11results about How to "Reduce feature redundancy" patented technology

The application discloses a cross-platform operation and maintenance replay scene intelligent identification method and a cross-platform operation and maintenance replay method. The identification method comprises the following steps: collecting operation and maintenance associated data from different platforms in real time, screening target associated feature data from the operation and maintenance associated data after preprocessing, determining time sequence features of the target associated feature data, and creating new features representing potential structures of the data; extracting key information from all the target associated feature data and the new features; extracting multi-scale features from the key information, performing feature fusion on the multi-scale features, and obtaining a plurality of composite features; screening a composite feature with the most independent features related to an identification target operation and maintenance scene from the plurality of composite features, and marking the composite feature as a target feature; and inputting the target feature into a target scene intelligent identification model to obtain a replay scene identification result. The method can automatically, efficiently and accurately identify an operation and maintenance operation scene that can be repeatedly executed between different operating systems and platforms.

The invention discloses a method and a system for detecting rice bacterial leaf blight based on unmanned aerial vehicle spatial spectrum fusion. The method comprises the following steps: firstly, constructing a rice bacterial leaf blight detection data set; a rice bacterial leaf blight severity grading detection double-branch model is constructed, and the model adopts a spectrum-space double-branch parallel coding and fusion framework and comprises a spectrum branch module, a space branch module and a cross attention fusion mechanism module. And inputting the complete image of the rice field into the trained rice bacterial leaf blight severity grading detection double-branch model, and obtaining severity grading detection results of the bacterial leaf blight in different rice growth periods of the target field. According to the invention, deep complementation and dynamic information balance of spectrum and spatial features of the multispectral unmanned aerial vehicle are realized, so that the accuracy of disease detection and the generalization ability of the model are effectively improved.

The invention relates to the technical field of agricultural remote sensing monitoring, in particular to a method and system for measuring the number (area) of plants in a tobacco field based on visible light images of an unmanned aerial vehicle, and is suitable for automatic and high-precision growth monitoring and resource accounting of a large-scale tobacco field. Comprising six steps of tobacco field visible light image acquisition, image preprocessing, tobacco field region segmentation, tobacco field plant target detection and plant number statistics, tobacco field area calculation, and result post-processing and output, and through combination of an improved target detection algorithm and an image segmentation technology, high precision of tobacco field plant number statistics and high accuracy of area determination are realized. Meanwhile, the detection efficiency is guaranteed, the actual requirement of large-scale tobacco field monitoring is met, data acquisition, processing, analysis and output can be automatically completed in the whole process, manual intervention is not needed, the monitoring efficiency is greatly improved, and the labor cost is reduced.

This invention discloses a method and device for diagnosing the health status of flow batteries based on particle swarm optimization (PSO), aiming to solve the problems of difficult modeling and poor real-time performance of traditional physical model-driven methods, and the unscientific feature selection and insufficient prediction stability of existing data-driven methods. The system is data-driven at its core, collecting multi-dimensional data such as voltage, current, and capacity of the flow battery during operation through sensors. After preprocessing, LASSO and grey relational analysis are used to jointly screen the optimal subset of health features. An extreme learning machine (ELM) is built as the main model for SOH prediction, using the ReLU activation function to improve nonlinear expression capabilities, and introducing a particle swarm optimization algorithm to optimize the weights and biases of the ELM, improving the instability caused by random initialization.

The invention relates to the technical field of oil well fracturing prediction, and solves the technical problems in the prior art that distribution of important features in fracturing data cannot be fully reserved generally through simple data segmentation, and low result accuracy in fracturing state prediction is easily caused. In particular to a fracturing state prediction system and method applying a machine learning intelligent decision-making system, and the method comprises the following steps: S1, obtaining original data of a gulongshale oil fracturing detection system, preprocessing the original data, and obtaining a unified table through an SQLJOIN method; the statistical feature extraction algorithm and the hierarchical clustering algorithm are used to construct a more excellent algorithm model, the accuracy and robustness of feature expression are improved by realizing feature extraction and clustering, the feature redundancy is reduced by combining statistical features and hierarchical clustering, the interpretability and prediction precision of a subsequent model are improved, and the prediction efficiency is improved. And meanwhile, the nonlinear relationship in the gulonium shale oil data is optimized, so that the subsequent steps are more accurate and smoother.

The invention discloses an obstetrical anti-phospholipid syndrome risk prediction method and system, and relates to the technical field of medical information processing, and the method comprises the steps: obtaining clinical sign data, laboratory specific antibody index data, past medical history data and diagnosis results of pregnant and lying-in women, and carrying out the preprocessing to obtain a standardized data set; extracting time sequence combination features and interaction features to form a fusion feature set; calculating a feature correlation intensity value and screening a core feature subset; extracting training features by using the core feature subset, and training a risk prediction model; extracting prediction features of a pregnant and lying-in woman to be predicted, and performing weight adjustment according to the pregnancy stage; and inputting the weighted features into a model to generate a risk score, determining a risk level and outputting early warning information. According to the invention, through multi-dimensional data analysis and dynamic weight adjustment, accurate prediction and timely early warning of the obstetrical anti-phospholipid syndrome risk are realized.

