals and the like can be checked by clicking a related icon.
When someone else and the user watch the application together, the user can not watch the asset condition of the user or can watch the asset condition slightly, so that the experience of the user is poor, the mind safety is low, the privacy of the user is not well protected, and certain risks are brought to the asset of the user.
Therefore, how to provide a new solution to the above technical problem is a technical problem to be solved in the art.
Disclosure of Invention
The embodiment of the invention provides an online asset security display locking method, which realizes accurate recommendation of an asset locking setting function, is beneficial to a new customer to know and use the asset locking function, improves the viscosity and security experience of the customer, and improves the safety coefficient of the user asset, and the method comprises the following steps:
acquiring customer set data;
determining a plurality of client subsets according to the client set data; wherein each customer subset includes its corresponding asset lock setup information;
analyzing each client subset, acquiring the active locking proportion in the corresponding asset locking setting condition information, calculating the probability of active locking of old clients which are not actively locked after receiving the locking setting push message, and sending the locking setting message to the old clients which are not set according to the probability;
training a machine learning model according to the customer set data and the corresponding asset locking setting condition information;
acquiring new client information, inputting a trained machine learning model, and determining a new client set locking probability estimated value;
according to the new client set locking probability estimated value, a locking setting message is sent to the new client;
acquiring an identity verification mode in a client history transaction of an old client, and locking an online asset by combining locking setting messages pushed to the old client and a new client; wherein after locking of the online property, authentication is required when viewing the online property.
The embodiment of the invention also provides an on-line asset security display locking device, which comprises:
the data acquisition module is used for acquiring customer set data;
a client sub-set determining module, configured to determine a plurality of client sub-sets according to client set data; wherein each customer subset includes its corresponding asset lock setup information;
the old client pushing and locking set message module is used for analyzing each client subset, acquiring the active locking proportion in the corresponding asset locking set condition information, calculating the probability of active locking of the old client which is not actively locked after receiving the locking set push message, and sending the locking set message to the old client which is not set according to the probability;
the machine learning model training module is used for training a machine learning model according to the customer set data and the corresponding asset locking setting condition information;
the new client setting locking probability estimation value determining module is used for acquiring new client information, inputting a trained machine learning model and determining a new client setting locking probability estimation value;
the new client locking setting message sending module is used for sending a locking setting message to the new client according to the new client setting locking probability estimated value;
the online asset locking module is used for acquiring an identity verification mode in a client history transaction of an old client, and locking online assets by combining locking setting messages pushed to the old client and a new client; wherein after locking of the online property, authentication is required when viewing the online property.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the above-mentioned method for locking the online asset security display when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program for executing the online asset security presentation locking method.
The embodiment of the invention provides a locking method and a device for online asset security display, which are characterized in that firstly, customer set data are acquired; then determining a plurality of client subsets according to the client set data; wherein each customer subset includes its corresponding asset lock setup information; then analyzing each client subset, obtaining the active locking proportion in the corresponding asset locking setting condition information, calculating the probability of active locking of the old clients which are not actively locked after receiving the locking setting push message, and sending the locking setting message to the old clients which are not set according to the probability; next, training a machine learning model according to the customer set data and the corresponding asset locking setting condition information; next, new customer information is acquired, a trained machine learning model is input, and a new customer set locking probability estimated value is determined; further according to the new client set locking probability estimated value, a locking setting message is sent to the new client; finally, acquiring an identity verification mode in the client history transaction of the old client, and locking the online asset by combining locking setting messages pushed to the old client and the new client; wherein after locking of the online property, authentication is required when viewing the online property. The embodiment of the invention analyzes the client set based on the existing client set data to obtain a plurality of client sub-sets; according to the locking setting condition of the assets corresponding to each client in the client sub-set, the locking proportion is obtained, locking setting information is recommended to old clients in the same sub-set, accurate pushing of locking setting can be achieved, and asset safety of the old clients is facilitated; for a new client, the client set data and the corresponding asset locking setting condition information are utilized to train a machine learning model, the locking probability estimated value set by the new client is identified, and the locking setting information is sent to the new client, so that the accurate recommendation of the asset locking setting function is realized, the new client can know and use the asset locking function, the client viscosity and the safety experience are improved, and the user asset safety coefficient is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
fig. 1 is a schematic diagram of an on-line asset security display locking method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a machine learning model training process of an online asset security display locking method according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of an online property locking process according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of a computer device running an on-line asset security display locking method embodying the present invention.
