CN116821350A - Trust-based social internet of things semantic blockchain consensus method - Google Patents
Trust-based social internet of things semantic blockchain consensus method Download PDFInfo
- Publication number
- CN116821350A CN116821350A CN202210712532.2A CN202210712532A CN116821350A CN 116821350 A CN116821350 A CN 116821350A CN 202210712532 A CN202210712532 A CN 202210712532A CN 116821350 A CN116821350 A CN 116821350A
- Authority
- CN
- China
- Prior art keywords
- trust
- semantic
- things
- social internet
- internet
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000003993 interaction Effects 0.000 claims abstract description 38
- 239000013598 vector Substances 0.000 claims abstract description 32
- 238000011156 evaluation Methods 0.000 claims description 37
- 230000001364 causal effect Effects 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 11
- 239000011159 matrix material Substances 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000012546 transfer Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 claims 1
- 230000007246 mechanism Effects 0.000 abstract description 5
- 238000007726 management method Methods 0.000 description 9
- 230000006855 networking Effects 0.000 description 7
- 230000007717 exclusion Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013524 data verification Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000013550 semantic technology Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Databases & Information Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Machine Translation (AREA)
Abstract
The application relates to a trust-based social Internet of things semantic blockchain consensus method, which comprises the steps of determining trust semantic relations among social Internet of things entities through trust semantic feature values and trust semantic feature vectors extracted from interaction data of the social Internet of things entities, and carrying out formal representation; and then, a trust mechanism between trust semantic relations is mined through an approximate reasoning algorithm and a Bayesian network, and a trust semantic consensus knowledge graph is constructed to form a semantic blockchain consensus protocol for selecting and accessing different scenes and applications.
Description
Technical Field
The application relates to the technical field of computer information security, in particular to a trust-based social Internet of things semantic blockchain consensus method.
Background
The semantic blockchain is to convert the data of the blockchain into a format of uniform resource description by adopting a semantic technology, so that the data has semantic characteristics, thereby providing a universal data format and exchange protocol for the blockchain and realizing the targets of data sharing, information interaction, resource scheduling, safety management and the like between different resources of the blockchain and different blockchains; the semantic blockchain builds a semantic processing layer on the basic blockchain architecture, and the main functions comprise semantic description of resources, exchange protocol of semantic data, storage of the semantic data, semanteme of intelligent contracts and the like; the semantic blockchain can solve the problems of heterogeneous data sharing, heterogeneous data interoperation, data verification, semantic search and the like in the blockchain.
Trust management is important content of social Internet of things research, and compared with the common Internet of things, the trust management of the social Internet of things faces new challenges due to the addition of social attributes; on one hand, the existing internet of things trust management technology and method are difficult to directly use for the trust management of the social internet of things, and the existing internet of things trust management modes mainly comprise modes of centralized trust authentication, third party trust management, trust domain management and the like; however, social Internet of things is a more free and open environment than Internet of things, and the collaboration among the cross-domain social Internet of things is more large-scale, heterogeneous and dynamic compared with the collaboration of general Internet of things, so that trying to strictly define the trust boundary of the social Internet of things is very difficult, and the credibility of different users in the processes of data acquisition, data transmission and data exchange of the Internet of things equipment cannot be effectively ensured; secondly, trust management using blockchains directly to social internet of things faces many insurmountable problems; because different platforms, different development languages, different protocols, different consensus mechanisms and privacy protection schemes are used, the existing blockchain is a closed ecological system, and an effective intercommunication mechanism between heterogeneous blockchains is not yet available, such as data fusion, data sharing and data interview problems of the heterogeneous blockchains, performance and efficiency problems, semantic indexing and searching problems of the blockchains and the like; finally, the social Internet of things is a very complex heterogeneous system, different social Internet of things application scenes have different trust consensus modes, and how to unify multiple modes with different trust consensus and flexibly select according to specific scenes is also a core problem of social Internet of things blockchain trust consensus. The main reason for the above problem is that social internet of things is a more open network, and social properties increase the diversity of network types, device types and data types. Thus, in order to enable trusted interactions and sharing between heterogeneous social networking, it is necessary to design a trust-based semantic blockchain consensus model.
