312 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.

A method for identifying a log file for resolution of a fault of a medical imaging device, is provided. The method includes: obtaining log file browsing data describing one or more log files of the medical imaging device already viewed by a user to resolve the fault of the medical imaging device; obtaining problem data describing the fault of the medical imaging device; inputting the problem data and the log file browsing data to a machine learning algorithm, the machine learning algorithm being trained to predict, for each of a plurality of log files of the medical imaging device, and based on the browsing data, a resolution probability indicating a likelihood that the log file will assist in resolution of the fault of the medical imaging device; obtaining a prediction result from the machine learning algorithm in response to the inputting, the prediction result comprising a resolution probability for one or more of the plurality of log files of the medical imaging device; and identifying a log file for resolution of the fault of the medical imaging device based on the obtained prediction result.

A computer-implemented method includes converting tabular data to a text representation, generating metadata associated with the text representation of the tabular data, outputting one or more natural language data descriptions indicative of the tabular data in response to utilizing a large language model (LLM) and zero-shot prompting of the metadata and text representation of the tabular data, outputting one or more summaries utilizing the LLM and appending a prompt on the one or more natural language data descriptions, selecting a single summary of the one or more summaries in response to the single summary having a smallest validation rate, receiving a query associated with the tabular data, outputting one or more predictions associated with the query, and in response to meeting a convergence threshold with the one or more predictions generated from the one or more iterations, output a final prediction associated with the query.

The invention provides an iron tower voiceprint intelligent detection method and system, and belongs to the voiceprint detection technology. The method comprises the following steps: firstly, outputting a sweep frequency signal in a specific frequency range, collecting iron tower vibration response data, extracting candidate frequency through spectral analysis, and dynamically adjusting excitation frequency by using a gradient descent algorithm; hardware filtering is carried out on collected sound signals, frequency band signals related to excitation frequency are reserved, and space beam forming is carried out through a microphone array to enhance iron tower voiceprint signals. Separating iron tower vibration components from the mixed signals by utilizing independent component analysis, performing time-frequency analysis, extracting an energy ratio of a specified frequency band by adopting wavelet packet transformation, and performing deep learning processing in combination with a one-dimensional convolutional neural network to generate an energy ratio and zero-crossing rate feature vector; and finally, inputting the feature vectors into a support vector data description model, setting an initial threshold value and dynamically adjusting the initial threshold value, thereby realizing graded judgment of bolt looseness and improving detection efficiency and stability.

The invention belongs to the technical field of system operation and maintenance monitoring, and particularly discloses a system health degree assessment method and system, a terminal and a medium. Comprising the following steps: collecting log stream data of a target system and a heterogeneous subsystem to obtain log feature data; inputting the log feature data into an exception detection engine of a preset training model, and performing exception judgment on the log feature data based on the exception detection engine to obtain an exception detection result; according to the anomaly detection result and the operation index of the target system, performing multi-dimensional comprehensive calculation on the operation index to obtain a system health degree score; and performing processing calculation according to the log feature data, the anomaly detection result and the system health degree score, and outputting a system operation state analysis result. By introducing the anomaly detection model based on the support vector data description and the improved grey wolf optimization algorithm, efficient analysis and anomaly recognition of complex heterogeneous system logs are realized, and the system stability and the operation and maintenance efficiency can be improved.

The invention relates to a data transmission method and device, electronic equipment, a chip and a medium, and the method comprises the steps: obtaining a target address of a target storage region in a first memory corresponding to an application processor; according to the target address, storing at least one piece of target data acquired by the modem into a target storage area through an interface between the modem and the application processor; determining a data description sequence according to each piece of target data in the target storage area; the data description sequence comprises data description of each piece of target data; storing the data description sequence into a target storage area; the data description sequence is used for indicating a processing sequence of at least one piece of target data; according to the method and the device, read-write operation processing on the second memory corresponding to the modem is avoided, the bandwidth requirement on the second memory is reduced, and the power consumption is reduced.

The embodiment of the invention provides a test case generation method and device, electronic equipment and a storage medium. The method comprises the steps that a requirement document and a technical document are analyzed according to the user story dimension, a scene set under the user story dimension is obtained, the scene set comprises multiple scene cases, each scene case comprises a set of data description information, the set of data description information is used for describing data details of multiple pieces of data needed by the scene case, and the data details of the multiple pieces of data are stored in the scene set; the data details of the data comprise interfaces corresponding to the data; generating a test data pair corresponding to the scene case according to the interface information of the interfaces corresponding to the data required by the scene case and the data description information included in the scene case; and generating a test case corresponding to the scene case based on the scene case and the test data pair corresponding to the scene case. Through the method, the test case can be automatically generated according to the demand document and the technical document corresponding to the demand document, and the generation efficiency and accuracy of the test case are greatly improved.

