1037 results about "Filter algorithm" patented technology

The invention discloses a vehicle-mounted Beidou positioning deviation self-calibration system fused with inertial navigation information, and relates to the technical field of navigation and positioning. The method is used for solving the problems of positioning deviation accumulation and signal failure in a complex environment. The system realizes space-time alignment of inertial navigation and Beidou observation data through timestamp interpolation and coordinate system conversion, and constructs a multi-source synchronous data stream. A difference value between an inertial navigation calculation speed and a Beidou Doppler speed is analyzed based on a vehicle incomplete constraint model, and a weight factor is generated in combination with a confidence threshold to suppress abnormal observation. A geometric matching error is calculated through a high-precision lane map curvature and a vehicle steering angle, and candidate tracks are screened to generate transverse calibration parameters. The noise covariance is dynamically adjusted through a tight coupling filtering algorithm, the mode is switched to a lane constraint calibration mode when a Beidou signal is interrupted, inertial navigation errors are compensated through error boundary constraint, and stable output is achieved. Multi-source data deep fusion and adaptive robust calibration are realized, and system robustness and positioning continuity are improved.

The invention discloses a multi-dimensional information integrated transformer health state monitoring method, which relates to the technical field of power systems, and comprises the following steps: deploying a multi-source sensor, collecting various signals of a transformer, and carrying out signal conditioning and analog-to-digital conversion on analog signals collected by the sensor; performing timestamp alignment on the received multi-source heterogeneous data, and eliminating data noise by adopting a filtering algorithm; key features which are sensitive to the health state of the transformer and complement each other are selected from the extracted feature values to form a health state evaluation index set; setting a sliding time window, obtaining historical data in the window, performing normalization processing on the data, calculating the information entropy and the variation coefficient of each index, and fusing the information entropy and the variation coefficient to obtain the dynamic weight of each index; obtaining a health state index of the transformer according to the normalized value and the dynamic weight; and displaying the health state index and the change trend thereof in real time, and setting an early warning threshold value and an alarm threshold value of the health state index.

The invention relates to the technical field of three-dimensional imaging, and discloses a high-precision underwater structured light three-dimensional point cloud imaging method and system, and the method comprises the following steps: deploying a plurality of sensor nodes, monitoring the temperature, salinity and turbidity key parameters of a water body in real time, selecting proper light source wavelength and power according to the collected data, and carrying out the three-dimensional point cloud imaging of the underwater structured light. It is ensured that the optimal signal-to-noise ratio is obtained under the current condition, and meanwhile the exposure time and gain setting of the camera are initialized. According to the method, the optimal signal-to-noise ratio is obtained by monitoring water body conditions in real time and dynamically adjusting light source characteristics, the optimal structured light pattern is predicted by using a machine learning model, scattering noise is removed, advanced registration and fusion algorithms and an accurate phase unwrapping method are applied, and a high-quality comprehensive structured light pattern is generated. Point cloud gaps are filled through a geometric correction algorithm and an interpolation / filtering algorithm, so that a reconstruction model is closer to the surface of a real object.

The invention relates to the technical field of inertial assistance, in particular to a Beidou high-precision space-time reference dynamic compensation system based on inertial assistance, which is characterized in that an inertial measurement unit module is used for measuring original measurement data, and the original measurement data comprises a carrier phase and a Doppler observation value; the processing unit module is used for fusing the inertial measurement unit, executing real-time environment perception and dynamic modeling, classifying an operation environment and predicting error characteristics; performing an adaptive robust fusion filtering algorithm that dynamically adjusts filter parameters and switches a filter model to estimate the position and attitude of the carrier; and in combination with radio wave precision single-point positioning enhancement service, super-tight combination signal tracking and integrity monitoring are realized, an estimated inertial assistance system state is fed back to a tracking loop of the multi-frequency Beidou receiving module, and real-time fault detection is performed on original measurement data.