Disclosure of Invention
The application solves the problem of how to construct a semantic blockchain consensus protocol for the social Internet of things. To solve the above problems, the present application provides a method comprising:
step 1, collecting interaction data between social Internet of things entities on the social Internet of things, and classifying and processing the interaction data to remove non-text data and form a standard text data set;
step 2, defining formalized terms of trust semantic ontology;
step 3, extracting trust semantic feature values and trust semantic feature vectors formed by the trust semantic feature values from the text data set, and carrying out formal description of trust semantic ontology;
step 4, calculating similarity for judging whether trust semantic relationships exist between the social Internet of things entities or not based on the trust semantic feature vectors;
step 5, determining trust semantic relations between social Internet of things entities according to the types of the concepts represented by the trust semantic ontology;
step 6, adopting an approximate reasoning algorithm to obtain marginal probability distribution among trust semantic relations, and adopting a Bayesian network to calculate conditional probability of causal relations among the trust semantic relations;
and 7, performing knowledge evaluation on any trust semantic relation obtained by reasoning in the step 6 by adopting an analytic hierarchy process, and constructing a semantic consensus knowledge graph according to the trust semantic relation with the causal relation to form a semantic blockchain consensus protocol for selecting and accessing different scenes and applications.
The beneficial effects of the application are as follows: determining trust semantic relations between social networking entities through trust semantic feature values and trust semantic feature vectors extracted from interaction data of the social networking entities, and carrying out formal representation; and then, a trust mechanism between trust semantic relations is mined through an approximate reasoning algorithm and a Bayesian network, and a trust semantic consensus knowledge graph is constructed to form a semantic blockchain consensus protocol for selecting and accessing different scenes and applications.
Preferably, the interaction data collected in the step 1 comprise text, pictures, semantics and data transfer; the standard text class data set comprises a user ID, a device ID, and an interactive trust semantic content text.
Preferably, the step 2 specifically includes a feature vector set for defining trust semantic relationships between entities of the social internet of things; the feature vector set includes ownership trust, dependency trust, direct trust, indirect trust, partnership trust, distrust, and deception.
Preferably, the extracting trust semantic feature values from the text dataset in the step 3 is:
r= (interaction trust semantic feature value);
the trust semantic feature vector is extracted from the text dataset as follows:
(interaction trust semantic feature value 2),
......
(interaction trust semantic feature value n)).
Preferably, the similarity in the step 4 is calculated by adopting a Pearson Correlation method, and the specific formula is as follows:
wherein a and b respectively represent two different social Internet of things entities; similarity (a, b) is the value of Similarity between social internet of things entities i and j; v is the total number of interaction trust semantic texts between the social Internet of things entities a and b; i is the ith trust semantic text of the interaction between the social internet of things entities a and b; (r) a ) Sum (r) b ) Text data given by entities a and b respectively,and->Representing the f-th feature extracted in the i-th interaction of a and b, respectively,/->And->Representing the mean of the feature vectors of a and b, respectively.
Preferably, in the step 5, the category of the trust semantic relationship is determined according to the type of the concept represented by the trust semantic ontology, and the category of the trust semantic relationship is determined by solving the maximum value of each category of semantic similarity between the entities of the social internet of things, wherein the specific calculation formula is as follows:
in Max (θ) f＝0,n ) The f value satisfying the requirement that θ be the largest is the category of the trust relationship.
Preferably, the calculating the marginal probability distribution between any trust semantic relationships by adopting the approximate reasoning method in the step 6 specifically includes:
construction of a simple distribution formula q * (z)：
Wherein omega is candidate distribution, an average field distribution family is adopted as candidate distribution, all trust semantic relations are variables in a simple distribution formula, x is a group of variables of trust semantic relations, and z is a group of independent variables z= { z of trust semantic relations 1 ,z 2 ,...z m ,...z M Q (z) is the product of the variable probability density functions of the respective random variables:q m the variation probability density, z, for each random variable m For the subset of other trust semantic relationships, M is the number of trust semantic relationship subsets; KL (q (z) |p (z|x) is KL divergence, and is used for measuring information loss when editing probability distribution calculation;
then, optimization calculation is performed through a expectation maximization algorithm, so that the trust semantic relation variable e= { e is known 1 ,e 2 ,...e n When }, other trust semantic relationship variables f= { f 1 ,f 2 ,...f n Conditional probability of };
in the step 6, a bayesian network is adopted to calculate the conditional probability with causal relation between any trust semantic relation, and the bayesian network formula is as follows:
wherein e and f are trust semantic relation variables, and z represents the rest trust semantic relation variables except e and f; and calculating the conditional probability with causal relation among all trust semantic relations through a Bayesian network.
