41 results about "Trust factor" patented technology

The invention relates to the technical field of Internet security, in particular to a dynamic trust evaluation method and system for heterogeneous fusion network nodes, and the method comprises the steps: carrying out the dynamic modeling of a network into a space-time heterogeneous graph based on a network architecture of a cloud-edge cooperative industrial Internet of Things, and enabling the space-time heterogeneous graph to comprise a sensing layer node, an edge layer node and a cloud node; performing joint embedding characterization on the dynamic topology by using a knowledge graph embedding technology, and extracting node attributes and spatial-temporal characteristics of interaction; multi-source feature interaction is fused based on a cross attention mechanism, and the weight of a trust factor is dynamically adjusted through a multi-time-slot feature; and updating the trust evaluation model through an incremental learning strategy, and establishing a dynamic mapping relationship between a trust evaluation value and an equipment time sequence behavior mode. According to the method, continuous and accurate evaluation of the dynamic trust of the network nodes under the zero-trust architecture is realized by fusing the space-time diagram representation learning and the cross attention mechanism, and the anti-attack capability and the safety performance of the industrial Internet of Things are enhanced.

Systems and methods are directed to an improved fraud detection feature in Mobile Application Verification (MAV) device enrollment process. The proposed solution corresponds to a two-prong approach involving a strength factor evaluation of the authentication inputs provided during the initial enrollment process, and a computation of a number of trust parameters values from archived transactional records. A computational model supplemented with a machine learning process is then used to determine a device stability period as a function of the strength factor associated with the user provided authentication data and the computed trust factors.

A data processing method relates to the field of media application, and comprises the following steps: judging whether one or more trust factors of media assets correspond to index requirements or not to obtain the credible information of the media assets. Wherein the media asset may include media content, the media asset further including at least one of metadata of the media content and a trust record of the media content; the trust factor is a factor related to the credibility of the media asset. And the user can judge the credibility of the media assets based on the credible information.

Systems, methods, and software described herein manage traffic rules in association with fully qualified domain names (FQDNs). In one implementation, a domain name system (DNS) security service obtains a FQDN associated with a DNS request by a computing device. The DNS security service determines a first score for the FQDN based on trust factors associated with the FQDN and determines whether the first score satisfies one or more criteria. When the first score satisfies the one or more criteria, the DNS security service evaluates host posture information associated with an IP address in the DNS response for the FQDN, updates the first score to a second score based on the host posture information, and determines a traffic rule for the FQDN based on the second score.

The invention provides an unmanned aerial vehicle pose estimation method based on credibility weighted strong tracking filtering. According to the method, the posterior error covariance after actual strong tracking filtering iteration and the theoretical true posterior covariance of algorithm iteration are utilized to define a trust factor used for describing filtering deviation under noise uncertainty, the trust factor acts on self-adaptive calculation of a forgetting factor, then a time-varying fading factor is corrected through calculation of an innovation error covariance, and therefore the filtering deviation under noise uncertainty is corrected. And tracking estimation under the condition of noise uncertainty is realized. Compared with traditional strong tracking series filtering, the method can quantify the uncertainty of noise and relieve experience dependence of forgetting factors. Meanwhile, the time-varying fading factor acts on the process noise covariance, and compared with a traditional action mode, the method has better interpretability. When the residual error and the fading factor are calculated, compared with the traditional fixed forgetting factor and the trust factor, the self-adaptive adjustment of the forgetting factor can be realized, the performance error is reduced, and meanwhile, the artificial experience assignment is reduced.

The invention discloses an account quotation management method and system based on account credible authentication. The method comprises the following steps: extracting account information from a data warehouse, extracting a credible factor, and constructing an account credible file; acquiring flow data of the account; performing account risk level evaluation according to the account credible file and the flow data to obtain an account risk level; determining a rating decision according to the account risk level to obtain a decision result; when the decision result is passing or manual service, the flow data is input to an account credibility rating model for credibility rating evaluation to obtain an account credibility rating; acquiring account related information; calculating the account quota according to the account credibility level and the account related information to obtain a calculation result; and outputting the account risk level and the calculation result. By implementing the method provided by the invention, more comprehensive, flexible and efficient risk control and limit management can be realized, various transaction and account risks can be better handled, and financial security and user experience are improved.

The invention discloses a hierarchical access authority management method of a behavior monitoring system, which relates to the technical field of authority management, and comprises the following steps: generating situation nodes according to multi-dimensional characteristics of behavior events, packaging changes into a traceability log, and incrementally updating affected sub-graphs; abstracting multi-level element roles according to activity and sensitivity threshold domains, maintaining a sorting double linked list and constructing a skip list index; mapping the element role into a fuzzy cognitive node, establishing a bidirectional fuzzy edge weight, and dynamically adjusting to generate a credible factor; constructing a cross authorization matrix storage basic permission set; and mapping an authorization request to a situation node, retrieving a latest state, positioning a user element role level, extracting a matrix basic permission set through a skip list index, and generating and issuing an authorization result after cutting based on a credible factor. Incremental maintenance is realized by updating a local sub-graph and a skip list index through a traceability log, and authorization efficiency, flexibility and security are improved by combining fuzzy cognitive mapping and dynamic credible factor fine permission through context mapping of a cross authorization matrix.

