87 results about "Parametric search" patented technology

Systems and methods for parameter search and adjustment are disclosed. A system can maintain a model configured to generate odds data for one or more live events. The system can receive a request to generate odds values for a first live event. The request can include a set of target values for the first live event. The system can determine that the model is to be updated based on the request. The system can generate, using a set of first parameters for the first live event and prior to updating the model, first odds for the first live event in response to the request. The system can update the model based on the set of target values in the request.

The invention discloses a production line layout scheme evaluation system based on a dynamic simulation technology, and the system comprises the steps: converting equipment coordinates, detecting equipment spacing and AGV channel interference, and generating a geometric data set containing position constraint; performing median filtering on the equipment state data to generate a state data set; drag-and-drop layout is carried out through the template, safety spacing and channel width are verified, and an initial layout parameter combination is generated; generating an orthogonal table through a Taguchi orthogonal experiment method, and constructing an optimization space less than or equal to 8 dimensions; performing parameter search by adopting a particle swarm optimization algorithm, and outputting optimized layout parameters; deploying a discrete event simulation engine, and generating a layout evaluation report; and generating a verification data set and performing t verification, and triggering parameter correction and updating the scheme label according to a verification result. The design can effectively improve the feasibility, efficiency and landing reliability of the layout scheme, and significantly reduce the trial and error cost and operation risk of enterprise production line planning.

The invention discloses a self-adaptive source network load storage integrated intelligent scheduling system and method, and the system comprises a data collection module, a prediction module, a multi-objective optimization module, an optimization algorithm module, a self-adaptive parameter adjustment module, a constraint processing module, and a result output module. The future power demand is predicted through the power demand prediction model, the future renewable energy power generation amount is predicted through the renewable energy power generation prediction model, and a multi-target model with cost, benefit and greenhouse gas emission and model constraint conditions are constructed; the multi-target model is optimized through a hybrid optimization algorithm model combining a genetic algorithm and a particle swarm optimization algorithm, algorithm parameters are dynamically adjusted, a global optimal solution is searched, the optimized power generation equipment output power, the optimized energy storage equipment charging and discharging power and the optimized power grid interaction power are output, and a scheduling decision is guided; according to the method, the optimization efficiency and robustness are improved, and the adaptive capacity and the multi-target optimization capacity of a power grid system are enhanced.

The invention discloses an optimization method for realizing a multi-performance architecture of a neural network at low cost. A data set input module, a parameter setting module, a multi-performance architecture hyper-parameter search module and a multi-performance neural network architecture recommendation module are included. According to the method, the error rate, the calculation complexity and the test time serve as targets, hyper-parameter combinations in the deep convolutional neural network are regarded as population individuals, and a corresponding multi-performance architecture scheme can be efficiently and automatically searched by designing PASAEA and inputting a corresponding data set, the deep convolutional neural network and a hyper-parameter search range. According to the method, normal form transformation from experience driving to data driving is realized, the bottlenecks of high subjective design cost and poor interpretability are broken through, and a scientific and high-stability solution is provided for the expensive training problem of the neural network. The method can be widely applied to construction and research in the fields of automation, signal processing and the like, manpower and time cost is remarkably reduced, and the method has extremely high engineering application value.

Systems and methods for selecting machine learning features using iterative batch feature reduction. In some aspects, the system receives training data intended for generating a machine learning model. Based on a preliminary model trained on the training data, the system defines a hyperparameter search space to generate a set of hyperparameter configurations. For each hyperparameter configuration, the system generates a feature vector by executing a feature selection method. Based on the set of feature vectors and the training data, the system generates a set of candidate models corresponding to the set of hyperparameter configurations. The system ranks the set of candidate models based on a performance metric to select the machine learning model from the set of candidate models.