The invention discloses a BIM model automatic generation method and system based on point cloud data, and belongs to the field of building information modelling, and the transmission method comprises the steps: obtaining original point cloud data, and employing a filtering algorithm based on point cloud density adaptive adjustment to carry out the preprocessing of the point cloud data; constructing a voxel octree structure for the preprocessed point cloud data, and performing semantic classification on the point cloud; geometric modeling is carried out based on the segmented point cloud subsets, and a fitting algorithm is adopted to carry out shape completion on a point cloud area; semantic annotation is carried out on the components subjected to geometric reconstruction, a corresponding relation between component types and spatial attributes is constructed, fusion features based on a point feature histogram and a local curvature are adopted, and classification is carried out; a standard BIM component family is converted, and a three-dimensional BIM model is constructed through the mapping relation. According to the method, the voxel octree data structure and the deep semantic segmentation neural network model are combined, division and semantic recognition are performed on the point cloud data, the intelligent degree of the model is improved, and manual intervention is reduced.

The invention discloses a ship inclination measurement method and system based on Kalman filtering fusion, and relates to the technical field of ship stability measurement, and the method comprises the steps: multi-sensor synchronous data collection, state space modeling and Kalman filtering fusion, adaptive dynamic adjustment of process noise, drift compensation, and fault tolerance and output. When the method and the system provided by the invention are used for measuring inclination, inclination angle error accumulation caused by gyroscope zero offset integral error, accelerometer dynamic interference, sensor noise and temperature drift, GNSS dependency and the like can be effectively avoided, available precision can still be maintained through multi-sensor depth coupling and an intelligent filtering algorithm, and the accuracy of inclination measurement is improved. The method is simple in operation, independent of satellite data, higher in stability, capable of greatly reducing hardware cost compared with GNSS and IMU schemes, and especially has the advantages of being simple in cost and installation configuration in ship refitting; and the method is also suitable for GNSS rejection or cost sensitive scenes such as inland ships, unmanned ships, offshore buoys and the like.

The invention relates to the technical field of geological monitoring, and discloses a side slope deformation monitoring method based on spaceborne synthetic aperture radar, which comprises the following steps: S1, data acquisition: acquiring multi-source spaceborne synthetic aperture radar (SAR) data of a side slope area, the multi-source spaceborne SAR data comprising C-band SAR data and L-band SAR data; s2, self-adaptive preprocessing: self-adaptive preprocessing is performed on the multi-source spaceborne SAR data according to a ground surface coverage scene of a slope area, and the ground surface coverage scene comprises a low vegetation area, a medium-high vegetation area, a flying dust interference area and a high and steep terrain area. According to the slope deformation monitoring method based on the spaceborne synthetic aperture radar, a self-adaptive wave band fusion strategy is adopted for different vegetation coverage scenes, a time sequence coherence enhancement algorithm is combined, the vegetation area monitoring precision is remarkably improved, and noise signals are effectively eliminated and interference errors are reduced through flying dust and construction interference dynamic identification and a multi-dimensional filtering algorithm.

The invention provides an industrial robot welding track real-time optimization method and system based on deep learning, and relates to the technical field of industrial robots, and the method comprises the steps: collecting three-dimensional point cloud data, carrying out the multi-scale processing to generate a high-precision curved surface, constructing a welding seam feature descriptor, and carrying out the real-time optimization of the welding track of an industrial robot; and the mapping relation between the technological parameters and the welding posture is optimized on the basis of a multi-target reward function, the mapping relation is converted into an initial movement track, the track is dynamically optimized through a segmented self-adaptive optimization model and a variable structure filtering algorithm, and self-adaptive adjustment of the welding process is achieved. The welding precision and quality of the complex curved surface are improved, and the adaptability and stability of robot welding are enhanced.

