387 results about "Extended Kalman filter" patented technology

The invention relates to the technical field of nondestructive testing, and discloses an ultrasonic nondestructive testing method and system for a welding seam of a steel structure. The method comprises the following steps: adopting a self-adaptive coupling ultrasonic probe to carry out signal acquisition on the surface of a welding seam of a steel structure to obtain original ultrasonic signal data; performing multispectral adaptive wavelet transform processing on the original ultrasonic signal data to obtain an enhanced ultrasonic signal; performing blind source separation on the enhanced ultrasonic signal to obtain a separated weld defect signal; performing triangular constraint average interpolation and adaptive extended Kalman filtering fusion processing on the separated weld defect signals to obtain filtered weld defect feature data; and performing regularization Gaussian field weld contour reconstruction and defect positioning processing based on the filtered weld defect feature data to obtain a weld contour model and defect position data. According to the invention, the stability and reliability of weld defect detection are improved, and high-precision detection of weld internal defects is realized.

The invention discloses a dynamic control method of a lithium battery module intelligent equalization management system, and relates to the field of lithium battery module equalization, and the method comprises the steps: constructing a hierarchical data fusion platform, and generating a battery state data set containing SOC, SOH and an inconsistency evaluation value; a credibility reasoning algorithm is adopted to calculate a single body imbalance credibility factor value to divide an equalization stage, and equalization current is dynamically adjusted; a charge and discharge curve health factor is extracted through an LAB health factor algorithm, SOH is updated online in combination with extended Kalman filtering, and an SOH attenuation rate is fed back to the battery model; in-module equalization is carried out through a bidirectional energy transfer circuit, inter-module equalization is carried out through a ring bus topology, and layered topology linkage control is formed. The method has the advantages that the consistency and the heat management efficiency of the battery pack are improved through the hybrid equalization strategy and the layered topology linkage control, the service life of the battery pack is prolonged, and the overall performance and the safety of the electric vehicle are improved.

The invention relates to the field of low-altitude airspace monitoring, and relates to a low-altitude monitoring system based on multi-source heterogeneous perception fusion and rule engine driving, which comprises a perception access layer used for completing data standardization and protocol adaptation and realizing data stream release through message middleware; the processing calculation layer is used for generating a structured alarm event; the application support layer is used for three-dimensional visual interactive display of target situation data and alarm events, unified access of multiple types of sensors such as radar, photoelectricity, radio frequency, ADS-B and meteorological sensors is realized through a plug-in protocol adapter, and target state estimation and track smoothing are realized by combining space-time registration and extended Kalman filtering. The limitation that an existing system can only process a single data source or simple superposition is overcome, and the target recognition precision and the track continuity are remarkably improved.

The invention relates to the technical field of data processing, and discloses a VICTS antenna attitude estimation and correction method and system based on satellite pointing feedback, and the method comprises the steps: constructing a tight coupling depth fusion system with extended Kalman filtering as a frame, introducing satellite communication antenna pointing information as a new observation source on the basis of traditional IMU / GNSS fusion, and carrying out the satellite communication antenna attitude estimation and correction. By deducing the Jacobian matrix of the carrier attitude, effective fusion of the observed quantity is realized, so that the attitude estimation precision and robustness of the carrier are reversely corrected and remarkably improved, the three-axis attitude error is estimated and corrected more comprehensively and steadily, and the problem of lack of roll angle observation is fundamentally solved. Observation information of different dimensions is unified into the same state estimation framework through accurate mathematical modeling, and the precision, autonomy and robustness in a complex environment of the system are greatly improved.

