2525 results about "Kalman filter" patented technology

The invention discloses an unmanned ship multi-agent collaborative obstacle avoidance method and system for complex sea conditions, and relates to the field of unmanned ship multi-agent collaborative obstacle avoidance, and the method comprises the steps: obtaining the real-time data of each unmanned ship and the surrounding environment; generating a candidate obstacle target point cloud cluster list based on a density clustering algorithm; on the basis of a Kalman filter, real-time absolute motion state estimation of the candidate obstacles is obtained, and an obstacle feature list is output; determining a safety radius compensation amount required by autonomous obstacle avoidance of each unmanned ship; obtaining the safe sailing space of each unmanned ship at the current moment; constructing a global synthetic potential field, and generating a group of optimal alternative paths of the unmanned ship from the current position to the target point; on the basis of adopting a consensus binding algorithm, an unmanned ship multi-agent collaborative obstacle avoidance path reaching a consensus is obtained. The method has the advantages that safe, efficient, cooperative and consistent intelligent obstacle avoidance of a multi-unmanned-ship cluster in a real complex marine environment is realized.

The invention relates to the technical field of construction surrounding rock stability evaluation, discloses a tunnel surrounding rock stability evaluation method and system based on multi-physics field parameter inversion, and aims to solve the problems that an existing method is insufficient in data collaboration, high in parameter inversion multiplicity and poor in surrounding rock stability evaluation accuracy and timeliness. According to the scheme, the method mainly comprises the steps that an acousto-optic electromagnetic vibration drill multi-mode sensing array is arranged, and time-space synchronization is implemented; establishing a mutual interference entropy spectral density model to realize multi-physics field collaborative excitation and acquisition; a unified feature vector is obtained through data correction, feature extraction and weighted fusion; a joint inversion objective function embedded with rock physical constraints is constructed, a three-dimensional physical property parameter field is obtained through inversion, and a dynamic permeability field is calculated in combination with acoustic emission energy; and finally, dynamically updating the model by utilizing ensemble Kalman filtering, and obtaining a final risk probability based on updated parameters and seepage-uncertainty coupling correction. According to the method, the accuracy, the real-time performance and the reliability of the stability evaluation of the surrounding rock of the deep-buried tunnel are improved.

The invention relates to a microphone array sound source positioning method and system based on cross-correlation-beam forming closed-loop optimization, and belongs to the technical field of sound source positioning. The method comprises the following steps: collecting multichannel sound signals through a microphone array and preprocessing the multichannel sound signals to extract time-frequency features and suppress noise interference; time delay information among the microphones is estimated by adopting a generalized cross-correlation phase transformation algorithm, and an optimization strategy is introduced to improve estimation stability and anti-interference performance; enhancing the target sound source signal in combination with a minimum variance undistorted response beam forming algorithm and an adaptive Kalman filtering mechanism; constructing a closed-loop feedback optimization mechanism based on the beam output signal to realize feedback adjustment; and adopting a hybrid network architecture, taking the beam output signal amplitude spectrum as input, and outputting the frequency spectrum or mask of the obtained target sound source signal. The method has the advantages of high calculation efficiency, high positioning precision and strong anti-interference capability, and is suitable for real-time acoustic signal processing in a complex environment.

The invention discloses a thermal management strategy management method and system based on an energy storage battery system, and relates to the technical field of battery thermal management, and the method comprises the steps: collecting temperature data, carrying out the preprocessing, generating and recording a measurement result and a convective heat transfer coefficient, taking the measurement result and the convective heat transfer coefficient as the input of a Kalman filter, and outputting an optimal estimation result of a battery. The temperature data refer to the environment temperature and the core temperature of the single battery, based on the optimal estimation result of the battery, establishing a fuzzy rule base for iteration and updating, outputting a dynamic temperature difference threshold value, constructing a three-dimensional thermodynamic potential function, calculating the cosine similarity of a cooling driving force vector and a cooling mode, and correcting the cosine similarity to obtain a cooling driving force vector. The mode switching list is generated, the cooling equipment decision mode is generated, starting judgment and optimization of the cooling mode are carried out, and the intelligent level, safety and energy efficiency ratio of thermal management of the energy storage battery system are remarkably improved.