The invention provides a high-precision intelligent welding system adapting to complex working conditions, and relates to the technical field of laser welding. The high-precision intelligent welding system adapting to the complex working conditions comprises the steps of S1, based on a high-resolution visual sensor and a laser scanner, S2, based on preprocessed point cloud data, S3, based on a workpiece three-dimensional model, S4, based on an optimized welding path and a material attribute database, S5, based on a dynamic welding parameter set, S6, based on a real-time welding track, and S7, based on multi-sensor monitoring data. S8, based on a defect detection report; and S9, based on a repaired welding result and historical welding data. According to the invention, the geometric data of the workpiece are collected through the high-resolution visual sensor and the laser scanner, the filtering algorithm is applied to remove noise to generate high-quality point cloud data, and a subsequent processing foundation is laid; workpiece features are identified by using a point cloud processing algorithm and a machine learning method based on the point cloud data, and an accurate digital model is generated through a three-dimensional reconstruction algorithm.

The invention provides a vehicle cluster tunnel access positioning method based on intelligent cooperation and a vehicle-mounted terminal, relates to the technical field of intelligent driving control, and solves the problems of positioning interruption, inertial navigation error accumulation and insufficient cooperative positioning reliability caused by satellite signal failure of a vehicle cluster in a tunnel scene. The method comprises the following steps: judging whether each vehicle enters or exits from a tunnel area, when part of vehicles enter a tunnel, dynamically selecting a reference node from vehicles with effective satellite signals outside the tunnel, and broadcasting original observation values of the satellite signals; establishing a relative position constraint model of a vehicle cluster, acquiring relative position data of adjacent vehicles in real time among the vehicles, and performing dynamic weighted fusion on observation values, IMU data and relative position data of the vehicles with effective satellite signals by adopting a combined filtering algorithm; the satellite signal failure vehicle performs positioning calculation by combining the broadcast observation value with the IMU data and the relative position constraint model; and pre-capturing satellite signal loop parameters when driving out of the tunnel.

The invention relates to the technical field of education management, in particular to a practical training teaching and evaluation management system, and aims to ensure that each task strictly corresponds to a teaching target by acquiring practical training task parameters and establishing a mapping relationship between practical training tasks and a teaching outline through a knowledge graph, and to realize practical training teaching and evaluation management by utilizing integration and mapping of multi-source data. A data basis is provided for subsequent analysis; a collaborative filtering algorithm is utilized to match student ability and training task requirements, and a matching degree score is output, so that the rationality of training task allocation is improved; the training process of the students is monitored and evaluated in real time through a multi-modal data intelligent analysis technology, and timely feedback is provided for teachers and the students; the skill tree is dynamically constructed and updated through a multi-modal evaluation result, a personalized practical training task recommendation sequence is generated, and the structure and node attributes of the skill tree are adjusted in time according to the actual learning condition and ability development of students, so that the skill tree has better pertinence and adaptability, and the learning efficiency is improved.

The invention discloses a method, a device and equipment for generating a stability control strategy of a wind power grid-connected system based on digital twinning and a medium. The method comprises the following steps: acquiring historical operation data and wind power operation data of the wind power grid-connected system; constructing a digital twinborn model and acquiring real-time measurement data according to the historical operation data in combination with a topological structure of the wind power grid-connected system; calling a preset filtering algorithm to perform state estimation optimization according to the real-time measurement data, and determining an optimal system state; extracting feature state vectors from the optimal system state and the wind power operation data; and predicting the feature state vector according to a built-in strategy through a preset target reinforcement learning model, generating a stability control strategy instruction, and issuing the stability control strategy instruction to a corresponding module in the wind power grid-connected system, thereby effectively improving the adaptability of wind power grid-connected control.