The invention provides an efficiency optimization control method of a permanent magnet synchronous motor and related equipment. The method comprises the following steps: acquiring an output current of an inverter; constructing an inverter loss model based on the output current of the inverter and the inverter loss coefficient; constructing a total loss model based on the electromagnetic loss model, the inverter loss model and an equivalent conversion method; constructing a current population by taking the plurality of d-axis current values as individuals; and performing optimization solution based on the total loss model, the current population, a PID search algorithm and an artemisinin optimization algorithm to obtain a global optimal solution and control the permanent magnet synchronous motor. According to the scheme, the driver loss is added into the total loss model, and efficiency optimization can be carried out on the whole motor driving system; in order to solve the problem that a traditional method is not suitable for an IPMSM, a PID search algorithm and an artemisinin optimization algorithm are combined, parameter search is carried out on a motor efficiency optimal point by simulating an incremental PID controller adjusting mechanism, a global optimal solution is obtained, and therefore optimal control over the click efficiency is achieved.

The invention discloses an unmanned aerial vehicle subsystem health assessment method and system, and relates to the technical field of unmanned aerial vehicle health management, and the method comprises the steps: obtaining the data of a multi-source sensor of an unmanned aerial vehicle, screening the features strongly correlated with a fault state through a Spearman rank correlation coefficient, and obtaining key monitoring parameters; according to the self-adaptive mutation mechanism, the virtual evolution strategy and the double-objective optimization strategy, an improved multi-objective genetic algorithm optimization model is constructed, then key monitoring parameters are input, and a visual report of a health state assessment result and confidence rating is obtained. According to the method, the problems that traditional single-target optimization neglects the misjudgment rate, the parameter search efficiency is low and the evaluation credibility is insufficient are solved, accurate evaluation is achieved, the evaluation accuracy is improved to 84.86%, the key misjudgment rate is reduced to 0.009%, the convergence speed is improved by 40% compared with a traditional method, the safety and real-time performance of unmanned aerial vehicle health state recognition are remarkably optimized, and the method is suitable for popularization and application. The method is suitable for embedded system deployment.

The invention discloses a two-stage algorithm selection and hyper-parameter joint optimization method, which comprises the following steps of: in the first stage, processing a training set and a test set through row sampling operation and column dimension reduction operation to form a reduced data set; randomly sampling a certain number of configurations in the hyper-parameter space of each candidate algorithm, evaluating the performance of each candidate algorithm by using the reduced data set, and extracting an optimal performance score; in the second stage, a previous algorithm is screened according to the optimal performance score to form a candidate set, and a pruned hyper-parameter search space is formed so as to reduce the calculation complexity of processor hyper-parameter search; and performing hyper-parameter optimization on the pruned hyper-parameter search space by using the original data set, and outputting an optimal algorithm adaptive to the target technical task and hyper-parameter configuration thereof. Algorithm screening and hyper-parameter tuning adaptive to a specific scene are realized through a two-stage optimization strategy, and meanwhile, the method is suitable for a traditional table type dichotomy task and aims at improving the deployment efficiency and performance of a machine learning model.

The invention relates to the technical field of data processing, and discloses a computing power server system layer optimization method and system oriented to a high-load scene. The method comprises the steps of collecting micro performance indexes such as the CPU instruction cycle number and the page table missing frequency, constructing a three-layer causal directed acyclic graph through Granger causal inspection, reducing a parameter search space based on bottleneck node reverse backtracking, generating an interpretable optimization decision with a causal path and contribution degree quantification, and carrying out optimization on the basis of the interpretable optimization decision. The problems that the performance bottleneck root cause cannot be accurately positioned and the optimization result lacks transparency in the prior art are solved. According to the method, bottleneck node reverse backtracking and parameter space pruning are performed based on the causal atlas, so that the problem of low optimization efficiency caused by incapability of accurately positioning a performance bottleneck root cause and blind exploration of a parameter space in the prior art is solved.

