12results about How to "Solve the sparse problem" patented technology

The invention discloses a three-dimensional target detection method, device and equipment based on an improved Pilllar structure and a medium, and relates to the field of three-dimensional point cloud processing and target detection. Dividing the original LiDAR point cloud data into a plurality of Pill units through a height perception double-branch coding module of the three-dimensional target detection model, and carrying out parallel branch feature coding to obtain a pseudo image feature map; global maximum and average pooling is carried out through a space attention feature extraction module to generate a space attention weight map, then the space attention weight map is multiplied by a pseudo image feature map point by point to obtain an enhanced weighted feature map, and the robustness of the model in a complex background and other scenes is enhanced; multi-scale context feature extraction fusion and receptive field expansion are carried out through the multi-scale feature extraction module, details can be seen clearly, the global context can be understood, and the problem that a long-distance target is difficult to detect due to sparse point clouds is solved; target prediction is carried out through the SSD detection head module, and the real-time requirements of applications such as automatic driving are met.

The invention provides a blasting vibration peak velocity prediction method fusing parameter uncertainty and data-driven optimization, which comprises the following steps of: firstly, acquiring data such as blasting parameters, lithologic indexes, geological conditions and actually measured vibration peak velocity (PPV), and establishing a basic database; a prior probability model is constructed, a joint uncertainty model is established in combination with probability disturbance and a fuzzy triangular number, a multi-dimensional disturbance sample is generated through a joint central value and a joint standard deviation and is fused with original data, and an extended database is formed. Feature analysis is carried out on the fused data, and input variables which have obvious influence on the vibration peak velocity (PPV) are screened; and constructing a BP neural network model based on the screened features, carrying out global optimization on a network weight and a threshold by adopting a PSO algorithm, and then carrying out local fine tuning by utilizing Adam. Finally, through training and verification, prediction of the vibration peak velocity (PPV) is realized, and model precision is evaluated through RMSE, MAE, MAPE, Rand other indexes. The influence of rock and soil parameter uncertainty on prediction precision can be effectively processed, and the reliability and applicability of blasting vibration prediction are improved.

This invention discloses a robust 3D hair reconstruction method with adaptive Gaussian ellipsoids and complex lighting, relating to the fields of 3D vision and computer graphics. Addressing the problems of sparse Gaussian ellipsoid distribution, unreliable orientation supervision signals, and geometric and texture coupling interference in existing multi-view hair reconstruction methods under non-ideal lighting, this invention employs a two-stage approach. It utilizes anisotropic Gaussian units to implicitly encode hair orientation and directly transfers the photometric constraint gradient to hair nodes through hair-aligned Gaussian dual representation, achieving efficient and high-precision hair-level reconstruction. Simultaneously, an adaptive lighting preprocessing flow is constructed, using a visual language model to locate low-quality viewpoints and dynamically setting the upper limit of Gaussian density. A three-stage adaptive density strategy is used to fill geometric gaps. Decoupled two-stage optimization is employed to eliminate lighting bias and restore true hair color. Furthermore, confidence-weighted orientation loss and 3D interpolation-guided upsampling are used to improve the orientation field quality and constraint coverage.

The invention relates to the technical field of computer and big data management, and discloses a data interaction method of a handheld tablet personal computer, which comprises the following steps: receiving a big data query instruction, and obtaining a high-dimensional data set; a three-dimensional point cloud is generated through graph neural network dimension reduction embedding; constructing and rendering a dynamic interactive three-dimensional topological mapping model; responding to the multi-finger gesture operation to adjust the spatial layout and the clustering structure of the data entity in real time; and dynamically calculating and visually coding the association strength between the data. The system comprises a query receiving module, a data acquisition module, a dimension reduction processing module, a model construction module, a rendering module, a gesture recognition module, an association calculation module, a visual coding module and the like. According to the method, through three-dimensional space mapping and multi-finger gesture interaction, the information density and association visibility are remarkably improved, local detail expansion, spatial memory anchor points and multi-user cooperation are supported, and efficient and visual big data exploration is achieved.

This application provides a method and system for intelligent identification of minerals in weak information areas based on transfer learning, relating to the field of transfer learning technology. First, a source domain mineral sample set and a set of mineral samples to be identified in weak information areas are obtained. Then, geological correlation elements between the two are extracted and a cross-domain feature mapping is constructed. The original features of the minerals in the weak information areas are subjected to transfer adaptation processing to generate intermediate features of the minerals in the weak information areas that are adapted to the feature dimensions of the source domain. Next, sparse feature dimensions in the intermediate features are identified, and the corresponding feature distribution patterns of the source domain are used to complete the features, generating completed features. The completed features are input into a pre-trained transfer learning identification model for cross-domain identification, generating preliminary identification results. Finally, source domain samples with similar geological correlation elements are selected to determine the transfer deviation, correct the preliminary identification results, and generate the final identification results, effectively improving the accuracy and reliability of mineral identification in weak information areas.

The invention discloses a big data intelligent recommendation method and system based on multi-scene dynamic adaptation. A dynamic user portrait is generated by constructing a three-dimensional scene feature space and fusing multi-source heterogeneous data; scene-feature weight adaptive adjustment is realized based on reinforcement learning, and a multi-scene model is trained in combination with a'shared-private 'neural network and transfer learning; and finally, dynamically allocating resources according to the scene priority to generate a recommendation result, and continuously optimizing through a feedback closed loop. The problems that a traditional recommendation system is insufficient in scene adaptation, poor in real-time performance and the like are solved, the recommendation precision and the resource utilization rate are remarkably improved, and the method is suitable for multiple fields such as e-commerce and content distribution.