Fig. 5 is a schematic diagram of an on-line asset security display locking device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
Fig. 1 is a schematic diagram of an online asset security display locking method according to an embodiment of the present invention, as shown in fig. 1, where the embodiment of the present invention provides an online asset security display locking method, which implements accurate recommendation of an asset locking setting function, facilitates a new client to know and use the asset locking function, and improves viscosity and security experience of the client, so as to improve a user asset security coefficient, and the method includes:
step 101: acquiring customer set data;
step 102: determining a plurality of client subsets according to the client set data; wherein each customer subset includes its corresponding asset lock setup information;
step 103: analyzing each client subset, acquiring the active locking proportion in the corresponding asset locking setting condition information, calculating the probability of active locking of old clients which are not actively locked after receiving the locking setting push message, and sending the locking setting message to the old clients which are not set according to the probability;
step 104: training a machine learning model according to the customer set data and the corresponding asset locking setting condition information;
step 105: acquiring new client information, inputting a trained machine learning model, and determining a new client set locking probability estimated value;
step 106: according to the new client set locking probability estimated value, a locking setting message is sent to the new client;
step 107: acquiring an identity verification mode in a client history transaction of an old client, and locking an online asset by combining locking setting messages pushed to the old client and a new client; wherein after locking of the online property, authentication is required when viewing the online property.
The embodiment of the invention provides an online asset security display locking method, which comprises the steps of firstly, acquiring customer set data; then determining a plurality of client subsets according to the client set data; wherein each customer subset includes its corresponding asset lock setup information; then analyzing each client subset, obtaining the active locking proportion in the corresponding asset locking setting condition information, calculating the probability of active locking of the old clients which are not actively locked after receiving the locking setting push message, and sending the locking setting message to the old clients which are not set according to the probability; next, training a machine learning model according to the customer set data and the corresponding asset locking setting condition information; next, new customer information is acquired, a trained machine learning model is input, and a new customer set locking probability estimated value is determined; further according to the new client set locking probability estimated value, a locking setting message is sent to the new client; finally, acquiring an identity verification mode in the client history transaction of the old client, and locking the online asset by combining locking setting messages pushed to the old client and the new client; wherein after locking of the online property, authentication is required when viewing the online property. The embodiment of the invention analyzes the client set based on the existing client set data to obtain a plurality of client sub-sets; according to the locking setting condition of the assets corresponding to each client in the client sub-set, the locking proportion is obtained, locking setting information is recommended to old clients in the same sub-set, accurate pushing of locking setting can be achieved, and asset safety of the old clients is facilitated; for a new client, the client set data and the corresponding asset locking setting condition information are utilized to train a machine learning model, the locking probability estimated value set by the new client is identified, and the locking setting information is sent to the new client, so that the accurate recommendation of the asset locking setting function is realized, the new client can know and use the asset locking function, the client viscosity and the safety experience are improved, and the user asset safety coefficient is improved.
When the method for locking the online asset security display provided by the embodiment of the invention is implemented, the method specifically comprises the following steps:
acquiring customer set data; determining a plurality of client subsets according to the client set data; wherein each customer subset includes its corresponding asset lock setup information; analyzing each client subset, acquiring the active locking proportion in the corresponding asset locking setting condition information, calculating the probability of active locking of old clients which are not actively locked after receiving the locking setting push message, and sending the locking setting message to the old clients which are not set according to the probability; training a machine learning model according to the customer set data and the corresponding asset locking setting condition information; acquiring new client information, inputting a trained machine learning model, and determining a new client set locking probability estimated value; according to the new client set locking probability estimated value, a locking setting message is sent to the new client; acquiring an identity verification mode in a client history transaction of an old client, and locking an online asset by combining locking setting messages pushed to the old client and a new client; wherein after locking of the online property, authentication is required when viewing the online property.
When the method for locking the online asset security display provided by the embodiment of the invention is implemented, in one embodiment, the acquiring the client set data includes:
and connecting a banking system, and acquiring asset information, app login data, transaction amount, transaction type and risk preference from the banking system to form customer set data.
In the embodiment, a banking system is connected, asset information, app login data, transaction amount, transaction type and risk preference are obtained from a database of the banking system, and customer set data is formed; and simultaneously acquiring the information of the locking setting condition of the asset corresponding to the customer set data, wherein the locking corresponds to 1, the unlocking corresponds to 0, and the customer is NULL if not set.
