7results about How to "Maximize operating efficiency" patented technology

The invention discloses a load optimization method and device for a supercritical traditional generator set in closed-loop operation. The method comprises the following steps: acquiring load operation data of the supercritical traditional generator set in a closed-loop operation state; analyzing a response relationship between each operation parameter and a load instruction in the load operation data, establishing a closed-loop operation model of the supercritical traditional generator set, and constructing an optimization constraint condition and a load optimization objective function in combination with a plurality of optimization dimensions; and solving the load optimization objective function by adopting a multi-objective evolutionary optimization algorithm in combination with the optimization constraint condition to obtain an optimal load instruction solution set and determine an optimal load instruction. By constructing a closed-loop operation model of the supercritical traditional generator set and combining an optimization constraint condition and a load optimization objective function, an issued load instruction is softened and corrected, so that the load instruction is more in accordance with an actually reachable dynamic behavior of the supercritical traditional generator set; therefore, stable and efficient operation of a supercritical traditional generator set in a high-proportion new energy system is supported.

The application discloses a kind of chip surface submicron dirty spot half-supervised automatic positioning method, comprising: S1, chip surface image is shot;S2, rough positioning: construct double memory bank driven semi-supervised AE defect positioning network, quickly resolve the two-dimensional center coordinates and radius of dirty spot;S3, fine positioning: the rough positioning area of dirty spot is carried out high-density depth data acquisition;S4, dirty spot three-dimensional model construction: the accurate depth value of each dirty spot is fused with the two-dimensional coordinate in rough positioning, and the high-precision point cloud model of micro dirty is generated.The method constructs double memory bank driven semi-supervised AE defect positioning network and based on galvanometer scanning image field telecentric spectral confocal detection system, effectively solve the detection problem caused by the scarcity of micro dirty sample in industrial scene, break through the bottleneck of low efficiency of traditional full-field scanning, realize the efficient, accurate three-dimensional reconstruction and quantitative analysis to the geometry appearance of chip surface submicron dirty.

The invention discloses an energy storage system storage battery charging and discharging strategy and parameter matching method, and relates to the technical field of energy storage systems, and the method comprises the following steps: constructing a multi-objective optimization model containing economical efficiency and reliability; solving the multi-objective optimization model through a non-dominated sorting genetic algorithm, and generating a leading edge solution set; establishing a scene classification library and a fuzzy rule library, collecting power grid state data in real time, and matching a current operation state with scene library features through a clustering algorithm; dynamically selecting a fuzzy rule base according to the scene type; selecting an optimal charging and discharging strategy from the leading edge solution set based on a fuzzy decision method; and dynamically controlling the charging and discharging power of the storage battery according to the optimal strategy, and matching parameter constraints of the storage battery. According to the energy storage system storage battery charging and discharging strategy and parameter matching method, through multi-objective optimization, the strategy is flexibly adjusted according to the actual condition of the power grid, and maximization of the operation benefit of the power grid is facilitated.

The application discloses a kind of logistics network balance optimization methods considering production, transportation, marketing, storage and use whole chain cooperation, comprising the following steps: obtaining the basic information of each board industry model in the integration range of relevant enterprises;Obtain the coal logistics network balance optimization model data mapping of production, transportation, marketing, storage and use whole chain cooperation;Based on the foregoing basic information and data mapping, the decision variables and constraint conditions of optimization problem are constructed, the constraint conditions include coal mine constraints, outsourcing constraints, loading platform constraints, railway constraints and sales area constraints;The objective function of optimization problem is constructed, and the objective function includes the maximum model of transportation quantity, the minimum model of cost, the maximum model of profit and multi-objective optimal model;Based on the decision variables and constraint conditions, the optimal value of each objective function is solved respectively.

This invention proposes an optimization method and system for energy storage systems based on a multi-objective ant colony optimization algorithm, belonging to the field of grid regulation technology for energy storage systems. It aims to efficiently solve the coordinated optimization problem of energy source, grid, load, and storage. The core of this method lies in constructing multiple heterogeneous heuristic information sets with different optimization biases, and dividing the ant colony into different groups. Each group searches for a scheduling scheme in parallel based on a specific heuristic information. This mechanism breaks through the limitations of traditional single search, greatly improves the diversity of global search, and can generate higher-quality and more widely distributed trade-off solution sets, thereby achieving optimal coordination between economic and environmental goals.

The invention discloses a linear Fresnel type heat collection field reflector parameter optimization design method and system. The method comprises the steps that a two-dimensional space coordinate system is established according to the arrangement direction of a linear Fresnel type heat collection field and the axis direction of a reflector; based on the two-dimensional space coordinate system, a linear Fresnel type heat collection field reflector parameter optimization model is constructed, constraint conditions are set, and the linear Fresnel type heat collection field reflector parameter optimization model takes annual average reflector field efficiency maximization as a target function; and solving the linear Fresnel heat collection field reflector parameter optimization model by combining a particle swarm algorithm and a genetic algorithm so as to determine reflector parameters which maximize annual average reflector field efficiency. By means of the scheme, a unified geometric reference is provided for optimization by establishing the accurate two-dimensional space coordinate system, the parameter optimization model with annual average mirror field efficiency maximization as the core target is established, and the long-term operation performance of the heat collection field can be comprehensively considered.

This invention discloses a method for predicting and planning operational risks of industrial robots based on probabilistic twins. The method includes: establishing an industrial robot model, defining at least one intrinsic physical parameter affecting the dynamic behavior of the industrial robot as a random variable with a prior probability distribution, thus forming a probabilistic twin model of the industrial robot; acquiring sensor observation data from the physical world during the industrial robot's task execution, and updating the probability distribution of the intrinsic physical parameter using a Bayesian calibration method based on the difference between the observation data and the prediction results of the probabilistic twin model; probabilistically predicting the future risks of the industrial robot executing candidate task paths based on the updated probabilistic twin model, and planning or adjusting the industrial robot's task path based on the risk prediction results; this invention enables path planning to intelligently balance safety, task completion, and active learning, significantly improving the robot's autonomous decision-making ability and operational efficiency.