613 results about "Online identification" patented technology

The invention discloses a 6G digital twinning online channel modeling method, which comprises the following steps of: based on information acquired from an environment data source, carrying out communication scene online identification by utilizing a machine learning model; matching channel parameters of a 6G universal channel model according to the identified communication scene, and mapping the channel parameters to the communication scene; reconstructing the digital twin online channel of the static scene in combination with the obtained channel parameters; dynamically sensing the physical environment, acquiring environment change information, and obtaining dynamic sensing data; and in combination with a static-dynamic mixed channel modeling algorithm and dynamic sensing data, dynamically updating the digital twin online channel of the reconstructed static scene to obtain a new real-time twin channel, and constructing a visual platform of the digital twin online channel. According to the method, the channel characteristics of the existing network communication environment can be reflected in real time, real-time sensing, prediction and decision making can be realized, the pilot frequency overhead is reduced, and the system performance is improved.

The invention discloses an FPSO stand pipe supporting structure stability online identification method and system, and belongs to the field of ocean engineering.The identification method comprises the following specific steps that firstly, the state of an FPSO stand pipe supporting structure is monitored in real time through a sensor system, and collected original sensor data are preprocessed; iI, training a stable recognition model by using historical sensor data, and globally optimizing the trained stable recognition model; the method can adapt to the rapid change of the environment in real time, improves the response capability of the whole system, effectively reduces the possibility of instability, remarkably reduces the probability of instability accidents, improves the safety of platform operation, achieves the dual optimization of safety and economy, and improves the reliability of the system. The comprehensive operation benefit of the system and the overall efficiency and accuracy of the stability online identification method are improved; the security risk of centralized storage of data can be avoided, the calculation efficiency of the system is improved, the network bandwidth pressure is reduced, the robustness and accuracy of the model are enhanced, and the fault-tolerant capability and stability of the system are improved.

The invention provides a generator set active power low-frequency oscillation online identification method and system, and relates to the field of operation safety of generator sets, and the method comprises the steps: obtaining active power data of a generator set, and carrying out the preprocessing of the active power data; setting a sliding window, scanning the preprocessed active power data through the sliding window, performing Prony decomposition on the data in the window, and obtaining a plurality of cosine components in each window; judging whether the window is a low-frequency oscillation suspected window or not according to a preset frequency threshold value, and calculating the maximum amplitude in all cosine components of the low-frequency oscillation suspected window; according to a preset continuity threshold value and an amplitude threshold value, the maximum amplitude mean value of the continuous low-frequency oscillation suspected windows is screened, and the screened continuous windows are low-frequency oscillation abnormal signals. On the basis of an existing Prony algorithm, a judgment rule of continuous window smoothness is introduced, the anti-noise performance of the algorithm is further optimized, and the accuracy and stability of low-frequency oscillation recognition are improved.

The invention relates to a mechanical arm tail end variable load dynamics self-diagnosis and compensation method in a quick change form. The mechanical arm tail end variable load dynamics self-diagnosis and compensation method is technically characterized in that a dynamics model is established based on iterative least square online identification; load parameter identification under variable working conditions is carried out based on Jacobian matrix mapping; establishing a hybrid dynamic model including connecting rod rigid body dynamics, connecting rod error compensation dynamics and load dynamics; establishing a variable load dynamical model; when the load at the tail end of the mechanical arm is changed, robot joints are controlled by updating parameters in the hybrid dynamic model; on-line identification and compensation are carried out by collecting sensor data in real time. The method can effectively improve the accuracy and robustness of dynamic representation of the robot when facing various uncertain factors such as connecting rod deformation, variable load and measurement noise, improves the safety and reliability of physical man-machine interaction of the robot, and can be widely applied to the fields of industrial automation, medical operation assistance and rescue robots.

The invention discloses a dynamic monitoring-based deaerator water supply control method and system for a nuclear power plant, and relates to the field of deaerator water supply control, and the method comprises the steps: firstly, through a hybrid online identification algorithm, analyzing a historical valve position instruction and a water supply flow sequence in real time, and dynamically estimating the local gain and hysteresis width of a valve under the current working condition; then, the parameters identified in real time are utilized to carry out prospective compensation on the expected flow variation calculated by the main controller. In other words, the instruction amplitude is adjusted according to the estimated local gain, and it is ensured that consistent flow response can be obtained under different loads; meanwhile, when the instruction is reverse, the compensation amount is actively applied according to the estimated hysteresis width so as to eliminate the adjustment dead zone and oscillation caused by hysteresis. In this way, the nonlinear object of the valve is equivalent to a linear link with consistent response, and the stability and accuracy of the deaerator water level control system in the full working condition range are remarkably improved.