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.

Systems and methods use dynamically generated validation rules. These validation rules comprise a first validation rule portion that is generated using a standardized validation process (e.g., corresponding to a standardized schema) and a second validation rule portion that is generated using a validation process selected based on a non-standardized schema that is specific to a respective asset type of the plurality of respective asset types.

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 discloses a hidden group relationship mining method and device based on a social platform and a medium, and relates to the technical field of big data. The method comprises the steps of collecting to-be-mined tweet data description information; respectively calculating weights corresponding to the current tweet release time, the current tweet source, the current tweet forwarding information, the current comment information and the current mention user information through a multi-dimensional weight calculation method, and generating a target multi-dimensional fusion weight in combination with a multi-dimensional weight fusion calculation method; obtaining a target weighted graph network through a graph network construction method according to the target multi-dimensional fusion weight; and performing community division processing on the target weighted graph network, and determining a hidden group relationship mining result in combination with the current tweet content. The problems that static data analysis is relied on, multi-dimensional user behavior characteristics cannot be fully considered, and hidden relations or coordination behaviors among users cannot be revealed are solved, and the accuracy and interpretability of hidden group relation mining results are improved.

An apparatus and methods for determining a resource distribution are provided. The apparatus includes a processor and a memory connected to the processor. The memory contains instructions configuring the processor to receive a first datum from a user device, where the first datum describes a first activity pattern of the user device, receive a second datum from a client device, where the second datum describes a second activity pattern of the user device, and to retrieve a third datum from the memory, where the third datum describes a prioritization value for adjusting the first activity pattern to match a threshold value. The processor may classify data to a label based on the prioritization value, where classifying includes modifying a sequence of activities in the first activity pattern and adjusting the second activity pattern.

Various examples are directed to systems and methods for utilizing relationship data. A computing system may receive a time-dependent query against a knowledge graph data structure. The computing system may access confirmation data from the knowledge graph data structure, the confirmation data describing a first plurality of confirmation points-in-time at which the first test relationship is true. The computing system may determine that at least one of a beginning or an end of a first time period associated with a test relationship of the time-dependent query is not defined by the knowledge graph data structure. The computing system may determine a response to the first time-dependent query indicating a veracity of the test relationship at a test point-in-time using the first plurality of confirmation points-and-time.

Data replication between databases is provided. The method comprises receiving, through an API integrated into a data replication tool, a request from a system of record (SoR) application for a replication setup and initialization. A number of data instances are imported through the API from a source database associated with the SoR. A manager engine integrated into the data replication tool initiates a separate replication process for each data instance. The manager engine is configured to support multiple data instances on a single virtual machine instance. If a difference between a data description language (DDL) of the replicated data instances and a DDL of a target database is detected, the DDL of the replicated data instances is automatically changed to the DDL of the target database. The replicated data instances are then exported to the target database.

A peripheral device workspace cloud orchestrator server includes a network interface device to receive detected peripheral device enrollment data describing peripheral device nodes to be registered with the peripheral device workspace cloud orchestrator server and hardware device operational policies describing how the registered peripheral device nodes are to operate. The hardware processor executes computer code of a cloud manageability orchestrator module to create and store, on a peripheral device workspace database, peripheral device workspaces with each of the peripheral device workspaces having a peripheral device workspace identification value, wherein the peripheral device workspaces are defined by a manifest of detected peripheral device nodes operatively coupled to an anchor node information handling system and a physical location of the anchor node information handling system.

Computer-implemented methods for efficiently deploying foundation models on resource-constrained edge devices are disclosed herein. Aspects include receiving a text-based service request for an artificial intelligence (AI) model for an edge client. Aspects further include generating model and data descriptions using the text-based service request. Aspects also include generating an AI task capacity profile. Aspects further include selecting a resource-optimal AI model for deployment on the edge device based on the AI task capacity profile.

