481 results about "Supplementary data" patented technology

A computer-implemented method, computer program product and computing system for receiving a message concerning an event within a computer platform, wherein the message concerns a technology type and includes raw data; defining a cipher for the technology type, thus defining an associated cipher; processing the raw data included within the message using the associated cipher to define supplemental data for the technology type; and forming enriched data for the technology type based, at least in part, upon the raw data and the supplemental data.

The invention discloses a multi-modal data driven general report generation method and system based on a large model, and belongs to the technical field of intelligent report generation. Firstly, texts, images and sensor data related to a report theme are obtained and subjected to standardized preprocessing; analyzing the report generation instruction, and matching and querying a task modal mapping library matching modal configuration scheme according to a task demand; quantitatively evaluating the data quality of each modal, dynamically calculating the final decision weight of each modal in combination with the basic weight, and distributing the final decision weight to a corresponding processing path to form dominant, supplementary and reference data; inputting the dominant data and the supplementary data into a multi-modal model for analysis to obtain a preliminary conclusion with confidence score, performing consistency judgment, if no conflict exists, performing fusion to form a comprehensive conclusion, and if the conflict exists, combining a quality evaluation result and an arbitration rule to complete conflict judgment; and finally, inputting the comprehensive conclusion and the reference data into a large language model to generate a report text, and outputting a complete report after typesetting and proofreading.

The invention discloses a data format conversion method and system, a storage medium and a program product, and relates to the field of electric digital data processing, and the method comprises the steps: receiving first format data, and determining a data format of the first format data and a target format of second format data as a conversion target; the data format is a DWG format or an SHP format; extracting feature data corresponding to the data format and mapping data corresponding to the target format in the first format data; inputting the mapping data and the target format into a data conversion model to obtain supplementary data; generating second format data based on the mapping data and the supplementary data; and extracting data different from the mapping data in the feature data as loss data, and storing the loss data to a conversion database corresponding to the second format data. By implementing the method, lossless conversion of the DWG format and the SHP format can be realized, and the utilization rate of heterogeneous data is improved.

The invention relates to a navigation anti-interference method based on IMU assistance. The method comprises the following steps: processing collected data to obtain a synchronous data stream; key sampling points are added in the compensated synchronous data stream to form a supplementary data stream; calculating the supplementary data stream according to the calculation parameter to obtain a compensation signal; carrying out IMU data acquisition, validity analysis, calibration processing and motion information extraction on the synchronous data stream to obtain carrier motion information; time-frequency features are extracted and analyzed to obtain signal spectrum features, abnormal feature recognition is carried out, and a recognition result and carrier motion information are compared and analyzed to obtain interference features; performing parameter processing on the compensation signal by using the anti-interference parameter to obtain an anti-interference signal; and carrying out quality index analysis on the anti-interference signal, obtaining a signal enhancement characteristic through characteristic enhancement, carrying out time-space domain reconstruction, establishing a corresponding relation with carrier motion information, and outputting an anti-interference navigation signal through region fusion. And the anti-interference performance in a complex environment is effectively improved.

Systems and methods for a programming language-agnostic data modeling platform that is both less resource intensive and scalable. Additionally, the programming language-agnostic data modeling platform allows for advanced analytics to be run on descriptions of the known logical data models, to generate data offerings describing underlying data, and to easily format data for compatibility with artificial intelligence systems. The systems and methods use a supplemental data structure that comprises logical data modeling metadata, in which the logical data modeling metadata describes the logical data model in a common, standardized language. For example, the logical data modeling metadata may comprise a transformer lineage of the logical data model.

The invention discloses an open source software risk detection method and device based on an SBOM list, equipment and a medium, and relates to the technical field of software detection. The method comprises the following steps: acquiring an SBOM of target software, and analyzing component information of the SBOM; generating a feature character string according to the component information, and calculating a feature hash value through a hash algorithm; based on a known open source component, analyzing component information and vulnerability information of the component, calculating a feature hash value of the component, and storing the feature hash value to form a feature database; searching a matching item in a feature database based on the feature hash value of the target software component, and if the matching item is found, obtaining a license and vulnerability information of the target software component; if not, marking, manually judging and supplementing the data to the feature database; constructing a license compatibility matrix, and checking the compatibility of the obtained license and the overall license of the project; and comparing the acquired vulnerability information with a feature database, calculating vulnerability severity by combining a CVSS scoring model, and generating a priority repair report. According to the method, the software security detection speed and accuracy are improved.