The invention discloses a hydroelectric generating set bolt service stress nephogram inversion optimization algorithm, and relates to the technical field of hydroelectric generating set bolt stress measurement, and the method comprises the following steps: S1, finite element simulation model construction: S2, carrying out data cleaning on physical field simulation data and field measurement data obtained in S1, and obtaining a physical field data set; s3, constructing a stress nephogram inversion model; and S4, searching for optimal parameters. According to the method, the defects in the aspects of actual environment modeling and structure optimization in the prior art are overcome, and a new technical approach is provided for maintenance and performance improvement of the bolts of the hydroelectric generating set. And high-precision simulation in an actual operation environment is realized, and the defects in the aspect of digital modeling in the prior art are overcome. Based on theoretical physical constraints, a reinforcement learning algorithm can be constructed to invert stress distribution of a bolt in long-term service, comprehensive information is provided for structural health monitoring, and certain limitation exists in the aspect in the prior art.

The invention discloses a partial discharge signal wavelet denoising method based on all-parameter space traversal optimization. The method comprises the following steps: S1, initializing parameters; s2, a noisy signal is read; s3, constructing a wavelet basis traversal cycle; s4, constructing a decomposition layer number traversal cycle; s5, denoising and index calculation are executed; s6, comparing and updating a global optimal solution; and S7, carrying out loop iteration until all preset wavelet base order and decomposition layer combinations are traversed. According to the method, the parameter search space containing various wavelet bases and different decomposition layer numbers is constructed, so that automation and optimal matching of denoising parameters are realized; compared with a traditional method of selecting wavelet parameters depending on artificial experience, the method has the advantages that the influence of subjective factors on the denoising effect is effectively avoided, and the adaptability and robustness of the algorithm to different field environments and different types of partial discharge signals are remarkably improved.

The invention relates to the technical field of industrial process automatic control, and discloses an automatic control method for a water sample analysis process, which comprises the following steps: establishing a unit step response reference model for representing a normalized standard track of a controlled object; iteratively searching an optimal amplitude scaling and time elastic factor in the two-parameter search space to minimize the fitting residual error of the reference model and the real-time data; the optimal amplitude scaling factor is utilized to determine a projection steady-state value to drive an execution unit, and the sampling interval is adaptively adjusted according to the optimal time elastic factor, the time elastic factor is introduced to realize closed-loop compensation of system kinetic parameter drift, a steady-state target can be accurately locked in an unbalanced state, and the stability of the system is improved. And the problem of control model mismatch caused by device aging or environment fluctuation is solved.

The embodiment of the invention provides an operator optimization processing method in a hyper-parameter search process, an electronic device and a computer program product, and the method comprises the steps that a corresponding target optimization algorithm is matched for a target operator through a matching rule base, and the rule base stores the mapping relation between the characteristics of different operators and the optimization algorithm; in the hyper-parameter searching process, an initial range is generated based on a heuristic algorithm, and the initial range is an initial hyper-parameter combination close to an optimal solution; based on the pruning optimization strategy, the hyper-parameter combination of the target operator is optimized in the initial range according to the target optimization algorithm, so that the problem of how to efficiently optimize the operator performance in a limited edge computing environment in related technologies can be solved, the hyper-parameter search process is accelerated by utilizing the pruning optimization strategy, and the search efficiency is improved. And in combination with a heuristic algorithm, the quality of an initial solution is improved, so that efficient operator optimization is realized.

The invention relates to a chaotic variation metamaterial parameter rapid optimization method based on a random forest model. The method comprises the following steps: S1, determining a structural parameter search space of a metamaterial and upper and lower bounds of optimization variables; s2, an initial population is generated, fitness values of individuals of the initial population are calculated, the individuals are metamaterial structure parameters, and the fitness values of the individuals are obtained through prediction of a random forest regression model; s3, variation and crossover operation is carried out on the current population, new individuals are obtained, fitness values of the new individuals are calculated, and the variation operation is hyperchaotic variation through hyperchaotic mapping; and S4, performing selection operation according to the fitness values of the individuals, obtaining a new population, returning to S3 until a termination condition is met, and obtaining the optimized metamaterial structure parameters. According to the method, the design and optimization efficiency of the metamaterial can be remarkably improved.

