99 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 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 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 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 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 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 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.

The invention provides an EKF attitude resolving method and system based on a rotation matrix and gravitational acceleration, and the method comprises the steps: reading data of an inertial measurement element, calculating an initial Euler angle, and converting the initial Euler angle into a quaternion; performing prediction updating through a quaternion differential equation according to gyroscope data and the latest quaternion, calculating and assigning a priori state vector through a quaternion gravitational acceleration extraction formula based on the predicted and updated quaternion, and assigning accelerometer data to a measurement matrix; jacobian matrixes A and W of a state transition equation are calculated according to gyroscope data and a gravitational acceleration differential equation derived based on a differential equation of a rotation matrix, Jacobian matrixes H and V are calculated according to a measurement equation, and then a posterior state vector and a posterior covariance matrix are calculated; and calculating a roll angle and a pitch angle according to the posterior state vector, and calculating a course angle according to the predicted and updated quaternion. According to the invention, EKF divergence can be avoided, and the anti-interference capability is high, so that the attitude calculation precision is ensured.

The application relates to a heading fusion method based on GNSS speed observation, which comprises the following steps: acquiring gyro angular velocity measurement values and accelerometer measurement values, preprocessing the gyro angular velocity measurement values, preprocessing the accelerometer measurement values, obtaining navigation system speed increments based on the preprocessed acceleration computer system, constructing a state transition equation according to the navigation system speed increments and calculating a prediction covariance matrix, calculating a heading angle, and switching a heading source in the operation of a UAV. The heading fusion method based on GNSS speed observation can solve the problems that the heading observation is invalid when the RTK is affected, the heading output is discontinuous in the invalidation and recovery process, the mutation response of the flight control closed loop is easily induced, the risk actions such as the rapid swing of the aircraft head are caused, and the problems that the deviation accumulation or the unstable updating is caused due to the differences between the time stamps and the updating frequencies of the GNSS and the IMU.

This invention discloses a high-precision power reconstruction method and system for low-frequency sampling of electricity meters, belonging to the field of electricity meter sampling. The method includes: acquiring low-frequency sampled current and voltage sequences; obtaining coarse-grained power baseline and residual data by performing two-dimensional phase point pairing and feature vector mining, and reconstructing using Gaussian process regression; importing the current and voltage sequences into a hybrid differential model, and calculating transient power increments through state transition equation propagation analysis; for the residual data, extracting harmonic amplitude, phase, and frequency parameters, and obtaining the power contribution of each harmonic through line impedance mapping inversion; and superimposing the coarse-grained power baseline, transient power increments, and the power contributions of each harmonic to obtain a full-band power sequence. This application solves the technical problem that existing low-frequency sampling electricity meters are unable to capture rapid transient processes, resulting in insufficient full-band power metering accuracy.

This invention discloses a dimension-reduction reliability assessment method for switched reluctance motors (SRMs) that considers repairability. This method analyzes the faults of the SRM at structural levels, comprehensively converting the failure probabilities of each underlying component into overall system state transition parameters. Based on this, the repairability rate is calculated according to the actual periodic maintenance cycle of the equipment, and a Markov state transition equation system containing maintenance recovery paths is established. To avoid complex calculus operations involving high-order matrices, this invention utilizes Laplace transform to convert the dynamic process into algebraic calculations in the complex frequency domain, achieving analytical order reduction of the model. By performing algebraic analysis on the complex frequency domain function, the dynamic reliability function and mean time between failures (MTBF) of the system are directly obtained. This invention overcomes the shortcomings of traditional models, such as cumbersome computation and unrealistic lifespan predictions. While effectively reducing the computational complexity of microcontrollers, it significantly improves the accuracy of lifespan prediction for motor drive systems and enhances their engineering application value.

The embodiment of the invention provides a control method, device and equipment of a thermal management system, a vehicle and a storage medium. The method comprises the following steps: acquiring a state input quantity corresponding to a model prediction controller, wherein the state input quantity is determined based on the actually measured temperature of an evaporator and the actually measured temperature of a battery water inlet; acquiring a control reference quantity corresponding to the model prediction controller, wherein the control reference quantity is determined based on the target temperature of the evaporator and the target temperature of the battery water inlet; based on the state input quantity corresponding to the model prediction controller, a state transition equation used for controlling the compressor to work is constructed, and based on the control reference quantity corresponding to the model prediction controller, a target optimization function and constraint conditions corresponding to the state transition equation are determined; the state transition equation is solved, the target compressor rotating speed meeting the target optimization function and the constraint condition is obtained, and the compressor is controlled to work based on the target compressor rotating speed. According to the method, the energy consumption of the compressor is reduced by controlling the heat management system, and the energy consumption economy of the heat management system is improved.

The application discloses a robot acceleration estimation method and device, electronic equipment and storage medium, wherein the method comprises the following steps: establishing a state transition equation and an observation equation according to a motion model; inputting feature data of a target robot into a preset neural network model to obtain torque values of each joint of the target robot, and determining an acceleration prediction value; generating a point set according to a state vector at a previous moment and a covariance at the previous moment; performing nonlinear transformation on the point set according to the state transition equation to obtain an updated point set; performing weighted transformation on the updated point set to obtain a new point set; calculating a prior distribution of the acceleration prediction value; obtaining measurement data of the motion model; obtaining a posterior distribution of the acceleration prediction value according to the measurement data and the prior distribution; and obtaining an estimation vector of a system state quantity. Thus, the problem that it is difficult to obtain high-precision state estimation in a noise and interference environment is solved, noise can be effectively suppressed, and the accuracy and stability of acceleration estimation are improved.

The invention discloses a multi-logistics vehicle cooperation method with transportation limitation based on a pheromone sharing ant colony system, and the method comprises the following steps: firstly, obtaining task and vehicle information, constructing a cost and authority matrix, and building a synchronous optimization model; secondly, setting ant colony parameters, and performing greedy initialization on an information matrix; thirdly, initializing a plurality of ant colonies and distributing a task taboo table; then, the ant colony selects tasks through a state transition equation according to the authority and the taboo table, winning ants are screened according to the cost probability, pheromones are updated, and a preliminary scheme is formed; secondly, optimizing the scheme by using a local search algorithm fused by 2-opt and point migration; then, evaluating an adaptive value, updating a global optimal scheme and global pheromones, and sharing the pheromones to adjacent edges; and finally, iteration is carried out until convergence, and a final cooperation scheme is output. According to the invention, efficient scheduling under transportation limitation is realized, and the cost difference between vehicles is reduced while the total cost is minimized.