89 results about "Random search" patented technology

The invention relates to the technical field of unmanned aerial vehicle trajectory planning, in particular to a bidirectional tree random search method based on a complex environment node cost function, which comprises the steps of surveying obstacle data in a flight area and modeling, then introducing a target deviation strategy and a bidirectional search tree mechanism, and combining with an improved minimum cost method to obtain a random search result. Alternately expanding the two trees to generate an initial path, and integrating the path length, the obstacle distance and the flight height energy consumption by a cost function; and finally, pruning the initial path, removing redundant nodes, smoothing the trajectory by adopting a cubic spline interpolation method, ensuring curvature continuity, and generating an optimal path meeting the flight performance of the unmanned aerial vehicle. According to the invention, through an improved bidirectional minimum cost fast expansion random tree algorithm, dynamic association of obstacle constraint, target deviation and path cost in unmanned aerial vehicle path planning in a complex environment is represented, and a more efficient search convergence speed and better path quality are obtained. And rapid convergence and global optimization of the track of the unmanned aerial vehicle in a high-dynamic environment are realized.

The invention relates to the technical field of intelligent vehicles, in particular to an intelligent vehicle automatic driving formation obstacle avoidance control method and device, and the method comprises the steps: constructing an intelligent vehicle formation controller according to the destination data and obstacle data of a target intelligent vehicle formation, so as to control the target intelligent vehicle formation to move towards the destination; establishing an intelligent vehicle formation space-time transformation strategy based on the queue expansion coefficient according to the queue expansion coefficient of the target intelligent vehicle formation and the driving safety boundary; performing dynamic optimization on the intelligent vehicle formation controller by adopting a Levy flight random search logic based on a cuckoo algorithm to obtain an optimized intelligent vehicle formation controller; and controlling the target intelligent vehicle formation to avoid obstacles by using the optimized intelligent vehicle formation controller and the intelligent vehicle formation space-time transformation strategy based on the queue expansion coefficient. Therefore, the problems that when intelligent vehicle formation obstacle avoidance is carried out through a traditional artificial potential field method, local minimum value and obstacle avoidance track jittering are likely to happen, and complex dynamic environments cannot be rapidly adapted are solved.

The invention discloses a vehicle transverse and longitudinal trajectory tracking collaborative optimization control method and system. The method comprises the following steps: establishing a vehicle monorail model and an error calculation module; respectively designing a transverse MPC controller, a longitudinal MPC upper controller and a lower controller based on an acceleration-driving force inverse dynamics model according to the vehicle monorail model; carrying out normalization processing on the transverse error and the yaw angle error which are calculated by the error calculation module, and taking a normalization processing result as a fitness function of a pelican algorithm; establishing a Latin-pelican optimization algorithm fusing population initialization of sine-Latin hypercube sampling, prey position updating of a grade system and a dynamic random search strategy; a fitness function and a Latin-pelican algorithm are utilized to optimize a transverse MPC controller and a longitudinal MPC upper controller respectively, physical mapping of control quantity is achieved in combination with a lower controller based on an acceleration-driving force inverse dynamics model, optimal weight matrix parameters are obtained, and transverse and longitudinal joint trajectory tracking control of the vehicle is completed.

A control method includes encircling a patient appendage with a pressurizable cuff pressurized via a valve, and directing emitted light through a patient appendage to a light sensor. A sensed plethysmogram signal is generated based on the received light level, and the valve is controlled via a closed feedback loop responsive to an error value computed as the difference between the sensed plethysmogram signal and a calibrated setpoint value. An unperturbed error value characteristic is recorded through a first time window, and a random vector perturbation in the closed-loop control space is generated. A perturbed error value characteristic is recorded through a second time window while temporarily adjusting closed-loop control parameters based on the random vector perturbation. Baseline closed-loop control parameters are updated in response to the unperturbed error value characteristic exceeding the perturbed error value characteristic.