The invention discloses a rock debris lithology analysis method based on a multilevel fusion image algorithm and X-ray diffraction. The method comprises the following steps: S1, acquiring rock debris image data under different resolutions; s2, generating a multi-level rock debris image feature matrix; s3, preliminarily generating a lithology classification result; s4, generating supplementary data of X-ray diffraction analysis; s5, fusing the supplementary data of the lithology analysis and the preliminarily generated lithology classification result, and finally generating a comprehensive lithology classification result; s6, the comprehensive lithology classification result is input into a dynamic adaptive analysis system, the dynamic adaptive analysis system adaptively adjusts an analysis strategy according to the rock debris sample complexity, and parameters of a dual-path convolutional neural network model and a multi-modal analysis algorithm are optimized through a real-time feedback and learning mechanism; and S7, outputting an analysis report containing mineral distribution, crystal structure and comprehensive lithology classification results. The classification accuracy of different types of rock debris samples is improved by about 15%.

The invention relates to the technical field of chip optical detection, and discloses a high-precision defect detection method and system for a semiconductor chip. Initial detection data are obtained through laser scattering and photo-thermal microscopic detection, key visual features are extracted and converted into a multi-dimensional visual feature matrix, and the multi-dimensional visual feature matrix is used for detecting the defects of the semiconductor chip; according to the method, a fuzzy sensing area is positioned and potential defects are identified by using a map clustering algorithm, adaptive analysis of a supplementary visual angle is carried out and supplementary detection data is generated, and finally defect identification is carried out through the supplementary data and popularity information to obtain a chip defect result. The method effectively improves the precision and reliability of defect detection, especially has obvious advantages in recognition of complex and fuzzy regions, and solves the problem of insufficient precision of single-path optical imaging detection in the prior art.

The invention relates to the technical field of medical image processing and computer vision, in particular to a medical image segmentation method and system based on deep learning, the method is based on a U-shaped encoder-decoder architecture, a DSAB module is introduced into an encoder, and context perception of a directional anatomical structure is enhanced through complementary directional space shift and CSA mechanism weighting; an MGCF module is designed in a decoder, and a parallel multi-scale convolution path and an AGCA mechanism are combined, so that multi-level features are efficiently fused to recover boundary details. Meanwhile, links of data preprocessing, Transform structure details, segmentation result post-processing and the like are supplemented, the model performance is improved through a mixed loss function and an optimization training strategy, and the method has remarkable advantages in segmentation precision and boundary definition and provides powerful support for clinical auxiliary diagnosis.

The invention relates to the technical field of vehicle image recognition, and discloses a multi-modal data enhanced vehicle recognition method and system based on a generative adversarial network, and the method comprises the steps: collecting vehicle multi-modal data (a visible light image, a thermal infrared image and three-dimensional laser point cloud data), and constructing a vehicle multi-modal data set; performing preprocessing and feature alignment on the data set to obtain standardized multi-modal data; constructing a cross-modal generator based on an adaptive attention mechanism, learning inter-modal feature association through a dynamic weight distribution module, and generating vehicle feature fusion data; designing a dual discriminator structure consisting of a perception consistency discriminator and a semantic fidelity discriminator, and optimizing the generator by adopting an alternate adversarial training strategy; generating a supplementary data sample for the complex scene by using the optimized generator, and constructing an enhanced data set; and constructing a multi-mode cooperative vehicle identification model based on the enhanced data set, and realizing high-precision vehicle attribute identification.

Systems and methods for a programming language-agnostic data modeling platform that is both less resource intensive and scalable. Additionally, the programming language-agnostic data modeling platform allows for advanced analytics to be run on descriptions of the known logical data models, to generate data offerings describing underlying data, and to easily format data for compatibility with artificial intelligence systems. The systems and methods use a supplemental data structure that comprises logical data modeling metadata, in which the logical data modeling metadata describes the logical data model in a common, standardized language. For example, the logical data modeling metadata may comprise a transformer lineage of the logical data model.

A mobile agricultural machine obtains an agricultural characteristic map indicative of agricultural characteristics of a field, wherein the agricultural characteristic map is based on data collected at or prior to a first time. The mobile agricultural machine obtains supplemental data indicative of characteristics relative to the worksite, the supplemental data collected after the first time. An agricultural characteristic confidence output, indicative of a confidence level in the agricultural characteristics indicated by the agricultural characteristic map, is generated based on the agricultural characteristic map and the supplemental data. In some examples, an action signal is generated to control an action of the mobile agricultural machine based on the agricultural characteristic confidence output.

