30results about How to "Complementary effect is good" patented technology

The invention provides a traffic missing data completion method based on a bidirectional recurrent neural network, and belongs to the field of traffic. The method comprises the following steps: firstly, the time sequence characteristic of data in time are utilized; meanwhile, the influence of data before and after the completion time point on the current time point is considered, the utilization and completion precision of the data is greatly improved, and secondly, the influence of external features and adjacent sensor data on the current sensor data is considered and added into the completion model, so that the completion precision is greatly improved. According to the method, the completion precision under the condition that the data missing rate is low is greatly improved, and the completion precision under the condition that the data missing rate is high is also improved.

The invention relates to a knowledge base completion method based on multi-modal representation learning, and the method comprises the steps of giving a knowledge base KB which comprises two parts: aknown knowledge set and an unknown knowledge set; performing data preprocessing on the data in the knowledge base; proposing a knowledge base completion model ConvAt, and firstly generating multi-modal representation of a head entity and a tail entity for the acquired data; splicing the multi-modal representation of the head entity, the structural feature vector of the relationship and the multi-modal representation of the tail entity according to columns, respectively processing through a convolutional neural network module, a channel attention module and a spatial attention module, and finally multiplying by a weight matrix to obtain a score of a triple (h, r, t); and training the completion model in the step S2 by using a loss function, and performing knowledge base completion by usingthe trained model. According to the algorithm provided by the invention, external information can be fused, and richer semantic information can be utilized.

The invention discloses a time-series-data completion method based on a distance matrix. The internal high-order time association relationship of time series data is mined and used, so that missing data is completed through similar data points in the time series data; the time-series-data completion method includes the specific steps that according to the time series data, a distance matrix D of time series is modeled based on some distance measure function, and a matrix element D<ij> located in an ith line and a jth row is the distance between an ith data point and a jth data point in the time series; based on the obtained distance matrix D, k segments closest to segments with missing are searched in original time series; through the computed k near segments, data of the segments with missing is completed. According to the method, the good completion effect can be obtained in real time-series-data missing scenes; meanwhile, the interpretability of the method is high, the physical meaning behind the method is clear, many extensions can be conducted on the basis of the method, and therefore the method is effectively applied to various real scenes.

A traffic missing data complementation method based on a space-time attention mechanism comprises the steps of firstly capturing the influence degree of all road sections in a road network on the road network traffic state at the current moment in an attention mechanism mode, capturing spatial correlation information again at different moments, and improving the data complementation precision; and secondly, considering the time sequence of the traffic data, the influence degrees of the traffic data at different moments on the data at the current moment are different, capturing the inconsistent time correlation information through a time attention mechanism, retaining the most effective information when the current missing data is complemented, and improving the complementation effect of the model. And finally, while capturing the spatial-temporal correlation of the traffic data by using a spatial-temporal attention mechanism, considering that the correlation between the data is attenuated due to the increase of the spatial distance and the time interval, and adding a spatial-temporal attenuation matrix to improve the completion precision. According to the method, the complementation precision under the condition that the data missing rate is low is greatly improved, and the complementation precision under the condition that the data missing rate is high is also improved.

The invention discloses a method for completing time series data, a device and an electronic device. The method comprises the following steps of: constructing a data set according to a set format of the collected data; the data set comprises at least a series of data; the data set comprises at least a series of data. Inserting a point in time at which each series needs to be completed into the data set; Repartition the data set inserted into the time point according to the series, and put the same series of data into the same partition; Sorting the data within the partition to obtain a data list; Traversing the data in the partition and completing the data respectively. Under the distributed computing framework, the method can complete the missing time series data forward or backward, andonly a small number of variables need to be traversed once to complete the data completion.