The invention relates to the technical field of forklift driving stability control, in particular to a forklift driving stability control system based on ultrasonic and laser composite ranging, which comprises a composite ranging module, a multi-modal data fusion module, a stability prediction and control module, an execution mechanism control module and an online learning system. The composite distance measuring module collects surrounding environment information and corrects motion errors through a dynamic compensation algorithm; the multi-modal data fusion module dynamically adjusts the weight of the sensor based on an environmental interference factor, and realizes space-time calibration in combination with extended Kalman filtering; the stability prediction and control module is fused with the vehicle dynamic characteristics to predict the rollover risk and generate a hierarchical control instruction; the actuating mechanism control module drives actuating components such as an active suspension and an electronic differential mechanism through a layered framework. The method can adapt to complex working conditions, the prediction precision and fault-tolerant capability are improved, safety and efficiency are balanced, the maintenance cost is reduced, and the method is suitable for various forklifts.

The invention discloses a multi-parameter coupling intelligent regulation and control method and system in a vanadium ore smelting process, relates to the technical field of non-ferrous metal smelting automatic control, and solves the problems of feeding deviation and resource chain scission caused by slow response of an existing heat balance model to ore impurity fluctuation, online sensing lag and process island. The impurity concentration and the dielectric constant are obtained in real time through an LIBS / XRF combined probe and a microwave dielectric spectrum, rheological disturbance is dynamically corrected through extended Kalman filtering, a dynamic coupling response model is input, a thermodynamic interference factor is calculated, and a leaching and precipitation strategy is generated; performing mother liquor tail gas material flow collaborative optimization in parallel, constructing a multi-dimensional control vector U, performing iterative updating through near-end strategy optimization in a digital twin and issuing the multi-dimensional control vector U to an edge PLC (Programmable Logic Controller) in a distributed manner, and finally correcting a model weight through online XRD (X-Ray Diffraction) feedback; according to the method, the adaptive capacity to complex vanadium ore component disturbance, the response precision of online control and the inter-process resource circulation efficiency are remarkably improved.

The invention relates to the technical field of aerocar mode switching, and discloses a distributed driving aerocar self-adaptive fault-tolerant control method for actuator multimode failure, which comprises the following steps: constructing a unified six-degree-of-freedom dual-mode state space model; residual signals are generated based on extended Kalman filtering and a sliding-mode observer, and fault types and positions are positioned in real time through a lightweight classifier; the method comprises the following steps: extracting residual time-frequency features, identifying hard faults by using a lightweight convolutional neural network, quantifying soft fault degrees through an incremental support vector machine, fusing multi-source information based on a Bayesian network to output fault types, levels and confidence coefficients, and introducing an incremental learning mechanism to realize self-evolution of a diagnosis model; a virtual control instruction is generated by adopting hierarchical sliding mode control, thrust and torque distribution of remaining actuators is optimized based on a dynamic quadratic programming algorithm, control parameters are adjusted online in combination with a Lyapunov adaptive law, aerodynamic interference and model uncertainty are inhibited, attitude stability and trajectory tracking in air-ground mode switching are guaranteed, and the method has the advantages of being high in reliability and high in reliability. And the fault-tolerant performance and the operation safety of the hovercar in the air-ground mode switching process are obviously enhanced.

The embodiment of the invention discloses a multi-sensor fusion anti-degradation SLAM mapping method and system. The method can effectively solve the problem of pose drift of a robot in a mapping process in structure degradation environments such as an indoor long corridor, constructs a globally consistent three-dimensional point cloud map and a robot trajectory, and comprises the following steps: realizing depth coupling of an IMU and a wheel speedometer based on extended Kalman filtering, and generating high-frequency pose prediction; denoising, down-sampling and motion distortion correction are carried out on the 4D laser radar point cloud, and the normal vector and intensity characteristics of the point cloud are extracted; a normal vector and intensity feature enhanced scanning matching algorithm is adopted, and a target function is optimized through a multi-feature weight, so that the matching precision in a degradation scene is improved; loopback detection is realized through candidate key frame screening and geometric registration verification, and a closed-loop constraint is incorporated into a factor graph for global correction; and finally, incrementally updating the global point cloud map and carrying out consistency optimization, and outputting a robust three-dimensional point cloud map and a high-precision robot track.