In a specific implementation of the method for locking online asset security display provided by the embodiment of the present invention, in one embodiment, the determining a plurality of client subsets according to the client set data includes:
and carrying out cluster analysis on the client set data to determine a plurality of client subsets.
In an embodiment, determining a plurality of subsets of clients from the client set data includes: and carrying out cluster analysis on the client set data to establish a plurality of client subsets. Through cluster analysis, clients of the same type can be divided into the same client sub-set, so that similar processing can be conveniently performed, for example, locking setting information is pushed to the clients in the same client sub-set; meanwhile, through cluster analysis, the information of the locking setting condition of the corresponding asset of each client sub-set can be obtained.
Fig. 2 is a schematic diagram of a machine learning model training process of an online asset security display locking method according to an embodiment of the present invention, and as shown in fig. 2, when the online asset security display locking method according to the embodiment of the present invention is implemented, in an embodiment, the training machine learning model according to client aggregate data and corresponding asset locking setting condition information includes:
step 201: setting the information of the locking setting condition of the asset corresponding to the customer set data as a target value, taking the customer set data as a characteristic attribute, training a machine learning model, and monitoring the convergence condition of the machine learning model;
step 202: after the machine learning model converges, stopping training to obtain a trained machine learning model.
In an embodiment, training the machine learning model according to the customer set data and the corresponding asset lock setup information may include: and setting the information of the locking setting condition of the asset corresponding to the customer set data as a target value, taking the customer set data as a characteristic attribute, training a machine learning model, continuously detecting the convergence condition of the machine learning model in the training process, stopping training after the machine learning model is converged, and obtaining the trained machine learning model.
Corresponding to a new customer, customer information is obtained, including assets, app login data, transaction amount, transaction type, risk preferences. And inputting the client information into the trained machine learning model, calculating to obtain a set locking probability estimated value of the new client, and recommending the set locking information to the new client according to the obtained probability estimated value.
Fig. 3 is a schematic diagram of a process of locking an online asset by using an online asset security display locking method according to an embodiment of the present invention, as shown in fig. 3, when the online asset security display locking method according to the embodiment of the present invention is implemented, in one embodiment, the foregoing method for obtaining identity verification in a client history transaction of an old client, in combination with a locking setup message pushed to the old client and a new client, locks the online asset, including:
step 301: when the old client receives the pushed locking setting message, acquiring an identity verification mode in the client history transaction of the old client, clicking an asset locking icon on an asset display page, calling the identity verification mode in the client history transaction of the old client to perform initial locking verification, and locking the online asset after the verification is successful;
step 302: when a new client receives the pushed locking setting message, the identity verification modes are sequenced from high to low according to the use frequency in the historical transaction of the old client, the identity verification modes are displayed to the new client, after the new client selects the identity verification modes and inputs personal verification information, the new client jumps to an asset display page, and the asset display page clicks an asset locking icon to lock the online asset.
In an embodiment, acquiring an authentication mode in a client history transaction of an old client, and locking an online asset in combination with a locking setting message pushed to the old client and a new client may include:
when the old client receives the pushed locking setting message, acquiring an identity verification mode in a client history transaction of the old client, wherein the identity verification mode in the history transaction can comprise: password, fingerprint, face recognition and other modes; then clicking an asset locking icon on an asset display page, calling an identity verification mode in a client history transaction of an old client, calculating the similarity of multiple verification modes, and when one of the verification modes is beyond a similarity threshold, performing initial locking verification by adopting the verification mode, and locking the online asset after verification is successful; for example, the similarity threshold is set to 0.9, and the similarity of the plurality of verification modes is calculated as follows: and the password is 0.3, the fingerprint is 0.5, and the face recognition is 0.95, and then the initial locking verification is carried out by adopting a face recognition mode.
When a new client receives the pushed locking setting message, the identity verification modes are sequenced from high to low according to the use frequency in the historical transaction of the old client, the identity verification modes are displayed to the new client, after the new client selects the identity verification modes and inputs personal verification information, the new client jumps to an asset display page, and the asset display page clicks an asset locking icon to lock the online asset.
