152 results about "State-transition equation" patented technology

The invention relates to an autonomous energy-saving soaring flight path planning method for a small-sized low-cost aircraft, belongs to the technical field of aircraft trajectory planning, solves the problem of low-cost wind field energy acquisition of the small-sized low-cost aircraft in the prior art, and comprises the following steps: S1, configuring a sensor for the aircraft, and measuring through the sensor to obtain observation parameters; s2, establishing a state vector of the aircraft; s3, establishing an aerodynamic force model, introducing a dynamic equation and a state transition equation, and performing accurate modeling on aerodynamic force; s4, performing multi-source data fusion by adopting extended Kalman filtering, establishing an extended Kalman filter of a nonlinear system, and executing real-time wind vector high-precision sensing; and S5, performing global wind field modeling, estimating a wind field environment, and performing energy-obtaining flight path planning to obtain an optimal energy-obtaining soaring flight path planning scheme.

The embodiment of the invention provides a construction hoist height measurement method and device based on double correction, and the method comprises the steps: building a pressure height mapping model for initial calibration through fusing the data of a high-precision air pressure sensor and a six-axis gyroscope; the weight of the sensor is dynamically adjusted by adopting a motion state evaluation matrix, and error correction is realized by combining null drift reference point records. And constructing a state transition equation and an observation equation by using an extended Kalman filter, and obtaining a high fusion result through a prediction updating step. Meanwhile, the system has the functions of abnormal state monitoring and sensor mode automatic switching, and double guarantees of measurement precision and reliability are achieved. The method effectively solves the problem that a traditional single sensor measurement scheme is unstable in precision in a complex environment.

The invention discloses an electric vehicle V2G micro-grid energy storage capacity optimization method based on dynamic planning, particularly relates to the technical field of micro-grid energy storage optimization, and aims to solve the problem of energy storage capacity optimization defects caused by spatial dynamic characteristics caused by mobility of an existing electric vehicle. Performing geographic grid division on the node position, generating a space-time matrix by combining with a timestamp, and identifying a high-frequency access node; constructing a state transition equation based on the inter-node impedance matrix, and setting a node-level safety boundary by taking a line capacity change rate as a dynamic constraint; a staged reverse dynamic programming algorithm is adopted, spatial dimension optimization is preferentially executed on the high-frequency access nodes, and charging and discharging constraints fusing voltage deviation and capacity overrun penalty terms are generated; carrying out global optimization by taking the minimization of the total operation cost as a target after the constraints are integrated, and outputting an energy storage capacity configuration strategy of each time period; and finally generating an energy storage deployment scheme.

The present application provides a method and system for predicting the remaining service life of an IGBT device, comprising: simulating an IGBT device power cycle aging experiment, collecting conduction voltage drop historical data on the basis of the experiment, and respectively obtaining a model parameter empirical value and a model parameter estimated value on the basis of the conduction voltage drop historical data; constructing a first state weight on the basis of the current operation state, and performing smooth adjustment on the model parameter estimated value on the basis of the model parameter empirical value and the first state weight to obtain a parameter correction result; and establishing a state transition equation on the basis of the parameter correction result, and predicting the remaining service life of an IGBT device on the basis of the state transition equation. Use of the present application can improve the accuracy of prediction of the remaining service life of the IGBT device.

The invention relates to the technical field of drivers, and discloses a low-delay control method, system, equipment and medium for a gallium nitride gate driver, and the method comprises the steps: carrying out the feature extraction and nonlinear mapping processing of the driving voltage, gate charge and switching time sequence parameters of the gate driver, and obtaining time domain characteristic data and a parameter distribution model; duty ratio modulation function calculation is carried out to obtain a rising edge duty ratio parameter and a falling edge duty ratio parameter; performing dynamic planning solution on the state transition equation to obtain an optimal control sequence; performing driving control on a gate driver according to the optimal control sequence, and acquiring running state data through a plurality of monitoring points to obtain an actual driving delay value and an actual switching loss value; the Gaussian process model is constructed based on the actual driving delay value and the actual switching loss value, the updated control parameters are obtained through iterative sampling calculation, the optimal control sequence considering multivariable constraints is obtained, and the accuracy of driving control is remarkably improved.