The invention discloses a communication sensing time slot allocation method and system based on a potential game. According to the invention, frequency spectrum hardware sharing of radar sensing and wireless communication is realized through system initialization, a time allocation mode is dynamically selected based on a 5G NR frame structure, and the time allocation mode comprises a segmentation mode and an aggregation mode, so that time slot resources are flexibly divided; constructing a non-cooperative potential game model, taking a vehicle as a participant, defining a utility function which takes mutual information maximization of a radar system as a target and is fused into communication capacity constraint, and ensuring collaborative optimization of perception and communication performance; and in combination with greedy initialization and a random search strategy, distributed algorithm iteration is executed, so that the calculation complexity is remarkably reduced, and the real-time response capability is improved.

The present invention belongs to the technical field of engineering structure analysis and optimization, and discloses a method for predicting vehicle body structural performance based on heterogeneous graph neural networks. The technical points include: sampling the structural samples required by the data set in the design domain to be predicted, using heterogeneous graph representation methods and numerical analysis methods to establish a heterogeneous graph data set; defining the HGNN design space with the HGT layer as the core; using part of the heterogeneous graph data set, controlled random search method and grid search method to construct the optimal HGNN model; using the entire heterogeneous graph data set and the optimal training configuration parameters to complete the training of the optimal HGNN model; using the trained HGNN model to predict the performance of any structural sample in the design domain. The present invention is not only applicable to homogeneous structures corresponding to homogeneous graphs, but also to more widely distributed structures corresponding to heterogeneous graphs, which greatly improves the versatility of the method and enhances the scalability of the method without losing the scope of application.

Embodiments of the present disclosure disclose a vehicle escape method and device, electronic equipment and storage medium, the method comprising: in the case where the vehicle is in a blocked state, determining a virtual end point according to the starting point, target lane, each obstacle and preset search distance of the vehicle; generating a sampling map based on the Frenet coordinate system according to the starting point, virtual end point, current lane width, other lane width and each lane line; determining an initial path based on the fast random search tree according to the starting point in the sampling map, virtual end point and each obstacle; determining a plurality of target escape points according to the starting point in the initial path, each initial escape point in the initial path and each obstacle; and performing escape processing on the vehicle according to the plurality of target escape points. The present disclosure can flexibly determine a plurality of escape points when the vehicle is in a blocked state, improve the rationality of the escape points, and improve the success rate of vehicle escape.

The invention provides an operation path planning method and system integrating environmental perception and occupational health, and the method specifically comprises the steps: obtaining a three-dimensional map of a farm, and building a harmful gas prediction model related to an exhaust state; taking a personnel starting and ending point as a root node, performing bias sampling on a low-concentration region according to the model, and respectively constructing a forward and backward random search tree, a path cost comprehensive geometric distance and a predicted average concentration; performing reconnection optimization on the double trees, and updating father nodes of lower-cost adjacent nodes; connecting the optimized dual trees to generate an initial path; accumulated harmful gas exposure of the path is calculated, the gas exposure is the integral of the product of the retention time and the concentration, and target operation parameters of all exhaust equipment along the line are determined accordingly; and updating the prediction model by using the new parameters, repeatedly executing path search and exhaust parameter optimization until the total cost change of adjacent iteration paths is smaller than a convergence threshold value, and outputting an operation path and exhaust cooperation scheme.

The invention discloses a construction industry warehouse order time consumption prediction method and system based on machine learning. The method comprises the steps of collecting order data and cleaning the order data; constructing a multivariable feature set and carrying out one-hot coding; an XGBoost model is adopted for training, and parameters are optimized through grid search and random search; a prediction interface is deployed to a warehouse management system, the system comprises a data acquisition module, a data preprocessing module, a feature engineering module, a model training module, a prediction interface module and a prediction module, and tests show that the prediction precision Rreaches 0.85, the MAE is 10.6 minutes, the scheduling conflict rate is reduced to 2%, and the operating cost is saved by 75% annually. The problems that a traditional method depends on manpower and is low in precision are solved.

