25results about How to "Robustness" patented technology

The invention belongs to the technical field of path planning and robot motion planning, and particularly relates to a multi-strategy fusion improved RRT * sampling planning method and device and a storage medium, and the method comprises the steps: constructing a dual-search tree through a starting point and a target point in a three-dimensional working space, and selecting a to-be-expanded search tree through a random mode; halton-Bridge mixed sampling is adopted to generate sampling points, so that the global coverage uniformity is improved, and the narrow channel sampling capability is enhanced; calculating the dynamic step length of the exponential decay based on the local obstacle proportion; adopting a hierarchical guidance expansion strategy, preferentially expanding towards a target, introducing an improved artificial potential field to guide expansion when the expansion fails, and taking the bottom with random expansion; performing RRT * neighborhood father node reselection and reconnection optimization on the new node; greedy pruning, self-adaptive interpolation and cubic B-spline smoothing are carried out on a path after the double trees are connected, and an executable smoothing track is output; according to the scheme, planning efficiency, success rate and path quality are improved.

The invention relates to the technical field of power distribution cabinet internal harmonic control, in particular to a power distribution cabinet harmonic suppression method based on multi-source data fusion. By extracting amplitude and phase information at each harmonic frequency point, branch equivalent admittance is calculated, and an admittance distribution state is formed; judging an abnormal branch with admittance deviation at each frequency point, and establishing a corresponding relation between the adjustable electrical parameters and the admittance deviation; in the operation process of the power distribution cabinet, the admittance distribution state and the abnormal branch set are periodically updated, the equivalent admittance of the target branch is dynamically adjusted according to the corresponding relation, and the distribution of the harmonic current among the branches is changed; through the method, the harmonic peak value and the multi-node superposition effect in the power distribution cabinet can be actively reduced, online operation under the condition of multi-frequency and multi-node load fluctuation is adapted, harmonic flow direction reconstruction is achieved on the premise that complex filtering or hardware devices are not added, and the electric energy quality and the system stability of the power distribution cabinet are improved.

The invention discloses a forklift path tracking control method based on MPC and ARC cooperation, and belongs to the technical field of industrial vehicle automatic control. Comprising the steps that an upper-layer MPC is designed based on a forklift kinematics model, and an expected steering angle and an expected speed instruction are obtained through solving; a lower-layer ARC is designed based on the pump control steering system dynamics model to track an expected steering angle, and a PID controller is designed to track an expected speed; a time delay compensation mechanism is integrated in the upper-layer MPC, and a high-frequency execution strategy is adopted. According to the method, interference of the steering system is accurately observed and compensated, parameter uncertainty is adaptively processed through parameters, nonlinear uncertainty is processed through robust items, a time delay compensation mechanism is integrated in MPC, all control rates are display expressions, online solution is not needed, and the method is suitable for large-scale popularization and application. The influence of steering resistance dramatic change, external interference, unmodeled dynamics and system time delay on the control precision is effectively reduced, and high-precision and low-time-delay path tracking performance is achieved.

This invention belongs to the field of medical signal processing technology, specifically a method and prediction system for constructing a three-dimensional tumor motion trajectory prediction model. It involves collecting temporal data of point clouds from the human chest and abdomen, as well as temporal location information of the tumor, and establishing a unified spatial coordinate system. The temporal data of the point clouds are divided into n time periods of total duration T1, and the temporal location information of the tumor is divided into n time periods of total duration T2, resulting in n training samples. The point clouds of the training samples are further divided into several pseudo-voxel sub-blocks according to the human projection plane, and K pseudo-voxel sub-blocks are selected based on motion saliency to obtain the temporal feature vector of the tumor motion. The temporal feature vector of the tumor three-dimensional motion trajectory prediction is used as input, and the corresponding tumor temporal location information is used as output to train an attention-enhanced dilated convolutional prediction network. This invention enables high-precision, non-invasive prediction of the three-dimensional tumor motion trajectory.

This application discloses an auxiliary classification system and method based on multi-view spatiotemporal interaction and difference compensation, relating to the field of auxiliary classification. The method includes: acquiring time-series data from multiple brain regions and calculating Pearson correlation matrices and partial correlation matrices; constructing first and second-dimensional time matrices based on features extracted from the time-series data; generating multiple spatial connectivity matrices based on the Pearson correlation matrix and partial correlation matrix, thereby constructing first and second-dimensional spatial matrices; performing bidirectional cross-attention interaction on the time and spatial matrices of the two dimensions respectively to obtain corresponding graph structure representations and node representations; generating compensation terms based on the differences between the graph structure representations and node representations of the two dimensions; enhancing the graph structure representation and node representation of the second dimension using the compensation terms to obtain fused features; and inputting the fused features into a graph convolutional network to output classification results. This invention improves the accuracy of auxiliary classification through two-dimensional spatiotemporal interaction and difference compensation.