The application discloses a collaborative filtering recommendation method based on hypergraph generative multi-granularity contrast learning. The method comprises the following steps: obtaining historical interaction data of users and items, and constructing initial node embedding; adaptively constructing a hypergraph structure through a dynamic hypergraph constructor, and aggregating high-order collaborative signals by using a hypergraph convolution propagation mechanism; based on the enhanced node representation, a graph variational autoencoder is used to generate node-specific contrast views for each node; multi-granularity contrast learning is performed, including node-level contrast learning for maximizing the consistency of the same node under different views, and feature-level contrast learning for reducing feature redundancy and improving diversity; the recommendation task and the contrast learning loss are jointly optimized, and the model is trained to generate a recommendation list. Through dynamic hypergraph modeling of high-order correlation, generative contrast views and multi-granularity learning, the application effectively improves the accuracy, robustness and generalization ability of the recommendation, and can be widely applied to personalized scenarios such as e-commerce and content recommendation.

This application discloses a sound source localization model training method, a sound source localization method, and an apparatus, belonging to the field of sound signal processing technology. This application is applied to sound source localization scenarios, such as predicting the spatial position of a loosely connected component, wherein the component is mounted on a mechanical connection structure. This application uses sound signals as the data processing object, trains a sound source localization model based on pre-built training samples, and achieves automatic sound source localization based on the trained model. Since this method requires no manual intervention, compared to manual inspection, it not only saves labor costs and avoids the risks of working at heights, but is also more efficient, enabling real-time monitoring of the fastening status of the connected component.

1. Invention name: Time series prediction feature selection method and system based on partial autocorrelation function (PACF) and mutual information (MI) dynamic cooperation 2. Technical field: The present invention belongs to the technical field of time series prediction, and relates to a method and system for dynamically screening linear and nonlinear features, which are suitable for industrial equipment predictive maintenance, energy scheduling, financial risk control and other scenes. 3. Technical scheme: By detecting the non-stationarity (ADF test) and memory (Hurst index) of time series data, the weight parameter α of PACF and MI is dynamically adjusted (non-stationary data α∈[0.2, 0.4], strong memory data α∈[0.6, 0.8]), the mixed score \text{Score}(k)=\alpha\cdot|\text{PACF}(k)|+(1‑\alpha)\cdot\text{MI}(k) is calculated, and the feature window is optimized by combining forward selection and backward pruning (correlation coefficient threshold 0.8). 4. Technical effect: The fault warning accuracy is improved by 17.7% (wind power data); the feature redundancy is reduced by 35%-53%; the data processing efficiency is improved by 50%.

The invention discloses a marine route planning method considering uncertain factors, and belongs to the field of marine traffic safety management, and the method comprises the steps: S1, carrying out the preprocessing and feature fusion of multi-source data; s2, carrying out the uncertainty modeling of the marine route, and quantifying the probability distribution of uncertainty parameters; s3, predicting a candidate route trajectory based on PCA hierarchical attention; s4, in combination with the probability distribution of the uncertainty parameters and the predicted candidate route trajectory, calculating the operability super probability of each segment of the route, and generating a probability operability index; and S5, by adopting an approximate MinSumA algorithm, outputting an optimal route by taking the integration of the minimum total navigation time and the maximum probability operability index as targets. By the adoption of the sea route planning method considering the uncertainty factors, a data-modeling-prediction-evaluation-optimization closed loop is constructed, the problems that a traditional method ignores uncertainty, is high in optimization complexity and poor in real-time performance are solved, and safety-efficiency-real-time performance collaborative optimization is achieved.

The application discloses a liver cancer prognosis evaluation model generation method and related devices, and relates to the technical field of data processing. The method obtains medical multi-modal data of a liver cancer patient, the medical multi-modal data including macroscopic magnetic resonance imaging data and microscopic pathological whole slice image data; based on a graph neural network, the medical multi-modal data is converted into multi-scale graph structure data and is subjected to cross-scale topological alignment through a GroMoVe optimal transport algorithm to obtain topological fusion features; the topological fusion features are input into a causal intervention module constructed based on a structural causal model, a counterfactual generation is performed to eliminate confounding factors, and causal invariant features are extracted; a continuous time evolution component is trained based on the causal invariant features, the continuous time evolution component represents time dynamic changes of a tumor recurrence risk based on a neural ordinary differential equation, and a liver cancer prognosis evaluation model is obtained. The application improves liver cancer recurrence prediction accuracy, enhances model generalization robustness and interpretability.