The invention discloses a short-term irradiance prediction method and device based on multi-source data fusion, and the method comprises the following steps: (1) data collection: obtaining meteorological data, actual measurement data of a radiation sensor and astronomical calculation data, and extracting time features; (2) data preprocessing: carrying out ADF unit root test on a global level irradiance sequence; (3) feature selection: screening features having a causal relationship with GHI through Granger causal test; based on XGBoost feature importance analysis, selecting an irradiance historical value and a solar azimuth angle as core input features; (4) model construction and training: processing a multivariable nonlinear relationship by adopting an XGBoost model, and dividing a training set and a test set according to a time sequence; time sequence cross validation is carried out to divide a validation set, and hyper-parameter tuning is carried out through grid search and random search; and (5) evaluation and optimization: using MAE, RMSE and MAPE index evaluation models to visualize a predicted value and actual value time sequence curve. According to the method, a night invalid data screening mechanism, ADF test and Granger causality analysis are introduced to perform feature selection, and time sequence cross validation is combined to perform hyper-parameter adjustment and optimization, so that the irradiance prediction model with high precision and strong generalization ability is realized, and the method is suitable for various photovoltaic application scenes and has wide application prospects. And reliable support is provided for power prediction and power station intelligent scheduling.

The invention discloses an unmanned equipment control method, device and equipment based on an improved MPC control model. The method comprises the following steps: constructing an unmanned equipment control model based on the MPC control model; improving a random search strategy, boundary processing and a variation mechanism of the whale optimization algorithm; improving a strategy updating mechanism of a near-end strategy optimization algorithm by using the improved whale optimization algorithm; using the optimized near-end strategy optimization algorithm to improve the prediction time domain of the unmanned equipment control model; and generating a control scheme of the unmanned equipment according to the improved unmanned equipment control model. According to the invention, adaptability and motion control precision of unmanned equipment in a dynamic environment, especially autonomous navigation and task execution capability in a complex scene, can be effectively improved.

The application discloses a ship ballast water adjusting method and system based on a simulated annealing algorithm, and comprises the following steps: obtaining ship ballast tank information and the state of current ballast water, and constructing a target function according to the ballast tank information and the state of current ballast water; establishing a ballast water constraint condition for the target function; solving the target function according to the ballast water constraint condition by using the simulated annealing algorithm until an optimal solution is obtained, wherein the optimal solution is a ballast water adjusting scheme; and adjusting the current ballast water according to the ballast water adjusting scheme. The application simulates the ballast water of a ship by using the simulated annealing algorithm, utilizes the random search characteristic of the annealing algorithm, realizes the global optimization of ballast water allocation, improves the calculation efficiency of the ballast water adjusting scheme of the ship, and can adapt to different complex conditions.

The application provides a pulse eddy current pipe wall thickness detection method based on an RSBO optimized LSTM network, pulse eddy current signals are used to estimate the thickness of the pipe, a data sample length evaluation criterion based on a least square fitting algorithm is used to determine the interval of the retained measurement signals, the processed signals are input into an LSTM model to detect the thickness of the pipe, and a combination optimization based on random search and Bayesian optimization is used to set the hyperparameters of the LSTM model.

The present invention discloses a facial expression recognition neural network search method, device, and application. The search method includes constructing a search space for facial expression recognition; generating a plurality of facial expression recognition candidate networks that meet preset parameter quantities using a random search method; selecting a preset number of zero-shot proxies that meet preset conditions from a NAS benchmark dataset; and selecting the optimal candidate network from the plurality of facial expression recognition candidate networks using the selected zero-shot proxies as the final facial expression recognition network. The application method of the facial expression recognition network includes searching for the facial expression recognition network using a construction search method, selecting a facial expression image dataset to train the facial expression recognition network, obtaining a facial expression image to be recognized, and preprocessing the facial expression image; and inputting the preprocessed facial expression image into a trained facial expression recognition network for recognition to obtain a recognition result.

This invention belongs to the technical field of railway tracks, specifically disclosing a method for identifying rail corrugation in rail transit based on the composite characteristics of train vibration and noise. The method includes the following steps: acquisition and processing of train vibration, noise, and related signals; enhancement and fusion of vibration and noise signals; mapping of vibration and noise composite data to corrugation relationships and establishment of a sample set; design and training of a convolutional neural network structure for the vibration and noise composite sample set; and identification of rail corrugation status. This invention uses a one-dimensional convolutional neural network to adaptively extract features from the sample set of vibration fusion data based on vibration and noise fusion data. Simultaneously, a random search method is used to determine the optimal parameters, shortening the sample training and identification time, and meeting the requirements for accuracy and timeliness in rail corrugation detection and monitoring.