The present invention relates to a strong adaptive knowledge base replenishment method. The method comprises the following steps: retrieving a data source from a knowledge base and performing partial subgraph traversal; setting path feature extractors, wherein the path feature extractors comprise a PRA feature classifier, a path binary feature extractor, a modified one-sided feature extractor, a bilateral contrast feature extractor and a generalized feature extractor, the extraction processes of all the path feature extractors are the same and comprise path feature extraction and path feature selection, the input is a partial subgraph and the output is a path feature; constructing a feature matrix according to the feature extractor; and selecting a classification model, transmitting the feature matrix to the classification model, training the classification model, outputting an established entity and a relationship type corresponding to the entity by using the classification model, and transmitting an output result to the knowledge base, so that the knowledge base replenishment is realized. The method provided by the present invention has relatively stable performance, namely, a relatively good knowledge base replenishment effect can be obtained on different data sets.

A method for complete time series data with label is disclosed and it is mainly used to solve the problem of time series data losing for a whole consecutive period in real scene, The core idea of themethod includes two aspects. Firstly, the low-dimensional time series is organized into high-dimensional form by using Hankel matrix technology, and the high-order time dependency relation is introduced. On this basis, the missing data is filled by using matrix decomposition method, thus the problem of data loss is effectively overcome. Secondly, the label information is modeled in the whole framework of the algorithm, and the label information is used to support the whole process of data complement, so that the complemented data is more in line with the real scene. By reasonably utilizing theideas of the above two aspects, the method proposed by the invention can obtain a better complement effect in a real time series data missing scene; At the same time, the method is interpretable, andcan be extended on the basis of the method, so it can be used in various real scenes effectively.

The invention provides a method for semantic completion of a single depth map point cloud scene, and belongs to the field of three-dimensional reconstruction in the field of computer vision. Accordingto the method, viewpoint repairing loopholes are converted in the process of mutual projection of a depth map, a depth segmentation map and point cloud, and high-resolution point cloud completion andsemantic segmentation are carried out at the same time. The problems that voxel representation form resolution is low and a point cloud representation form cannot give consideration to semantic segmentation in the scene semantic completion problem are solved, and a high-resolution geometric structure and semantic information details of the scene can be replenished at the same time by performing scene semantic completion on the three-dimensional point cloud; based on a single depth map, tasks of three-dimensional point cloud completion and semantic segmentation can be completed at the same time; effectiveness of semantic information and three-dimensional geometrical information constraints on semantic completion of the point cloud scene is verified.

The invention discloses a tensor low-rank model non-smooth three-dimensional image completion method based on manifold optimization, and the method comprises the steps: setting a tensor Qnuclear normTQN and an orthogonal projection basis in a low-rank completed non-smooth three-dimensional image as learnable image dependent optimization variables through manifold optimization, and updating a datadependent orthogonal projection basis; wherein the input is a limited observation image sample of the non-smooth three-dimensional image under the action of a projection operator, and the output is ato-be-recovered non-smooth low-rank three-dimensional image, so that the low-rank recovery of the non-smooth three-dimensional image is efficiently realized. The method is used for recovering the low-rank image, the applicability of image completion is improved, and the low-rank completion effect of the non-smooth three-dimensional image is improved.

The invention discloses a knowledge graph completion method based on graph perception tensor decomposition, and the method comprises the following steps: extracting the representation information of triple data (es, r, eo) from a graph neural network, and constructing a graph coding model with an entity and a relation, i.e. G = (V, E); constructing a three-order tensor decomposition model for thetwo-dimensional representation information of the graphic coding model through a Tucker decomposition method; namely, the three-order tensor decomposition model takes the maximum probability of prediction (es, r,) as the probability output that a triple is true, knowledge graph complementation is achieved, the problems that in an existing knowledge graph library, the relation between data is speculated, and the implicit connection relation between entities is difficult to mine are solved. High-precision completion of a large-scale knowledge graph data set is realized.