The invention provides a system and method for geological survey recording and automatic mapping and a medium, and relates to the technical field of geological survey, and the system comprises one or more of a field outcrop system and a drilling core system; the field outcrop system is used for recording field outcrop data and automatically generating a field outcrop histogram according to the field outcrop data; the well drilling core system is used for recording well drilling core data and automatically generating a well drilling core histogram according to the well drilling core data. Thus, the system can be applied to mobile electronic equipment, geological survey recording can be efficiently and timely carried out, the problem of incomplete data description caused by forgetting and omission is solved, the system can automatically generate a field outcrop histogram and a drilling core histogram, the convenience and efficiency of histogram drawing are improved, and the system is suitable for popularization and application. The use experience of geologists is improved, and the geological description is more accurate.

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.

The invention belongs to the technical field of intelligent control, and particularly relates to a cooperative robot injection molding defect intelligent detection control method which comprises the following steps: acquiring surface point cloud data of an injection molding part through a three-dimensional vision sensor; acquiring injection molding process parameters in real time, wherein the injection molding process parameters comprise mold temperature, mold locking force and melt flow velocity; a depth support vector data description model is adopted to calculate an abnormal score, and when the abnormal score exceeds a threshold value, defect classification is triggered; classifying the defects through the meta-learning prototype network model and outputting a process adjustment suggestion; the adjustment suggestion is fed back to an injection molding machine control system to complete closed-loop control; according to the scheme, a grading processing mechanism from bimodal acquisition and defect classification from anomaly detection is realized, a closed-loop process adjustment system is formed, namely, a closed loop from detection to classification, suggestion and execution is formed, the technical problems of defect identification lagging and process adjustment isolation in a traditional detection method are solved, and the detection efficiency is improved. And intelligent tracing adjustment control of early-stage processing parameters is realized.

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 relates to a production process-oriented data semantic model construction method and a data storage method thereof, and relates to the field of data modeling. Under the condition that enterprise production needs multi-system collaborative data calling, data of each system is collected, the collected data comprises metadata and a data ontology, mapping processing is conducted on the collected metadata information through a data dictionary, and semantic description is unified; meanwhile, endowing the data ontology with a unique identifier, and mapping the data into a data semantic model according to a data dictionary; the semantic model is divided into a data description layer, a data state description layer and a data context description layer from top to bottom; uniform data semantic modeling is performed on the operation data of the industrial internet, so that massive heterogeneous operation data can be described and managed in a unified manner, and the problem that data between different systems or between the system and equipment is not intercommunicated is solved.

The invention relates to a data communication method and device for automatic driving, electronic equipment and a storage medium, relates to the technical field of automatic driving, and is used for solving the problem of low data transmission efficiency in related technologies. The method comprises the following steps: preprocessing sensor data collected by a vehicle sensor to obtain first UDL data of a unified data description language, wherein the first UDL data comprises a time-space stamp, sensor coordinate system information, a data quality factor and the preprocessed sensor data; based on the value score of the first UDL data, second UDL data corresponding to the current transmission window is determined from the first UDL data, the data size of the second UDL data is smaller than or equal to the data size supported by the current transmission window for transmission, the second UDL data are the first N UDL data with the highest value score in the first UDL data, and N is a positive integer; and the encrypted second UDL data is sent to the cloud server in the current transmission window, and the second UDL data is used for assisting the cloud server to make an automatic driving decision, so that the data transmission efficiency is improved.

Devices, systems, and methods for predicting fire spread in a facility are described herein. In some examples, one or more embodiments include a computing device comprising a processor and a memory having instructions stored thereon which, when executed by the processor, cause the processor to receive an indication of a fire detected by an event device of a plurality of event devices installed in a facility, sort the plurality of event devices into a plurality of priority levels based on a determined likelihood of fire spread to each of the plurality of event devices, receive data collected by the plurality of event devices during the fire, and provide, via an interface, graphical information descriptive of a spread of the fire based on the received data.

The invention discloses a fault detection method based on recursive feature elimination, and the method comprises the steps: carrying out the standardized preprocessing of collected single-class data, obtaining training data, calculating the Gaussian kernel of the training data, and carrying out the learning of the training data, a recursive feature elimination algorithm described by density-induced support vector data based on a dual target is utilized to sort function values of the dual target to obtain a feature sorting set, and 10-fold cross validation is performed on the feature sorting set to obtain an optimal feature subset related to a fault; extracting an optimal feature subset from the training data to obtain recombined training data, and learning to obtain a model for detection; and extracting test data according to the optimal feature subset, and performing fault judgment by a model for detection. The invention also discloses a computer storage medium and a program for implementing the method. According to the invention, faults can be rapidly detected, and the fault detection accuracy is improved.

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.