9results about How to "Fully portrayed" patented technology

The application discloses an emotion recognition method based on online cross-modal knowledge distillation, comprising the following steps: acquiring electroencephalogram and electrocardiogram original signals and windowing and cutting; constructing electroencephalogram and electrocardiogram student models, extracting intermediate features from each modal data segment through an encoder, and obtaining non-normalized prediction output through a classifier; constructing a teacher probability distribution through a joint encoder fusion; introducing adaptive contrast loss to align the cross-modal intermediate features, introducing distillation loss to constrain the prediction probability distribution of each modal to align with the teacher probability distribution; synchronously optimizing new student model parameters through online collaborative training; and performing actual inference prediction based on the student model after training. The application combines double modal signals to make up for the defects of single modal information, excavates the complementarity of modes, realizes dynamic generation of teacher supervision signals and real-time collaborative learning of modes through online distillation, does not increase test calculation overhead, effectively improves the recognition accuracy, model robustness and generalization ability, and has good application prospect.

This invention relates to the field of fault early warning technology, and provides a method and system for early warning of faults in large oil-filled equipment, comprising: Step 1, collecting acoustic fingerprint data throughout the entire lifecycle of the large oil-filled equipment and constructing an acoustic fingerprint sample set; Step 2, preprocessing the acoustic fingerprint sample set, extracting acoustic fingerprint feature parameters, and establishing a standardized acoustic fingerprint database; Step 3, constructing a multi-channel deep learning acoustic fingerprint recognition model and a discharge severity assessment model based on deep learning; Step 4, based on the output of the trained acoustic fingerprint recognition model and combined with a preset multi-level early warning threshold system, performing real-time assessment of the operating status of the large oil-filled equipment, and generating corresponding early warning information when the assessment result meets the early warning triggering conditions; Step 5, pushing the early warning information to the operation and maintenance terminal, and automatically generating operation and maintenance suggestions based on the defect type and severity. This invention can effectively provide early warning of faults in large oil-filled equipment.

This invention discloses a rapid detection method and system for the reheating degree of meat dishes based on electrochemical sensing, belonging to the field of food testing technology. Addressing the problem of difficulty in rapidly and accurately evaluating the reheating degree of meat dishes in existing technologies, this invention first places the meat dish to be tested in a space, generating headspace gas at a preset equilibrium temperature and time. An electrochemical sensor unit collects the complete time-response curve of the interaction between the headspace gas and the sensitive layer, extracting dynamic characteristic parameters such as response rise time, peak response value, time to reach peak value, curve integral area, and half-life of the fall phase. Simultaneously, it acquires ingredient attribute parameters and reheating method parameters. These parameters are then input into a pre-trained deep learning prediction model, which outputs the reheating degree level and displays it on a display unit. This method can be used in the catering industry, food processing, and home cooking for rapid detection of the reheating degree of meat dishes, ensuring food quality and food safety.

The application discloses an AI image discrimination method based on frequency domain and noise domain consistency, and belongs to the technical field of image processing, computer vision and artificial intelligence security. The method comprises the following steps: acquiring an image to be discriminated and performing pretreatment, converting the image into a brightness channel and acquiring frequency spectrum information; inputting the image into a dual-domain physical guidance feature extraction module to extract directional spectrum features and multi-scale noise flow features; inputting the directional spectrum features and the multi-scale noise flow features into a cross-domain physical consistency module to generate a local consistency distance map and a global consistency score through shared embedding mapping and feature distance calculation, and obtaining consistency perception features; inputting the directional spectrum features, the multi-scale noise flow features and the consistency perception features into a dual-flow fusion module for cross-flow interactive fusion and global feature aggregation to obtain discrimination feature representation; and inputting the discrimination feature representation into a classification head to output a discrimination result of whether the image to be discriminated is a real image or an AI generated image. The application realizes effective discrimination of AI generated images by jointly modeling the physical consistency relationship between frequency domain features and noise domain features, and improves the robustness and generalization ability of the model under the conditions of cross-generator, cross-dataset and image compression, blurring and other post-processing conditions while ensuring detection accuracy.