This invention discloses a method and apparatus for active discharge of a DC bus. The method includes: acquiring the bus voltage signal of the DC bus in the y-th sampling period and calculating the actual discharge energy of the DC bus in the y-th sampling period; dynamically adjusting the reference discharge energy based on the actual discharge energy of the DC bus and the target total discharge time; and dynamically adjusting the d-axis current in the (y+1)-th sampling period based on the actual discharge energy of the DC bus in the y-th sampling period and the reference discharge energy. In this invention, the target total discharge time is preset, making the discharge duration of the DC bus controllable and eliminating the need for manual calibration, thus achieving the effect of reducing the bus voltage to a safe threshold within a specified time. Based on the closed-loop feedback of the actual discharge energy and the dynamic adjustment of the reference discharge energy, the d-axis current is dynamically adjusted, thereby achieving dynamic adjustment of the actual discharge energy in each sampling period and improving discharge safety.

This invention belongs to the field of Internet technology and relates to a distributed task monitoring method and system based on state transition and targeted push. It includes receiving subscription requests initiated by clients, adding clients to communication groups associated with the task status corresponding to a tab, and maintaining the group affiliation information of each client in real time; based on the group affiliation information, when a task status transitions from its original state to a new state, locating the original state group and the new state group, pushing a removal notification to the original state group and a move-in notification to the new state group; when a task in the execution state experiences a progress update, locating the execution state group and pushing a progress update message only to that group; and using the removal notification, move-in notification, and progress update message as outputs to trigger clients to perform removal, addition, and progress refresh operations on the corresponding tasks in their local task lists, respectively. This solves problems such as list synchronization issues and numerous invalid pushes, achieving real-time list consistency and accurate push notifications.

The invention relates to a permanent magnet synchronous motor model prediction control method and system, belongs to the technical field of permanent magnet synchronous motor driving, and solves the problem that parameter robustness and dynamic performance cannot be considered when parameter mismatch occurs in permanent magnet synchronous motor model prediction control in the prior art. Comprising the steps of obtaining sampling values of stator current, stator voltage and electrical angular velocity at a current sampling moment and two previous moments and a direct current bus voltage at the current sampling moment, further updating incremental current prediction errors and time sequence sampling data of incremental voltage, and further obtaining a first sample vector and a second sample vector at the current sampling moment; and then a small-batch stochastic gradient descent method is adopted to obtain an identification value of a parameter mismatch coefficient at the current sampling moment, then stator current prediction values of different voltage vectors at the next moment are obtained, then an optimal voltage vector is obtained, and permanent magnet synchronous motor model prediction control at the next moment is carried out based on the optimal voltage vector.

The application discloses an AFS, ARS and DYC cooperative control method of a distributed electric drive vehicle based on DDPG. The method utilizes a cooperative control method and a deep reinforcement learning method, designs and develops a vehicle stability control method based on a centroid side slip angle and a yaw angular velocity, takes driving signals provided by a driver and measured or estimated vehicle state information as input, and judges the stability of the vehicle. When the active safety system needs to intervene, a DDPG-AAD model trained offline by a strategy deep gradient algorithm is used to coordinately control AFS, ARS and DYC systems, precise and independent control of each wheel is realized, and thus the active safety driving stability of the vehicle is improved.

The invention discloses a curved surface self-adaptive grinding and polishing method based on a two-degree-of-freedom force control grinding and polishing robot. The method comprises the steps that a surface image of a workpiece to be machined is acquired, and local curvature information is extracted; calculating a feed-forward compensation amount according to the local curvature information, and correcting a preset expected position and a preset expected speed based on the feed-forward compensation amount to obtain a corrected expected position and a corrected expected speed; on the basis of the position deviation between the corrected expected position and the actual position and the speed deviation between the corrected expected speed and the actual speed, inertial parameters and damping parameters of a position-based impedance model are adjusted in real time through a fuzzy adaptive algorithm; and generating a control instruction according to the adjusted impedance model, and driving the two-degree-of-freedom linear motion module to perform compensation motion. According to the method, through cooperation of curvature feedforward compensation and fuzzy variable impedance control, force control lag at the curvature sudden change position is reduced, and force control stability and machining consistency of complex curved surface grinding and polishing are improved.