The invention discloses a multi-device behavior interoperation verification system and method based on an Internet of Things platform, and relates to the technical field of Internet of Things, and the method comprises the steps: collecting the operation state data and interaction behavior data of each device in real time according to the device interoperation demand of the Internet of Things platform; establishing an equipment behavior prediction model, and constructing a state transition equation of the equipment; according to the equipment operation state data and the interaction behavior data, verifying an interoperation consistency index of the multi-equipment behavior, and calculating an equipment behavior deviation coefficient; when the behavior deviation coefficient exceeds a preset threshold value, generating an equipment interoperation abnormal alarm, and positioning an abnormal interaction node; a three-dimensional dynamic model of the Internet of Things equipment is established through a digital twinning technology, and a multi-equipment behavior interoperation verification result and abnormal early warning information are displayed in real time. The method has the advantages that the problem of state transition consistency verification in multi-device behavior collaboration is mainly solved by constructing a dynamic matching mechanism of the device state transition equation and the operation state feature library.

The invention provides a Q / R dual-adaptive hybrid quantum filtering method for an OTFS (Over the The Over the File System) communication and inductance integrated system, and the method comprises the following steps: constructing an OTFS communication and inductance integrated signal model, and obtaining a target measurement vector; establishing a state vector for describing a target motion state, and constructing a state transition equation and a measurement equation; the method comprises the following steps: constructing a Q / R mixed quantum adaptive adjustment module based on dual-channel features to construct a dual-channel feature input vector, generating a dual-path adjustment factor through a mixed quantum neural network, and constructing an adaptive Q matrix and an adaptive R matrix; and performing adaptive state prediction and updating at each time step by using the adaptive Q and R matrixes constructed in the step 3 to realize tracking of the motion state of the target. According to the method, the hybrid quantum neural network is introduced for driving, collaborative optimization of filter parameters is achieved, and therefore the precision, robustness and response speed of target tracking are remarkably improved in a complex dynamic noise environment.

The invention provides a power battery SOC estimation method and system based on a characteristic model, and relates to the technical field of SOC estimation, and the method specifically comprises the steps: obtaining three characteristic parameters of an open-circuit voltage, an internal resistance and a polarization voltage of a battery, introducing a temperature and aging correction mechanism, building a relation curve between the characteristic parameters and the SOC, and calculating the SOC of the battery according to the relation curve; mapping the characteristic parameters into SOC values through a fitting formula; constructing an SOC fusion model based on a relation formula of the calibrated characteristic parameters and the SOC, synthesizing SOC estimated values of the characteristics into a comprehensive estimated value by using a weighted linear combination method, dynamically adjusting weights of the characteristics through a particle swarm optimization algorithm, and determining an optimal weight coefficient; through the extended Kalman filtering, the comprehensive estimation value output by the model is corrected in real time, the SOC change is predicted based on the state transition equation in the correction process, the deviation between the estimation value and the true value is updated in combination with the measurement equation, and the real-time performance and reliability of SOC estimation are improved.

The invention relates to a flight control technology of an unmanned aerial vehicle suspension load system, and provides a multi-target nonlinear model prediction control method for an unmanned aerial vehicle-load suspension system, so as to realize accurate track tracking, active suppression of lifting rope swinging and collaborative optimization of flight smoothness. According to the multi-state multi-target model prediction control method for the unmanned aerial vehicle-load suspension system, firstly, a nonlinear model prediction control equation is constructed; discretizing the continuous kinetic model by adopting a fourth-order Runge-Kutta method to obtain a high-precision state transition equation under discrete time as a dynamic constraint; forming an optimal control function of a flight nonlinear model predictive control equation of the unmanned aerial vehicle-load suspension system; adding thrust amplitude constraint and moment boundary constraint as inequality constraint conditions of a nonlinear model predictive control equation; and finally, multi-target optimal control of the unmanned aerial vehicle-load suspension system is realized. The method is mainly applied to design and manufacturing occasions of unmanned aerial vehicle hanging load systems.