The invention provides a vibration active control method based on a secondary channel online identification FxLMS algorithm, a vibration active control system comprises a sensor, a vibration signal conditioner, an actuator, a piezoelectric power amplifier, a real-time control simulation system and a computer, and a built SPMFxLMS algorithm physical model is embedded in a computer controller. The SPMFxLMS algorithm physical model comprises a vibration response error signal calculation module, a control channel adaptive filter, an identification channel adaptive filter and an unknown secondary channel model, and the unknown secondary channel model is constructed according to the identification channel adaptive filter and updated in real time. The algorithm physical model constructed by the invention adopts an improved power scheduling strategy and an identification adaptive filter to update and calculate the vibration response time domain data of the controlled object, so that the obtained control voltage signal is reliable, the residual vibration quantity caused by identification noise is avoided, the vibration suppression effect is excellent, and the method has excellent noise immunity and applicability.

The invention provides a motor parameter online identification method based on an improved golden section method, and belongs to the technical field of motor parameter identification, and the method comprises the steps: S1, obtaining a residual function of a recursive least square method with a forgetting factor when parameter identification is not carried out on a motor model for the first time; s2, detecting whether the residual function exceeds a preset threshold value or not, and executing a step S3 when the residual function exceeds the preset threshold value; s3, taking the residual function as a target function, and adjusting the forgetting factor by using a golden section method to obtain an optimized forgetting factor; and S4, carrying out motor parameter online identification by using a recursive least square method with the optimized forgetting factor. The method has the advantages that the forgetting factor is dynamically adjusted through the golden section method, the residual function serves as the target function, the optimal solution of the forgetting factor under the corresponding working condition can be found when the system changes rapidly, the tracking speed is increased, the steady-state precision is further improved, and therefore the overall performance of parameter identification is improved.

The invention relates to the technical field of underground engineering safety monitoring and disaster simulation, in particular to a digital twinborn deduction platform for underground engineering disaster chain evolution simulation, which comprises a multi-disaster coupling numerical simulation module used for acquiring multi-source data including design drawing data, geological data and engineering monitoring data, and based on the multi-source data, establishing an initial stress field model by using a numerical calculation method combining a finite element and a finite difference, and carrying out leakage-settlement-structure failure multi-disaster coupling numerical simulation. According to the method, the partial differential equation for describing the evolution law of the underground engineering is used as a constraint term to be embedded into the deduction model, and online identification and dynamic extrapolation of parameters are executed by fusing real-time monitoring data, so that the accuracy of disaster evolution simulation under complex working conditions is remarkably improved; and the problem that the traditional numerical calculation method is difficult to meet the real-time requirement of digital twinning is solved.

The invention relates to a low-voltage transformer area phase sequence recognition graph connection prediction method fusing time sequence coding and graph attention. The method comprises the following steps: step S101, acquiring voltage time sequence data of all users; step S102, converting a traditional'node classification 'problem of allocating a phase label to each user into a'graph connection prediction' problem of judging whether any two users are connected to the same phase; step S103, constructing an end-to-end deep learning model; step S104, constructing a pairwise prediction module (MLP classifier); and step S105, deploying the finally trained model to an embedded computing device, and performing low-cost and high-efficiency online identification and effect test on the user phase relationship of an unknown transformer area in an actual or simulated environment. The method gets rid of dependence on physical parameters of a power grid, and realizes high-precision automatic phase identification through an innovative problem modeling mode and a deep learning architecture.

The invention belongs to the technical field of industrial control, and particularly relates to a brushless direct current motor high-efficiency control method and system based on an FOC algorithm, and the method comprises the steps: superposing high-frequency sine disturbance in a voltage instruction, extracting a response component, achieving the online identification of the equivalent impedance of a motor, and dynamically solving and updating the estimated values of resistance and inductance; and correcting d-axis and q-axis voltage feed-forward items in real time by using the updated parameters, and correspondingly adjusting a current loop proportion and an integral gain. The method can adapt to the time-varying characteristics of motor parameters in an industrial field, so that the accuracy of feed-forward compensation and the stability of current loop bandwidth are effectively guaranteed, and the robustness and the control precision of a control system under complicated industrial working conditions such as variable temperature and variable load are remarkably improved; and the requirements of high-end industrial equipment on high performance and high reliability of the motor driving system are met.