The invention discloses an unmanned aerial vehicle position optimization method and system, a terminal and a storage medium, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: obtaining the number of unmanned aerial vehicles corresponding to an unmanned aerial vehicle cluster, an activity space range, a minimum vehicle spacing, a synthetic beam main lobe target direction, and a working carrier frequency of airborne equipment of the unmanned aerial vehicles in the unmanned aerial vehicle cluster; an initial interval is determined according to the working carrier frequency, a set of virtual position points are determined according to the initial interval and a one-dimensional linear range defined by the activity space range, and the number of the virtual position points is larger than the number of the unmanned aerial vehicles; and optimizing the positions of the unmanned aerial vehicles in the unmanned aerial vehicle cluster through an iterative fast Fourier transform algorithm according to the number of the unmanned aerial vehicles, the minimum distance between the unmanned aerial vehicles, the target direction of the synthetic beam main lobe and the virtual position point, and obtaining a group of target position points corresponding to the unmanned aerial vehicle cluster until a preset iteration termination condition is met. In this way, a large number of random search processes are not involved, and the efficiency of unmanned aerial vehicle position optimization is improved.

The invention provides a substation project multi-objective optimization method and device. The method comprises the following steps: acquiring a process set, a process preposition and postposition logic relation, selectable construction modes of each process and corresponding parameter information; introducing the logical relationship as a constraint, taking a construction mode selection result as a discrete decision variable, and constructing a multi-objective optimization model including a construction period, a cost present value, quality and a carbon emission objective; an improved swarm intelligence algorithm is adopted for iterative solution and layered sampling to generate an initial population and establish an external file, a target function is calculated in iteration and feasibility is judged, non-dominated sorting and congestion degree are executed after merging, the external file is updated, and a leader individual is selected; and candidate positions are generated based on nonlinear convergence control and hunting, random search and spiral bubble net updating, discretization and constraint restoration are performed after fusion, and an external file non-dominated solution set is output. According to the technical scheme, multi-target scheme optimization is achieved, and scheme generation efficiency and evaluation quality are improved.

The application relates to the technical field of artificial intelligence, in particular to a behavior decision-making method and device for human-computer interaction, equipment and a storage medium, wherein the method comprises the following steps: acquiring at least one target event in a human-computer interaction scene; randomly searching a target reward function space of the at least one target event to obtain a plurality of behavior preferences, generating a bias strategy set based on the plurality of behavior preferences, sequentially selecting different types of bias strategies from the bias strategy set according to a preset behavior index, and constructing a strategy pool with strategy diversity according to the different types of bias strategies and a preset reward strategy; training an adaptive strategy of a target intelligent agent by randomly sampling a strategy from the strategy pool, deciding a behavior of human-computer interaction with the target intelligent agent based on the adaptive strategy, and obtaining a behavior decision-making result. Thus, the embodiment of the application can realize strong interaction and collaborative generalization centered on people, and group behavior decision-making generalization of large-scale multi-intelligent agents in an open scene.

This invention relates to the field of computing power scale prediction technology, specifically disclosing a method and system for predicting urban computing power scale based on a sled dog-optimized MLP. This invention constructs a parameter optimization architecture for an MLP driven by a sled dog optimization algorithm, encoding the number of hidden layer neurons, truncation quantiles, and year-specific switch variables as decision vectors. It designs a multi-objective fitness function that integrates training set error, validation set error, generalization gap penalty, model complexity penalty, and stability penalty. This simulates the dynamic selection, movement, obstacle avoidance, disorientation, training, and retirement behaviors of a sled dog population through iterative optimization. This solves the problems of traditional grid search and random search easily getting trapped in local optima, and the reliance on human experience for key MLP parameter configuration. It also overcomes the shortcomings of insufficient fitting of statistical models and overfitting of conventional neural networks in small sample scenarios, achieving improved accuracy in urban computing power scale prediction and enhanced model generalization performance.

