307 results about "State-transition matrix" patented technology

The invention relates to the technical field of operation prediction, in particular to an optical storage flexible direct current operation prediction method based on deep learning, and the method achieves the precise evaluation of photovoltaic power fluctuation through the analysis of historical operation data and the combination of a power state conversion matrix, guarantees that the energy storage charging and discharging rate can be matched with the load demand distribution, and improves the prediction precision. Power scheduling is optimized, topology analysis and power flow path identification are carried out, topology weight calculation is carried out by using a graph convolutional network, power flow can be dynamically adjusted, the adaptability of an operation mode is improved, the influence of power fluctuation on system stability is reduced, power matching is carried out by adopting an adversarial generative network, photovoltaic output is optimized, and the system stability is improved. The energy storage release path is more reasonable, the power loss is reduced, the overall power distribution efficiency is improved, and the load compensation strategy is optimized through short-period fluctuation detection and long-period trend fitting. And variable correction is performed based on error calculation and anomaly detection, so that the reliability of regulation and control response is improved.

The invention discloses a Doppler and pseudo-range fusion positioning method and system for low-orbit satellite conduction signals, and relates to satellite positioning. The method comprises the steps that a Doppler and pseudo-range observation equation is established; performing least square residual calculation on the calculated Doppler and pseudo-range theoretical values and the observed Doppler and pseudo-range measured values, and taking a grid center point coordinate corresponding to a two-norm minimum value as a receiver position initial value; performing Taylor series expansion on the Doppler and pseudo-range observation equation, and representing a state updating matrix by using the established state transition matrix and receiver position, clock error and frequency offset correction; the inverse matrix of the observation noise covariance is used as a weight matrix to be distributed to the state transition matrix; and iterating the initial position value of the receiver, updating the state updating matrix, and stopping iteration until the two-norm of the residual solution is smaller than a set threshold value, so as to obtain the position, clock error and frequency offset results of the receiver. According to the method, the low-orbit satellite positioning algorithm model can be optimized, the convergence time is shortened, and the positioning precision is improved.

The invention discloses an unmanned aerial vehicle cluster patrol path decision-making method under resource constraint, and relates to the technical field of unmanned aerial vehicle path planning, and the method comprises the steps: carrying out the discretization of an actual to-be-patrolled physical position, and constructing an undirected topological graph; generating steady-state distribution of each patrol node according to topological constraints and importance degrees of the nodes; generating a plurality of transfer matrixes with the same steady-state distribution and different transfer characteristics according to a multi-stage entropy driving random matrix optimization algorithm; initializing the position of a navigator according to the transfer matrix and determining a selected path; according to the reference path of the navigator, the self-adaptive active positioning decision is realized under the positioning constraint to ensure the path tracking effect; according to path selection and tracking of the navigator, the follower and the navigator form a humanoid marshalling cluster through a reward function; and according to the multi-state transfer matrix and the humanoid marshalling, automatically switching to the next transfer matrix when a transfer frequency threshold value is reached, and realizing unmanned aerial vehicle cluster intelligent patrol path decision under resource constraint.

The invention provides a breakwater monitoring data preprocessing method and system based on Kalman filtering, and relates to the technical field of breakwater structure safety monitoring. The method comprises the following steps: acquiring original motion data of acceleration, inclination and displacement through a motion attitude sensor to obtain an original data sequence; initializing a state vector and an error covariance matrix; dynamically correcting the state transition matrix and calculating a prediction state vector and a prediction error covariance matrix; a Kalman gain is generated; updating a state vector and an error covariance matrix; and extracting the filtered motion data as a preprocessing result. According to the method, the state transition matrix is dynamically corrected by introducing the wave force feedback, so that the Kalman filtering algorithm can adapt to the wave impact environment, noise interference in monitoring data is effectively inhibited, and the accuracy and reliability of key motion parameter data of the breakwater are remarkably improved.