The invention relates to the field of signal processing, and discloses an FPGA-based dynamic multi-modal signal fusion algorithm and an adaptive noise suppression system, and the algorithm comprises a signal collection module which is used for collecting signals of different modals through a multi-modal sensor, adding a timestamp and a sensor identifier to each modal signal, and obtaining an original signal; the space-time calibration module is used for performing time synchronization and space external parameter calibration on the original signal and outputting a space-time aligned single-mode signal; and the noise suppression module is used for mapping high-dimensional signals to low-dimensional Riemannian manifolds through a manifold embedding algorithm for each single-mode signal aligned in time and space, and suppressing non-Gaussian noise by using a manifold thermonuclear filtering algorithm. Space-time labels are given to multi-mode signals by means of a signal acquisition module, time deviation and space position difference between sensors are eliminated through a space-time calibration module, and non-Gaussian noise is filtered out through a noise suppression module based on manifold learning and intrinsic characteristics of the signals are reserved.

The invention discloses an intelligent hardware equipment monitoring method based on the Internet of Things, which comprises the following steps of: acquiring data such as sensor signals, temperature power consumption of a processor, voltage and current of a power supply module and the like in real time, processing the data by applying technologies such as a filtering algorithm, a causal inference model and time sequence analysis and judging the performance change of a component; and inputting the processed data into a pre-training regression model, predicting the residual life of each component and generating a health score, integrating the health scores of each component, introducing a dynamic correlation analysis and adaptive weight adjustment mechanism, constructing a system-level health state report, and generating a priority maintenance plan for high-risk components based on the report. Intelligent evaluation and prediction of the health state of the hardware component are realized, interruption of operation of the monitoring system due to hardware faults is effectively avoided, and the reliability and maintenance efficiency of the system are improved.

The invention discloses a battery capacity performance testing method and device and a storage medium, and relates to the technical field of battery capacity performance testing. By constructing a dynamic load model and a multi-load test protocol and combining a high-precision data acquisition and filtering algorithm, adaptive data analysis and a machine learning regression model, the problems of difficulty in balancing precision and efficiency, large data acquisition error, fixed test protocol and the like in the traditional battery test are solved; by dynamically adjusting test parameters and environmental conditions, complex working conditions of the battery in actual use are accurately simulated, meanwhile, dynamic updating and optimization of a test protocol are achieved, the test precision, the data reliability and the test efficiency are remarkably improved, and a comprehensive, efficient and high-adaptability solution is provided for battery performance evaluation and health management.

The invention discloses a wavefront reconstruction method for improving the dynamic range of a Shack-Hartmann wavefront sensor. Belongs to the technical field of wavefront detection. According to the method, a new Shack-Hartmann wavefront detection method is needed, the effective dynamic range of a Shack-Hartmann sensor can be expanded on the premise that the hardware complexity is not remarkably increased, local mismatching accumulation can be avoided through a reasonable matching strategy, and the detection accuracy is improved. Therefore, high-precision and high-reliability wavefront reconstruction can be realized in a large-amplitude wavefront distortion scene. Comprising the following steps: S1, detecting by a Shack-Hartmann wavefront sensor to obtain a light spot distribution diagram H (x, y); s2, extracting a light spot centroid pixel-level position through a matched filtering algorithm; s3, calculating the sub-pixel-level position of the mass center of the light spot through Gaussian interpolation; s4, a mass center-subaperture matching relation is established through a Kuhn-Munkres algorithm; and S5, calculating the wavefront slope according to the corresponding relationship between the centroid and the sub-aperture, and then recovering the wavefront according to the wavefront slope.

The invention belongs to the technical field of safety monitoring, and discloses a gas pipe network construction site safety monitoring method and related equipment, and the method comprises the steps: collecting the original inclination angle data of different positions of a construction region in real time through distributed monitoring nodes, and carrying out the filtering processing of the original inclination angle data through a moving average filtering algorithm, the differential settlement value is calculated according to the filtered inclination angle data of the adjacent monitoring nodes, the differential settlement value and the dynamic settlement difference threshold value are compared to determine the settlement risk and give an early warning, real-time monitoring of gas pipe network construction site ground settlement is achieved, and the settlement monitoring accuracy is improved.

