33 results about "Viewdata" patented technology

The invention discloses a motor imagery electroencephalogram decoding method based on multi-view space-time convolution and an attention mechanism, which comprises the following steps of: firstly, constructing multi-view data by utilizing a filter bank, and enhancing training data by adopting a frequency band sensing fragment splicing strategy so as to expand sample distribution; secondly, respectively and accurately capturing specific time sequence features and spatial features of different frequency bands through a frequency adaptive convolution kernel and depth separable convolution; meanwhile, in combination with a channel attention mechanism and a time sequence statistics module, feature channel weights are effectively utilized, and a mean value and a logarithmic variance in a time window are calculated, so that the modeling capability of the model for key spatio-temporal information is enhanced. According to the method, the problem of insufficient spatio-temporal feature mining in the motor imagery task is effectively solved, and a competitive result is obtained.

A data transferring method comprises monitoring (210, fig 2) the contents of a data buffer 110, 120, determining (220, fig 2) that the contents contains sensitive data, obfuscating (230, fig 2) the se

The invention discloses a fusion representation method for incomplete multi-view data, and belongs to the technical field of information technology service. The method comprises the following steps: aiming at the problem that a traditional multi-view learning method cannot be suitable for incomplete multi-view data learning when views are missing, constructing a multi-view representation matrix aiming at an incomplete multi-view data set; optimizing a single view representation matrix; a single-view reconstruction learning model is constructed, and complementation of a missing view representation matrix is realized; constructing a target function by using the fusion representation model; the fusion representation model is trained, an optimal fusion representation matrix is obtained through model optimization, and the fusion representation matrix can be directly used as input of downstream tasks. According to the method, the key technology of fusion expression of multi-source heterogeneous multi-view data is broken through, the purposes of making full use of multiple views and reducing influences of incomplete views are achieved, and then downstream tasks in a network can be supported.

To provide a method and a device for encoding multi-view data for immersive video, a method and a device for decoding multi-view data, and a corresponding bit stream including metadata.SOLUTION: A method of encoding multiview data for immersive video includes generating metadata including a field indicating whether a patch data unit of the multiview data includes in-painted data. This metadata provides a way to distinguish between patch data units containing original texture and depth data and patch data units containing in-painted data (in-painted texture and depth data). Providing such information in the metadata of an immersive video addresses issues related to blending and pruned view reconstruction.SELECTED DRAWING: Figure 1

The application discloses a small sample robust modulation recognition method based on asymmetric double-view enhancement and AD-HCL loss, and belongs to the technical field of automatic modulation recognition of wireless communication signals. The method solves the problems that the prior art is strongly dependent on large-scale labeled data, is insufficient in robustness in a complex electromagnetic environment, and does not fully utilize unlabeled data. Technical scheme points include: a deep neural network fusing a convolution residual network, a bidirectional long short-term memory network and an attention mechanism is constructed for feature extraction; the network is unsupervised pre-trained by using asymmetric double-view data enhancement and an AD-HCL loss function; and the network is supervised fine-tuned by removing a projection head and connecting a new classifier and using a small amount of labeled data. The application can effectively utilize unlabeled data and realize high-precision and robust modulation recognition under a small sample condition.

The invention provides a method, device and equipment for modifying BIM link model data and a storage medium, and relates to the technical field of BIM design, and the method is applied to a BIM link scene and comprises the steps that an axis net of a BIM link model is loaded in a current document, when the axis net is in a model state, execution of editing operation on the axis net is forbidden, and in a current view, editing operation on the axis net is forbidden. And in response to a first state switching operation for the axis net in the current view, switching the axis net from a model state to a view state, and when the axis net is in the view state, in response to an editing operation for the axis net, modifying the axis net view data corresponding to the current view in the second data area without changing the axis net model data in the first data area. The invention provides the method. In a BIM model link scene, on the premise of not changing basic data of an axis network in a link document, flexible display and controlled editing of the axis network of the link model in a specific view of the current document are realized.

The application provides a processing method and device of video data based on left and right views, equipment and medium, the method comprises: determining left view data and right view data of a to-be-processed video; wherein the left view data is an image frame of a left eye view, and the right view data is an image frame of a right eye view; determining a parallax fusion feature of the to-be-processed video according to the left view data and the right view data; wherein the parallax fusion feature represents a left eye view and a left eye parallax map; determining a quality score of the to-be-processed video according to the parallax fusion feature and a preset text description; wherein the preset text description represents an evaluation dimension of the to-be-processed video, and the quality score represents a video quality of the to-be-processed video. The method of the application determines the parallax fusion feature of the to-be-processed video through the left view data and the right view data, and the parallax fusion feature of the to-be-processed video can improve the accuracy of data processing.