The invention discloses an intelligent welding control system and method based on multiple sensors and electronic equipment, and belongs to the technical field of automatic welding, the system comprises a sensing layer, a decision-making layer and an execution layer, through cooperative work of a front laser vision sensor, a rear laser vision sensor and a molten pool image sensor, a groove three-dimensional model can be accurately established before welding, and the welding precision is improved. And dynamic information of the molten pool is continuously collected in the welding process, a dual monitoring mechanism for the geometric characteristics of the welding line and the state of the molten pool is formed, and multi-source data collection and fusion in the whole welding process are achieved. The control method comprises the steps of pre-scanning modeling, feedforward parameter planning, real-time feedback adjustment of a molten pool, data fusion optimization and the like, and high-precision self-adaptive control over welding parameters is achieved by combining deep learning and the Kalman filtering technology. According to the method, the welding adaptability, the control precision and the intelligent level can be remarkably improved, and the method is suitable for high-precision welding scenes such as pipelines, pressure containers and steel structures and has remarkable engineering application value and popularization prospects.

The invention discloses an intelligent power prediction method considering dynamic load change, and relates to the technical field of power grid load prediction, and the method comprises the steps: collecting original load data, carrying out the preprocessing, constructing a VMD constraint optimization model, carrying out the four-stage improvement of optimization parameters through employing an improved dung beetle optimization algorithm, so as to generate IMF components, reconstructing the IMF component by calculating a sample entropy to obtain a low-frequency component and a high-frequency component; establishing a Kalman filtering state space model based on the low-frequency component, and decomposing the low-frequency component into a residual component and a pseudo trend component through a Kalman filtering recursive algorithm; external features are obtained, the high-frequency component, the residual component and the pseudo trend component are aligned and spliced with the external features, multi-component collaborative prediction is carried out through a local-global interactive attention mechanism, and a final load prediction result is obtained; and generating a power demand visualization chart based on the final load prediction result. And reliable decision support is provided for power dispatching and energy management.

The invention discloses an electric power control method based on a diesel generator, commercial power and photovoltaic grid connection. The method comprises the following steps: acquiring operation data of commercial power, a diesel generator and photovoltaic-energy storage through an energy management system, constructing a multi-target dynamic optimization model, and distributing power by taking the lowest comprehensive operation cost, the highest power supply reliability and the minimum carbon emission as targets; the power analysis and regulation unit adopts a double-loop mechanism of outer-loop Kalman filtering prediction compensation and inner-loop model prediction control, synchronizes the voltage, frequency and phase of the side to be connected with the grid and the power grid side, and smoothly switches the power change rate of each energy source in the process through a self-adaptive sliding mode control strategy; and the grid-connected switching unit intelligently pre-judges the grid-connected switching opportunity by using a long short-term memory (LSTM) network and fuzzy logic fusion model. According to the invention, the energy utilization efficiency and the power supply stability are improved, multi-energy intelligent dynamic regulation and control are realized, the comprehensive operation cost of the system is effectively reduced, and reliable power supply under complex working conditions is ensured.

The invention discloses a multi-source navigation data fusion method for an unmanned loader, which comprises the following steps: data acquisition: acquiring environmental perception data of satellite navigation, an inertial measurement unit IMU, a wheel type odometer and a laser radar and camera in real time; performing space-time synchronization preprocessing, realizing multi-source data time synchronization, unifying environment sensing data to a body coordinate system, eliminating abnormal data, and complementing missing data; carrying out dynamic weight calculation, establishing an error model of each sensor, and adjusting a fusion weight by using an error reciprocal exponential weighting method; layering fusion is carried out, a satellite and an IMU are fused through bottom-layer extended Kalman filtering (EKF), a laser radar and a high-precision map are fused through middle-layer ICP, a middle-layer result and a wheel type odometer are integrated through high-layer federated filtering, and high-precision fusion is achieved; and outputting and optimizing a result, outputting navigation data, performing closed-loop optimization, monitoring the health degree of the sensor, and executing redundancy switching when a fault occurs. The system comprises a corresponding processing unit, and a storage medium stores a program for executing the method.

