101 results about "Particle filtering algorithm" patented technology

The invention discloses a lithium battery health assessment method based on an adaptive extended particle filter algorithm. The method comprises the following steps: constructing a second-order RC equivalent circuit model of a lithium battery; determining all parameters in the lithium battery second-order RC equivalent circuit model by using a recursive least square algorithm with a forgetting factor to obtain a determined lithium battery second-order RC equivalent circuit; estimating SOC parameters of the lithium battery at the moment by combining a particle filtering algorithm with an adaptive extended Kalman filtering algorithm; correcting the terminal voltage of the second-order RC equivalent circuit of the lithium battery at the moment to obtain the terminal voltage of the equivalent circuit; and based on the lithium battery second-order RC equivalent circuit and the corrected terminal voltage, estimating the SOE parameter of the lithium battery at the moment k by using a particle filtering algorithm in combination with an extended Kalman filtering algorithm. The SOC and the SOE of the lithium battery are accurately estimated, and the sampling efficiency and the estimation stability of particle filtering are improved. Cooperative joint estimation of SOC and SOE complements each other, and the accuracy of overall state estimation is further improved.

The invention discloses an urban particulate matter migration path identification method based on data fusion of a dynamic diffusion model and multi-source sensing. According to the method, firstly, multi-source pollution related data of a fixed monitoring station, a mobile monitoring device, a meteorological observation node and a traffic sensing system are collected, and standardized input is constructed after time synchronization, coordinate mapping and exception handling. And then establishing a hybrid dynamic diffusion model fusing a two-dimensional Gaussian plume model and a Lagrange particle tracking mechanism, and realizing assimilation of monitoring data and model output in combination with an improved particle filtering algorithm to obtain a correction concentration field. Based on concentration gradient analysis and particle trajectory superposition, a pollution migration path is extracted, then indexes such as a migration intensity index (MII), a path stability factor (PSF) and path average correlation are calculated, and recognition and sorting of a migration direction, a pollution source position and path stability are achieved. Meanwhile, an alpha dynamic weight factor is introduced, adaptive weighting is realized between the Gaussian model and the LPDM model, and the path strength and consistency are considered. Experimental results show that the method is superior to a traditional method in average absolute deviation (MAD) and hot spot overlap ratio (IoU) indexes, diffusion and migration rules of particulate matters under complex urban conditions can be more accurately revealed, and the method has important environmental governance and emergency management application value.

The invention relates to a tunnel construction progress model generation method based on artificial intelligence. The method comprises the following steps: acquiring multi-source heterogeneous data such as tunnel field equipment operation and geological change, and performing multi-modal fusion to form a unified data set; based on this, a particle filter algorithm is used to correct equipment positioning deviation, and a real-time position optimization result is generated. Comparing the time and space with the progress plan, and identifying and quantifying deviation points through a critical path method and a deviation analysis algorithm to form deviation data. And constructing a construction progress model by combining construction basic parameters and adopting a genetic algorithm, generating an adjustment scheme by taking a construction period and resources as targets, and obtaining an optimal scheme through compliance screening and fitness calculation iterative optimization. By the adoption of the method, efficient integration of heterogeneous data can be achieved, the equipment positioning precision is improved to the centimeter level, multi-dimensional quantification of progress deviation is achieved, progress deviation recognition and resource allocation efficiency is improved, and a technical scheme is provided for tunnel intelligent construction.

The invention relates to the technical field of hybrid power system power distribution, and provides a hybrid power operation platform power distribution method suitable for narrow lane operation. A state acquisition module, a fuzzy preprocessing unit, a particle filter estimation unit, a model prediction control unit and a self-adaptive control unit are designed, vehicle inclination angle, load and wheel speed data are acquired in real time, and terrain uncertainty factors are output through fuzzy logic processing; estimating hidden state variables such as the wheel slippage rate and the energy margin by adopting a particle filtering algorithm; performing multi-stage power distribution optimization by applying a model predictive control technology to realize coordinated control of the diesel engine and the electric pump; and finally, self-adaptive feedback and switching are executed, wherein the power modes are switched according to the uncertainty factors, inclination angle and load safety protection is implemented, and braking energy is recycled. The terrain adaptability, the power distribution precision and the energy utilization efficiency of the working platform in the narrow lane complex environment are effectively improved, and meanwhile the operation stability and the environment friendliness are enhanced.

