214 results about "Data description" patented technology

The invention relates to the technical field of ophthalmology optics, in particular to a myopia control lens optical system integrating ray tracing and aberration correction, which comprises a wavefront phase acquisition module, a wavefront feature statistics module, a ray tracing simulation module, a microstructure form regulation and control module and a feedback control module. According to the method, accurate identification and datamation description of wavefront change under a dynamic vision condition can be realized by acquiring an eyeball wavefront phase matrix, performing gridding partition analysis and extracting local phase offset amplitude and time sequence characteristics, and a light propagation path model is constructed by combining statistical characteristics of an abnormal wavefront region; the self-adaptive compensation is carried out on the local focus deviation, and the spatial distribution optimization of the refractive index and the curvature is further realized based on the microstructure form regulation and control, so that the real-time matching of the optical parameters and the wavefront state is maintained during the processing and use of the lens, and therefore, the imaging quality is stabilized, the visual definition is improved, and the myopia control reliability is enhanced.

The invention relates to the technical field of information and data processing, in particular to an enterprise digitalization-oriented data management and application method, which comprises the following steps of: acquiring multi-source heterogeneous data and recording metadata; establishing a cross-department management system to standardize the treatment process; building an asset catalog based on the business section and the data field classification; formulating unified data description of a multi-level data standard system; designing a quality control rule to filter low-quality data; constructing a business index system and embedding the business index system into a business system; identifying a cross-system and cross-type association mode through a metadata association technology and a rule engine; and dynamically adjusting the treatment process by adopting a multi-objective optimization algorithm in combination with service feedback and treatment parameters. According to the method, the defects of existing data governance in comprehensiveness, depth and business adaptability are overcome, standardized governance and potential value mining of multi-source data are achieved, and effective data support is provided for intelligent decision making of enterprises.

The invention provides a method and a device for generating a customized report of an SAAS (Software as a Service) system based on dynamic template configuration. The method comprises the following steps: acquiring a customized visual report template for realizing business data decoupling; editing the report template based on a preset editor, and generating a template configuration file; analyzing a structured marker in the template configuration file to generate a metadata description file; querying a business database through a unified data interface according to a data requirement defined by the metadata description file, and obtaining and assembling customized business data corresponding to the tenant ID; and at least based on the customized business data, controlling a rendering process through a dynamic position calculation algorithm, and dynamically filling the data to a corresponding position of the report template to generate a customized report. According to the method, the problems of code redundancy and difficulty in maintenance of the SAAS multi-tenant report are solved, zero code configuration is realized, and report generation efficiency and tenant personalized adaptation capability are improved.

The invention discloses a single-classification industrial control system anomaly detection method based on double-view information bottleneck fusion, which belongs to the technical field of industrial control system anomaly detection and comprises the following steps of: in a training stage, simultaneously constructing a time sequence view and a pseudo image view; respectively extracting characterization through a time dynamic encoder and a pre-trained visual backbone; performing cross-view compression in the variational information bottleneck fusion module and establishing projection branches only depending on sequence representation; a normal subspace is learned around a center vector, an accurate hypersphere is established to describe normal data, and a center distance is used as an abnormal score and a quantile threshold is used to complete discrimination; in the detection stage, online recognition can be performed only by inputting a time sequence view. According to the method, dual-view prior and variational information bottleneck are fused, end-to-end collaborative optimization is carried out on the dual-view prior and the variational information bottleneck and deep support vector data description, an accurate decision boundary can be established under the condition of a small number of samples, and a solution for industrial control system anomaly detection under the condition that the samples are limited is provided.

The invention provides a multimedia operation state early warning method and system, and relates to the technical field of multimedia, and the method comprises the steps: collecting multi-dimensional operation parameters of a terminal device and a cloud service platform in real time, carrying out the preprocessing of the multi-dimensional operation parameters, and obtaining the preprocessed operation parameters; constructing a feature vector of a state monitoring index based on the preprocessed operation parameters, and generating an initial threshold evaluation result containing a normal working condition range and an abnormal judgment standard through a support vector data description method based on the feature vector; and extracting depth state features based on the operation parameters of which the alarm is not triggered in the initial threshold evaluation result, and identifying a potential abnormal evolution mode by analyzing the change trend of the depth state features to obtain a predictive early warning signal. According to the invention, a full-process early warning mechanism of the multimedia operation state is constructed, so that a precise closed loop from monitoring and evaluation to fault positioning and co-processing is realized.

