A validation approach for content generated across DIKW modalities

Abstract

The present invention provides a verification method for generating content across DIKW modalities, and the specific steps are: obtaining user verification domain requirements, constructing a user DIKW model according to the user verification domain requirements, the user DIKW model including a data model, an information model and a knowledge model; when a user initiates a verification request, generating a verification keyword according to the user DIKW model of the user who initiates the request; obtaining typed resources for verification according to the verification keywords, and constructing a verification DIKW model according to the typed resources for verification; the verification DIKW model generates verification content and feeds it back to the user for verification, the verification DIKW model is constructed according to the user's verification keywords, and the verification keywords contain the user's personal privacy content, so the generated verification content is customized for the user, and only the user himself can quickly and accurately perform verification, avoiding others or scripts from logging into the user account or performing operations such as payment, thereby improving the privacy security of individuals and networks.

Description

Technical Field

[0001] The present invention relates to the field of privacy security protection technology, and in particular to a verification method for cross-DIKW modality generated content. Background Art

[0002] The rapid development of the big data era has brought new challenges to the privacy protection of personal data. With the continuous expansion of the Internet circle, the number of various portals and APP software is also growing exponentially. In order to facilitate management and provide a better experience, websites and APP software will require users to register personal accounts. Users need to ensure privacy and security when logging in to their accounts, exchanging information, and making payments. A common way to improve security is to set a verification code. However, the verification codes currently set are too simple. Criminals can automatically identify and fill in the verification code through specific scripts, thereby logging into the user's account and using the user's account to interact, place orders, and make payments, which seriously affects the privacy and security of individuals and the Internet.

[0003] Data Information Knowledge Wisdom Graph (DIKW) is a new type of system composed of Data, Information, Knowledge and Wisdom. Everyone has his or her own data resources, information resources, knowledge resources and wisdom resources. By modeling the above four types of resources, we can get the DIKW graph system. The DIKW graph is closely related to the user's own privacy. If the corresponding content can be generated according to the privacy in the DIKW graph for user verification, it will greatly enhance the privacy protection. Therefore, how to combine DIKW with privacy protection is an urgent problem to be solved. Summary of the invention

[0004] In view of this, the present invention proposes a verification method for generating content across DIKW modalities, generating verification content that meets the user's verification field requirements, preventing criminals from filling in verification codes to steal accounts and perform information interaction and payment operations, thereby ensuring the privacy security of individuals and the network.

[0005] The technical solution of the present invention is achieved in this way:

[0006] The validation method for generating content across DIKW modalities includes the following steps:

[0007] Step S1: Obtain user verification domain requirements, and construct a user DIKW model according to the user verification domain requirements, wherein the user DIKW model includes a data model, an information model, and a knowledge model;

[0008] Step S2: When a user initiates a verification request, a verification keyword is generated according to the user DIKW model of the user initiating the request;

[0009] Step S3: Obtain verification typed resources according to the verification keywords, and construct a verification DIKW model according to the verification typed resources;

[0010] Step S4: the verification DIKW model generates verification content and feeds it back to the user for verification;

[0011] The specific steps of step S2 include:

[0012] Step S21: When a user initiates a verification request, the account ID of the user initiating the verification request is obtained;

[0013] Step S22: Select the corresponding user DIKW model according to the account identifier;

[0014] Step S23: The user DIKW model analyzes and converts the basic information, character information and website information, and obtains verification keywords;

[0015] The specific steps of step S23 include:

[0016] Step S231: The user DIKW model searches the basic information to determine whether the basic information contains the verification method set by the user.

[0017] Step S232: if the basic information contains a verification method set by the user, output the verification method as a verification keyword;

[0018] Step S233: If the basic information does not contain the verification method set by the user, the user DIKW model analyzes and converts the basic information and / or character information and / or website information, and obtains corresponding verification keywords;

[0019] The specific steps of step S3 include:

[0020] Step S31: Acquire typed resources for verification according to the verification keyword, wherein the typed resources for verification include data resources, information resources, and knowledge resources;

[0021] Step S32: constructing a data model, an information model and a knowledge model for verifying the DIKW model based on data resources, information resources and knowledge resources;

[0022] The specific steps of step S31 to obtain typed resources for verification according to the verification keywords are: traversing each verification keyword, searching for related fields in the network based on each verification keyword, classifying the content contained in the field according to data, information and knowledge, and forming typed resources.

[0023] Preferably, the specific steps of step S1 to obtain the user verification domain requirements are:

[0024] Step S11, extracting the basic information filled in when the user registers the account;

[0025] Step S12, obtaining character information that appears more frequently in the user input method;

[0026] Step S13, obtaining information about websites that are frequently browsed by users;

[0027] Step S14: forming a user verification domain requirement based on basic information, character information and website information.

