36 results about "State-transition matrix" patented technology

The application discloses a kind of communication power supply operating state intelligent monitoring methods based on machine learning, comprising the following steps: S1, obtains multiple-source monitoring data and pre-processes;S2, sequence segmentation is carried out using sliding time window and statistical feature vector is extracted, forms state variable set;S3, each state variable is encoded to construct state node and establish weighted directed connection, and construct operating state evolution diagram;S4, state transition matrix is constructed, main transition feature is extracted and low rank is approximately reconstructed, sequence learning is carried out to construct state update function;S5, to state node, multiple-step recursion is carried out, and the Euclidean distance between recursive state and risk boundary is calculated, to determine state monitoring result;S6, error is calculated and state update function parameter is iteratively updated.The application can realize the multiple-step recursion prediction and risk trend identification of communication power supply operating state, improve the accuracy and stability of communication power supply operating state monitoring.

This invention discloses an electronic image sharpening method based on image processing, comprising: mapping the image to an initial continuous signal function continuously distributed in coordinates; constructing a liquid time constant prediction branch and generating liquid response parameters based on local gradient and contrast features; reconstructing the discrete state transition matrix into a dynamic evolution kernel, constructing a liquid state space model architecture and performing bidirectional scanning to obtain a latent space state feature sequence; calculating an edge gain adjustment factor and coupling it to the input weight matrix to generate an evolutionary feature tensor; performing spatiotemporal smoothing correction in conjunction with sensor motion parameters; and outputting the electronic image through spatial dimension recovery and implicit function resampling. This invention achieves enhanced edge details while suppressing background noise, eliminating displacement deviations and artifacts, and significantly improving the sharpness and structural fidelity of electronic images.

The application provides a pilot landing operation analysis method and device, electronic equipment and a storage medium, comprising: obtaining external environment parameters and aircraft operation state parameters formed in the process of the pilot operating the aircraft to land; calling a neural network model and inputting the external environment parameters and the aircraft operation state parameters into the neural network model to obtain a state transition matrix at each time point; based on the state transition matrix, obtaining a hidden state corresponding to the state transition matrix to complete the pilot landing operation analysis, wherein the hidden state is used to represent each state of the attention allocation mode of the pilot, and the hidden state includes any one or several of a state without attention loss, a state of missing attention to a yaw angle, a state of missing attention to an altitude, and a state of missing attention to an airspeed. The pilot landing operation is accurately and objectively analyzed, so as to facilitate risk control and improve the flight safety level of the pilot.

The application provides a power meter anomaly analysis method and system, and relates to the technical field of smart grid and power metering. The method steps include: collecting voltage, current and chip surface temperature data of the electric energy meter, extracting the load rate change rate, temperature rise rate and waveform distortion rate as feature vectors; determining a dynamic state transition matrix based on the feature vectors and a preset function relationship, and establishing a state space model with the metering error estimated value as the state variable; using Kalman filter recursion algorithm for state prediction and measurement update to obtain the optimal estimated value of the metering error at the current time; according to the optimal estimated value, generating a multi-dimensional compensation instruction through a preset compensation mapping relationship, and executing the instruction to real-time offset the estimated metering error. The application realizes real-time tracking and closed-loop compensation of dynamic error, and significantly improves the long-term operation accuracy and stability of the electric energy meter under complex working conditions.

A method of quantum probability estimation of an event in a stochastic process is provided. The stochastic process is modelled based on a finite state-machine model. A classical predictive model for the event is modelled based on the stochastic process and includes a set of states and a set of state transition matrices associated with the finite state-machine model. The method includes: generating a circuit description of a quantum state-preparation operator based on a past observation sequence of the stochastic process and the set of state transition matrices of the classical predictive model; generating a circuit description of a quantum search operator based on the circuit description of the quantum state-preparation operator and an event indication function, whereby the event indication function is configured to indicate whether the event is to occur with respect to the future sequence of the stochastic process; generating a plurality of circuit descriptions for a plurality of measurement quantum circuits, respectively, for compiling the plurality of measurement quantum circuits on a quantum computer, whereby each circuit description of the measurement quantum circuit is generated based on the circuit description of the quantum state-preparation operator and the circuit description of the quantum search operator; for each of the plurality of circuit descriptions of the plurality of measurement quantum circuits: compiling the circuit description of the measurement quantum circuit on the quantum computer to form the measurement quantum circuit thereon; and executing the measurement quantum circuit formed on the quantum computer to obtain a measurement result associated with the measurement quantum circuit that indicates a number of times that the measurement quantum circuit produces an indication that the event is to occur; and estimating a probability of the event based on the measurement results obtained. There is also provided a corresponding system for quantum probability estimation of an event.

