484 results about "Data description" patented technology

The invention discloses a communication network performance prediction method and system based on a large model, and relates to a communication network and artificial intelligence. The method comprises the following steps: acquiring performance time series data from a historical database of a network management system, preprocessing the data in an offline training stage, and generating data description according to a prompt structure; the preprocessed performance time sequence data are respectively input into a time sequence large model TimeGPT for model fine tuning and a patch reprogramming module to generate a group of new vector representations; converting the data description into a time sequence prompt prefix by using a pre-trained large language model, and splicing the time sequence prompt prefix with a vector output by a patch reprogramming module to form a new time sequence to represent the understanding ability of the pre-trained large language model training model to the time sequence data; in the real-time stage, the TimeGPT carries out anomaly and performance prediction according to input performance time sequence data, detection and prediction results are input into a pre-trained large language model, and efficient and accurate detection results, anomaly description, prediction results and potential fault description are output.

The invention provides a track prediction method and device based on a pre-trained large language model, and relates to the field of artificial intelligence. The method comprises the following steps: acquiring historical flight path data of a target aircraft and carrying out data preprocessing to obtain a flight path sequence; constructing a cue word, and inputting the cue word into a pre-trained large language model to obtain a word embedding representation; the cue word comprises track prediction task description and track data description; encoding the track sequence to obtain an encoded track sequence and a track position sequence, and performing word segmentation on the track position sequence; constructing a space-time diagram of the track sequence based on the encoded track sequence, and extracting space-time dependent feature representation of the track sequence; fusing the word embedding representation and the space-time dependent feature representation by using a pre-trained large language model to generate a track information feature representation; and inputting the track information feature representation into a trained prediction module to obtain predicted values of the longitude, the latitude and the height of the target aircraft at the future moment.

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 an LED spectrum fingerprint open set identification method based on Transform and support vector data description and related equipment. The method comprises the following steps: collecting an optical signal emitted by at least one LED device, converting the optical signal into spectral data, and dividing the spectral data into a training set and a test set; inputting the training set into a preset feature extraction network to carry out network training so as to learn and obtain a spectral feature vector; wherein the feature extraction network comprises a Transform branch, a CNN (Convolutional Neural Network) branch and a feature interaction module; based on the spectral feature vectors of the training set, constructing a hypersphere model by using an SVDD algorithm, and optimizing parameters of the hypersphere model to obtain an optimal hypersphere model; and inputting test samples in a test set into the trained feature extraction network to obtain corresponding spectral feature vectors, and inputting the spectral feature vectors of the test samples into the optimal hypersphere model for classification.

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.

Techniques for computer science, data science, data analytics, computer software, algorithmic analysis, and networked technologies for sourcing, procurement, manufacturing, and supply chain management in small-to-medium manufacturing (“SMM”) industries that involve processes whereby implementing user input and extracted image and text from engineering drawings may be used to map manufacturing requirements to a subset of manufacturers via natural language agent programs. More specifically, natural language agent programs may be implemented to apply input and extracted image and text from engineering drawings to a large language model (“LLM”). An example method may include receiving a user input including data representing requirements to manufacture a physical structure, concatenating an engineering drawing summary data, a shoptype description, and estimated part size instruction data to combine the at least two of the engineering drawing summary data, the shoptype description, and the estimated part size instruction data to generate a subset (e.g., a list) of qualified manufacturers (e.g., SMMs).

The present application provides an image processing method and apparatus, a computer device, a storage medium and a product. The method comprises: obtaining an input image containing a preset object, and obtaining text data; performing encoding processing on the text data to obtain text embedding features; performing image feature extraction processing on the input image according to a plurality of predefined data dimensions, so as to obtain identity embedding features of the preset object under each data dimension; and, by means of using an interlaced condition mechanism, performing fusion and recognition processing on the identity embedding features of the preset object under each data dimension and the text embedding features to generate an output image comprising the preset object and comprising the features described by the text data. The present application can extract multi-dimensional identity features and, according to the interlaced condition mechanism, fuse the extracted multi-dimensional identity features and text features to generate personalized images, thus improving the accuracy of the personalized images.

