383 results about "Prior information" patented technology

The invention discloses an electromechanical equipment abnormal behavior detection and fault prediction method based on a causal time-space Transform, and belongs to the technical field of electromechanical equipment abnormal detection, and the method comprises the steps: S1, constructing an initial causal graph according to the causal relationship between the physical structure and the functional part of electromechanical equipment; s2, correcting the initial causal graph based on historical operation data of the electromechanical equipment to generate a causal graph; s3, introducing the causal graph as prior information into a Transform model, predicting sensor time sequence data of the electromechanical equipment to be detected through the trained Transform model, and outputting corresponding high-dimensional feature representation; and S4, according to the high-dimensional feature representation, calculating the abnormal weight of each component, and carrying out component-level abnormal identification and fault prediction. The method breaks through the limitation that only the data correlation is fitted and the causal relationship is ignored in the electromechanical equipment anomaly detection of a traditional time sequence model, and explicit modeling of an equipment fault chain propagation mechanism is realized by fusing a causal reasoning mechanism and feature modeling.

The invention discloses a document table extraction method and device, equipment and a medium, and relates to the technical field of computer information processing. The extraction method comprises the following steps: performing OCR (Optical Character Recognition) on a to-be-processed document table image to obtain a text block; performing visual feature coding on the document table image to obtain deep visual features; performing semantic feature coding on the text sequence of the text block to obtain a semantic feature vector; performing spatial feature coding on the bounding box of the text block to obtain a spatial feature vector; performing feature fusion processing on the deep visual features, the semantic feature vectors and the spatial feature vectors to obtain multi-modal guide features; and performing structured decoding processing on the multi-modal guide features to obtain structured representation of the table. According to the method, the text and the position information pre-recognized by the OCR are fused with the visual features of the document table, so that the visual features are guided to be expressed again and are actively aligned to the logic structure defined by the prior information, and the extraction accuracy of the table logic structure is improved.

The invention discloses a large-model-driven cross-modal power insulator defect detection method and system, and the method comprises the steps: constructing an image-text dual-channel feature learning frame based on a pre-trained vision-language large model, introducing fine-grained semantic description as prior information, and carrying out the detection of the defects of a cross-modal power insulator. Feature enhancement and space focusing under semantic guidance are realized through a cross-modal attention fusion mechanism, and the robustness and discrimination ability of the model in a complex scene are further improved in combination with a multi-scale pyramid structure and a channel re-calibration mechanism. The method can effectively solve the practical problems of difficult recognition of tiny targets, strong environmental interference, limited sample data and the like.

The invention relates to a vehicle and unmanned aerial vehicle combined scheduling method for wide-range low-cost inspection. The method comprises the following steps: acquiring prior information; modeling the unmanned aerial vehicle inspection problem of each target area according to the prior information to obtain a mixed integer non-convex optimization problem with the goal of minimizing the weighted sum of the total execution time and the energy consumption of all the inspection unmanned aerial vehicles; performing linearization on a non-convex bilinear term in the mixed integer non-convex optimization problem, and performing discretization processing on a nonlinear function by adopting piecewise linear approximation; an approximate mixed integer linear programming problem is obtained and solved, and an unmanned aerial vehicle scheduling strategy is obtained; modeling according to the unmanned aerial vehicle scheduling strategy and the prior information to obtain an inspection vehicle path planning problem taking the comprehensive driving cost as a target; the routing inspection vehicle path planning problem is converted and modeled into a Markov decision process, a routing inspection vehicle is used as an intelligent agent, a state, an action and a reward function are defined, and a routing inspection vehicle scheduling strategy is obtained. Therefore, combined inspection of the inspection vehicle and the unmanned aerial vehicle is realized, and the inspection range is expanded.

The invention discloses a physical information neural network hyperspectral band selection method for target identification, which uses physical information SDI as prior information to be combined with a channel attention mechanism added in a deep neural network to guide band selection, and evaluates the advantages and disadvantages of band weights through a reconstruction module after the weights are generated. Wave band selection is carried out accordingly; according to the hyperspectral image target recognition waveband selection method based on the physical information neural network, compared with a traditional method, the process is simplified and the precision is improved through end-to-end learning, the utilization of spatial context information can be enhanced by adding an attention mechanism, and the model has interpretability by adding prior SDI information.