The invention relates to the technical field of agricultural machinery control, in particular to a multi-variety vegetable harvester dynamic identification and feeding control system based on image processing, which comprises an image acquisition module, an environment sensing module, an image processing module, a data fusion module, a dynamic identification and tracking module and a feeding control module. Through binocular stereo vision and millimeter wave radar fusion, three-dimensional space positioning and dynamic tracking of the brassica oleracea corm are realized. Track prediction is carried out in combination with extended Kalman filtering and a long-short-term memory network, and the clamping path of the mechanical arm is optimized; the clamping force is adaptively adjusted through fuzzy PID control, and accurate feeding of the cabbages is achieved in combination with speed control of the conveying belt. The automatic cabbage harvesting machine improves the automation level of cabbage harvesting, effectively reduces the damage rate, improves the operation stability and harvesting efficiency, and is suitable for intelligent harvesting operation of a large-scale cabbage planting base.

The invention discloses a cavity environment real-time monitoring method based on multi-mode perception and a related device. The method comprises the following steps: synchronously acquiring cavity vision, contact force and electromagnetic positioning data; respectively refining the three types of data; extracting YOLO features from the refined visual data, outputting dynamic tactile features from the contact force data by using a mamba model, and refining the positioning data into positioning features through extended Kalman filtering; respectively generating classification results based on the three types of features, fusing through weighted voting, and mapping the visual and tactile features to a unified three-dimensional coordinate for association marking; and finally, generating an environment state decision according to judgment and association results. According to the invention, multi-mode real-time perception and risk early warning of the cavity environment are realized, and the operation safety and efficiency are improved.

The invention discloses a fusion filtering attitude estimation method based on EKF and Mahony, and the method comprises the steps: firstly obtaining the original data information of an IMU sensor at the current moment, carrying out the normalization of the attitude information of an accelerometer and a magnetometer, carrying out the error calculation, and compensating the error to the angular velocity of a gyroscope through a PI controller, the method comprises the following steps: performing Mahony filtering correction on an angular velocity and a corrected attitude quaternion to obtain an angular velocity subjected to Mahony filtering correction and a corrected attitude quaternion, constructing a state equation of an EKF by using the corrected attitude quaternion and a gyroscope zero offset value, and constructing an observation equation by using data of an accelerometer and a magnetometer so as to obtain an optimally estimated attitude quaternion and a gyroscope zero offset value; and finally, compensating the optimally estimated zero offset value of the gyroscope to the angular velocity correction process in the Mahony filtering process.

The invention discloses a real-time power generation load estimation method and system for photovoltaic power generation, relates to the technical field of photovoltaic power generation load estimation, and aims to solve the problems of state judgment delay and model mismatching and misestimation caused by short-time cloud shadow rapid movement. The method comprises the following steps: collecting power, current, voltage, irradiance and temperature signals through a multi-array sensor, separating a high-frequency characteristic signal from a low-frequency characteristic signal through high-pass / low-pass filtering, and generating a static baseline multi-dimensional characteristic vector; based on the power spectrum entropy, adaptively adjusting a time window and extracting dynamic characteristics; fusing the static and dynamic characteristics to generate discrimination input; performing state judgment by adopting a logistic regression model, and starting extended Kalman filtering online correction when an energy residual exceeds a threshold; and model drift is detected based on CUSUM and Shewhart algorithms, and an incremental training set is automatically constructed to update model parameters. And high-precision and low-delay real-time power generation load estimation and operation and maintenance support under a short-time cloud shadow condition are realized.

