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This invention discloses a parametric design method for server racks, comprising: decomposing parameters based on an existing standard rack model to obtain the parameters required for building the rack model; dividing the required parameters into three groups: a total dimension parameter group, a dependent variable parameter group, and a default value parameter group; the total dimension parameter group and the server dimension parameters together form the independent variable parameter group; mapping and associating the dependent variable parameter group with the independent variable parameter group to obtain the calculation relationship between the dependent variable parameter group and the independent variable parameter group; and generating a three-dimensional model of the rack using software based on the parameter values ​​of the obtained default value parameter group, total dimension parameter group, and dependent variable parameter group. This invention simplifies the rack modeling process, saves manpower, and improves work efficiency.

The present application belongs to the field of artificial intelligence geoscience application and dense reservoir evaluation technology, and particularly to a dense reservoir structure porosity inversion method based on rock physical prior embedding. The method comprises the following steps: S1: obtaining conventional logging data and calibration data; S2: logging data preprocessing; S3: constructing rock physical prior features; S4: constructing a rock physical prior embedding input matrix; S5: constructing an integrated coupling model; S6: extracting bidirectional sequence features in the well depth direction; S7: performing nonlinear inversion; S8: constructing a rock physical prior constraint loss function; S9: performing joint global optimization; and S10: outputting intelligent inversion results of structure porosity. The continuous curves of free porosity, micro-microporosity and clay porosity output by the present application can directly serve reservoir effectiveness evaluation, movable fluid identification, reserve calculation and development interval optimization, and provide reliable parameter support for oil and gas exploration and development.