5results about How to "Automate selection" patented technology

The invention relates to the technical field of intelligent analysis, and discloses a preheating point location determination method, device and equipment, and the method comprises the steps: determining the playing frequency corresponding to each playing point location and the first screen playing time consumption according to historical playing behavior data; selecting candidate preheating point locations from the playing point locations based on the playing frequency and the first screen playing time consumption; based on the popularity parameters, determining popularity corresponding to each candidate preheating point location; and selecting a preheating point position from the candidate preheating point positions based on the heat degree of each candidate preheating point position. As the candidate preheating point locations which are accessed at high frequency and have relatively long first screen playing time can be quickly determined according to historical playing behavior data analysis, and further selection is performed according to the popularity of each candidate preheating point location in combination with related settings, the candidate preheating point locations can be reasonably selected as the preheating point locations, and the accuracy of the candidate preheating point locations is improved. Automatic selection of the preheating point positions is achieved, and the labor burden is reduced.

The invention discloses an automatic replacement device for a liquid flow test tool. The automatic replacement device comprises a rotary switching unit, a horizontal connection unit and an operation table, a switching tool is loaded on a tool wheel disc in the rotary switching unit, and tool selection is achieved through an ejection air cylinder and a clamping air cylinder. A clamping air cylinder is arranged on a tool moving plate in the horizontal connection unit and used for clamping the tools selected in the rotary switching unit, and transfer of the tools and the experiment testing unit is achieved through a linear moving module. The operation table top is used for installing the rotary switching unit, the horizontal connection unit, the test unit and the gas distribution block, the selection, transfer and test functions of test tools are integrated, and the control system is used for achieving automatic switching of the test tools.

The invention relates to the technical field of shale oil casing deformation prediction, in particular to a shale fracturing casing deformation machine learning prediction method based on a mechanism model, and the method comprises the steps: obtaining a target to-be-processed simulation data set meeting an acquisition standard; preprocessing the target to-be-processed simulation data set, correcting each to-be-processed simulation feature set according to a preprocessing result to obtain a target actual simulation data set, and performing initial numerical simulation on each actual simulation feature set, target shale oil well reservoir slip casing variables under different influence factors are determined according to the initial numerical simulation result; obtaining influence factors which cannot be measured actually to obtain a plurality of invisible simulation feature sets, and performing inversion based on each invisible simulation feature set so as to optimize the initial numerical simulation result to obtain an actual numerical simulation result; and performing prediction training and prediction testing on the actual numerical simulation result based on the selected initial prediction standard to optimize the initial prediction standard so as to obtain an actual prediction standard.

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.