The invention provides a task alarm processing method and system based on intelligent grading, and relates to the technical field of task scheduling alarm, and the method comprises the steps: collecting execution behavior data of each task in a task scheduling system in a plurality of time windows, carrying out the time sequence coding, and converting the execution behavior data into a state transition matrix, identifying an abnormal task and quantifying an alarm intensity value by analyzing non-stationary features; constructing a data flow direction and resource competition relation heterogeneous graph between the tasks, and mapping the abnormal tasks to corresponding nodes; generating a context sensing vector through multi-hop neighborhood aggregation, forming an alarm cluster based on semantic distance clustering, and determining a core node; and determining a control instruction according to the alarm intensity of the core node, performing conflict detection in combination with a resource competition relationship, adjusting a task execution time sequence, and predicting a failure probability output risk type based on a state transition path. According to the invention, intelligent grading and accurate processing of alarms can be realized, and the reliability and resource utilization efficiency of a task scheduling system are improved.

The invention relates to the field of data traceability, and particularly discloses a natural resource data dynamic monitoring traceability method and system, and the method comprises the steps: building a multi-tense database, automatically writing real-time data into a corresponding partition according to a timestamp, and carrying out the incremental updating of a workflow; data confidence coefficient weights are introduced into SQL screening statements according to attribute tracing logic, and data legality is judged through weighted logic; constructing a land class evolution state transition matrix, determining a legal change path and an illegal path, and carrying out path deduction on attribute data missing to judge whether the attribute data missing is illegal change or not; and detecting land type mutation according to NDVI index change of a target remote sensing image, forming a three-dimensional tracing database of attribute data, space evidence and business files according to the attribute data and the spatial relationship, and dynamically monitoring and tracing natural resource data. The problems that the legal source of the construction land cannot be accurately traced due to land judgment errors and real-time data analysis and traceability are difficult in the prior art are solved.

The invention provides a bridge group maintenance priority dynamic decision-making method and device based on reinforcement learning, and relates to the technical field of bridge intelligent maintenance. The method comprises the following steps: constructing a topological structure of a bridge network and a road; defining a state space, a maintenance action space and a state transition matrix of the bridge; defining a reliability index corresponding to the state of the bridge, and designing a comprehensive reward function based on maintenance cost, asset risk and traffic network capacity loss risk based on the topological structure; constructing a bridge maintenance decision problem; the method comprises the following steps of: describing a bridge maintenance decision problem as a Markov decision process, establishing a pointer network strategy model by adopting a pointer network, and training the pointer network strategy model by adopting an Actor-Critic algorithm to obtain a maintenance decision model based on reinforcement learning; and training the maintenance decision model based on reinforcement learning until convergence, and outputting a bridge maintenance action sequence under limited constraints. By adopting the method, the limitation problem of traditional single bridge assessment can be solved.

The invention discloses a missile erection filtering method, system and device and a readable storage medium, and relates to the technical field of inertial navigation and the field of initial alignment. Based on the reference inertial unit pitch angle before erection, the reference inertial unit pitch angle at the current moment, the inertial unit angular velocity on the missile and the trajectory launching inclination angle, whether the missile is in any one of the non-target states of the non-erection state, the just erection state, the to-be-erected in-place state and the erection completion state or not is judged; if yes, measurement updating is not carried out in the Kalman filtering calculation process of the current moment, and time updating is carried out based on the state transition matrix of the current moment, the state vector of the previous moment, the mean square error matrix of the previous moment and the process noise variance matrix; and attitude information output by the inertial navigation system at the current moment is used as a missile attitude result for calculation at the next moment. According to the method, shaking between the guided missile and the launching box in the erecting process before launching is considered, and the attitude precision of initial alignment of the guided missile is improved.