The invention discloses a multi-target tracking method combining camera motion compensation and pseudo depth estimation, belongs to the field of computer vision, and is suitable for a complex automatic driving road environment. The method comprises the following steps: constructing a training set and a test set; detecting the image by using a deep learning detector and extracting features; a Kalman filter is adopted to correct a motion modeling state vector, and the target position and size prediction precision is improved; solving a homography matrix through feature point matching, performing global camera motion compensation, and reducing camera jitter and displacement interference; target pseudo depth information is calculated, hierarchical cascade matching is carried out, and association performance in dense and shielding scenes is optimized; a three-level cascade strategy is adopted to complete high confidence degree, low confidence degree and residual target matching in sequence; and finally, outputting a tracking result with a detection frame and identity information to obtain a trained model. According to the invention, accurate detection and stable tracking of multi-category targets can be realized in a complex environment, and identity switching is effectively reduced.

The invention discloses a multi-modal measurement data fusion method for an unmanned aerial vehicle. The method comprises the following steps: synchronously acquiring multi-modal measurement data through a plurality of sensors carried on the unmanned aerial vehicle; performing preprocessing including time synchronization and space alignment on the data; based on the environmental reliability evaluation model, dynamically analyzing reliability parameters of each sensor in the current environment, and generating a dynamic weight for each sensor data; and utilizing a fusion module to carry out adaptive weighted fusion on the preprocessed data according to the dynamic weight, and generating a fusion result containing unmanned aerial vehicle global pose estimation and an environment map. The device comprises functional modules corresponding to the steps. The core of the method is that through dynamic reliability evaluation and weight generation, the weight is introduced into the Kalman filter as an observation noise covariance adjustment factor, adaptive optimization of the fusion strategy to the environment is realized, and the pose estimation precision, the system robustness and the long-term operation stability of the unmanned aerial vehicle in a complex scene are remarkably improved.

The invention discloses a temperature measurement sensor data processing method and system in a chemical synthesis environment. The method comprises the steps of original data acquisition and preprocessing, sensor drift compensation, dynamic response optimization and anomaly detection and adaptive calibration. The system comprises an original data acquisition and preprocessing module, a sensor drift compensation module, a dynamic response optimization module and an anomaly detection and adaptive calibration module. Through multi-module deep fusion of LSTM drift compensation and adaptive Kalman filtering, + / -0.5 DEG C measurement precision is realized in a range of 80-300 DEG C; the MPC predictive control compresses the thermal inertia lag of the sensor from 500ms to less than or equal to 80ms, so that the sudden rising / dropping temperature in the synthesis reaction can be effectively captured, and the product decomposition caused by local overheating is avoided; isolated forest anomaly detection and least square calibration are combined, so that the maintenance period of the sensor in a strong corrosion environment is prolonged from 7 days to 90 days, and the annual maintenance cost is reduced by 65%.

The invention discloses a photovoltaic module service life prediction method and a health management system. The life prediction method comprises the following steps: acquiring degradation data of a photovoltaic module, and segmenting the degradation data based on a sliding window; reconstructing the degradation data in each sliding window in combination with Kalman filtering and an RTS smoothing algorithm to obtain a degradation track; according to the degradation track, based on a degradation model and a preset failure threshold value, probability distribution of the remaining life of the photovoltaic module is determined; wherein the degradation model is constructed based on a Wiener process, and posterior distribution of parameters is estimated by using a variational inference method. According to the invention, accurate evaluation of the running state of the photovoltaic module and high-precision prediction of the residual life can be realized, so that reliable support is provided for intelligent operation and maintenance and predictive maintenance of a photovoltaic power station.

The invention relates to the technical field of spacecraft orbit control and space safety, and discloses a space debris orbit prediction and avoidance method, which comprises the following steps that: a ground computing center screens candidate targets and generates enhanced data packets for uploading; the spaceborne computer combines real-time navigation data to carry out geometric feature screening, and an instant high-risk target list is generated; controlling the star sensor to execute optical observation, and resolving an optical measurement relative orbit state by utilizing displacement superposition and Kalman filtering; selecting a high confidence coefficient or a conservative probability threshold according to the optical measurement state acquisition condition, and generating an avoidance instruction when the collision probability exceeds the limit; and the propulsion system responds to the instruction to execute avoidance, verifies the effect by confirming the semi-major axis variation and executes track recovery by selecting an aircraft. According to the method, the ground-air collaborative screening and shift superposition enhancement technology is adopted, the problems that the low-precision ephemeris false alarm rate is high and the detection capability of a satellite-borne sensor on a dark and weak target is insufficient are solved, and the accuracy of evasion decision making and the execution reliability are improved.