The invention discloses a complex text OCR (Optical Character Recognition) error recognition and repair method based on a large language model, and relates to the technical field of text processing, the method comprises the following steps: step 1, a text preprocessing module recognizes and excludes a non-text area in a preliminary text result generated by OCR to ensure that residual content is pure text input, and a text preprocessing module is used for preprocessing the residual content; obtaining a text result of the plain text data; step 2, an error recognition module performs entity recognition on a text result of the plain text data by using a large language model, and provides revision suggestions for recognized suspected errors; 3, the database provides knowledge recall for the recognized entities and errors, and supplementation is provided for follow-up repair; and 4, correcting errors in a text result of the plain text data by an error repairing module through the acquired context information and supplementary data provided by the database, and outputting a document.

Methods and systems are disclosed herein to address the problems discussed above and in particular to provide coordination for uncoordinated content. Beyond simply archiving and / or organizing available content, the methods and systems generate composite media assets that provide enriched details and supplemental data that extends beyond any given source content. The methods and systems achieve this through a novel data filtration and synchronization process.

Knowledge-based audio-text modeling via automatic multimodal graph construction is performed. An audio dataset is received, the audio dataset including clips of audio data, wherein each of the clips of the audio data is paired with corresponding metadata descriptive of the audio contents of the respective clip of the audio data. Graph nodes of interest are identified from a sematic network, the graph nodes being descriptive of semantics of the knowledge domain of the contents of the audio dataset. A large language model (LLM) is utilized for categorizing the metadata into the graph nodes and for inferring supplemental data for the graph nodes for which there is no metadata, producing an extracted knowledge graph. The extracted knowledge graph is validated utilizing the LLM to perform relation verification of edges between the graph nodes of the extracted knowledge graph, thereby mitigating hallucination effects in the categorizing and inferring of the supplemental data.

The invention provides a nuclear power container mechanical property iterative optimization method and system, and the method comprises the steps: training a mechanical property prediction model based on machine learning based on a pre-constructed historical sample data set, and obtaining a trained mechanical property prediction model; based on the mechanical property prediction model, constructing a quantitative relationship between a mechanical property optimization objective function and design index parameters; on the basis of the quantitative relation, optimization of the design index parameters is executed in a parameter space, and the initial optimal point of the design index parameters is obtained; on the basis of the obtained initial optimal point of the design index parameter and the working condition point in the preset neighborhood, combined with the working condition parameters of random and manual supplementation, obtaining supplementation data; and performing mechanical simulation calculation on the basis of the obtained supplementary data, judging whether the initial optimal point of the design index parameter is locally optimal or not on the basis of a simulation calculation result, if so, introducing the supplementary data into a historical sample data set, and iteratively executing the process until the global optimal point of the design index is obtained.

A system for digital authentication may include an authentication server. The system may be configured to receive a login request from a user device associated with a user. The login request may include a user identity data element and a login request data element. The system may be configured to retrieve, based on the login request, a supplemental data element associated with the user from a recording server and determine whether the login request data element matches the supplemental data element and in response to a determination that the login request data element matches the supplemental data element: perform an authentication on the user device; configure the user device as authenticated in response to the authentication; and in response to configure the user device as authenticated: configure a first login mode as an authenticated status; and configure a second login mode as the authenticated status.

The invention relates to the technical field of computer platform load balancing, and discloses a comprehensive risk monitoring method and system for a transformer substation, and the method comprises the steps: collecting first feature data of target equipment; performing anomaly detection on the first feature data, and triggering a dynamic acquisition mechanism according to an anomaly detection result to obtain supplementary data; and constructing an anomaly evaluation model based on the first feature information and the supplementary data, performing quantitative evaluation on the target equipment, and outputting a risk monitoring report. According to the method, feature data of target equipment are collected in real time, anomaly detection is carried out, a dynamic collection strategy is triggered based on a detection result, and supplementary collection is carried out on appearance and environment data of the target equipment. The dynamic acquisition strategy adjusts the acquisition priority and frequency in real time according to the risk level, so that the acquired data is more accurate and targeted, the comprehensiveness and timeliness of equipment state monitoring are ensured, and the accuracy of risk assessment and the efficiency and safety of substation operation and maintenance are improved.

The invention discloses an industrial product surface defect traceability method, system and device, and relates to the technical field of industrial product surface defect traceability, and the method comprises the steps: collecting actual defect time sequence data of a product and supplementary data generated by a digital twin system, and building a multi-view topological expression; extracting consistency characteristics of each view to obtain space-time integration characteristics of defect evolution; performing instance mining according to the multi-view topological expression and the space-time integration characteristics to establish a surface defect quality analysis knowledge graph; defining type vectors of head entities and tail entities for various relationships in the knowledge graph, and generating initial semantic representations of newly added entities; constructing a knowledge graph evolution process of dynamic graph updating to perform sub-graph level updating, and performing graph enhancement by generating soft facts and confidence thereof; a reinforcement learning recommendation framework is constructed, and product surface defect traceability is realized; according to the method, the reasoning capability of the model under the sparse atlas is improved, and the reliability and interpretability of a reasoning path are maintained.