The present application discloses an encoding method for low-density parity-check (LDPC) codes. The method comprises: acquiring an information bit sequence; and on the basis of a check matrix, performing LDPC encoding on the information bit sequence to obtain an encoded bit sequence, wherein the check matrix is determined by a base matrix, the base matrix comprises a fundamental sub-matrix, and the fundamental sub-matrix is formed by concatenating at least two basic unit blocks with cyclic or translational properties. Compared with existing 5G new radio (NR) matrices of which shifting values are all obtained by random search without guidance from a fixed algebraic structure, the base matrix having cyclic block structures or translational block structures designed in the present application can be guided by a fixed algebraic structure, thereby solving the technical problems of the lack of practical code design for LDPC and the difficulty in finding a cycle-free exponent matrix by means of random search.

The invention discloses a permanent magnet motor multi-objective optimization method based on an improved fuzzy ellipse parameter method. The method comprises the steps of global parametric modeling; key parameter screening: screening a key parameter set P with the highest sensitivity to cogging torque Ct and torque pulsation Tr from X by adopting an improved fuzzy ellipse parameter method; the key parameter set P is optimized through the hybrid optimization algorithm, global optimization is conducted on the P through the hybrid optimization algorithm, the hybrid optimization algorithm combines model-free random search, particle swarm optimization and a genetic algorithm, and collaborative optimization of the three methods is achieved; and performance verification: verifying an optimization result through finite element analysis, and if the cogging torque Ct is reduced, the torque pulse Tr is reduced compared with the cogging torque and the torque pulsation which are initially designed, and the average torque change is not large, outputting a final parameter. According to the method, the subjectivity of optimization parameter selection is reduced, and the optimization efficiency of the cogging torque and the torque pulsation is improved.

A computing system generates directives of a program. Beam nodes are selected one level at a time from multiple nodes in the tree structure. Each node represents a subset of operations in the program. A first number of the beam nodes are selected at a given level of the tree structure. The selection of the first number of the beam nodes uses a cost model that is based on a neural network. A second number of the beam nodes are selected using a random search. The ratio of the first number to the second number is determined based on a search completion percentage at the given level. A path is identified that passes through respective beam nodes at multiple levels of the tree structure. The path represents a schedule for executing the program on a target machine. Then the directives corresponding to the schedule are generated.

The invention provides a large-scale farmland scale design method based on double-target constraint, and relates to the technical field of farmland standardized construction, and the method comprises the steps: carrying out the fitting of a farmland actual irrigation sample and an agricultural machinery operation actual sample, and obtaining an irrigation performance prediction model and an agricultural machinery operation efficiency prediction model; judging actual constraint conditions, and if the actual constraint conditions do not exist, performing simulation by utilizing the paddy field specification prediction model to obtain a paddy field specification prediction data sample so as to obtain a paddy field specification suitable range; and if the actual constraint condition exists, based on the irrigation performance prediction data sample and the agricultural machine operation efficiency prediction data sample, performing multi-objective optimization and global random search by using the paddy field specification optimization model to obtain a suitable paddy field specification, and analyzing a paddy field specification suitable range and the suitable paddy field specification to obtain a farmland scale design result. The problem that an existing farmland design technology lacks large scale, whole-process mechanization and high intensification is solved.

The application discloses a kind of covalent crystal structure intelligent prediction method based on molecular unit topological assembly, by regarding molecular unit as rigid body and using hybrid gene coding system, topological connection and geometric pose decoupling are realized, compared with traditional atomic coordinate random search, the application can keep the internal configuration of molecule stable during evolution, avoid the collapse of geometric configuration of complex molecule during search process, and improve the calculation efficiency of large-scale, non-spherical molecular system;The method does not depend on specific empirical parameters, and can cover the structure prediction of fullerene polymer, covalent organic framework and complex porous material through general topological assembly logic, and has strong cross-field universality.