The invention provides a multivariate data-based printing machine top project approval design method, which relates to the technical field of printing equipment, and comprises the following steps of: acquiring printing machine operation data comprising printing quality parameters, equipment working condition parameters and environment parameters; performing feature analysis and parameter extraction through an adaptive fuzzy clustering algorithm, dynamically adjusting a sample classification membership degree, identifying a key parameter combination influencing the printing quality, and constructing a correlation matrix; constructing a dynamic Bayesian probability network model based on a parameter mapping relation, constructing a core network structure through an information entropy value and information gain, and establishing probability association between a printing quality parameter and an equipment working condition parameter; and determining the optimal parameter configuration of the function modules of the printing machine by utilizing a bidirectional constraint dynamic programming algorithm and combining the coupling weight and the state transition equation among the function modules, and generating a top design scheme.

The invention belongs to the technical field of shared bicycles, and particularly relates to a station type shared bicycle system dynamic management and control method and system considering platform game, and the method comprises the steps: obtaining the related data of a target operation range, and initializing a simulation module; defining the state, action, income and state transition probability of the intelligent agent of the platform in a Markov state transition equation according to the operation target and management and control means of each platform, and initializing a neural network used for evaluating the action quality of the intelligent agent in a specific state to realize initialization of an optimization control module; supply and demand deduction and strategy optimization are carried out in the simulation module; judging whether the income of each platform in the simulation module tends to be stable or not; according to the shared bicycle-electric bicycle dynamic management and control method, a traditional shared bicycle static management and control normal form is broken through, and dynamic management and control of a shared bicycle-electric bicycle system are achieved in a high-dimensional random scene through the Markov decision process model and the deep reinforcement learning algorithm.

The invention discloses a permanent magnet synchronous motor temperature estimation method based on model data dual driving. A model driving module, a data driving module and a state estimation module are included. The model driving module models the thermal behavior of the motor so as to determine a thermal model of the motor; the data driving module identifies online parameters of the motor by using real-time data of a motor sensor to obtain real-time parameters of a motor thermal model; a self-adaptive state transition equation of the state estimation module is driven by a motor thermal model and real-time parameters of the motor thermal model at the same time, and meanwhile a state estimator achieves motor temperature estimation according to the self-adaptive state transition equation. The permanent magnet synchronous motor temperature estimation method has the beneficial effects that the permanent magnet synchronous motor temperature estimation method with high reliability is provided, the permanent magnet temperature and the stator winding temperature are accurately monitored, and the control performance and the safety of a permanent magnet synchronous motor system are improved.

The invention relates to the technical field of ship shafting measurement, and discloses a polar region ship shafting comprehensive measurement system and method, and the system comprises a data collection module, a preprocessing module, a data processing module, and an evaluation module. The data acquisition module acquires a strain signal, a vibration acceleration signal, a temperature signal, an axial displacement signal and a radial displacement signal of a shaft system through a sensor group, and the preprocessing module performs wavelet packet decomposition and adaptive threshold denoising, mean filtering and triple standard deviation criterion abnormal value elimination processing on the signals. The data processing module fuses the data of the same physical quantity based on an improved Kalman filtering algorithm and dynamically adjusts the weight of the fused data to obtain final optimized data, and the improved Kalman filtering algorithm comprises the step of introducing a temperature drift compensation factor into a state transition equation, and the evaluation module predicts potential fault types and development trends through an operation state evaluation model based on the optimization data.

