4results about How to "Choose science" patented technology

This invention belongs to the field of cutting parameter optimization technology, specifically relating to a multi-objective cutting parameter optimization method, including: acquiring cutting parameters and machine tool spindle power signals, calculating cutting specific energy, and simultaneously acquiring surface roughness; constructing a BP neural network model, optimizing the connection weights and thresholds of the BP neural network model using the Harris Eagle optimization algorithm to minimize the fitness function, and establishing a cutting specific energy prediction model; constructing a surface roughness prediction model with the encoded variables of cutting parameters as input and surface roughness as output, and establishing a multi-objective cutting parameter optimization model; solving the multi-objective cutting parameter optimization model to obtain the Pareto non-dominated solution set, using the entropy weight method for comprehensive decision-making, and selecting the solution with the largest comprehensive evaluation value as the optimal cutting parameter combination. This invention, through methods such as the BP neural network improved by the Harris Eagle optimization algorithm, reduces machining energy consumption, improves surface roughness, and enhances overall machining performance.

The present application relates to the vulnerability evaluation method of immovable cultural relics, comprising the following steps: 1, determining the vulnerability evaluation index; 2, determining the evaluation index vulnerability value grading standard; 3, obtaining evaluation index data and vulnerability value assignment; 4, determining the vulnerability evaluation index weight; 5, constructing the vulnerability evaluation model; 6, dividing the vulnerability level; 7, calculating the vulnerability index; 8, determining the vulnerability level of immovable cultural relics. The method of the present application can well evaluate the vulnerability of immovable cultural relics under the influence of natural disasters such as flood, earthquake and other disasters at different scales, and has universal applicability.

The invention provides a screening method and system for a blast furnace heat prediction model, and relates to the technical field of blast furnace smelting. The method comprises the following steps: training a plurality of candidate prediction models; calculating a combined evaluation index of each model for furnace heat abnormity early warning, wherein the indexes comprise three indexes for measuring prediction accuracy, abnormity capture capability and early warning advance; and determining an optimal prediction model according to an evaluation result of the combined evaluation indexes. According to the method, the combined evaluation indexes oriented to abnormal early warning are constructed, so that the model evaluation standard is aligned with the actual production demand, the screened optimal model has the capability of accurately early warning the key furnace heat abnormality in advance, precious reaction time can be won for field operators, and the method has relatively high practical value.

The present application belongs to the technical field of oil and gas development and exploitation engineering, and particularly relates to a method for selecting a sand control mode of an oil and gas well in a loose sandstone reservoir. The method is based on stablely obtainable geological and production parameters in an engineering field, and a parameter obtainability index PAI is introduced i The sand control evaluation indexes are screened, and based on historical operation data and the screened sand control indexes, main sand control indexes of different sand control modes are screened, and the main sand control indexes of the screened sand control modes are divided into positive indexes and negative indexes. On this basis, an applicability index (UI), a failure risk index (FRI) and a comprehensive adaptation index (CSI) are respectively calculated, so as to realize quantitative evaluation and optimization of the sand control mode, and overcome problems of the existing sand control mode selection method, such as small coverage of the selected sand control mode, single factor considered, strong subjectivity, large calculation amount, and difficult parameter acquisition.