The invention provides a time step calibration method of a refracturing well output Transform prediction model based on a trust factor. The method comprises the following steps: (S1) preparing data and inputting Tokens; (S2) Token embedding and position embedding are carried out; (S3) selecting the size of a sliding window; (S4) calibrating an initial predicted value; and (S5) repeatedly calibrating until the precision meets the expectation. Through the combination of a sliding window method and a real-time calibration mechanism, the deviation between initial prediction and real yield can be effectively reduced, the training efficiency of the model is improved, unnecessary computing power investment is reduced, and cost and efficiency are reduced.

Systems and methods are provided utilizing a consensus protocol that assesses a trust factor of one or more nodes, and which executes a block creation process by selecting and assigning trusted nodes, based on the trust factor assessment.

The present invention provides a node credibility assessment method for an underwater acoustic sensor network based on node behavior. The method uses node behavior and interaction success rate during information interaction as trust factors, aggregates trust factors using information entropy, and obtains direct trust, indirect trust, and historical trust. It then calculates node trust and establishes a trust assessment model based on node behavior. This allows for rapid and accurate detection of malicious nodes, effective identification and isolation, and ensures the safety and reliability of nodes in routing paths. The present invention detects tampering attacks, deception attacks, and black hole malicious nodes in underwater acoustic sensor networks. Combined with opportunistic routing protocols, it can rapidly and accurately detect malicious nodes, effectively identify and isolate them, ensuring the safety and reliability of nodes in routing paths.

The invention belongs to the field of software testing, and relates to a software testing method and device based on artificial intelligence, computer equipment and a storage medium, and the method comprises the following steps: obtaining big data for software testing, setting a data trust factor, and preprocessing the big data according to the data trust factor; performing feature extraction and selection on the preprocessed big data; based on the big data subjected to feature extraction and selection, constructing a test model and a test case based on artificial intelligence; performing software testing according to the test model and the test case; recording, tracking and managing defects found in a test execution process by comparing a prediction result of the test model with an actual software operation condition, and classifying the defects; and based on software defects, feeding back and updating the test model, the test case and the trust factor. By setting and updating the trust factor, the use of historical data can be debiased, and the test efficiency and accuracy are improved.

The invention discloses a commodity traceability and counterfeit identification system and method based on a block chain, and relates to the field of commodity traceability, and the system comprises a block chain storage module which is used for receiving commodity verification information, and carrying out the updating and storage of the commodity verification information; the trust factor evaluation module is used for traversing the updated content in the commodity verification information and evaluating a commodity trust factor based on the updated content of the commodity verification information; the monitoring module is used for recording the commodity trust factor evaluation result in the commodity trust factor evaluation module and monitoring the commodity transportation risk based on the recorded commodity trust factor evaluation result; commodity verification information is stored in the block chain, in the transportation process, transportation personnel regularly verify the identity through a secret key and carry out operations such as location, time, electronic signature and commodity image shooting, it is guaranteed that the commodity verification information is dynamically updated, and the system comprehensively analyzes the information, evaluates commodity trust factors and monitors transportation risks; and whether the commodity has a counterfeit risk is judged in time.

The invention discloses an operation data analysis method and system based on an intelligent energy storage system, and relates to the technical field of data analysis and management, and the method comprises the steps: collecting original data, carrying out the preprocessing, extracting a feature index, and generating a confidence feature vector; based on the confidence feature vector, calculating a comprehensive confidence score to judge a credible state variable, then generating a trust factor, then performing correction based on the trust factor, obtaining a comprehensive trust factor, and performing grade division to obtain a trust result; based on the trust result, obtaining an average trust factor value of each grade, calculating an initial weight and a fuzzy membership weight of each grade, fusing the initial weight and the fuzzy membership weight to obtain a comprehensive weight, and combining the average trust factor value to generate overall trust strength for data analysis to obtain an analysis result; according to the method, the accuracy and the self-adaptability of energy storage operation data analysis can be effectively improved, and the real-time monitoring and intelligent decision-making capabilities of the system are remarkably enhanced.

