15 results about "Markov transition" patented technology

The application discloses an omnidirectional guiding interaction multi-model positioning method and system for a motorized mother ship to recover an underwater robot and a storage medium, the method obtains the state information and distance data of the mother ship by integrating a water acoustic ranging and a water acoustic communication device, constructs an interaction multi-model singular value decomposition unscented Kalman filtering algorithm fusing a uniform straight line and a uniform bow turning model, and realizes real-time estimation of the position of the mother ship under a mouth-shaped maneuvering track. An event-triggered communication mechanism is designed, information is broadcast only when the turning state of the mother ship changes, so as to reduce the communication load and maintain real-time performance; meanwhile, a Markov transition matrix is updated, the turning model weight is enhanced, and the estimation accuracy and model matching are improved. According to the model probability and the estimation result, a LOS guiding law with an adaptive look-ahead distance is constructed, and the autonomous guiding recovery of the underwater robot to the motorized mother ship is realized. The method provided by the application can realize dynamic estimation and guiding based on the measurement data of a single water acoustic device under the conditions of the mother ship maneuvering and communication limitation.

This invention discloses a memory-based dynamic event triggering control method for a single-link robotic arm system based on spoofing attacks. The method first establishes a Markov model of the single-link robotic arm system based on Markov jump system theory, considering a system model with a general transition rate. Next, a mode-dependent memory controller is designed to overcome the influence of spoofing attacks and external disturbances on the system. A memory-based dynamic event triggering mechanism is also designed to reduce communication transmission frequency. Compared with existing memoryless event triggering schemes, this scheme utilizes a series of recently released signals and introduces a threshold function and an internal dynamic factor, which can automatically adjust according to the triggering error. Finally, vertex separator processing is introduced to address the uncertainty in the Markov transition rate. A mode-dependent state feedback controller is designed to control the stochastic stability of a single-link robotic arm system. When applied to a single-link robotic arm system, this method ensures the normal operation of the system under spoofing attacks and disturbances.

This invention discloses a personalized location trajectory privacy protection method based on mutual information, belonging to the field of crowdsourced sensing technology. It includes: Step 1: Data preprocessing; Step 2: After generating the Markov matrix in Step 1, the matrix is ​​normalized, and a location privacy protection mechanism is established by setting a distortion threshold and regularization parameters; Step 3: Measuring the effectiveness of location privacy protection. This method generates a Markov transition matrix by merging user location trajectory data, analyzes implicit privacy information and adds distortion perturbations, and uses mutual information to measure the degree of privacy leakage, achieving a trade-off between privacy and usability. It solves the problem that existing technologies cannot effectively overcome the risk of location information privacy leakage.

This invention relates to the field of equipment operation status monitoring technology, and discloses a method for early warning of photovoltaic terminal sealing failure based on mechanical fingerprint features, including the following steps: A simulated strain signal is acquired using a microelectromechanical system strain gauge, and a basic mechanical fingerprint is output using a preprocessing module; a gateway module calculates an instantaneous reward value to generate a global timestamp, sends the basic mechanical fingerprint and the global timestamp to a cloud server, and stores a pending tuple containing the instantaneous reward value and the global timestamp in a buffer module; the cloud server uses parameterized quantum circuits to map the basic mechanical fingerprint to a quantum state input quantum support vector machine, outputs a sealing failure probability distribution, calculates the quantum state measurement entropy value to generate a delayed reward value for distribution; the gateway module extracts the pending tuple using the global timestamp, merges the reward values ​​to generate a Markov transition tuple, updates the deep Q-network model, and issues control commands; the cloud server calculates a failure risk index for early warning.