The invention relates to the technical field of the Internet of Things, and discloses an animal physiological feature data acquisition system and analysis method based on the Internet of Things, and the method comprises the following steps: cleaning and standardizing original sensor data, including filling missing data, eliminating noise interference through a dynamic filtering technology, unifying the dimension of multi-source data, and analyzing the data; the method comprises the following steps of: acquiring a Beidou time service time reference, eliminating redundant features, interpolating and aligning asynchronous sampling data to the Beidou time service time reference, correcting a time sequence phase difference of physiological and behavior data through a dynamic path matching technology, and correcting GPS / Beidou positioning drift in combination with Kalman filtering. Through multi-source sensing fusion and an edge-fog-cloud cooperative computing architecture, the effective collection rate of animal physiological data is achieved, abnormal response is delayed and compressed, the flexible energy supply technology and dynamic communication optimization are combined, the endurance and communication success rate is maintained in a pasture, and the false alarm rate is reduced compared with a traditional method; common diseases such as abnormal body temperature, digestive system diseases and the like are accurately warned and covered.

The invention provides an outbound routing method and system based on multi-dimensional index coupling and large model decision, and the method comprises the steps: carrying out the preprocessing of the historical data of each line provider, predicting the future performance index through an LSTM neural network, and optimizing the prediction result through a Kalman filter, and eliminating the noise. In the route decision-making link, the system calculates the Q value of each line provider based on the deep Q learning network, comprehensively considers the call completing rate, average cost, negative feedback rate and other factors of each line provider, and intelligently selects the optimal line provider in combination with the user portrait, scene recognition and real-time load state. The system also constructs a complete feedback closed loop, continuously optimizes the model by collecting call result data, and automatically adjusts decision parameters. The call completing rate and the service quality of the outbound service are improved, and the communication cost is reduced.

The invention provides a photovoltaic power station equipment health state assessment method, which belongs to the technical field of photovoltaic power stations, and comprises the following steps of: after performing wavelet transform decomposition and Kalman filtering noise reduction processing on power data, establishing a dynamic statistical baseline and extracting a power attenuation trend index; converting real-time power into standard test condition power by using an environment normalization model so as to eliminate the influence of environmental factors, constructing a game optimization model, obtaining an optimal detection threshold and a sensitivity parameter through iterative solution of upper-layer detection optimization and lower-layer threshold optimization, and outputting an equipment degradation type and degree score in combination with a health state recognition model. And finally, aggregating the health indexes of the equipment through an analytic hierarchy process to form a power station level comprehensive health index, thereby solving the technical problem of high false alarm rate caused by difficulty in balancing environmental factor interference and early fault detection sensitivity in a photovoltaic power station equipment health state evaluation process.

The invention provides an intelligent positioning system for precise butt joint of fabricated building components, and belongs to the technical field of fabricated building construction. The system comprises a multi-source heterogeneous sensing module, an intelligent positioning host, an execution adjustment module, a BIM cooperation module and an environment adaptive calibration module. The multi-source heterogeneous sensing module is integrated with a laser radar, a visual sense, a UWB and an attitude sensing unit, and data synchronous acquisition is realized through a self-created time synchronization mechanism; the intelligent positioning host adopts a three-layer architecture of preprocessing, feature fusion and decision output, and processes data in combination with improved Kalman filtering and a dynamic deviation correction model; the execution adjustment module realizes six-degree-of-freedom posture adjustment based on a cross type hydraulic driving structure, and is matched with force feedback compliant control; the BIM collaboration module constructs twin mapping and generates a deviation thermodynamic diagram; and the environment self-adaptive calibration module realizes quick calibration of environment abrupt change. The problems that traditional positioning is low in precision, poor in efficiency, weak in environment adaptability and the like are solved, and the method is suitable for precise butt joint of various assembly type components.