The invention relates to a land investigation, analysis and comparison method and system, and belongs to the technical field of land investigation and data analysis, and the method comprises the steps: receiving an investigation and analysis instruction, and obtaining multi-source data of an investigated object, the multi-source data at least comprising a remote sensing image and land utilization attribute information; preprocessing the multi-source data to obtain processed data; based on a preset comparison logic, comparing the processed data with specified reference data, and generating and outputting a comparison result for an investigator to know; wherein the specified reference data refers to multi-source data of the investigated object with the same description as the processed data; the comparison result is at least used for representing the deviation between the processed data and the specified reference data, and the deviation is used for representing the change condition of the investigated object. According to the invention, the accuracy and high efficiency of land investigation and data analysis can be improved.

The invention discloses a data enhancement method and device based on multiple agents, and the method comprises the steps: a planning agent obtains task description and data description of sample data, and the data description comprises data distribution statistics and data feature details; the planning agent generates an initial zero sample thinking chain according to data distribution statistics and data feature details of the sample data, and the zero sample thinking chain comprises an agent type and an execution strategy called by each execution level; planning an execution strategy of the agent based on a zero sample thinking chain, determining an execution level of calling an enhancement strategy agent, and generating a new sample; and the planning agent evaluates the enhancement effect of the new sample on the downstream deep learning model, if the enhancement effect does not reach a set condition, the execution hierarchy sequence of the zero sample thinking chain and the data enhancement step in the execution hierarchy are adjusted, and the step of determining the execution hierarchy of the agent for calling the enhancement strategy based on the zero sample thinking chain is returned to the step of determining the execution hierarchy of the agent for calling the enhancement strategy based on the zero sample thinking chain. The efficiency of the deep learning model is improved.

This invention provides a method and apparatus for trajectory prediction based on a pre-trained large language model, relating to the field of artificial intelligence. The method includes: acquiring historical trajectory data of a target aircraft and performing data preprocessing to obtain a trajectory sequence; constructing prompt words and inputting the prompt words into a pre-trained large language model to obtain word embedding representations; the prompt words contain a description of the trajectory prediction task and a description of the trajectory data; encoding the trajectory sequence to obtain an encoded trajectory sequence and a trajectory position sequence, and segmenting the trajectory position sequence into words; constructing a spatiotemporal graph of the trajectory sequence based on the encoded trajectory sequence and extracting spatiotemporal dependency feature representations of the trajectory sequence; fusing the word embedding representations and the spatiotemporal dependency feature representations using the pre-trained large language model to generate a trajectory information feature representation; and inputting the trajectory information feature representations into a trained prediction module to obtain predicted values ​​of the longitude, latitude, and altitude of the target aircraft at future times.

A vulnerability documentation system detects vulnerabilities having outdated or undocumented formatted descriptions for corresponding remediation actions. A web crawler crawls the Internet for configuration data for software / firmware affected by the detected vulnerabilities and descriptive content for the remediation actions. The vulnerability documentation system prompts and LLM with a prompt for each detected vulnerability comprising instructions to generate a formatted description for remediation actions using the crawled configuration data / descriptive content. The vulnerability documentation system then populates natural language descriptions of remediation actions from the formatted descriptions and pushes the natural language descriptions to affected devices.

The invention provides an anomaly detection method based on deep multi-ellipsoid support vector data description, which can be applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring a multi-channel time sequence signal generated by equipment to be detected in operation; inputting the multi-channel time sequence signal into a pre-trained deep multi-ellipsoid support vector data description model to obtain an abnormal score of the to-be-detected equipment at the current moment; and determining an anomaly detection result according to the anomaly score, wherein the depth multi-ellipsoid support vector data description model is configured to extract high-dimensional features of the multi-channel time sequence signals based on a depth feature extraction network, and the high-dimensional features are used for representing a coupling relationship between the multi-channel time sequence signals and the operation state of the to-be-detected equipment; and calculating the minimum value of the square of the mahalanobis distance from the high-dimensional feature to the plurality of hyper-ellipsoid regions as an abnormal score. The invention further provides an anomaly detection device based on depth multi-ellipsoid support vector data description.