[0028] Preferably, the specific steps of constructing the user DIKW model in step S1 are: mapping user verification domain requirements into user typed resources, the user typed resources include data resources, information resources and knowledge resources, and constructing data models, information models and knowledge models based on data resources, information resources and knowledge resources.

[0029] Preferably, the specific steps of step S4 include:

[0030] Step S41, the verification DIKW model determines the user's cognitive level based on basic information, character information and website information;

[0031] Step S42: determining the presentation form of the verification content according to the user's cognitive level;

[0032] Step S43: The verification DIKW model combines verification content according to its internal resources, and feeds back the verification content to the user in the determined form of expression.

[0033] Preferably, the verification content is expressed in the form of text, pictures and sounds.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention provides a verification method for generating content across DIKW modalities. A user DIKW model is constructed according to the verification field requirements of different users. Each user DIKW model includes 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 to construct a verification DIKW model. Finally, the verification DIKW model generates verification content and feeds it back to the user for verification. Since the verification DIKW model is constructed based on the data output by the user DIKW model, the verification content finally generated is closely related to the user himself. If the user himself is not using it, the verification code cannot be filled in accurately. Therefore, the privacy security of the user and the network can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 A flowchart of a verification method for generating content across DIKW modalities of the present invention;

[0038] Figure 2 A flowchart of step S1 of the verification method for generating content across DIKW modalities of the present invention;

[0039] Figure 3 A flowchart of step S2 of the verification method for generating content across DIKW modalities of the present invention;

[0040] Figure 4 A flowchart of step S23 of the verification method for generating content across DIKW modalities of the present invention;

[0041] Figure 5 A flowchart of step S3 of the verification method for generating content across DIKW modalities of the present invention;

[0042] Figure 6 Flow chart of step S4 of the verification method for generating content across DIKW modalities of the present invention. DETAILED DESCRIPTION

[0043] In order to better understand the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described in conjunction with the accompanying drawings.

[0044] See also Figures 1 to 6 The verification method for generating content across DIKW modalities provided by the present invention comprises the following steps:

[0045] Step S1: Obtain user verification domain requirements, and construct a user DIKW model according to the user verification domain requirements, wherein the user DIKW model includes a data model, an information model, and a knowledge model;

[0046] Step S2: When a user initiates a verification request, a verification keyword is generated according to the user DIKW model of the user initiating the request;

[0047] Step S3: Obtain verification typed resources according to the verification keywords, and construct a verification DIKW model according to the verification typed resources;

[0048] Step S4: the verification DIKW model generates verification content and feeds it back to the user for verification.

[0049] A verification method for generating content across DIKW modalities of the present invention is used for verification when a user logs into an account or performs information interaction, order placement, payment and other operations after logging into an account. A corresponding verification code can be generated for the user to input information for verification. Unlike traditional digital verification codes, the generated verification content is obtained based on the user's own attributes and privacy information, so only the user himself can quickly fill in the verification content, thereby preventing criminals from stealing accounts for payment and privacy theft, and ensuring the privacy security of individuals and networks.

[0050] Specifically, each user will have his or her own account, which contains the user's own privacy information. Through these privacy information, the user verification domain requirements can be obtained, and the user verification domain requirements are used to build a user DIKW model. The DIKW model contains a data model, an information model and a knowledge model. The data model, the information model and the knowledge model can be converted into each other. Through the combination and conversion of data, information and knowledge, a user DIKW model containing a large amount of user privacy content can be constructed. When the corresponding user initiates a verification request, the user DIKW model can generate corresponding verification keywords according to the user's privacy content. After combining and processing the verification keywords, typed resources for verification can be obtained. According to the typed resources for verification, a verification DIKW model can be constructed. Since the verification DIKW model is constructed based on the user's verification keywords, when it generates verification content, it will be generated based on the user's own privacy content, ensuring that only the user himself can obtain the correct verification code to fill in, thereby improving the strength of privacy and security protection.

[0051] Preferably, the specific steps of step S1 to obtain the user verification domain requirements are:

[0052] Step S11, extracting the basic information filled in when the user registers the account;

[0053] Step S12, obtaining character information that appears more frequently in the user input method;

[0054] Step S13, obtaining information about websites that are frequently browsed by users;

[0055] Step S14: forming a user verification domain requirement based on basic information, character information and website information.

