133 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 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 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 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 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 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 application discloses a method for designing the number of countermeasure devices in an unmanned aerial vehicle countermeasure system, comprising the following steps: S1. constructing a queuing model of the unmanned aerial vehicle countermeasure system service and giving relevant conditions of the queuing model; S2. for the queuing model of the unmanned aerial vehicle countermeasure system service, establishing a target number state transition graph by using a Markov process and listing state transition equations, analyzing the relationship of the to-be-processed target number and the relationship of the number state probabilities; S3. solving the steady-state solution of the state transition equation, and analyzing the success probability of intercepting targets in a long-time interception process of the unmanned aerial vehicle countermeasure system; S4. determining the efficiency and cost ratio of the unmanned aerial vehicle countermeasure system; and S5. analyzing the total interception probability derivation formula in combination with the efficiency and cost ratio formula, and selecting the optimal number N of countermeasure devices. The application can determine the optimal number of sets of countermeasure devices when the unmanned aerial vehicle countermeasure system is designed and constructed, and provides conditions for the construction of the unmanned aerial vehicle countermeasure system.

The invention belongs to the technical field of vehicle control, and particularly relates to an intelligent driving lane changing decision-making method and system for a heavy-duty car, and the method comprises the steps: defining an automatic driving heavy-duty car as a main car, and recognizing an adjacent car having a potential lane changing conflict with the main car as a competitive car; constructing a probabilistic game model including participation of a main vehicle, a competitive vehicle and environmental disturbance; wherein a state vector and an action set of the main vehicle are defined in the probabilistic game model, and a state transition equation of the main vehicle is established; uncertainties such as vehicle motion states, road curvatures and meteorological conditions are modeled into environmental disturbance in a unified mode, a state evolution law after a main vehicle executes actions is quantitatively described by establishing a state transition equation based on vehicle kinematics, random fluctuation in actual traffic is simulated in combination with Gaussian random disturbance terms, and the real-time performance of the vehicle is improved. And the model can dynamically adapt to complex traffic scenes.

The invention discloses a combined system error correction and target state estimation method based on multi-angle measurement, which is applied to a three-dimensional passive multi-sensor multi-target tracking system. Comprising the following steps: constructing an augmented state vector based on a target state vector, an angle measurement deviation vector, a time deviation vector and a sensor position vector, and constructing an augmented state transition equation; performing modeling by taking azimuth angle measurement, pitch angle measurement and sensor position measurement as functions of an augmented state vector, constructing an augmented measurement vector and an augmented measurement equation, and determining a nonlinear relationship between the constructed augmented measurement vector and augmented state vector; azimuth angle measurement, pitch angle measurement and sensor position measurement are obtained through actual measurement, and an augmented state vector is estimated through linearization of a non-linear relation between a constructed augmented measurement vector and an augmented state vector and sequential filtering. The method can be applied to multi-source information fusion under a passive multi-sensor system, and the tracking and positioning precision of the target is improved.

The invention relates to an aviation turbine engine fault prediction method based on space-time fusion Transform, and relates to the technical field of fault prediction and health management. Due to the fact that the extracted features are enhanced, the output dynamic features are obtained, in further features obtained based on the dynamic features, dynamic evolution is achieved through a gating circulation unit, and a state transition equation is established; the hidden state is mapped to a TFT feature space through a full connection layer, and an observation equation is established; and the dynamic features output by the observation equation and the first feature vector are spliced to obtain further features, so that more comprehensive feature information is captured based on state space enhanced time series prediction Transformers, the sensitivity of the model to gradual change and sudden change faults is enhanced, key fault features can be better adaptively screened, and the fault detection accuracy is improved. And the accuracy of fault prediction is improved.

The invention provides a reservoir area water level monitoring method and device based on Beidou signals and water level change characteristics, belongs to the technical field of water level monitoring, and can improve the monitoring precision of the reservoir area water level and the reliability of a monitoring result. The method comprises the steps of constructing a state transition equation by adopting water level change characteristics of a reservoir area; an inversion observation equation is constructed through the elevation angle of the signal receiver in the reservoir area and the observation signal received by the signal receiver; and solving to obtain the water level of the reservoir area at the current moment through a Kalman filtering method in combination with the state transition equation and the inversion observation equation.

This application discloses a knowledge- and data-driven elastic positioning method, system, and storage medium. The method includes: generating a knowledge graph based on a knowledge base in the positioning domain; the knowledge base includes multiple pieces of first knowledge information, including entity pairs in the positioning domain, relationships between entity pairs, and a first confidence level of the relationships; nodes in the knowledge graph represent entities, edges in the knowledge graph represent relationships between different entities, and the attributes of the edges include the first confidence level of the relationship represented by the edge; inferring a positioning model applicable to the target object based on currently available information sources, observation data of the available information sources for the target object, and the type of observation data; the positioning model includes positioning parameters, observation equations, and state transition equations; and locating the target object based on the observation data and the positioning model to obtain the positioning result of the target object.

The invention discloses a robust visual servo control method and verification system based on interference estimation. The method comprises the following steps: acquiring an environment RGB image and depth information; constructing a state transition equation and a pseudo-linear measurement equation, and performing joint estimation on the target position and speed by adopting a Kalman filter; according to the estimated target space position, establishing a geometric mapping relation between a rotary table coordinate system and a target coordinate system, and calculating an attitude angle required by the rotary table; and designing a time-varying disturbance observer, estimating unmodeled dynamics and external disturbance of the system in real time, performing feed-forward compensation based on a disturbance estimation value, and generating an anti-disturbance control instruction in combination with an expected attitude angle. The system is applied to the servo control method and comprises the two-degree-of-freedom rotary table, the depth camera, the target, the connecting firmware, the laser director, the balancing weight and the ground station. According to the invention, the precision of servo control can be improved. The method can be widely applied to the field of sensing control.