The invention relates to a circuit board defect identification method and system based on multi-dimensional image data, and belongs to the technical field of data identification processing, and the method comprises the following steps: obtaining synchronous image data of a circuit board to be detected in a plurality of imaging modes; performing space-spectrum joint registration on each modal image to generate a multi-dimensional image cube with a unified coordinate system and pixel alignment; inputting the multi-dimensional image cube into a pre-trained multi-branch heterogeneous fusion neural network; generating a pixel-level defect probability graph by utilizing a defect sensing context decoder, and performing geometric constraint optimization on the probability graph by combining prior information of a circuit board design layout; outputting defect types, positions and confidence coefficients, and establishing an interpretable defect fingerprint database according to the multi-dimensional response characteristics of the defects; the method has the beneficial effects that false defect signals generated by image noise and circuit board surface texture interference can be effectively inhibited, the omission ratio and the false detection ratio are greatly reduced, and pixel-level accurate defect positioning is realized.

The invention discloses a ground penetrating radar pipeline intelligent inversion method based on multi-scale deep learning, and belongs to the technical field of ground penetrating radar pipeline data inversion. The method aims at solving the problems that a traditional inversion method is low in resolution, high in calculation complexity, depends on prior information and the like. The method comprises the specific steps that underground simulation model data sets are constructed in batches; forward modeling calculation is carried out based on time domain finite difference, and a matching data pair of the radar B-scan image and dielectric constant distribution is generated; constructing a multi-scale deep neural network comprising a residual attention module, a dual-path space attention module, a multi-scale feature fusion module and a reconstruction module; through end-to-end training and model test optimization, an optimal model is finally stored to realize inversion output from original radar data to dielectric constant distribution. According to the method, through a multi-scale feature extraction and fusion mechanism, the recognition precision of pipelines of different sizes is effectively improved, the inversion efficiency is greatly improved, and an efficient and accurate technical scheme is provided for underground pipeline detection.

The invention relates to the technical field of computer vision, and discloses a precise medicine delivery method and system, and the method effectively strips illumination artifacts through the construction of a local gradient structure tensor field and an anisotropic screening mechanism, remarkably improves the robustness of a visual front end in a light and dark alternating environment, and improves the accuracy of medicine delivery. Rigid body motion constraint and Lie algebra continuous modeling are utilized, high-precision space-time alignment between heterogeneous sensors is realized on the premise that scene depth does not need to be recovered, hardware delay errors are eliminated, a physical-driven probability weight dynamic allocation mechanism is constructed by introducing a multipath scattering index and a structural information entropy flux, and a dynamic space-time alignment algorithm is established. Environment degradation is sensed in real time, the observation weight is adjusted in a self-adaptive mode, the influence of the non-line-of-sight multipath effect and visual texture missing is effectively restrained, high-precision inertial dead reckoning can still be maintained in a blind area where a sensor is in full failure in combination with momentum prior information generated through Schel complement marginalization operation, and the method has the advantages of being high in precision and high in precision. And the navigation continuity and reliability of the distribution robot in a complex scene are ensured.

The invention relates to the technical field of artificial intelligence, and discloses an unmanned aerial vehicle flight control-oriented intelligent obstacle recognition method, which comprises the following steps of constructing an adaptive wind disturbance fuzzy kernel based on unmanned aerial vehicle attitude data, and synthesizing a degraded image for training; a specific obstacle recognition model integrating a frequency domain and space domain joint feature decomposition module, a turbulence invariant feature enhancement module guided by physical prior information and a double-branch anti-fuzzy feature extraction network is adopted, and a fuzzy robustness contrast loss function is combined to carry out progressive training so as to learn feature representation insensitive to fuzziness; after the trained model is deployed, the wind disturbance fuzzy intensity of a real-time input image is estimated on line, the internal workflow of the model is dynamically adjusted according to the wind disturbance fuzzy intensity, the attention weight is adaptively adjusted, and the output of a trunk or an anti-fuzzy branch is selected. According to the invention, through coupling of the physical model and deep learning, the recognition robustness, accuracy and stability of the model in a dynamic wind disturbance environment are improved.