The invention relates to an unmanned aerial vehicle anti-interference passive positioning method and system based on GRU and particle filtering fusion, belongs to the field of navigation and positioning, and solves the problems that an unmanned aerial vehicle positioning signal is unstable and is easy to drop in a complex environment in the existing method. Comprising the following steps: acquiring the time difference of arrival of radio communication signals of at least one unmanned aerial vehicle by using a plurality of TDOA passive positioning nodes, and generating a historical state sequence of each unmanned aerial vehicle based on the time difference of arrival; wherein the state value in each historical state sequence is a TDOA observation value and comprises three-dimensional coordinate data and three-dimensional speed data; inputting the historical state sequence of each unmanned aerial vehicle into a plurality of pre-trained GRU neural networks, and generating proposed distribution of the state of each unmanned aerial vehicle at the next moment based on the GRU state sequences output by the plurality of networks; and obtaining a state prediction result of each unmanned aerial vehicle based on a proposal distribution fusion particle filtering algorithm of the state of each unmanned aerial vehicle at the next moment. And continuous, smooth and high-precision positioning of the unmanned aerial vehicle under the condition of discontinuous signals is realized.

The invention relates to the technical field of machining cutter wear prediction, and provides a milling cutter wear prediction method based on parallel spatial-temporal feature extraction and physical description, and the method comprises the steps: collecting a cutter wear monitoring signal, carrying out the denoising of the signal through VMD decomposition in combination with a PCC coefficient, and constructing a data set; inputting data in the data set into a CNN-BiGRU parallel structure prediction model, performing joint modeling on local and global features of tool wear, and introducing an improved space-time double attention mechanism to enhance features; selecting a prediction model hyper-parameter and constructing a training unit to carry out performance evaluation to obtain an optimal prediction result; and constructing a staged physical degradation model based on tool wear characteristics, introducing a particle filter algorithm, and dynamically updating parameters of the physical degradation model by taking a prediction result of a prediction model as an observed quantity to realize residual service life estimation. According to the method, the prediction precision, the physical interpretability and the result credibility are improved.

The invention discloses a virtual reality (VR)-based landscape design dynamic display method and system, and the method comprises the steps: collecting real-time environment data and plant growth cycle information through a multi-source sensor, carrying out the processing through a particle filtering algorithm, and obtaining a preliminary time-space alignment dynamic data set; according to the dynamic data set subjected to the preliminary time-space alignment, a neural network model is adopted to train the mutual influence relation among the key feature parameters, and a coupling coefficient matrix is determined; obtaining a logic rule of environment interaction from the coupling coefficient matrix to obtain an interaction graph containing time-space consistency; on the basis of a node connection structure in the interaction map, updating a dynamic state value of each node through iterative simulation equipment to obtain an optimized scene evolution sequence; if a part with inconsistent data exists in the optimized scene evolution sequence, determining a collaborative driving model; and generating a rendering data stream of virtual display according to the collaborative driving model, and generating a continuous landscape dynamic result.

The invention discloses a thermal management method for a power battery module of a new energy automobile, which comprises the following steps of: acquiring current and voltage data and temperature readings of each single battery through a sensor array to obtain an initial heating state data set; determining a real-time internal resistance estimation value of each single battery based on the initial heating state data set; the instantaneous heating power of each single battery is calculated by combining the real-time internal resistance estimation value with the current and voltage data, and a heating power distribution matrix is obtained; determining a local high temperature distribution pattern according to the heating power distribution matrix; obtaining total available flow resources of the cooling medium according to the local high-temperature distribution pattern to obtain a flow resource pool; according to the flow resource pool and the local high-temperature distribution pattern, a particle filtering algorithm is used for optimizing the distribution proportion, and the cooling medium flow share corresponding to each single battery is determined; and the opening degree of an actuator control valve is adjusted through the determined cooling medium flow share, and the overall temperature equilibrium state of the module is obtained.