This invention discloses a flexible control method for digital twin manufacturing systems addressing uncertainties in the production process, belonging to the field of intelligent manufacturing system technology. The method includes: statistically analyzing the time of the four stages of perception, analysis, decision-making, and execution; determining the advance prediction time by combining system complexity and simulation speed; constructing an scalable system state transition matrix; performing accelerated simulation based on real-time states and injecting uncertain events according to trigger probabilities to obtain multiple future states; selecting the top n high-probability states in descending order of occurrence probability for parallel decision-making; binding decision instructions with corresponding states and issuing them in advance; and executing the corresponding instructions through state matching when the physical system reaches the predicted time, achieving flexible control. This invention can improve the consistency and robustness of "virtual control of the real" in digital twin systems under uncertain conditions and is applicable to multi-level digital twin systems such as machine tools, robots, production lines, and workshops.

This invention relates to the field of computer data processing technology and discloses an intelligent review method for science and technology competitions and an evaluation method for the transformation of educational achievements. By collecting process data from participating projects, an innovation trajectory event sequence is constructed, and multi-layer trajectory entropy is calculated based on a state transition matrix. A process innovation index is generated by combining this with a time consistency coefficient. Based on this, technology-related features, education-related features, and market-related features are extracted to construct a three-dimensional transformation coordinate system and calculate the transformation potential energy. A system reference score is generated by combining this with the basic completion degree. A review convergence mechanism is executed based on the system reference score to obtain the converged review result, ultimately generating a comprehensive review result. This invention solves the problem in existing technologies where review relies on result data and fails to reflect the innovation process by collecting process data from the entire process of participating projects and constructing an innovation trajectory event sequence, combined with a state transition matrix and multi-layer trajectory entropy to quantify the project evolution process.

Methods, systems, and apparatus, including medium-encoded computer program products, for electrical power grid simulation. One of the methods includes obtaining a state-transition matrix model of an electrical power grid and executing a simulation of electric power grid behaviors by executing the state-transition matrix model using a parallel processing device that includes multiple cores.

The application relates to the technical field of communication, and particularly discloses an Internet of Things touch screen controller supporting multiple communication protocols and a communication method thereof. The Internet of Things touch screen controller comprises a protocol identification module, a data analysis module, a data conversion module and a control execution module. The protocol identification module adopts a multi-stage matching algorithm of a weighted decision tree to determine a current communication protocol type. The data analysis module analyzes input data by using a corresponding protocol analysis algorithm, generates a corresponding real-time state transition matrix based on a current analysis state path of the input data, analyzes real-time coincidence degrees between the current analysis state path and an expected analysis state path based on the real-time state transition matrix, judges whether an analysis abnormality processing procedure is triggered based on the real-time coincidence degrees, and extracts payload data until the payload data is extracted. The data conversion module converts the payload data into a unified internal data format to obtain target instruction content. The control execution module executes corresponding device control operations or state feedback operations based on the target instruction content, thereby meeting control requirements in different scenes.

This invention provides a massive data processing method for DSGE analysis and decision-making related to marine industries and ecology. Belonging to the technical field of massive data processing for DSGE models, this invention collects multi-source heterogeneous data streams and performs spatiotemporal alignment preprocessing. It then performs threshold cointegration tests on the preprocessed data to identify nonlinear threshold points and establish a state transition matrix. The data is input into a heterogeneous computing scheduler for resource allocation and grid partitioning. A multi-objective decomposition framework combined with particle swarm optimization and simulated annealing algorithms is used to complete parameter calibration. A sliding window mechanism is established, and a recursive least squares algorithm is used for incremental updates. A low-rank approximate decomposition is performed on the high-dimensional weight matrix, and the optimized low-rank submatrix is ​​used as the weight parameters of the DSGE model for decision analysis. This solves the technical problem that massive heterogeneous data is difficult to efficiently support DSGE model parameter calibration and decision optimization under real-time processing conditions.

