Encryption and verification method of DIKW resources for essential computing and reasoning

Abstract

The present invention provides an encryption and verification method for DIKWP resources for essential computing and reasoning, comprising the following steps: S1, establishing a DIKWP model of a user, wherein the DIKWP model comprises a data model, an information model, a knowledge model, a wisdom model and an intention model; S2, obtaining a verification request issued by a user, wherein the DIKWP model performs intention recognition on the verification request of the user and generates verification questions of different difficulties; S3, the user inputs an answer to the verification question, and the correctness of the answer is judged by the DIKWP model of the user. If the answer of the user is correct, the verification request of the user is passed, and corresponding operations are performed according to the needs of the user.

Description

Technical Field

[0001] The present invention relates to the field of privacy security protection technology, and in particular to an encryption and verification method of DIKW resources for essential computing and reasoning. 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, users need to be verified when requesting different operations on the Internet, but the current verification method is relatively simple. Criminals use specific scripts to crack or intercept users' verification information and use users' accounts to perform illegal operations, which seriously affects the privacy security of individuals and the network and poses great security risks.

[0003] At present, the Data Information Knowledge Wisdom Purpose Graph (DIKWP) is a new type of system, which consists of Data, Information, Knowledge, Wisdom and Purpose. Everyone has his or her own data resources, information resources, knowledge resources, wisdom resources and intention resources. By modeling the above five types of resources, the DIKW graph system can be obtained. 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. Summary of the invention

[0004] The purpose of the present invention is to provide an encryption and verification method for DIKW resources oriented to essential computing and reasoning, so as to solve the problems raised in the above background technology.

[0005] The present invention is implemented by the following technical solution: an encryption and verification method of DIKW resources for essential computing and reasoning, comprising the following steps:

[0006] S1. Obtain the user's needs to be verified and establish the user's DIKWP model, wherein the DIKWP model includes a data model, an information model, a knowledge model, a wisdom model and an intention model;

[0007] S2. Obtaining a verification request from a user, the DIKWP model identifies the intent of the user's verification request and generates verification questions of different difficulty levels;

[0008] S3. The user inputs the answer to the verification question, and the correctness of the answer is judged by the user's DIKWP model. If the user's answer is correct, the user's verification request is passed, and corresponding operations are performed according to the user's needs.

[0009] Optionally, the establishing of the user's DIKWP model specifically includes the following steps:

[0010] S101, searching for content directly or indirectly generated by a user on the network, recorded as the first search content;

[0011] S102, mapping the first search content into a type resource, where the type resource includes data resources, information resources, knowledge resources, wisdom resources, and intention resources;

[0012] S103. Construct the user's data model, information model, knowledge model, wisdom model and intention model based on data resources, information resources, knowledge resources, wisdom resources and intention resources. The data model, information model, knowledge model, wisdom model and intention model constitute the DIKWP model.

[0013] Optionally, the DIKWP model performs intent recognition on the user's verification request, specifically by traversing the user intent model nodes and identifying the user intent.

[0014] Optionally, based on the user's verification needs, the DIKWP model generates verification questions of different difficulty levels, specifically: based on the user's intention, the DIKWP model makes a trust judgment on the user's needs; the more times the user's needs at a certain address are repeated, the more frequently the address appears in the DIKWP model; at this time, the DIKWP model has a higher degree of trust in the user's needs at a certain address; for needs with higher trust levels, the DIKWP model outputs verification questions with lower difficulty.

[0015] Optionally, when the number of repetitions of a user's demand for a certain address exceeds a threshold value, the DIKWP model begins to reduce the trust in the user's demand for a certain address, and for demands with low trust, the DIKWP model outputs difficult verification questions.

[0016] Optionally, the DIKWP model generates verification questions of different difficulty levels based on the user's verification needs, which specifically includes: the DIKWP model analyzes the user's verification needs, determines the importance of the needs relative to the user and quantifies them, and generates verification questions of different difficulty levels according to different quantification results.

[0017] Optionally, the first search content, the user's requirement to be verified, the user's verification question, and the user's answer to the verification question are mapped to a new type resource, and the user's DIKWP model is updated based on the new type resource.

[0018] Optionally, the correctness of the answer is judged by the user's DIKWP model, specifically by traversing the user's data resources and information resource model nodes, and based on the answer given by the user, identifying and matching in the node content of the data resources and information resource models. If the match is consistent, it means that the answer given by the user is correct.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The encryption and verification method of DIKW resources for essential computing and reasoning provided by the present invention establishes DIKWP models for different users, analyzes the user's intentions through the DIKWP model, generates verification questions of different difficulty levels according to different intentions and the trust analysis of user behaviors and addresses, and only when the user passes the verification of the verification questions can the corresponding request be operated. The above method enhances the security of the user in performing the request operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] 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.

[0022] Figure 1 A flowchart of the encryption and verification method of DIKW resources for essential computing and reasoning provided by the present invention;

[0023] Figure 2 A flow chart of establishing a user's DIKWP model provided by the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical scheme and advantages of the present invention more obvious, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described in the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the protection scope of the present invention.

