19results about How to "Strong representation ability" patented technology

The invention provides a multispectral image classification method based on an adaptive feature fusion residual network, and belongs to the technical field of computer vision and remote sensing image processing. The method comprises the following steps: firstly, preprocessing an original multispectral image; then, a novel adaptive feature fusion residual network model is constructed, the core of the model comprises a dual-path feature extraction backbone network, and spectral features and spatial context features are extracted respectively; a self-adaptive feature fusion module is introduced, multi-level features from double paths are dynamically learned and fused through a channel attention mechanism, and optimal integration of spectral information and spatial information is achieved; and finally, completing pixel-level or image-level classification by using the fused advanced features. According to the method, the problems of insufficient utilization of spectrum-space joint features of multispectral data and weak model generalization ability of a traditional method are effectively solved, and the precision and robustness of multispectral image classification are remarkably improved.

The invention discloses a lithium battery state estimation method and system based on a PNGV model and a DEKF algorithm, and the method comprises the steps: firstly, carrying out the systematic offline test of a battery, and obtaining an open-circuit voltage-state of charge curve of the battery and the equivalent capacitance of a second-order PNGV equivalent circuit model; secondly, discretizing a continuous state equation of the model into a difference equation through bilinear transformation, and identifying other model parameters of the battery in different charge states by applying a recursive least square method; and finally, constructing a state filter and a parameter filter by adopting a double-extended Kalman filtering algorithm, and realizing collaborative estimation of the charge state and the health state through data interaction and feedback of the two filters. According to the method, through a progressive process of parameter identification and cooperative state estimation, the problems of time-varying characteristics and state coupling of battery model parameters are effectively solved, and the joint estimation precision and robustness of the state of charge and the state of health of the lithium battery under complex working conditions are remarkably improved.

The application provides a Raman spectrum urine iodine concentration detection method based on machine learning, and relates to the technical field of Raman spectrum urine iodine concentration detection.In the application, first, standardized spectrum data is acquired;second, multi-scale wavelet decomposition is performed to extract energy features, mean features and standard deviation features of different decomposition levels, and multi-scale statistical features are formed;based on the characteristic vibration wave band of urine iodine molecules, physical prior features of the corresponding wave number interval are extracted;then, the multi-scale statistical features and the physical prior features are fused to form a fusion feature vector;finally, the fusion feature vector is taken as the input of a pre-trained machine learning model to acquire a urine iodine concentration prediction value.The method extracts multi-scale statistical features by performing multi-scale wavelet decomposition on the Raman spectrum, combines the physical prior features of the characteristic vibration wave band of urine iodine, and constructs a fusion feature vector with stronger representation ability, so that the accuracy and stability of urine iodine concentration detection are significantly improved by using machine learning technology.

This invention discloses a method and system for terrain accessibility assessment of unmanned ground vehicles in unstructured environments, belonging to the field of terrain accessibility analysis technology. The method includes: acquiring image data and LiDAR point cloud data using data acquisition equipment, and extracting features from the image data and LiDAR point cloud data using a pre-trained model to obtain image feature maps and point cloud feature maps; the data acquisition equipment includes a camera and a LiDAR; fusing the image feature maps and point cloud feature maps to obtain multimodal feature maps; spatially transforming the multimodal feature maps to obtain transformed multimodal feature maps; classifying and calculating the transformed multimodal feature maps to obtain an elevation map and a accessibility map, using the accessibility map as the final output of the terrain accessibility assessment. This invention is applicable to unstructured scenarios with complex terrain types and frequent dynamic environmental changes.

The application discloses an electric power transaction data mining system based on artificial intelligence, which comprises a time sequence construction module, an association mining module, a hidden space coding module and an abnormal positioning module.The time sequence construction module acquires electric power transaction historical data and constructs a transaction state time sequence.The association mining module inputs the transaction state time sequence into an adaptive graph convolution network based on an attention mechanism, extracts dynamic association features between electric power transaction participants, and generates a preliminary transaction behavior feature graph.The hidden space coding module inputs the preliminary transaction behavior feature graph into a variational autoencoder based on adversarial training, maps the preliminary transaction behavior feature graph to a hidden space through an encoder, and obtains hidden variables by sampling.The abnormal positioning module inputs the hidden variables into a decoder to reconstruct a reconstruction error matrix, identifies electric power transaction time periods and participants with abnormal transaction modes according to the reconstruction error matrix, and takes the electric power transaction time periods and participants as initial anchor points for data mining.The system belongs to the technical field of electric power transaction data mining and can effectively mine dynamic association features and locate abnormal transactions.