The application belongs to the technical field of traffic flow prediction, and particularly relates to a spatio-temporal traffic flow prediction method fusing multi-graph structure and knowledge enhancement, comprising the following steps: obtaining historical observation data of traffic monitoring nodes at continuous multiple time steps; constructing enhanced features based on the historical observation data; splicing the enhanced features and the historical observation data to obtain enhanced input features; inputting the enhanced input features into a pre-trained traffic flow prediction model; and the traffic flow prediction model predicting traffic flow at one or more future time steps. The application realizes joint modeling of multi-source spatial correlation, complex time dynamics and historical period knowledge in traffic flow data, and improves the accuracy and robustness of future multiple time step traffic flow prediction.

This invention discloses a digital health diagnosis method for diesel engines based on multi-source data fusion, comprising the following steps: collecting signals generated during diesel engine operation and preprocessing them to generate multi-source combustion response data sequences; mapping events to combustion propagation nodes to generate a set of combustion propagation nodes; establishing propagation connections between nodes to generate a set of combustion propagation paths; constructing a combustion propagation topology; calculating the frequency of combustion propagation path occurrences and the stability of propagation time differences to generate a stable domain for the combustion propagation structure; performing structural matching between the current combustion propagation topology and the stable domain for the combustion propagation structure to generate a topology deviation structure; determining the diesel engine health status based on the topology deviation structure and generating a digital health diagnosis result for the diesel engine. This invention utilizes multi-source data fusion and combustion propagation topology analysis methods to achieve abnormal identification of diesel engine combustion structures, possessing advantages such as high diagnostic accuracy and strong structural characterization capabilities.

The application discloses a domain-invariant multi-modal emotion recognition method based on multi-time scale contrast learning, which comprises a pre-training stage and a fine-tuning stage; wherein the pre-training stage is to input the physiological signals of M modalities of different subjects into a modal encoder to extract features, and to align the multi-modal physiological signals under the same emotional stimulus through time contrast learning and cross-modal contrast learning to obtain emotion-related representations; meanwhile, the high-dimensional features output by the encoder are learned through an adversarial domain adaptation mechanism to suppress the information related to the subject identity in the features and to reduce the influence of individual differences, so as to obtain multi-modal feature representations which are invariant to subjects. The fine-tuning stage is to model the multi-scale time features by using the encoder obtained through pre-training, and to complete emotion recognition training through modal fusion and a classifier. The application can effectively improve the emotion recognition capability of the model in the cross-subject scenario.

This invention provides a method for analyzing the spatial transmission effect of flood control resilience in watersheds, involving the interdisciplinary fields of watershed flood control analysis, spatial econometric analysis, and water resources management. The method includes: obtaining a set of indicators to be used based on multi-source basic spatial data; acquiring the flood control resilience influencing factor vector and flood control resilience value for each administrative unit; correlating and mapping the flood control resilience influencing factor vector and flood control resilience value at the administrative unit scale to obtain the flood control resilience influencing factor vector and flood control resilience characterization value for each sub-watershed; constructing a spatial weight matrix and a spatial econometric model to identify target sub-watersheds with spatial transmission effects; and characterizing the impact of spatial transmission effects at the administrative unit scale based on the model parameter estimation results for the target sub-watersheds and all sub-watersheds. This invention can clearly identify the main driving factors of flood control resilience within a watershed and their spatial transmission paths.

A method for predicting coal spontaneous combustion temperature based on adaptive fusion of multi-source heterogeneous data is proposed. This method collects four data sources: time-series data of indicator gas concentrations, distributed fiber optic temperature field data, infrared thermal imaging data, and environmental parameter data. Modal features are extracted using bidirectional long short-term memory networks, one-dimensional convolutional networks, two-dimensional convolutional networks, and fully connected networks, respectively. Data quality scores are calculated in real-time for each data source, generating adaptive fusion weights. A cross-modal multi-head attention mechanism is used to deeply fuse the multi-modal features. The fused features are then propagated multiple times forward using a Monte Carlo random deactivation method, outputting the predicted temperature value and its confidence interval. A risk assessment index is constructed by combining the temperature rise rate, enabling graded early warning of coal spontaneous combustion temperature. This invention overcomes the shortcomings of existing methods, such as reliance on a single data source, lack of sensor fault tolerance, and lack of confidence assessment for prediction results, thus improving the accuracy, robustness, and scientific validity of coal spontaneous combustion temperature prediction and early warning.