Preferably, in the step 7, performing knowledge evaluation on the trust semantic relationship by using an analytic hierarchy process specifically includes:
step 701, establishing an evaluation index level based on a feature vector set;
step 702, quantifying the evaluation indexes, respectively constructing judgment matrixes according to each layer of evaluation indexes, calculating the maximum eigenvalue of each judgment matrix as the weight of each layer of evaluation indexes, and obtaining an evaluation result vector for judging the relation type between each trust semantic relation according to the weight.
Drawings
FIG. 1 is an interaction diagram between social networking views;
FIG. 2 is a schematic flow chart of the present application;
Detailed Description
In order that the above objects, features and advantages of the application will be readily understood, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the social internet of things scene shown in fig. 1, interaction data is generated by interaction between social internet of things entities, and the social internet of things semantic blockchain consensus method is shown in fig. 2 and comprises the following steps:
step 1, collecting interaction data between social Internet of things entities on the social Internet of things, wherein the interaction data comprise texts, pictures, semantics, data transfer and the like, non-text data are removed by processing interaction data classification, and a standard text data set is formed, and comprises the following steps: text, picture, semantic, and data transfer; the standard text class data set comprises a user ID, a device ID and an interactive trust semantic content text;
step 2, defining formalized terms of trust semantic ontology based on background knowledge of semantic ontology in the prior art; the method specifically comprises a characteristic value and a characteristic vector set for defining trust semantic relation between social Internet of things entities; the semantic ontology concept set to which the feature value belongs comprises ownership trust, dependency trust, direct trust, indirect trust, collaboration trust, distrust and deception; thus, the trust semantic ontology in this particular embodiment is represented as:
TrustObject[].FORALL User1,Device1,User2,Device2
User::TrustObject.
User1:User[dir-trustWith->>User2]，
User2:User[dir-trustWith->>User1]，
User1:User[indir-trustWith->>User2]，
User2:User[indir-trustWith->>User1]，
User1:User[holdWith->>Device1]，
User2:User[holdWith->>Device1]，
User1:User[dir-trustWith->>Device2]，
User2:User[dir-trustWith->>Device1]，
User1:User[distrustWith->>User2]，
User2:User[distrustWith->>User1]，
User1:User[che-trustWith->>User2]，
User2:User[che-trustWith->>User1]，
Device::TrustObject.
Device1:Device[belongTo->>User1]，
Device2:Device[belongTo->>User2]，
Device1:Device[cooperatesWith->>Device2]，
Device2:Device[cooperatesWith->>Device1]；
step 3, extracting trust semantic feature values and trust semantic feature vectors from the text class data set, wherein the method specifically comprises the following steps: the trust semantic feature values are extracted from the text dataset as follows:
r= (interaction trust semantic feature value);
performing formal representation of trust semantic ontology on the interaction trust semantic content; formalized representation of trust semantic ontology is an existing basis and is not described in any detail here; after formal representation of the trust semantic ontology, trust semantic relationships are determined in the trust semantic feature values according to the interaction trust semantic content;
the trust semantic feature vectors are then extracted from the text dataset as:
(interaction trust semantic feature value 2),
......
