194 results about "Kalman filter algorithm" patented technology

The application provides a kind of detection method and system for liquid nitrogen liquid level, it is related to liquid level measurement technical field, including with the first data information of liquid nitrogen environment that is obtained by real-time acquisition using pre-set target sensor;Based on the first data information and the evaporation characteristics of liquid nitrogen in the present environment, after obtaining the empirical equation corresponding to the relationship between liquid nitrogen level and time, temperature, the target state equation and observation equation are constructed;Introduce Kalman filtering algorithm, and combine the target state equation and observation equation to estimate the real-time liquid level state of liquid nitrogen.Combined with Kalman filtering algorithm, the corresponding evaporation characteristics of the environment based on the storage of liquid nitrogen is used to obtain the empirical equation corresponding to the relationship between liquid nitrogen level and time, temperature, and then the sensor liquid level meter is used to realize the accurate estimation of the continuous real-time liquid level state of liquid nitrogen.

This invention provides a microgrid power coordination method and system based on state-enhanced Kalman filtering, relating to the fields of new energy, power electronics, power conversion, and control technologies. The method includes: constructing a state-space model of an AC microgrid and modeling feeder impedance as an augmented state, thus constructing an augmented state-space model of the AC microgrid; based on the augmented state-space model, using an improved Kalman filtering algorithm, calculating the optimal estimate of the feeder impedance of each distributed power source unit, and using an optimal impedance adjustment algorithm to calculate the virtual impedance of each distributed power source unit; and adjusting the output voltage of each distributed power source unit according to its virtual impedance to achieve reactive power regulation of the AC microgrid. This invention effectively solves the problem of the difficulty in directly obtaining feeder impedance in practical engineering and compensates for feeder impedance mismatch by allocating virtual impedance, thereby achieving reactive power balance in the AC microgrid.

This invention discloses a method for the collaborative optimization of turbine governor parameters and hydraulic system parameters. The method involves synchronously collecting multi-source operating data from a hydropower station using a sensor network to construct a hydraulic-governing coupled state-space model. Based on the UKF unscented Kalman filter algorithm, the method identifies the water flow inertial time constant, water hammer wave velocity, and turbine integrated transfer coefficient online, and evaluates the reliability of the identification results using a parameter identification confidence index. In a joint high-dimensional space of the control parameter domain and the hydraulic parameter domain, the method uses regulation quality, hydraulic safety, and grid stability as three objective optimization functions to solve for the Pareto front candidate set, and selects robust parameter schemes using a robust stability margin index. A three-level differentiated push strategy is implemented based on the parameter change amplitude, and real-time monitoring and an automatic anomaly backoff mechanism are implemented during the gradual push process. This invention achieves full-cycle collaborative optimization and safe tuning of hydraulic system parameters and governor control parameters.

The application discloses a new energy commercial vehicle adaptive torque distribution output method and system, and belongs to the field of new energy vehicles. The method comprises the following steps: acquiring vehicle acceleration and vehicle speed data; based on the vehicle acceleration and vehicle speed data, vehicle weight is calculated by using an extended Kalman filtering algorithm and a least square method to obtain a vehicle weight estimation value; based on the vehicle weight estimation value, a target torque value is obtained by table lookup of a preset vehicle weight-torque mapping table, and the target torque value is sent to a motor control unit; the motor control unit controls the power output of the vehicle according to the received torque value; a vehicle reference vehicle weight inputted from outside is acquired; and based on the vehicle reference vehicle weight, the vehicle weight calculation process is adaptively corrected. The application realizes adaptive torque distribution output suitable for different load conditions.

The application relates to a river flood discharge capacity dynamic evaluation and hydrological early warning method. The method comprises the following steps: based on a parameter sensitive observation subset, sequentially updating an initial hidden variable in a low-dimensional hidden space through an unscented particle filter algorithm to obtain an optimized hidden variable, and inputting the optimized hidden variable into a river dynamic resistance field generator based on an initial dynamic roughness field to perform decoding processing to obtain a dynamic roughness field of a current period; based on the dynamic roughness field and a state sensitive observation subset, performing analysis and update processing on a state variable field of a hydrodynamic model through a deterministic ensemble Kalman filter algorithm to obtain an assimilated water level field and a flow velocity field; according to the water level field, the flow velocity field and the dynamic roughness field, the maximum flood discharge capacity of a section is calculated in combination with dike top elevation data, and a flood discharge capacity dynamic index is calculated based on the maximum flood discharge capacity of the section and a current actual flow. The method can realize real-time dynamic quantification of river flood discharge capacity and predictive evaluation of flood risk.