The invention discloses a low-orbit satellite joint frequency offset estimation and channel equalization method in a high-speed moving scene, which comprises the following steps of: constructing a dynamic coupling state space model to realize joint estimation of frequency offset and channel parameters; associating a frequency offset change rate with the dynamic change of multipath time delay through a state transition equation; the association is based on relative motion parameters and radio wave propagation characteristics of the satellite and the mobile terminal; according to the method, state estimation is carried out by adopting a filtering algorithm, channel multipath sparsity constraint is introduced into the filtering algorithm, and satellite orbit parameters and navigation data of a mobile terminal are fused to serve as priori information, so that joint estimation of frequency offset and channel parameters is realized through a dynamic coupling state space model; the problem of parameter error accumulation in traditional separated processing is solved, and the estimation precision is improved; a pilot frequency-data dual-drive mechanism is combined with time-frequency orthogonal pilot frequency and data feedback, and the estimation real-time performance and the dynamic adjustment capability of the equalizer are both considered.

The invention relates to the technical field of high-pressure gas filling, in particular to a high-pressure gas filling equipment temperature anomaly detection method based on wireless sensing. Comprising the steps of constructing a state transition equation and an observation equation; collecting temperature data of a key area in the high-pressure gas filling equipment; the acquired temperature data is preprocessed by using an improved robust adaptive unscented Kalman filter; carrying out local fusion on the preprocessed temperature data based on a serialization fast covariance overlapping algorithm; global fusion is carried out on the temperature data after local fusion based on an improved zebra optimization algorithm; and comparing the global fusion result with a preset threshold value, and outputting a temperature detection result in the high-pressure filling equipment. The method can cope with the extreme environment in the high-pressure gas filling equipment, and ensures the accuracy and reliability of temperature monitoring. Based on the design of the wireless sensor network, the system has the characteristics of flexible deployment, low cost and strong expansibility, and can be widely applied to multi-point temperature monitoring in the fields of energy and chemical industry.

The invention discloses a method for dynamically adjusting copper concentrate ingredients to control the antimony content in matte. The method comprises the steps of data collection and calculation, target control and stage division, state transition equation calculation and a dynamic adjustment mechanism. Production data shows that when the antimony content in the matte is controlled not to exceed 0.4%, the antimony content in the anode copper of the product does not exceed the standard, and therefore the method for dynamically adjusting the copper concentrate ingredients to control the antimony content in the matte is provided, the antimony content in the matte is controlled in real time, and the antimony content of the anode copper of the product is prevented from being accumulated to exceed the standard. Based on the antimony content feedback in the matte smelted in each stage, the proportion of copper concentrate, smoke dust and cold charge is dynamically adjusted, so that the antimony content in the matte is kept within a safe range, and the subsequent process is prevented from being influenced.

The invention discloses a BIM-based large-space pipeline installation method and system, and relates to pipeline installation: performing three-dimensional gridding processing on BIM model data, generating a gridding space model, and constructing a pipeline installation space; calculating the load per unit length of the pipeline according to the self weight of the pipeline and the weight of a medium in the pipeline in the physical parameters; a dynamic planning method is adopted for optimization arrangement of the supports and hangers; constructing a state transition equation according to the load per unit length of the pipeline and the structural mechanics continuous beam model; according to the state transition equation, an optimal decision sequence is obtained through reverse recursion solution, backtracking is conducted from the tail end of the pipeline to the starting point, in each decision stage, the position of a supporting point enabling accumulated deflection of all follow-up pipe sections to be minimum is selected, and a globally optimal supporting and hanging bracket arrangement sequence is generated; according to the globally optimal arrangement sequence of the supports and the hangers and the load per unit length of the pipeline, the deflection value of each pipe section is calculated; according to the method, the problem of non-uniform deflection distribution caused by a traditional local optimization method is avoided.