The invention discloses an intelligent robot trajectory planning method and system based on visual inspection. The method comprises the following steps: acquiring obstacle motion state data through a sensor array, performing time alignment and multi-modal fusion, and constructing an initial state vector; iteratively updating the state by adopting a particle filter algorithm, and generating a smooth trajectory sequence through continuity correction and time sequence integration; determining a potential collision area based on polynomial fitting, and performing extension to obtain a predicted collision boundary; extracting passable grids to construct a dynamic risk map, screening low-risk routes, and sequencing to determine an optimized path; performing correction according to deviation feedback by simulating a forward verification path to generate a final execution instruction; state deviation is continuously monitored in the execution process, and the control scheme is dynamically adjusted. According to the invention, real-time trajectory planning and intelligent path optimization of the robot in a dynamic obstacle environment are realized, and the movement safety of the robot is remarkably improved.

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

The application belongs to the technical field of target tracking and fusion, and relates to a nonlinear filtering distributed target tracking method based on variational inference. Measurement noise parameters of a conventional nonlinear filtering distributed algorithm are assumed to be known, and the application extends the nonlinear filtering distributed algorithm based on variational inference to a single-target tracking scene under unknown measurement noise parameters. Under the distributed algorithm, weight likelihood parameters of each sensor to the target state tend to be consistent, so that the algorithm has strong robustness, small calculation amount, high flexibility and timeliness, and in combination with the variational inference method, the algorithm can achieve high-precision target tracking in a distributed multi-sensor scene under unknown measurement noise variance. The algorithm has close tracking precision to a distributed particle filtering algorithm with known measurement noise variance, and enhances practicability.

The application discloses a large-scale unmanned aerial vehicle group close-coupling positioning method based on IMU / TDOA fusion, which comprises the following steps: step 1, establishing an unmanned aerial vehicle system general model, including a kinematics model of unknown unmanned aerial vehicles, a time difference of arrival (TDOA) and an inertial navigation (IMU) observation model, and preprocessing TDOA measurement values through a moving average filtering (MAF); step 2, according to the measurement information of each unknown unmanned aerial vehicle, an improved particle filtering algorithm is adopted to perform particle initialization, state prediction, measurement update, resampling and state estimation on each unknown unmanned aerial vehicle, so that the optimal position estimation value of each unknown unmanned aerial vehicle at the current moment is obtained; and step 3, each unmanned aerial vehicle moves and continues to estimate the position of the next moment. The application can realize high-precision cooperative positioning of a large-scale unmanned aerial vehicle group in a GNSS denial environment, and the positioning accuracy and robustness are improved compared with a traditional algorithm.

The application discloses a molten silicon liquid level estimation method based on a hybrid adaptive resampling particle filter, and comprises the following steps: firstly, processing laser spot images collected by a CCD sensor to obtain observation data of the liquid level; then, establishing a system state equation of the molten silicon liquid level according to a kinematic principle to describe the movement of the liquid level; performing state estimation on a laser centroid vertical coordinate by using a particle filter algorithm, and obtaining a new particle set by using a hybrid adaptive resampling method; after re-normalizing all particle weights, outputting an optimal estimation value; and finally, smoothing the filtered data by using a moving weighted average method, and the smoothed result is the estimation value of the molten silicon liquid level. The application solves the problem that in the prior art, the variance of Gaussian variation is too large, particles jump out of a sampling range, and it is difficult to accurately estimate a real liquid level.