The present application relates to the technical field of pipeline structure reliability analysis, in particular to a pipeline structure reliability analysis method under interval uncertainty, comprising: collecting basic parameters affecting the vibration fatigue life of the pipeline structure; establishing a function function in reliability analysis; standardizing the input vector and the function function; establishing a reliability model of the pipeline structure under interval uncertainty with the standard function function as a constraint; introducing an intermediate variable for equivalent conversion; solving by using the dichotomy combined with the random search feasibility judgment method to obtain the reliability index of the pipeline structure. The present application solves the problems of difficulty in solving the black box function and difficulty in ensuring global convergence in the vibration fatigue life analysis of the engine pipeline structure by using the interval reliability analysis framework of the dichotomy combined with the random search feasibility judgment.

This invention relates to the fields of artificial intelligence and medical clinical decision support, specifically a method and system for predicting postoperative delirium risk in elderly patients based on machine learning. The method includes: acquiring perioperative data of the patient to be predicted, including clinical indicators from the preoperative, intraoperative, and postoperative stages; preprocessing and preliminary feature screening of the data to obtain a structured feature set; constructing and optimizing a machine learning-based postoperative delirium prediction model, forming a modeling pipeline by combining multiple feature selection methods with a classifier, determining the optimal hyperparameters using random search and k-fold hierarchical cross-validation, and selecting the best pipeline based on feature stability assessment and multiple evaluation indicators; training and evaluating the performance of the final model; outputting the postoperative delirium risk prediction results and providing model interpretation. This invention is applicable to scenarios such as perioperative risk assessment of elderly patients, early warning of high-risk patients with postoperative delirium, individualized intervention plan formulation, and clinical auxiliary decision systems, providing reliable technical support for reducing the incidence of postoperative delirium and optimizing the allocation of medical resources.

This invention proposes a method for predicting tree fall specifications based on historical meteorological data and UAV post-disaster inspection surveys. It integrates historical typhoon data, UAV inspection data, and geographic information data to form a comprehensive and multi-dimensional dataset. Through in-depth analysis of various influencing factors, key features are identified, and advanced machine learning methods such as regression and deep learning models are used to construct the model. Simulation optimization is employed to handle complex data relationships, improving prediction accuracy and reliability. Scientific evaluation indicators such as accuracy, recall, F1 score, and mean squared error are set, and k-fold cross-validation is used to comprehensively evaluate the model's generalization ability. Grid search and random search methods are used to fine-tune the model parameters. The constructed real-time monitoring and early warning system, along with post-disaster response and recovery strategies, effectively reduces tree fall damage and plays a positive role in protecting urban greening and the ecological environment.

The invention discloses a method for extracting parameters of a three-diode model of a photovoltaic cell, and the method comprises the steps: building a three-diode equivalent circuit model of the photovoltaic cell, and determining a plurality of to-be-recognized parameters of the photovoltaic cell according to the three-diode equivalent circuit model; output current-voltage I-V curve data of the three-diode equivalent circuit model are obtained, and the curve data at least comprise three feature point data, namely open circuit point voltage, short circuit point current and voltage and current of the maximum power point; and solving the three-diode equivalent circuit model based on the obtained I-V curve data, and obtaining an optimal solution of the to-be-identified parameters. The whole parameter extraction step has no matrix inversion operation and no random search process, the calculation complexity is low, continuous automatic model monitoring can be realized, and a parameter feasible region does not need to be manually set.

The invention relates to the technical field of motor optimization design, in particular to a motor optimization design method and system based on a multi-objective myxomycete algorithm, and the method comprises the steps: determining an optimization objective and a design variable; constructing a target function vector; iterative optimization is carried out based on an improved multi-target myxomycete algorithm, and the position of a myxomycete individual is updated based on a guiding individual and a self-adaptive exploration mode. According to the method, the elitist strategy is introduced and the elitist pool is updated on the basis of the traditional multi-target myxomycete algorithm, so that directional retention and utilization of the historical high-quality solution are realized, the loss of the high-quality solution in random search is avoided, a stable reference direction is provided for algorithm evolution, the optimization efficiency is improved, and the method is suitable for large-scale popularization and application. And the robustness of the optimization result and the engineering practicability are enhanced.