This invention discloses a multi-sensor fusion adaptive control method and system for a micro-agricultural box. The method simultaneously collects three types of data: environmental, cultivation medium, and crop images. It utilizes an improved unscented Kalman filter algorithm for multi-source data fusion and multi-parameter coupling calculation, combined with a BP neural network-optimized adaptive PID controller to achieve closed-loop control. The system also includes an image recognition dynamic correction unit and a fault diagnosis and tolerance unit. This invention addresses the pain points of existing micro-planting equipment, such as low control precision, large parameter coupling interference, inability to adapt to the entire growth cycle, and poor reliability for unattended operation. This invention improves temperature and humidity control precision to ±0.5℃ / ±3%RH, reduces pest and disease incidence by more than 95%, and achieves pesticide-free, unmanned adaptive planting, suitable for home and commercial micro-agricultural scenarios.

The application provides an assembled trolley control system, and relates to the technical field of trolley intelligent control systems.The technical scheme points of the application are as follows: an environment sensing module, a posture detection module, an intelligent control module and an execution driving module are communicated;the environment sensing module comprises a laser radar submodule, a visual camera submodule, an ultrasonic sensor submodule and a data fusion submodule;the laser radar submodule is used for scanning the three-dimensional profile of a work area;the visual camera submodule is used for identifying component marker points;the ultrasonic sensor submodule is used for detecting close-range obstacles;and the data fusion submodule adopts a Kalman filtering algorithm to perform noise reduction processing on the multi-source sensor data.The assembled trolley control system provided by the application has the advantages of high positioning accuracy, strong environmental adaptability and good control coordination, and can meet the high-precision assembly requirements.

This invention provides a safety assurance method and system for unmanned aerial vehicles (UAVs) used for medical examinations of high-rise buildings in cities. The method includes: continuously monitoring the flight parameters of the UAV, sampling the flight parameter data at a frequency of ≥100Hz, and performing fusion processing using a Kalman filter algorithm; employing a fault diagnosis algorithm, based on a dual judgment mechanism of threshold and trend analysis, to analyze the collected flight parameter data in real time; comparing power redundancy and attitude stability indicators with preset thresholds to determine the fault level, and performing graded emergency handling according to the fault level, triggering corresponding handling and response strategies for non-catastrophic or catastrophic faults; after the UAV lands, confirming the safety of the landing point through visual SLAM or GPS positioning, and initiating onboard communication to report the status. This invention provides a complete solution from fault prevention to collision buffering through redundant design, graded emergency response, and dual-level protection, maximizing the protection effect and facilitating industrial implementation and verification.

This invention provides a high-precision fatigue testing method for hinges, belonging to the field of hinge fatigue testing technology. This invention utilizes a crack identification and prediction model to output a three-dimensional defect distribution map and a crack initiation risk score, adjusting the stress amplitude accordingly. A multi-channel decoupling controller calculates the coupling coefficient based on the Jacobian matrix and employs an inverse kinematics compensation algorithm to eliminate inter-channel interference when applying combined bending and torsional loads. During the fatigue cycle, adaptive zero-point calibration is performed at intervals. A Kalman filter algorithm is used to predict and compensate for the sensor's zero-point drift, and a temperature compensation coefficient is combined to correct for thermal drift effects. An improved rainflow counting method is used to extract cycle pairs, and the direction of the maximum damage surface is identified using the critical plane method to calculate the cumulative fatigue damage value. When the cumulative fatigue damage value reaches the damage threshold, a crack is identified. This invention solves the problem of insufficient testing accuracy caused by multi-axis load coupling interference during hinge fatigue testing.