The invention discloses a distributed particle filtering algorithm based on Gaussian mixture and Gossip fusion, which relates to the technical field of distributed state estimation and comprises the following steps: acquiring local posteriori distribution by a sensor node through local particle filtering, exchanging sufficient statistics of GMM between the node and a randomly selected neighbor node based on a Gossip protocol, and obtaining a distributed state estimation result. Iterative fusion of the GMM is realized through weighted mixed importance sampling; and finally, optimizing a global state estimation result through a posterior correction step. According to the method, only full statistics such as a mixed coefficient, a mean vector and a covariance matrix of a Gaussian mixture model (GMM) are exchanged among nodes, original particle transmission is replaced, the communication overhead is remarkably reduced, and the communication complexity is linearly increased along with the network scale; meanwhile, an adaptive GMM component adjustment, a regularization weighting EM algorithm and an iteration updating strategy of random weight matrix control are adopted, the calculation process is simplified on the premise that the estimation precision is guaranteed, the single-node calculation burden is reduced, and large-scale sensor network deployment with limited adaptive resources is achieved.

The invention discloses a hybrid energy storage capacity optimal configuration method considering degradation cost. The method comprises the following steps: acquiring an annual wind power output data set; clustering the annual wind power output data set; determining a hybrid energy storage system power curve according to the annual wind power output data set and the target grid-connected fluctuation quantity limit value; introducing an improved particle filter algorithm to solve the service life of the lithium battery, and calculating by combining with the annual degradation cost of the super capacitor to minimize the annual loss cost of the hybrid energy storage system, the charge state constraint of the hybrid energy storage system, the charge and discharge power constraint of the hybrid energy storage system, the power constraint of the hybrid energy storage system and the power curve of the hybrid energy storage system. And establishing a hybrid energy storage system capacity configuration model, and determining the rated capacity of the hybrid energy storage system, including the rated capacity of a super capacitor and the rated capacity of a lithium battery. According to the method, the optimal capacity configuration of the hybrid energy storage system can be found, the energy storage defect of the existing hybrid energy storage system is improved, and the benefit maximization of the hybrid energy storage system during semi-suppressed wind power fluctuation is realized.

The invention relates to a robot positioning method and device in a degraded tunnel scene, and belongs to the technical field of robot positioning.The robot positioning method in the degraded tunnel scene comprises the steps that prior path information and road sign structure position information are obtained from a tunnel design drawing, constructing a path likelihood model, a relative distance constraint model and a pose constraint model; fusing the path likelihood model, the relative distance constraint model and the pose constraint model based on a particle filtering algorithm to obtain an odometer pose calculation result of the robot; extracting a multi-fine-grained feature from a tunnel side view image collected by a camera arranged on the robot, and carrying out loopback detection based on a Viterbi algorithm and the multi-fine-grained feature to obtain a loopback detection pose calculation result of the robot; and determining the optimal estimation of the pose of the robot based on the odometer pose calculation result and the loop-back detection pose calculation result. According to the invention, high-precision positioning of the robot in a degraded tunnel scene can be realized.

The present application relates to the technical field of positioning measurement, and discloses a kind of coal mine underground personnel accurate positioning method and system, including obtaining the ranging information set between the personnel label to be positioned and positioning base station;Construct the mixed probability model of line-of-sight and non-line-of-sight propagation, the particle filtering algorithm is used to iteratively estimate personnel location, based on the confidence of the positioning result determined in last period, adjust the process noise covariance of particle propagation process;The joint likelihood function of particle under all positioning base stations is calculated using the mixed probability model and the weight is updated;The density clustering algorithm is executed to the particle set whose weight is higher than the preset threshold, and the weighted centroid of the maximum density cluster is output as the personnel positioning coordinates of the current period, and the positioning result confidence is calculated.The present application can accurately represent the ranging error under line-of-sight and non-line-of-sight propagation, and adaptively adjust the process noise using confidence, overcome the particle distribution multi-peak problem caused by NLOS interference, and improve the accuracy of underground personnel positioning.