A vehicle-mounted GNSS positioning method based on multi-motion model interaction includes: establishing a position-constant velocity (PCV) model and a position-constant steering angular velocity (PCSAV) model for two attitudes of a carrier (i.e., linear motion and turning motion) respectively to obtain a state estimation vector and a state transition matrix of the carrier of the PCV model and the PCSAV model at a previous moment, introducing an interacting multiple model (INM), establishing a heuristic position-velocity filtering (HPV)-IMM model based on the IMM model to achieve an information filtering interaction between the PCV model and the PCSAV model, and obtaining a state estimation vector and an error covariance matrix of the carrier at a current moment, so as to obtain a position and velocity of the carrier at the current moment. The present disclosure solves the problem of low accuracy of a traditional single kinematic model in multi-motion attitude vehicle positioning.

The invention discloses an application sequence prediction method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a historical use behavior sequence corresponding to a target application program; establishing a first-order state transition matrix based on the historical use behavior sequence; determining a reference application program from the historical use behavior sequence, and obtaining an application program set corresponding to the reference application program from the first-order state transition matrix; determining a reference application program sequence based on the historical use behavior sequence; and splicing the application program set with the reference application program sequence to obtain a target application program set, and determining a second number of application programs from the target application program set as an application program sequence prediction result. According to the method, the application program sequence to be used by the user is predicted by directly combining the matrix transfer algorithm and the segmentation statistical algorithm, a neural network model does not need to be introduced for prediction, and the problem of deploying the neural network model on marginalized equipment with limited computing power resources is solved.

The invention provides a construction method and system of a settlement landscape evolution database based on multivariate spatial-temporal characteristics. According to the method, historical literatures, aerial photography, satellite images and landscape data are integrated to form a multivariate data set; constructing a dynamically updated database by using MySQL, and realizing data source associated storage by using a relational model; establishing a settlement landscape state machine model, and extracting spatio-temporal data to identify typical states and conversion conditions; constructing a state transition matrix, and analyzing the influence of social economic and environmental changes on evolution; the database is updated through field investigation, and a visual tool is developed to display the evolution process. According to the method, full-process dynamic analysis of settlement landscape evolution is realized, and efficient and accurate support is provided for decision making.

The invention discloses a self-adaptive integrated navigation method based on geometric accuracy factors, which belongs to the technical field of underwater navigation and positioning, is used for underwater navigation and positioning, and comprises the following steps: initializing an inertial navigation system and an integrated navigation filter, and setting a state vector and a noise covariance matrix; predicted navigation parameters of the recursive carrier are mechanically arranged through inertial navigation, and the state transition matrix is used for state prediction; and dynamically adjusting an observation noise covariance matrix according to the geometric precision factor value, further calculating a Kalman gain, performing optimal estimation and correction on a prediction state by fusing acoustic observation information, and finally outputting a high-precision carrier position, speed and attitude. According to the method, the statistical characteristics of observation noise are dynamically remodeled by calculating and feeding back geometric precision factor values in real time, so that the Kalman filter has the capabilities of knowing the own geometric situation and adjusting the trust degree of each information source, and the global optimal navigation precision and reliability are realized under any motion track.

The embodiment of the invention provides a combined navigation method and device fused with a double-antenna GNSS, and the method comprises the steps: obtaining various error propagation matrixes related to attitude angles, which are obtained through the collection of a carrier by the double-antenna GNSS, and the carrier comprises a vehicle; calculating and determining a state transition matrix at the current moment at least based on various error propagation matrixes of the attitude angle; performing Kalman filtering time updating on the basis of the state transition matrix and Kalman filtering state data of the carrier at the previous moment to obtain an updating result; determining a pitch angle and a course angle, which are output by the double-antenna GNSS, of a carrier relative to the ground; calculating and determining a Kalman filtering gain based on the updating result, the pitch angle and the course angle; and performing navigation optimal estimation of the attitude angle, the speed and the position based on the Kalman filtering gain.

