2results about How to "Improve coverage quality" patented technology

This invention discloses a distributed beamforming method, system, and apparatus for low-Earth orbit satellites, comprising: acquiring ground user location information and satellites to be communicated with; calculating the transmission paths between multiple satellites and ground users and obtaining the superimposed signal of electromagnetic waves at the ground end; calculating the superimposed power distribution of the ground electromagnetic waves based on the superimposed signal; constructing an objective function that maximizes the average received power of all ground users and the fairness of ground users based on the calculated superimposed power distribution of the ground electromagnetic waves; calculating the optimal satellite and its corresponding initial transmission phase within the selectable set, indirectly controlling the transmission distance from the satellite to the ground end to control the final phase of the electromagnetic waves received by the receiver, thereby maximizing the overall power gain of multiple ground receiving terminals and the fairness among each individual ground user, providing ideal power gain regardless of the location of the ground user, and simultaneously covering multiple users.

The invention relates to the technical field of software testing, and particularly discloses a knowledge distillation-based large language model fine tuning and software testing method and system, and the method comprises a supervision fine tuning stage, a knowledge distillation stage and a reasoning stage. In the supervised fine tuning stage, taking the pre-trained large model as a backbone, inserting an LoRA low-rank adapter, and learning a structured unit test generation normal form under to-be-tested codes and demand conditions to obtain a student model; in the knowledge distillation stage, a teacher model is introduced, and soft target distillation, hard target supervision, Jensen-Shannon distillation and semantic keeping consistency regularization are jointly used as constraint conditions to train a student model; in the reasoning stage, the software code to be tested is tested based on the trained student model. According to the method, variation-sensitive and semantic-robust test generation can be realized under the constraint of actual engineering only by depending on supervised fine tuning and distillation.