The embodiment of the invention also provides a process for setting and locking the online assets by using the online asset security display locking method, which mainly comprises the following steps:
1. the user logs in online banking application such as a mobile phone bank;
2. clicking to the position of my asset when the user needs to view the asset data;
3. an asset lock icon may be set on the location side of my asset;
4. the user may set the lock of the asset lock icon to open, that is, the asset data does not need to be locked, and the user opens the data that is directly visible or (in this embodiment, set to an asterisk, or other forms of symbols may be used). If the user sets that the lock of the asset locking icon is closed, step 6 is performed;
5. if the user wants to further view the asset data, clicking the previous icon with the number displayed as the number;
6. the user sets the lock of the asset locking icon, and the user needs to verify the asset data to check, for example, inputting a password or fingerprint or face brushing or short message verification code, and the like, so that the asset data can be displayed later. Each unit can be designed by itself without adding a number;
7. the initial condition of the lock can be set according to the specific condition of a client, the lock state setting can be pushed to the client, and the lock state setting can be carried out in the following manner;
8. no verification may be set for the new customer that the lock is open, as yet this function is not used. The client settings are subsequently pushed to lock according to 10 and subsequent steps. The old customer is not set with a locking default key to be opened, and the customer can be pushed to set with the locking according to the step 10 and the following steps;
9. the verification switch function is set in the application, so that a client can adjust whether verification is needed according to own preference.
Can be transacted on an intelligent machine or counter;
10. collecting client collection data, including assets, app login data, transaction amount, transaction type, risk preference, and corresponding locking icon setting conditions (locking corresponds to 1, unlocking corresponds to 0, and NULL if the client is not set) and the like;
11. and carrying out cluster analysis on the client set according to the client set data. Acquiring a corresponding client sub-set;
12. and for each client subset, calculating the probability of setting locking corresponding to the old clients which are not set to be locked according to the proportion of the locking icons actively set by the clients in the subset. Pushing a locking setting message to the old client according to the probability;
13. setting the locking setting condition of the client set as a target value, taking other data as characteristic attributes, and training a machine learning model;
14. corresponding to a new customer, his customer information is obtained, including assets, app login data, transaction amount, transaction type, risk preferences. His customer information is input to the machine learning model obtained in step 13, and a probability estimate of his set-up lock is calculated. And recommending the setting locking information to the user according to the acquired probability estimated value.
15. When the client sets up the locking, the corresponding mode of locking the authentication identity is set according to the authentication identity means in the historical transaction of the client. For example, the face authentication is performed before, the similarity threshold is 0.9, and the face authentication can also be set for locking correspondence, and the threshold is also 0.9.
The default state of the lock can be designed according to other schemes, and whether the setting of the lock is opened or closed needs to be authorized or not is also designed according to each specific situation.
The method is simple in setting, and the user can use the authorization in the same way as the prior checking mode if the user uses the authorization, and can use the authorization in the same way as the prior checking mode if the user does not set the authorization, so that the method is convenient for the user. For developers, adding a layer of authorization is an existing mechanism, and the implementation is simple. However, the user does not worry about other functions of the application or the problem of asking for education before other people, so that the user's enhanced safety experience is brought, and the safety coefficient of the user's assets is also improved.
Fig. 4 is a schematic diagram of a computer device for running an on-line asset security display locking method implemented in the present invention, and as shown in fig. 4, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, where the processor implements the above-mentioned on-line asset security display locking method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program for implementing the method for locking the online asset security demonstration.
The embodiment of the invention also provides an online asset security display locking device, which is described in the following embodiment. Because the principle of the device for solving the problem is similar to that of an on-line asset security display locking method, the implementation of the device can be referred to the implementation of the on-line asset security display locking method, and the repetition is omitted.
Fig. 5 is a schematic diagram of an on-line asset security display locking device according to an embodiment of the present invention, and as shown in fig. 5, the embodiment of the present invention further provides an on-line asset security display locking device, including:
a data acquisition module 501, configured to acquire client set data;
a client subset determining module 502, configured to determine a plurality of client subsets according to the client set data; wherein each customer subset includes its corresponding asset lock setup information;
the old client pushing and locking set message module 503 is configured to analyze each client subset, obtain the proportion of active locking in the corresponding asset locking set condition information, calculate the probability that the old client that is not actively locked is actively locked after receiving the locking set push message, and send the locking set message to the old client that is not set according to the probability;
a machine learning model training module 504 configured to train a machine learning model according to the customer set data and corresponding asset lock setup condition information;
the new client setting locking probability estimation value determining module 505 is configured to acquire new client information, input a trained machine learning model, and determine a new client setting locking probability estimation value;
a new client locking setting message sending module 506, configured to send a locking setting message to the new client according to the new client setting locking probability estimated value;
the online asset locking module 507 is configured to obtain an authentication mode in a client history transaction of an old client, and lock an online asset in combination with a locking setting message pushed to the old client and a new client; wherein after locking of the online property, authentication is required when viewing the online property.
