7results about How to "Effective exploration" patented technology

This invention belongs to the field of electrical digital data processing technology, specifically relating to a multi-objective optimization method for improving motor torque performance. The method includes: establishing a parameterized model of the motor; extracting geometric harmonic prior features based on the harmonic analysis principle of the motor's air gap magnetic flux density, and using these as a screening criterion to obtain an initial high-quality sample set; constructing a Kriging surrogate model reflecting the mapping relationship between design variables and performance response, introducing an energy efficiency boundary risk function during the construction process; based on the Kriging surrogate model, using a multi-objective optimization algorithm to search for a Pareto front solution set, selecting potential sample points from the Pareto front solution set for simulation verification, and then adaptively updating the Kriging surrogate model until the stopping criterion is met, at which point the target motor design parameters are output. This invention can achieve accurate optimization of electromagnetic parameters by screening samples through geometric harmonic prior features and utilizing the energy efficiency boundary risk function.

The application discloses a PM2.5 prediction method and system of an adaptive geographical weighted artificial neural network model, and relates to the technical field of spatial data analysis. Specifically, the method comprises the following steps: a data acquisition step: acquiring historical PM2.5 data sets of a to-be-measured region, and dividing the historical PM2.5 data sets into a training data set and a test data set; a step of constructing an AS-GWNN model: constructing a network architecture of the AS-GWNN model, including an input layer, a hidden layer and an output layer; a training step: inputting the training data set into the network architecture of the AS-GWNN model to obtain a trained AS-GWNN model; and a test step: inputting the test data set into the trained AS-GWNN model to obtain a predicted PM2.5 value. The application solves the problems that machine learning technology ignores the inherent spatial non-stationarity of spatial data, and that regression models considering spatial non-stationarity lack nonlinear fitting capability.

The application discloses a kind of based on hovering underwater vehicle's underwater unknown environment active mapping method, belong to robot environment exploration technical field.The underwater unknown environment active mapping method based on hovering underwater vehicle provided by the application utilizes hovering underwater vehicle to obtain the environmental data of unknown area of ocean in cooperation with sensor, determines the best next viewpoint according to information gain value, and determines the safe and feasible path to the best next viewpoint according to the cost of each adjacent node on the current tree as the parent node when the best next viewpoint is reached.Updating grid map in real time in this way, efficiently, quickly and accurately realize the construction and dynamic updating of map, and then assist underwater vehicle to realize the navigation of target area, and has broad application prospect in underwater environment exploration task and underwater structure inspection based on underwater vehicle and similar tasks.

The application discloses a game virtual role behavior decision-making method based on deep reinforcement learning, comprising the following steps: S1, obtaining the current environment state information of a game virtual role, and obtaining standardized environment state features after preprocessing the environment state information; S2, performing hierarchical reinforcement decision-making based on an intention-risk double-layer decoupling architecture, and generating a candidate behavior action set matching the current environment state; and S3, performing robustness screening on the candidate behavior action set through opponent counterfactual simulation, and the application relates to the technical field of game artificial intelligence. The game virtual role behavior decision-making method based on deep reinforcement learning performs robustness screening on the candidate behavior action through opponent counterfactual simulation, ensures that the selected behavior not only conforms to the current environment state, but also exhibits good stability and benefits under various possible opponent strategies, and greatly improves the ability of the game virtual role to cope with complex situations.

The application discloses a fissure medium permeability measuring method and system, relates to the field of fissured rock mass permeability calculation, and combines high-resolution three-dimensional imaging of fissure medium, direct modeling based on images, finite element seepage process numerical simulation and other technologies to design a fissure medium permeability measuring method, so that the permeability of fissured rock mass in different directions can be accurately measured on the basis of considering the complexity of fissure structure and the distribution heterogeneity, and the effective measurement of the permeability of strongly fissured rock mass in different directions under the condition of non-damage can be realized by processing various three-dimensional fissure structure data bodies, the method can be applied to technical researches such as quantitative characterization of seepage characteristics of reservoirs in complex structure regions, researches on gas production rules of fissure gas reservoirs and optimization of drainage and production systems, and has important reference significance for effective exploration and efficient development and utilization of unconventional natural gas resources and other engineering practices.

The application discloses a kind of based on colony intelligent algorithm's fishery pond water temperature control optimization method, belong to the technical field of control optimization, including the following steps: S1, constructs and introduces the fishery pond water temperature PID control system of improved narwhal optimization algorithm module;S2, improved narwhal optimization algorithm is introduced, and improved narwhal optimization algorithm module is constructed;S3, the improved narwhal optimization algorithm is used to the setting optimization of pond water temperature PID control parameter, and the best control parameter is obtained;S4, the best control parameter obtained using improved narwhal optimization algorithm optimization is set as the parameter of pond water temperature PID controller, and the effect of pond water temperature regulation control is optimized.The application introduces a colony intelligent optimization algorithm, i.e.improved narwhal optimization algorithm, realizes the high-performance control of pond water temperature, and provides reliable control technical support for guaranteeing the efficient, stable and safe operation of aquaculture.

A self-adaptive sorting strategy optimization method based on artificial intelligence and a machine learning algorithm comprises the steps that 1, a sorting system based on multiple agents is established in a production workshop, and the sorting system comprises a plurality of auxiliary agents and a main agent located in the production workshop; 2, a reward function is constructed for each sorted sample, and dynamic weights are distributed for all indexes in the reward functions; and step 3, based on the currently established dynamic weight reward function, the slave agent carries out sorting by adopting a probability distribution-based hybrid exploration strategy. The method has the following beneficial effects: 1, the adaptability of the system is improved: by introducing sample quality stability indexes and dynamic weight distribution, the method can automatically adjust a sorting strategy to adapt to different production environments; 2, the sorting efficiency is optimized, an exploration strategy based on probability distribution and an exploration strategy based on counting are adopted, the intelligent agent can more effectively explore and learn the optimal sorting strategy, and the sorting efficiency is improved;