The invention relates to the technical field of aero-engines, and particularly provides an aero-engine gas path multi-combination fault data enhancement method and system, and the method comprises the steps: obtaining the original data of a gas path fault of an aero-engine, and dividing the original data into a plurality of groups of fault data according to the fault type; capturing time sequence characteristics of the fault data by using a TimeGAN model, and generating extended data based on the time sequence characteristics; mapping the fault data and the corresponding extended data to a potential space, and screening out a high-density data region from the potential space through KDE; and establishing a minimum hyper-sphere based on the high-density data region by using support vector data description, and extracting extended data in the minimum hyper-sphere to obtain optimal extended data. According to the invention, a large amount of fault data consistent with actual gas path multi-combination fault data distribution can be generated.

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 relates to a computing device (3) for an assistance system (2) for the automated lateral guidance of a vehicle (1), wherein the computing device (3) is configured to: recognize a traffic situation requiring an automated lane change maneuver, plan the automated lane change maneuver into a free gap (19), issue an offer signal to issue an offer for the automated lane change maneuver to a driver at an offer time and / or issue a steering signal to control a steering system (9) of the vehicle (1) to carry out the automated lane change maneuver at a lane change time, receive attention data, wherein the attention data describes a gaze direction and / or a head orientation of the driver,prior to issuing the offer signal and / or controlling the steering system (9), to recognize the driver's intention to perform the automated lane change maneuver based on the attention data and to adjust the offer time and / or the lane change time depending on the recognized intention of the driver.

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 relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses a business report generation method, which comprises the following steps: acquiring business data, inputting the business data into a text generation model, and generating a plurality of business description texts; inputting the business data into an image generation model to generate a plurality of business display images; performing association matching on the service description text and the service display image to obtain a text and an image which are associated with each other; and integrating the mutually associated text and image to generate a complete report. According to the method, the text generation model and the image generation model are combined, and the generated text and image are intelligently matched and integrated, so that the content richness and readability of the report are improved. Meanwhile, through automatic data processing and generation steps, the generation efficiency is improved, and the accuracy and consistency of data description and image display in the report can be ensured.

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 discloses a large-scale embedded index data desensitization method suitable for a forestry database, and belongs to the technical field of data desensitization, and the method comprises the steps: obtaining a forestry data description document D1, splitting the document D1 into sentences, and processing the sentences into embedded vectors; a user customizes a query sentence containing the desensitization word, and a query vector is generated; performing hash coding on the embedded vector and the query vector to obtain a corresponding hash function cluster; searching data to be desensitized through cosine similarity calculation; and inputting to-be-desensitized data and the selected desensitization method into the large language model to generate desensitized data, and replacing the corresponding sentences in the D1 to obtain a desensitized forestry data description document D2. According to the method, the input data is converted into the high-dimensional vector through the embedding layer, so that the semantic features of the data can be effectively expressed in the vector space, the retrieval speed and accuracy of large-scale data are improved by means of the locality sensitive hashing technology, the calculation overhead is reduced, and a technical guarantee is provided for real-time desensitization processing of a large-scale data set.

The invention discloses a database-based financial budget comparison method and apparatus, a device and a medium. The method comprises the steps of receiving a first budget mapping table which is sent by a financial system and indicates a mapping relationship among a financial item, a reference budget, first data description information and a first budget calculation demand including a first formula identifier; obtaining target data corresponding to the first data description information in a database; calling a first reference code corresponding to the first formula identifier in a formula code library, combining to obtain a first target code, and calculating to obtain a target budget based on the target data and the first target code; and comparing the target budget with the reference budget to obtain a target comparison result. According to budget calculation requirements of different financial items sent by a financial system, the target component freely combines the codes of the formula database as required to obtain the target code to calculate the target budget, and compared with a mode that developers write codes according to the budget calculation requirements to calculate the budget, the workload of the developers is reduced.