Methods and systems for generating a synthetic trends dataset by securely combining a healthcare dataset with a supplementary dataset, each pertaining to a common individual. A system receives the healthcare dataset and a supplementary dataset from databases in respective segregated data environments of a federated data cleanroom. The system anonymizes each dataset, stores the anonymized data in respective segregated data environments, and generates numerical representations of data features that include sequences of embedding vectors. The system transforms the sequences of embedding vectors by reducing the information with respect to the original sequences of embedding vectors. Upon determining a risk of disclosure is above a threshold, the system modifies the data features. The system generates a synthetic trends dataset that includes the transformed sequence of embeddings associated with each segregated data environment and outputs values from a machine learning model that is trained with the synthetic trends dataset.

The invention discloses an irrigation prediction method and device, equipment and a medium. The method comprises the following steps: acquiring an initial noise action sequence; when the obtained current iteration step length number is smaller than or equal to the total number of model iteration step lengths, determining a current noise action sequence corresponding to the current iteration step length number; determining current supplementary data corresponding to the current iteration step length; inputting the current supplementary data and the current noise action sequence into an irrigation prediction model, and determining a predicted noise action sequence corresponding to the current iteration step length; when the current iteration step length number is greater than the total number of model iteration step lengths, obtaining a predicted action sequence corresponding to the current iteration step length number; and in response to the fact that the current iteration step length number is smaller than or equal to the total number of model iteration step lengths, taking the predicted action sequence corresponding to the current iteration step length number as a current noise action sequence corresponding to the next iteration step length number, and accumulating 1 on the current iteration step length number. According to the embodiment of the invention, the irrigation prediction accuracy can be improved.

The invention discloses a method, a system and a device for updating data in a database and a program product. The method comprises the following steps: controlling a data synchronization module to generate a data acquisition request; in the first data processing area, in response to the data acquisition request, acquiring data to be supplemented from a first database corresponding to the first data processing area, and transmitting the data to be supplemented to a data synchronization module, and physical isolation does not exist between the first data processing area and the host system; the data synchronization module is controlled to synchronize the data to be supplemented into a second data processing area, and physical isolation exists between the second data processing area and the host system; and in the second data processing area, according to the to-be-supplemented data, updating the data in the second database corresponding to the second data processing area. The technical problem of low business data processing efficiency is solved.

The invention relates to the technical field of natural gas turbine intelligent monitoring, in particular to a natural gas turbine operation data analysis method and system, and the method comprises the following steps: S1, data acquisition and preprocessing: collecting natural gas turbine operation data through a sensor, carrying out the standardization processing of the data, and removing and supplementing abnormal values and missing values in the data; s2, data extraction and calculation; according to the method, the data quality and the fault feature recognition capability are remarkably improved through the multi-modal data processing and dynamic feature extraction technology, accurate elimination of abnormal values and dynamic filling of missing values are achieved based on the isolated forest algorithm and time sequence linear interpolation, the data integrity is greatly improved, and the method is suitable for large-scale popularization and application. The problem of misjudgment caused by noise interference in a traditional threshold value method is solved, a dynamic weight optimization mechanism is combined, the method adapts to complex working conditions of large fuel calorific value fluctuation and large load sudden change, the fault recognition rate is greatly improved, and the evaluation error is greatly reduced.

The invention discloses a method and device for improving reliability of data in a solid state disk and a storage medium, and the method for improving the reliability of the data in the solid state disk comprises the steps that programming progress detection is conducted on migration data migrated from a single-layer unit flash memory to a four-layer unit flash memory in the solid state disk, and the current programming progress of the migration data is obtained; in response to the situation that the current programming progress does not meet the complete programming progress of the migration data, timing processing is conducted on the current programming progress, and programming waiting time is obtained; in response to the fact that the programming waiting time is larger than a preset time threshold value, determining to-be-supplemented data according to the current programming progress of the migration data and the complete programming progress of the migration data; and migrating the data to be supplemented into the four-layer unit flash memory. According to the scheme, the data reliability in the solid state disk can be improved.