The invention discloses an AC excitation system rotor initial position on-line identification method and system, and the method comprises the steps: collecting a stator three-phase voltage signal when a stator is no-load, carrying out the Clarke conversion of the signal, obtaining a voltage component under a static coordinate system, and carrying out the low-pass filtering processing; calculating a stator flux linkage based on the stator current and the processed voltage component, and calculating a rotor flux linkage based on the rotor current; the rotor initial position offset is continuously accumulated through closed-loop adjustment by calculating the stator and rotor flux linkage angle and the phase difference until the phase difference approaches zero, and the accumulated rotor initial position offset is output; and combining the mechanical angle output by the encoder with the accumulated rotor initial position offset to obtain a corrected rotor position angle, and updating the corrected rotor position angle to an excitation system. According to the method, the in-phase characteristics of stator and rotor flux linkages are utilized, and the optimally designed low-pass filter is combined, so that the identification precision of the initial position of the rotor is remarkably improved, and the zero offset of the encoder is effectively compensated.

The invention relates to the technical field of lithium battery cell online nondestructive testing, and discloses a lithium battery cell lamination process online quality detection method, which is used for solving the problems of high difficulty in real-time online identification of hidden defects in a lithium battery cell lamination process and high omission ratio in a traditional method. The method comprises the following steps: firstly, synchronously acquiring an optical image of the surface of a pole piece and an X-ray perspective image of an internal structure on a lamination production line to obtain a multi-modal image sequence, carrying out time sequence registration and space alignment fusion on the two types of images, and extracting a pole piece layer, a diaphragm layer boundary and a density abnormal region; according to the method, an S-shaped deformation template and an internal foreign matter template are combined to carry out mode comparison, three-dimensional coordinate positioning, density grading and severity marking are carried out on an abnormal area, a structured abnormal catalogue is fed back to a production line control system, technological parameters are adjusted in a linkage mode, online nondestructive testing and closed-loop regulation and control of hidden defects are achieved, and the production efficiency is improved. The omission ratio is reduced; and the consistency and the safety of the lamination process are improved.

The invention relates to a heading machine load self-adaptive control method and system, and the method comprises the steps: a data analysis module is disposed on a heading machine equipment layer, collects the heading data of the heading machine equipment layer in real time, and calculates a load coefficient for self-adaptive adjustment; and the control module is connected with the data analysis module and the heading machine equipment layer, and analyzes the accessed load coefficient into a control signal for adjusting rotation and lifting of a cutting arm in the heading machine equipment layer. The method comprises the following steps: determining a motion trail model of a cutting operation surface of a heading machine equipment layer by acquiring perception data of a manual cutting section, determining a load coefficient generated by the motion trail model according to the state change of the cutting operation surface in the cutting process, and adjusting rotation and lifting of a cutting arm in the heading machine equipment layer based on the load coefficient. The self-adaptive control of the equipment layer of the heading machine is realized, the accuracy and the stability of on-line identification of the heading working face are greatly improved, the equipment failure rate and the safety risk of operation are reduced to the greatest extent, and the method has wide applicability.

The invention provides a self-adaptive MPC vehicle trajectory tracking control method for danger level online identification. The method comprises the following steps: acquiring a vehicle reference trajectory of a controlled vehicle; establishing a three-degree-of-freedom vehicle dynamics model; acquiring vehicle state information of the controlled vehicle; identifying an overall danger level of the current controlled vehicle based on the vehicle state information; state error weight coefficients of the prediction time domain and the target function are adaptively adjusted by taking the total danger level and the lateral error of the controlled vehicle as input variables of an ant algorithm; an MPC trajectory tracking controller is established according to the three-degree-of-freedom vehicle dynamics model and the reference trajectory, the prediction time domain and the state error weight coefficient are given to the MPC trajectory tracking controller, the MPC trajectory tracking controller carries out rolling optimization on vehicle state information of a controlled vehicle with the vehicle reference trajectory as reference information, and a self-adaptive MPC vehicle trajectory is obtained. And the accuracy of MPC vehicle trajectory tracking is improved.