The application discloses a kind of underwater man-machine shared teleoperation control methods based on bilateral trust mechanism.Method includes: master end force feedback device teleoperation control underwater mechanical arm moves, so that underwater mechanical arm end underwater hand-eye camera identifies underwater target, first, the master end evaluation model of master end force feedback device is established to obtain master end trust factor;Establish the visual evaluation model of underwater hand-eye camera, and finally obtain visual trust factor by density-based visual measurement outlier rejection method;Master end trust factor, visual trust factor and each control instruction are collectively processed by man-machine shared teleoperation method based on fuzzy logic, and the desired control instruction of underwater mechanical arm is obtained to carry out man-machine shared teleoperation control.The application can effectively eliminate the outlier of visual identification, ensure the reliability of visual servo system, introduce visual auxiliary system, can further reduce the burden of operator, improve the task success rate and operation efficiency of teleoperation system.

The present invention discloses a terminal trust evaluation method and device based on trust aggregation of a consensus mechanism, including: calculating objective historical trust evaluation values ​​of login nodes and historically visited nodes based on location trust factors, energy trust factors, and interaction trust factors; selecting nodes to form a related node list based on the reference value of the objective historical trust evaluation values; selecting nodes from the related node list to form a trusted consensus node list using a voting screening mechanism; performing trust consensus based on the trusted consensus node list to obtain a consensus trust value; and performing weighted averaging based on the consensus trust value and the objective historical trust evaluation value to obtain a total trust value of the terminal where the login node is located. When determining the consensus node, the node is evaluated and selected from the related node list determined based on the objective historical trust evaluation value, thereby improving the security of consensus node selection; and determining the total trust value based on the weighted averaging of the consensus trust value and the objective historical trust evaluation value, thereby improving the accuracy of trust value determination.

The application discloses an INP file analysis method. Wherein, the method comprises: fusing the first block size predicted by the machine learning model and the second block size calculated based on the feedback formula through the trust factor to obtain the third block size; dividing the first INP file into the third data block and the second INP file, identifying the complete data block and the separated data from the third data block according to the lightweight boundary identification model, splicing the separated data and the second INP file to form a new first INP file; analyzing the complete data block in parallel and storing the final analysis result; repeating the above process until the size of the first INP file is less than or equal to the third block size, at this time, the first INP file as a whole is analyzed as a complete data block; the final analysis results of all complete data blocks are merged into the complete analysis result of the INP file for storage. The application solves the problems of low analysis efficiency and poor analysis accuracy of the existing INP file analysis technology.

The invention discloses a resource quality control system based on learner trust evaluation in an open environment, which is used for calculating the credibility of a learner based on the historical contribution of the learner, granting resource control rights of different permissions to the learner and exciting the learner to continuously provide high-quality resources and valuable contribution. The system calculates historical contribution records of the learner through a trust factor algorithm under the guidance of a bidirectional interactive feedback model, and establishes a dynamic trust scoring mechanism of the learner. And according to the credibility score of the learner, the system automatically allocates corresponding resource management authority and resource sharing range for the learner, such as resource browsing range, uploading authority, editing authority, auditing authority, sharing authority and the like. According to the system, a data storage module, a resource packaging module, a learner trust evaluation module, a resource cooperative control module, an open access module and other modules are matched with one another to form a dynamic feedback circulation system, so that the resource quality of the whole system is continuously optimized.

The invention discloses an underwater man-machine sharing type teleoperation control method based on a bilateral trust mechanism. The method comprises the steps that main end force feedback equipment remotely operates and controls the underwater mechanical arm to move, so that when an underwater hand-eye camera at the tail end of the underwater mechanical arm recognizes an underwater target object, a main end evaluation model of the main end force feedback equipment is firstly established, and a main end trust factor is obtained; establishing a visual evaluation model of the underwater hand-eye camera, and finally obtaining a visual trust factor through a density-based visual measurement abnormal value elimination method; and the main end trust factor, the visual trust factor and all the control instructions are jointly processed through a man-machine sharing type teleoperation method based on fuzzy logic, and then an expected control instruction of the underwater mechanical arm is obtained so that man-machine sharing type teleoperation control can be carried out. Abnormal values of visual identification can be effectively eliminated, the reliability of a visual servo system is ensured, a visual auxiliary system is introduced, the burden of an operator can be further relieved, and the task success rate and the operation efficiency of a teleoperation system are improved.

A data processing method, relating to the field of media applications. The method comprises: acquiring media assets, the media assets comprising media content, and the media assets further comprising at least one of metadata of the media content and a trust record of the media content; and determining one or multiple trust factors of the media assets, the one or multiple trust factors being used to evaluate the credibility of the media assets. Therefore, the extracted trust factor can be used for evaluating the credibility of the media assets and accordingly, in subsequent credibility evaluation of the media asset, there is no need to read the media assets again, but to read information from the trust factor.