The invention relates to the technical field of medical information processing, and discloses an acute pulmonary embolism auxiliary diagnosis decision-making system based on a large model, and the system comprises a data collection and preprocessing module which is used for receiving multi-source clinical data and preprocessing the multi-source clinical data; the large model reasoning module is used for inputting the preprocessed multi-source clinical data into a large model fused with a multi-modal causal attention and identification adversarial learning mechanism, and outputting a reasoning result; the dynamic learning module is used for accessing desensitized case data of a hospital electronic medical record system, continuously learning new cases through federal learning, and optimizing the recognition capability of a large model pair; the diagnosis suggestion generation module is used for generating structured diagnosis suggestions according to the reasoning result output by the large model; the result output and interaction module is used for displaying diagnosis suggestions to doctors and updating reasoning results in real time by a large model when the doctors supplement data; the diagnosis efficiency is improved.

The invention provides an unmanned aerial vehicle riverway inspection method based on artificial intelligence, and belongs to the technical field of unmanned aerial vehicle riverway inspection. A riverway environment multi-scale feature sensing system is established to respectively construct a fine-grained feature matrix for recording surface texture changes and a coarse-grained environment matrix for storing overall form information; different scale feature matrixes are mapped to corresponding high-dimensional feature spaces by adopting a radial basis kernel function and a polynomial kernel function to form a fine-grained feature mapping matrix and a coarse-grained environment mapping matrix, result quality is evaluated through an inspection result judgment function, and a recollection instruction matrix is generated to guide supplementary data collection when confidence is insufficient. And finally, trend analysis is performed in combination with a historical data fusion module to provide parameter optimization guidance for a subsequent inspection task, and the technical problem that multi-scale feature information is difficult to adaptively process in the unmanned aerial vehicle river channel inspection process is solved.

The invention discloses a text steganography method introducing a large language model, a related method and a related device. The method comprises the following steps: segmenting a secret information bit stream into a plurality of segmented bit streams according to a preset segment length; based on the secret information bit stream and a preset segment length, collecting a knowledge graph, and constructing a candidate database and a supplementary database; coding is carried out according to all knowledge maps in the candidate database, so that the knowledge map corresponding to the first segmented bit stream is determined to serve as a selected knowledge map; inputting the selected knowledge graph into a preset knowledge graph text generation model to obtain a steganographic text corresponding to the first segmented bit stream; deleting the selected mapping knowledge domain from the candidate database, selecting one mapping knowledge domain from the supplementary database, and adding the selected mapping knowledge domain into the candidate database; and the knowledge graph coding and text steganography processes are executed again until all the segmented bit streams are processed. According to the method, the quality and naturalness of text generation are improved, and high-capacity and hidden secret information hiding is realized.

The invention relates to the technical field of enterprise credit evaluation, in particular to an enterprise credit model optimization method and system based on big data, and the method comprises the steps: obtaining training data corresponding to a target enterprise credit model, and determining a model data state according to the coverage integrity and data singularity of the training data, determining an optimization analysis mode according to the model data state; in the data supplementary analysis, determining a search mode corresponding to each search keyword according to the data richness and the effective evaluation index so as to obtain search data, and determining a supplementary mode according to the effective search data quantity and the dislocation coefficient of the associated search data combination; determining an adjustment mode according to the correlation coefficient and the comparison coefficient of the supplementary data; in data rejection analysis, noise indexes are rejected according to an index fluctuation coefficient and an index offset rate; training the target enterprise credit model by adopting the optimized data; the stability and reliability of the enterprise credit model can be improved.

The invention relates to the technical field of open channel gate flow prediction, and discloses an open channel gate flow prediction method based on multivariate data fusion driving, and the method comprises the following steps: carrying out the hour-level processing and preprocessing of actually measured water regimen data, and generating a basic data set; simulating gate water regimen data based on the hydrodynamic model, and constructing a supplementary data set; the basic data set and the supplementary data set are fused, and a BiLSTM multi-factor flow prediction model is constructed; inputting the fused data set into the model, and fitting a traffic prediction model through iterative training; and carrying out traffic prediction by using the trained model, and carrying out reverse normalization to output a final result. According to the method, the actually measured water regimen data and the hydrodynamic model simulation data are subjected to multivariate fusion, so that the problems of precision and robustness of a single data source in gate flow prediction are solved. The simulation data of the hydrodynamic model is used as an effective supplement for actually measured data, and comprehensive gate water regimen information is provided, so that the accuracy and generalization ability of the prediction model are improved.

Systems and methods for a programming language-agnostic data modeling platform that is both less resource intensive and scalable. Additionally, the programming language-agnostic data modeling platform allows for advanced analytics to be run on descriptions of the known logical data models, to generate data offerings describing underlying data, and to easily format data for compatibility with artificial intelligence systems. The systems and methods use a supplemental data structure that comprises logical data modeling metadata, in which the logical data modeling metadata describes the logical data model in a common, standardized language. For example, the logical data modeling metadata may comprise a transformer lineage of the logical data model.