This application provides a method, apparatus, terminal, and medium for calculating navigation heading angle based on a combination of GNSS and IMU, including: acquiring the real-time heading angle and real-time forward speed of a mobile device at the current moment to construct a motion model of the mobile device; obtaining the state-space equation of the motion model based on the state transition matrix, velocity increment matrix, and white noise sequence; obtaining a Kalman filter measurement model based on the state-space equation of the motion model and using GNSS navigation and positioning results as observation vectors; and obtaining the optimal navigation heading angle at the current moment based on the Kalman filter measurement model, Kalman gain, GNSS navigation and positioning results, and IMU kinematic calculation results. This invention effectively controls the influence of large GNSS noise, and the use of incremental data to calculate prior values ​​also avoids the cumulative divergence phenomenon of IMU random drift errors.

This invention provides a method for predicting linear wave loads in ship structural design, relating to the field of ship engineering structural design technology. Based on optimized Latin hypercube sampling and three-dimensional potential flow calculation, this invention constructs a dataset of hydrodynamic pressure fields and load components under regular wave conditions. It extracts the basis function system and establishes a linear mapping relationship between the combination coefficients and the loads. For irregular wave sea states, it interpolates to determine the state transition matrix and control matrix. Combined with the main wave disturbance force, it performs a time-series recursion of the combination coefficients, triggering local transient calculations at preset intervals, and performing sparse verification corrections on the recursive state. Finally, it outputs the load history in real time through the mapping relationship and statistically analyzes the extreme values ​​to generate a wave load design parameter table. This method replaces full-order solutions with low-dimensional recursion, triggering a small number of full-order verifications at preset intervals. While maintaining prediction accuracy comparable to full-order solutions, it significantly reduces computation time, providing efficient and reliable load input for ship structural design.

This invention provides a method for reliability assessment and maintenance strategy optimization of vehicle door systems based on the PH distribution. The method includes: acquiring fault data recorded by vehicle repair departments; estimating PH distribution parameters based on the dwell time of components in different states within the door system following a PH distribution; obtaining the state set of components in the door system based on the PH distribution parameters; constructing the state transition matrix of the components in the door system; generating a general generating function for the door system; constructing the reliability structure of the door system; obtaining the output performance and maintenance cost of the door system; establishing an optimization objective function for the maintenance strategy of the door system with the goals of minimizing the maintenance cost rate and maximizing the expected output performance; solving the optimization objective function using the NSGA-II algorithm based on dynamic congestion distance; and obtaining the optimal maintenance strategy for the door system. This invention can analyze the reliability of vehicle door systems and optimize maintenance strategies for door systems with both minimum cost rate and maximum output performance as dual objectives.

This application belongs to the field of wind power generation technology, and specifically discloses a method for analyzing the mechanical-electrical coupling characteristics of large wind turbine units. The method includes: constructing a nonlinear time-varying model of the wind turbine unit's machine-side system, which includes mechanical and electrical subsystems, based on the wind turbine unit's mechanical parameters, electrical parameters, control parameters, operating data, and periodic aerodynamic loads caused by wind shear and tower shadow effects; performing time-domain simulation on the nonlinear time-varying model to obtain the periodic steady-state trajectory of the machine-side system under set operating conditions; linearizing the nonlinear time-varying model based on the periodic steady-state trajectory to establish a linear periodic time-varying state-space model, and calculating the state transition matrix within one fundamental period; calculating the Floquet characteristic index of the wind turbine unit using Floquet-Lyapunov theory based on the state transition matrix, and determining the small-disturbance stability of the machine-side system based on the real part of the Floquet characteristic index.

This invention discloses an adaptive robustness method for IGGⅢ parameters suitable for GNSS positioning in complex environments, belonging to the field of satellite navigation and positioning technology. The method first establishes double-difference observation equations and state equations based on satellite observations and the state transition matrix; it then estimates parameters using extended Kalman filtering to obtain post-hoc residuals and Mahalanobis distance statistics; it standardizes the post-hoc residuals to construct a test metric for the IGGⅢ model; it performs a chi-square test on the Mahalanobis distance statistics of the current epoch to obtain adjusted IGGⅢ model tuning parameters, and performs outlier satellite deletion and observation weight reduction processing; when the preset maximum number of iterations is met or no outliers are found in the current test, a floating-point solution is output, and ambiguity is fixed using the LAMBDA algorithm to obtain a fixed solution or a floating-point solution. The method provided by this invention can effectively reduce the adverse effects of NLOS and multipath errors in complex environments on precise GNSS positioning, improving GNSS positioning performance and reliability.