The application discloses a small sample geothermal resource prediction method and device, system and storage medium, comprising: learning the Bayesian prior knowledge with generalization ability from limited source data by meta-learning, migrating to a new small sample target area through a rapid adaptation mechanism, and utilizing multi-scale differential similarity calculation and mixed student distribution to realize high-precision point prediction and reliable uncertainty interval estimation. By adopting the technical scheme of the application, under the objective constraints of small sample, high cost and strong regional heterogeneity in geothermal resource exploration, high-precision, high-robustness and energy-based uncertainty reserve prediction can be realized.

The invention discloses oil seal air tightness detection equipment for an electric vehicle driving motor. The oil seal air tightness detection equipment comprises a rack, a conveying module and an air tightness detection module. The conveying module is matched with a jacking limiting assembly through a roller conveying line, and automatic feeding, accurate positioning and rigid locking of the motor are achieved. The air tightness detection module comprises an air tightness instrument, an air tightness pressing module and an automatic point inspection module. A pressing air cylinder drives an air tightness pressing head to conduct sealing detection on the motor oil seal. The equipment innovatively adopts a jacking-support changing structure, stable stress is provided by a supporting plate, and detection micro-motion is eliminated; an automatic point inspection function is integrated, a zero leakage comparison tool can be periodically called to carry out on-line self-calibration on the system, and precision drift is effectively early warned. The motor oil seal airtightness detection device realizes full automation of the detection process, has the characteristics of high precision, high stability and intelligent self-maintenance, and ensures the consistency and long-term reliability of motor oil seal airtightness detection.

The invention provides an all-vanadium redox flow battery pump fault detection method based on a self-adaptive GCN, and the method comprises the following steps: carrying out the operation experiment of an all-vanadium redox flow battery in a plurality of operation states, collecting a battery voltage signal and an electrolyte flow signal in the experiment process, and marking the corresponding battery state as a training set; performing feature extraction and feature fusion on the battery voltage signal and the electrolyte flow signal to obtain a multi-modal node feature matrix of the all-vanadium redox flow battery; the Euclidean distance between the nodes is calculated to serve as the similarity between the nodes, a similarity matrix is constructed, neighbors with the highest similarity are screened out for each node from the similarity matrix, and an adjacent matrix is constructed; constructing a graph convolutional neural network model based on the adjacent matrix, and introducing an adaptive smoothing factor used for dynamically adjusting the smoothing strength of neighborhood features during volume accumulation; and inputting to-be-detected battery operation data into the trained graph convolutional neural network model to obtain a battery pump fault diagnosis result.

The application provides a multi-modal electric power form recognition method and device. In the execution of the method, first, the image of the electric power form to be recognized is obtained, then the image of the electric power form to be recognized is preprocessed to obtain a preprocessed image, then the CTPN neural network model is used to extract the text line position information of the preprocessed image to obtain the position information of the text line of the preprocessed image; and based on the position information of the text line, the CRNN model is used to recognize the preprocessed image to obtain the recognition result of the preprocessed image; finally, the multi-modal feature information extraction algorithm is used to extract the recognition result of the preprocessed image, and the multi-modal feature information is taken as the recognition result of the electric power form to be recognized. The application can automatically recognize the multi-modal feature information of the electric power form with the nature of the table in multiple scenes, solve the problem of low work efficiency of manual extraction, improve the electric power form recognition efficiency, and has strong robustness.

The present disclosure provides a terminal initialization method, device, electronic equipment and storage medium. The terminal initialization method comprises: in response to an initialization event of a terminal, acquiring image data of the terminal from an initial time to an end time and inertial data measured in an inertial coordinate system, the inertial coordinate system being fixed relative to the position of the terminal; establishing a linear constraint equation of a feature point according to a constraint equation of the coordinates of the feature point in a normalized plane and the coordinates of the feature point in a camera coordinate system, and a representation of the coordinates of the feature point in the camera coordinate system converted into the inertial system at the same time in the form of inertial data; projecting the linear constraint equation to a left null space of the corresponding feature point for dimension reduction; superimposing the linear constraint equations of each feature point to obtain an integrated constraint equation; and solving an initialization parameter of the terminal at the initial time according to the integrated constraint equation. The method of the present disclosure can improve the solving speed compared with the existing tight coupling initialization method, and takes into account robustness and efficiency.