The invention discloses a polymer formula analysis method and device based on a graph Q learning network, and the method comprises the steps: building a Markov decision process of formula generation through defining a state space, an action space, a state transition probability and a reward function; constructing a graph Q learning network model based on the Markov decision process; training the graph Q learning network model through a double-Q learning algorithm and a strategy function to obtain a trained graph Q learning network model; and performing state transition and action selection processing through the trained graph Q learning network model to generate a polymer formula. According to the method, the problems of low learning speed, high computing resource consumption, difficulty in accurately matching multi-target performance and the like in the prior art are solved.

The application relates to the technical field of unmanned aerial vehicle scheduling and intelligent optimization, and discloses a multi-unmanned aerial vehicle cooperative task allocation method and device for complex task scenarios, which has the technical scheme as follows: global multi-source data acquisition and space-time heterogeneous hypergraph representation, complex task causal emergence decoupling and counterfactual deduction optimization, hierarchical federated heterogeneous matching degree accurate representation, distributed external generalization causal element reinforcement learning dynamic allocation decision, distributed conflict resolution and digital twin closed loop iterative optimization; by adopting frontier technologies such as space-time heterogeneous hypergraph representation, causal emergence reasoning, hierarchical federated learning, causal element reinforcement learning and digital twin closed loop optimization, a causal driving full-link algorithm architecture is constructed to break through the inherent limitations of traditional correlation-based data-driven algorithms, so that efficient cooperative operation of a large-scale heterogeneous unmanned aerial vehicle cluster in a strong coupling task and an extreme dynamic environment can be realized.

The embodiment of the invention discloses a radar radiation source individual identification method and device for airport low-altitude security and protection. A specific embodiment of the method comprises the following steps: generating a radar signal fragment group set; performing self-supervised pre-training on the radiation source feature extraction network to update model parameters; constructing a radar radiation source individual identification model; performing joint optimization on the radar radiation source individual identification model through the known radiation source signal group set to generate a known radiation source center feature set; generating a known radiation source boundary parameter set according to the radar radiation source individual identification model and the known radiation source center feature set; and according to the known radiation source boundary parameter set and the radar radiation source individual identification model, performing radiation source individual identification on the real-time radar signal to generate a radar radiation source identification result. According to the embodiment, the generalization ability of the recognition model can be improved under the small sample condition, so that the individual recognition accuracy of the radiation source is improved, and the low-altitude security and protection safety of an airport is improved.

The invention provides a flight control sensor electric signal abnormity detection method and system based on electric signal analysis, and the method comprises the steps: collecting the voltage and current signal data of an aircraft control sensor in real time, and carrying out the data preprocessing of the voltage and current signal data; inputting the preprocessed electric signal data into a pre-constructed nonlinear four-rotor kinetic model for analogue simulation, and outputting an electric signal data set containing fault information; based on the real-time feature vector in the electric signal data set and a pre-stored historical feature sample set, constructing a global adjacency graph containing real-time nodes and historical nodes; inputting the global adjacency graph into a pre-constructed graph convolutional network model, and extracting space-time fusion features of the real-time nodes; and carrying out classification processing on the space-time fusion features, and outputting an abnormal state category of a sensor electric signal, so as to achieve the purposes of online high-precision diagnosis and autonomous fault-tolerant control of aircraft sensor faults and guarantee safe and reliable operation of a complex equipment system.