The application provides a crane safety monitoring system and method, the system comprising: an airborne monitoring terminal for real-time acquisition of multi-dimensional operation data of the crane; an operation and maintenance monitoring platform comprising an operation monitoring module for fusion processing of the pre-processed multi-dimensional operation data by using a Kalman filtering algorithm to obtain real-time operation state estimation; and an updated model is established for each movement mechanism of the crane to calculate a theoretical operation state; an alarm signal of the corresponding movement mechanism is generated by comparing the real-time operation state estimation with the theoretical operation state, and a historical operation database is constructed; a warning prediction model is trained by using the historical operation data to predict a fault risk probability and generate a warning signal of the corresponding movement mechanism; the system realizes equipment state monitoring, fault warning, safety protection and management by comprehensively monitoring and analyzing the operation parameters, energy consumption data and operation behavior of the crane, and effectively improves the operation safety and operation and maintenance efficiency of the crane.

This application relates to a method and system for dynamic calibration of the state of charge (SOC) of lithium batteries for electric tricycles. The method includes: determining whether the current operating conditions meet the triggering conditions for a dynamic calibration event based on the total voltage, current, and temperature of the lithium battery, and the speed, motor power, and load status of the electric tricycle; when any dynamic calibration event is triggered, identifying the ohmic internal resistance and polarization internal resistance of the lithium battery using a parameter identification algorithm, calculating the virtual open-circuit voltage using a compensation calculation formula, querying a preset SOC table based on the virtual open-circuit voltage and temperature to obtain a reference SOC value, estimating the predicted SOC value using the ampere-hour integration method, and fusing the reference SOC value and the predicted SOC value using a Kalman filter algorithm to obtain the calibrated SOC value. This solves the problems of large errors, poor environmental adaptability, and inability to address SOC drift during dynamic operation in traditional battery SOC calibration methods, significantly improving the SOC estimation accuracy throughout the entire lifespan.

The application provides a slope vehicle distance dynamic compensation control method and system of an electric vehicle, and relates to the technical field of electric vehicle control. The method steps include: acquiring the vehicle longitudinal speed, vehicle longitudinal acceleration, pedal opening, slope angle, relative distance between the vehicle and the front vehicle, and relative speed between the vehicle and the front vehicle in real time; calculating the feedforward torque based on the slope angle, and calculating the PI feedback torque based on the error between the vehicle longitudinal speed and the preset reference value for proportional integral adjustment; obtaining the actual distance between the vehicle and the front vehicle through geometric compensation, dynamic correction, extended Kalman filtering algorithm, and recursive least squares method, and then calculating the distance compensation torque; dynamically switching between the maintaining mode, the accelerating mode, the transition mode, or the front vehicle sensing mode, and outputting the total control torque to each wheel hub motor of the vehicle to perform slope auxiliary control by comprehensively considering the feedforward torque, the PI feedback torque, and the distance compensation torque.

The application relates to a kind of moving base star sensor and high-precision time synchronization method of inertial navigation, and is mainly related to the technical field of integrated navigation, and the main steps include: real-time acquisition of the motion parameter data output by IMU;Using second-order extrapolation algorithm, based on IMU historical data, the equivalent IMU data corresponding to the star sensor data update time is calculated, the output frequency difference is adapted;Through asynchronous Kalman filtering algorithm, combined with IMU data and star sensor measured data, time update and measurement update are completed, and dynamic error is inhibited;Using high-precision crystal oscillator to optimize the clock source of inertial navigation system, the inherent error of clock output is reduced;Through multi-link cooperation, the time delay error of star sensor is compensated;Using FPGA interrupt latch, time alignment at the hardware level of star and inertial is realized.The method can effectively solve the time asynchronous problem of star sensor and inertial navigation data in the online calibration stage during flight, and guarantee the navigation accuracy and stability of integrated navigation system.