This invention provides a method and system for automatically picking up the first arrival time of ultrasonic waves, relating to the field of ultrasonic signal processing. The method involves: acquiring ultrasonic signals and constructing a three-channel input tensor; constructing a mask target sequence and a heatmap target sequence based on the actual first arrival time; constructing and training an encoder-decoder network, employing a residual attention mechanism at skip connections to output modulated features in residual form; inputting the decoder output into the mask branch and the heatmap branch respectively for collaborative learning; fusing the dual-branch outputs during the inference stage, and determining the first arrival time position based on a joint scoring function. The system includes an ultrasonic signal acquisition and preprocessing module, a target label construction module, a picking network construction and training module, and a first arrival time position determination module, used to execute the above method. This invention achieves automatic, high-precision picking of the first arrival time of ultrasonic waves in complex noise environments.

The invention discloses a complex sound scene target voice selectivity enhancement method and system based on eye movement guidance, and the method comprises the steps: synchronously collecting a gazing direction and a multi-channel audio signal through a miniature eye movement tracking module and an annular quaternary microphone array, and constructing a dynamic sound source positioning mechanism based on eye movement guidance; based on visual confidence score adaptive switching or fusion beam forming and deep learning enhancement strategies, voice intelligibility evaluation and bone conduction tactile feedback closed loops are introduced, and children are actively guided to adjust gazing behaviors. The system comprises an eye movement tracking module, a sound source positioning module, an adaptive enhancement module, a feedback prompt module and the like, and is integrally light, low in delay and high in comfort. According to the invention, through multi-mode fusion and closed-loop interaction, the selective perception ability and communication efficiency of hearing-impaired children to the voice of the target speaker in a complex sound scene are improved.

The application belongs to the technical field of composite machining, and discloses a composite workpiece element automatic extraction method based on three-dimensional point cloud semantic segmentation, which comprises the following steps: obtaining a two-dimensional image and a three-dimensional point cloud of a composite workpiece element; respectively performing data preprocessing on the two-dimensional image and the three-dimensional point cloud of the composite workpiece element to obtain a co-configuration image set composed of a three-dimensional point set and a two-dimensional image; extracting the composite workpiece element in the two-dimensional image based on a convolutional neural network and obtaining a two-dimensional feature mapping set; establishing the corresponding relationship between the two-dimensional image and the three-dimensional point cloud of the composite workpiece element by using a multi-view aggregation model based on deep learning; and performing semantic segmentation on the three-dimensional point cloud to obtain corresponding point cloud sets of different composite workpiece elements. The application can realize the automatic denoising segmentation and extraction of a point cloud file scanned by a laser after the point cloud file is imported into an algorithm, and can segment the workpiece element according to the characteristics of the composite material, mark the belonging element into the belonging information of each point, and facilitate the extraction and calling.

This invention discloses a power system dispatch optimization method and system based on deep reinforcement learning, belonging to the field of smart grid optimization and dispatch technology. It includes: receiving and synchronizing real-time operational data, meteorological data, equipment health indicators, and renewable energy output data; constructing a power grid diagram and generating a node time-series matrix, encoding it as a multi-temporal embedding vector; generating short-term output prediction values ​​and uncertainty indicators based on meteorological and renewable energy output data, converting them into compensation factors; calculating risk scores based on equipment health indicators and meteorological data and mapping them to dynamic weights; inputting the multi-temporal embedding vectors, compensation factors, and dynamic weights into a deep reinforcement learning model to generate a dispatch strategy and performing feasibility verification; if the verification passes, the strategy is executed and the results are returned for online model updates. This invention effectively improves the security and robustness of power system dispatch by integrating multi-source data and a risk perception mechanism.

The invention discloses a color image encryption method based on deep learning and optical chaotic signals, and the method comprises the steps: firstly carrying out the feature extraction of a color image through a deep learning model, and obtaining feature maps of RGB channels; secondly, the side emitting laser emits light with a set wavelength, an optical chaos phenomenon is caused through a chaos system, and a chaos optical signal is output. Then, the three channel feature maps are respectively coded into holograms through a spatial light modulator, a chaotic light signal is used as an illumination light source for irradiation, an encrypted chaotic light signal is generated through phase modulation and wavelength division multiplexing, and the encrypted chaotic light signal is separated into three independent channel signals through a beam splitter according to RGB wavelengths. And finally, performing inverse modulation decryption on the channel signal, restoring the optical information, converting the optical information into an electric signal, performing image restoration on the electric signal based on a random phase mask, and decrypting the image. According to the method, the security problem of encrypted information in the image encryption process can be effectively solved, and the encryption is more targeted and robust.