A method for verifying system behavior and correcting design flaws in a finite state machine (FSM)-modeled system or any other representational method that includes receiving from a user device via a model verification platform, data associated with the system design. The data describes states, state transitions, events, and outputs of the FSM-modeled system. The method includes searching the data for predetermined behavior of the FSM-modeled system, including predetermined state(s) and / or mode changes. The method includes flagging the predetermined behavior as a verified behavior and performing a control action in response to the verified behavior, including transmitting a notification to the user device that is indicative of the verified behavior, and a design recommendation where applicable. Instructions for the method may be recorded in a computer readable storage medium and executed by a processor to cause the model verification platform to perform the method.

An example method includes (a) obtaining sensor data descriptive of an environment of an autonomous vehicle; (b) obtaining a plurality of travel way markers from map data descriptive of the environment; (c) determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment; and (d) generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object.

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.

The invention relates to a cable partial discharge defect identification method based on a support vector machine, and belongs to the technical field of electrics. The problems that an existing method is low in recognition rate and prone to interference are solved. The method comprises the following steps: firstly, carrying out interference suppression on an acquired partial discharge high-frequency signal, wherein the interference suppression comprises narrowband interference suppression by subspace reconstruction and frequency slice wavelet transform white noise suppression based on ensemble empirical mode decomposition; then, carrying out support vector multi-classification by utilizing an optimized support vector data description algorithm, and constructing a plurality of classifiers; and finally, calculating the generalized distance from a test sample to each classifier, and making a decision, thereby achieving the recognition of the partial discharge defect. According to the method, the partial discharge defect recognition rate is effectively improved, the anti-interference capability is high, and a reliable technical means is provided for power cable insulation state detection.

An online concierge system receives unstructured data describing items offered for purchase by various warehouses. To generate attributes for products from the unstructured data, the online concierge system extracts candidate values for attributes from the unstructured data through natural language processing. One or more users associate a subset candidate values with corresponding attributes, and the online concierge system clusters the remaining candidate values with the candidate values of the subset associated with attributes. One or more users provide input on the accuracy of the generated clusters. The candidate values are applied as labels to items by the online concierge system, which uses the labeled items as training data for an attribute extraction model to predict values for one or more attributes from unstructured data about an item.

The invention relates to the technical field of data annotation, and particularly provides a multi-modal data annotation method and device, a storage medium, equipment and a product, and the method can comprise the steps: mapping data in each modal in multi-modal data to a same feature space, and obtaining a feature vector in each modal, the multi-modal data comprises image data, text data and audio data; determining a to-be-fused feature vector in each mode by evaluating the correlation degree between the feature vectors in each mode; fusing the to-be-fused feature vectors in each mode to obtain a fused feature vector; the fusion feature vector is labeled, a labeling result is obtained, and the labeling result comprises a data type label and / or a data description text. According to some embodiments of the invention, accurate labeling of cross-modal data can be realized.

The invention provides a data infringement detection and ownership tracing method and system, and the scheme supports the efficient data infringement judgment outside a chain, achieves the release of data description and the registration of data ownership through a block chain, and employs an on-chain and off-chain cooperation mode. Data infringement judgment operation is carried out in a trusted execution environment based on SGX, a judgment result is returned to an intelligent contract, data ownership tracing is carried out by the intelligent contract, and an infringement judgment result is securely stored in an uplink mode, and the scheme is suitable for scenes of data transaction and infringement judgment under a user. According to the method, infringement judgment on personal transaction data by a data owner can be completed, and the data ownership and the data control right of a user can be maintained while the security and privacy of the transaction data are ensured.

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.

Systems, methods and apparatus of machine-learning-predictive-analytics, the method performed by a predictive analytic control computer and including receiving from a second computer a training-profile data that describes one or more contributions of resources that are associated and identified with particular entities, receiving from a third computer a training-profile data that are associated and identified with the particular entities, that does not describe one or more contributions of resources the training-profile data, and that includes data that is that is received from additional computers that host websites and applications that focus on communication, community-based input, interaction, content-sharing and collaboration that describe a first set of features and representations of issues of interest of the particular entities, generating a machine-learning-predictive-analytic model by a machine learning-predictive-analytic trainer in reference to the training-profile data, generating predictions from the machine-learning-predictive-analytic model and from a second set of features and representations of issues of interest.

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.