381 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 an industrial data quality evaluation method based on micro-service, a medium and a system, and belongs to the technical field of electrical digital data process.The industrial data quality evaluation method comprises the steps that firstly, multi-source heterogeneous data is collected through a distributed network, and an evaluation system containing deviation rate, completeness rate and timeliness indexes is established; extracting features by using a deep learning model, and generating a feature probability distribution matrix; then, constructing a state transition matrix based on the characteristic entropy and the fluctuation coefficient, deploying an evaluation unit through the micro-service architecture, and establishing a transverse and longitudinal evaluation network to calculate information gain and association strength; and finally, performing feature fusion through an improved pyramid structure, realizing quality scoring by adopting a three-layer evaluation equation set, ensuring scientificity of a scoring result through logarithm mapping, and realizing dynamic evaluation of industrial data quality. The technical problem that in the prior art, an industrial data quality evaluation method is difficult to adapt to dynamic changes of multi-source heterogeneous data is solved.

The invention provides a pilot landing operation analysis method and device, electronic equipment and a storage medium. The pilot landing operation analysis method comprises the steps that external environment parameters and aircraft running state parameters formed in the process that a pilot operates an aircraft to land are obtained; calling a neural network model and inputting the external environment parameters and the aircraft operation state parameters into the neural network model to obtain a state transition matrix at each time point; based on the state transition matrix, hidden states corresponding to the state transition matrix are obtained, so that pilot landing operation analysis is completed, the hidden states are used for representing all states of an attention distribution mode of a pilot, and the hidden states comprise the state without attention loss, the state of attention loss of a yaw angle and the state of attention loss of a height; and any one or more of states in which attention to airspeed is missing. The landing operation of the pilot is accurately and objectively analyzed, so that the risk is conveniently managed and controlled in a targeted manner, and the flight safety level of the pilot is improved.

The invention discloses a Markov chain-based power dispatching boundary data behavior anomaly detection method and system. The method comprises the following steps: monitoring historical data of each information object of network boundary equipment of a power dispatching system in an IEC104 protocol, and extracting each dispatching service according to a time interval of a remote control instruction data packet of the information object; constructing a state transition matrix based on all historical remote control instructions for each information object based on a Markov chain algorithm, wherein the state transition matrix records all possible transitions among states and probability distribution thereof; when a new scheduling service is received, calculating the probability that the scheduling service belongs to a normal behavior by using the state transition matrix, and if the calculated probability is lower than a preset safety threshold, determining that the scheduling service has an abnormal behavior; and updating the corresponding state transition matrix according to the abnormal behavior detection result. According to the scheme, the anomaly detection capability of the power dispatching control system is remarkably improved.

The invention discloses a charger communication test method and system, a storage medium and a program product, and the method comprises the steps: obtaining a state parameter of a to-be-tested charger; collecting a communication message of the charger to be tested, and constructing a message time sequence queue; generating a message interval sequence reflecting the time sequence characteristics of the message; calculating a discrete degree coefficient of a message time sequence according to the message interval sequence; recording record data at an abnormal moment when the communication abnormality occurs, and inputting the record data into a preset state transition matrix to obtain a corresponding relationship; calculating probability distribution of communication abnormity under different state parameters to obtain a correlation model; generating a message sequence for testing based on the association model, wherein the time sequence characteristic of the message sequence for testing is the same as the state parameter and the dispersion degree coefficient corresponding to the high-probability abnormal scene of which the probability is greater than a preset probability threshold in the association model; and sending the test message sequence to the to-be-tested charger to detect response data of the to-be-tested charger. And the accuracy and efficiency of the communication test of the charger are improved.

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 relates to the field of data processing, in particular to a high-voltage cable production process parameter management method and system, and the method comprises the steps: obtaining a measurement value of the same process parameter at the current moment through a plurality of sensors, and correcting the measurement values of the plurality of sensors at the current moment to obtain an optimal measurement value for the management of a production process; the method for correcting the measured value at the current moment comprises the following steps of: constructing an optimal measurement matrix by utilizing the optimal measured value at the previous moment, taking the product of the optimal measurement matrix and a preset first state transition matrix as a predicted value of the technological parameter at the current moment, and calculating the adaptive Kalman gain of each sensor. The method has the effect of improving the accuracy of process parameter acquisition.