When the on-line asset security display locking device provided by the embodiment of the invention is implemented, in one embodiment, the data acquisition module is specifically configured to:
and connecting a banking system, and acquiring asset information, app login data, transaction amount, transaction type and risk preference from the banking system to form customer set data.
When the online asset security display locking device provided by the embodiment of the invention is implemented, in one embodiment, the client subset determining module is specifically configured to:
and carrying out cluster analysis on the client set data to determine a plurality of client subsets.
When the on-line asset security display locking device provided by the embodiment of the invention is implemented, in one embodiment, the machine learning model training module is specifically configured to:
setting the information of the locking setting condition of the asset corresponding to the customer set data as a target value, taking the customer set data as a characteristic attribute, training a machine learning model, and monitoring the convergence condition of the machine learning model;
after the machine learning model converges, stopping training to obtain a trained machine learning model.
When the online asset security display locking device provided by the embodiment of the invention is implemented, in one embodiment, the online asset locking module is specifically configured to:
when the old client receives the pushed locking setting message, acquiring an identity verification mode in the client history transaction of the old client, clicking an asset locking icon on an asset display page, calling the identity verification mode in the client history transaction of the old client to perform initial locking verification, and locking the online asset after the verification is successful;
when a new client receives the pushed locking setting message, the identity verification modes are sequenced from high to low according to the use frequency in the historical transaction of the old client, the identity verification modes are displayed to the new client, after the new client selects the identity verification modes and inputs personal verification information, the new client jumps to an asset display page, and the asset display page clicks an asset locking icon to lock the online asset.
In summary, the method and the device for locking the online asset security display provided by the embodiment of the invention firstly acquire customer set data and corresponding asset locking setting condition information; then determining a plurality of client subsets according to the client set data and the corresponding asset locking setting condition information; then analyzing each client subset, obtaining the active locking proportion in the corresponding asset locking setting condition information, calculating the probability of active locking of the old clients which are not actively locked after receiving the locking setting push message, and sending the locking setting message to the old clients which are not set according to the probability; next, training a machine learning model according to the customer set data and the corresponding asset locking setting condition information; next, new customer information is acquired, a trained machine learning model is input, and a new customer set locking probability estimated value is determined; further according to the new client set locking probability estimated value, a locking setting message is sent to the new client; finally, acquiring an identity verification mode in the client history transaction of the old client, and locking the online asset by combining locking setting messages pushed to the old client and the new client; wherein after locking of the online property, authentication is required when viewing the online property. The embodiment of the invention analyzes the client set based on the existing client set data to obtain a plurality of client sub-sets; according to the locking setting condition of the assets corresponding to each client in the client sub-set, the locking proportion is obtained, locking setting information is recommended to old clients in the same sub-set, accurate pushing of locking setting can be achieved, and asset safety of the old clients is facilitated; for a new client, the client set data and the corresponding asset locking setting condition information are utilized to train a machine learning model, the locking probability estimated value set by the new client is identified, and the locking setting information is sent to the new client, so that the accurate recommendation of the asset locking setting function is realized, the new client can know and use the asset locking function, the client viscosity and the safety experience are improved, and the user asset safety coefficient is improved.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. An on-line asset security display locking method, comprising:
acquiring customer set data;
determining a plurality of client subsets according to the client set data; wherein each customer subset includes its corresponding asset lock setup information;
analyzing each client subset, acquiring the active locking proportion in the corresponding asset locking setting condition information, calculating the probability of active locking of old clients which are not actively locked after receiving the locking setting push message, and sending the locking setting message to the old clients which are not set according to the probability;
training a machine learning model according to the customer set data and the corresponding asset locking setting condition information;
acquiring new client information, inputting a trained machine learning model, and determining a new client set locking probability estimated value;
according to the new client set locking probability estimated value, a locking setting message is sent to the new client;
acquiring an identity verification mode in a client history transaction of an old client, and locking an online asset by combining locking setting messages pushed to the old client and a new client; after the online assets are locked, identity verification is needed when the online assets are checked;
acquiring an identity verification mode in a client history transaction of an old client, and locking an online asset by combining locking setting messages pushed to the old client and a new client, wherein the method comprises the following steps of:
when the old client receives the pushed locking setting message, acquiring an identity verification mode in the client history transaction of the old client, clicking an asset locking icon on an asset display page, calling the identity verification mode in the client history transaction of the old client to perform initial locking verification, and locking the online asset after the verification is successful;
when a new client receives the pushed locking setting message, the identity verification modes are sequenced from high to low according to the use frequency in the historical transaction of the old client, the identity verification modes are displayed to the new client, after the new client selects the identity verification modes and inputs personal verification information, the new client jumps to an asset display page, and the asset display page clicks an asset locking icon to lock the online asset.
