CN114386021A - Verification method for generating content in cross DIKW mode - Google Patents
Verification method for generating content in cross DIKW mode Download PDFInfo
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- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
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- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
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Abstract
The invention provides a verification method for generating contents in a cross-DIKW mode, which comprises the following specific steps: acquiring user verification field requirements, and constructing a user DIKW model according to the user verification field requirements, wherein the user DIKW model comprises a data model, an information model and a knowledge model; when a user initiates an authentication request, generating an authentication keyword according to a DIKW model of the user initiating the request; obtaining typed resources for verification according to verification keywords, and constructing a DIKW verification model according to the typed resources for verification; the DIKW verification model generates verification contents and feeds the verification contents back to the user for verification, the DIKW verification model is constructed according to verification keywords of the user, and the verification keywords contain privacy contents of the user, so that the generated verification contents are customized for the user, only the user can quickly and accurately verify the verification contents, operations such as logging in a user account or payment are avoided for others or scripts, and privacy safety of the individual and the network is improved.
Description
Technical Field
The invention relates to the technical field of privacy security protection, in particular to a verification method for generating contents in a DIKW-crossing mode.
Background
The quick development of the big data era presents an updated challenge to the privacy protection of personal data, along with the continuous expansion of a network circle, the number of various types of web portals and APP software is exponentially multiplied, the management is convenient and better experience is provided, the websites and the APP software can require a user to register a personal account, the user can ensure privacy safety protection when logging in an account, information interaction, payment and other steps, the common mode for improving the safety is to set a verification code, however, the verification code set at present is too simple, lawless persons can automatically identify and fill the verification code through a specific script, thereby logging in the account of the user, and the user's account is used for interaction, ordering, payment and other operations, and the privacy safety of individuals and the network is seriously influenced.
Data Information Knowledge Wisdom atlas (DIKW) is a neotype system, by Data (Data), Information (Information), Knowledge and Wisdom (Wisdom) are constituteed, everyone has the Data resource who belongs to self, Information resource, Knowledge resource and Wisdom resource, can obtain DIKW atlas system through modeling to the resource of above-mentioned four kinds, DIKW atlas is closely relevant with user's self privacy, if can supply the user to verify according to the corresponding content of privacy generation in the DIKW atlas, the dynamics of improvement privacy protection that will be very big, how to combine the DIKW to the problem that needs the solution at present in the privacy protection.
Disclosure of Invention
In view of the above, the invention provides a verification method for generating content in a DIKW-crossing mode, which generates verification content meeting the requirements of the user in the verification field, avoids lawless persons from filling verification codes to steal accounts and carrying out operations such as information interaction and payment, and ensures privacy safety of individuals and networks.
The technical scheme of the invention is realized as follows:
the verification method for generating contents across DIKW modalities comprises the following steps:
step S1, acquiring user verification field requirements, and constructing a user DIKW model according to the user verification field requirements, wherein the user DIKW model comprises a data model, an information model and a knowledge model;
step S2, when a user initiates a verification request, generating a verification keyword according to a DIKW model of the user initiating the request;
step S3, acquiring typed resources for verification according to the verification keywords, and constructing a DIKW verification model according to the typed resources for verification;
and step S4, generating verification contents by the DIKW verification model, and feeding the verification contents back to the user for verification.
Preferably, the specific step of acquiring the requirement of the user authentication field in step S1 is as follows:
step S11, extracting basic information filled when the user registers the account;
step S12, acquiring character information with more frequency in the user input method;
step S13, acquiring website information of more browsed websites of the user;
and step S14, forming the user authentication field requirement according to the basic information, the character information and the website information.
Preferably, the step S1 is a specific step of constructing a user didw model, and the specific step is as follows: mapping the user verification field requirements into user type resources, wherein the user type resources comprise data resources, information resources and knowledge resources, and constructing a data model, an information model and a knowledge model based on the data resources, the information resources and the knowledge resources.
Preferably, the specific step of step S2 includes:
step S21, when the user initiates the verification request, the account identification of the user initiating the verification request is obtained;
s22, selecting a corresponding user DIKW model according to the account identifier;
and step S23, analyzing and converting the basic information, the character information and the website information by the user DIKW model, and obtaining verification keywords.
Preferably, the specific step of step S23 includes:
s231, searching basic information by using a user DIKW model, and judging whether the interior contains a verification mode set by a user;
step S232, if the basic information contains the verification mode set by the user, outputting the verification mode as a verification key word;
and step S233, if the basic information does not contain the verification mode set by the user, analyzing and converting the basic information and/or the character information and/or the website information by the DIKW model of the user, and obtaining the corresponding verification keywords.