A method of determining locations of devices in a peer-to-peer network includes, at a first device, performing an intrinsic location measurement using a plurality of inertial measurement units of the first device and broadcasting to other devices first location information of the first device based on the intrinsic location measurement. The method further includes, at a second device, receiving the first location measurement information of the first device from the first device, receiving second location measurement information of the first device from a third device, and comparing the first and second location information of the first device. Based on whether the first and second location information match, the method includes, at the second device, adjusting a trust factor for the first device and broadcasting the adjusted trust factor and consensus location information of the first device.

Systems, methods, and software described herein manage traffic rules in association with fully qualified domain names (FQDNs). In one implementation, a domain name system (DNS) security service obtains notifications associated with an FQDN included in DNS requests. In response to the notifications, the DNS security service generates scores for the FQDN based on trust factors associated with the FQDN and determines traffic rules based on the scores.

The application discloses a multi-model adaptive ship position prediction method and system based on a credibility evaluation mechanism. For short-term prediction of the position of a ship, a credible UKF model is introduced to perform credibility processing on decomposed low-noise part position data, and a GRU neural network is used to predict decomposed high-noise part position data, so as to form a hybrid driving structure algorithm in a self-adaptive multi-model manner. Meanwhile, the application also uses an overall trust factor to evaluate the credibility of overall hybrid driving prediction, thereby improving the prediction effect of the model. Compared with a conventional single prediction algorithm, the application solves the problem that inaccurate modeling has an influence on a prediction result to a great extent.

Systems and methods are provided utilizing a consensus protocol that assesses a trust factor of one or more nodes, and which executes a block creation process by selecting and assigning trusted nodes, based on the trust factor assessment.

Systems and methods are directed to an improved fraud detection feature in Mobile Application Verification (MAV) device enrollment process. The proposed solution corresponds to a two-prong approach involving a strength factor evaluation of the authentication inputs provided during the initial enrollment process, and a computation of a number of trust parameters values from archived transactional records. A computational model supplemented with a machine learning process is then used to determine a device stability period as a function of the strength factor associated with the user provided authentication data and the computed trust factors.

The invention provides a network identity continuous trust assessment method and system, relates to the technical field of network security, and solves the security risk problem that an existing network identity authentication mechanism lacks dynamic and continuous assessment of a user trust state. In the method, a network identity association factor and a network identity influence factor are obtained, the association factor describes an association factor set influencing a network identity trust state, and the influence factor describes a network identity security risk degree influenced by the association factor; inputting the two types of factors into a network identity trust formula, wherein the formula is used for calculating the network identity trust state in real time; network identity weight factors are introduced during calculation, and the importance of correlation factors and influence factors participating in calculation in a trust formula is described respectively; and calculating to obtain a network identity trust factor, thereby completing continuous trust evaluation of the network identity. According to the method, the trust state of the network identity is accurately evaluated, and an effective trust basis is provided for security control based on the network identity.

Computer systems, methods, and non-transitory computer-readable storage devices for trust analysis and content reliability of online media are disclosed. A computerized method comprises: receiving an article that a user is viewing on a user device; analyzing the article to determine one or more trust factors related to the article; determining a content reliability score of the article using a contextually-trained trust analysis artificial intelligence (AI) model based on the one or more trust factors; and outputting the content reliability score of the article for display in a user interface of the user device.

The embodiment of the invention provides an equipment communication identity authentication method and related equipment, and belongs to the technical field of communication security. The method comprises the following steps: analyzing a received equipment authentication request to obtain first identity information of requesting equipment; querying and acquiring a dynamic credible factor of the request equipment according to the first identity information; determining the equipment credibility type of the request equipment according to the dynamic credibility factor; under the condition that the equipment credibility type is credible equipment, a first authentication strategy is adopted to authenticate the request equipment; the first authentication strategy is realized by verifying the digital signature based on an asymmetric encryption technology; under the condition that the equipment credible type is non-credible equipment, a first authentication strategy and a second authentication strategy are adopted to authenticate the request equipment; the second authentication policy is implemented based on an authentication protocol of challenge and response interaction. According to the invention, the equipment identity authentication efficiency and the equipment communication security can be improved.

A data processing method, relating to the field of media applications. The method comprises: determining whether one or more trust factors of a media asset correspond to indicator requirements, so as to obtain credibility information of the media asset, wherein the media asset may comprise media content, the media asset further comprises at least one of metadata of the media content and a trust record of the media content, and the trust factors are factors related to the credibility of the media asset. Users can determine the credibility of the media asset on the basis of the credibility information.

Systems and methods are directed to an improved fraud detection feature in Mobile Application Verification (MAV) device enrollment process. Systems and methods comprise a two-prong approach involving a strength factor evaluation of the authentication inputs provided during the initial enrollment process and a computation of a number of trust parameters values from archived transactional records. A computational model supplemented with machine learning is then used to determine a device stability period as a function of the strength factor associated with the user provided authentication data and the computed trust factors.