[0056] The premise for generating verification content related to the user's privacy content is to obtain a large amount of user privacy content, and form user verification domain requirements based on the privacy content to build the user DIKW model. As for the user's privacy content, first of all, we can use the basic information left by the user when registering an account, including age, gender, date of birth, position, company, hobbies, etc., and then obtain the character information that appears more frequently in the user's daily chat and the website information that is browsed more frequently, so as to form the user verification domain requirements and build the verification DIKW model.

[0057] For character information, the user's occupation, hobbies, etc. can be inferred based on the character information that appears more frequently. For example, if the first few characters with higher frequency are "patent", "invention", "utility model", etc., it can be inferred that the user is engaged in work related to intellectual property patents. Therefore, when generating verification content in the future, some patent-related verification content can be generated accordingly, so that users can fill in the verification code in a timely and fast manner, while avoiding the automatic filling of verification content by scripts, thereby improving personal privacy and security protection.

[0058] For website information, the user's occupation or hobbies can also be inferred based on the number of websites the user browses. For example, if the website that the user browses most frequently every day is a patent search website, then it can be determined that the user's occupation is related to patents; or if the user often browses football or basketball programs, it can be inferred that the user's hobbies are football or basketball. Therefore, when generating verification content, content related to the occupation or hobby can be generated accordingly to prevent others or scripts from stealing personal information or making payments and placing orders after passing verification.

[0059] Preferably, the specific steps of constructing the user DIKW model in step S1 are: mapping user verification domain requirements into user typed resources, the user typed resources include data resources, information resources and knowledge resources, and constructing data models, information models and knowledge models based on data resources, information resources and knowledge resources.

[0060] The DIKW model includes data, information, knowledge and wisdom. The various parts of the DIKW model can be converted into each other.

[0061] Preferably, the specific steps of step S2 include:

[0062] Step S21: When a user initiates a verification request, the account ID of the user initiating the verification request is obtained;

[0063] Step S22: Select the corresponding user DIKW model according to the account identifier;

[0064] Step S23: The user DIKW model analyzes and converts the basic information, character information and website information, and obtains verification keywords.

[0065] The specific steps of step S23 include:

[0066] Step S231: The user DIKW model searches the basic information to determine whether the basic information contains the verification method set by the user.

[0067] Step S232: if the basic information contains a verification method set by the user, output the verification method as a verification keyword;

[0068] Step S233: If the basic information does not contain the verification method set by the user, the user DIKW model analyzes and converts the basic information and / or character information and / or website information, and obtains corresponding verification keywords.

[0069] After building the user DIKW model for each user, wait for the user to initiate a request. When the verification request is never initiated, first obtain the account ID of the user who initiated the verification request, such as obtaining the verified account name. The correct user can be determined based on the account ID, and the corresponding user DIKW model is selected. Then, the basic information, character information and website information contained in the user DIKW model are parsed and converted to obtain the verification keywords.

[0070] In the step of obtaining the verification keyword, the user DIKW model will first search the basic information. The basic information will store the verification methods set by the user in advance, including the security questions filled in by the user when registering an account. If the basic information contains the verification method set by the user, the verification method can be directly output as the verification keyword. If it is not included in the basic information, the user DIKW model will need to search from the basic information, character information and website information to obtain the corresponding verification keyword.

[0071] Preferably, the specific steps of step S3 include:

[0072] Step S31, obtaining typed resources for verification according to the verification keyword, traversing each verification keyword, searching for fields related to each verification keyword in the network, classifying the contents contained in the fields according to data, information and knowledge, and forming typed resources, wherein the typed resources for verification include data resources, information resources and knowledge resources;

[0073] Step S32: constructing a data model, an information model and a knowledge model for verifying the DIKW model based on the data resources, the information resources and the knowledge resources.

[0074] For each verification keyword, there will be one or more corresponding fields. Therefore, it is necessary to search for each verification keyword, traverse the verification keywords and search for related fields on the Internet, and then combine the contents contained in the fields. The combined content is the typed resource, which is used to verify the construction of the DIKW model. Since there are multiple fields, the verification DIKW model will also internally count the fields that are most involved, and generate verification content with the content of the fields that are most involved. For example, the verification DIKW model contains 15 patent fields and 20 basketball fields. When generating verification content, you can choose basketball as the main content.

[0075] In addition, the present invention can also set the verification method and set the verification content to a multi-level form. For example, the first level is a verification method set by the user himself, the second level can be set according to the user's basic information, such as asking the user's date of birth, and the third level can be set according to the user's occupation or hobbies. The multi-level verification method can further enhance the user's privacy protection.