The present application provides a kind of vital signs detection signal denoising method and device, it is related to physiological perception and signal processing technical field, including: based on millimeter wave radar's Doppler technique, obtains the original phase signal containing physiological signal;Original phase signal is carried out variational mode decomposition based on the search algorithm of variational mode decomposition super parameter optimization algorithm optimized by particle swarm optimization algorithm, obtain the vibration modal function information corresponding to the original phase signal;Wherein, the particle swarm optimization algorithm uses permutation entropy and fuzzy entropy as fitness function;After denoising processing is carried out to the vibration modal function, the vibration modal function after denoising processing is recombined, and high-precision denoising processing is obtained after physiological signal;From the high-precision denoising processing physiological signal, extract respiratory and heartbeat physiological signal.

The invention discloses a Xinanjiang model multi-target parameter calibration method, system and device based on multi-department collaboration and a medium, and belongs to the technical field of hydrology and water conservancy. The method comprises the steps of obtaining a drainage basin characteristic index value of a target drainage basin; according to the drainage basin characteristic index value, the actually measured drainage basin total outflow is calculated; based on the drainage basin characteristic index value of the target drainage basin, the Xinanjiang model and the actually measured drainage basin total outflow, a multi-department multi-target optimization model including multi-department target spaces is constructed, and the multi-department target spaces include multi-target spaces corresponding to all decision-making departments; and taking all parameters in a pre-obtained Xinanjiang model as a public parameter space, and performing parameter search in the public parameter space by adopting an evolutionary optimization algorithm based on the multi-department multi-objective optimization model until the maximum number of iterations is reached, thereby obtaining an optimal parameter solution set. According to the invention, in one calibration process, the parameter solution set meeting the differentiated requirements of different decision departments is obtained at the same time.

The application discloses a method for globally optimizing 3D coordinate conversion parameters of multi-source three-dimensional data in a trusted space, acquires two groups of source point cloud data to be converted, target reference point cloud data, conversion quality constraint data and conversion parameter constraint interval, and performs standardization; constructs a trusted space unit according to the number of points, point cloud density and covariance eigenvalue of the space grid unit, and divides search trusted space units and verification trusted space units; in a joint conversion parameter search domain, a distance error lower bound and an overlap rate upper bound pruning parameter box are used, a processing order is determined according to the trusted space characteristics, and the parameter box is subdivided, so that candidate joint conversion parameters are obtained; verification error, overlap rate and parameter distinguishability are used to determine the joint conversion parameters to be output, coordinate conversion, quantization and post-quantization rechecking are performed, original coordinate conversion matrix and converted point cloud data are output, and the risk of mispairing of heterogeneous point clouds is reduced.

The invention relates to the field of integrated circuit layout design, provides a circuit device model parameter extraction and optimization method based on artificial intelligence, and aims at solving the technical problems that a traditional EDA tool is high in artificial dependence, low in optimization efficiency, poor in algorithm adaptability and split in tool. Automatic data interaction among the modules is realized through a standardized API; preprocessing and intention recognition are carried out on the multi-modal input, and user requirements are converted into formatted data; searching similar circuit historical parameters based on the knowledge base and generating a recommendation list; extracting a target curve through an image recognition model and carrying out standardization processing; dynamically matching an optimization algorithm according to circuit characteristics and adjusting hyper-parameters in real time; multi-tool collaborative optimization control is realized, and the parameter search progress is dynamically updated; and outputting the optimal parameter combination and carrying out verification iteration. The method is used for high-precision automatic optimization and full-process closed-loop verification of circuit parameters.

The invention provides a dam seepage parameter inversion method based on an improved reptile search algorithm, which is characterized by comprising the following steps of: aiming at prototype monitoring data of dam seepage characteristics when loads comprise water pressure, temperature and rainfall factors, firstly, separating an accurate hydrostatic pressure deformation component from the prototype monitoring data; independent fitting is carried out on effect components caused by water pressure to enhance the physical interpretability of an inversion process, so that a feasible path is provided for accurate identification of seepage parameters in a multi-field coupling environment; the method comprises the following steps: S1, separating historical monitoring data; s2, constructing a dam seepage numerical simulation model as an agent model; s3, performing seepage parameter search based on an improved reptile search algorithm RSA; according to the method, the precision and efficiency of seepage parameter inversion are remarkably improved, the physical interpretability of the inversion process is enhanced, and the dam seepage parameters are accurately and efficiently obtained.