Methods and apparatus are disclosed herein to synthesize six degree-of-freedom views from sparce RGB-depth inputs. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to reproject a first red-green-blue (RGB) depth view and a second RGB depth view to a target camera position to obtain a first reprojected view and a second reprojected view, combine the first reprojected view and the second reprojected view into a blended view data based on a first weight map and a second weight map, the blended view including missing RGB depth information due to at least one of an occlusion or a disocclusion, and generate a six degree-of-freedom synthesized view of the blended view data, the synthesized view including the missing RGB depth information.

The invention discloses an electric energy substitution potential customer type identification method and device, a terminal and a medium, and the method comprises the steps: obtaining multi-dimensional original data from different data sources, converting the multi-dimensional original data into corresponding view data, screening out a representative anchor point in each view, calculating the similarity between each sample point and the anchor point, and carrying out the recognition of the type of a customer with the electric energy substitution potential. And constructing an initial similarity matrix of each view. And inputting the multi-view similarity matrix into a feature selection model, and gradually eliminating the noise influence of the low-weight views through model optimization. According to the scheme, firstly, the calculation complexity is reduced through anchor point screening, meanwhile, the view contribution degree is dynamically adjusted through the feature selection model, irrelevant feature interference is effectively restrained, multi-dimensional information such as power loads and equipment states can be integrated, and the method and the device have the advantages that the method and the device are easy to implement. The customer energy utilization potential under different environment conditions can be accurately identified, and the classification accuracy is improved.

The invention provides a remote 3D image data transmission and display method and device based on the Internet, and belongs to the technical field of 3D image data transmission. Environment images are collected by using a binocular fisheye camera synchronously configured at a robot end, are spliced into a single panoramic image after being cut, and are subjected to single-stream coding; an RTP protocol is adopted as a carrier and is transmitted to a client in combination with a differentiated reliability strategy of data classification, so that low delay and synchronism of the data are ensured; double-view data capable of being displayed are generated through single-time rendering processing, and the method is suitable for various 3D image display devices. Therefore, the traditional double-path flow is replaced by the single-flow coding, and the network congestion is effectively relieved; binocular images are spliced into a single-frame panoramic image at a robot end, and subsequent coding, transmission, decoding and rendering full links are all based on the same frame of data, so that the problem of frame desynchrony caused by delay jitter in traditional double-path transmission is solved, the teleoperation accuracy is improved, and meanwhile, the resource overhead of a client is greatly reduced.

The invention discloses a small sample robust modulation identification method based on asymmetric double view enhancement and AD-HCL loss, and belongs to the technical field of wireless communication signal automatic modulation identification. The problems that in the prior art, dependence on large-scale labeled data is high, robustness is insufficient in a complex electromagnetic environment, and unlabeled data is not fully utilized are solved. According to the technical scheme, the method comprises the following steps: constructing a deep neural network fusing a convolutional residual network, a bidirectional long-short-term memory network and an attention mechanism for feature extraction; carrying out unsupervised pre-training on the network by adopting asymmetric double-view data enhancement and an AD-HCL loss function; by removing a projection head and connecting a new classifier, supervised fine tuning is performed by using a small amount of marked data. According to the method, label-free data can be effectively utilized, and high-precision and robust modulation identification is realized under a small sample condition.

The invention discloses a base station evaluation method and a computer program product, and relates to the technical field of wireless. The base station evaluation method comprises the following steps: processing obtained multi-dimensional index data of a base station to obtain a multi-view data matrix; on the basis of the multi-view data matrix, a public view map is constructed, and the public view map is used for representing the weight of each view; clustering processing is carried out on the public view map to obtain a clustering result of the base station, and the clustering result is the category of the base station in a preset number of clustering clusters; and based on the clustering result and the weight of each view in the base station, evaluating the base station to obtain the grade of the base station.