The present invention relates to the fields of PID control and Kalman filtering, and disclosed is an improved PID control method based on Kalman filtering. The present invention comprises: in the process of acquiring a target value of an expected signal, taking the difference between the target value and a current feedback quantity of a controlled object to obtain an error value, acquiring an output of PID from the error value, applying the output of PID to the controlled object to obtain a noisy output signal, then, after Kalman filtering, feeding the output back to PID, continuously iterating to make the feedback quantity approach the target value, and finally obtaining a stable output signal. Variable-speed integration and integral separation algorithms are added to an integral term of PID to change an integration speed on the basis of a system deviation, and an incomplete differential algorithm and a low-pass filter are added to a differential term of PID to prevent high-frequency interference. According to the present invention, the optimal estimation of a system state is obtained by means of prediction of a current state and updating of observation data, thereby reducing the influence of measurement errors and noise, and improving the performance and stability of a PID controller.

The invention provides a road inspection target positioning method based on multi-source data fusion, and relates to the field of road daily inspection and infrastructure performance monitoring, and the method comprises the following steps: obtaining multi-source data based on a preset multi-source sensor; through deep coupling of satellite positioning carrier double-difference observation and inertial navigation solution of an inertial sensor, error correction is carried out in combination with Kalman filtering recursion, and fusion navigation data is determined; according to the fused navigation data, carrying out fusion processing on the two-dimensional visual observation data and the three-dimensional space point cloud data to obtain a forward view depth map; determining the national geodetic coordinates of the disease by combining a depth value back calculation method and utilizing a forward view depth map; and obtaining a starting kilometer pile number in a road inspection process, and determining a mileage pile number of the disease by combining the national geodetic coordinate of the disease and utilizing a Gaussian positive algorithm. According to the invention, centimeter-level geographic coordinate positions of diseases and corresponding mileage stake numbers can be output.

The invention provides a wetland ecosystem health evaluation method based on multi-source remote sensing data, and belongs to the technical field of wetland ecosystems, and the method comprises the steps: carrying out the dense dark pixel and Kalman filtering cooperative atmospheric correction of a multi-spectral remote sensing image to obtain a surface reflectance image, and achieving the precise segmentation of a wetland landscape through a graph cut theory, a spectral unmixing model based on an improved Gaussian kernel is utilized to embed physical constraints to invert water quality parameters, a laser radar canopy height priori constraint three-dimensional radiation transmission model is combined to invert vegetation parameters, and a mixed pixel decomposition result is optimized through a Markov random field. A water quality, vegetation and landscape three-dimensional comprehensive health evaluation system is constructed, degradation dominant factors are identified, and the technical problem that it is difficult for multi-source remote sensing data to cooperatively invert wetland ecosystem multi-dimensional health state parameters is solved.

The invention relates to the technical field of attitude determination of a photoelectric pod of an unmanned aerial vehicle, in particular to an MEMS-IMU dual-mode correction attitude determination method for the photoelectric pod of the unmanned aerial vehicle. Comprising the following steps: establishing a damping system second-order transfer function model, and outputting an attenuation coefficient matrix; based on MEMS-IMU three-axis motion parameters, distinguishing a large maneuver turning state and a linear flight state; respectively compensating a roll angle, a pitch angle and a course angle of the MEMS-IMU by using the main inertial navigation according to different states; converting the corrected attitude angle into a quaternion, and optimizing and outputting a fused quaternion through a gradient descent method; taking the fused quaternion as an input, constructing a seven-dimensional state space model, and outputting an error compensation amount through Kalman filtering; dynamically adjusting the main inertial navigation weight according to the accelerated speed root mean square; and outputting a final attitude angle in combination with the error compensation amount and the weight, and verifying the final attitude angle through GNSS (Global Navigation Satellite System) position inversion. The method has the advantages that cross interference is avoided; therefore, the method is suitable for the photoelectric pod scene of the unmanned aerial vehicle.