The invention discloses a product key production parameter mining method and system based on user demand classification, and relates to the technical field of artificial intelligence. The method comprises the following steps: obtaining user preference data containing paired preference samples and a candidate parameter space formed by mass-producible parameter combinations; based on the data and the space, an improved self-adaptive direct preference optimization algorithm is adopted to train a strategy model, a dynamically calculated self-adaptive reward margin is introduced in the training process so as to adjust a model updating gradient according to a sample differentiation degree, and a Coubeck-Leibler divergence constraint is applied to limit parameter search in a candidate parameter space; and after the strategy model is converged, determining a group of product parameter combinations with the maximum posterior probability from the candidate parameter space according to the optimized model, and outputting the product parameter combinations as key production parameters.

The invention belongs to the related technical field of unmanned surface vehicle intelligent control and reinforcement learning optimization, and particularly relates to a gradient-free reinforcement learning method based on an acceleration and deceleration strategy, which comprises the following steps: reducing an original high-dimensional parameter search space to an effective low-dimensional parameter search space, evaluating the quality of an optimization result under the effective low dimension, and if the quality is not satisfied, executing the step 1; if the to-be-learned neural network does not exist, the effective low-dimension dimensionality is increased until an effective low-dimension parameter search space meeting the quality requirement is determined, a set of initial parameter solutions are randomly generated in the space, and each initial parameter solution is composed of values of all parameters of the to-be-learned neural network; determining a parameter solution updating direction based on the current group of initial parameter solutions; and moreover, the current group of initial parameter solutions are mapped back to the original high-dimensional parameter search space to serve as a group of strategy parameter solutions, and the accumulated total reward of each strategy parameter solution is independently evaluated, so that the initial parameter solutions are updated in combination with the parameter updating direction, and the updating operation is repeatedly executed. The sampling cost is low.

The invention belongs to the field of intelligent computing, and relates to a federal fuzzy system optimization method based on particle information cooperation. The invention provides a particle information collaboration-based fuzzy system optimization method (PICFFS) aiming at the challenge of finding a global optimal solution on the premise of ensuring data privacy and high calculation efficiency in federal fuzzy learning. According to the method, a double-layer optimization mechanism is innovatively designed: on a client side, a particle swarm optimization algorithm is adopted to carry out parameter search on a local fuzzy rule base, and the limitation that a traditional gradient descent method is prone to falling into local optimum is broken through; on a server side, through a particle distance information federation aggregation strategy, only inter-particle relative position features rather than original data are exchanged so as to realize privacy enhancement. Compared with the prior art, the method has the advantages that the global optimal solution can be quickly found without depending on initial value setting while the data privacy is ensured, and the method shows superiority in a plurality of evaluation indexes.

This invention discloses an optimization algorithm for stress cloud map inversion of hydropower unit bolts during service, relating to the field of hydropower unit bolt stress measurement technology. The algorithm includes the following steps: S1: Finite element simulation model construction, including the following steps: S2: Data cleaning of the physical field simulation data and field measurement data obtained in step S1 to obtain a physical field dataset; S3: Stress cloud map inversion model construction; S4: Optimal parameter search. This invention overcomes the shortcomings of existing technologies in actual environment modeling and structural optimization, providing a new technical approach for the maintenance and performance improvement of hydropower unit bolts. It achieves high-precision simulation in actual operating environments, overcoming the shortcomings of existing technologies in digital modeling. Based on theoretical physical constraints, it can construct a reinforcement learning algorithm to invert the stress distribution of bolts during long-term service, providing comprehensive information for structural health monitoring, while existing technologies have certain limitations in this regard.

The invention provides a die-casting island process parameter optimization method and system based on machine learning, and relates to the technical field of die-casting processes, and the method comprises the steps: obtaining process parameters in a die-casting process, calculating an energy loss value and an entropy weight of each process stage, recognizing a target process stage, and carrying out the local optimization. Constructing a low-dimensional space for the historical process parameters, and clustering based on geodesic distances; selecting a historical parameter with the optimal quality as a search starting point, and executing parallel parameter search to obtain an optimal process parameter combination; and an adjusting instruction is issued to the die-casting island control system. According to the method, the quality of die-casting products can be improved, energy consumption is reduced, and self-adaptive optimization is achieved.