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 a satellite formation baseline control method based on inter-satellite ranging information, which aims at a satellite formation without a GNSS (Global Navigation Satellite System), and can realize accurate control of a baseline by depending on the inter-satellite ranging information without depending on real-time navigation information. The baseline control method mainly comprises three parts of establishment of a state transition matrix, establishment of a distance measurement equation and correction control pulse calculation. Compared with navigation data acquisition, distance measurement data acquisition is more convenient, and the distance measurement precision of the inter-satellite low-gain link reaches 1m. The method does not depend on real-time navigation information, and the accurate control of the base line is realized by fully utilizing inter-satellite distance measurement information.

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 discloses a hydrogen fuel cell hybrid power system grid-connected control method, which comprises the following steps of: analyzing and establishing a plurality of models of a system, comprehensively considering a plurality of cost weights on the basis of a hydrogen consumption model of the system, and constructing a comprehensive cost function; establishing a system operation state model; the speed, the acceleration and the system operation state serve as input of a self-adaptive Elman neural network together, a state transition matrix real-time correction factor of the system in the state is obtained, and a basic state transition matrix is adjusted through the real-time correction factor to obtain a real-time state transition matrix after the influence of the current driving condition on system state transition is considered; and according to the real-time state transition matrix, predicting the system demand power within a period of time, solving the comprehensive cost to enable the cost to be minimum, and completing the power distribution between the multiple stacks of fuel cells and storage batteries. According to the method, the fuel economy of the system is ensured, the internal balance of the multi-stack fuel cell system is improved, the service life of the system is prolonged, and the real-time prediction capability of the system is improved.

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 an ultra-short-term wind power prediction method and system for a complex climatic environment, and relates to the technical field of wind power, and the method comprises the steps: extracting the historical environment and power data of a target scene, and carrying out the analysis to obtain a typical environment condition set; main environmental components are obtained through principal component analysis, and key environmental factors are extracted to construct a basic prediction model library; predicting an ultra-short-term environment condition based on the environment state transition matrix, and configuring a model calling parameter; and according to the calling parameters and the basic model library, configuring a wind power prediction model and performing prediction. Therefore, the technical effects of improving the environment adaptability and the prediction accuracy and improving the response delay are achieved.

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 relates to a power enterprise data full-link fault positioning method based on data consanguinity. The method comprises the following steps: step 1, constructing a data consanguinity map; 2, based on the data consanguinity map constructed in the step 1, metadata consanguinity full links are collected and monitored in real time; 3, based on the data blood relationship map constructed in the step 1 and the real-time monitoring data collected in the step 2, a state transition matrix is constructed by analyzing state change information, and a Markov chain model is defined; by using the Markov chain model, the system can identify potential fault points and carry out accurate positioning. According to the invention, the problems of high data link complexity, difficulty in fault positioning, low troubleshooting efficiency and the like in the prior art can be solved.

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 discloses a non-linear scene multi-target tracking method and system and a storage medium, and the method comprises the steps: constructing a non-linear motion model and a measurement model, and specifically, representing a non-linear state process and a measurement process through a non-linear model; according to multi-target tracking in a two-dimensional plane, determining a motion equation of each target, setting the motion equation as a collaborative turning model, and determining a motion equation of the target; constructing a system state transition matrix, a process noise distribution matrix and a measurement equation; according to the nonlinear motion model and the measurement model, extraction of multi-target state estimation is carried out through an FAUKF-PHD filter, a state vector of each target is obtained, and tracking of each target is completed; and verifying the tracking result of each target based on the multiple pieces of preset scene information. The method is high in tracking precision and low in cost, can improve the effectiveness of the method, and can be widely applied to the technical field of computers.