The invention discloses an adaptive detection system and method for a spaceborne computer, relates to the technical field of space flight and aviation, and adopts a nonlinear state space model to establish a state transition equation and an output equation so as to realize accurate modeling and real-time detection of the spaceborne computer. The adaptive detection middleware integrates heartbeat detection, power supply power-on detection, multi-protocol communication, RS422, CAN, TCP and the like, supports remote interaction and intelligent task scheduling, and improves the automation degree of detection; compared with a traditional method, data interaction is optimized through a multi-protocol self-adaptive processing mechanism, the detection efficiency is improved, the system maintenance cost is reduced, the data interaction real-time performance is improved, the problems that detection equipment is complex, data interaction is not flexible, detection precision is low and the like are solved, the integration level, the intelligence and the remote control capability of the detection system are improved, and the detection efficiency is improved. The method is suitable for the fields of spacecraft ground testing, spaceborne computer research and development, testing and maintenance and the like, and has important engineering practical value.

The invention relates to the technical field of engine control and state monitoring, and discloses a diesel engine in-cylinder pressure multi-source fusion estimation method based on adaptive Kalman filtering, which comprises the following steps: acquiring working condition parameters and torque signals of a diesel engine; based on the collected working condition parameters, model prediction cylinder pressure is obtained; based on the collected torque signals, single-cylinder contribution is extracted, and torque reverse thrust cylinder pressure is obtained through reverse thrust; according to the real-time information entropy change of the model prediction cylinder pressure and the torque reverse thrust cylinder pressure, dynamically distributing a fusion weight, and generating a fusion observed quantity; forming an extended characteristic quantity based on the characteristic that the fusion observed quantity changes along with the crank angle; establishing a state transition equation and an observation equation; and outputting an in-cylinder state prediction and estimation result after fusion optimization. According to the method, the in-cylinder pressure multi-source fusion estimation method based on adaptive Kalman filtering is adopted, fusion weights of different data sources can be dynamically adjusted in real time, and the technical effect of considering steady-state accuracy and transient response speed is achieved.

The invention discloses a wide-working-condition steam turbine system operation data correction method based on digital twinning, and relates to the technical field of steam turbine equipment digitalization, and the method comprises the steps: collecting operation data of a wide-working-condition steam turbine system in real time; constructing a digital twinborn model of the steam turbine system; performing prior covariance estimation on the operation data according to the statistical distribution condition and the design parameters; generating a Sigma point by adopting an unscented Kalman filtering algorithm, iteratively updating a state vector and a covariance in combination with a state transition equation and an observation equation, and outputting an updated known variable; performing smoothing processing on the updated known variable in a plurality of time point regions by adopting an RLOESS algorithm to obtain corrected operation data; according to the method, the real dynamic characteristics of the wide-working-condition steam turbine are approached by building the digital twinborn model, the calculation complexity of dynamic data fusion of a steam turbine system can be remarkably reduced, and the problems that the convergence speed is low and calculation is complex due to strong nonlinearity of a traditional method are solved.

The invention provides a bolt attitude detection method and system, and the method comprises the steps: firstly, detecting the position attitude angle, the motion state and the speed of a bolt, and obtaining a state transition matrix according to the position attitude angle, the motion state and the speed of the bolt; and obtaining an observation matrix according to the bolt position attitude angle. Secondly, building a Kalman filtering bolt attitude space model and predicting a future state and a future covariance of the bolt according to a state transition equation; by correcting an innovation measurement noise covariance matrix and a residual measurement noise covariance matrix and adopting fuzzy logic to calculate a self-adaptive adjustment factor, the Kalman gain is dynamically adjusted, the bolt state is updated in real time, the bolt detection precision and working efficiency are improved, and the method is suitable for complex working conditions.