This invention discloses an adaptive tracking method and device for anti-dropped decoys based on a multi-model approach to measurement errors, belonging to the field of radar target tracking technology. The method includes constructing motion and centroid interference models for passive radar dropped decoys to determine the impact of interference on radar measurements; constructing a set of passive radar measurement models characterizing the time-varying characteristics of measurement noise, and establishing a hybrid system with uncertain measurement noise by combining the target motion model; employing an interactive multi-model architecture combined with a capacitive Kalman filter algorithm to perform interactive and parallel filtering on each sub-model of the hybrid system; updating the posterior probability of the measurement noise in each sub-model at the current moment in real time based on the measurement residuals and information covariance; and performing weighted fusion of the state estimates of each sub-model to obtain the adaptive tracking result. This invention effectively solves the tracking divergence problem caused by weak observability and time-varying measurement errors in passive radar under dropped decoy interference, significantly improving the tracking accuracy and robustness of the system.

This application relates to the field of equipment condition monitoring technology in fully mechanized coal mining faces, and discloses a method for monitoring the upward and downward movement of a scraper conveyor head based on lidar. The method includes: constructing a global spatial coordinate system and benchmark model of the roadway based on lidar point clouds; secondly, deploying a positioning sensing unit on the transfer machine connected to the scraper conveyor head to calibrate the relative position vector between the machines; then collecting inertial data and local point clouds, and using an error state Kalman filter algorithm to calculate the real-time pose of the carrier; furthermore, using the carrier attitude matrix to perform a spatial rotation transformation on the relative position vector to deduce the absolute coordinates of the scraper conveyor head; finally, calculating the component of this coordinate along the tilt direction, and combining the offset and rate to determine the upward and downward movement state. This invention solves the problems of limited direct measurement and long-term operational drift through indirect measurement and multi-source fusion strategies, achieving high-precision continuous monitoring under harsh working conditions.

The application discloses a superheat degree identification method for an evaporator outlet of a refrigeration system, a refrigeration system and a computer program product. The method comprises the following steps: obtaining the evaporator outlet pressure, the evaporator outlet temperature, the reheater outlet pressure, the reheater outlet temperature, the refrigerant mass flow and the input power of the internal electric heater of the reheater through sensors, mass flow meters and power meters; calculating the evaporator outlet superheat degree measurement value, the reheater outlet superheat degree measurement value and the evaporator outlet superheat degree calculation value according to the evaporator outlet pressure, the evaporator outlet temperature, the reheater outlet pressure, the reheater outlet temperature, the refrigerant mass flow and the input power; performing superheat degree identification based on the Kalman filtering algorithm according to the evaporator outlet superheat degree measurement value and the evaporator outlet superheat degree calculation value, and outputting the optimal estimation value of the superheat degree. The technical scheme provided in the embodiment of the application improves the superheat degree identification precision in the oscillation interval.

The application discloses a vehicle classification method and system, the method comprises the following steps: S1, acquiring a plurality of real-time data of an observation area, and acquiring corresponding point cloud data set D according to the real-time data; S2, matching the point cloud data in the point cloud data set D with target vehicle data predicted by an extended Kalman filtering algorithm, and adding corresponding target vehicles into a target vehicle set O; S3, using a DBSCAN algorithm to perform cluster analysis on the point cloud data that are not matched successfully in the step S2, and adding corresponding target vehicles into the target vehicle set O; S4, acquiring the distance between any two point cloud data, and taking the maximum distance as the vehicle length; S5, according to the size of the vehicle length and a threshold value, configuring a size label for the vehicle, configuring a large vehicle label for the target vehicle with the vehicle length greater than or equal to the threshold value, and configuring a small vehicle label for the target vehicle with the vehicle length less than the threshold value. The application can realize vehicle size classification, and has the characteristics of small calculation amount and simple classification.

This application relates to the field of medical and health monitoring technology, and discloses a system for assessing the body fluid status of hemodialysis patients based on multi-frequency bioelectrical impedance analysis. The system includes: a body fluid prediction module that uses a preprocessed impedance vector to solve for Cole model parameters through nonlinear fitting, combines real-time ultrafiltration rate data, and uses an unscented Kalman filter algorithm to dynamically optimize the Cole model parameters, obtaining optimized Cole model parameters. Based on these optimized parameters, time-series features are extracted and a fusion prediction model is used to obtain the predicted body fluid volume. A body fluid assessment and early warning module is used to determine the patient's current body fluid volume quantification index based on the optimized Cole model parameters and basic information, providing real-time early warning and short-term trend warning based on the predicted body fluid volume. This application helps improve the real-time performance and accuracy of body fluid status assessment.