The invention relates to the technical field of servo drivers, in particular to a servo driver control method, and the technical scheme comprises the steps: carrying out the online identification of load inertia and external disturbance through a self-adaptive observer, generating a dynamic compensation parameter, constructing a multi-model parallel self-adaptive observer group, and carrying out the online identification of the load inertia and external disturbance through the self-adaptive observer group, a dynamic confidence evaluation mechanism is designed, disturbance dynamic compensation is realized, parameter sudden change and slow change scenes can be taken into consideration, the real-time performance of dynamic response can be ensured, compensation parameters are input into a fuzzy neural network controller, an optimal control quantity is generated in combination with a preset control target, a multi-source compensation parameter input channel is constructed, and the optimal control quantity is generated. A dynamic input weighting module is designed, fuzzy rule confidence is updated through an online strategy gradient algorithm to generate an optimized control quantity, the working condition adaptability can be enhanced, a dynamic target can be dealt with, finally, a sliding mode variable structure algorithm is adopted to carry out high-frequency buffeting suppression on the control quantity, and a final driving signal is output to a power module. And the control precision of the servo driver is improved.

The invention relates to the field of motor fault diagnosis, and particularly discloses a permanent magnet synchronous motor fault diagnosis method and system, which dynamically update motor physical model parameters through an online identification algorithm so as to reflect the characteristic change of a motor in real time. Thirdly, predicting the theoretical voltage of the healthy motor under the working condition by using the self-adaptive model and the real-time operation data, and calculating a residual sequence subjected to working condition normalization between the theoretical predicted voltage and the actual voltage; the residual signal is essentially free from the influence of working condition change, and only the abnormal characteristics caused by the fault are highlighted. And finally, inputting the high-robustness residual error sequence into a convolutional recurrent neural network, deeply fusing space-time fault features, and realizing accurate judgment on a motor fault type and confidence thereof, thereby effectively improving variable working condition adaptability and weak fault detection capability of diagnosis.

The application discloses an online error calibration method for a hemispherical resonator gyro based on frequency superposition, which solves the problem that the traditional error calibration method for a hemispherical resonator gyro cannot realize online calibration of gain error, bias angle error and phase error in the same working mode. The application utilizes high-frequency cosine and sine reference signals to demodulate two-channel detection signals and extract high-frequency components in the detection signals. Subsequently, an associated model is established by using the high-frequency cosine and sine given control signals and the demodulated signals to realize online identification of the gyro error, and the identification result is applied to online calibration of the channel gain error, bias angle error and phase error, thereby enhancing the anti-interference ability of the hemispherical resonator gyro under variable temperature conditions. The application is mainly used for simultaneous online calibration of the channel gain error, bias angle error and phase error.

The invention discloses a torque ripple suppression method and control device based on permanent magnet flux linkage harmonic identification, and the method comprises an online identification process and a torque ripple suppression process, and is used for suppressing the torque ripple of a built-in permanent magnet synchronous motor. The online identification process comprises a permanent magnet flux linkage harmonic identification step, a dead zone voltage harmonic compensation step and a harmonic component extraction step; the torque ripple suppression process comprises the steps of inputting a pre-recognized flux linkage harmonic lookup table to obtain fifth and seventh permanent magnet flux linkage harmonics, predicting torque ripple according to a magnetic co-energy model, outputting an optimal stator current reference value, obtaining sixth harmonic voltage for suppressing the torque ripple, and driving the motor by using an SVPWM strategy after a fundamental component and a harmonic component are superposed.

The invention discloses a video online identification method and device for a locomotive, and relates to the technical field of railway vehicles, and the method comprises the steps: receiving a steward monitoring video stream sent by a front camera and a pantograph monitoring video stream sent by a pantograph camera; based on the steward monitoring video stream, determining whether the steward has a view deviation abnormal condition and a discontinuous lookout abnormal condition, and determining whether the front camera has a shielding abnormal condition; based on the pantograph monitoring video stream, determining whether the pantograph has a structure abnormal condition, a spark abnormal condition and a foreign matter invasion abnormal condition; in response to determining that at least one of the visual field deviation abnormal condition, the intermittent outlook abnormal condition and the shielding abnormal condition exists, carrying out online real-time reminding on the steward; and in response to determining that at least one of the structure abnormal condition, the spark abnormal condition and the foreign matter invasion abnormal condition exists, determining a fault sub-video from the pantograph monitoring video stream, and sending the fault sub-video to the client.