The embodiment of the invention provides a data display method, device and equipment, a readable storage medium and a program product, and can be applied to fields or scenes such as artificial intelligence and application programs, the method comprises the steps that a data acquisition request is sent to a server, and the data acquisition request carries a data display position identifier of the application program; receiving data content and a first data style template determined by the server according to the data display position identifier, and analyzing the first data style template to obtain an analysis result; if the analysis result indicates that the analysis of the first data style template fails, determining a target view object meeting an object selection requirement from candidate view objects stored in a local storage space; and generating first view data according to the data content and the target view object, and performing display processing on the first view data. Through the embodiment of the invention, the data display effect and the object experience can be improved.

The application provides a stream view classification method and device, terminal equipment and medium, the method comprises the following steps: receiving stream view data arriving in a streaming order; inputting a data view arriving at a current time into a pre-constructed evidence deep neural network model to generate a current evidence vector through a non-negative activation transformation; performing linear transformation on the current evidence vector by using a preset label transition matrix to obtain a noise-adaptive evidence vector; calculating a group of Dirichlet distribution parameters based on the noise-adaptive evidence vector, and generating a subjective logical opinion of the data view according to the Dirichlet distribution parameters; synthesizing the subjective logical opinion and a comprehensive opinion output at a previous time through a fusion operator defined based on the subjective logic to obtain a new comprehensive opinion; and determining a final classification identifier of an entity according to a belief quality vector in the new comprehensive opinion. The application can improve the accuracy of stream view classification.

Embodiments of the present application provide a training method and device of a conversion model for incomplete multi-view data, the method comprising: obtaining first description information and second description information of a sample object. First feature information and first conversion information are obtained by performing feature extraction and feature conversion processing on the first description information through a first conversion model. Second feature information and second conversion information are obtained by performing feature extraction and feature conversion processing on the second description information through a second conversion model. Model parameters of the first conversion model and the second conversion model are updated according to the first feature information, the first conversion information, the second feature information, the second conversion information, the first description information and the second description information. The technical solution of the present application can automatically generate data of a missing view according to existing view data, thereby effectively improving the accuracy of clustering processing.

The invention discloses an after-sales service SLA performance countdown monitoring system. The system comprises a multi-source performance data acquisition module, a multi-source data spectrum embedding fusion module, a performance resource scheduling calculation module, an SLA data fusion learning module, a big data performance monitoring module and a performance countdown monitoring module. The multi-source performance data acquisition module acquires multi-dimensional data such as equipment fault records and maintenance personnel scheduling information, the multi-dimensional data are fused by the multi-source data spectrum embedding fusion module and then transmitted to the performance resource scheduling calculation module to dynamically allocate resources through an algorithm, and then the SLA data fusion learning module carries out feature learning fusion on multi-view SLA data and carries out feature fusion on the multi-view SLA data. A fusion result is sent to the big data performance monitoring module for storage, retrieval and analysis, and finally the performance countdown monitoring module calculates and displays the remaining time of performance. According to the invention, the SLA performance management accuracy is improved, and reliable support is provided for after-sales service.

The application discloses a kind of gantry crane's lifting appliance pose determination method of intelligence, it includes constructing gantry crane digital model and constructing camera module, and then obtain the video sequence of gantry crane lifting appliance descent angle of view and construct gantry crane view data training set;To the angle column calibration of gantry crane view data training set, obtain calibration angle column value;View data training set is input into neural network, and angle column detection is carried out using loss function, and angle column position value is obtained;Segment angle column position value, and obtain angle column pixel space position;The depth value of lifting appliance is obtained using three-point model, and the length value of lifting appliance, width value is estimated by scale estimation, for adjusting lifting appliance pose;Through the comparison of angle column pixel space position, the depth value of lifting appliance, length value and width value of gantry crane view data training set, whether the four angle columns of lifting appliance are aligned container is judged.The present application reduces positioning error by angle column calibration and neural network training, and does not need to rely on artificial visual inspection to adjust lifting appliance.

Concepts for encoding and decoding multi-view data for immersive video are disclosed. In an encoding method, metadata is generated comprising a field indicating if a patch data unit of the multi-view data comprises in-painted data for representing missing data. The generated metadata provides a means of distinguishing patch data units comprising original texture and depth data from patch data units comprising in-painted data (e.g. in-painted texture and depth data). The provision of such information within the metadata of immersive video may address problems associated with blending and pruned view reconstruction. Also provided are an encoder and a decoder for multi-view data for immersive video, and a corresponding bitstream, comprising metadata.