The invention discloses a post-stack seismic data denoising method based on multi-dip-angle structure guided filtering, and belongs to the field of seismic data denoising, and the method comprises the following steps: carrying out the processing of input seismic data through guided filtering, and obtaining a fault likelihood attribute; dividing the seismic data into a fault region and a continuous stratum region based on fault likelihood attributes; in the fault region, performing filtering processing on the seismic data by using an edge-preserving smoothing algorithm to obtain a filtering result of the fault region; in the continuous stratum area, calculating a local inclination angle of the seismic data, and performing filtering processing on the seismic data by adopting a structure guiding filtering algorithm which performs smoothing along the direction of the local inclination angle to obtain a filtering result of the continuous stratum area; and fusing the filtering result of the fault region and the filtering result of the continuous stratum region to obtain final de-noised seismic data. According to the method, efficient noise suppression can be performed on post-stack seismic data, and meanwhile, the protection capability on a complex geological structure is remarkably improved.

The invention discloses a multi-source heterogeneous learning path planning method based on personalized constraints, which solves the problems of single path, data staticization and the like in the prior art, and comprises the following steps: acquiring multi-source behavior data of a learner, carrying out feature modeling, and constructing a high-dimensional behavior portrait; according to the high-dimensional behavior portrait, utilizing a neural collaborative filtering algorithm to predict the interest and mastering probability of a learner to any knowledge point, and generating a preliminary learning path; carrying out dominant and implicit evaluation on the knowledge mastering state of the learner by adopting a cognitive diagnosis model, and carrying out dynamic correction on the preliminary learning path to obtain a corrected learning path; performing dimension reduction on the multi-source behavior data by adopting a principal component analysis algorithm to extract a core factor, and forming an evaluation basis for path optimization; and constructing a multi-objective path function model, and obtaining an optimal dynamic learning path by adopting a multi-objective optimization path algorithm in combination with the corrected learning path and the core factor. The method has high practical value and popularization value in the technical field of learning path planning.

The invention relates to the technical field of water meter flow, in particular to an ultrasonic water meter flow curve fitting system, which is characterized in that an edge computing device is used for primarily processing original data at a data acquisition end, a mean filtering algorithm is used for removing noise data, and the data after acquisition is subjected to filtering, denoising, abnormal value elimination and normalization processing; a dynamic evaluation model is constructed by adopting a time sequence prediction algorithm, the model calculates and predicts various data of future ultrasonic water meter flow by continuously inputting real-time data, and the model is optimized according to a prediction result, so that errors of the model are continuously reduced in the training process, and the prediction accuracy of the model is improved; modeling and fitting are conducted on the flow curves, calculated by the neural network model, of the ultrasonic water meter in different working states through the fitting model, the fitted curves can serve as a basic data source of a user water consumption behavior and system load characteristic advanced analysis model, and energy-saving analysis and fault positioning are facilitated.

The invention relates to the technical field of image processing, in particular to an intelligent recognition and positioning system for battery support welding defects. Obtaining defect degrees and edge pixel points of the pixel points according to gradient features and neighborhood gray features of the pixel points in the welding image; obtaining change complexity according to the change characteristics of the local edges of the edge pixel points; edge sharpness is obtained according to the local gradient features and the gray level change features of the edge pixel points; obtaining local uniformity according to the local gray level distribution characteristics of the edge pixel points; obtaining a confidence coefficient according to the change complexity, the edge sharpness and the local uniformity; and obtaining an adjustment factor of the edge pixel point according to the confidence coefficient and the defect degree. The method comprises the following steps: adjusting a weighting coefficient in a high-lift filtering algorithm according to an adjustment factor to obtain a self-adaptive weighting coefficient; performing image enhancement and defect identification according to the adaptive weighting coefficient; and the accuracy of image enhancement and defect identification is improved.

