8results about How to "Reduce data collection costs" patented technology

The invention discloses a blue-green algae identification and quantification method and device based on unmanned aerial vehicle remote sensing images and deep learning and a medium, and relates to the technical field of information data processing. Combining meteorological data and water quality monitoring data to construct an adaptive dynamic environment algorithm to extract EXIF metadata including camera parameters and attitude angle information, and establishing a geometric projection model to perform coarse orthographic correction on an original aerial image; the method comprises the following steps: extracting a multi-scale cyanobacterial bloom image feature map by stages based on a ResNet architecture and in combination with a feature pyramid network FPN fused with an attention mechanism, and obtaining an instance mask of cyanobacterial bloom through an anchor-free region proposal network, ROI Align and a head network; based on an improved GIS space projection and deep learning algorithm, carrying out high-precision area measurement and calculation on a binary mask image converted from the instance mask; according to the method, the influence of irrelevant interference on blue-green algae identification can be reduced, and the accuracy and comparability of blue-green algae identification and area calculation are improved.

This invention provides a method and system for reconstructing the full-band electrochemical impedance spectroscopy (EIS) of lithium-ion batteries. The method includes: performing distributed relaxation time (DRT) analysis on each group of battery EIS samples to obtain DRT spectra; aligning the DRT spectra on the logarithmic time constant axis and performing ensemble statistics, selecting peaks and / or valleys that stably appear under multiple operating conditions based on statistical stability, and mapping them to obtain a set of conditionally invariant characteristic frequencies; selecting characteristic frequency points that satisfy the sampling budget from the set of conditionally invariant characteristic frequencies, and performing sparse EIS measurements on the target battery at these characteristic frequency points to obtain sparse complex impedance inputs; generating a full-band complex impedance sequence based on the sparse complex impedance inputs using an autoregressive reconstruction model; and performing battery state estimation and fault diagnosis based on the full-band complex impedance sequence. This invention significantly improves the continuity of the spectral shape and the generalization ability across operating conditions under sparse sampling conditions, while reducing the full-spectrum testing time and sampling burden.

This invention relates to a data-driven method for estimating the driving range of new energy vehicles, belonging to the field of battery technology. The method includes: collecting operational data of new energy vehicles of corresponding models to establish a new energy vehicle operational database; establishing an estimation model based on charging data and data augmentation technology; calculating the vehicle's current available total capacity based on the estimation results and the ampere integral method; extracting charging and driving feature sets correlated with the driving range from the driving range estimation feature set based on the Pearson correlation coefficient index; constructing a driving range estimation model based on XGBoost based on the selected charging and driving feature sets; and estimating the driving range using the driving range estimation model. This invention utilizes historical vehicle operating data to calculate the current available total battery energy, effectively considering the impact of battery aging on driving range, extracting features from battery status and driving behavior, and comprehensively reflecting the actual operating conditions of the vehicle.

The present application relates to the technical field of underground engineering ventilation and heat and humidity environment control, and discloses a tunnel heat exchange real-time prediction method and system based on physical guidance machine learning, comprising: obtaining standardized simulation data related to tunnel heat exchange and target tunnel measured data and preprocessing to obtain first time series data; based on air enthalpy and annual cycle, day cycle time characteristics, the first time series data is subjected to physical enhancement feature construction to obtain model input features including encoder input features and decoder input features; an LSTM-Seq2Seq-Attention prediction model is constructed, and a two-stage training strategy of simulation data pre-training and target tunnel measured data fine-tuning is used to train the prediction model; the trained prediction model is used to output tunnel outlet temperature and humidity prediction results at multiple future time points at one time; according to the comparison result of the prediction result and the same period measured data, effective measured samples are screened to obtain a model update data set, and the prediction model is periodically fine-tuned and dynamically updated.

The invention discloses a double-stage pre-training system for reading of an industrial inspection instrument, and belongs to the technical field of industrial visual inspection and intelligent inspection. The system comprises a structure interpretable parameterized instrument data synthesis module, a mask auto-encoder pre-training module, a multi-task joint pre-training module and an industrial deployment fine tuning module. Constructing a plurality of pointer type instrument structure templates based on a parametric modeling mode, randomizing parameters to generate high-diversity synthetic instrument data, and automatically generating corresponding structure and semantic annotations; performing unsupervised pre-training by using a mask auto-encoder to learn the relationship between the underlying structure features of the instrument image and the global space; then, multi-task joint pre-training is introduced into the shared backbone network, and multi-task collaborative optimization is carried out; in the industrial deployment stage, through multiple task heads and newly added target detection task heads, small sample fine adjustment is carried out in combination with a small amount of field data, and rapid adaptation to a specific industrial environment is realized. The method is suitable for an automatic instrument reading scene in a complex industrial environment.

The application provides a three-dimensional model construction method and system for topographic survey, and belongs to the technical field of model construction. The method comprises the following steps: segmenting satellite images of a target survey area based on the complexity of the terrain to obtain complex terrain areas and open terrain areas; obtaining laser scanning data of the complex terrain areas and low-altitude image data of the open terrain areas; fusing the laser scanning data and the low-altitude image data to obtain a target data set corresponding to the target survey area; constructing a regular grid model corresponding to the target survey area based on the target data set; constructing an irregular triangular grid model corresponding to the target survey area based on the laser scanning data; and fusing the regular grid model and the irregular triangular grid model to obtain a three-dimensional model of the target survey area. The three-dimensional model construction method and system for topographic survey can improve the modeling accuracy.

The radio frequency signal background noise elimination method based on multi-mode space-time decoupling comprises the following steps: S1, collecting an original radio frequency reflection signal of a target object, and synchronously collecting a depth image containing scene information; s2, encoding the original radio frequency reflection signal and the depth image, and extracting the spatial-temporal characteristics of the original radio frequency reflection signal and the spatial-temporal characteristics of the depth image; s3, by taking the spatial-temporal characteristics of the depth image as guidance, aligning the spatial-temporal characteristics of the original radio frequency reflection signal with the spatial-temporal characteristics of the depth image through a multi-head cross attention mechanism to obtain multi-modal fusion characteristics; and S4, inputting the multi-modal fusion feature into a pure signal decoder module, enhancing the original radio frequency reflection signal through a predictive attention mask, and reconstructing a pure signal from the original radio frequency reflection signal in combination with residual connection to realize elimination of the background noise of the radio frequency signal. According to the invention, a multi-mode deep learning technology is adopted, and low-cost and high-efficiency radio frequency signal background noise elimination is realized.

The application discloses an old equipment fault prediction model construction method based on transfer learning, relates to the technical field of industrial equipment fault prediction and machine learning, and solves the problems of sample scarcity, low precision caused by data distribution difference and high construction cost of existing old equipment fault prediction model construction. The method comprises the steps of data collection preparation, data preprocessing, source model training, model transfer, target model fine-tuning and optimization, and model evaluation output; target domain data is acquired through a multi-sensor acquisition terminal, a general model is trained by using source domain data of a new type of equipment, and a model is fine-tuned in combination with limited data of old equipment by using transfer learning and field self-adaptation technology. The application can construct a high-precision fault prediction model only by using a small amount of old equipment data, has high construction efficiency, low cost and strong adaptability, and is suitable for fault prediction of various types of old industrial equipment with scarce data.