The invention discloses a shaft cage guide collision monitoring method based on artificial intelligence acoustic recognition. According to the system and the method, the pipeline operation sound is continuously collected in real time through the acoustics collecting device, the data cleaning, feature processing and model recognition processes of the sound are rapidly carried out, the abnormal collision can be rapidly alarmed in a short time, and the safety of shaft operation is effectively improved. The background noise and invalid signals in the sound are effectively filtered by cleaning the multi-index data of the original sound signal, and the characterization capability and the distinguishing capability of the feature vector to the cage guide collision sound are enhanced by calculating a plurality of acoustic features, so that the accuracy of the trained model in a complex shaft environment is improved. The normal acoustic mode and the abnormal acoustic mode are distinguished through the support vector data description method, a large number of abnormal sample training is not needed, the method is suitable for the situation that abnormal collision samples are single and normal samples are complex and diverse in an actual shaft scene, and good environment adaptability and expansibility are achieved.

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: performing natural language processing to process a text string of a user, wherein the text string specifies characteristics of a container image to be built; processing, with use of natural language processing, instances of text-based data that describe respective ones of a plurality of container images stored within a container image repository; selecting, in dependence on a result of the performing natural language processing, and the processing, a base image from the plurality of container images; and presenting prompting data to the user that prompts building of a new container image, wherein the prompting data references the base image.

The invention discloses a marine geological environment-oriented multi-source heterogeneous data acquisition quality control method and system, and belongs to the technical field of marine exploration. According to the method, a unified data description model is constructed, and the data structure and the space-time reference of the sensor are standardized; executing an adaptive collaborative acquisition strategy based on a preset task, and dynamically adjusting sensor parameters according to data characteristics to realize multi-source collaboration; and through multi-dimensional verification of integrity, range, time sequence stability, association consistency and the like, real-time evaluation and quality marking are carried out on the acquired data, and the data quality is guaranteed from the source. And the processed data and metadata are packaged into a standardized data product, so that the data usability and the reliability of system operation are ensured. By means of the method, pre-processing of quality control is achieved, the follow-up cleaning workload is reduced, support is provided for efficient data fusion analysis and scientific interpretation, and the automation level and the intelligent level of marine geological survey are improved.

The invention discloses a unified unsupervised depth forgery detection method based on prototype guidance and double hyperspheres, and the method comprises the following steps: S1, extracting visual artifact features generated by depth forgery, and achieving the generation of pseudo labels through the clustering of a Gaussian mixture model; s2, performing comparative learning through a category prototype of momentum updating; and S3, realizing effective fusion of a feature space and a geometric decision by respectively constructing independent hyper-spheres for real and forged samples, and constructing a dual-depth support vector data description framework. The system has the beneficial effects that the system is composed of three core modules: a visual artifact feature-based pseudo label generator provides a reliable supervision signal, and the visual artifact feature-based pseudo label generator provides a visual artifact feature-based pseudo label description framework; a prototype guided contrast learning (PGCL) module enhances the discrimination capability through a prototype of momentum update, and a dual-depth support vector data description (Dual-DeepSVDD) module constructs a dual-hyperspherical decision boundary of true and false samples, thereby realizing effective integration of feature learning and geometric decision.

An image processing method includes: obtaining an input image comprising a preset object, and obtaining text data; encoding the text data to obtain a text embedding feature; performing image feature extraction on the input image according to a plurality of predefined data dimensions, to obtain identity embedding features of the preset object in the data dimensions; and fusing and recognizing the identity embedding features of the preset object in the data dimensions and the text embedding feature by using an interlaced condition mechanism, to generate an output image that includes the preset object and that includes a feature described by the text data.

The invention discloses a geospatial data geometric topological relation evaluation method and system based on a large language model, and belongs to the technical field of spatio-temporal information processing, and the method comprises the following steps: S1, data preprocessing and model construction: processing a training data set containing geospatial data and a corresponding geometric topological relation thereof, and constructing a model; the geometric topological relation of the geographic space data is coded into text or vector representation which can be understood by a large language model; the coded training data is utilized to finely adjust a pre-trained large language model, so that the pre-trained large language model can evaluate a topological relation according to input geographic space data description; s2, geographic space data input and feature extraction; and S3, large language model reasoning and topological relation evaluation. According to the method, by combining the language understanding ability of the large model and the spatial topology calculation technology, intelligent and high-precision evaluation of the geographic spatial data geometrical relationship is achieved, and a brand-new and self-adaptive evaluation technology is provided for the field of geographic spatial data processing.