The invention relates to the technical field of electric heat tracing intelligent temperature control, and discloses a control box electric heat tracing band intelligent temperature control system and method. The method comprises the steps of collecting data of a plurality of temperature sensors in a control box, constructing a temperature sequential sequence, and forming a sequential diagram structure reflecting correlation strength among the sensors by calculating spatial proximity and time correlation. And inputting the sequential graph into a state space model, and dynamically adjusting a state transition matrix of the model according to an edge weight of the graph so as to accurately simulate a heat transfer dynamic process in the box. A state output by the model is separated into a periodic component and a trend component, the periodic component is matched with a reference pattern library to identify an abnormal fluctuation phase, and multi-scale decomposition and slope curvature analysis are performed on the trend component to quantify a change trend. According to the method, dynamic and accurate modeling of the complex thermal environment is realized, the temperature can be controlled more accurately, and abnormity can be warned in advance.

The invention provides an oxidation kettle temperature prediction control method. The method comprises the following steps: S1, establishing a differential equation model; s2, converting the differential equation model into a discretized space prediction model, enabling the space prediction model to be equivalent to a first-order inertia plus lag system, determining state transition matrix parameters through system identification, and generating an oxidation kettle temperature prediction model; s3, obtaining a target function of a model prediction controller according to an error between the predicted temperature and the reference temperature; s4, improving an artificial bee colony algorithm by adopting a variation strategy of a differential evolution algorithm; s5, in the rolling optimization process of model prediction control, the improved artificial bee colony algorithm is adopted to update the optimal control input sequence in a rolling mode until the target function is minimized, and the optimal control input sequence is obtained; and S6, applying the optimal control input sequence to an oxidation kettle control system in real time, and dynamically adjusting the temperature of the oxidation kettle. The selection of the control input sequence is optimized, and the temperature control in the reaction process is more accurate and efficient.

The invention relates to a target track association method in a multi-target environment. The method comprises the following steps: firstly, preprocessing a new received trace point; secondly, predicting the next position of each track by using the existing track historical information and adopting a Kalman filtering algorithm, meanwhile, adjusting a state transition matrix and a process noise covariance matrix in real time by considering factors such as ocean current and wind direction in an offshore environment, and adjusting an observation noise covariance matrix according to meteorological information; then comprehensively considering distance, speed difference and motion direction difference to calculate a correlation degree magnitude between a new receiving trace point and each predicted track position; and finally, judging whether the newly received trace point belongs to the existing target track or forms a new target by adopting a threshold judgment method according to the correlation degree value, and updating the state of the track. The method can effectively cope with multi-target intersection, observation loss and clutter interference, improves the stability of a matching result, can realize continuous tracking of a marine target, can adjust parameters according to the environment and target characteristics, and has high adaptability.

The invention discloses a real-time SLMP fuel cell hybrid electric vehicle energy management method. The method comprises the following steps: firstly, collecting vehicle operation data in real time, constructing an operation state vector containing kinetic parameters, and initializing a state transition matrix as agent action input; on the basis, the speed and the acceleration are predicted through a self-learning Markov predictor, and dynamic self-adaptive power distribution of the fuel cell and the power cell is achieved in combination with a multi-objective optimization function and power distribution constraints. The method passes hardware-in-the-loop (HIL) verification, aims at reducing hydrogen consumption and improving efficiency, and exits if two times of circulation are not passed. Compared with a traditional method based on rules or static optimization, the method innovatively embeds a self-learning mechanism, has the advantages of being high in adaptability, high in prediction precision and good in generalization performance, and is suitable for energy management optimization under complex working conditions.