This invention discloses a low-temperature charging and insulation device for unmanned aerial vehicles (UAVs) in high-altitude areas, belonging to the field of UAV charging and protection technology. The device includes a charging module, an insulation module, an environmental monitoring module, a control module, and a power supply module. The charging module provides adaptive charging services for the UAV battery, the insulation module provides low-temperature protection and temperature maintenance for both the battery and the charging module, the environmental monitoring module collects high-altitude environmental and device operating parameters, the control module dynamically adjusts the charging and insulation strategies based on these parameters, and the power supply module provides stable power. This invention achieves precise control of charging parameters and insulation temperature through the synergistic effect of PID control algorithms, fuzzy PID algorithms, and Kalman filtering algorithms, effectively solving the problems of low charging efficiency, easy damage, and poor insulation effect of UAV batteries in low-temperature, low-pressure, and strong interference environments at high altitudes, and is compatible with different UAV models.

This invention relates to the field of unmanned aerial vehicle (UAV) flight control technology and discloses a Kalman attitude evaluation method for UAVs. Addressing the problems of low accuracy and poor real-time performance in attitude evaluation of UAVs under complex weather conditions and varying terrain, this method deploys multiple types of sensors (barometric pressure, angular velocity, acceleration, and attitude angle sensors) on the UAV to collect flight data in real time, which is then transmitted to a central processing unit (CPU). The CPU constructs a state-space model based on the Kalman filter algorithm, performs prediction and update fusion processing on the sensor data, and optimizes the evaluation results using a PID control algorithm, achieving real-time, high-precision attitude evaluation of the UAV. This method effectively improves the attitude perception capability and stability of UAVs in complex environments and can be widely applied in aerial surveying, material delivery, environmental monitoring, and other fields, providing reliable attitude data support for efficient and safe UAV operations.

The application discloses a kind of asynchronous fusion method and system of deep-sea biological space-time trajectory analysis, the method is extracted from the single video data stream collected by underwater equipment biological target using visual detection module concurrently, and using video text recognition module extracts the space-time metadata superimposed in video picture;Then using asynchronous data fusion module based on a confidence-aware asynchronous extended Kalman filtering algorithm, the visual feature information and space-time metadata are fused, and the unified space-time trajectory point with geographic registration is generated.The application solves the technical problems that biological visual information and accurate space-time information are difficult to automatically integrate from single video source in the prior art, and through innovative asynchronous data fusion algorithm and diversified visualization output, the robustness, accuracy and application breadth of the automatic ecological monitoring and analysis system are significantly improved.

The application discloses a kind of based on multi-modal perception's combustion equipment adaptive control system and method, belong to equipment control technical field.The application gathers air duct and fan parameter, constructs air duct flow mapping model of primary air and secondary air independence, and is calibrated to form the benchmark control parameter table of each combustion stage under standard condition;Real-time acquisition equipment operation data calculates fuel supply amount, based on energy conservation principle combined with extended Kalman filter algorithm backstepping current particle actual low calorific value;Based on real-time calorific value, predict the trend of furnace temperature change, calculate the optimal flow sequence of primary air and secondary air by multi-objective optimization and back solution as damper and fan instruction;Establish the mapping relationship between flue oxygen content and furnace oxygen gradient, deduce the optimal secondary air injection interval and correct execution instruction.The application realizes the accurate control of combustion process, significantly improves the combustion efficiency and fuel adaptability, optimizes the oxygen distribution of furnace, effectively reduces pollutant emission.

The application relates to the field of intelligent agricultural machinery and automatic driving technology and discloses a livestock industry feed pushing robot feed pushing control system based on deep learning. The system comprises a perception layer, a decision layer and an execution layer. The perception layer fuses a laser radar, ultrasonic waves and a camera; an edge computing unit runs an instance segmentation model to fit a feed edge; the decision layer utilizes an extended Kalman filtering algorithm containing a dynamic adjustment mechanism of an observation noise covariance matrix, adjusts a weight in real time according to a satellite signal quality, realizes seamless navigation indoors and outdoors, and executes a double closed loop PID strategy of a visual position loop and a torque limiting loop, and adaptively adjusts a pushing plate and a vehicle speed according to visual feedback and motor current. The application solves the problems of weak perception ability of the feed pushing robot, unstable navigation switching and poor operation flexibility, and realizes precise and efficient unmanned feed pushing.