The invention discloses a massage chair with an external counterpulsation function, which does not influence the normal massage use of the massage chair and is convenient to realize household and community popularization. Massage can be carried out after counterpulsation is finished, so that the conditions of numbness, discomfort and the like of legs after counterpulsation can be overcome or reduced, and a better blood circulation effect can be achieved; besides, for a few patients with high heart rate and high blood pressure, the minority of patients can be massaged and relaxed firstly, and external counterpulsation is performed after the blood pressure is stable and the heart rate is reduced; when counterpulsation is carried out, the waist massage function of the massage chair can be selectively started, and in combination with blood circulation during counterpulsation, waist and back muscle groups and spines can be better massaged and subjected to health care. The combination of thetwo is not only the superposition of functions, but also has a better complementary effect.

The invention discloses a three-dimensional spectrum situation completion method based on a generative adversarial network, and the method comprises the steps: obtaining stored historical or experience radio monitoring data offline, and obtaining a plurality of complete three-dimensional spectrum situations or field intensity training data; performing iteration and adversarial offline training on the generative adversarial network variation to obtain the generative adversarial network variation of which the three-dimensional spectrum situation or the field intensity completion mechanism is learned; acquiring actual radio monitoring data of the current three-dimensional target area on line, and preprocessing to obtain defect spectrum situation or field intensity measured data of the three-dimensional target area; inputting the obtained measured data into the generative adversarial network variation to obtain output data of the generative adversarial network variation, and processing to obtain the current three-dimensional target area completion spectrum situation or field intensity. The method is applied to the three-dimensional spectrum situation or the field intensity in the space-air-ground information network, and the complementation error of the three-dimensional spectrum situation or the field intensity can be effectively reduced.

The embodiment of the invention discloses a data processing method and device, equipment and a medium. The method comprises the following steps: carrying out the rasterization of a target region according to the digital map information of the target region, obtaining S grid regions with the same area, and S is an integer greater than or equal to 1; determining the moving point quantity of each grid region according to the position data of the target region obtained by the base station in the preset first time period; complementing the missing fragment movement point quantities of all the gridregions in the preset first time period to obtain a complete movement point quantity of the target region, the fragment movement point quantity being a movement point quantity of one grid region in apreset duration, and the preset duration being a time period in the preset first time period.

The invention provides a vegetation index time sequence reconstruction method in combination with matrix completion and trend filtering, which comprises the following steps of: firstly, determining a missing position in time sequence data through quality marker data in a vegetation index product; the method comprises the following steps of: converting a one-dimensional vector into a two-dimensional matrix through matrix change, establishing an optimal energy equation for low-rank matrix completion for a matrix after each pixel is converted, and realizing matrix completion through an imprecise augmented Lagrange algorithm to obtain a time sequence completion matrix which does not contain data missing preliminarily; and finally, vectorizing the completion matrix, establishing an energy optimization equation of weighted trend filtering on the basis of a one-dimensional vector, and solving the model through an alternating direction multiplier method so as to further filter residual noise and obtain smooth and clean high-quality vegetation index time sequence data. And high-precision reconstruction of the long-time remote sensing vegetation index sequence is realized.

The invention relates to the technical field of organic solar cells, and particularly discloses a chlorine atom-modified benzobisthiophene derivative two-dimensional donor material for organic solar cells and a preparation method of the material. By introducing chlorine atoms on conjugate side chains of donor units, the molecular energy level of the polymer material is effectively adjusted so as to increase the open circuit voltage Voc of a device, and the donor material and an acceptor material have a good optical absorption complementary effect so as to significantly improve the performanceand photoelectric conversion efficiency of the solar cell device. The two-dimensional donor material of the organic solar cells has a structural formula as follows.

The invention discloses a method for finely complementing a defect optical flow graph and application thereof. The method comprises the following steps: acquiring a smoke picture I in real time, processing the smoke picture I to obtain an optical flow image, and positioning a smoke dynamic shielding area; extracting an image region from the center of the minimum bounding square of the dynamic occlusion region; inputting the image region into a trained improved GAN network to obtain a replacement image of the dynamic occlusion region, wherein the improved GAN network refers to replacing confrontation loss in a loss function of a context network module of the GAN network with WGAN loss in a loss function of a context network module of the improved GAN network; processing the replacement image of the dynamic occlusion area through a VGG-19 network, and adding texture information; and mapping the image obtained by adding the texture information to an optical flow image to obtain a completeglobal optical flow graph. The method disclosed by the invention is good in completion effect and high in processing speed; the electronic equipment is simple in structure, low in cost and good in application prospect.