The invention discloses a reactor core neutron field ex-situ explicit inversion method based on a graph structure, and belongs to the technical field of reactor core safety monitoring and control. According to the method, reactor core space coordinates, energy spectrum grouping, material physical properties and prior information are fused through an explicit graph structure, a lightweight graph convolutional network architecture is adopted, and accurate inversion of a high-dimensional reactor core neutron field is achieved. And meanwhile, the inversion precision under the high-dimensional resolution can be remarkably improved, and reliable technical support can be provided for safe operation and intelligent monitoring of an advanced reactor type. Compared with an existing deep learning black box model, the method has the advantages that the occurrence probability of local high deviation can be remarkably reduced at different reactor core change positions, and more uniform and stable prediction performance can be realized in the whole space. And under the condition that the reactor core has multi-region change, better inversion performance is still shown. When the middle and low complexity changes, the fine multi-region features of the reactor core can be effectively and stably captured, and the method has better overall error suppression capability.

The invention relates to a radar target recognition method and device under main lobe jamming based on prior information assistance, and the method comprises the steps: constructing a point distribution function under jamming capable of depicting jamming characteristics and an HRRP distortion rule, effectively extracting jamming prior information, and combining a multi-scale feature extraction network layer and a cross attention fusion mechanism to recognize a radar target under main lobe jamming. Deep fusion of HRRP features and interference prior information is realized, so that the target recognition accuracy and robustness of the model under typical interference conditions such as intermittent sampling and forwarding are remarkably improved. Therefore, the problems that in the prior art, under the typical interference condition, the radar target high-resolution range profile recognition robustness is poor, and the recognition accuracy is reduced are solved.

An online system uses a pose prior model and a pose objective function to estimate the pose of a client device. A pose prior model is a model for prior information known about client devices and their poses without reference to a particular client device and its pose data. The online system receives pose data from a client device and computes an estimated pose for the client device based on the received pose data, the pose prior model, and a generated initial candidate pose for the client device. The online system uses these as inputs to a pose objective function and optimizes the pose objective function to estimate a pose for the client device. The online system transmits this estimated pose to the client device, and may use the estimated pose as the pose for the client device for the purposes of delivering content to the user.

The invention discloses a frequency offset estimation method and device, electronic equipment and a storage medium, and relates to the technical field of communication. The method comprises the following steps: receiving a baseband spread spectrum signal from a low earth orbit satellite to obtain a received signal; performing segmented sweep frequency compensation on the received signal to obtain a compensated measured signal; despreading and descrambling the compensated measured signal by using prior information determined from the received signal, and completing coarse frequency offset estimation on the measured signal despread and descrambled for the first time; and de-spreading and descrambling the signal data after the coarse frequency offset estimation again, and finishing fine frequency offset estimation on the signal data after the second time of de-spreading and descrambling. According to the invention, prior information is introduced to directly lock and track a specified target beam signal, so that the directionality and efficiency of measurement are remarkably improved; by designing a low-complexity signal processing flow, the algorithm burden can be remarkably reduced, it is ensured that adjacent satellite beam frequency offset estimation is rapidly completed in service proceeding, and the real-time requirement of seamless switching is met.

The invention discloses a road intelligent diagnosis method based on an AI large model, and relates to the technical field of road engineering detection, and the method comprises the following steps: building a unified time base line, constructing a phase reference field, injecting a calibration pulse, separating dynamic shadow time sequence texture and crack initial features in a road image sequence, and obtaining a road image sequence; generating prior information of dynamic shadows and cracks; under the constraint of prior information, polarization spectrum three-dimensional coding imaging is carried out, polarization angle distribution and spectrum gradient are extracted, a feature dictionary of dynamic shadows and cracks is constructed, and phase drift in the prior information is calibrated. According to the method, a unified time base line and a phase reference field are constructed, and accurate distinguishing of dynamic shadows and cracks is realized by combining polarization spectrum imaging and anti-fact distillation; the point cloud and acceleration data are fused for geometric reconstruction, and through phase conjugate extinguishing and closed-loop feedback, the continuity and accuracy of diagnosis are significantly improved, and maintenance error decisions caused by misjudgment are avoided.