This invention belongs to the field of computer data clustering technology and discloses a multi-view clustering method, apparatus, electronic device, and storage medium. The method includes: acquiring multi-view data to be clustered, wherein the multi-view data is one of images, videos, audio, and text; determining the Laplacian matrix, depth decomposition matrix, and global consensus embedding matrix for each view based on the original data matrix of the multi-view data; performing hypergraph regularized deep non-negative matrix decomposition on each layer of the original data matrix through the depth decomposition matrix to obtain the decomposition result; performing high-level semantic alignment and consensus representation fusion on the decomposition result through the global consensus embedding matrix to obtain the final layer representation of the view; determining the clustering indicator matrix of the final layer representation according to the objective function; and processing the clustering indicator matrix using the k-means clustering algorithm to obtain the multi-view clustering result. Through the technical solution of this invention, the accuracy of multi-view clustering is achieved.

The application discloses a multi-view target tracking detection method and device, a terminal and a medium. The scheme provided by the application first inputs multi-view data obtained to an encoder module containing a specific feature reconstruction pyramid model, wherein the feature reconstruction pyramid model comprises a bottom-up network unit, a top-down network unit and a feature reconstruction unit, the feature reconstruction unit is arranged on a transverse connection channel between the bottom-up network unit and the top-down network unit, information is more efficiently fused through the feature reconstruction pyramid model, important spatial features and refined channel features are extracted, feature representation of a key region is optimized, thereby eliminating redundant information, improving the accuracy of feature aggregation, and further improving the target detection and tracking capability in a multi-view scene.

The invention discloses an urban international propagation influence multi-source data integration and structured storage method, which is realized based on a five-layer decoupling architecture originally embedded on a large language model. The method comprises the following steps: S1, collecting multi-source heterogeneous data such as social media and news, retaining original data, collecting metadata, and carrying out partitioned storage; s2, cleaning and annotating the data through a large language model, and generating a semantic-rich record containing semantic annotations and quality confidence fields; s3, constructing a topic model containing details and pre-aggregated data, and adding a data quality watermark; s4, dynamically generating a materialized view based on the query hot spot, and executing cross-source intelligent bit complementing when the data source is missing; and S5, packaging data product packages with different granularities and providing a data service interface. According to the method, efficient conversion of data from an original state to semantic enhanced assets is realized, and the method has the advantages of low cost, high precision and high adaptability.

The invention provides a stream view classification method and device, terminal equipment and a medium. The method comprises the following steps: receiving stream view data arriving in a stream sequence; inputting the data view arriving at the current moment into a pre-constructed evidence deep neural network model, and generating a current evidence vector through non-negative activation transformation; performing linear transformation on the current evidence vector by using a preset label transfer matrix to obtain a noise adaptive evidence vector; calculating a group of Dirichlet distribution parameters based on the noise adaptation evidence vector, and generating subjective logic opinions of the data view according to the Dirichlet distribution parameters; synthesizing the subjective logic opinions and the comprehensive opinions output at the last moment through a fusion operator based on subjective logic definition to obtain new comprehensive opinions; and determining a final classification identifier of the entity according to the belief quality vector in the new comprehensive opinion. According to the invention, the accuracy of stream view classification can be improved.

The invention relates to the technical field of electric energy substitution, and discloses an electric energy substitution potential user contribution degree evaluation method and device, equipment and a storage medium, which are used for solving the technical problem of insufficient reliability of electric energy substitution potential user contribution degree evaluation. The method comprises the following steps: acquiring view data of multiple types of samples of electric energy replacement potential users under multiple views; inputting view data in the aspects of power consumption behaviors, industry attributes, economic potential and equipment conditions into a pre-trained cross-view affinity and rejection coding model, and outputting hidden codes; inputting the implicit codes generated by the view data into a pre-trained discriminator, and calculating a reliability score of each view data; calculating a multi-view reliability score corresponding to each sample according to the reliability score; inputting the implicit codes into a pre-training classifier to obtain a preliminary classification result; and calculating contribution degree classification of the electric energy replacing potential users by adopting the preliminary classification result and the multi-view reliability score.

The invention discloses a data authorization method and device, a program and a storage medium. The method comprises the following steps: responding to a received data authorization application form, and generating an application number; storing the application number, the applicant information and the application authorization time period in an application form information table; determining a mechanism field name corresponding to each target data table according to each target data table and a preset mechanism information table; determining an allowable parameter set of each target data table based on respective mechanism field names and respective expected parameter sets of the plurality of target data tables; according to the application number and an allowable parameter set of each target data table, creating view data and a view name of each target data table, and storing the view data and the view name in a data authorization information table; and in response to an access request for the target data table sent by the front end, based on the access request, the application form information table and the data authorization information table, determining effective view data and a view name of the target data table, and sending the effective view data and the view name to the front end. According to the invention, the security of data authorization can be improved.