The invention provides a face incompleteness scanning completion method based on deep learning. The method comprises the following steps: taking a depth image and a color image collected by a depth camera; detecting two-dimensional face feature points in the color image, generating three-dimensional face feature points of face scanning according to an internal reference of the camera and depth information in the depth image, and roughly aligning the face scanning to a standard coordinate system where a template face is located according to the three-dimensional feature points; more accurately aligning the face scanning with the template face by using an iterative nearest point algorithm; fitting the template face to the aligned face for scanning by using a Laplace deformation algorithm; setting a distance threshold value on a fitting result, and removing a redundant surface in the face scanning; performing geometric shape completion on incomplete point cloud of a face area by using a PointNet auto-encoder to generate a complete face point cloud. According to the method and the device, the problems of different face scanning poses and redundant surfaces are solved, and geometric face shapes are complemented by using a neural network, so that a better complementation effect is obtained.

A traffic missing data completion method based on the spatio-temporal attention mechanism. First, through the attention mechanism, capture the degree of influence of all road sections in the road network on the traffic state of the road network at the current moment, and recapture the spatial data at different times. Relevance information to improve the accuracy of data completion. Secondly, considering the timing of traffic data, traffic data at different times have different influences on the data at the current moment. This inconsistent time correlation information is captured through the time attention mechanism and retained when completing the current missing data. The most effective information improves the completion effect of the model. Finally, while using the spatiotemporal attention mechanism to capture the spatiotemporal correlation of traffic data, considering that the correlation between data is attenuated by the increase of spatial distance and time interval, adding a spatiotemporal attenuation matrix improves the completion accuracy. The present invention not only greatly improves the completion accuracy in the case of low data missing rate, but also improves the completion accuracy in the case of high data missing rate.

The embodiment of the present invention discloses a data processing method, device, equipment and medium. The data processing method includes: performing grid processing on the target area according to the digital map information of the target area to obtain S grid areas of equal area , S is an integer greater than or equal to 1; according to the position data of the target area acquired by the base station in the preset first time period, determine the amount of moving points in each grid area; for all grids in the preset first time period The fragment moving point amount missing in the grid area is complemented to obtain the complete moving point amount of the target area. The fragment moving point amount is the moving point amount of a grid area within a preset time length, and the preset time length is A time period within the first time period is set.

The invention discloses a three-dimensional spectrum situation completion method based on a generative confrontation network, which includes: acquiring and storing historical or empirical radio monitoring data off-line, and obtaining several complete three-dimensional spectrum situation or field strength training data; and iterating on the variant of the generative confrontation network And adversarial off-line training, get a variant of the generative adversarial network that has learned the 3D spectrum situation or field strength complement mechanism; collect the actual radio monitoring data of the current 3D target area online, and preprocess to get the 3D target area defect spectrum situation or field strength Measured data: Input the obtained measured data into the variant of the generated confrontation network to obtain the output data of the variant of the generated confrontation network, and process it to obtain the current three-dimensional target area to complete the spectrum situation or field strength. The invention is oriented to the application of the three-dimensional spectrum situation or field strength in the space-space-ground information network, and can effectively reduce the complement error of the three-dimensional spectrum situation or field strength.

The present invention provides a system and method for complementing a laser radar three-dimensional point cloud target. The global characteristics of the laser radar target are obtained through three layers of shared multi-layer perceptrons and three layers of point-by-point maximum pooling layers, so that the objects obtained by laser radar scanning The point cloud distribution is denser and more uniform, and the details of the outline of the object are more complete, so as to achieve the purpose of detection, recognition and measurement calculation.