The invention belongs to the technical field of gravity data noise reduction, discloses a gravity data noise reduction method based on a U-Net network, and provides a U-Net network structure fusing a multi-scale feature fusion module and an attention mechanism aiming at the noise reduction problem of gravity and gravity gradient data. And a physical consistency constraint is introduced in a model training process. A Laplace equation met by the gravitational field potential in geophysics is used as prior information to be fused into a loss function, and a Laplace item is designed. The operation effectively improves the physical interpretability and generalization ability of the noise reduction result of the neural network. Meanwhile, the network structure adopts multi-scale feature fusion and an attention mechanism, and information of different spatial scale features and key spatial positions in complex geological signals is better captured.

The invention provides a passive radiation noise multipath channel estimation method based on a complex residual neural network. According to the method, complex number time-frequency characteristics of ship radiation noise are fully utilized, in a non-cooperative scene without prior information, time delay and amplitude parameters of a multipath channel are accurately estimated through a deep neural network, and the problems that a traditional method is unstable in performance and low in estimation precision under the condition of a low signal-to-noise ratio are solved. According to a simulation experiment, the estimation precision, robustness and path resolution capability of the algorithm are systematically analyzed from multiple angles of different signal-to-noise ratios, path strength and the like, the result shows that the method has stable and reliable performance under the ship radiation noise background, and the effectiveness and popularization potential of the method are verified.

The invention relates to the technical field of power grid new energy consumption and demand side management, and particularly discloses a user energy consumption excitation method and system based on a dynamic game model, and the method comprises the steps: constructing a user model considering a dynamic participation rate; calculating the probability that the user is selected after the posterior information is corrected based on the posterior information and the prior information, and constructing an IESP model considering a dynamic selection game mechanism; constructing a dynamic game model based on the user model considering the dynamic participation rate and the IESP model considering the dynamic selection game mechanism; acquiring basic data influencing the incentive price; inputting the basic data influencing the incentive price into the dynamic game model to obtain an optimal incentive price; and exciting the user to use energy based on the optimal excitation price. According to the method, the IESP serves as a dominant, the user selection probability is adjusted according to user historical response data and posterior information in the game process with the user, the dynamic self-adaptive capacity of the IDR strategy is improved, and therefore the user is effectively stimulated to use energy.

A dynamic SLAM robustness improvement method for a degraded motion scene is characterized in that on the basis of an ORB-SLAM2 architecture, a lightweight target detection thread is introduced, YOLO-FASTEST with high reasoning speed and small model size is combined with an NCNN reasoning framework, efficient semantic perception is realized, and prior information is provided for dynamic point elimination. According to the method, the epipolar constraint and the degradation consistency constraint are further fused, a dynamic judgment mechanism fusing camera motion prior residual modeling and Bayesian reasoning is provided, dynamic feature judgment under degradation motion is achieved, and the applicability defect of traditional geometric constraint in a degradation motion scene is effectively overcome. Meanwhile, the system designs a multi-index-driven key frame insertion strategy, the pose change amplitude, the image entropy change and the constraint pixel proportion are jointly considered, and the mapping efficiency and the real-time performance are improved.

The invention discloses a mine rescue multi-user VR practical training interaction method based on cloud computing, and the method comprises the following steps: collecting multi-source sensing data and task prior information, and constructing a rescue task data set; based on a cloud computing platform, constructing an initial task graph by using an improved Grapher model, and generating a global task graph in combination with a time sequence dependency relationship and a resource constraint relationship between nodes; performing decomposition processing on the global task graph by adopting a hypergraph division algorithm to obtain a plurality of local task graphs; binding the local task graph with the role identity information to generate a corresponding role task sequence; based on global progress management of a cloud computing platform, issuing a role task sequence to a VR training environment, and driving a multi-user interaction process; and carrying out real-time monitoring and data recording on the multi-user interaction process in the cloud computing platform, generating a training evaluation result and outputting the training evaluation result to the practical training management end. According to the invention, the dynamic adaptation capability of task scheduling and the response efficiency of multi-user collaborative practical training are improved.