The invention provides a train operation state estimation method and system based on a high-order extended Kalman filter considering noise correlation, and belongs to the technical field of rail transit train operation management. According to the method, correlation analysis between high-order terms is introduced, the filtering precision is remarkably improved, and particularly, excellent performance is shown under a strong nonlinear system; by establishing a multi-mode train state model, the dynamic characteristics of a train in operation modes such as traction, cruising, sliding and braking are comprehensively covered, and a more accurate input basis is provided for a filtering algorithm; by introducing a sliding window error criterion, the filter can quickly switch operation modes according to real-time errors, so that the filter is ensured to keep high adaptability in a complex and changeable operation environment; the comprehensive performance evaluation framework based on the error covariance matrix not only evaluates the mean square error and the mean absolute error, but also quantifies the contribution of the high-order correlation to the precision of the filter, and provides a more scientific and comprehensive evaluation system.

The invention discloses a communication data transmission method and system for an intelligent charging pile, and relates to the technical field of intelligent charging pile communication, and the method comprises the steps: constructing a reverse power line transmission channel model based on an equivalent channel response matrix, extracting channel frequency response, generating a channel equalization configuration parameter set through a joint optimization equalization algorithm, and transmitting the channel equalization configuration parameter set to a communication module. Subcarrier modulation orders are dynamically allocated according to the signal-to-noise ratio parameter, and a pre-balanced OFDM time domain signal is generated; on the basis of the channel equalization configuration parameter set and the pre-equalization OFDM time domain signal, dynamically monitoring power frequency harmonic noise of the cable, and inhibiting interference through a filtering algorithm to generate a clean power line carrier signal; and generating a verified V2G communication control instruction through a cyclic redundancy check method based on the clean power line carrier signal in combination with the main polarization direction feature vector. According to the method, an accurate channel model is constructed by combining an equivalent channel response matrix and cable characteristics, and dynamic subcarrier distribution is realized based on signal-to-noise ratio parameters.

The invention relates to the technical field of image processing, in particular to an engineering building component model construction method and system based on BIM, and the method comprises the steps: collecting initial point cloud data of a target engineering building component; performing feature analysis processing on the initial point cloud data to correct a noisy value of each point cloud when an existing filtering algorithm is used for calculation; and according to the corrected noisy value, judging a noise point cloud and removing the noise point cloud to obtain effective point cloud data which is used for constructing a BIM engineering building component model. According to the method and the device, the denoising result of the radius filtering algorithm using the fixed radius is corrected by analyzing the point cloud features, so that a more reliable denoising result is obtained.

The invention relates to the technical field of software-defined wide area network data transmission, and discloses a software-defined wide area network data transmission method and system based on edge computing. According to the method, network state data are collected through distributed probes deployed at edge nodes, and a dynamic transmission path between the nodes is calculated after data cleaning and feature extraction. A collaborative filtering algorithm is utilized to analyze historical and real-time data to generate a transmission optimization strategy, and a fuzzy logic decision maker is combined to generate a dynamic routing strategy. And predicting a future congestion state based on network characteristics and a routing strategy, and adjusting flow scheduling parameters accordingly. And finally realizing visual display and time sequence storage of the network state data, the routing strategy and the scheduling parameters. The method can effectively reduce transmission delay and improve the utilization rate of network resources.

The invention relates to the technical field of monitoring, and discloses a multi-modal video public opinion intelligent monitoring method and system, and the system comprises a multi-modal data collection module, a data processing module, a coefficient generation module, an intelligent analysis module, a dynamic threshold module, and a public opinion visual platform. According to the method, a visual, voice and character cross-modal cooperative computing framework is established, and the one-sidedness problem of traditional single-modal analysis is solved; a recognition-tendency-propagation three-dimensional index system is constructed, and accurate quantitative evaluation of public opinion evolution is achieved; designing a static comprehensive coefficient threshold value and a dynamic growth threshold value of the dual-threshold grading system, and cooperating with an imaginary main body filtering algorithm and KOL polarization value calculation, so that the false alarm rate is remarkably reduced, and meanwhile, rapid early warning response is realized; a three-dimensional propagation map is generated through propagation path concentration analysis, content feature evolution tracking and influence radiation, and a complete public opinion evolution chain and hotspot traceability are provided.