The invention discloses an EM-Kalman method-based PT error online solving method and device and a storage medium, and the method comprises the steps: firstly collecting the secondary voltage data, measured at a plurality of moments, of a plurality of in-phase voltage transformers of which the primary ends are connected with the same bus, and analyzing the amplitude, phase and other information; an error parameter estimation model comprising a basic physical model, a state transition equation and an observation equation is constructed, model parameter initialization is completed through a truncation average method, robust statistics (MAD) and the like, iterative optimization is performed through an EM algorithm, Kalman smoothing is alternately executed to estimate standard voltage and a maximum likelihood function is alternately executed to estimate overall error offset parameters until iteration convergence is performed, and the overall error offset parameters are estimated. And accurate solving of the ratio difference value and the angle difference value in the error parameters of the mutual inductor is realized. The method does not need to depend on an external standard source, eliminates the influence of the preset dynamic parameter through parameter self-learning, separates the cyclic dependence of the standard value and the error parameter, and improves the accuracy and reliability of PT error calculation in an online monitoring scene.

This application provides a target localization method, apparatus, electronic device, and storage medium. The method obtains the initial position of the target object by allocating distance through optimal sub-patterns; it pre-constructs a motion state model of the target object, predicts the state vector of the target object at the next moment based on the state transition equation of the motion state model, and determines the prior position of the target; it filters the current real observation set based on the prior position of the target to obtain a first observation set; it searches and locates the target based on the first observation set, using the previous position of the target object as the center, and determines the real-time target position; it narrows the search range to improve the real-time performance of localization; it fuses the real-time target position with the prior position of the target to obtain a corrected target position, and uses the corrected target position as the target localization result. By fusing the prediction and observation, the respective errors are offset, solving the problem of excessive localization error and improving localization accuracy and reliability.

The application discloses a power distribution network prediction auxiliary state estimation method and system based on an improved iteration process, and the method comprises the following steps: establishing a nonlinear discrete time state space model; generating a group of Sigma points through an unscented transformation, and substituting the Sigma points into a state transition equation to calculate a state prediction value and a state prediction error covariance matrix at a current time; substituting the Sigma points into a measurement equation to obtain a measurement prediction value, and calculating a Kalman gain; calculating a process noise covariance matrix at a next time; and correcting the state prediction value and the state prediction error covariance matrix at the current time to obtain an optimal state estimation value and an error covariance matrix thereof. The application avoids the risk of losing semi-positive definiteness of the covariance matrix in the iteration process from the algorithm mechanism, significantly enhances the numerical stability and robustness of the UKF algorithm, and ensures reliable application in high-dimensional complex power distribution network state estimation.

The application discloses a dynamic programming-based electric vehicle V2G microgrid energy storage capacity optimization method and particularly relates to the technical field of microgrid energy storage optimization, and is used for solving the problem of the existing electric vehicle mobility-induced spatial dynamic characteristics leading to energy storage capacity optimization defects, collecting power grid topology parameters and electric vehicle moving track data, performing geographical grid division on node positions, generating a space-time matrix in combination with time stamps and identifying high-frequency access nodes; constructing a state transition equation based on an impedance matrix between nodes, setting a node-level safety boundary with a line capacity change rate as a dynamic constraint; adopting a phased reverse dynamic programming algorithm, preferentially performing spatial dimension optimization on the high-frequency access nodes, generating charging and discharging constraints that fuse voltage deviation and capacity overrun penalty items; performing global optimization with the minimum total operation cost as the target after integrating the constraints, outputting energy storage capacity configuration strategies in each period; and finally generating an energy storage deployment scheme.

The invention provides a multi-rate ore grinding classification process control method based on a Koopman operator. The method comprises the following steps: solving a Koopman operator to obtain a multi-rate linear predictor; establishing a multi-step prediction equation of the predictor and solving the multi-step prediction equation; judging whether each moment is a frame period starting moment or not; if yes, constructing a target function and solving an optimal control sequence; taking the first control quantity of the optimal control sequence as a current frame period control law, executing the first control quantity, and storing the first control quantity in a zero-order holder; if not, keeping executing the control law in the zero-order holder; updating a multi-rate linear predictor, performing incremental correction on a state transition equation correlation matrix, evaluating parameter estimation uncertainty in combination with a covariance matrix, and performing recursive updating; judging whether the control task is completed or not; and if not, returning to the multi-step prediction equation solution to continue rolling execution. According to the method, unified modeling and stable optimization control of a nonlinear system in the multi-rate ore grinding classification process are realized, and the prediction precision and robustness are improved.