The present application relates to the technical field of industrial intelligent monitoring and fault diagnosis, and particularly relates to an unbalanced fault diagnosis method based on attention perception and decision coupling. The technical scheme comprises the following steps: obtaining a one-dimensional vibration time series observation signal of a target industrial equipment in a running state, and pre-processing the signal; and inputting the pre-processed time series signal into a Gram angle field sine transformation, a Gram angle field difference transformation and a Markov transition field for parallel coding. The present application can autonomously capture weak fault fingerprints submerged in strong background noise without relying on manual resampling or prior threshold setting, can guarantee the reliability of the diagnosis of majority class samples, can significantly improve the upper limit of the recognition of a small number of key fault patterns, and can provide an intelligent diagnosis solution with high sensitivity, strong robustness and without manual intervention for complex industrial scenes such as wind power, aviation and rail transportation which have high reliability requirements and difficulty in obtaining fault data.

This invention discloses a disturbance-free sliding mode control method for a multimodal Boost converter circuit, belonging to the field of automation technology. Based on a discrete implicit stochastic semi-Markov transition system model, a disturbance-free transfer sliding mode control strategy based on an observer is designed. Since real-world modal information is difficult to obtain in actual environments, an implicit semi-Markov transition system is used to describe the system, which has lower conservatism than a semi-Markov transition system with fully known system modes. To better match the system modes, a mode-dependent sliding surface is selected. Expressions for the disturbance-free transfer performance during operation and at transition moments are also provided. By integrating the sliding mode controller with the disturbance-free transfer performance, disturbances and impacts in the control input are effectively suppressed. This invention can accurately describe the dynamic characteristics of a multimodal Boost converter circuit under conditions of mismatched system and observed modes, effectively suppressing the effects of disturbances and jolts in the control input, and improving the fast response speed and dynamic performance of the Boost converter circuit.

This invention belongs to the technical field of lead-bismuth cooled reactor control system design, and specifically relates to an interactive multi-model weighted fusion method for core power control. The invention includes the following steps: S1, constructing a core model and performing lead-bismuth heat transfer calculations; S2, constructing transfer functions, building four power step transfer functions to characterize the lead-bismuth reactor model, converting the transfer functions into state-space form to obtain a linearized model; S3, dynamically mixing the initial states of each model using Markov transition probabilities, independently calculating the predicted values ​​and residuals of each model using Kalman filtering; updating the model weights in real time based on residual covariance and Bayes' theorem, and weighting and fusing the outputs of each model. This invention obtains the system's transfer function from input to output based on the constructed core model, fuses real-time measurement data through a Markov transition mechanism, and weights and fuses the outputs of each model, ultimately achieving real-time matching of the optimal model to support core power control in lead-bismuth reactors.

This invention provides a robot localization method based on background pulse Kalman filtering, comprising: constructing a linear dynamic system with multiple motion states and a Markov transition probability matrix; applying an interactive multi-model Kalman filter algorithm based on the linear dynamic system and the Markov transition probability matrix to obtain the estimated values ​​of the state vectors of the motion models and the model probabilities of the motion models; obtaining the estimated values ​​of the state vectors based on the estimated values ​​of the state vectors of the motion models and the model probabilities of the motion models; applying the background pulse Kalman filter algorithm to each motion model to obtain the estimated values ​​of the state vectors of the robot's motion models and the covariance matrix of the state vectors of the motion models; and iteratively solving to obtain the estimated values ​​of the state vectors of the motion models and the covariance matrix of the state vectors of the motion models at the next time step, thereby achieving high-precision localization of the robot.

The application relates to the technical field of power consumption anomaly detection, and specifically provides a Markov model-based improved power consumption anomaly detection method for clustering, which comprises the following steps: for power data preprocessing, a symbolic aggregate approximation method is used to reduce the dimension and symbolize the power consumption time sequence, and a Markov state transition probability model is constructed to represent the power consumption behavior mode of a user; for the problem that a traditional density peak value clustering algorithm relies on manual experience to select a truncation distance, a clustering anomaly detection model is provided; the relative distance is used to measure the difference in the Markov transition probability distribution of the user; a mycoparasitism optimization algorithm and a Bonferroni index are introduced to automatically optimize the truncation distance parameter; and the density peak value is used to identify the clustering center to complete user clustering and identify abnormal points. The application improves the precision and efficiency of power consumption anomaly detection, and provides technical support for power consumption safety management and anti-electricity stealing.