Preferably, the specific step of step S3 includes:
step S31, obtaining typed resources for verification according to the verification keywords, wherein the typed resources for verification comprise data resources, information resources and knowledge resources;
and S32, constructing a data model, an information model and a knowledge model of the DIKW model to be verified based on the data resources, the information resources and the knowledge resources.
Preferably, the step S31 of obtaining the typed resource for verification according to the verification keyword includes the specific steps of: and traversing each verification keyword, searching a related field in the network based on each verification keyword, classifying the contents contained in the field according to data, information and knowledge, and forming typed resources.
Preferably, the specific step of step S4 includes:
step S41, the DIKW model is verified to determine the cognitive level of the user according to basic information, character information and website information;
step S42, determining the expression form of the verification content according to the cognitive level of the user;
and step S43, combining the verification contents by the DIKW verification model according to the internal resources thereof, and feeding the verification contents back to the user according to the determined expression form.
Preferably, the presentation form of the verification content includes text, pictures and sound.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a cross-DIKW modal generated content verification method, which comprises the steps of constructing user DIKW models corresponding to the requirements of different users in the verification field, wherein each user DIKW model comprises a data model, an information model and a knowledge model, when a verification request is initiated with a user, a verification keyword is obtained based on the user DIKW model of the user, the obtained verification keyword is used for constructing the verification DIKW model, and finally the verification DIKW model generates verification content and feeds the verification content back to the user for verification.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only preferred embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a flow chart of a verification method of generating content across DIKW modalities of the present invention;
FIG. 2 is a flowchart of step S1 of the method of the present invention for verifying the generation of content across DIKW modalities;
FIG. 3 is a flowchart of step S2 of the verification method of generating content across DIKW modalities of the present invention;
FIG. 4 is a flowchart of step S23 of the verification method of generating content across DIKW modalities of the present invention;
FIG. 5 is a flowchart of step S3 of the verification method of generating content across DIKW modalities of the present invention;
fig. 6 is a flowchart of step S4 of the verification method of generating content across a DIKW modality of the present invention.
Detailed Description
For a better understanding of the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described with reference to the accompanying drawings.
Referring to fig. 1 to 6, the method for verifying content generated across a DIKW modality provided by the present invention includes the following steps:
step S1, acquiring user verification field requirements, and constructing a user DIKW model according to the user verification field requirements, wherein the user DIKW model comprises a data model, an information model and a knowledge model;
step S2, when a user initiates a verification request, generating a verification keyword according to a DIKW model of the user initiating the request;
step S3, acquiring typed resources for verification according to the verification keywords, and constructing a DIKW verification model according to the typed resources for verification;
and step S4, generating verification contents by the DIKW verification model, and feeding the verification contents back to the user for verification.
The verification method of the cross-DIKW modal generated content is used for verifying information interaction, order placing, payment and other operations after a user logs in an account or logs in the account, and can generate a corresponding verification code for the user to input information for verification.
Specifically, each user has an account, the account contains the private information of the user, the private information can obtain the field requirement of user verification, the field requirement of user verification is used for constructing a user DIKW model, the DIKW model comprises a data model, an information model and a knowledge model, the data model, the information model and the knowledge model can be obtained through mutual conversion, the user DIKW model comprising a large amount of private contents of the user can be constructed through combination and conversion among the data, the information and the knowledge, when the corresponding user initiates a verification request, the user DIKW model can generate corresponding verification keywords according to the private contents of the user, the verification keywords can be combined and processed to obtain the typed resources for verification, and the verification DIKW model can be constructed according to the typed resources for verification, because the DIKW verification model is constructed based on the verification keywords of the user, when verification content is generated, the verification content can be generated according to the privacy content of the user, so that the user can acquire correct verification codes to fill in the verification codes, and the privacy safety protection strength is improved.
Preferably, the specific step of acquiring the requirement of the user authentication field in step S1 is as follows:
step S11, extracting basic information filled when the user registers the account;
step S12, acquiring character information with more frequency in the user input method;
step S13, acquiring website information of more browsed websites of the user;
and step S14, forming the user authentication field requirement according to the basic information, the character information and the website information.
The premise of generating verification content related to the user privacy content is that a large amount of user privacy content needs to be acquired, user verification field requirements are formed according to the privacy content and used for building a user DIKW model, for the user privacy content, firstly, basic information including age, gender, birth year and month, position, company, hobby and the like can be left when a user registers an account, then, character information frequently appearing in daily chatting of the user and website information frequently browsed can be acquired, user verification field requirements are formed according to the character information and the website information, and the verification field requirements are used for building a verification DIKW model.