The invention discloses a lake water quality parameter inversion method based on a remote sensing image, a medium, equipment and a product, and relates to the field of water quality parameter inversion, and the method comprises the steps: obtaining a Sentinel-2 remote sensing image and water quality sampling data, re-sampling a wave band in the remote sensing image to a uniform resolution, and carrying out the time-space matching of the wave band and the water quality sampling data, selecting a dual-band combination, a three-band combination and a four-band combination related to the water quality sampling data from the matched resampling bands, and constructing a training set; a deep learning inversion model is constructed, the deep learning inversion model comprises three feature extraction sub-networks, an attention mechanism and a fusion main network, wave band combinations are input into the three feature extraction sub-networks respectively, obtained three feature vectors are subjected to weighted fusion through the attention mechanism and then input into the fusion main network, and predicted water quality parameter concentration is output; and training the model by using the training set, and performing lake water quality parameter inversion by using the trained model. According to the invention, the inversion precision of water quality parameters is improved.

The application provides a multi-feature fusion oral bioavailability prediction method, device, equipment and medium. Four-dimensional features of a candidate drug, including a graph structure feature, a fingerprint feature, a physicochemical descriptor and a pharmacophore feature, are fused to comprehensively depict drug molecular characteristics, so that the representation capability is stronger, and the prediction accuracy is improved. In addition, the fused features are processed by a plurality of different expert processing models corresponding to a plurality of preset different result types, so that different processing strategies can be adopted for different chemical structure types, and different application scenarios can be adapted.

The invention relates to the field of fire early warning, and provides an industrial park-oriented electrical fire comprehensive monitoring and early warning method and a corresponding product. The method comprises the following steps: synchronously acquiring multi-source data of the industrial park power supply network; processing the collected multi-source data of the industrial park power supply network so as to respectively extract electrical transient characteristics and thermal hidden danger space characteristics, fusing the characteristics with the multi-gas concentration data based on an attention mechanism, and outputting a high-dimensional hidden danger characteristic vector; carrying out parallel processing on the high-dimensional hidden danger feature vectors to respectively obtain a physical inconsistency score and a data-driven hidden danger probability score; dynamically adjusting the weight of the physical inconsistency score and the weight of the data-driven hidden danger probability score according to the signal-to-noise ratio and the feature uncertainty of the real-time data through an adaptive weighted fusion mechanism, and performing fusion to generate a dynamic electrical fire risk index; and according to the threshold interval in which the dynamic electrical fire risk index is located, generating early warning information of a corresponding level.

The application relates to an aviation laser point cloud semantic segmentation method and device based on a multi-level context feature fusion network, which comprises the following steps: S1, data down-sampling preprocessing is performed on acquired target region point cloud; S2, the processed aviation laser point cloud data is input into a pre-established multi-level context feature fusion network coding layer, and each layer feature is input into a cross-layer attention module to obtain cross-layer connection features; S3, the coding features are up-sampled to the size of the point cloud to be segmented through a decoding layer, multi-level feature fusion modules are used for fusing the features of all levels, and then a Softmax function is used to obtain the final segmentation result of the aviation laser point cloud; and S4, a hybrid loss function is constructed to obtain the error between the segmentation result and the true value, and according to the error value, a random gradient descent method is used for back propagation training, model parameters are dynamically adjusted, and a stable segmentation result is obtained. The method and device can effectively enhance the association of point feature expression and spatial information, improve the information perception ability of multi-scale context features of the point cloud, and obtain a more fine semantic segmentation result.

The invention discloses an oil field station environment personnel intrusion face recognition method and system and electronic equipment, and the method comprises the steps: selecting feature maps outputted by a plurality of branches on a multi-stage trunk feature extraction network from face image features, merging the deep features of the feature maps to the shallow layer step by step, and obtaining multi-scale features; shielding information in the features is decoded into feature masks through a mask decoder; weighting the feature masks and features in the original feature map, and endowing useful feature information with a larger weight; connecting the obtained features, and adjusting the number of feature channels through convolution to enable the connected features to be consistent with the original image; connecting the feature masks, and enabling the channel number of the feature masks to be consistent with that of the original image through convolution adjustment; the classification loss of face recognition and the predicted shielding mode loss are weighted and then added to obtain comprehensive loss; and judging whether the person is intruded or not according to the face image of the person by using the shielded face recognition network model obtained by training. According to the invention, the recognition accuracy of the shielded face can be improved.