The application relates to the technical field of navigation positioning, and discloses an unmanned ship cluster cooperative navigation method based on an interactive multiple model, which comprises the following steps: establishing a state model and a measurement model of an unmanned ship cluster cooperative navigation system; determining initial state estimation according to model probability and Markov transition probability of the state model based on an interactive multiple model algorithm; determining predicted measurement data of each sub-state model according to the measurement model and the initial state estimation, and obtaining Markov distance between the predicted measurement data and actual measurement data obtained through a relative navigation sensor; updating the model probability of the state model according to the Markov distance and a likelihood function of the state model, and updating the Markov transition probability of the state model according to a change amount of the model probability; and determining state estimation of the unmanned ship cluster cooperative navigation system at a target time through the model probability of the state model at the target time, so that cooperative navigation of the unmanned ship cluster is realized, and navigation precision is improved.

The application provides a disease classification model construction method and device, electronic equipment and storage medium. The method comprises the following steps: performing wave band screening on a plasma ATR-FTIR spectrum; dividing the spectrum into an original training set and a test set by using a KS algorithm; performing image processing by using a Markov transition field method; converting a two-dimensional matrix into a one-dimensional feature vector; performing dimension reduction processing on the one-dimensional feature vector by using principal component analysis; performing sample augmentation on minority class samples in the dimension reduction training set by using a synthetic minority over-sampling technique; combining the dimension reduction training set and virtual samples to obtain an augmented training set; and constructing a classification model by using a joint sparse boundary Fisher regularization classification algorithm. The application amplifies the weak spectral difference between majority class samples and minority class samples, and can balance the number of majority class samples and minority class samples in the training set, significantly improves the recognition sensitivity of target class patients, and is used for classifying mental diseases or neurodegenerative diseases.

This invention relates to the fields of stochastic processes and Monte Carlo computation, and particularly to a method for constructing a Markov transition operator. The aim is to improve the spectral properties of traditional Markov operators by introducing a higher-order transition structure, thereby enhancing sampling efficiency. First, starting with a basic Markov transition operator that satisfies the invariance of the target distribution, this method, based on the Metropolis–Hastings (MH) framework, introduces a two-step hybrid update mechanism to construct a polynomial Markov operator. Through spectral structure analysis of this operator, its eigenvalue transformation relationship is established, and it is proven that it has a larger spectral gap and better convergence performance compared to the original operator, while also reducing the asymptotic variance of the corresponding statistics. Second, further analysis of the autocorrelation function and integration time shows that the Ishikawa-MCMC algorithm can effectively suppress linear dependencies between samples, resulting in a faster decay rate of the autocovariance, thereby reducing the Monte Carlo covariance. The operator construction method proposed in this invention overcomes the problem of slow convergence of traditional single transition operators under high-dimensional or complex distributions by integrating multi-order transition information. It provides a new operator design framework for Markov chain Monte Carlo algorithms and can be widely applied in fields such as Bayesian statistical inference, complex probability distribution sampling, and stochastic simulation.

The application provides a time sequence knowledge graph explainable extrapolation method, system, computer device and medium, and belongs to the technical field of knowledge graph reasoning. The method comprises the following steps: constructing a relation-driven Markov transition matrix based on facts in a historical time sequence knowledge graph; taking a seed edge sampled according to a specific relation as a starting point, guiding a sampling path based on the Markov transition matrix, and executing a bidirectional time sequence walking strategy; mining three context-aware logical rules, i.e., a leading rule, a bridging rule and a tracing rule, containing a timestamp constraint condition based on the sampling path; and performing reasoning prediction on a future query based on the context-aware logical rules. Through the time sequence knowledge graph explainable extrapolation method, system, computer device and medium, the accuracy and explainability of time sequence knowledge graph extrapolation prediction can be improved simultaneously, and effective technical support is provided for event prediction and dynamic decision-making.