The invention provides a three-dimensional tracking method applied to a single-photon laser radar to detect a far-field dynamic unmanned aerial vehicle point target, and belongs to the technical field of laser radar detection and target tracking. The problems that in the prior art, under the long-distance condition, the target imaging size is smaller than one pixel, and a detection and tracking algorithm based on shape, texture or edge features fails are solved. The method comprises the following steps: constructing a six-dimensional state vector containing a three-dimensional position and a three-dimensional speed of a target; predicting a state vector according to a state transfer function containing air resistance and turning acceleration; and an extended Kalman filtering framework is utilized to update the predicted state and obtain state estimation of the target, and the extended Kalman filtering framework comprises a self-adaptive process noise adjustment mechanism based on innovation feedback and is used for adjusting a process noise covariance matrix on line. The method is mainly used in the field of detection and multi-dimensional tracking of airspace moving targets.

The invention discloses an underground combined positioning method based on UWB and IMU, and the method comprises the following steps: arranging a UWB base station and a motion capture system in a positioning area, obtaining the distance between the base station and a label through UWB distance measurement, collecting IMU speed data through the movement of a person, recording the position of the label on the person through the motion capture system, and constructing a data set; constructing a positioning model based on the data set and the bidirectional long-short-term memory neural network; positioning the personnel through the positioning model to obtain a predicted positioning result; and fusing the predicted positioning result and IMU data through extended Kalman filtering to obtain a final positioning result. According to the invention, the accurate position of the underground coal mine personnel can be accurately positioned.

The invention relates to an agricultural robot, and discloses an animal husbandry pushing robot autonomous navigation method based on a laser radar, which comprises a microsecond-level multi-sensor time synchronization architecture based on an FPGA (Field Programmable Gate Array); in a navigation layer, seamless switching between the GNSS and the laser SLAM is realized by constructing a virtual fusion observation value by utilizing extended Kalman filtering and a self-adaptive dynamic weight algorithm; in the sensing layer, an RGB-I multi-mode model fused with laser reflectivity and a biological micro-motion detection technology are adopted, and feed and biological obstacles are accurately distinguished; and in the execution layer, flexible self-adaptive material pushing is achieved in combination with material groove edge fitting and force-position hybrid control, and the navigation precision, the operation quality and the safety of the material pushing robot in the all-weather and unstructured pasture environment are effectively improved.

The invention relates to the technical field of remote monitoring, and discloses a high-precision holder control method and system. Comprising the steps of collecting a target scene image, performing denoising, distortion correction and resolution adjustment, obtaining a target pixel coordinate and confidence through a lightweight target detection model, and calling a corresponding image enhancement model to perform preprocessing when an abnormal environment is detected; transmitting a part of detection results as key frames to an edge end based on a frame extraction strategy, caching the rest of the detection results locally, and switching to a local tracking mode and reducing control frequency when network delay exceeds a threshold value; fusing visual detection data and inertial measurement data, and predicting a target position through extended Kalman filtering; carrying out sub-pixel-level refinement on the target position, and calculating a three-dimensional orientation in combination with a participated holder attitude in a camera; and adjusting the attitude of the holder in real time based on a prediction result and an anti-saturation integral separation PID controller.

The invention relates to the technical field of positioning navigation, in particular to a vehicle-mounted IMU (inertial measurement unit) data compensation method for intelligent driving inertial navigation, which comprises the following steps: acquiring various attitude data of a vehicle-mounted IMU through a rotating platform, acquiring a reference quantity, establishing a target function, and acquiring an offline calibrated rotating error matrix and a scale factor matrix by adopting an optimization algorithm; the method comprises the following steps: acquiring operation original measurement in a vehicle operation process, defining a system state vector, analyzing a Taylor expansion approximation relation between a cross multiplication matrix corresponding to an error small angle and a rotation error matrix, and constructing a discrete time state equation and an observation equation based on an IMU kinematics model; acquiring an error small angle after each update by adopting extended Kalman filtering; and carrying out real-time compensation on the vehicle-mounted IMU data in combination with the operation original measurement at each moment. The invention aims to improve the precision and robustness of inertial navigation and meet the requirement of intelligent driving for continuous and stable positioning.