(interaction trust semantic feature value n);
the interaction trust semantic feature value 1 and the interaction trust semantic feature value 2 are subjected to formal representation of trust semantic ontology;
step 4, calculating similarity for judging whether trust semantic relationships exist between the social Internet of things entities or not based on the trust semantic feature vectors; the similarity of the specific embodiment is calculated by adopting a Pearson Correlation method, and the specific formula is as follows:
wherein a and b respectively represent two different social Internet of things entities; similarity (a, b) is the value of Similarity between social internet of things entities i and j; v is the total number of interaction trust semantic texts between the social Internet of things entities a and b; i is the ith trust semantic text of the interaction between the social internet of things entities a and b; (r) a ) Sum (r) b ) Text data given by entities a and b respectively,and->Representing the f-th feature extracted in the i-th interaction of a and b, respectively,/->And->Mean values of feature vectors respectively representing a and b;
step 5, determining trust semantic relationships between social networking entities according to the types of concepts represented by trust semantic ontology, wherein in the embodiment, judging the category of the trust semantic relationships is obtained by solving the maximum value of each category of semantic similarity between social networking entities, and the specific calculation formula is as follows:
in Max (θ) f＝0,n ) The f value which satisfies the maximum theta is the category of the trust relationship;
in the specific embodiment, the similarity range of the trust semantic relationship is (0, 1), whether the similarity calculated in the step 4 is within a preset range is judged, if so, the maximum value of each class f is calculated, and the corresponding f type is the type of the trust relationship;
step 6, adopting an approximate reasoning algorithm to obtain marginal probability distribution of any trust semantic relation, and adopting a Bayesian network to calculate conditional probability with causal relation between any trust semantic relation; the method for solving the marginal probability distribution between any trust semantic relations by adopting the approximate reasoning method specifically comprises the following steps:
construction of a simple distribution formula q * (z)：
Wherein omega is candidate distribution, an average field distribution family is adopted as candidate distribution, all trust semantic relations are variables in a simple distribution formula, e is a group of variables of trust semantic relations, and q (z) is the product of variation probability density functions of random variables:z i to trust a subset of semantic relationships z, q, other than e i (z i ) The probability distribution of the subsets is that m is the number of the trust semantic relation subsets; KL (q (z) ||p (z|e) is KL divergence, so as to measure information loss during editing probability distribution calculation, and z is a hidden variable;
then, optimizing calculation is carried out through an expectation maximization algorithm, and q meeting the condition is obtained * (z) and use q * (z) to approximate p (z|e); thus, when knowing the trust semantic relationship variable e= { e 1 ,e 2 ,...e n When }, other trust semantic relationship variables f= { f 1 ,f 2 ,...f n Conditional probability of };
calculating the conditional probability with causal relation between any trust semantic relation by adopting a Bayesian network, wherein the Bayesian network formula is as follows:
wherein e and f are trust semantic relation variables, and z represents the rest trust semantic relation variables except e and f; calculating to obtain conditional probabilities with causal relationships among all trust semantic relationships through a Bayesian network;
step 7, carrying out knowledge evaluation on any trust semantic relation obtained by reasoning in the step 6 by adopting an analytic hierarchy process, and constructing a semantic consensus knowledge graph according to the trust semantic relation with causal relation to form a semantic blockchain consensus protocol for selecting and accessing different scenes and applications: the indication evaluation specifically comprises the following steps:
step 701, establishing an evaluation index level based on a feature vector set;
in the specific embodiment, the trust semantic feature value in the feature vector set is used as a second-level evaluation index; adding a first-level evaluation index on the basis of a second-level evaluation index, wherein the first-level evaluation index comprises an ownership trust relationship, a parental trust relationship, a cooperative trust relationship and a mutual exclusion trust relationship; wherein the ownership trust and the subordinate trust membership ownership trust relationship, the direct trust membership parent trust relationship, the indirect trust and the cooperative trust membership cooperation trust relationship, and the untrustworthy and the deceptive membership mutual exclusion trust relationship; as shown in graph 1:
TABLE 1
Step 702, respectively constructing judgment matrixes according to the importance degree of each layer of evaluation indexes to quantify judgment, calculating the maximum eigenvalue of each judgment matrix as the weight of each layer of evaluation indexes, and then obtaining an evaluation result vector for judging the relation type between each trust semantic relation according to the weight; in this embodiment, the judgment matrix B is constructed according to the importance level of the evaluation index for quantization, as shown in table 2:
TABLE 2
Establishing a judgment matrix of an evaluation result and four first-level evaluation index judgment matrices according to the quantized values of the table 2; calculating the maximum eigenvalue of each judgment matrix to obtain a weight vector W of the evaluation result and a weight W of the first-level index l The method comprises the steps of carrying out a first treatment on the surface of the And according to weights W and W l Calculating an evaluation result, wherein the calculation formula is as follows:
S l ＝W l *R l
S＝W*R
in which W is l Weight vector for first-order evaluation index, R l Judging a matrix for the first-level evaluation index, S l Evaluation result vector for first-order evaluation indexL is the number of first-order evaluation indexes, W is the weight vector of the evaluation result, and R= [ S ] 1 ,S 2 ,S 3 ,S 4 ]S is an evaluation result vector; and determining the relation type among the trust semantic relations according to the value of the evaluation result vector to carry out validity judgment.