The application relates to an indoor multi-radar fusion positioning method and a related device. The method comprises the following steps: acquiring radar target positions detected by multiple radar devices in a current frame; acquiring fusion state information of a human target in a previous frame to predict tracking prediction positions of each human target in the current frame through a Kalman filtering algorithm; for each human target, the tracking prediction position is matched with each radar target position respectively to determine at least one radar target position associated with the human target; the weight coefficient of each associated radar target position is determined to fuse the radar target positions associated with the same human target to obtain a fusion observation position of the human target in the current frame; the fusion observation position is taken as an observation value and input into the Kalman filtering algorithm for updating to determine the fusion positioning information of the human target in the current frame. The method can realize accurate positioning and stable tracking of the human target in an indoor environment in a multi-radar deployment scene.

The application discloses an omnidirectional guiding interaction multi-model positioning method and system for a motorized mother ship to recover an underwater robot and a storage medium, the method obtains the state information and distance data of the mother ship by integrating a water acoustic ranging and a water acoustic communication device, constructs an interaction multi-model singular value decomposition unscented Kalman filtering algorithm fusing a uniform straight line and a uniform bow turning model, and realizes real-time estimation of the position of the mother ship under a mouth-shaped maneuvering track. An event-triggered communication mechanism is designed, information is broadcast only when the turning state of the mother ship changes, so as to reduce the communication load and maintain real-time performance; meanwhile, a Markov transition matrix is updated, the turning model weight is enhanced, and the estimation accuracy and model matching are improved. According to the model probability and the estimation result, a LOS guiding law with an adaptive look-ahead distance is constructed, and the autonomous guiding recovery of the underwater robot to the motorized mother ship is realized. The method provided by the application can realize dynamic estimation and guiding based on the measurement data of a single water acoustic device under the conditions of the mother ship maneuvering and communication limitation.

This invention discloses a label-free real-time blood vessel tracking method driven by dynamic blood oxygenation features, relating to the fields of medical image processing and computer vision. The method includes: acquiring dynamic blood oxygenation signals from a target tissue region; preprocessing the blood oxygen concentration change data; constructing a spatiotemporal matrix of blood oxygenation features; performing adaptive feature extraction using an improved 3D convolutional neural network; establishing a multi-scale blood vessel contour prediction model; dynamically predicting and updating the blood vessel contour using a Kalman filter algorithm; outputting the real-time trajectory of the blood vessel center and boundary contours; and introducing a dynamic feedback mechanism to achieve continuous and stable blood vessel tracking. This invention achieves highly secure blood vessel tracking, significantly improving tracking accuracy, robustness, and real-time performance under tissue movement and complex background interference by utilizing dynamic blood oxygenation features.

The application relates to the technical field of power load regulation, and discloses a method and system for dynamic scheduling of charging pile power. The method comprises the following steps: obtaining a real-time data sequence containing a basic load and a charging pile load, and obtaining a load fluctuation trend after denoising by using a Kalman filtering algorithm; calculating an instantaneous change rate of the basic load, and determining a power offset amount that needs to be compensated on the charging pile side when the instantaneous change rate exceeds a preset change threshold; inputting the power offset amount into a proportional-integral-derivative control algorithm to generate a preliminary power scheduling instruction for each charging pile; verifying and amplitude correcting the preliminary power scheduling instruction in combination with an upper limit of a total load of a transformer area to obtain an optimized power scheduling instruction; controlling each charging pile to adjust output power, generating a scheduled total load curve and performing stability verification; and when a verification result does not satisfy a preset safety threshold, updating Kalman filtering algorithm parameters and using the updated Kalman filtering algorithm parameters for subsequent real-time data processing. The method can improve the real-time performance, stability and safety of transformer area load regulation.