The invention discloses an underway real-time GNSS acoustic underwater positioning method, which belongs to the technical field of underwater positioning, is used for acoustic underwater positioning, and comprises the following steps: constructing an extended Kalman filtering solution model, constructing a constraint equation by taking coordinate prior information of a seabed transponder as a constraint, and solving an initial state parameter and an initial covariance matrix; and processing ill-conditioned problems in the initial state parameter and initial covariance matrix solving process by adopting a Tikhonov regularization method, substituting the initial state parameter and an initial covariance matrix value into an extended Kalman filtering solution model, and carrying out real-time GNSS acoustic underwater positioning on the submarine transponder. The real-time positioning method for the GNSS-acoustic seabed transponder solves the possible ill-conditioned problem in the solving process, has good performance of short convergence time and high precision, can improve the real-time positioning efficiency of the GNSS-acoustic seabed transponder, and is expected to provide powerful technical support for seabed plate movement, geological disasters, disaster early warning and seabed geodetic datum updating.

The invention discloses an online self-calibration filtering method for inertial navigation, and relates to the technical field of inertial navigation, and the method comprises the following steps: obtaining multi-modal data, and establishing an inertial device error mathematical model and an inertial navigation system error mathematical model; carrying out recursive estimation on the state variables by the extended Kalman filtering model to obtain error parameters of the inertial device and the navigation system; constructing a fuzzy adaptive model based on the error parameters, generating an observation noise correction factor through a fuzzy logic rule, and correcting an observation noise covariance matrix in the extended Kalman filtering model in real time; carrying out online estimation and compensation on error parameters of the inertial navigation system by utilizing an extended Kalman filtering model, and outputting compensation parameters containing zero offset and scale factors in real time; correcting the original data based on the compensation parameters, injecting the original data into the navigation solution, and updating the fuzzy adaptive model according to the compensated error data.

The invention discloses a micro-action three-dimensional sensing method and system based on WiFi channel state information, and relates to the technical field of wireless signal sensing. The method comprises the following steps: collecting CSI data, preprocessing, and outputting a CSI amplitude matrix and a phase matrix; the phase difference of adjacent antenna pairs is used for counteracting common noise, a linear mapping model of displacement increment and phase change is constructed, and displacement calculation is achieved; designing a space-time TransFormer model, fusing CSI amplitude, phase difference, displacement increment and Doppler frequency shift characteristics through a time sequence and space joint attention mechanism, and outputting an initial three-dimensional trajectory; optimizing the initial three-dimensional trajectory by using a self-supervised learning mechanism based on physical consistency constraint; and finally, constructing a dynamic path filter, carrying out real-time filtering correction on the optimized trajectory in combination with extended Kalman filtering and Bayesian multipath fusion, and outputting a final three-dimensional trajectory, thereby realizing high-precision and low-delay trajectory reconstruction without labeling in a complex scene.

The invention discloses an AGV-six-degree-of-freedom mechanical arm operating system and method based on double-vision fusion, the system comprises a control system and a six-degree-of-freedom mechanical arm, and the control system comprises a master control unit, a slave control unit and a dynamic error compensation module; the method comprises the steps of AGV autonomous navigation and material transfer, cross-station accurate docking and dynamic error compensation, high-precision biological medicine operation under double-vision fusion guidance, and material recovery and circulation. According to the invention, through dual-vision dynamic fusion of eyes outside the hand and eyes on the hand, adaptive switching from global coarse positioning to local fine positioning is realized; due to the integrated design of the AGV and the six-degree-of-freedom mechanical arm, the operation flexibility is greatly improved; an extended Kalman filtering dynamic error compensation algorithm is innovatively adopted, so that the comprehensive error of the AGV, the mechanical arm and the visual system can be corrected in real time; and by combining with a double-layer distributed control architecture, the system realizes multi-task intelligent scheduling and modular collaboration.