The invention belongs to the technical field of intelligent vehicles, and discloses an autonomous mobile robot multi-target tracking method and system based on a dynamic adaptive motion model. Detecting object information in the environment, and obtaining detection frame data of each frame of laser radar and camera; laser and camera data are projected to the ground to calculate the Euclidean distance so as to realize multi-sensor fusion and different frame data association operation. A corresponding adaptive state transition matrix is set for a previous frame trajectory, and a prediction bounding box and a covariance matrix of the previous frame trajectory in a current frame are calculated by using a Kalman filtering technology. The long-term and short-term memory module stores historical information of a management target and dynamically adjusts a tracking strategy and parameters. According to the invention, the tracking effect of the multi-target tracking algorithm on the autonomous mobile robot is successfully improved, powerful technical support and guarantee are provided for the intelligent robot to realize accurate multi-target tracking and efficient operation in a complex environment, and further development of the intelligent robot technology in practical application is powerfully promoted.

The invention discloses an electric vehicle charging load scene clustering method and system, and belongs to the technical field of load prediction and data analysis, and the method comprises the steps: collecting the historical time series data of an electric vehicle charging load, and constructing a feature vector; training under multiple confidence levels to obtain a probability distribution model of the charging load; generating a plurality of charging load time sequence scenes; performing dynamic modeling on each scene by applying a Gaussian hidden Markov model, extracting a state transition matrix, a state mean value and an initial state probability parameter, and combining the state transition matrix, the state mean value and the initial state probability parameter into a feature vector representing dynamic features of the scene; clustering analysis is carried out on the feature vectors representing the dynamic features of the scenes, and the scene closest to the clustering center is selected from each cluster to serve as a typical scene to be output. By accurately describing the uncertainty of the charging load, a charging load time sequence scene conforming to statistical distribution is generated, and a reliable and efficient decision basis is provided for power grid dispatching, planning and risk assessment.

The invention discloses a communication link fault detection method and device and electronic equipment. The method comprises the following steps: determining a node state matrix corresponding to a network element node in a first time step in a communication link; determining a state transition matrix corresponding to the network element node in the first time step; according to the node state matrix, the state transition matrix and a state success rate mapping matrix corresponding to the network element node, determining an end-to-end success rate corresponding to the communication link in the second time step, the state success rate mapping matrix being used for representing communication success rates of the network element node in different node states, and the state success rate mapping matrix being used for representing communication success rates of the network element node in different node states; the end-to-end success rate is used for representing the overall communication success probability of the communication link; and when the end-to-end success rate is lower than a preset success rate threshold, determining that the communication link has a fault. The technical problem that a communication network fault detection method in the prior art is poor in dynamic adaptability and lacks prediction capability is solved.

The invention relates to the field of power system state evaluation and equipment intelligent operation and maintenance, and provides an electric energy meter state evaluation method and system based on big data analysis. The big data analysis-based electric energy meter state evaluation method comprises the steps of obtaining electric energy meter operation error data, and extracting typical period trend and residual disturbance information; constructing a state transition matrix according to the typical period trend and the residual disturbance information; converting the state transition matrix into a dynamic structure tensor to quantify evolution strength and path activeness between error states; constructing a trend function based on the dynamic structure tensor and the error multi-scale trend function, and depicting the evolution strength of the error under the action of each structure path; and based on the typical period trend, the residual disturbance information, the trend function and the dynamic structure tensor, constructing a prediction function, and carrying out continuous prediction and state evaluation judgment on the future error change trend. According to the invention, continuous, batch and automatic analysis of the error state of the electric energy meter running in the network can be realized.

Along with diversification and complexity of underwater target motion forms, an existing interactive multi-model algorithm has the problems of slow model switching and insufficient tracking precision when facing target state switching. Therefore, the invention provides a parallel interactive multi-model target tracking algorithm based on time sequence information by taking a classical IMM algorithm as a main body framework. According to the algorithm, model probability change trends at adjacent moments are compared, parameters of a state transition matrix are dynamically corrected, and self-adaptive updating of the state transition matrix is achieved through normalization processing. And meanwhile, the model probability is dynamically updated by utilizing a parallel IMM framework and information entropy, so that the tracking precision reduction caused by excessive correction of a state transition matrix is avoided. Simulation results show that compared with an existing algorithm, the algorithm provided by the invention has the advantage that the prediction precision of the target is improved by 3.52% to 7.87%. And meanwhile, the switching speed of the model is higher, and the underwater target tracking precision is effectively improved.