The invention belongs to the technical field of artificial intelligence personalized services, and particularly relates to a reinforcement learning-fused user portrait dynamic construction and personalized recommendation implementation method, which comprises the following steps of: acquiring multi-dimensional user data; using the multi-dimensional user data to construct a multi-dimensional dynamic user portrait through a preset algorithm; based on the multi-dimensional dynamic user portrait, combining a collaborative filtering algorithm and a deep learning algorithm to generate a scene recommendation strategy; and monitoring user feedback and behavior data in real time, and optimizing the scenarized recommendation strategy by using the real-time monitored user feedback and behavior data. According to the method, the dynamic user portrait is constructed, scene recommendation is generated, and the strategy is optimized in real time by relying on reinforcement learning, so that the individuation degree, recommendation accuracy and user satisfaction of the service are remarkably improved, and the limitation of a traditional service mode is effectively broken through.

The invention relates to the technical field of 5G communication, in particular to a 5G network signal low-altitude coverage test method and system, and the method comprises the steps: obtaining regional geographic information and base station layout information; based on geographic information and base station layout, constructing a three-dimensional digital map, dividing a dynamic test grid, and determining an initial flight path; according to a multi-source sensor of the unmanned aerial vehicle, in combination with the initial flight path, collecting 5G signal parameters and environmental data of the grid points through a preset frequency; a wavelet transform filtering algorithm is adopted for calibration, abnormal values are removed, and feature vectors are generated; according to the feature vectors, a coverage evaluation model is constructed in combination with a three-dimensional digital map, and a low-altitude coverage blind area and an interference type are identified; and if a low-altitude coverage blind area or abnormal interference is detected, dynamically adjusting the flight path of the unmanned aerial vehicle through an A-star algorithm, testing a target area, and generating a coverage optimization scheme based on a test result. Therefore, the problems of low test efficiency, lagging data processing and analysis and the like in the prior art are solved.

The invention discloses an EPB anti-lock control method and system based on a self-adaptive wheel speed filtering algorithm, and belongs to the field of anti-lock control, and the method comprises the following steps: S1, collecting driving parameters in a vehicle driving process in real time, extracting feature vectors in the driving parameters after preprocessing, inputting the feature vectors into a pre-trained driving condition classification model, and obtaining a driving condition classification model; outputting the current driving condition type; s2, correcting a wheel speed signal based on an extended Kalman filter with determined parameters; s3, calculating a tire pressure deviation ratio based on the corrected wheel speed signal; and S4, when it is detected that the tire pressure deviation ratio exceeds a set threshold value, an EPB anti-lock control system is triggered. By the adoption of the EPB anti-lock control method and system based on the self-adaptive wheel speed filtering algorithm, the wheel speed information can be accurately extracted under the complex road and environment conditions, the calculation precision of the tire pressure deviation ratio is remarkably improved, and therefore a more reliable basis is provided for starting emergency braking.

The invention discloses a geotechnical engineering model optimization training method and system based on a multi-source knowledge base. The method comprises the following steps: constructing a geotechnical engineering multi-source knowledge base; collecting field sensor monitoring data in real time and preprocessing to obtain standardized time sequence monitoring data; performing time sequence feature extraction on the standardized data by adopting a Mamba model to generate a time sequence state feature data set; generating initial computable prior information and parameter constraint conditions of the geotechnical engineering model based on the knowledge base, and establishing an initial particle state set; iteratively updating the particle state by adopting a particle flow filtering algorithm to obtain geotechnical engineering model parameter posterior distribution; carrying out model numerical simulation and calculating a simulation error evaluation index; and judging whether the posterior distribution reaches a predetermined optimization convergence condition according to the error evaluation index, and outputting the geotechnical engineering model after optimization training. According to the method, the accuracy and convergence stability of geotechnical engineering model parameter optimization are effectively improved.