3results about How to "Accurate life" patented technology

The application discloses a kind of unmanned mine car reliable life prediction methods of fusion-field theory, comprising: based on object-field theory, multiple source performance degradation parameters are classified and extracted, and initial performance degradation dataset is constructed;And normalization processing;Using mutual information method, the correlation measure between each performance degradation characteristic and mine car reliable life after normalization processing is calculated, and according to the set threshold, the key object-field performance degradation factor is filtered out, and constitutes key factor dataset;Using piecewise linear model, the reliable life label of sample in key factor dataset is set;With the reliable life label corresponding to key factor dataset as training data, a deep neural network prediction model is constructed and trained;Using the trained deep neural network prediction model, according to the real-time or historical acquisition key object-field performance degradation factor data, the reliable life of unmanned mine car is predicted.The more accurate, dynamic prediction of the reliable life of unmanned mine car is realized.

The invention discloses a method and system for testing the service life of an acetabular cup, and the method comprises the steps: outputting a dynamic stress distribution cloud picture according to the three-dimensional structure parameters and material attributes of an acetabular cup implant; outputting a damage evolution feature vector based on the dynamic stress distribution cloud picture; outputting a cross-scale fatigue damage prediction model according to the damage evolution feature vector; outputting a dynamic damage accumulation index based on the cross-scale fatigue damage prediction model; and according to the dynamic damage accumulation index, optimizing a test parameter space through reinforcement learning, generating an accelerated life test protocol, driving a six-degree-of-freedom test board to execute multi-modal coupling loading, and outputting a life prediction confidence interval report. According to the embodiment of the invention, more accurate service life prediction and reliability evaluation can be realized.

This invention discloses a battery life prediction method and system based on a multi-scale state-space model, belonging to the field of battery health management technology. The method includes acquiring battery operating data; calculating relative characteristics at various time scales based on the operating data; generating a battery identification code based on the battery's specifications and attribute characteristics; inputting the relative characteristics at each time scale and the battery identification code into a multi-scale state-space model for fusion and processing; and outputting the battery life prediction result and health status assessment. This invention can simultaneously process batteries of different models, capacities, and production batches, significantly reducing model deployment and maintenance costs; through multi-scale feature fusion and physical constraints, it provides more accurate life prediction and health status assessment; the model output used in this invention includes degradation mode classification and uncertainty estimation, providing users with more comprehensive decision support.