[0025] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features well known in the art are not described.

[0026] It should be understood that the present invention can be implemented in different forms and should not be interpreted as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to make the disclosure thorough and complete and to fully convey the scope of the present invention to those skilled in the art.

[0027] The purpose of the terms used herein is only to describe specific embodiments and is not intended to be limiting of the present invention. When used herein, the singular forms "one", "an" and "said / the" are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "consisting of" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0028] In order to fully understand the present invention, a detailed structure will be proposed in the following description to illustrate the technical solution proposed by the present invention. The optional embodiments of the present invention are described in detail as follows, but in addition to these detailed descriptions, the present invention may also have other implementations.

[0029] See also Figure 1 , the encryption and verification method of DIKW resources for essential computing and reasoning includes the following steps:

[0030] S1. Obtain the user's needs to be verified and establish the user's DIKWP model, where the DIKWP model includes a data model, an information model, a knowledge model, a wisdom model and an intent model.

[0031] See also Figure 2 In step S1, the establishment of the user's DIKWP model specifically includes the following steps:

[0032] S101. Search for content directly or indirectly generated by a user on the network, which is recorded as the first search content.

[0033] When users are active on the Internet, they will generate a lot of content and record it on the Internet. The content generated directly or indirectly by users on the Internet can be the content recorded in this system or obtained from other third-party sites and platforms. The content directly generated by users on the Internet is the content edited and sent by the users themselves, while the content indirectly generated by users is the content related to the users on the Internet but published by third parties.

[0034] S102. Map the first search content to type resources, where the type resources include data resources, information resources, knowledge resources, wisdom resources, and intention resources.

[0035] Type the user-related content obtained - that is, abstract and conceptualize it into data resources, information resources, knowledge resources, wisdom resources and intention resources.

[0036] The data resources are discrete objects without binding intentions collected by directly observing a segment of existing semantics. Data resources are divided into numerical data, probabilistic data, logical data, and range data according to different attributes and structures. Numerical data is a specific numerical value obtained by observation, measurement or simple calculation, including Arabic numerals, time, date, or numerical data associated with character data. Probabilistic data represents an inference or prediction of the development trend of other things based on personal cognition, and is an uncertain description. Logical data uses (yes / no) to represent data resources, which can filter and classify things and use digital codes to represent each category. Range data is a data type used to describe a value of a certain data type. It can express multiple data of a single data object. The higher the overlap of range data, the more similar it is. Range data is divided into bounded ranges and unbounded ranges according to whether there are boundaries.

[0037] The information resource is formed by data directly or indirectly associated with one or more intentions and is time-sensitive. Information can be partial order information, usage information and change information. Partial order information is the comparison of data objects of the same type or different types based on the comparison intention at the same standard level under the premise of the same attributes. Usage information is to analyze the functional attributes of data based on the usage intention to achieve the ultimate goal. Change information is a description of the process of modifying the selected object and causing changes.

[0038] The knowledge resource is a structured and formalized category reasoning of data resources and / or information resources or empirical rules derived from probability statistics. The wisdom resource reflects the user's value orientation and is used to describe universal values ​​and the user's personal values. The intention resource is used to reflect what the user wants to do and what purpose the user wants to achieve.

[0039] In addition, the type resources can be converted through modal operations to obtain new type resources. The modal operations include homomodal operations and cross-modal operations. Homomodal operations are to obtain new type resources by operating on type resources of the same modality. Cross-modal operations are to obtain new type resources by operating on type resources of different modalities. For example, new information resources can be obtained by performing cross-modal operations on data resources and information resources.

[0040] S103, constructing the user's data model, information model, knowledge model, wisdom model and intention model based on data resources, information resources, knowledge resources, wisdom resources and intention resources. After the user-related content is categorized, the corresponding DIKWP model is constructed according to the type resources, for example, a data model is constructed according to data resources, an information model is constructed according to information resources, and so on. The data model, information model, knowledge model, wisdom model, intention model, etc. can all be represented in the form of a graph.

[0041] S2. Obtain a verification request from a user. The DIKWP model identifies the intent of the user's verification request and generates verification questions of different difficulty levels.

[0042] In step S2, each node in the user intent model corresponds to an intent. By typifying the user-related content, multiple user intents may be obtained at the same time. Each intent can be further subdivided into multiple specific intents. The further splitting of intents in the intent model is manifested in the form of parent nodes and child nodes. The purpose of traversing the user intent model nodes in this step is to find out the intent content related to the user verification request, so as to determine how to generate the associated verification questions in the subsequent steps.

[0043] Based on the user's intention, the DIKWP model determines the trust of the user's needs. The more times the user's needs at a certain address are repeated, the higher the frequency of the address appearing in the DIKWP model. At this time, the DIKWP model has a higher trust in the user's needs at a certain address. For needs with higher trust, the DIKWP model outputs verification questions with lower difficulty. For example, when a user makes a payment operation request at the address "Tiananmen Square, Beijing" for the first time, this address appears in the DIKWP model for the first time, and the DIKWP model has a low trust in this address. At this time, the DIKWP model will output questions that are more relevant to the user as verification questions. When the user makes a payment operation request at the address "Tiananmen Square, Beijing" for the second and third time, the frequency of this address appearing in the DIKWP model increases, and the DIKWP model's trust in this address increases. At this time, the verification questions output by the DIKWP model become less difficult.