This invention belongs to the field of power line fault detection technology and discloses a method, system, equipment, and medium for diagnosing power distribution line faults. The method includes: acquiring initial fault signal data corresponding to the power distribution line, preprocessing the initial fault signal data, and outputting fault signal data; decomposing the fault signal data using joint decomposition technology to obtain intrinsic mode components (IMCs); introducing information mining technology to extract the distribution change feature vector of the fault signal and fusing it with the IMCs to obtain a change image; extracting local features of the change image through a convolutional neural network and combining it with a strategy function optimized by a Huffman tree for fault classification, and outputting the fault diagnosis result. This invention applies the Softmax strategy of Huffman trees, improving the efficiency and accuracy of judgment, enabling early identification and warning of fault signals in power distribution lines, and improving the efficiency of power distribution line fault diagnosis.

This invention discloses a power quality disturbance location and identification method based on phase sensing and multimodal fusion, relating to the field of power system monitoring and fault diagnosis technology. This power quality disturbance location and identification method based on phase sensing and multimodal fusion includes the following steps: acquiring the raw voltage signal of a three-phase power system; extracting the sample embedding vector of each phase based on the raw voltage signal and comparing it with the normal sample vector, and determining the disturbed phase of the power quality disturbance by combining an adaptive threshold; extracting time-series feature vectors and bispectral image features from the disturbed phase, and aligning and co-fusing them to obtain fused features; identifying the power quality disturbance type based on the fused features, achieving phase-level location of the three-phase PQD: through an innovative phase-sensing embedding and prototype comparison mechanism, it achieves accurate and rapid location of abnormal phases in a three-phase system for the first time without relying on the disturbed phase label, solving the core pain points of traditional methods.

The present application relates to a kind of multi-modal remote sensing image multi-class feature recognition methods based on feature decoupling, belong to remote sensing image recognition and understanding field.The method includes the following steps: S1: obtaining multi-modal remote sensing image data;S2: multi-modal feature extraction, including convolution module, Fourier orthogonal attention module, center feature fusion module;S3: calculate multi-modal contrast decoupling loss;S4: feature fusion and feature recognition, obtain feature recognition result chart.The performance of the method and system described in the present application is better than other multi-class feature recognition methods, and the present method can better obtain and identify feature type, and has advantage in identifying multi-class feature than other methods.

The invention provides a training method and device for a multi-mode semantic communication model. The method comprises the following steps: performing semantic grouping on a plurality of users according to semantic vectors of the users; encoding task request data of each user of the multicast group to obtain modal semantic features, and calculating group-level shared semantic features according to each modal semantic feature and the weight; coding and transmitting the group-level shared semantic features, decoding coding features output by a channel, inputting decoding results to detection heads corresponding to different user requirements, calculating loss values corresponding to the output of the detection heads by using a target loss function, and updating parameters of a trainable module by using the loss values, so as to achieve the purpose of training the detection heads. And a multi-modal semantic communication model is obtained. The method is suitable for multi-user transmission or task requirements of different modes, effective modeling can be carried out on user semantic requirement differences, the obtained multi-mode semantic information is high in characterization capacity, and semantic communication stability can be improved.

The application relates to a display panel Mura quantitative evaluation method based on space-time multi-dimensional response modeling. The method comprises the following steps: controlling a display panel to be tested to display a target test image, and collecting images according to a time sampling sequence; performing dark field correction, flat field correction, geometric registration and exposure normalization processing on the original collected images; performing background modeling on the pretreated images, calculating a normalized residual error, and constructing a Mura response tensor covering the space, time, gray scale, color and state dimensions; performing time domain decomposition on the response tensor to obtain static and dynamic defect components, and calculating a time fluctuation graph; performing multi-dimensional response feature extraction including time persistence, dynamic fluctuation and recovery lag, and inputting the normalized and homodirectional sub-indicators into a comprehensive scoring model to obtain a comprehensive Mura score. The application improves the Mura from two-dimensional static defect characterization to multi-dimensional response modeling, and can effectively identify lag-type, fluctuation-type and working condition trigger-type defects.