The invention discloses a tensor low-rank model non-smooth three-dimensional image completion method based on manifold optimization. The manifold optimization is used to combine the tensor Q-kernel norm TQN and the orthogonality in the low-rank completion non-smooth three-dimensional image. The projection basis is set as a learnable image-dependent optimization variable, and the data-dependent orthogonal projection basis is updated. The input is the restricted observation image sample of the non-smooth 3D image under the action of the projection operator, and the output is the non-smooth to be restored. Low-rank 3D images, thereby efficiently realizing low-rank restoration of non-smooth 3D images. The invention is used for low-rank image restoration, improves the applicability of image completion, and improves the low-rank completion effect of non-smooth three-dimensional images.

The present invention discloses a method for completing tagged time series data, which is mainly used to solve the problem of missing a continuous segment of time series data common in real scenes. The core idea of ​​the data completion method includes two aspects: first, using Hankel matrix technology organizes low-dimensional time series into high-dimensional forms, introduces high-order time dependencies, and uses matrix decomposition to complete missing data on this basis, thereby effectively overcoming the problem of data loss; second, in the overall algorithm The tag information is modeled in the framework, and the tag information is used to support the data completion process, so that the completed data is more in line with the real scene. By rationally using the above two ideas, the method proposed by the present invention can achieve a better completion effect in the real time series data missing scene; at the same time, the method has strong interpretability, and can also be based on the method More expansions are carried out so that it can be effectively used in various real scenarios.

The invention discloses a dam safety monitoring data completion method based on spatiotemporal multi-view fusion. Abstract the view model; fuse the four models using lasso regression to generate a spatiotemporal multi-view fusion model; use the spatiotemporal multi-view fusion model to generate complementary data. In the case of strong correlation between temporal and spatial characteristics, this method can well solve the problems of block deletion and local deletion in dam safety monitoring data, and has been verified on real dam safety monitoring data. Algorithms and traditional spatiotemporal models have smaller errors and better completions.

The invention discloses a color image completion method based on tensor block cyclic unrolling, and belongs to the technical field of image processing. First, input the image to be completed and initialize the missing pixels with n-nearest neighbors to obtain the target image; then initialize the model parameters, estimate the block cyclic unrolling rank of the target image, and set the weight coefficient. Then, the target image is input into the image completion model in the form of tensor, and the alternating direction multiplier method is used to solve the convex optimization of the model through iteration. The image completion model is a low-rank matrix factorization model based on tensor block cyclic unrolling. . Finally, convert the data format of the iterative tensor so that it is output in the format of the image to be completed. This method increases the connection between image slices when tensor block circular unrolling is performed, thereby reducing the loss of image structure information caused by the unrolling operation to a certain extent; the peak signal-to-noise ratio of the completed image is significantly improved , and the texture and detail information is richer.

The invention discloses a time series data complement method, device and electronic equipment. The method includes: constructing a data set according to a set format for the collected data, and the data set contains at least one series of data; The time point to be completed is inserted into the data set; the data set at the inserted time point is repartitioned according to the series, and the data of the same series are put into the same partition; the data in the partition is sorted to obtain the data list; Traverse the data in the partition and complete the data. Under the distributed computing framework, this method completes the missing time series data forward or backward. When performing data completion, only a small number of variables need to be traversed once to complete the data completion.

The invention relates to the technical field of clustering analysis, in particular to a bidirectional clustering method for risk control system data complementation, example clustering mainly takes intra-cluster high similarity and inter-cluster low similarity as targets, sample points are distributed to different clusters, attribute clustering performs attribute dimension clustering on centroidsobtained by example clustering, information of example dimensions and attribute dimensions is fully considered, clustering is combined to effectively capture potential rules between rows and columns,a local matrix is constructed accordingly, users in the local matrix have high correlation with projects, the local matrix is filled with a potential factor model, and the method has the advantage ofbeing good in noise robustness through bidirectional clustering and improving the accuracy of the processing result by capturing the features of multiple dimensions,.