The invention provides an incremental multi-view clustering method and system based on anchor point alignment, and belongs to the technical field of data analysis mining, and the method comprises the following steps: incrementally receiving a multi-view data stream, and uniformly mapping each view data to a public feature space by using a Gaussian kernel function; initializing a consensus anchor map and an anchor point feature matrix for the first view; for the tth view which arrives subsequently, taking a historical consensus matrix as guidance, solving a joint optimization problem, and synchronously learning an anchor map matrix, an anchor point feature matrix and a permutation matrix for alignment of the tth view; the joint optimization problem minimizes the sum of a data reconstruction error, an anchor image alignment error and a key semantic reconstruction error so as to ensure cross-view semantic coherence; according to the method, the consensus is solved and updated by adopting an alternating optimization strategy, and finally the consensus anchor graph is clustered, so that efficient and accurate clustering of flow type multi-view data is realized, and the method has linear time complexity and excellent clustering performance.

The invention discloses an xBOM-based airplane multi-dimensional visual view rapid construction method and system, and belongs to the field of airplane multi-dimensional visual views. The objective of the invention is to solve the problem that professional view generation is not flexible and low in efficiency due to the fact that an existing xBOM top layer structure is fixed and node permissions cannot be customized. The method comprises six steps of creating a view, constructing a view structure, defining a data collection rule, designing an authority control rule, publishing the view and updating view data. The system comprises a view construction unit, a view data updating unit and a view viewing unit. According to the method, personalized view design is supported, cross-platform data extraction and convergence can be realized, differentiated authority control is realized, the view can be embedded into a multi-service platform, high efficiency, safety and flexibility are realized, and the method is suitable for product structure view construction of multi-service scenes in aircraft scientific research and production.

The application provides a micro-service calling method and device, a server and a storage medium. It relates to the technical field of micro-service. The method comprises the following steps: in response to receiving the configuration information sent by each micro-service, configuring the corresponding database, and sending the corresponding database to each micro-service. In response to receiving the micro-service calling request, the corresponding calling information is determined. When the first transaction code exists in both the global addressing view database and the consumption transaction code list of the first micro-service, the target publishing environment identifier corresponding to the first transaction code is obtained from the global addressing view database, the registered component information corresponding to the target publishing environment identifier is obtained from the transaction configuration database, the target service instance corresponding to the first transaction code is addressed in the second micro-service based on the registered component information, the micro-service calling request is processed through the target service instance, and the processing result sent by the second micro-service is received and sent to the first micro-service, thereby solving the problem of low micro-service calling efficiency in the prior art.

Embodiments of the present application provide an application rollback method and device and electronic equipment, and relate to the technical field of computers, to improve the diagnosis efficiency of abnormal problems and user satisfaction. The method comprises: obtaining session data of the current session of the application, the session data representing a set of interaction behaviors of an interaction object with the application within the current time period, the session data including session view data and session basic data, the session view data including native view data and H5 view data, and the session basic data including multidimensional session data and associated information of the multidimensional session data; storing the session basic data and providing an API interface supporting multidimensional session data queries, so that a target object can query the multidimensional session data through the associated information; and synchronously rendering playback content of the native view and the H5 view according to the session view data through a front-end player, so that the target object can locate abnormal problems based on the playback content.

The invention belongs to the technical field of computer data clustering, and discloses a multi-view clustering method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-view data to be clustered, the multi-view data being one of an image, a video, an audio and a text; determining a Laplacian matrix, a depth decomposition matrix and a global consensus embedding matrix of each view according to the original data matrix of the multi-view data; performing hypergraph regularization depth non-negative matrix factorization on each layer of the original data matrix through the depth factorization matrix to obtain a factorization result; performing high-level semantic alignment and consensus expression fusion on a decomposition result through a global consensus embedding matrix to obtain a final layer expression of the view; determining a clustering indication matrix represented by a final layer according to the objective function; the clustering indication matrix is processed through a k-means clustering algorithm, a multi-view clustering result is obtained, and the accuracy of multi-view clustering is achieved through the technical scheme.