The application discloses a carbon fiber fatigue life estimation method based on an improved particle filtering algorithm, constructs a carbon fiber state observation equation containing unknown parameters of state errors and measurement errors, and realizes dynamic estimation of one-way carbon fiber composite material fatigue life by combining with an improved particle filtering PF algorithm of an IVYO algorithm of an evergreen optimization. δ ce , a proportional parameter beta, and a damage related parameter B are system state values, and the carbon fiber resistance change rate is associated with the fatigue cycle number. The application is suitable for data scarce conditions: without relying on a large amount of fatigue cycle experiment data, with the dynamic parameter updating capability of the particle filtering algorithm, precise fatigue life prediction can be realized only through a small amount of resistance measurement data, the product research and development cycle is greatly shortened, the experiment platform building cost and the test cost are reduced, and the application is especially suitable for the scene that the super high-rise elevator carbon fiber traction belt needs to bear millions of cycles.

The application provides an indoor positioning method and system based on multi-modal data fusion of RMLICT. First, in the mapping stage, a fingerprint map is constructed from unbalanced crowd-sourced trajectory data. In the offline stage, trajectory data enhancement, dimension reduction and clustering processing are performed. In the online positioning stage, direction matching filtering is performed first, and then enhanced particle filtering is used to establish the mapping relationship between the RSS signal and single-step motion. This method optimizes data preprocessing and uses advanced particle filtering algorithms, which not only improves the positioning accuracy and robustness, but also significantly improves the real-time performance of the system, and is suitable for various complex indoor environments, such as scenes with severe multipath effect and signal obstruction.

This application discloses a method and system for predicting wind turbine icing power based on sensitivity and hyperbolic residuals, belonging to the field of wind power generation technology. The method includes: collecting wind turbine datasets; calculating theoretical ice-free power based on blade element momentum theory; obtaining weighted equivalent ice thickness from the wind turbine dataset using a pre-calibrated sensitivity function, and constructing a power correction model based on the theoretical ice-free power to obtain the corrected power; fitting the residual between the observed power and the corrected power using a generalized hyperbolic distribution; constructing a state-space model and updating the parameters of the state-space model based on a particle filter algorithm incorporating physical consistency constraints; outputting the final power prediction value based on the determined parameters of the state-space model, and calculating the uncertainty confidence interval and icing risk probability index; improving the accurate characterization of asymmetric ice layers, making the confidence interval coverage of the state-space model more accurate when predicting extreme icing events, and enhancing robustness.

The invention provides a CNN-DBN pseudo measurement modeling-based power distribution network prediction auxiliary state estimation method and a related device, and the method comprises the steps: collecting the historical data, meteorological data, date data and economic data of the node injection power of a power distribution network, carrying out the data preprocessing, and constructing an input vector; performing feature extraction on the input vector by using a pre-constructed convolutional neural network to obtain data after feature extraction; inputting the data after feature extraction into a pre-trained deep belief network to obtain prediction data of the node injection power of the power distribution network; and using the prediction data as pseudo measurement data, combining the real-time measurement data, and adopting an unscented particle filter algorithm to estimate the prediction auxiliary state of the power distribution network. According to the method, pseudo measurement data is generated through CNN-DBN modeling, state estimation is carried out through unscented particle filtering in combination with real-time measurement data, and the accuracy and reliability of real-time state estimation of the power distribution network are improved.

The invention provides a multi-detector radioactive source positioning method and system based on a particle filter algorithm, and the method comprises the following steps: defining a to-be-monitored physical space as a search region, and building a radiation measurement model; randomly generating a particle set in the search area, wherein each particle comprises assumed radioactive source position coordinates and estimated activity parameters; a mobile platform carrying a plurality of radiation detectors is controlled to move in the area, the total count value of actual measurement of each detector is obtained, and the weight of each particle in the particle set is calculated by using the radiation measurement model and the actual measurement values; acquiring an environment background count value, calculating a ratio of an actual measurement value to a background value, and only when the ratio exceeds a preset signal-to-noise ratio threshold value, performing resampling on the particle set; judging whether the particle set meets a positioning ending condition or not, and if yes, determining a radioactive source positioning result by using the particle parameter with the highest weight and outputting the radioactive source positioning result; and if not, taking the weight gathering center as the next target position, and controlling the mobile platform to move towards the next target position.