[0076] Preferably, the specific steps of step S4 include:

[0077] Step S41, the verification DIKW model determines the user's cognitive level based on basic information, character information and website information;

[0078] Step S42: determining the expression form of the verification content according to the user's cognitive level, wherein the expression form of the verification content includes text, picture and sound;

[0079] Step S43: The verification DIKW model combines verification content according to its internal resources, and feeds back the verification content to the user in the determined form of expression.

[0080] When generating verification content, the verification DIKW model also needs to consider the user's own cognitive level and determine the form of expression of the verification content according to different cognitive levels. Different forms of expression correspond to different verification complexities and corresponding privacy and security protection strengths. If the user is a child, the child himself does not have a high cognitive ability, so you can choose pictures or simple texts to express the verification content, such as asking children to distinguish the number of animals they often watch, etc. For people with higher cognitive levels, sound can be used to express the verification content. For example, if the user is a fan of a certain band, the verification DIKW model can output a song of the band, and the user can determine the title of the song for verification, thereby greatly improving the strength of privacy and security protection.

[0081] The present invention collects, extracts and combines the privacy content of users, and then constructs a user DIKW model for backup. When a user initiates a verification request (including a login request, a payment request, etc.), the user DIKW model corresponding to the user can generate verification keywords related to the privacy content of the user. The verification keywords can be the content in the verification method pre-set by the user, or can be the field content related to the user's occupation or hobbies extracted according to the user's privacy content. A verification DIKW model can be constructed accordingly based on the verification keywords. The verification DIKW model can judge and classify the user's cognition, adopt different verification content expressions for users of different levels, and simultaneously adopt one-level or multi-level verification methods to improve the privacy and security protection.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A verification method for generating content across DIKW modalities, characterized in that The following steps are involved: Step S1: Obtain user verification domain requirements, and construct a user DIKW model according to the user verification domain requirements, wherein the user DIKW model includes a data model, an information model, and a knowledge model; Step S2: When a user initiates a verification request, a verification keyword is generated according to the user DIKW model of the user initiating the request; Step S3: Obtain verification typed resources according to the verification keywords, and construct a verification DIKW model according to the verification typed resources; Step S4: the verification DIKW model generates verification content and feeds it back to the user for verification; The specific steps of step S2 include: Step S21: When a user initiates a verification request, the account ID of the user initiating the verification request is obtained; Step S22: Select the corresponding user DIKW model according to the account identifier; Step S23: The user DIKW model analyzes and converts the basic information, character information and website information, and obtains verification keywords; The specific steps of step S23 include: Step S231: The user DIKW model searches the basic information to determine whether the basic information contains the verification method set by the user. Step S232: if the basic information contains a verification method set by the user, output the verification method as a verification keyword; Step S233: If the basic information does not contain the verification method set by the user, the user DIKW model analyzes and converts the basic information and / or character information and / or website information, and obtains corresponding verification keywords; The specific steps of step S3 include: Step S31: Acquire typed resources for verification according to the verification keyword, wherein the typed resources for verification include data resources, information resources, and knowledge resources; Step S32: constructing a data model, an information model and a knowledge model for verifying the DIKW model based on data resources, information resources and knowledge resources; The specific steps of step S31 to obtain typed resources for verification according to the verification keywords are: traversing each verification keyword, searching for related fields in the network based on each verification keyword, classifying the content contained in the field according to data, information and knowledge, and forming typed resources.

2. The verification method for cross-DIKW modality generated content according to claim 1 is characterized in that: The specific steps of step S1 to obtain the user verification domain requirements are: Step S11, extracting the basic information filled in when the user registers the account; Step S12, obtaining character information that appears more frequently in the user input method; Step S13, obtaining information about websites that are frequently browsed by users; Step S14: forming a user verification domain requirement based on basic information, character information and website information.

3. The verification method for cross-DIKW modality generated content according to claim 2 is characterized in that: The specific steps of constructing the user DIKW model in step S1 are: mapping the user verification domain requirements into user typed resources, the user typed resources include data resources, information resources and knowledge resources, and constructing data models, information models and knowledge models based on data resources, information resources and knowledge resources.

4. The verification method for cross-DIKW modality generated content according to claim 2 is characterized in that: The specific steps of step S4 include: Step S41, the verification DIKW model determines the user's cognitive level based on basic information, character information and website information; Step S42: determining the presentation form of the verification content according to the user's cognitive level; Step S43: The verification DIKW model combines verification content according to its internal resources, and feeds back the verification content to the user in the determined form of expression.

5. The verification method for cross-DIKW modality generated content according to claim 4 is characterized in that: The verification content may be presented in the form of text, pictures, and sounds.

Images