The standardized description of the trust semantic feature value and the feature vector specifically comprises the following steps:
and carrying out the property division of the secondary trust relationship according to the trust relationship range in the secondary trust relationship in the trust semantic relationship standard library according to the trust relationship value between the social Internet of things entities in the trust relationship set, and then carrying out standardized description in a formal mode of the semantic ontology.
The formalized trust semantic feature values need to be standardized so as to be subjected to related calculation and processing; in this embodiment, the feature value is normalized based on the average value and standard deviation of the feature value, and the standard deviation of r of the feature value used for normalization is calculated, where the standard deviation is defined as:
wherein n is the number of eigenvalues in the text,is the average value of the f-th class of characteristics, and +.> An f-th class feature value representing i-th data;
presettingThe normalized values are:
specific application
Scenes and applications in social networking involve a wide range of very broad areas. In different scenes and different applications, the common identification protocols of the similar scenes and the same application are generally the same, but the common identification protocols of the different scenes and the different applications are different, the application fields of the social Internet of things comprise intelligent home, internet of vehicles, life entertainment and the like, and under different application scenes, different requirements are met on the common identification mechanism of the blockchain; depending on the scenario and application, the alternative rules for trust semantic blockchains are any combination of four trust relationships, ownership trust relationship, parental trust relationship, collaborative trust relationship, mutual exclusion trust relationship, which make up a consensus protocol for trust semantic blockchains, the rules for combining include:
{
combination 1 (ownership trust relationship),
combination 2 (parental trust relationship),
combination 3 (cooperative trust relationship),
combination 4 (non-mutually exclusive trust relationship),
combination 5 (ownership trust relationship, parental trust relationship),
combination 6 (ownership trust relationship, collaborative trust relationship),
combination 7 (ownership trust relationship, non-mutually exclusive trust relationship),
combination 8 (parental trust relationship, collaborative trust relationship),
combination 9 (parental trust relationship, non-mutually exclusive trust relationship),
combination 10 (cooperative trust relationship, non-mutually exclusive trust relationship),
combination 11 (ownership trust relationship, parental trust relationship, collaborative trust relationship),
combination 12 (ownership trust relationship, parental trust relationship, non-mutually exclusive trust relationship),
combination 13 (ownership trust relationship, collaborative trust relationship, non-mutually exclusive trust relationship),
combination 14 (parental trust relationship, collaborative trust relationship, non-exclusive trust relationship),
combination 15 (ownership trust relationship, parental trust relationship, collaborative trust relationship, non-mutually exclusive trust relationship)
}
Wherein the relationships within the group are "and" relationships, i.e., the group of rules is selected, then all conditions within the group need to be satisfied simultaneously; the relation among the groups is 'OR' relation, and the step 1 to the step 7 of the application are carried out to obtain a trust-based trust-description entity semantic state, and a trust consensus protocol combination facing the semantic blockchain, thereby being capable of meeting the same situation of the same scene of the social Internet of things and the same consensus protocol in the same application and meeting the situation of the consensus protocol among different scenes and different applications.
Although the present disclosure is described above, the scope of protection of the present disclosure is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the disclosure, and these changes and modifications will fall within the scope of the application.