The invention provides a Q / R dual-adaptive hybrid quantum filtering method for an OTFS (Over the The Over the File System) communication and inductance integrated system, and the method comprises the following steps: constructing an OTFS communication and inductance integrated signal model, and obtaining a target measurement vector; establishing a state vector for describing a target motion state, and constructing a state transition equation and a measurement equation; the method comprises the following steps: constructing a Q / R mixed quantum adaptive adjustment module based on dual-channel features to construct a dual-channel feature input vector, generating a dual-path adjustment factor through a mixed quantum neural network, and constructing an adaptive Q matrix and an adaptive R matrix; and performing adaptive state prediction and updating at each time step by using the adaptive Q and R matrixes constructed in the step 3 to realize tracking of the motion state of the target. According to the method, the hybrid quantum neural network is introduced for driving, collaborative optimization of filter parameters is achieved, and therefore the precision, robustness and response speed of target tracking are remarkably improved in a complex dynamic noise environment.

The invention discloses a transmission conductor three-dimensional attitude estimation method, system and equipment based on RTK and inertial measurement coupling and a medium, and belongs to the technical field of three-dimensional state monitoring, and the method comprises the following steps: fusing RTK absolute positioning and IMU inertial measurement data, and realizing accurate estimation of the transmission conductor three-dimensional attitude by using extended Kalman filtering; and abnormal identification and risk early warning are carried out based on an attitude calculation result, and finally real-time monitoring and intelligent warning of the state of the power transmission line are realized through a three-dimensional visual platform. According to the method, RTK and IMU data are fused, accurate sensing and continuous tracking of the three-dimensional attitude of the power transmission line are achieved through extended Kalman filtering, the defect that a single technology is insufficient in reliability in a complex environment is overcome, and finally a complete monitoring closed loop of the safety state of the power transmission line is achieved through intelligent early warning and three-dimensional visualization.

The invention provides an automatic tracking laser bird repelling barrier lamp which comprises a lamp body and a detecting, tracking and repelling module, and a barrier warning module is arranged in the lamp body and can emit high-brightness flashing signals meeting safety standards. The detection tracking driving module is integrated with a millimeter-wave radar and a visible light camera, the millimeter-wave radar collects target distance, speed and azimuth information in real time, and the visible light camera carries an AI bird recognition algorithm to recognize and position birds; the intelligent control unit fuses multi-source data through extended Kalman filtering, predicts the future motion trail of a target based on a Kalman filtering model, drives the servo holder to adjust the laser emission direction, and meanwhile, automatically adjusts the diameter of a laser beam spot through the focusing lens group according to the target distance to ensure that the energy density at different distances maintains an effective driving threshold value. The device adopts a multi-modal fusion detection technology to cooperate with a dynamic tracking algorithm, has the functions of obstacle warning and intelligent bird repelling, is suitable for scenes such as towers, building tops, airports and the like, and realizes efficient safety protection in a complex environment.

The invention discloses an environment monitoring method based on a low-altitude aircraft, belongs to the technical field of low-altitude aircrafts, and realizes high-precision monitoring through cooperation of anti-interference hardware, edge calculation, navigation communication and cloud modeling. According to the system, a hexagonal prism cabin body is integrated with multiple types of sensors, and data are dynamically calibrated through a BP neural network. And aligning the multi-source spatio-temporal data by using extended Kalman filtering, and generating a three-dimensional grid model containing obstacles and a flow field. Navigation adopts Beidou / IMU combination and visual SLAM redundancy switching, and communication is guaranteed through a 5G private network and UWB double links. And the cloud constructs a three-dimensional AQI thermodynamic diagram based on inverse distance weighting and a Kriging algorithm, predicts pollutant diffusion in combination with a random forest, and drives the aircraft to execute a spiral / grid composite route. The problems that traditional monitoring is low in density, complex in model and insufficient in precision are solved, and a full-link anti-interference perception-decision closed loop is achieved.