[0044] Furthermore, when the number of repetitions of the user's needs at a certain address exceeds the threshold, the DIKWP model's trust in the user's needs at a certain address begins to decrease. For needs with low trust, the DIKWP model outputs difficult verification questions. For example, when the user makes a payment operation request at the address "Beijing Tiananmen Square" for the sixteenth time, this address appears in the DIKWP model many times and has exceeded the threshold, and the DIKWP model's trust in this address decreases instead. At this time, the difficulty of the verification question output by the DIKWP model will be greater than that of the verification question output for the fifteenth time.

[0045] Specifically, in some embodiments of the present invention, the DIKWP model analyzes the user's verification needs, determines the importance of the needs relative to the user and quantifies them, and generates verification questions of different difficulty levels according to different quantification results. For example, when a user requests to pay "RMB 100", since the DIKWP model includes the user's fund balance, it is determined that "RMB 100" is not very important to the user according to the fund balance. At this time, the DIKWP model quantifies the importance and outputs a verification question with a lower difficulty level;

[0046] It is understandable that when a user requests to pay "RMB 10,000", the importance of "RMB 10,000" to the user is relatively high based on the fund balance. At this time, the DIKWP model quantifies the importance and outputs a more difficult verification question.

[0047] S3. The user inputs the answer to the verification question, and the correctness of the answer is judged by the user's DIKWP model. If the user's answer is correct, the user's verification request is passed, and corresponding operations are performed according to the user's needs.

[0048] In this step, the user data resource and information resource model nodes are traversed, and based on the answer given by the user, identification and matching are performed in the node content of the data resource and information resource model. If the match is consistent, it means that the answer given by the user is correct and the user verification is passed.

[0049] Specifically, in some embodiments of the present invention, the method further includes: mapping the first search content, the user's requirements to be verified, the user's verification questions, and the user's answers to the verification questions into new type resources, and updating the user's DIKWP model based on the new type resources.

[0050] 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 principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The encryption and verification method of DIKW resources for essential computing and reasoning is characterized by: The following steps are involved: S1. Establish a DIKWP model for the user, wherein the DIKWP model includes a data model, an information model, a knowledge model, a wisdom model and an intention model; S2. Obtaining a verification request from a user, the DIKWP model identifies the intent of the user's verification request and generates verification questions of different difficulty levels; S3. The user inputs the answer to the verification question, and the correctness of the answer is judged by the user's DIKWP model. If the user's answer is correct, the user's verification request is passed, and the corresponding operation is performed according to the user's needs; The DIKWP model performs intent recognition on the user's verification request, specifically: traversing the user intent model nodes to recognize the user intent; Based on the verification needs of the user, the DIKWP model generates verification questions of different difficulty levels, specifically: based on the user's intention, the DIKWP model makes a trust judgment on the user's needs. The more times the user's needs at a certain address are repeated, the higher the frequency of the address appearing in the DIKWP model. At this time, the DIKWP model has a higher trust in the user's needs at a certain address. For needs with higher trust levels, the DIKWP model outputs verification questions with lower difficulty levels. When the number of repetitions of the user's demand at a certain address exceeds the threshold, the DIKWP model begins to reduce the trust in the user's demand at a certain address. For the demand with low trust, the DIKWP model outputs a difficult verification question. Based on the verification requirements of the user, the DIKWP model generates verification questions of different difficulty levels, which specifically includes: the DIKWP model analyzes the verification requirements of the user, determines the importance of the requirements relative to the user and quantifies them, and generates verification questions of different difficulty levels according to different quantification results.

2. The encryption and verification method of DIKW resources for essential computing and reasoning according to claim 1 is characterized in that: The establishment of the user's DIKWP model specifically includes the following steps: S101, searching for content directly or indirectly generated by a user on the network, recorded as the first search content; S102, mapping the first search content into a type resource, where the type resource includes data resources, information resources, knowledge resources, wisdom resources, and intention resources; S103. Construct the user's data model, information model, knowledge model, wisdom model and intention model based on data resources, information resources, knowledge resources, wisdom resources and intention resources. The data model, information model, knowledge model, wisdom model and intention model constitute the DIKWP model.

3. The encryption and verification method for DIKW resources for essential computing and reasoning according to claim 2 is characterized in that: The first search content, the user's need to be verified, the user's verification question, and the user's answer to the verification question are mapped into new type resources, and the user's DIKWP model is updated based on the new type resources.

4. The encryption and verification method of DIKW resources for essential computing and reasoning according to claim 3 is characterized in that: The correctness of the answer is judged by the user's DIKWP model, specifically: traversing the user's data resources and information resource model nodes, and based on the answer given by the user, identifying and matching in the node content of the data resources and information resource models. If the match is consistent, it means that the answer given by the user is correct.

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