25 results about "Kinetic information" patented technology

The invention provides a driving anti-slip optimization control method based on vehicle-road cooperation road fusion estimation, and the method comprises the following steps: S1, fusing vehicle-mounted and roadside visual recognition information, and obtaining a vehicle-road cooperation road state recognition result through employing a weighted voting method in combination with road invariant hypothesis and historical information assistance judgment; s2, adaptively adjusting parameters of a dynamic estimator by using a pavement state recognition result, establishing a sample set based on a pavement fusion estimation result, introducing a fuzzy clustering algorithm of particle swarm optimization to realize self-evolution optimization of the structure and the parameters of the estimator, and outputting a fused attachment coefficient estimation result; and S3, designing a slip rate and wheel acceleration combined control strategy to deal with potential slip in advance, and outputting a control torque result of a slip rate controller. Compared with the prior art, the method integrates visual perception and dynamic information to realize accurate estimation of the pavement state, supports advanced intervention of the slip rate controller before slip, and improves the driving stability of the whole vehicle.

The invention provides a robot control method and device, computer equipment and a storage medium, and relates to the technical field of computers. According to the method, joint driving torque information of a robot is introduced, and a force sense condition mark corresponding to a noise adding action sequence is constructed as a condition constraint in a diffusion denoising process, so that the generated target action sequence can explicitly consider force sense and dynamics information, and the noise is reduced. And the stability and safety of the robot in physical interaction tasks such as contact, grabbing and assembling are effectively improved.

The application provides an aerodynamic parameter identification method based on an improved physical information neural network, comprising: obtaining a target flight state quantity in a flight process of an aircraft; inputting the target flight state quantity into the improved physical information neural network to obtain an aerodynamic parameter perturbation quantity, the aerodynamic parameter perturbation quantity being a difference quantitative value between a nominal aerodynamic parameter and an actual aerodynamic parameter in flight, a loss function of the improved physical information neural network being an integral loss function, the integral loss function establishing a connection between a dynamic model containing an estimated value and a real dynamic characteristic; and determining the actual aerodynamic parameter according to the aerodynamic parameter perturbation quantity and the corresponding nominal aerodynamic parameter. Through implementation of the application, the dynamic model containing the estimated value is connected with the real dynamic characteristic in an integral manner, correct dynamic information is ensured to be learned by the neural network in a training process, the accuracy of the aerodynamic parameter is improved, and the training difficulty is reduced.

The present application relates to a kind of device and method for measuring solid-liquid two-phase flow dynamics information, including measuring plate, glass plate, pore pressure sensor, filter screen, triaxial force sensor, force plate, outer pressure strip, inner pressure strip, sealing film, sensor bracket, dynamics information includes the pressure of liquid component in two-phase flow and the force of overall to force plate;The present application can ensure fluid seal when the mechanical sensor is reasonably measured dynamics information;Meanwhile, the present application can also realize the simultaneous measurement of solid-liquid two-phase flow kinematics and dynamics information by mechanical sensor cooperation high-speed camera technology.

The invention relates to a train spacing determination method and device, computer equipment and a storage medium, and belongs to the technical field of railway transportation. The method comprises the steps that dynamics information and positioning measurement information of adjacent trains are acquired; wherein the adjacent trains comprise the first train and the second train. And according to the dynamic information and the positioning measurement information, a first interval and a second interval between the adjacent trains are determined. And determining the train spacing between the adjacent trains according to the first spacing and the second spacing. According to the method, when the distance between the adjacent trains is determined, the positioning measurement information of the adjacent trains is considered, the dynamic information of the adjacent trains is also considered, and compared with a traditional method for determining the distance between the adjacent trains only by means of a satellite positioning system, the method is more comprehensive in consideration, and the accuracy of the distance between the adjacent trains is improved. And the obtained train distance between the adjacent trains is more accurate.

This invention discloses a simulation method and related products for the influence of irradiation defects on the micromechanical properties of Zr-Nb alloys. The simulation method includes the following steps: S1, obtaining Zr-Nb alloys containing typical irradiation defects and obtaining stable configurations of Zr-Nb alloys containing typical irradiation defects based on classical molecular dynamics; S2, calculating the micromechanical properties of the stable configurations obtained in step S1 by simulating a nanoindentation experiment, and obtaining the micromechanical properties of each system; S3, based on the micromechanical properties obtained in step S2, and combined with the kinetic information of the internal microstructure changes, establishing the correspondence between micromechanical properties and microstructure. This invention, through this simulation method, can establish the correspondence between micromechanical properties and microstructure, helping to reveal the influence of irradiation defects on their micromechanical properties.