The invention belongs to the field of robot motion control, and discloses a dynamic sliding mode motion control method for a mobile manipulator based on dynamic online identification feedforward, which comprises the following steps of: 1, designing a physical consistency linearization coupling dynamic model of the mobile manipulator; 2, designing a self-adaptive Kalman filtering algorithm based on modeling error covariance dynamic updating, and realizing high-precision online identification of the physical consistency kinetic parameters of the mobile mechanical arm; 3, designing a recursive dynamics feedforward algorithm of the mobile mechanical arm; 4, a self-adaptive over-torque dynamic sliding mode control algorithm based on recursive dynamics feedforward is designed, the continuity of the control law is guaranteed, and high-precision finite time convergence of the motion error of the mobile mechanical arm is achieved; and 5, the stability of the controller is achieved through a Lyapunov design method, a paradigm framework of robot dynamics feedforward finite time motion control is formed, and the response speed and precision of robot motion control can be effectively improved.

The invention provides a digital twinning five-axis machine tool contour error prediction method and system and electronic equipment, and belongs to the technical field of numerical control machine tool precision control and digital twinning. According to the method, servo axis data are collected in real time at a numerical control system end, electromechanical parameters are identified on line, comprehensive disturbance is estimated, and an autoregressive exogenous (ARX) model is updated accordingly; an interpolation instruction is generated in a digital twin body end mirror image numerical control system process, an updated model is utilized to predict a shaft position with deviation, a tool path is obtained through kinematics positive solution, and finally contour errors are calculated through geometric alignment. According to the digital twinning five-axis machine tool contour error prediction method and system and the electronic equipment, dynamic and high-precision prediction of the five-axis machine tool machining track contour error is achieved through the online identification and digital twinning technology, the limitation of a traditional static compensation method is overcome, and the precision of the digital twinning five-axis machine tool contour error prediction is improved. And a key basis is provided for prospective process optimization and precision control, and the machining precision and efficiency are effectively improved.

The invention relates to a mechanical arm self-adaption compliance control method and system based on model reference self-adaption and impedance parameter online identification, and belongs to the field of robots. The method comprises the steps that a mechanical arm task space dynamics model is established; designing a reference model; a recursive least square method is adopted, and an equivalent stiffness matrix and a damping matrix of the interaction environment are identified online; designing a composite adaptive law; the expected rigidity of the reference model is dynamically adjusted according to the environment rigidity obtained through identification; and according to sensor data collected in real time, the mechanical arm task space dynamics model, the robot parameter self-adaption law, the environment rigidity, the environment damping and the environment parameter identification robust item, control force is generated, and the mechanical arm is driven through the control force. According to the method and the system, the environment characteristics can be sensed online, and the compliant behaviors can be adaptively adjusted, so that safe, efficient and high-robustness interaction of the mechanical arm in a complex and unknown environment is realized.

The invention discloses a battery SOC prediction method fusing online identification and an adaptive algorithm, relates to the technical field of power battery monitoring, and aims to solve the problem of insufficient SOC prediction precision of an electric vehicle power battery under a dynamic working condition. The prediction method comprises the steps of obtaining lithium battery charging and discharging experiment data, establishing a dual-polarization equivalent circuit model to simulate a strong nonlinear behavior in the battery, and fitting an OCV-SOC curve based on the experiment data; performing online parameter identification on the equivalent circuit model by using a forgetting factor recursive least square method based on grid search-real-time voltage difference improvement; sOC prediction is carried out by combining online parameter identification with a self-adaptive expansion Kalman filtering algorithm; and the proposed prediction algorithm is verified under a mixed pulse power characteristic test working condition. According to the method, the accuracy and the real-time performance of SOC prediction are remarkably improved, and the method has a wide application prospect.

The invention relates to the technical field of cable defect identification, and discloses an incremental OS-ELM cable defect online identification method and system. The method comprises the steps of collecting cable joint monitoring signals to extract time-frequency features, calculating feature importance weighting initialization model weight, screening samples to adjust forgetting factor recursion update weight, constructing mixed batch optimization weight from a multi-scale time window, and fusing multi-classifier evaluation values to output a defect identification result. According to the invention, the accuracy and stability of cable defect online identification and the long-term knowledge retention capability are improved.

