7results about How to "Verify rationality" patented technology

A method for constructing a damage constitutive model to analyze the damage evolution of easily argillaceous rocks includes: preparing ore specimens; conducting acoustic emission tests on the ore specimens under uniaxial compression; obtaining the displacement, load, and acoustic emission parameters of each specimen; calculating and analyzing the characteristic stresses of easily argillaceous rocks under uniaxial compression; revealing the evolution characteristics of acoustic emission parameters under uniaxial compression; defining the damage variables at different characteristic stress stages; and constructing a damage constitutive model of the entire stress-strain process of easily argillaceous rocks. This method can accurately and comprehensively quantitatively analyze the strength evolution characteristics of easily argillaceous rocks under uniaxial compression, monitor the damage evolution characteristics and damage degree of easily argillaceous rocks in real time, and provide a reference for maintaining the stability of underground goaf areas in mines.

The application discloses a kind of power power balance calculation method and system based on machine learning, belong to electric power system optimization scheduling technical field, will deep neural network and optimization calculation be combined, adopt the strategy of integer variable reduction, the operation state of most thermal power units is determined in advance by Seq2Seq-Attention model, the input of model is the relaxation solution of operating state and scene curve data, and the output is the operating state of thermal power unit, so as to reach the effect of improving the calculation speed of power power balance problem, the acceleration effect evaluation index designed and the acceleration effect verification method based on variable time scale scene generation are used to verify the acceleration effect of the fast calculation method, which has high precision and strong practicability.

The invention provides a blade vibration test device and test method, and the device comprises an installation seat which is used for installing a tenon of a blade; the vibration exciter is used for applying vibration to the blade on the mounting seat; and the loading device is used for applying torque and / or pulling force to the blade crown of the blade positioned on the mounting seat. It can be seen that by applying torque or tension in a blade vibration test, the vibration test can be close to a real blade working state as much as possible, obtained test data approach to real blade vibration characteristics, the influence of blade crown pre-twisting, pre-tensioning and the like on the vibration characteristics is effectively verified, and the reasonability of blade crown design is verified; and test data support is provided for blade optimization design.

The invention relates to the technical field of ancient building protection and repair, and discloses an ancient building wood framework deformation grading tension reset construction and acceptance method, which comprises the following steps: scanning a wood framework by adopting three-dimensional laser scanning equipment, quantitatively analyzing deformation and calculating a deviation correction amount; according to the deviation correction amount, a graded tensioning scheme including local advanced deviation correction and overall synchronous deviation correction is formulated, closed-loop control of graded loading and load-holding voltage stabilization is executed through a collaborative tensioning and monitoring system in construction, and a tensioning strategy is adjusted in real time; after resetting is completed, a model is generated through three-dimensional laser scanning again, and quantitative evaluation of multi-dimensional indexes such as column grid resetting precision, mortise and tenon joint sealing degree and elevation levelness is carried out; and long-term stability monitoring is carried out on the wood framework. According to the method, through full-process digitization and closed-loop control, the precision, safety and controllability of resetting are improved, an objective quantitative evaluation basis is provided for the repairing effect, and long-term structural stability after resetting is guaranteed.

This invention discloses a finite element analysis method and system for the yield buckling strength of a marine integrated test platform. The method includes establishing a finite element model of the platform for marine integrated testing; obtaining the peak value of the wave load transfer function based on the design wave method and the hydrodynamic calculation results of the hydrodynamic model, selecting design wave parameters, and applying the corresponding wave loads to the entire platform finite element model; solving the structural finite element model to obtain the calculated results of nodal stresses and nodal displacements; and analyzing the calculated results of the nodal stresses and nodal displacements to generate a stress contour map. This invention enables the structure to withstand environmental loads within its design service life and meets the strength requirements under design conditions.

The invention discloses a large model evaluation method and device, electronic equipment and a computer program product. The method comprises the steps that a directed acyclic graph is constructed based on a target task of a robot, the directed acyclic graph comprises at least one legal execution path corresponding to the target task, and each legal execution path comprises at least one action node; according to the directed acyclic graph, determining a current executable action node set of the robot; the target task is planned through the large model, and a current prediction action is obtained; evaluating the predicted action according to the executable action node set to obtain an evaluation result; under the condition that the evaluation result indicates that the prediction is correct, triggering the robot to execute a prediction action; the above process is continuously repeated until a preset ending condition is met, and then the evaluation of the large model based on the target task is ended. According to the scheme, systematic deviation caused by a unique truth value in an existing evaluation method can be eliminated, and objective measurement of the real planning ability of the large model is achieved.

The application belongs to the field of agricultural information technology, and particularly relates to a Canada goldenrod prevention and control method based on leaf vein feature fine-grained identification. A high-resolution image dataset containing four types of seedling leaves of species is constructed, and the characteristics of tri-foliate veins of Canada goldenrod and the characteristics of pinnate veins of other species are highlighted and labeled. A deep learning model with a leaf vein structure attention module is designed, multi-scale feature extraction and triple comparison learning are used to force the model to focus on the vein direction, bifurcation mode and topological structure difference. Real-time images are collected by a drone and are divided into grids, and the species category, seedling confidence and spatial distribution are output in grid units. Based on the identification result, a differentiated and accurate prevention and control scheme is automatically matched. The seedling identification accuracy of the Canada goldenrod reaches 96.8%, the false alarm rates of the sumacweed, the small bentgrass and the annual meadowgrass are as low as 3.2%, 3.5% and 2.8% respectively, the pesticide saving rate reaches 52%-68%, and the other plant injury rate is reduced from 18.5% of the traditional method to 2.1%.