The invention provides an integrated navigation method combining starlight refraction positioning with inertial navigation in a near space, and belongs to the technical field of starlight refraction navigation positioning. The method comprises the following steps: firstly, determining a state model of starlight refraction positioning in a near space by taking an error equation of an inertial navigation system of a near space aircraft as a state equation; designing a near space starlight refraction positioning algorithm, identifying a refraction star by using a star sensor on the aircraft, acquiring a refraction angle as a measurement, and establishing a measurement model of starlight refraction positioning in a near space; and obtaining the optimal estimation value of the system state variable by using an extended Kalman filter (EKF) method. The method has the advantages of being suitable for near space, high in reliability, high in autonomy and the like, and can be finally used for pure astronomical positioning and astronomical / inertial integrated navigation.

The invention relates to a lunar satellite formation collaborative navigation method, which comprises the following steps of: inputting an orbit state and an observation residual error of a lunar satellite formation at a previous moment into an LSTM (Long Short Term Memory) prediction network of a navigation algorithm to predict a process noise covariance matrix and an observation noise covariance matrix, and multiplying the process noise covariance matrix and the observation noise covariance matrix by a scaling factor for scaling, obtaining a scaled prediction process noise covariance matrix and a scaled observation noise covariance matrix; and inputting the orbit state of the lunar satellite formation at the previous moment, the multi-source observation data at the current moment and the scaled process noise covariance matrix and observation noise covariance matrix into a self-adaptive extended Kalman filtering module in a navigation algorithm to predict the orbit state of the lunar satellite formation at the current moment.

The invention discloses a micro-grid safety monitoring method and system based on attack detection data, and relates to the field of power grid safety monitoring, and the method comprises the steps: outputting a state vector of each node; obtaining a predicted attacked result of each node; outputting an estimated value set and a measured value set of the operation data of each prediction node; and obtaining an attack detection result of each prediction node and executing corresponding operation. According to the method, the data channel safety degree and the multi-dimensional evaluation parameters are fused, a state estimation and attack probability recognition mechanism combining extended Kalman filtering and a time sequence model is introduced, a hierarchical response mechanism is set according to attack levels, the elastic defense and active recovery capability of the system under different attack intensities is improved, and stable operation of the micro-grid is guaranteed.

The invention provides three-dimensional vehicle-mounted navigation equipment and a navigation method based on the equipment. The method comprises the following steps: carrying out equipment initialization and reference calibration, arranging communication equipment on a vehicle roof, arranging acquisition equipment on a vehicle head grid, and arranging a calibration and calculation module in a vehicle cabin; synchronously acquiring multi-source scene and body state data by using hardware; identifying an operation scene through a CNN-LSTM hybrid model, and dynamically allocating a sensor fusion weight based on a reinforcement learning algorithm; a BP neural network is utilized to construct nonlinear mapping of the vibration frequency and the positioning error, and a dynamic error value is calculated; the dynamic error is substituted into extended Kalman filtering for real-time correction, and vibration influence is eliminated; the method comprises the following steps: pre-judging a GNSS signal trend by using an LSTM model, and initializing a backup module in advance to realize multi-mode seamless switching; dynamically adjusting map updating frequency according to environment change and compressing data by using an octree; and outputting a three-dimensional navigation instruction on the display screen. The problems of high-precision positioning and smooth switching of the engineering vehicle under strong vibration and complex scenes are effectively solved.

The invention provides an urban traffic dynamic deduction and real-time planning method and system based on multi-source position data fusion. Time-space references of signaling, MDT and MR data are unified through a dynamic rasterization mechanism, and the problem of data isomerism is solved by adopting anchor point selection of signal weighting and a triple criterion time expansion algorithm; generating a minute-level updated dynamic OD matrix based on the hierarchical road network constraint model; a METANET macroscopic traffic flow model and EKF extended Kalman filtering are fused to realize traffic state deduction, and parameter online calibration is supported; signal timing optimization, path induction and emergency control schemes are generated in combination with real-time deduction results, and a sensing-deduction-decision-evaluation closed loop is formed. The method can improve road network traffic efficiency, and is especially suitable for response of sudden traffic events.