For character information, occupation, hobbies and the like of a user can be combined and inferred according to character information with more occurrence frequency, for example, the first few character information with more occurrence frequency are patents, inventions, utility models and the like, so that the user can be inferred to be related to the intellectual property patents, and therefore some verification contents related to the patents can be correspondingly generated when verification contents are generated subsequently, so that the user can conveniently and rapidly fill in verification codes in time, meanwhile, the verification contents can be prevented from being automatically filled in by scripts, and personal privacy safety protection is improved.
For the website information, the occupation or hobby of the user can be inferred according to the number of websites browsed by the user, for example, if a website browsed frequently every day by the user is a patent retrieval website, the occupation of the user can be judged to be related to the patent, or if the user frequently browses football or basketball programs, the interest and hobby of the user can be inferred to be football or basketball, so that when verification content is generated, the content related to the occupation or hobby can be correspondingly generated, and operations of stealing personal information or paying and placing orders and the like after verification of other people or scripts can be avoided.
Preferably, the step S1 is a specific step of constructing a user didw model, and the specific step is as follows: mapping the user verification field requirements into user type resources, wherein the user type resources comprise data resources, information resources and knowledge resources, and constructing a data model, an information model and a knowledge model based on the data resources, the information resources and the knowledge resources.
The DIKW model comprises Data (Data), Information (Information), Knowledge (Knowledge) and wisdom (wisdom), and contents of all parts in the DIKW model can be converted mutually.
Preferably, the specific step of step S2 includes:
step S21, when the user initiates the verification request, the account identification of the user initiating the verification request is obtained;
s22, selecting a corresponding user DIKW model according to the account identifier;
and step S23, analyzing and converting the basic information, the character information and the website information by the user DIKW model, and obtaining verification keywords.
The specific steps of step S23 include:
s231, searching basic information by using a user DIKW model, and judging whether the interior contains a verification mode set by a user;
step S232, if the basic information contains the verification mode set by the user, outputting the verification mode as a verification key word;
and step S233, if the basic information does not contain the verification mode set by the user, analyzing and converting the basic information and/or the character information and/or the website information by the DIKW model of the user, and obtaining the corresponding verification keywords.
After the user DIKW model of each user is built, the user is waited for initiating a request, when a verification request is never initiated, the account identification of the user initiating the verification request is firstly obtained, for example, a verified account name is obtained, the correct user can be determined according to the account identification, the corresponding user DIKW model is selected, and then basic information, character information and website information contained in the user DIKW model are analyzed and converted to obtain a verification keyword.
In the step of obtaining the verification keyword, the user DIKW model firstly searches basic information, a verification mode which is set by a user in advance and comprises secret protection problems and the like filled by the user when the user registers an account is stored in the basic information, if the basic information contains the verification mode set by the user, the verification mode can be directly output as the verification keyword, and if the basic information does not contain the verification mode, the user DIKW model needs to be searched from the basic information, character information and website information to obtain the corresponding verification keyword.
Preferably, the specific step of step S3 includes:
step S31, obtaining typed resources for verification according to the verification keywords, traversing each verification keyword, searching the related field in the network based on each verification keyword, classifying the contents contained in the field according to data, information and knowledge, and forming typed resources, wherein the typed resources for verification comprise data resources, information resources and knowledge resources;
and S32, constructing a data model, an information model and a knowledge model of the DIKW model to be verified based on the data resources, the information resources and the knowledge resources.
For each verification keyword, there may be one or more fields corresponding to the verification keyword, and for this reason, each verification keyword needs to be searched, the verification keyword is traversed, and after a field related to the verification keyword is searched in a network, contents included in the fields are combined, and the combined contents are typed resources for verifying the construction of a DIKW model, and since there are a plurality of fields, the verification DIKW model may also count the fields related to the most internally, and generate verification contents with the contents related to the most fields, for example, the verification DIKW model includes 15 patent fields and 20 basketball fields, and when generating the verification contents, a basketball may be used as a main content for selection.
In addition, the invention can also set the verification mode, and set the verification content into a multi-stage mode, for example, the first stage is the verification mode set by the user, the second stage can be set according to the basic information of the user, for example, inquiring the birth year and month of the user, the third stage can be set according to the occupation or hobby of the user, and the privacy protection of the user can be further improved by the multi-stage verification mode.
Preferably, the specific step of step S4 includes:
step S41, the DIKW model is verified to determine the cognitive level of the user according to basic information, character information and website information;
step S42, determining the expression form of the verification content according to the cognitive level of the user, wherein the expression form of the verification content comprises characters, pictures and sound;
and step S43, combining the verification contents by the DIKW verification model according to the internal resources thereof, and feeding the verification contents back to the user according to the determined expression form.