The invention discloses an online identification method for kinetic parameters of a joint type series robot, which comprises the following steps: S1, for a to-be-identified joint type series industrial robot, establishing a robot kinetic model considering friction; s2, linearization is carried out on the joint type series industrial robot dynamic model; s3, designing an optimal excitation track for an identification experiment, and obtaining an excitation track by using five finite Fourier series and solving a constrained nonlinear optimization problem; and S4, the series robot runs the excitation track, meanwhile, the position information of each joint of the robot and the driving current of each joint at each moment are collected, and to-be-identified parameters are calculated on line at each moment. According to the method, online identification of the robot kinetic parameters can be carried out, the identification efficiency of the robot kinetic parameters is improved, and the method can be used for industrial scenes such as robot moving and carrying, polishing and grinding and heavy-load hydraulic pressure.

The invention provides an industrial process-oriented PID controller online setting method, and aims to improve control precision and online setting efficiency and meet industrial control requirements. Due to the time-varying characteristic of an industrial system, firstly, a control object is subjected to online identification, and a model structure and parameters are determined to construct an accurate mathematical model; thirdly, a Ziegler-Nichols empirical formula method is introduced to preliminarily set controller parameters, the convergence speed of an optimization algorithm is increased through a set initial value, and then the setting efficiency is improved; and finally, optimizing in an area adjacent to the initial constant value by using a particle swarm algorithm to obtain an optimal controller parameter. Compared with an existing control strategy, the method has higher control precision, also has a good self-adaptive characteristic, can flexibly cope with a complex and changeable industrial environment, and provides an efficient and reliable solution for industrial process control.

The invention relates to the technical field of copper bar machining equipment, and discloses a copper bar bending control system and device.The copper bar bending control method comprises the following control steps that S1, the initial temperature of a to-be-machined copper bar, the real-time temperature of a bending template and the actual pressure in a working cavity of a hydraulic cylinder are collected; s2, the actual pressure collected by the multi-source state sensing module is recorded to obtain pressure attenuation data, and the final accurate stress relaxation time constant of the current workpiece is calculated based on the pressure attenuation data; s3, generating a target pressure set value according to the final accurate stress relaxation time constant; s4, calculating in real time, and obtaining a control signal according to a deviation value between the generated target pressure set value and the actual pressure collected in S1; and S5, a control signal is received, and the pressure in the working cavity of the hydraulic cylinder is adjusted. According to the method, the springback difference caused by temperature fluctuation is effectively restrained through online identification and dynamic adjustment of the pressure maintaining strategy, and the product bending consistency and precision are improved.

The invention provides a driving style online identification method and system based on an LSTM network, and the method comprises the steps: carrying out the online identification of the driving style of a driver based on a driving style online identification model which is trained, verified and tested in advance; the pre-training, verification and testing steps of the driving style on-line identification model are as follows: dividing a large amount of free driving data into a training set, a verification set and a testing set; training the LSTM network based on the training set; and based on the verification set and the test set, verifying and testing the trained LSTM network, and determining a driving style online identification model. According to the driving style online identification method and system based on the LSTM network, efficient and accurate online identification of the driving style of the driver is realized.

The invention discloses a latex skinning defect online detection method and system based on machine vision, and relates to the technical field of machine vision, and the method comprises the following steps: obtaining image acquisition parameters, and constructing an industrial camera imaging model based on a latex production environment; based on the industrial camera imaging model, collecting target area image data of the latex surface; sequentially executing edge enhancement processing and connected region extraction operation on the target region image data to obtain a candidate defect region set; sequentially extracting geometric morphological features from the candidate defect region set, and screening suspected defect regions through a hierarchical judgment rule; and identifying a target defect area according to the number and spatial distribution characteristics of the suspected defect areas. The technical problem that the skinning defect in the latex production process is difficult to identify online can be effectively solved, and the method has good application prospects and popularization value.

The invention provides a road traffic state online identification method based on time sequence clustering, and the method comprises the steps: obtaining historical traffic flow state parameter time sequence data collected by each traffic detection station installed in a target road corridor in a target time period; after the collected data is preprocessed, based on dynamic time warping and an FCM clustering algorithm, the similarity between global and local traffic state evolution characteristics implied in high-resolution traffic flow state parameter time sequence data and a traffic flow state parameter time sequence is mined, and a historical traffic state mode is extracted; on the basis, a traffic flow state parameter time sequence obtained in real time and an online traffic state smoothing algorithm based on dynamic planning are combined, and reliable online traffic state identification is achieved. According to the method, the stability of traffic state recognition is effectively improved, the manual participation degree and the labor cost are remarkably reduced, and a new technical support choice is provided for intelligent traffic decision making.