The invention discloses a GRACE and Swarm time-varying gravity field fusion filtering method based on a state space model, and the method comprises the steps: taking a spherical harmonic coefficient as a state quantity, constructing a random walk process equation, introducing three types of observations, employing a quantization parameter for observation noise and process noise covariance, constructing according to an order / power law, and carrying out the self-adaptive updating along with the monthly; a Nelder-Mead method is adopted to search for spectral index parameters, and an EM algorithm and a statistical method are utilized to update other parameters in a closed / quasi-closed mode; obtaining the state and posterior covariance of a full time sequence by using Kalman filtering and RTS smoothing; in the GRACE and GRACE-FO window period, continuous reconstruction is carried out by means of a process model and Swarm; and outputting quality evaluation information including monthly gravity field coefficients, posterior covariance, innovative variance ratio, residual whitening test, space power spectrum, uncertainty band and the like. According to the method, while physical rationality and calculation feasibility are ensured, a continuous and stable monthly time-varying gravitational field sequence with quantifiable uncertainty is realized.

The invention relates to the technical field of dynamic visual target motion tracking control, in particular to a dynamic visual target motion tracking control method and system based on deep learning, and the method comprises the steps: synchronously collecting continuous multi-frame target scene image data through a visual multi-frame collection module; and performing time sequence association and memory fusion on target features in continuous multi-frame target scene image data through a cross-frame feature memory fusion module, and constructing a target feature model. According to the invention, the current and historical stable features are dynamically fused through the cross-frame feature memory fusion module, time sequence association is realized in combination with the long and short-term memory network, and the problem of slow feature model updating under target deformation and shielding is solved; the deformation-shielding bimodal recognition module accurately recognizes a scene state, provides a basis for the multi-branch Kalman filtering prediction module, enables the multi-branch Kalman filtering prediction module to call a corresponding branch, corrects a prediction equation through a compensation factor, and improves the position prediction accuracy.

The invention relates to the technical field of mobile energy storage charging piles, and discloses a fault detection method and system for a mobile energy storage charging pile, and the method comprises the steps: collecting a voltage and current original sequence during an operation period, and executing the Kalman filtering to obtain a smooth electrical parameter; adaptively adjusting a process noise covariance matrix by using numerical dispersion to obtain a stable internal resistance value sequence; extracting a fluctuation trend and matching a working condition mode to filter periodic disturbance to obtain a pure internal resistance offset sequence; extracting a health degradation rate slope, establishing a dynamic health reference range by using kernel density estimation, and judging an abnormal migration event according to the dynamic health reference range; the offset severity is calculated through severity probability weighted fusion, the fault early warning level is obtained, and an event trigger counter reset mechanism is driven to judge the overall health state. The method can solve the problem of low fault detection accuracy in the prior art.

The invention provides a satellite weak and small target on-orbit observation system and method based on intelligent closed-loop feedback, and the system comprises a detection module which carries out the image enhancement and time sequence consistency enhancement of an infrared image, obtains an enhancement feature, and obtains a candidate target set based on the enhancement feature; the tracking module is used for carrying out target matching in the search area; when the matching succeeds, the candidate position is used as an observation value, and the observation value is input into a Kalman filter to obtain a target state vector; when the matching fails, taking a prediction state of the Kalman filter as a target state vector, expanding a search window by taking a prediction position as a center, and executing re-identification; the control module is used for mapping the target state vector into an attitude error and generating a control instruction by adopting a single-neuron self-adaptive PID (Proportion Integration Differentiation) controller; and the closed-loop scheduling module is used for dynamically adjusting operation parameters of at least one module according to the execution error and the detection confidence coefficient. According to the invention, the continuous tracking precision and attitude control stability of the weak and small target are improved.