The present application relates to a kind of VLC-RF network resource matching method based on martingale theory and reinforcement learning, the present application constructs visible light communication (Visible Light Communication, VLC)-radio frequency (Radio Frequency, RF) heterogeneous network switching service mechanism, deduces the state transition matrix of system service process.Based on martingale theory, network QoS parameters are evaluated, and queue reliability QoS, delay reliability QoS and jitter reliability QoS are combined to construct user satisfaction utility function.Finally, the service rate combination required by the system network is obtained using reinforcement learning to achieve optimal allocation of resources and improve network service efficiency.

The application discloses a wireless communication service social mobile behavior privacy risk quantification method and belongs to the technical field of user privacy risk quantification. The wireless communication service social mobile behavior privacy risk quantification method comprises the following steps: step one, obtaining the social mobile trajectory of the user; step two, marking the stay behavior semantics of the user; step three, obtaining the implicit state transition matrix; step four, constructing a mapping observation matrix; step five, performing social mobile random process representation; step six, designing a behavior semantics reasoning algorithm to reason the possibility of the stay and transition behaviors of the user and the most possible behavior trajectory. The application solves the problem that the prior art only considers the behavior semantics of the user facing a single place and lacks behavior mode calculation based on the trajectory hidden in the user mobility, and provides a privacy protection deployment for revealing and quantifying the user social mobile privacy leakage risk in a mobile scene and supporting rigorous behavior semantics.

The application discloses a GNSS spoofing jamming detection method based on multi-step inertial navigation prediction, and comprises the following steps: constructing a GNSS / INS tight coupling model, and performing time updating and observation updating through an extended Kalman filter (EKF); adding a new independent inertial navigation (INS) unit in the GNSS / INS tight coupling model, constructing a spoofing detection method model based on innovation, and generating un-contaminated pseudo-range estimation values within N steps; replacing contaminated elements in an EKF observation matrix and a state transition matrix to obtain a reconstructed observation matrix and a state transition matrix; calculating improved innovation values based on the reconstructed observation matrix and the state transition matrix, predicting innovation deviation by using the average value of the innovation at the previous epoch at the current time, obtaining unbiased improved innovation values, constructing a spoofing detection factor based on the unbiased improved innovation values, generating a detection statistic, comparing the detection statistic with a preset threshold, and determining spoofing jamming. The application has better robustness.

This invention discloses a method for estimating the patrol strategy of an unmanned system by an attacker in an adversarial environment, belonging to the field of unmanned system patrol strategy analysis and estimation technology. It includes: the attacker continuously observes the movement trajectory of the patroller and records the state sequence; based on the state sequence, a maximum likelihood estimator for transition frequency is used as the basic estimation method, by statistically analyzing the frequency of transitions between states, constructing a transition count matrix, and normalizing it to estimate the state transition matrix from a state sequence; and on top of the maximum likelihood estimator framework, an adaptive time decoupling ensemble estimator is employed, introducing a time interval parameter. t Temporal decoupling of the state sequence and adaptive generation of candidate data based on data size. t value set, through K Cross-validation to select the optimal t The values ​​are weighted and integrated according to the scoring weights to obtain the final state transition matrix estimation result. This invention provides a purely data-driven patrol policy estimation method from the attacker's perspective.

The application discloses a beam angle prediction method based on neural network and Kalman filtering fusion, and belongs to the technical field of millimeter wave communication.The method adopts a single-layer neural network without an activation function to learn the behavior of LSTM, extracts a weight matrix of the single-layer neural network as a state transition matrix of a Kalman filtering algorithm, and performs beam tracking by executing the Kalman filtering algorithm.The application fuses the strong robustness of the Kalman filtering algorithm on the basis of accurate prediction of the beam tracking angle; and since the LSTM has strong learning ability, dense observation of a communication target is not needed, the observation interval can be artificially set according to cost, the prediction cost is greatly reduced, and the performance of the beam tracking system is improved.