The invention relates to the technical field of syndrome evolution prediction, in particular to a syndrome evolution prediction method, system and device based on multi-modal data driving and a medium, the method comprises the steps that multi-modal syndrome data is acquired and preprocessed, and the multi-modal syndrome data comprises clinical symptom data, tongue condition feature data and pulse condition feature data; extracting bidirectional time sequence characteristics in the preprocessed multi-modal syndrome data through a bidirectional long-short-term memory network, and generating a hidden state sequence; weighting the hidden state sequence by using an attention mechanism to obtain context vectors of contribution weights of different time steps; and inputting the context vector into a hidden Markov model of which the state transition matrix is constrained and corrected by the traditional Chinese medicine theory, performing syndrome state reasoning and evolution trend prediction, and outputting a prediction result. The objective of the invention is to improve the prediction precision and interpretability of syndrome evolution.

This invention relates to the field of data prediction technology, specifically to a collaborative control method for the opening degree of hydropower station gates based on BeiDou technology. This method constructs state parameters and analyzes the transition of these parameters over historical periods to obtain the transition probability for each state parameter transition. By combining the correlation between meteorological data and opening data, as well as the correlation between historical meteorological data and predicted meteorological data, the transition probabilities are continuously weighted to obtain a second weighted transition probability. The quantitative characteristics of the state transition of the flood discharge channel gate under its predicted state transition are statistically analyzed for the non-flood discharge channel gate, thereby obtaining the upper-level influence weight, obtaining the second state transition matrix of the flood discharge channel gate, and performing state prediction. This embodiment of the invention, by combining the influence of historical meteorological data and further combining the control influence between gates, obtains accurate gate state prediction results.

The invention provides a decoding device and method. The method is suitable for the condition that periodic repeated code elements exist in original information code elements. The method comprises the steps that scrambled information code elements k0, k1, k2,..., kt-1, kt,..., km-1 with the length of m, the initial state M (0) of a long code generator and a state transition matrix B are received; constructing auxiliary data k0 ', k1', k2 ',..., k' 82 according to the scrambled information code elements k0, k1, k2,..., kt-1, kt,..., km-1; wherein ki '= ki + kt-1 + i, and i = 0, 1,..., 82; according to the auxiliary data k0 ', k1', k2 ',..., k' 82, the initial state M (0) and the state transition matrix B, a 42 * 42 full-rank matrix D is obtained through calculation; wherein the first behavior of the D is CBn, and then a scrambling code # imgabs0 # with the length of m is obtained through sequential analysis, the scrambling code obtained through analysis and scrambled information code elements k0, k1, k2,..., km-1 are subjected to modulo 2 addition operation, and an original information code element with the length of m is obtained.

The invention provides a learning enhanced Kalman filtering method for forest emergency positioning and a related device. The method comprises the following steps: acquiring time sequence measurement data of an unmanned aerial vehicle and a ground target node; inputting time sequence measurement data into an error compensation network, and outputting a ranging error compensation amount and an observation noise covariance matrix; performing distance measurement compensation on the time sequence distance measurement data, feeding back the distance measurement data subjected to error compensation and a state error of the ground target node to a state prediction network, and outputting a state transition matrix and a process noise covariance matrix; state prediction is carried out based on a state transition model and a process noise covariance matrix, Kalman filtering fusion is carried out on a prediction state, compensated distance measurement data and an observation noise covariance matrix, three-dimensional position estimation of a ground target node is determined, combined improvement of measurement correction and prior prediction precision is realized, and the accuracy of measurement correction and prior prediction is improved. On the basis of keeping the optimality of traditional filtering, modeling is carried out on time-varying noise and non-linear motion, and the positioning precision in a forest complex environment is improved.