When the verification DIKW model generates verification contents, the cognitive level of a user per se is also required to be considered, the expression forms of the verification contents are determined according to different cognitive levels, the verification complexity corresponding to different expression forms is different, the corresponding privacy safety protection strength is also different, if the user is a child, the child does not have higher cognitive ability, pictures or simple characters can be selected to express the verification contents, for example, the child can distinguish the number of animals which the child often watches, for the crowd with higher cognitive level, sound can be adopted to express the verification contents, for example, the user is a fan of a certain band, the verification DIKW model can output a certain song of the band, the song name of the song is judged by the user to be verified, and the privacy safety protection strength is greatly improved.
According to the method, the private contents of the users are collected, extracted and combined, then the DIKW models of the users are built to be reserved, when a certain user initiates an authentication request (comprising a login request, a payment request and the like), the DIKW models of the users corresponding to the users can generate authentication keywords related to the private contents of the users, the authentication keywords can be contents in an authentication mode preset by the users, field contents related to occupation or hobbies of the users and the like can be extracted according to the private contents of the users, the authentication DIKW models can be correspondingly built according to the authentication keywords, the authentication DIKW models can judge and grade the cognition of the users, different authentication content expression modes are adopted for the users at different levels, and one-level or multi-level authentication modes are adopted to improve the privacy security protection.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. The verification method for generating the content in the DIKW-crossing mode is characterized by comprising the following steps of:
step S1, acquiring user verification field requirements, and constructing a user DIKW model according to the user verification field requirements, wherein the user DIKW model comprises a data model, an information model and a knowledge model;
step S2, when a user initiates a verification request, generating a verification keyword according to a DIKW model of the user initiating the request;
step S3, acquiring typed resources for verification according to the verification keywords, and constructing a DIKW verification model according to the typed resources for verification;
and step S4, generating verification contents by the DIKW verification model, and feeding the verification contents back to the user for verification.
2. The method for verifying content generated across DIKW modalities of claim 1, wherein the step S1 comprises the following steps:
step S11, extracting basic information filled when the user registers the account;
step S12, acquiring character information with more frequency in the user input method;
step S13, acquiring website information of more browsed websites of the user;
and step S14, forming the user authentication field requirement according to the basic information, the character information and the website information.
3. The method for verifying cross-DIKW modality generated content according to claim 2, wherein the step S1 is a specific step of constructing a user DIKW model, and comprises the following steps: mapping the user verification field requirements into user type resources, wherein the user type resources comprise data resources, information resources and knowledge resources, and constructing a data model, an information model and a knowledge model based on the data resources, the information resources and the knowledge resources.
4. The method of claim 2, wherein the step S2 includes the following steps:
step S21, when the user initiates the verification request, the account identification of the user initiating the verification request is obtained;
s22, selecting a corresponding user DIKW model according to the account identifier;
and step S23, analyzing and converting the basic information, the character information and the website information by the user DIKW model, and obtaining verification keywords.
5. The method of claim 4, wherein the step S23 includes the following steps:
s231, searching basic information by using a user DIKW model, and judging whether the interior contains a verification mode set by a user;
step S232, if the basic information contains the verification mode set by the user, outputting the verification mode as a verification key word;
and step S233, if the basic information does not contain the verification mode set by the user, analyzing and converting the basic information and/or the character information and/or the website information by the DIKW model of the user, and obtaining the corresponding verification keywords.
6. The method of validating content generated across a DIKW modality according to claim 1, wherein the specific steps of step S3 include:
step S31, obtaining typed resources for verification according to the verification keywords, wherein the typed resources for verification comprise data resources, information resources and knowledge resources;
and S32, constructing a data model, an information model and a knowledge model of the DIKW model to be verified based on the data resources, the information resources and the knowledge resources.
7. The method for verifying content generated across DIKW modalities of claim 6, wherein the step S31 is to obtain the typed resource for verification according to the verification keyword by the specific steps of: and traversing each verification keyword, searching a related field in the network based on each verification keyword, classifying the contents contained in the field according to data, information and knowledge, and forming typed resources.
8. The method of claim 2, wherein the step S4 includes the following steps:
step S41, the DIKW model is verified to determine the cognitive level of the user according to basic information, character information and website information;
step S42, determining the expression form of the verification content according to the cognitive level of the user;
and step S43, combining the verification contents by the DIKW verification model according to the internal resources thereof, and feeding the verification contents back to the user according to the determined expression form.
9. The method of claim 8, wherein the presentation form of the verification content comprises words, pictures and sounds.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115952354A (en) * | 2022-12-28 | 2023-04-11 | 海南星捷安科技集团股份有限公司 | Management system for medical instrument sales representative workbench |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115952354A (en) * | 2022-12-28 | 2023-04-11 | 海南星捷安科技集团股份有限公司 | Management system for medical instrument sales representative workbench |
| CN115952354B (en) * | 2022-12-28 | 2024-04-12 | 海南星捷安科技集团股份有限公司 | Management system for workbench of medical instrument sales representative |
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