3 results about "Stability index" patented technology

This invention provides a cloud service assurance method and system based on eBPF observable data and a prediction model. The method includes: collecting historical observable operational status data of the kernel, containers, and application layers and mapping and associating them; aligning and concatenating the associating data to generate a time-series feature vector; using the time-series feature vector to train and test the constructed model, calculating the residuals based on the predicted values ​​and corresponding actual observed values ​​during the testing period, and analyzing the residual probability distribution characteristics; using the model's prediction results for cloud services to calculate the residuals, and generating anomaly confidence scores based on the residual probability distribution characteristics; under the condition that the anomaly confidence score is greater than a threshold, analyzing the anomaly type and its impact range, then matching a remediation plan in the policy library and automatically executing the remediation plan; monitoring the execution effect of the remediation plan and feeding it back to the model; calculating a stability index based on key stability indicators to form a stability change curve, and then evaluating the execution effect of the remediation plan.

This invention relates to the field of loading control technology and discloses a method and system for disturbance-free switching of loading control. The method involves collecting motion state data of the loading system, including displacement and force deviations; calculating a set of switching decision indices based on the motion state data to determine whether the loading system meets the switching conditions; the set of switching decision indices includes a first stability index for displacement deviation and a second stability index for force deviation; setting a gradual switching operation strategy based on displacement and force deviations; and monitoring the smoothness index of the switching transition process, which includes the amplitude of control signal fluctuations and the continuity of the loading system's response. When the smoothness index meets the baseline disturbance-free condition, the switching is confirmed to be complete. This achieves intelligent decision-making based on state assessment, gradual smooth transition, and self-learning optimization of compensation quantities, ensuring disturbance-free switching between displacement feedback and force feedback control and avoiding impacts or disturbances to the loading system.

The application relates to the technical field of servo control, and discloses a servo steering engine adaptive parameter adjustment method and system and a storage medium. The method comprises the following steps: fusing environment data into a comprehensive environment vector to generate a smoothed environment feature set; calculating a deviation vector to determine a significant feature in an environment change period; if the significant feature exceeds a preset threshold, extracting specific data from the environment feature set to calculate a parameter change rate range; mapping a parameter adjustment sequence in combination with historical operation data to calculate a parameter mapping error; if the parameter mapping error exceeds a preset tolerance, iteratively updating an adaptive adjustment coefficient to obtain an optimized adjustment coefficient set; verifying parameter consistency and boundary compliance under a constraint condition, adjusting parameter combination weight distribution and optimizing to obtain a final control parameter combination; and analyzing a stability index to judge system adaptability and generate an operation state evaluation result. The application improves the precision of servo steering engine adaptive parameter adjustment.