The invention discloses a sliding hybrid model construction method, and particularly relates to the technical field of natural language processing and artificial intelligence, and the method specifically comprises the following steps: S1, voice-to-text and small model prediction; s2, performing Flag preliminary judgment and output; s3, threshold table judgment and model selection; and S4, building and predicting a large model prompt. The invention relates to a sliding hybrid model construction method, aims to solve the problems of low efficiency, poor accuracy, large resource consumption and the like in related business applications, and provides a sliding hybrid model construction method through construction of a long-tail intention threshold table, information extraction and classification based on prompt, 'prior information + PR curve 'threshold analysis and the like. The long-tail intention is quickly processed, the multi-label classification accuracy is improved, and the model performance is stabilized. The application effect is good in scenes such as automobile sales and after-sales, an efficient and intelligent solution is provided for multi-label classification and related services, and user experience and enterprise benefits are effectively improved.

The invention discloses a multi-modal speech enhancement method and device based on a deep learning model, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the head posture data, binaural audio signals and visual context information of a user in a virtual reality environment, coding the binaural audio signals into three-dimensional space acoustic features, and carrying out the coding of the three-dimensional space acoustic features; and extracting virtual sound source position features and lip motion features from the visual context information, inputting the features into an immersive fusion enhancement network, selectively enhancing or inhibiting acoustic features from different spatial directions, generating an enhanced audio stream, and outputting the enhanced audio stream through a binaural rendering engine. According to the method, the technical problems that in the prior art, due to the fact that multi-modal prior information cannot be effectively fused, voice enhancement lacks spatial selectivity, and an interference sound source irrelevant to vision is difficult to restrain are solved, and head posture dynamic attention and lip motion cross-modal constraint are fused; the technical effects of natural binding of auditory attention and a visual focus and effective suppression of an interference sound source are achieved.

The invention discloses a geologic body boundary extraction method based on gravity total horizontal derivative anomaly, and belongs to the technical field of geophysical data processing and interpretation. Calculating gravity total horizontal derivative anomaly based on actually measured gravity anomaly data; in the sliding window, detecting whether the gravity total horizontal derivative abnormity meets a preset extreme value structure condition in multiple directions, and screening out boundary points based on an amplitude threshold value and a slope threshold value; according to the invention, a geologic body boundary extraction method based on gravity total horizontal derivative anomaly is established by using directional extremum features of gravity total horizontal derivative anomaly; compared with a traditional boundary recognition method, the method does not depend on prior information, is high in anti-noise performance, can quickly and accurately recognize the boundary position of the geologic body under a complex background, and has higher boundary resolution.

The invention belongs to the technical field of visual inertial odometers in the fields of robot navigation, automatic driving, virtual reality and augmented reality, and discloses a dynamic environment adaptive visual inertial odometer method with zero-speed updating capability. Performing multi-resolution static state detection analysis on the inertial data; in a motion state, executing pre-integration calculation based on the inertial data; a pre-integration result of inertial data is used as pose prior information between adjacent frames, epipolar constraints are constructed, and a dynamic feature set and an original static feature set in a dynamic environment are obtained; based on the dynamic feature set, clustering calculation is carried out to obtain a plurality of clustering centers as cue words, and instance segmentation of a plurality of dynamic objects in the scene is guided to be completed; after a dynamic object instance segmentation mask is generated, static information is rechecked, and a selected static feature set is obtained based on the mask; in a static state, executing a zero-speed updating strategy; and in the motion state, executing an iterative extended Kalman filtering updating strategy.

The invention provides an electromechanical composite transmission system fault federated migration diagnosis method and system, and belongs to the technical field of armored vehicle electromechanical composite transmission system fault diagnosis. Initializing a global model; performing gradient prior guidance on cross-client updating of the global model; in a model parameter selection mode, a stable area with higher generalization potential is searched in a parameter space through a flat minimum value optimization strategy so as to enhance the robustness and adaptive capacity of the model at an unknown client; on the basis of federated learning framework design, a cross-client gradient interaction mechanism is designed, the updating direction of a global model at each client is constrained by utilizing generalization gradient prior information, feature representation collaborative alignment of each client is promoted, the updating efficiency and generalization ability of the global model are improved, and the robustness and generalization ability of the model are improved. And the diagnosis requirements that heterogeneous data cannot be concentrated and a target domain is invisible in an actual industrial environment are met.

