2results about How to "Automate selection" patented technology

The utility model provides an automatic selection equipment of excavator body label relates to excavator manufacturing technical field, this excavator body label automatic selection equipment, include: model scanning device for scanning excavator body's model instruction information, obtains model information, sucking mechanism is used for sucking out excavator body label in at least one label box in multiple label boxes with model information matching, sucking out one excavator body label each time, and the excavator body label that sucks out is placed in the material box, driving mechanism is used for driving sucking mechanism to move to the top of at least one label box with model information matching in turn, control device is connected with model scanning device, sucking mechanism and driving mechanism respectively electricity, is used for obtaining model information, and according to model information sends work instruction to driving mechanism and sucking mechanism respectively. The utility model's scheme can avoid appearing excavator body label to take, take less or take wrong hidden danger.

The application discloses a two-dimensional layout algorithm selection method and system based on machine learning, and the method comprises the following steps: constructing and training XGBoost model and full connection neural network model, and establishing a two-dimensional layout algorithm library; acquiring real-time layout data, extracting features and calculating characteristic values, inputting the XGBoost model to obtain the selection probability of each algorithm, and screening out the candidate algorithm sequence exceeding the preset probability threshold; sorting the candidate algorithm according to whether the time factor needs to be considered, calling the solution in sequence, determining the final algorithm if successful, and trying the subsequent algorithm in sequence if failed; if all the algorithms fail, inputting the characteristic values into the full connection neural network to generate the first n optimal algorithms, and constituting a new sequence to solve again. The application solves the problems that in the two-dimensional layout algorithm selection method depending on artificial experience or trial-and-error method, the artificial experience may lead to subjective bias, thereby affecting the accuracy and optimality of algorithm selection; and the trial-and-error method is easy to introduce a large amount of invalid calculation, thereby leading to the problem of low algorithm selection efficiency.