The invention discloses a teenager long-distance running strategy method fusing deep learning and an improved swarm intelligence optimization algorithm, and the method comprises the following steps: collecting multi-dimensional time sequence features in the historical training and competition process of athletes, and constructing and training a CNN-LSTM-Attention prediction model based on the multi-dimensional time sequence features; establishing an integral form long-distance running strategy optimization model framework with the goal of minimizing the game completion time; dividing the long-distance running process into a plurality of continuous time sequence windows, calling a CNN-LSTM-Attention model at the starting moment of each window to generate a state prediction result as a dynamic constraint, and constructing a state transition equation; and based on the state transition equation, solving the optimal speed under the current time sequence window through an improved dung beetle optimization algorithm, rolling to the next window after execution, and carrying out iterative execution until the optimal speeds under all time sequence windows are output.

The invention discloses a millimeter wave beam tracking method, a computer readable storage medium and node equipment, and belongs to the technical field of wireless communication, and the method comprises the steps: constructing a beam pointing error interval at an initial moment; at the next moment, predicting the interval through a state transition equation; acquiring a channel observation value under the current beam pointing based on a channel model; carrying out shrinkage processing on the predicted error interval; and estimating a beam pointing angle according to the contracted interval, and transmitting the interval as priori information to the next moment so as to repeat the steps. A computer program for executing the method is stored in the computer readable storage medium. The node equipment comprises at least one processor, a memory and a bus, wherein the memory stores a program for executing the method. According to the method, the tracking performance in a nonlinear and non-Gaussian scene can be kept under the condition that the calculation load is not increased, and the method is suitable for the conditions of uncertain and incomplete channel state information introduced by multipath, analog-to-digital conversion quantization, hardware nonlinear errors and the like.

The invention discloses an anomaly detection method for textile equipment, and relates to the technical field of anomaly detection of the textile equipment, and the method comprises the following steps: building a system model of a linear encoder; representing a system model of the linear encoder as a state space form, wherein the state space form comprises a state vector, a state transition equation and an observation equation; prior to operation of the linear encoder, a state estimate and a covariance matrix of the Kalman filter are initialized. According to the invention, by establishing the hidden danger perception model, intelligent perception of abnormity in the operation process of the linear encoder is realized, so that the equipment can timely give out an early warning prompt when a hidden danger risk of position information feedback errors occurs, and related technical personnel are notified to know, so that measures are timely taken for overhaul and maintenance, and the maintenance efficiency is improved. And the position deviation condition in the production process is avoided. According to the intelligent abnormity sensing method, the textile quality problem caused by abnormity can be greatly reduced, and the stability and reliability of the production process are improved.

The present invention belongs to the field of communication, and discloses a video routing optimization method, device and electronic device based on dynamic programming. The method includes constructing a network topology diagram according to the network structure, in which vertices represent network nodes and edges represent unit transmission paths between network nodes; the network nodes include source nodes, intermediate nodes and destination nodes; constraining the input flow and output flow of the network nodes to obtain constraint conditions; constructing a state transfer equation according to the flow loss of the unit transmission path between each network node and its adjacent network nodes; and solving the state transfer equation to obtain the optimal video routing by minimizing the flow loss from the source node to each destination node as the optimization goal when the constraint conditions are met, and effectively utilizing network resources and reducing the overall transmission cost through global optimization decision-making. Moreover, the network topology diagram can be adjusted according to the changes in the network structure, so that the present invention can adapt to network structures of different scales and types.