This invention relates to a sequential filtering method for data fusion in a rotorcraft unmanned aerial vehicle (UAV) attitude reference system, belonging to the field of UAV attitude estimation. In this invention, a low-pass filter is used to process the acquired sensor data to obtain the initialized horizontal attitude angle and heading angle, thus initializing the attitude reference system. During the calculation process, Euler angle differential equations are used to construct the system's state transition equations, defining relevant state variables and obtaining the state transition matrix. Measurement equations are constructed based on the acceleration output and magnetometer output of the carrier's observations, yielding the corresponding observation matrix. Following the steps required for Kalman filtering, the state variables required during the Kalman filter's operation are mapped to the system, and calculations are performed using the Kalman filter data update method to complete the calculation derivation of the corresponding quantities, thus realizing the sequential processing method of Kalman filtering. Compared to traditional Kalman filtering algorithms, this algorithm implements Kalman filtering sequentially, requiring only partial data calculations during attitude updates, and does not involve matrix operations throughout the process, thereby improving the algorithm's computational efficiency.

The application relates to the field of electric digital data processing, and discloses an electric digital data processing and visualization system for hot processing process data, which comprises the following modules: a data alignment module which acquires multi-source time sequence signals under hot processing conditions and completes time domain alignment and packaging; a topology control module which stores a space topology correlation matrix, maps sample values to nodes, completes feature assignment, determines the algebraic operation relationship of the product of a state transition matrix and a historical feature vector plus an input excitation vector by calculating a topology state feature vector of a current period; and an incremental rendering module which calculates a differential feature vector of adjacent periods, extracts element coordinates whose absolute values exceed an amplitude threshold, and completes local data coverage of a display memory storage module. The application converts space correlation reconstruction into matrix state evolution, avoids global redundant recalculation and large-area pixel flushing, eliminates data bus occupation pressure, and improves the real-time performance of process situation reconstruction.

The application discloses a kind of unmanned system patrol path decision-making method under hostile environment, belong to autonomous unmanned system and intelligent decision-making technical field.The method includes: obtaining the undirected patrol topology graph of the physical environment to be patrolled, target steady-state distribution is calculated based on the value parameter of each node, attack time parameter and degree;Define the patrol strategy unpredictability index formed by the weight of strategy internal entropy and strategy entropy PSUI , to quantify the uncertainty of strategy in micro-randomness and macro-diversity;Using simulated annealing double-entropy balance optimization algorithm, to maximize PSUI As target, generate state transition matrix strategy set under the constraints of meeting topology adjacency, line normalization and target steady-state distribution;Control unmanned system to carry out space-time decoupling patrol by random switching matrix and probabilistic transition in matrix.The application realizes the unity of patrol strategy evaluation and optimization, significantly improves the unpredictability of patrol path, and enhances the protection ability to key infrastructure.

This invention discloses a seamless switching positioning method integrating RTK and PPP-RTK algorithms. This method achieves seamless switching between RTK and PPP-RTK algorithms by designing a dual-state model within a Kalman filtering framework. When network connectivity is available, the system automatically uses NRTK corrections to achieve high-precision RTK positioning; when the network is interrupted, the system seamlessly switches to satellite-based PPP-RTK corrections for PPP-RTK positioning without requiring a software restart. This invention, through the design of a state transition matrix and an adaptive noise adjustment mechanism, ensures positioning continuity and accuracy stability during the switching process, solving the problem of traditional methods requiring re-initialization during switching, which leads to positioning interruptions. This significantly improves the robustness and availability of GNSS positioning systems in complex environments.

This invention relates to the field of digital information transmission technology, specifically to a bandwidth-adaptive dynamic rate-limited data distribution system. The system includes a physical layer state acquisition front-end, a signal-to-noise ratio (SNR) trend prediction front-end, a cross-layer rate-limiting decision front-end, and a distribution execution front-end. The physical layer state acquisition front-end acquires the SNR sequence of the target distribution link; the SNR trend prediction front-end inputs the SNR sequence into a Kalman filter model and outputs a predicted value of the available bandwidth of the link, the state transition matrix parameters of which are dynamically determined by the multipath delay spread value of the current link; the cross-layer rate-limiting decision front-end compares the predicted value of the available bandwidth of the link with the current sliding window congestion window value of the transport layer and takes the smaller value as the dynamic rate-limiting threshold; the distribution execution front-end adjusts the data packet transmission interval of the transmission queue according to the dynamic rate-limiting threshold. This scheme reduces the transmission rate in advance based on the bandwidth prediction results before physical layer channel fading causes packet loss at the transport layer.