The invention provides a terrain recognition method, which belongs to the field of intelligent driving and comprises the following steps: acquiring visual data, dynamic data and weather and vehicle working condition data; inputting the visual data into the recognition model to obtain a visual terrain scoring result, and inputting the dynamic data into the recognition model to obtain a dynamic terrain scoring result; inputting weather and vehicle working condition data into a recognition model to obtain a weight coefficient; according to the scheme, a vision and dynamics fusion method is used for identifying the terrain, and the defect that a single information source scheme is limited by weather or working conditions is overcome. Visual information and dynamic information are subjected to weighted fusion, weight coefficients are obtained through training according to factors such as weather and working conditions, the adaptive capacity is achieved, the difficulty of manually setting threshold values and weights is avoided, and the misrecognition proportion is reduced.

The invention belongs to the technical field of intelligent traffic systems, and discloses an expressway truck transverse oscillation amplitude prediction method based on trajectory data, which fuses high-frequency floating truck data, road design data and truck driving dynamics information, and obtains trajectory data through data preprocessing and abnormal data elimination. Extracting a vehicle transverse oscillation index from the trajectory data, selecting a plurality of independent variables, judging the influence relationship of the independent variables on the vehicle transverse oscillation index based on a hierarchical regression method to obtain significant influence independent variables, and finally constructing a prediction model of the significant influence independent variables and the transverse oscillation amplitude based on a Gaussian regression process. The method effectively solves a problem that the modeling capability of the lateral shift behavior of the truck is insufficient in an existing method, and improves the scientificity and robustness of prediction.

The application discloses a kind of robot optimal human-computer interaction impedance control method based on joint learning, storage medium and robot, method includes the following steps: constructing task space reference regression trajectory, human-computer interaction second-order impedance model and task space auxiliary trajectory, establish human-computer interaction task space augmented system and corresponding evaluation index function;Based on integral reinforcement algorithm, human-computer interaction second-order impedance model parameters are updated online until optimal parameters are obtained;For second-order impedance model, construct adaptive neural network impedance controller;Based on the theory of deterministic learning, experience knowledge is obtained, and a constant neural network impedance controller is constructed.This method effectively solves the accurate impedance control of human-computer interaction under the condition of unknown dynamics information and unknown human motion characteristics of robot, and realizes the online optimal adjustment of human-computer interaction parameters under different task scenarios and different interactive objects by using reinforcement learning mechanism.

The invention provides a robot safety control method based on fault-tolerant control, which is applied to a robot control system comprising a multi-degree-of-freedom joint driver and a tail end executing mechanism, and comprises the following steps: constructing a state observer based on a robot model, and comparing by combining joint measurement data and a redundant sensor to obtain fault state information; the space relation and the movement speed of the person and the robot are collected, fault state information and space and dynamics information are fused, and a comprehensive safety risk index is calculated; according to comparison between the index and a multi-gear risk threshold value, the function safety state machine is driven to be switched among multiple operation states; the motion parameters are subjected to amplitude limiting in the amplitude limiting operation state, constraint is applied to the fault joint in the degradation operation state, and a joint control instruction meeting the safety constraint is generated based on the residual degree of freedom. According to the invention, the robot can be controlled to decelerate to stop in a safe shutdown state, and graded safety protection and safe operation with faults under a fault working condition are realized.

The application provides an interception guidance strategy acquisition method and device, equipment and a storage medium. The method comprises the following steps: obtaining a first Bellman equation, which is an off-policy integral reinforcement learning Bellman equation obtained based on a nonlinear interception dynamics equation, a game value function and an iterative control strategy; obtaining control input data and state output data; and iteratively solving the first Bellman equation based on the control input data and the state output data to obtain an optimal interception guidance control strategy. In this way, the optimal control strategy is found in a data-driven manner without relying on specific dynamics information, so that the optimal control strategy is used to achieve the purpose of high-precision interception of a high-maneuvering escape party by a pursuit party.

The invention relates to the field of power load fluctuation prediction, and provides a power load prediction method and system based on dynamic driving. The power load prediction method based on dynamic driving comprises the following steps: performing dynamic modeling on an obtained historical power load time training sequence by adopting a deterministic learning algorithm, and extracting internal dynamic information of data; on the basis of dynamic information, a multi-step forward iteration predictor based on a fourth-order Runge-Kutta model is adopted, and a power load prediction sequence composed of prediction values from the step 1 to the step 2 is obtained; a corrector is adopted to perform periodic correction on a power load prediction sequence formed by the prediction values from the step 1 to the step 2, and a power load prediction sequence correction value of the step 1 is obtained; and obtaining a power load prediction result based on the prediction value and the correction value. And the power load prediction precision is improved.