2. The method of claim 1, wherein obtaining customer set data comprises:
and connecting a banking system, and acquiring asset information, app login data, transaction amount, transaction type and risk preference from the banking system to form customer set data.
3. The method of claim 1, wherein determining a plurality of subsets of clients based on the client set data comprises:
and carrying out cluster analysis on the client set data to determine a plurality of client subsets.
4. The method of claim 1, wherein training a machine learning model based on customer set data and corresponding asset lock setup information comprises:
setting the information of the locking setting condition of the asset corresponding to the customer set data as a target value, taking the customer set data as a characteristic attribute, training a machine learning model, and monitoring the convergence condition of the machine learning model;
after the machine learning model converges, stopping training to obtain a trained machine learning model.
5. An on-line asset security display locking device, comprising:
the data acquisition module is used for acquiring customer set data;
a client sub-set determining module, configured to determine a plurality of client sub-sets according to client set data; wherein each customer subset includes its corresponding asset lock setup information;
the old client pushing and locking set message module is used for analyzing each client subset, acquiring the active locking proportion in the corresponding asset locking set condition information, calculating the probability of active locking of the old client which is not actively locked after receiving the locking set push message, and sending the locking set message to the old client which is not set according to the probability;
the machine learning model training module is used for training a machine learning model according to the customer set data and the corresponding asset locking setting condition information;
the new client setting locking probability estimation value determining module is used for acquiring new client information, inputting a trained machine learning model and determining a new client setting locking probability estimation value;
the new client locking setting message sending module is used for sending a locking setting message to the new client according to the new client setting locking probability estimated value;
the online asset locking module is used for acquiring an identity verification mode in a client history transaction of an old client, and locking online assets by combining locking setting messages pushed to the old client and a new client; after the online assets are locked, identity verification is needed when the online assets are checked;
the online asset locking module is specifically used for:
when the old client receives the pushed locking setting message, acquiring an identity verification mode in the client history transaction of the old client, clicking an asset locking icon on an asset display page, calling the identity verification mode in the client history transaction of the old client to perform initial locking verification, and locking the online asset after the verification is successful;
when a new client receives the pushed locking setting message, the identity verification modes are sequenced from high to low according to the use frequency in the historical transaction of the old client, the identity verification modes are displayed to the new client, after the new client selects the identity verification modes and inputs personal verification information, the new client jumps to an asset display page, and the asset display page clicks an asset locking icon to lock the online asset.
6. The apparatus of claim 5, wherein the data acquisition module is specifically configured to:
and connecting a banking system, and acquiring asset information, app login data, transaction amount, transaction type and risk preference from the banking system to form customer set data.
7. The apparatus of claim 5, wherein the client subset determination module is specifically configured to:
and carrying out cluster analysis on the client set data to determine a plurality of client subsets.
8. The apparatus of claim 5, wherein the machine learning model training module is specifically configured to:
setting the information of the locking setting condition of the asset corresponding to the customer set data as a target value, taking the customer set data as a characteristic attribute, training a machine learning model, and monitoring the convergence condition of the machine learning model;
after the machine learning model converges, stopping training to obtain a trained machine learning model.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 4 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.
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| CN111507723A (en) * | 2020-06-18 | 2020-08-07 | 海南安迈云网络技术有限公司 | Digital asset management transaction encryption method |
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| CN109726763A (en) * | 2018-12-29 | 2019-05-07 | 北京神州绿盟信息安全科技股份有限公司 | A kind of information assets recognition methods, device, equipment and medium |
| CN111159694A (en) * | 2019-12-17 | 2020-05-15 | 上海七印信息科技有限公司 | Private use authorization method of block chain digital assets based on zero knowledge proof |
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