The application discloses an automatic driving training method based on a difficult case pool and a decision model, and belongs to the technical field of driving training. The application comprises the following steps: S1, obtaining automatic driving observation information, training, obtaining driving actions, and calculating trajectory-level evaluation indexes; S2, determining a difficult case pool according to the trajectory-level evaluation indexes; S3, generating a difficult case robust training branch according to the difficult case pool; S4, updating an automatic driving decision model according to a regularization term of the difficult case robust training branch and a training loss of a main training branch, and obtaining a robust automatic driving decision model; and S5, outputting driving actions or behavior decisions by using the robust automatic driving decision model. The application comprises a closed-loop training mechanism composed of ordinary sample training, difficult case identification, difficult case pool updating, difficult case sampling, strong disturbance triggering and opponent robust regularization, and can simultaneously consider normal driving capability and robustness under observation disturbance.

This application relates to the field of broadband digital beamforming technology, and discloses a method and system for beam decoupling and grating lobe suppression of large-scale broadband digital subarrays. The method includes: a subarray array synthesis unit generating intra-subarray synthesized beam data based on the received digital signal and array weights within the subarray; an array beam synthesis unit generating inter-subarray synthesized beam data based on the received intra-subarray synthesized beam data and array weights between subarrays; and a beam verification unit calculating the main lobe amplitude and grating lobe amplitude based on the inter-subarray synthesized beam data, calculating the main grating ratio based on the main lobe amplitude and grating lobe amplitude, evaluating whether the current subarray partitioning result meets the actual engineering requirements based on the main grating ratio, until all valid subarray partitioning results are traversed, and taking the subarray partitioning result corresponding to the maximum main grating ratio as the final subarray partitioning result. This application significantly reduces resource consumption and hardware costs, and improves grating lobe suppression performance.

This invention provides an adaptive reactive power droop control method and system based on state of charge (SOC) sensing. By sensing the SOC of the energy storage units in real time, an adaptive reactive power droop controller is constructed to adjust the power distribution weights of each unit, achieving SOC balance and consistency among the energy storage converters. Simultaneously, to meet the demands of frequency-sensitive loads, frequency locking is achieved by directly modifying the phase angle in the reactive power control loop, ensuring frequency stability during dynamic power regulation. This invention enables precise and autonomous power balancing of distributed energy storage converter clusters in off-grid operation, providing voltage and frequency grid support for the common coupling point, and improving the operational reliability and power quality of the energy storage converter cluster.

The application discloses a planar array FDA-MIMO radar main lobe interference suppression method, and the Capon technology is used to estimate target signal power to dynamically adjust a DL loading factor; based on eigenvalue distribution of a sample covariance matrix, an adaptive subspace dimension selection strategy is introduced to balance robustness and main lobe suppression performance under different mismatch conditions; the beamformer can adjust the projection subspace and the diagonal loading coefficient according to actual conditions, and can solve the mismatch problem of the steering vector caused by the distance-angle error, the array element position error, the frequency offset and the coherent local scattering, effectively suppress the main lobe interference while improving the beam robustness, obtain better SINR performance, and has wide application value and promotion prospect.

A kind of motion information and visual information fusion mechanical arm space pose self-adapting estimation method, first, the inner parameter matrix of binocular camera is combined, and three-dimensional reconstruction of two-dimensional image features is realized based on polar line constraint method;Then, the "hand-eye calibration" is completed by the combined measurement mode of laser tracker and binocular camera, and the relative position relationship between the machining end of mechanical arm and the center of workpiece is calculated based on the calibration relationship;Finally, an adaptive extended Kalman filter is proposed, the known binocular camera measurement noise covariance matrix is combined, the state noise covariance matrix of the end of mechanical arm at different positions is adaptively adjusted, the smooth estimation of the end position of mechanical arm during motion is realized, and finally the end of mechanical arm is accurately moved into the effective field of view range of binocular camera.The method solves the problem that the end position of mechanical arm is difficult to accurately estimate under complex working conditions, and the adaptive accurate estimation of the end position of mechanical arm can be realized without prior accurate observation and motion noise covariance matrix.