This invention belongs to the field of robot path planning and discloses a ground robot path planning method based on deep reinforcement learning. Based on deep reinforcement learning and combined with a robot dynamics model, it aims to propose a safe and reliable path in a field environment. The method first constructs a 3D scene and preprocesses it, then performs dynamic modeling of the robot to build a deep reinforcement learning model. This model uses the Asynchronous Advantage Actor-Critic method to learn the optimal path planning strategy and the optimal state value function, designs a reward function considering terrain and distance, and designs an exploration strategy based on the interaction between the robot and different terrains. Finally, a deep convolutional neural network model is trained to drive the robot to obtain the maximum reward value during operation. This method comprehensively considers terrain and robot dynamics information to plan a safe and reliable path for the robot in a 3D scene.

The invention provides a fixed time control method for a flexible double-connecting-rod mechanical arm, which is applied to the field of robot intelligence and comprises the following steps: establishing a flexible system dynamics model with dead zone input; deducing an initial control moment depending on the flexible system dynamic model with dead zone input by combining a backstepping method; designing a first radial basis function neural network update rate based on unknown dynamic information in the radial basis function neural network approximate initial control torque; designing a second radial basis function neural network update rate based on a radial basis function neural network approximation unknown dead zone function; the fixed time control torque of the novel flexible double-connecting-rod mechanical arm is obtained on the basis of the two radial basis function neural network update rates and the initial control torque, the fixed time control strategy provided by the invention ensures rapid convergence of the flexible double-connecting-rod mechanical arm, and even under the operation condition involving system constraints, the control torque of the flexible double-connecting-rod mechanical arm can be effectively controlled. And the trajectory tracking precision and the vibration suppression performance can be improved at the same time.

The application discloses a differential dynamic microscopic image acquisition processing method based on a phase spectrum and a related device, relates to the technical field of particle microscopic image processing, and can measure kinetic information such as a diffusion coefficient of Brown particles. Compared with traditional microscopic image technology, the application is a new technology combining light scattering and optical microscopy. The experimental equipment used in the application is simpler, and the experimental steps are fewer; the application is more convenient to combine with other technologies; the application can well solve the sample concentration limitation problem; and the application can measure a wider variety of particles. The method is to use a high-speed camera to shoot time sequence nanoparticle Brown motion microscopic images, then calculate phase changes of two images with different time intervals through a cross-correlation function, find the relationship between the data obtained in the previous steps and the diffusion coefficient, and thus obtain the diffusion coefficient and other kinetic information of the Brown particles.

The application discloses a kind of machine dog joint gap online compensation method and system, it is related to machine dog joint control technical field.A kind of machine dog joint gap online compensation system, including have: machine dog movement monitoring module and machine dog movement compensation module.The application obtains joint dynamics information and foot end dynamics information by dynamic, can real-time monitoring machine dog in complex environment Motion state, to provide accurate basis for subsequent compensation;By dynamics topological decoupling to machine dog, complex multi-joint system is decomposed into multiple independent joint control unit, improve control accuracy and response speed, make system more stable under different load conditions;By real-time distinguishing joint is in clearance stagnation period or rigid engagement period, can dynamically adjust compensation strategy, different compensation measures are taken for different motion state, to significantly improve motion accuracy and stability.

The application discloses a self-adaptive adjusting split-belt dual-track treadmill belt speed system, which comprises an information acquisition module, an information analysis module, a controller module, a speed control module and a visual feedback module; the information acquisition module further comprises a motion signal acquisition unit and a pressure signal acquisition unit. The application calculates the average mapping error and the error average change between the real-time symmetry coefficient and the target symmetry coefficient in the training process of the patient in the information analysis module through the kinematic information and the dynamic information collected by the information acquisition module in real time, analyzes the training difficulty change required by the patient at present according to the fuzzy logic controller of the controller module, controls the running speed of the two belts in the speed control module respectively and individually based on the training difficulty change, and provides the patient with the visual feedback with implicit distortion as the training target in the visual feedback module, so as to improve the symmetry and improve the walking ability of the patient.

The invention relates to the technical field of automobile safety crash test data acquisition and analysis, in particular to a crash test dummy head 6D attitude data robust acquisition system and method. The system comprises an edge AI analysis module, a visual depth perception unit, a dummy head inertial measurement unit and a redundant power supply and data preservation module, and further comprises an inertial wake-up and attitude reference unit. According to the method, accurate 6D pose measurement is realized, and the problem of visual / inertial data interruption or inaccuracy caused by air bag shielding, environment change and inertial drift is effectively solved through visual depth fusion and deep fusion of inertial measurement data of the dummy head by unscented Kalman filtering; robustness and continuity of 6D attitude data and high-precision dynamic information of the head in the whole collision process are guaranteed, a second scheme still serves as a guarantee mechanism to obtain related data of the dummy head under the extreme condition, and high-precision high-dimensional analysis data can be provided for automobile manufacturers.