The invention relates to the technical field of high-precision positioning, in particular to a high-precision six-degree-of-freedom magnetic positioning system and method, and the method comprises the steps: generating a three-dimensional low-frequency magnetic induction field with a preset frequency in a space through a three-axis orthogonal transmitting coil; detecting the three-dimensional low-frequency magnetic induction field through a three-axis orthogonal receiving coil to obtain an induced voltage signal; transmitting the digitized induced voltage signal to a control layer; in the control layer, the position and attitude of the receiver are estimated in real time by using a joint estimation algorithm fusing error state extended Kalman filtering and particle swarm optimization; the error state extended Kalman filtering comprises the steps of constructing a state transition matrix F of a target carrying a receiver, and constructing a process noise covariance matrix Qk and a parameterized measurement noise covariance matrix R of the system through translation acceleration noise sigma a and rotation noise sigma omega. According to the invention, the PSO module is utilized to dynamically and adaptively adjust the key parameters of the ESKF, and high-precision positioning and low-delay response are realized.

The invention discloses a bridge cluster multi-agent full-period maintenance decision-making method under deep reinforcement learning, and the method comprises the steps: firstly dividing a bridge into an upper structure, a lower structure and a bridge deck system, and constructing a simulation environment of bridge cluster maintenance; defining the condition and bridge age of each bridge structure in the bridge cluster; defining three intelligent agents, namely agentsup, agentsub and agentdeck, respectively corresponding to the three part structures, and carrying out training and execution of bridge cluster maintenance; defining a state space and an action space of the deep reinforcement learning method; designing a reward function and determining a state transition matrix; constructing a plurality of agent networks based on a deep Q network algorithm, and training an agent neural network; and finally, testing the capability of the intelligent agent in practical application, and displaying a test result to a user. According to the invention, the structure complexity is greatly simplified, and the method is an important basis for practical application of the maintenance framework.

The invention relates to the technical field of attitude estimation, in particular to an attitude estimation method in a magnetic interference environment, which comprises the following steps of: obtaining a conversion relation between an Euler angle and a quaternion; calculating the quaternion at the next sampling moment; respectively solving a first state transition matrix, a first process noise matrix and a first prior covariance according to the first state equation; respectively solving a second state transition matrix, a second process noise matrix and a second prior covariance according to a second state equation; elements in the nb-series rotation matrix are used as observed quantities of state variables in the parameter-series rotation matrix, a corresponding Kalman gain is calculated, and a Jacobian matrix is designed by using the observed quantities; calculating an observation noise covariance matrix by using the Jacobian matrix; updating the Kalman gain by using the observation noise covariance matrix; and updating the state equation and the posterior covariance by using the updated Kalman gain and the observed quantity. The method solves the problem of how to prevent the calculation of the roll angle and the pitch angle from being influenced by magnetic interference when the magnetic interference exists.

The invention discloses an optimal distribution method for electric vehicle charging stations, and the method comprises the steps: constructing a state transition matrix based on the travel time of a driver of a to-be-charged electric vehicle, the charging cost, the number of available charging piles of the charging stations, the comprehensive position of the charging stations, and the attraction of the charging stations, and generating charging demand probability distribution in real time; calculating a reachable radius according to the residual electric quantity and the energy consumption rate of the to-be-charged electric vehicle, screening a candidate charging station set, fusing a driver preference weight, and establishing a multi-target distribution cost evaluation mechanism; constructing an optimal distribution model of the electric vehicle charging stations; an initial population is generated through greedy random adaptive search, a multi-parent cross strategy or a directional exchange strategy is dynamically executed according to the remaining time of a driver, and a corresponding optimal charging station distribution scheme is output. According to the method, the optimal charging station is allocated for the electric vehicle user, and the driving distance cost, the charging currency cost and the destination walking distance cost are reduced to the maximum extent.