The invention discloses a degradation scene-oriented multi-residual fusion laser radar positioning method, which comprises the following steps of: firstly, performing state prediction by adopting an iterative extended Kalman filtering framework and an IMU (Inertial Measurement Unit), and constructing three complementary observation models of a global map matching residual, a local point-to-plane geometry residual and a luminosity residual; secondly, designing a degradation sensing mechanism based on a covariance ellipsoid, representing absolute and relative degradation degrees through a condition number and an information entropy respectively, realizing quantitative evaluation of system observability, and dynamically adjusting fusion weights of observation residuals; meanwhile, a self-adaptive weight strategy based on luminosity Jacobi intensity is introduced; and finally, performing anomaly detection through deviation comparison between the IMU predicted pose and the IEKF estimated pose, inhibiting pose jump, and ensuring continuity of a positioning time sequence. The method effectively overcomes the challenges of geometric constraint deficiency, positioning drift accumulation and the like of the LiDAR positioning system in the geometric degradation environment, does not need to adjust parameters for a specific scene, and improves the precision, robustness and real-time performance of global positioning in the degradation environment.

The invention relates to the technical field of sensor data fusion, in particular to a real-time dynamic attitude stability control method and system based on multi-sensor fusion, and the method comprises the following steps: collecting multi-source sensor data, and correcting to generate a synchronous sensor data set; calculating a stability coefficient, a signal quality coefficient and a consistency coefficient, weighting the signal quality coefficient and the consistency coefficient to generate a dynamic fusion weight coefficient, executing weighted fusion based on the weight, outputting a fusion attitude state estimation result through adaptive extended Kalman filtering, and generating an optimization control instruction sequence by combining with dynamic constraint model rolling optimization; according to the method, a dynamic weight mechanism based on noise and signal strength is introduced to achieve self-adaptive fusion, the synchronization precision and reliability are improved by combining consistency verification, abnormal signals are corrected through self-adaptive filtering, the attitude estimation stability is enhanced, and the performance of the system is improved. And a control instruction is generated through rolling optimization, so that rapid and accurate adjustment is realized, and attitude keeping and control consistency in a complex environment is improved.

The invention discloses an automatic driving carrying equipment scheduling system and method for an industrial robot. The system comprises a plurality of automatic driving carrying devices, a central dispatching device and corresponding communication modules. The system adopts a multi-agent reinforcement learning framework and combines a graph neural network processing environment topological structure to realize dynamic path planning and multi-device collaborative scheduling; integrating an energy consumption prediction mechanism based on a Kalman filter, and bringing energy consumption factors into a task allocation decision; meanwhile, a fault-tolerant management mechanism based on a distributed account book and federated learning is established, and fault detection and rapid recovery are achieved. The technical modules are deeply coupled, and a unified collaborative optimization framework is formed through reward function design, utility function optimization and fault probability calculation of multi-agent reinforcement learning. According to the method, the problems of poor dynamic environment adaptability, isolated decision making of each module, extensive energy consumption management and the like in the prior art are effectively solved, and the overall efficiency, energy efficiency and reliability of a scheduling system are remarkably improved.

The invention discloses an unmanned aerial vehicle tracking method and device based on vision and point cloud fusion and a medium, and relates to the field of unmanned aerial vehicles, and the method comprises the steps: building a total energy function based on each frame of binocular image and each frame of laser radar point cloud, and taking the minimum total energy function as a target, and determining each frame of camera-radar extrinsic parameter matrix; inputting the binocular image sequence into a target detection model to obtain a plurality of target detection results corresponding to each frame of binocular image, and obtaining a bounding box through three-dimensional coarse positioning; projecting the bounding boxes into a radar coordinate system by using a projection expression based on the camera-radar extrinsic parameter matrix to obtain three-dimensional positions of the plurality of bounding boxes in the radar coordinate system; and processing the local point cloud subset by using a bisecting K-means algorithm and a Kalman filter to obtain three-dimensional positions and attitude parameters of all target unmanned aerial vehicles, and completing unmanned aerial vehicle tracking. According to the invention, the detection precision and tracking precision of the unmanned aerial vehicle can be improved in a GNSS limited environment.

The invention provides an energy storage battery management system and a control method, belongs to the field of energy storage battery management, and is used for solving the problems of low battery state estimation precision, insufficient fault diagnosis sensitivity and poor full life cycle adaptability in related technologies. By combining a neural network-Kalman filtering cascaded state estimation algorithm and a twin structure feature coding fault diagnosis algorithm and matching with an edge-cloud collaborative model self-evolution mechanism, accurate estimation of a battery state and timely diagnosis and positioning of a fault are realized, and the full-working-condition adaptability and operation reliability of a system are improved.