Claims (8)
1. The trust-based social Internet of things semantic blockchain consensus method comprises social Internet of things entities and is characterized by comprising the following steps:
step 1, collecting interaction data between social Internet of things entities on the social Internet of things, and classifying and processing the interaction data to remove non-text data and form a standard text data set;
step 2, defining formalized terms of trust semantic ontology;
step 3, extracting trust semantic feature values and trust semantic feature vectors formed by the trust semantic feature values from the text data set, and carrying out formal description of trust semantic ontology;
step 4, calculating similarity for judging whether trust semantic relationships exist between the social Internet of things entities or not based on the trust semantic feature vectors;
step 5, determining trust semantic relations between social Internet of things entities according to the types of the concepts represented by the trust semantic ontology;
step 6, adopting an approximate reasoning algorithm to obtain marginal probability distribution among trust semantic relations, and adopting a Bayesian network to calculate conditional probability of causal relations among the trust semantic relations;
and 7, performing knowledge evaluation on any trust semantic relation obtained by reasoning in the step 6 by adopting an analytic hierarchy process, and constructing a semantic consensus knowledge graph according to the trust semantic relation with the causal relation to form a semantic blockchain consensus protocol for selecting and accessing different scenes and applications.
2. The trust-based social internet of things semantic blockchain consensus method according to claim 1, wherein the interaction data collected in step 1 comprises text, pictures, semantics and data transfer; the standard text class data set comprises a user ID, a device ID, and an interactive trust semantic content text.
3. The trust-based social internet of things semantic blockchain consensus method according to claim 2, wherein the step 2 specifically comprises a feature value and a feature vector set for defining trust semantic relationships between social internet of things entities; the semantic concept sets described in the features include ownership trust, dependency trust, direct trust, indirect trust, partnership trust, distrust, and deception.
4. The trust-based social internet of things semantic blockchain consensus method according to claim 3, wherein the extracting trust semantic feature values from the text dataset in step 3 is:
r= (interaction trust semantic feature value);
the trust semantic feature vector is extracted from the text dataset as follows:
(interaction trust semantic feature value 2),
......
(interaction trust semantic feature value n)).
5. The trust-based social internet of things semantic blockchain consensus method according to claim 4, wherein the similarity in step 4 is calculated by adopting a Pearson Correlation method, and the specific formula is as follows:
wherein a and b respectively represent two different social Internet of things entities; similarity (a, b) is the value of Similarity between social internet of things entities i and j; v is the total number of interaction trust semantic texts between the social Internet of things entities a and b; i is the ith trust semantic text of the interaction between the social internet of things entities a and b; (r) a ) Sum (r) b ) Text data given by entities a and b respectively,and->Representing the f-th feature extracted in the i-th interaction of a and b, respectively,/->And->Representing the mean of the feature vectors of a and b, respectively.
6. The trust-based social internet of things semantic blockchain consensus method according to claim 5, wherein the category of the trust semantic relationship in step 5 is determined according to the type of the concept represented by the trust semantic ontology, and the category of the trust semantic relationship is determined by solving the maximum value of each type of semantic similarity between social internet of things entities, and the specific calculation formula is as follows:
in Max (θ) f＝0,n ) The f value satisfying the requirement that θ be the largest is the category of the trust relationship.
7. The trust-based social internet of things semantic blockchain consensus method according to claim 6, wherein the solving the marginal probability distribution between any trust semantic relationships by adopting the approximate reasoning method in step 6 specifically comprises:
construction of a simple distribution formula q * (z)：
Wherein omega is candidate distribution, an average field distribution family is adopted as candidate distribution, all trust semantic relations are variables in a simple distribution formula, x is a group of variables of trust semantic relations, and z is a group of independent variables z= { z of trust semantic relations 1 ,z 2 ,...z m ,...z M Q (z) is the product of the variable probability density functions of the respective random variables:q m the variation probability density, z, for each random variable m For the subset of other trust semantic relationships, M is the number of trust semantic relationship subsets; KL (q (z) |p (z|x) is KL divergence, which is used for measuring information loss when editing probability distribution calculation, and arg represents that KL (q (z) |p (z|x) is the most important valueWhen the value is small, the value of z is taken;
then, optimization calculation is performed through a expectation maximization algorithm, so that the trust semantic relation variable e= { e is known 1 ,e 2 ,...e n When }, other trust semantic relationship variables f= { f 1 ,f 2 ,...f n Conditional probability of };
in the step 6, a bayesian network is adopted to calculate the conditional probability with causal relation between any trust semantic relation, and the bayesian network formula is as follows:
wherein e and f are trust semantic relation variables, and z represents the rest trust semantic relation variables except e and f; and calculating the conditional probability with causal relation among all trust semantic relations through a Bayesian network.