The invention discloses a data encapsulation method, a data rendering method and a related product. The data packaging method comprises the steps that a data file is sent, the data file comprises rendering space parameters and data description parameters, the rendering space parameters are used for describing attribute information of a rendering space, and the data description parameters are used for describing three-dimensional Gaussian splash 3DGS; the data description parameters and the rendering space parameters are used to render a digital object in the rendering space. The method is used for enriching the rendering effect and improving the rendering quality.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for indexing actions for resources. In an aspect, a method receiving, for each resource a plurality of resources that are each accessible by a user device, action data describing actions supported by each resource, each action being an action that can be performed by the user device processing the resource; for each resource: determining, from the action data, an action type for each action supported by the resource, determining, from the action data, action parameters for each action supported by the resource, and indexing the action data, action types and action parameters for the resource in an index that is searchable by a search engine.

The invention provides a transformer state detection method and device and electronic equipment, and relates to the technical field of transformer state detection. The method comprises the following steps: acquiring multi-dimensional target detection features; the target detection feature is extracted and generated based on multi-dimensional operation state data of a to-be-detected transformer; inputting each target detection feature into a multi-head contraction graph attention network trained to be convergent, and generating a target fusion feature irrelevant to a load condition; and generating a transformer state detection result according to the target fusion feature and the support vector data description detector trained to be converged. According to the method, target detection features of all dimensions are input into a multi-head contraction graph attention network, target fusion features irrelevant to load working conditions are generated, and meanwhile, a transformer state detection result is generated according to the target fusion features and a support vector data description detector trained to be converged. Therefore, the influence of different working conditions on transformer state detection can be reduced, and the accuracy of transformer state detection is improved.

Systems and methods described herein provide for identifying a skillset to augment in a learner agent system; selecting, from a pool of candidate instructor agent systems associated with a multiagent training platform, one or more instructor agent systems based on instructor skills documentation data descriptive of respective skill capabilities of the candidate instructor agent systems; obtaining at least one training task representative of the skillset; generating a learner solution to the at least one training task using the learner agent system; generating instructor feedback data using the one or more instructor agent systems and the at least one training task, the instructor feedback data descriptive of a qualify of the learner solution; and training the learner agent system based on the instructor feedback data.

Providing a service to obtain attributes of memory frames includes receiving a query indicating a range of memory locations from a program. The method also includes identifying, in response to the query, page frame table data for one or more real memory frames corresponding to the range of memory locations. The method also includes generating, based on the page frame table data, defined output data describing attributes of the one or more real memory frames.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for indexing actions for resources. In an aspect, a method receiving, for each resource a plurality of resources that are each accessible by a user device, action data describing actions supported by each resource, each action being an action that can be performed by the user device processing the resource; for each resource: determining, from the action data, an action type for each action supported by the resource, determining, from the action data, action parameters for each action supported by the resource, and indexing the action data, action types and action parameters for the resource in an index that is searchable by a search engine.

The disclosed technology provides solutions for optimizing a hardware configuration of an autonomous vehicle (AV) using continuous learning machine. Processes of the disclosed technology can include steps for collecting AV sensor data, receiving hardware profile data relating to the AV sensor data, wherein the hardware profile data describes a hardware configuration of an AV, and providing the AV sensor data and the hardware profile data to a continuous learning machine model of an AV stack. The process can further include receiving an output of the continuous learning machine model based on the AV sensor data and the hardware profile data and determining an updated hardware configuration to replace the hardware configuration based on the output of the continuous learning machine model. Systems and machine-readable media are also provided.

The invention relates to a method for providing a trained support function (15) for an image-monitored medical procedure on a procedure arrangement (31) comprising at least one technical procedure device (32, 35), at least one of which is a medical image acquisition device (33) for image monitoring of the procedure, to support an operator, comprising: - providing a pre-trained generative function (1) for several data types, including images, videos and structured text, which generates output data (2) related to the input data (3) from input data (3) of the data types, as an untrained support function, - training the support function with training data sets (8) related to the image-monitored medical procedure,wherein the training data sets (8) for several time points of an intervention duration of each intervention described in the training data sets (8) contain at least the following input data (14) for the support function: o at least one image data set (10) of the image monitoring with the image acquisition device (33) and o at least one structured text data set (11) that describes at least one technical operating action performed and / or at least one intervention-related intervention information, and wherein the training is carried out such that, for training data containing input data (14) up to a certain time point, the output data describe at least one recommended activity for the operator and the recommended activity includes the operator's next activity according to the training data and / or a best-rated activity when using reinforcement learning, - providing the trained support function (15).