The application belongs to the technical field of intelligent transportation systems, and discloses a highway truck transverse oscillation amplitude prediction method based on trajectory data. The application fuses high-frequency floating truck data, road design data and truck driving dynamics information, obtains trajectory data through data preprocessing and abnormal data elimination, extracts a vehicle transverse oscillation index from the trajectory data, selects multiple independent variables, judges the influence relationship of the independent variables on the vehicle transverse oscillation index based on a layered regression method, obtains significant influence variables, and finally constructs a prediction model of the significant influence variables and the transverse oscillation amplitude based on a Gaussian regression process, effectively solving the problem of insufficient modeling capability of the truck transverse deviation behavior in the existing method, and improving the scientificity and robustness of the prediction.

The disclosure generally relates to motion analysis systems and methods of use thereof. In certain aspects, the system includes an image capture device, at least one accelerometer, and a central processing unit (CPU) with storage coupled thereto for storing instructions that when executed by the CPU cause the CPU to receive a first set of motion data from the image capture device related to at least one joint of a subject while the subject is performing a task and receive a second set of motion data from the accelerometer related to the at least one joint of the subject while the subject is performing the task. The CPU also calculates kinematic and / or kinetic information about the at least one joint of a subject from a combination of the first and second sets of motion data, and outputs the kinematic and / or kinetic information for purposes of assessing a movement disorder.

The present application relates to the field of power load fluctuation prediction, and provides a power load prediction method and system based on dynamics driving. The power load prediction method based on dynamics driving comprises the following steps: adopting a deterministic learning algorithm to perform dynamics modeling on a historical power load time training sequence obtained, and extracting inherent dynamics information of data; based on the dynamics information, adopting a multi-step forward iteration predictor based on a fourth-order Runge-Kutta model to obtain a power load prediction sequence composed of prediction values from the first step to the nth step; adopting a corrector to perform periodic correction on the power load prediction sequence composed of the prediction values from the first step to the nth step, and obtaining a corrected value of the power load prediction sequence of the first step; and based on the prediction value and the corrected value, obtaining a power load prediction result. The power load prediction accuracy is improved.

The invention discloses a multi-path dynamic information fusion wheel-rail force measurement method and system, and relates to the technical field of rail transit vehicle monitoring, and the method comprises the steps: collecting multi-path dynamic data in the vehicle operation process through a sensor; carrying out normalization processing on the collected dynamic data; discretizing and visualizing the normalized data, and converting the normalized data into a two-dimensional matrix format suitable for the input of a convolutional neural network; performing feature extraction on the preprocessed data by using a multi-dimensional dynamic feature extraction network, and classifying and identifying the change of the wheel-rail force; analyzing the multi-path dynamic data through a multi-dimensional dynamic feature extraction network, and extracting key features related to the wheel-rail force; under the complex line working condition, the train wheel-rail force is monitored and recognized in real time through multi-path dynamic information fusion. According to the multi-path dynamic information fused wheel-rail force measurement method provided by the invention, through fusion of the multi-path dynamic information, wheel-rail force changes under different working conditions can be effectively processed, the adaptability of a measurement system is improved, and the maintenance and use cost is reduced.

The invention relates to a method for determining luminescence kinetic information of at least one reversibly photo-convertible luminescent species, comprising: - illumination of the sample with at least a first and second excitation light, the intensities of the first and second excitation lights being periodically modulated at different frequencies, - detection of the luminescence light and extraction, for a plurality of modulation frequencies of the first excitation light, of an intensity amplitude of the luminescence light, - determination of a kinetic information of the species from the luminescence amplitude. The invention also relates to another method for determining luminescence kinetic information of such species, comprising:- illumination of the sample with at least a first excitation light, the intensity of the first excitation light being periodically modulated, - detection of the luminescence light and extraction of an average luminescence intensity, - determination of a kinetic information of the species from the average luminescence intensity.

The invention discloses a vehicle control method, device and equipment and a storage medium, and relates to the technical field of vehicle control, and the method comprises the steps that when the current vehicle speed of a vehicle is larger than a preset vehicle speed threshold value, the current working condition of the vehicle is determined according to the current state data of the vehicle, and the current state data represents the vertical dynamics information of the vehicle; target control damping force of each axle of the vehicle is determined according to the current working condition; and performing suspension control on the vehicle according to the target control damping force when the current working condition of the vehicle is the flying slope coping working condition. A vehicle suspension is controlled in advance to prevent the occurrence of slope flying by identifying the working condition that the vehicle is about to fly on a slope in advance, or when it is identified that the vehicle cannot avoid the occurrence of slope flying, the posture of the vehicle during slope flying is controlled to improve the stability of the vehicle after slope flying and reduce the impact on the vehicle, and the stability of the vehicle after slope flying is controlled.