Approaches are disclosed that can automatically tune parameters in an application pipeline. An application pipeline and datasets with labels can be accepted as input. An application pipeline can include various modules and information such as their interconnections and a set of parameters to be tuned. The input data can be fed into a preprocessing module and then fed into a parameter search module, which can navigate through the parameter space and search for improved and / or optimal parameters. The search can progress to informed selections based on outcomes of previous evaluations. The parameters identified can be used by an execution module to execute the pipeline. The results produced can be evaluated by an evaluation module that condenses its findings into a single score, which is passed back to a parameter search module to inform the next round of parameter predictions. Such an iterative process can continue until certain criteria are met, with final output corresponding to a set of automatically tuned parameters.

The invention discloses an active power failure monitoring method and system based on machine learning. The method comprises the steps of data acquisition, data processing, establishment of an active power failure monitoring model, adaptive optimization and active power failure monitoring. The invention relates to the technical field of power failure monitoring, in particular to a power failure active monitoring method and system based on machine learning, according to the scheme, noise data is removed by calculating a deviation item, constructing an entropy supplementing item and calculating noise intensity, and the data quality and the accuracy of a monitoring result are improved; the model is established by constructing a depth mapping function, designing a depth feature learning layer and constructing a loss function, so that the prediction performance, precision and stability of the model are enhanced; parameter search is carried out by designing an efficiency coordination factor, a direction transformation function and a noise reduction function, the search process is guided to evolve towards a more efficient direction, the search efficiency is improved, the diversity in the search process is increased, and jumping out of a local optimal solution and exploration of a global better solution are facilitated.

Provided are a hyperparameter search method and a hyperparameter search device. The hyperparameter search method comprises: obtaining a hyperparameter set for a predetermined task feature dimension, each task feature dimension in the predetermined task feature dimension comprising a predetermined plurality of feature values, the hyperparameter set comprising a hyperparameter corresponding to each task feature combination in a plurality of task feature combinations, the plurality of task feature combinations being obtained by one-to-one combination of each feature value under each different task feature dimension, and the hyperparameter corresponding to each task feature combination being an optimal hyperparameter pre-searched in a first hyperparameter search range for each task feature combination; searching, from the hyperparameter set, a hyperparameter corresponding to a predetermined K task feature combinations according to a first task feature combination of a to-be-processed task, the first task feature combination being a combination of feature values of each task feature dimension in the predetermined task feature dimension of the to-be-processed task; and searching a hyperparameter corresponding to the to-be-processed task based on the searched hyperparameter.

A motor parameter search system and a motor parameter search method are provided. The motor parameter search system includes a processing device and an input device. The processing device may execute a motor design parameter search engine to iteratively perform a parameter search operation based on a plurality of design parameters, a plurality of optimization objectives, a plurality of restriction conditions, and a plurality of historical recommended parameter combinations to generate a plurality of design parameter combinations. The design parameter combinations are sequentially input into a simulation software, so that the simulation software generates a plurality of simulation results. The processing device searches a plurality of historical simulation results in the database according to the plurality of optimization objectives to generate a recommended parameter combination.

The application belongs to the technical field of natural language processing, and specifically designs an integrated retrieval enhancement method of a large language model, an electronic device and a storage medium. The integrated retrieval enhancement method specifically is that a controller calls multiple retrievers according to user input, returns multiple-source multiple-document, and integrates and disturbs different documents into multiple knowledge sections through multiple processing modes, and respectively inputs a generation module. After the generation module returns multiple replies, the control module selects the optimal reply through the consistency between the replies and an objective scorer. The optimal parameter search is converted into a non-derivable optimization problem of a target function, the optimal retrieval enhancement configuration can be found through a meta-heuristic search algorithm, and the ability of the large language model on factual problems is effectively improved.