The embodiment of the invention discloses a data label display method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: acquiring data description information; determining classification and grading information by using the data classification and grading information; generating a relation graph; displaying the relation graph on an information display page; obtaining an adjustment relation graph set after the information display page is adjusted; generating an atlas feature information set; for each piece of map feature information, executing a map generation step: generating a label set; adding the label graph layer to the graph layer under the corresponding adjustment relation graph to obtain an added label graph; and in response to determining that the corresponding label display component in the information display page is clicked, popping up the added label atlas. According to the embodiment, the explicit adjustment is performed on the relation graph on the information display page, and the tag set is generated under the graph feature information, so that the tag graph under explicit adjustment can be accurately generated by utilizing a graph layer adding technology.

The application discloses a kind of firewall log security detection method and system, it is related to the technical field of network firewall, comprising: the original log record data obtained by collecting to the firewall to be detected;Original log record data is preprocessed to obtain triad structured data;Triad structured data is converted into numerical value and is spliced to obtain log feature vector;Once detection is carried out through deep support vector data description to obtain once detection result, generate preliminary abnormal log subset;Based on preliminary abnormal log subset, event trace construction obtains XES preliminary screening event log, and the workflow network to be detected is generated by inductive mining algorithm;Consistency check is calculated by edit distance algorithm to obtain secondary detection score, and secondary judgment obtains security detection result.The application can accurately identify hidden security threats, can capture complex abnormal behaviors of combination and serialization, balance high efficiency, high accuracy and interpretability, effectively reduce false alarm rate.

A smart cart system accounts for edge cases in user interactions by leveraging sensor data and machine-learning models of a smart cart system. For example, a smart cart system uses sensor data to detect when a user removes an item from the smart cart system and presents content to the user on a display of the smart cart system based on the removed item. The smart cart system captures images of the storage area and applies an item identification model to the images to identify the item removed from the storage area. The smart cart system identifies a set of candidate items based on location sensor data describing a location of the smart cart system when the item was removed and computes presentation scores for each of the set of candidate items based on item data for each item the removed item.

The present application relates to a kind of data processing methods for expressway hazard source data correlation analysis, the method comprises the following steps: step S1, obtain expressway engineering construction demand data;Step S2, select the object to be evaluated and its belonging attribute set in combination with attribute topology theory, construct formal context by data classification and data description;Step S3, the formal context is preprocessed, and the adjacency matrix and the correlation matrix are obtained by conversion;Step S4, draw initial attribute topology graph using adjacency matrix, and based on initial attribute, constantly update until all causal relationships are analyzed, obtain the final data correlation visual inference result.Compared with prior art, the safety management target of expressway from construction period to operation period is considered as a whole in the present application, and the information silo in the safety management process is broken, which is convenient for the integrated management of expressway construction and maintenance.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive configuration information indicating that the UE is to report a number of machine learning processing units (MPUs) for each of a plurality of machine learning (ML) operations. The UE may transmit data indicating the number of MPUs for each of the plurality of ML operations and a maximum number of MPUs supported by the UE. Numerous other aspects are described.

Embodiments of the present specification provide a data query method, device, big data platform, equipment, medium and program product, wherein the data query method comprises: receiving a data query request, determining at least one data source corresponding to the data query request; determining a corresponding security query operator based on the data query request; obtaining data description information of the data source, determining a target query plan from candidate query plans of the at least one data source based on the data description information and the security query operator, and obtaining a query result corresponding to the data query request based on the target query plan, wherein the candidate query plan is used to indicate an execution order of the at least one data source under the security query operator. By using the security query operator and combining the data description information to dynamically select the target query plan, the operator execution order is optimized while ensuring data security, the efficiency bottleneck of traditional non-security scene operators in an encrypted environment is solved, and the effective improvement of query performance in a security protection scene is realized.