The invention provides a method and system for applying uncertain prior information in a deep learning network, relates to the technical field of model training and application, and solves the technical problem that prior information cannot be applied in the deep learning network in the prior art. The method comprises the following steps: inputting prior information into a prior information mapping network, and mapping to obtain a first prior feature; verifying the prior information to obtain a reliable state of the prior information; if the reliable state represents that the prior information is reliable, determining the first prior feature as a target prior feature; if the reliable state characterizes that the prior information is unreliable, determining a default second prior feature as a target prior feature; the second prior feature is consistent with the data feature of the first prior feature; and embedding the prior information mapping network and the target prior features into the target deep learning network.

The invention discloses a control and communication combined maximum posterior probability detection analysis method and device. The method comprises the following steps: calculating and estimating probability density of transmission data according to a control model and a filtering algorithm; performing maximum posterior probability detection according to the probability density and the coding scheme; and analyzing the upper and lower bounds of the maximum posterior probability detection of the control and communication combination according to the probability density and the coding scheme. According to the control and communication combined maximum posterior probability detection and analysis method and device disclosed by the invention, combined detection of communication and control is realized, the transmission error probability can be reduced by utilizing the prior information of the control model, and the transmission reliability and the control performance are improved. According to the invention, the requirements of low-delay and high-reliability control scenes can be effectively met.

This invention discloses a clutter discrimination and suppression method based on multiple echo features, comprising: acquiring radar front-end clutter and target echo detection video data and dividing it into several connected regions; extracting multi-dimensional features and labeling prior information for each connected region; using the Relief feature selection algorithm to calculate and filter the weights of the multi-dimensional features, removing redundant features to obtain the optimal feature vector; using this feature vector to train a support vector machine classifier, and obtaining the optimal parameter model through cross-validation; acquiring measured clutter data, extracting corresponding features and inputting them into the model; and determining whether to remove clutter and retain targets based on the output. This invention effectively filters out dynamic clutter spots, avoids false deletion of weak targets, significantly reduces the false alarm rate of the system, and alleviates the computational burden of subsequent track processing.

The application discloses a neural network embedding method and device for power distribution network state estimation, electronic equipment and medium, wherein the method comprises: acquiring an input sequence; embedding time information and node type information into a vector through space-time prior information embedding to obtain a space-time embedding vector; sampling a node of interest according to a power flow direction of optimal power flow and fusing node information to obtain a node embedding vector; using a graph isomorphism neural network to capture the local of a graph and embedding it into a feature vector to obtain a structure embedding vector; fusing the input sequence and the three vectors and inputting them into a graph space-time prediction network to output a prediction result. Through the introduction of space-time prior information, graph node embedding based on the optimal power flow direction and graph structure embedding of the graph isomorphism neural network, the application realizes the modeling of the characteristics of the power distribution network, makes up for the deficiency of the prior art in the specific modeling of the power distribution network and improves the accuracy of the power distribution network state estimation.

The present application relates to a kind of individual future health state prediction method, device and medium based on proteomics, wherein the method comprises the following steps: obtaining proteomics data and preprocessing;Shared network construction: construct individual past and future comorbidity condition prediction neural network based on twin network framework;Trunk network construction: construct health-specific outcome prediction neural network based on multilayer perceptron method;Health assessment network integrates the shared network and trunk network architecture, and extracts features thereof to fuse and fine-tune in latent space, updates fine-tuning network parameters by further training, and outputs the risk assessment probability of multiple health-specific outcomes in the future.Compared with prior art, the present application focuses on proteomics data processing and modeling method, uses past and future health estimates as prior information, and realizes individual health condition assessment with multiple diseases and death as outcome.

The application discloses a low-complexity deep learning MIMO detection method based on partial MAP, comprising the following steps: obtaining multiple groups of training and test samples, and performing hierarchical and data preprocessing on the training and test data samples through QR decomposition to obtain K training sublayers and calculate the input and output labels of each training sublayer; training a DNN for generating local LLR in each detection stage of the K training sublayers based on a partial MAP method; performing layer-by-layer inspection from the Kth layer to the first layer, for each detection stage, calculating the local LLR by using the trained DNN, updating the LLR result of the current layer in combination with the prior information transmitted by the previous layer, and transmitting the LLR result to the next layer as prior information until the final LLR result is obtained; inputting the final LLR result into an FEC decoder to perform error correction and obtain a final signal estimation result; and the partial MAP directly outputs log-likelihood ratio soft information, and the combination of the partial MAP and a forward error correction code can obtain better detection performance.