The application discloses a kind of infrared interference signal equal optical path difference interval reconstruction methods based on optical path difference model, belong to Fourier infrared spectrometer signal processing technical field.Method includes: to reference signal is filtered and transformed, and the extraction index of quarter wavelength interval is obtained by peak searching;Linear region and nonlinear region are determined using the Taylor expansion of optical path difference mathematical model;Infrared interference signal is directly sampled or interpolation correction in linear region at equal interval, and high times interpolation reconstruction is carried out in nonlinear region.The present application makes full use of the optical path difference mathematical model of rotating mirror spectrometer, overcomes the defects that the existing method only relies on reference signal, resulting in less sampling points, low precision, improves the sampling density and spectral inversion accuracy of equal optical path difference interval reconstruction.

The application discloses a weaving method of a back-hanging fabric and the fabric, the fabric is woven by a circular knitting machine, the back-hanging fabric comprises at least one cycle unit, the cycle unit is first woven by hooking the face yarn in a positive direction through a needle cylinder, then is woven by hooking the back-hanging yarn in a positive direction and a reverse direction in sequence through the needle cylinder, is then woven by hooking the face yarn in a reverse direction through the needle cylinder, and finally is woven by hooking the back-hanging yarn in a positive direction and a reverse direction in sequence through the needle cylinder; the above technical scheme makes each face yarn weaving row have a back-hanging yarn weaving row formed by the positive direction and the reverse direction of the back-hanging yarn, further makes the back-hanging yarn weaving row in the fabric have no interval, solves the problem of fabric sparseness, and meanwhile keeps the original weaving positions of the face yarn and the back-hanging yarn unchanged, avoids many flying lines in the cycle weaving process, and even avoids the problem of entanglement.

The invention discloses a causal federation element reinforcement learning intelligent decision-making method and system for people-benefiting insurance claim settlement, and relates to the field of insurance intelligent claim settlement, and the method comprises the steps: building and adaptively generating a regional causal knowledge graph based on a multi-dimensional policy text; a causal federation element reinforcement learning framework is adopted, through element strategy network initialization, and federation learning is utilized to carry out fine adjustment on small samples in an encryption mode on the premise that data does not go out of a domain, so that local intention recognition model adaptation is realized; according to the claim consultation text of the user, performing intention recognition in combination with neural symbol double-track reasoning and knowledge graph causal chain matching; and training the decision tree, and generating a visual report through anti-factual reasoning to assist in decision making. According to the method, the user intention recognition accuracy and the rule interpretability are enhanced, the response adaptation efficiency of policy dynamic updating is improved, privacy is guaranteed on the premise that data are not out of a domain, small sample learning is achieved, then the people-benefiting insurance claim settlement processing efficiency and the customer satisfaction degree are optimized, and the operation cost is reduced.

The application discloses a traditional Chinese medicine technical element importance evaluation method and system based on patent citations, and relates to the field of traditional Chinese medicine information technology. The method comprises the following steps: obtaining the composition of the target traditional Chinese medicine compound and the monarch-minister-assistant role label of each medicinal material; constructing a technical element graph containing medicinal material layer nodes and component layer nodes; assigning an initial weight value to each medicinal material node based on the monarch-minister-assistant role; obtaining patent citation relationship data, including the citation edge between patent nodes and the mapping relationship between patent nodes and technical element nodes; calculating the final weight value of each technical element node based on the initial weight value and the citation frequency information; and outputting according to the final weight value sorting. The application solves the problems of strong subjectivity and sparse objective data in traditional Chinese medicine technical element importance evaluation by combining the subjective weight of the monarch-minister-assistant theory with the objective data of the patent citation relationship, and the evaluation result can be applied to FTO retrieval strategy generation, patent navigation analysis and other scenes.

This invention discloses an immediate reward learning method based on self-supervised reinforcement learning, belonging to the field of artificial intelligence reinforcement learning technology. First, the invention calculates a first prediction error between the agent's predicted state and the actual state. Second, it reconstructs action data using an inverse dynamics model and calculates a second prediction error. Then, it constructs a causal confidence factor based on the second prediction error to generate an effective prediction error. Next, it generates a fast-channel reward and calculates the volatility of the reward signal using an oscillation index. Finally, it generates the final immediate reward. By integrating a multi-layered mechanism of prediction error correction, causal relationship modeling, reward stability assessment, and policy stochastic adjustment, the stability and reliability of the reward signal can be effectively enhanced, noise interference reduced, and training oscillations avoided. Simultaneously, it significantly improves the quality of immediate reward generation in sparse reward environments, accelerates policy convergence, and enhances the efficiency and stability of self-supervised reinforcement learning in complex tasks.

The application discloses a kind of based on missing cross-modal data class incremental learning unmanned aerial vehicle inspection method and device, method includes: to the missing data that unmanned aerial vehicle collects is completed;The data after completion is preprocessed, obtains the characteristics of each mode hidden in data;The feature channel of different mode in input feature map is mixed;Each group is assigned a set of convolution kernel in the input feature map after mixing, after grouping convolution, adaptive average pooling is applied to feature map, and the final output feature map is obtained;The output feature map is flattened, then it is input into fully connected layer, and activation function is applied to introduce nonlinearity, generate final classification result, subsequently apply loss function to calculate loss, and use gradient descent method to update model parameters;After model training is completed, it is deployed to unmanned aerial vehicle, and unmanned aerial vehicle identifies scene and entity.The device includes: processor and memory.The application improves the identification and analysis capability of unmanned aerial vehicle in complex environment.