This invention belongs to the field of pose measurement, specifically relating to a pose measurement calculation method, system, and electronic device for a smart plunger in a gas well. It aims to solve the problem of the lack of effective means to measure the real-time position and attitude of the plunger during operation in a gas well. The invention includes: obtaining the attitude quaternion of the carrier coordinate system relative to the navigation coordinate system based on the navigation coordinate system and the carrier coordinate system; calculating the plunger's state variables based on the attitude quaternion and the plunger's zero bias in the carrier coordinate system; calculating the plunger's state transition matrix based on the plunger's angular velocity in three directions in the navigation coordinate system, the attitude quaternion, and the algorithm's calculation cycle; calculating the measurement matrix based on the attitude quaternion and the plunger's acceleration in the carrier coordinate system; and performing time updates and measurement updates on the plunger based on the measurement variables, the state transition matrix, and the measurement matrix. This application uses gravity field data to complete the filter measurement update, suppressing attitude error divergence and solving the problem of long-term attitude measurement.

This invention discloses a method and system for determining the commissioning boundary during substation expansion and renovation. The method includes: acquiring the entire substation configuration file; mapping intelligent devices and virtual loops to nodes and directed edges in a directed graph; calculating the sub-cyclic redundancy check (CRC) code corresponding to each directed edge; dividing nodes into expansion domains, directly associated domains, and indirectly associated domains based on configuration change identifiers and topological relationships; performing sub-CRC comparison and constructing a state transition matrix based on the dataset priority attributes; starting with high-risk seed nodes, calculating the attenuation probability of configuration change risk propagating outwards based on the state transition matrix; iteratively adding nodes with probabilities greater than the safety confidence threshold to the target test boundary set until convergence. This invention integrates underlying communication semantics with graph theory probabilistic inference to define the minimum safety test boundary, avoiding unnecessary shutdowns of primary equipment on-site, and improving the safety and efficiency of power grid commissioning.

This invention relates to a time-varying frequency offset dynamic tracking method in a VDES satellite link, comprising: acquiring the VDES satellite link baseband digital signal and performing coarse frequency offset estimation to obtain a coarse frequency offset estimate; setting a processing window that slides along the sampling time, calculating the frequency offset change rate based on the coarse frequency offset estimate within the window, and adaptively adjusting the window length according to the frequency offset change rate; constructing a Kalman filter with the frequency offset change rate and the optimal frequency offset estimate as the state vector, and establishing a frequency offset estimation state transition matrix based on a uniformly accelerated motion model; performing iterative calculation using the Kalman filter, outputting the posterior frequency offset estimate as the optimal frequency offset estimate at the corresponding time to achieve time-varying frequency offset dynamic tracking, and introducing a fine frequency offset estimate during iteration to optimize the prior frequency offset estimate at the current time. This invention can achieve fast and high-precision tracking of time-varying frequency offset in VDE-SAT links, improving the communication stability of VDES satellite links.

The application discloses a kind of based on data fusion's slope displacement estimation method, comprising: based on multiple sensors data acquisition and pre-processing, the data time and space alignment of acquisition;Based on the data after alignment, discrete observation point extraction and reference sampling section generation are carried out, and high-quality data section is screened by sliding window method;Based on neural differential equation, dynamic modeling is carried out to the data after screening, and nonlinear modeling is carried out to the deformation process of slope by neural network;Based on the prediction-update process of Kalman filter enhanced model, state transition matrix and observation matrix are dynamically adjusted;Based on multiple sensor data, fusion estimation and adaptive error correction are carried out;Through the combination of distributed computing and edge computing, real-time calculation of slope displacement estimation is carried out.The application also discloses a kind of based on data fusion's slope displacement estimation device, equipment and storage medium.The application can realize high-precision, high-reliability slope displacement monitoring by integrating data fusion enhancement technology.