The invention discloses an inspection robot SLAM positioning mapping method based on deep learning multi-sensor fusion, and belongs to the technical field of synchronous positioning and mapping. The method comprises the following steps: collecting data of a laser radar, an IMU (Inertial Measurement Unit) and a visual camera, extracting inspection environment characteristics, and carrying out dynamic target detection and semantic segmentation to obtain dynamic target and static background semantic information; the information is combined with multi-sensor data by using a particle filter algorithm to realize robot positioning; an octree map is constructed, geometric constraint loopback detection and semantic information are combined to optimize the map, and a dynamic target state is updated; the limitation of a single sensor is overcome, and stable positioning can be achieved in a complex environment; static structure retention loss is introduced into the YOLOv8n model, and the static edge misjudgment dynamic probability is reduced; particle filtering and semantic consistency scoring are carried out, correction can be carried out during visual deficiency or dynamic interference, and robustness is improved; loopback detection uses a geometric and semantic fusion distance, the recall rate and accuracy are improved, multiple sensors are combined with deep learning to adapt to a complex environment, and routing inspection reliability is guaranteed.

This invention relates to the field of mathematical modeling technology, and more particularly to a method, apparatus, device, and computer-readable storage medium for predicting the remaining lifespan of lithium-ion batteries. The method includes: establishing a segmented empirical degradation model based on inflection point data of the lithium-ion battery; obtaining an initial prediction result of the remaining lifespan of the lithium-ion battery according to a preset particle filter algorithm and the segmented empirical degradation model, and obtaining an original error sequence based on the initial prediction result; determining a reconstruction error sequence corresponding to the capacity of the lithium-ion battery according to the original error sequence and a preset discrete wavelet transform algorithm; constructing a prediction model using a support vector regression algorithm based on the reconstruction error sequence, and correcting the initial prediction result based on the prediction model to determine the final prediction result of the remaining lifespan. This invention improves the prediction efficiency and accuracy of the remaining lifespan of lithium-ion batteries.

The embodiment of the application belongs to the field of multi-camera systems, and relates to a multi-target region dynamic vehicle and pedestrian identification method, which comprises the following steps: acquiring image data of a target region collected by multiple cameras; pre-processing the image data to extract features of the target region; performing target detection on the image data to identify vehicle and pedestrian targets in the image data and determine positions and features of the targets; preliminarily positioning each target and predicting a motion trajectory of the target through a Kalman filtering algorithm or a particle filtering algorithm; and performing space-time correlation on the targets identified by the multiple cameras based on a joint data correlation method of the positions, features and motion trajectories of the targets. The application also provides a multi-target region dynamic vehicle and pedestrian identification system. The application improves the coverage and accuracy of target identification.

The invention discloses a deep reinforcement learning target positioning method, and the method comprises the steps: carrying out the multi-scale feature extraction and attention weighted fusion of an original image containing rain and fog through an FFA-Net model, outputting a denoised clear image, calculating the weight of particles through the edge intensity based on the clear image through a particle filtering algorithm, and carrying out the weighted average, thereby obtaining a target positioning result. Screening a high-weight pixel region in combination with a historical state and a visual attention mechanism, completing target focusing, carrying out region coarse adjustment under the dominance of a deep Q network until conditions are met, outputting a coarse positioning region, repairing missing features of the region, inputting the repaired region features into a regression network to predict a coordinate offset, and completing target focusing. And outputting a final high-precision positioning result. According to the deep reinforcement learning target positioning method provided by the embodiment of the invention, the problems of imaging blur, feature deficiency, positioning deviation and the like caused by temperature can be solved.

