US 12,487,583 B2
Parameter optimization method, device, and storage medium
Armin Roux, Erlangen (DE); Bin Zhang, Beijing (CN); Zhong Yang Sun, Beijing (CN); Shun Jie Fan, Beijing (CN); and Ming Jie, Beijing (CN)
Assigned to SIEMENS AKTIENGESELLSCHAFT, Munich (DE)
Appl. No. 18/041,442
Filed by Siemens Aktiengesellschaft, Munich (DE)
PCT Filed Aug. 14, 2020, PCT No. PCT/CN2020/109361
§ 371(c)(1), (2) Date Feb. 13, 2023,
PCT Pub. No. WO2022/023682, PCT Pub. Date Feb. 17, 2022.
Prior Publication US 2023/0315052 A1, Oct. 5, 2023
Int. Cl. G05B 19/4155 (2006.01)
CPC G05B 19/4155 (2013.01) [G05B 2219/33099 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A parameter optimization method comprising:
establishing a one-to-one functional relationship between each parameter of a set of parameters and each performance index of a set of performance indexes based on function relationships between each performance index and all parameters of the set of parameters;
determining each current correlation coefficient between the parameter and each performance index based on the one-to-one function relationships;
obtaining a current weight of each performance index;
according to the current weights and the current correlation coefficients, obtaining a current influence coefficient of each parameter on a comprehensive performance of the performance indexes;
determining a parameter whose current influence coefficient reaches a set high threshold as an important optimization parameter, or determining a first one of the one or more parameters with a highest current influence coefficient of the one or more parameters as the important optimization parameter;
for each two parameters, calculating a current correlation coefficient of the two parameters according to the current weights and the current correlation coefficient between each of the two parameters and each performance index, and determining the parameter whose current correlation coefficient with the important optimization parameter meets set requirements as an adjustment parameter;
performing parameter optimization based on the important optimization parameters and the adjustment parameters;
establishing a knowledge map of the parameters based on an association coefficient of each of the two parameters;
wherein the knowledge map comprises nodes representing the parameters and connecting lines between nodes representing an association relationship between parameters; and
in the knowledge map, the size of each node is directly proportional to the value of the influence coefficient of the parameter represented by the node, and the nodes corresponding to the important optimization parameters and the adjustment parameters are highlighted.