17 results about "Trust factor" patented technology

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 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 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 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 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.

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.

The invention discloses a relay protection device DDoS attack detection method and system. The method comprises the following steps: acquiring a network message; preprocessing the network message to form 64 * 64 two-dimensional matrix data, inputting the 64 * 64 two-dimensional matrix data into the CNN neural network model, and performing reasoning analysis to obtain an attack type and a corresponding first attack probability value; the attack types comprise ARP, ICMP, UDP, TCPSYN, other attacks and normal traffic; carrying out attack type classification on the network messages according to protocol types, counting the number of various messages in a time window, and comparing the number of various messages with a corresponding security threshold to obtain a second attack probability based on a traffic threshold; and for each attack type, obtaining the probability of each attack type according to the first attack probability value and the second attack probability value in combination with the trust factor, and determining a DDoS attack detection result. And the advantages of the two are combined, so that the accuracy and robustness of DDoS attack detection are remarkably improved.

The invention discloses an INP file analysis method. The method comprises the steps of fusing a first block size, predicted according to a machine learning model, of a first INP file and a second block size, calculated based on a feedback formula, through a credibility factor, so as to obtain a third block size; dividing the first INP file into a third data block and a second INP file, identifying a complete data block and separated data from the third data block according to a lightweight boundary identification model, and splicing the separated data with the second INP file to form a new first INP file; analyzing the complete data block in parallel and storing a final analysis result; the process is repeated until the size of the first INP file is smaller than or equal to the size of the third block, and at the moment, the whole first INP file serves as a complete data block to be analyzed; and combining the final analysis results of all the complete data blocks into a complete analysis result of the INP file, and storing the complete analysis result. According to the method and the device, the problems of low analysis efficiency and poor analysis precision of the existing INP file analysis technology are solved.

The invention discloses an interactive electric power material knowledge unit construction method and device, and the method comprises the steps: obtaining electric power material data, and carrying out the vectorization, and obtaining an original data unit; based on an original data unit, considering a credible factor and a time sequence factor, executing dynamic weighted aggregation, and giving a basic knowledge unit; on the basis of the basic knowledge unit, spatial correlation, topological correlation and time correlation among the electric power materials are obtained, and comprehensive correlation is given in combination with a fault conduction mechanism; fusing the basic knowledge unit and the comprehensive association, generating an enhanced knowledge unit, and constructing an electric power material knowledge unit library; and adopting a pre-training language model, according to the interaction unit, combining power rule base constraints, generating answers, responding to answer feedback of a user, updating the power material knowledge unit library, and iteratively optimizing the pre-training language model to convergence to realize construction of the interactive power material knowledge unit. The electric power material data is accurately integrated, depth association is excavated, and real-time and intelligent management and control are realized.

The invention belongs to the technical field of distributed testing, and particularly relates to an MAS-based microwave signal analyzer test task intelligent allocation method, which adopts a multi-agent system protocol of an extended contract network, introduces a trust mechanism, and analogy task allocation into an engineering bid invitation process. The whole process is further decomposed into four stages of bid invitation, bidding, bid winning and contract signing, namely four stages of test task issuing, signal analyzer autonomous response, intelligent selection of a target signal analyzer and test task starting, the test task is used as input, and the indexes of the signal analyzer, the test task completion time and quality and the like are used as evaluation standards; according to the method, intelligent distribution of the test tasks is carried out, trust factors of corresponding signal analyzers are modified according to results of completing the tasks, iterative optimization is carried out continuously, the accuracy and efficiency of intelligent test task distribution are improved, and therefore the overall test efficiency is improved.

The application discloses a kind of converter system network attack detection method and system based on electric signal, the method includes: obtaining data set under historical steady state condition and historical network attack data set;According to the minimum value of the periodicity rule change of the trust factor calculated according to the data set under the steady state condition, steady-state trust boundary is obtained;According to the historical network attack data set and grey correlation degree method screening training data set, input machine learning model training obtains network attack type detection model;Real-time monitoring and collecting three-phase current signal and three-phase voltage signal of converter system, calculating trust factor, comparing trust factor and steady-state trust boundary to determine whether to suffer network attack, if suffer, disconnect server from attack source, and according to network attack type detection model, the type of network attack is judged in real time.The application solves the problem that existing network attack detection is difficult to consider detection real-time and detection accuracy.