This invention discloses an AIS-based UAV search and rescue deployment method and apparatus, relating to the field of data processing technology. The method includes: decoding the AIS signal to obtain decoded information; performing ship information filtering and analysis on the decoded information using a particle filter algorithm to obtain target ship information; obtaining ballistic trajectory calculation information based on the target ship information through ballistic trajectory analysis and ballistic calculation; preprocessing real-time wind field data; constructing a layered wind disturbance model based on the preprocessed real-time wind field data; correcting and optimizing the ballistic trajectory information based on the ballistic calculation information and the layered wind disturbance model to obtain optimized ballistic trajectory information; and generating an execution trajectory command set based on B-spline curve smoothing using the optimized ballistic trajectory information to perform UAV search and rescue deployment in conjunction with real-time control feedback. This invention achieves ballistic trajectory correction control under wind field disturbances, thereby achieving precise UAV deployment of distressed targets under complex sea conditions.

The invention provides an AGV obstacle dynamic and static state judgment method and system, and relates to the technical field of AGV obstacle dynamic and static state judgment, and the method comprises the steps: obtaining obstacle scanning data based on a single-line laser radar installed on an AGV; in combination with AGV operation data, an obstacle motion state is judged based on a particle filtering algorithm, that is, a plurality of particles are randomly selected in a potential obstacle existence area to generate a particle set; predicting the prediction state of the particles through a state transition model; calculating an obstacle prediction observation state according to the prediction state of the particles, and calculating a particle weight through observation likelihood in combination with the actual observation state of the obstacle; resampling is carried out based on the particle weight, and new particles are selected to generate a new particle set; and the obstacle motion state is judged based on the speed distribution of the new particle set after multiple times of iteration convergence, and a corresponding AGV control strategy is generated, so that the position and motion characteristics of the obstacle are effectively detected, the dynamic and static states of the obstacle are accurately judged, and effective obstacle avoidance is realized.

The invention relates to the technical field of digital operation and maintenance, in particular to an underground pipe network facility life prediction system and method based on multi-parameter time sequence analysis, and the method comprises the steps: generating a holographic state set through a multi-scale window and a tensor filling algorithm, building a pipe network topology through three-layer logic verification, and paying attention to network quantification risk conduction characteristics based on a space-time diagram. And performing state recursion and continuous integration by using a particle filter algorithm to predict the remaining life. According to the method, heterogeneous monitoring data is converted into a low-rank tensor model, and sparse graph matrix operation is combined, so that the calculation complexity and memory overhead in a high-dimensional spatial-temporal feature extraction process are effectively reduced; meanwhile, based on a numerical integration strategy of a Bayesian sequence Monte Carlo method, the particle degradation problem in nonlinear system state estimation is solved, and convergence and numerical stability of a residual life prediction result in a computer simulation environment are guaranteed.

This application belongs to the field of UAV tow rope technology, and discloses a method and device for controlling the tow rope reel of a UAV. The method includes: real-time acquisition of UAV hovering altitude data and calculation of corresponding real-time elevation difference data; using a particle filtering algorithm to remove noise from the real-time elevation difference data to obtain a terrain elevation difference change sequence; calculating the sag depth of each elevation difference sampling point in the terrain elevation difference change sequence to obtain a sag depth distribution curve; obtaining a sequence of abrupt change positions including multiple abrupt change locations based on the sag depth distribution curve; calculating the tension increment or tension decrement at each abrupt change location to obtain a tension change rate distribution; obtaining dynamic characteristics of tension growth based on the tension change rate distribution and baseline trend line analysis at each abrupt change location; and generating a set of control commands for the tow rope reel based on the dynamic characteristics of tension growth, a preset safe tension range, and the rated parameters of the tow rope reel. This application can avoid the risk of sag loss of control in the tow rope.