8. The trust-based social internet of things semantic blockchain consensus method according to claim 7, wherein the step 7 of performing knowledge evaluation on trust semantic relationships by using a hierarchical analysis method specifically comprises:
step 701, establishing an evaluation index level based on a feature vector set;
step 702, quantifying the evaluation indexes, respectively constructing judgment matrixes according to each layer of evaluation indexes, calculating the maximum eigenvalue of each judgment matrix as the weight of each layer of evaluation indexes, and obtaining an evaluation result vector for judging the relation type between each trust semantic relation according to the weight.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210712532.2A CN116821350A (en) | 2022-06-22 | 2022-06-22 | Trust-based social internet of things semantic blockchain consensus method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210712532.2A CN116821350A (en) | 2022-06-22 | 2022-06-22 | Trust-based social internet of things semantic blockchain consensus method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116821350A true CN116821350A (en) | 2023-09-29 |
Family
ID=88119041
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210712532.2A Pending CN116821350A (en) | 2022-06-22 | 2022-06-22 | Trust-based social internet of things semantic blockchain consensus method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116821350A (en) |
-
2022
- 2022-06-22 CN CN202210712532.2A patent/CN116821350A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104008174B (en) | A kind of secret protection index generation method of massive image retrieval | |
| Yang et al. | A Novel TOPSIS Method Based on Improved Grey Relational Analysis for Multiattribute Decision‐Making Problem | |
| Su et al. | Neural graph matching based collaborative filtering | |
| Deng et al. | Knowledge-aware group representation learning for group recommendation | |
| Liu et al. | Hybrid‐patent classification based on patent‐network analysis | |
| Ozturk et al. | From existing trends to future trends in privacy‐preserving collaborative filtering | |
| Şensoy et al. | Stage: Stereotypical trust assessment through graph extraction | |
| Meng et al. | A method to solve cold-start problem in recommendation system based on social network sub-community and ontology decision model | |
| CN116109372B (en) | Cold chain logistics product federal recommendation method and device based on multi-level block chain | |
| Kang et al. | Enhanced privacy preserving for social networks relational data based on personalized differential privacy | |
| Pan et al. | Contextual client selection for efficient federated learning over edge devices | |
| Qi et al. | An inverse collaborative filtering approach for cold-start problem in web service recommendation | |
| CN116821350A (en) | Trust-based social internet of things semantic blockchain consensus method | |
| Petkos et al. | Social circle discovery in ego-networks by mining the latent structure of user connections and profile attributes | |
| Yu et al. | Large-scale group decision-making | |
| Noughabi et al. | Predicting Students' Behavioral Patterns in University Networks for Efficient Bandwidth Allocation: A Hybrid Data Mining Method (Application Paper) | |
| Ma et al. | Hybrid attention mechanism for few‐shot relational learning of knowledge graphs | |
| Sheugh et al. | Merging similarity and trust based social networks to enhance the accuracy of trust-aware recommender systems | |
| Chen et al. | ACTSSD: social spammer detection based on active learning and co-training | |
| Jin et al. | Web Log Analysis and Security Assessment Method Based on Data Mining | |
| Cong et al. | LGBM: An Intrusion Detection Scheme for Resource‐Constrained End Devices in Internet of Things | |
| Helal et al. | An efficient algorithm for community detection in attributed social networks | |
| Wang et al. | A Survey on Federated Analytics: Taxonomy, Enabling Techniques, Applications and Open Issues | |
| Kazmi et al. | Evaluation of role detection approaches in terrorist networks | |
| Gupta et al. | A model‐based approach to user preference discovery in multi‐criteria recommender system using genetic programming |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |