282 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 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 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.

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.

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.

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 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 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 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.

The invention discloses a data processing method and system based on a monitoring bracelet, and the method comprises the steps: obtaining physiological monitoring data of a monitored object through a built-in chip module of the monitoring bracelet; performing abnormal interval identification and abnormal interval classification on the physiological monitoring data; according to the classification of all the abnormal intervals and the features of the abnormal intervals under each classification, constructing adaptive features of the monitoring object; performing parameter mapping through the self-adaptive features of the monitoring object to generate parameters of a data supplement model; and after data clearing is carried out on each abnormal interval, data supplementation is carried out by utilizing the data supplementation model after parameter assembly. Feature differences of individuals in different abnormal states can be reflected, and personalized data analysis is realized; more reasonable supplementary data is generated for different abnormal characteristics; the generated data is ensured to be consistent with normal data in numerical characteristics and dynamic trends, and the data continuity and physiological rationality are effectively improved.

The invention discloses an electrical automation monitoring data optimization method and system based on edge calculation, and belongs to the field of data optimization. According to the method, the electrical, positioning and communication state data of the equipment are synchronously acquired through the edge computing node, the link quality index is generated based on the geometric precision attenuation factor and the communication state, and the joint feature sequence is constructed by associating the spatial position. And predicting the link risk in the first time period in the future according to the sequence by using a risk prediction model based on an attention mechanism long short-term memory network. And when the predicted risk meets the condition, adding a to-be-reconstructed label to the first electrical monitoring data with the high priority, and performing local caching. And in a second time period after communication recovery, the cache data and the risk information are sent to a central cloud platform, the cloud platform reconstructs the historical data and the supplementary transmission data according to the risk information, and finally complete target electrical monitoring data is generated, so that the integrity and effectiveness of the electrical automation monitoring data can be improved.

The invention discloses a data analysis method for intelligent police law enforcement glasses, and belongs to the technical field of data analysis, and the method comprises the steps: extracting a video stream from a multi-source data set for recognition, judging whether a recognition result meets a preset threshold value or not, taking a target feature as a video feature set if the recognition result meets the preset threshold value, if not, triggering supplementary data collection and obtaining a video feature set in combination with the original target features; performing space-time alignment on key elements in the video feature set and the voice instruction to obtain a comprehensive event description vector; performing sequence analysis on the comprehensive event description vector to obtain an event trend vector, and obtaining an event analysis result; converting the event analysis result according to the priority to obtain real-time voice feedback content; and generating a voice broadcast signal of the real-time voice feedback content. According to the data analysis method of the intelligent police law enforcement glasses, the problem that the efficiency and safety of the law enforcement process are difficult to guarantee due to information lag caused by insufficient data processing speed is solved.

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 data processing and financial science and technology, and particularly discloses a vehicle insurance claim data processing method and device, a storage medium and computer equipment. The method comprises the following steps: acquiring vehicle insurance claim settlement application data corresponding to a vehicle insurance claim settlement behavior, vehicle feature supplementary data of a claimed vehicle and user feature supplementary data of a claim settlement user; according to the vehicle insurance claim settlement application data, the vehicle feature supplementary data and the user feature supplementary data, generating a vehicle insurance claim settlement feature vector, and based on the vehicle insurance claim settlement feature vector, determining a first fraud probability through a preset fraud behavior identification model; according to the vehicle feature supplementary data and the user feature supplementary data, constructing a multi-subject claim relation graph, and based on the multi-subject claim relation graph, determining a second fraud probability corresponding to the claim user; and determining a target fraud probability of the car insurance claim settlement behavior according to the first fraud probability and the second fraud probability.

The invention discloses a power grid inspection method and device, and relates to the technical field of electric power. The method comprises the steps of detecting whether a user instruction is received or not; determining a communication protocol with each inspection point in the power grid based on the distributed service architecture if the inspection point information is received, and collecting power grid data through the communication protocol; processing missing values and abnormal values in the power grid data, and integrating the processed power grid data through supplementary data to obtain integrated power grid data; predicting the integrated power grid data based on a prediction model to obtain an analysis result; wherein the analysis result at least comprises the comprehensive operation state of the power grid; the prediction model is a data analysis model constructed based on a machine learning algorithm; according to the analysis result, determining and executing an inspection strategy; wherein the inspection strategy comprises an optimal inspection path; the optimal inspection path is obtained by predicting the inspection time, the energy consumption and the equipment health state through a path planning model. The system is used for realizing a power grid inspection function.

Techniques are disclosed relating to improving user privacy using machine learning (ML) models. In various embodiments, a device processes a query using a locally stored large language model (LLM) operable to use supplemental data provided by one of a plurality of assisting server systems. The device verifies a set of public-key attestations, each attesting to a public key of a respective one of the assisting server systems. The device sends, based on the verifying, a request for the supplemental data to the assisting server systems. The request includes intermediary data produced by the processing and is encrypted using the attested-to public keys. The device processes the received supplemental data using the LLM to produce a result of the query. In some embodiments, the device encrypts the intermediary data with a symmetric key and encrypts a respective instance of the symmetric key with each of the attested-to public keys.

The application discloses a high-precision earthwork measurement algorithm and system for constructing a three-dimensional model based on unmanned aerial vehicle aerial survey, and the method comprises the following steps: arranging image control points and check points in a to-be-measured area in advance; starting unmanned aerial vehicle aerial survey image data collection, and performing supplementary measurement on an obstacle-shielded area to obtain supplementary data; importing the aerial survey image data to generate a real scene three-dimensional model; finding an image control point mark position on the three-dimensional model and corresponding puncture points of actual measured image control points, converting model latitude and longitude coordinates into project coordinates after matching all the image control points, regenerating a three-dimensional model, and importing check point coordinate data to verify the model precision. Seed points are added to remove obstacles and restore the ground terrain, and then drawings and design elevations are imported to perform high-precision earthwork calculation, so that the problems of complex calculation operation, easy errors and large time consumption of manual operation combined with other software are solved, accurate data support is provided for earthwork construction, and the project construction period is saved.

There is provided a data analytics function comprising a processor and a receiver. The processor is arranged to generate analytics data for an analytics service using at least one data source. The receiver is arranged to receive an event related to the analytics service. The processor is further arranged to determine a rating of the at least one data source, the rating based on supplementary data.

The invention discloses a fused salt energy storage optimization management method and system based on nuclear power waste heat, and relates to the technical field of energy storage management, and the method comprises the steps: monitoring a fused salt energy storage system through a sensor, and obtaining monitoring data; performing independence judgment on the monitoring item, performing data validity judgment on the monitoring data of the monitoring item with independence, performing data extraction on an influence source influencing the monitoring item without independence to obtain supplementary data, and performing joint judgment on the supplementary data and the corresponding monitoring data to determine the validity of the monitoring data; if the monitoring data is valid, performing security judgment on the monitoring data, and determining the security of the fused salt energy storage system; and if it is judged that the fused salt energy storage system is not safe, outputting a corresponding alarm signal according to a monitoring item corresponding to the monitoring data. The method has the effect of improving the safety judgment accuracy.

The invention provides a medical data intelligent query analysis method, system and device and a medium, and belongs to the technical field of medical data query. The method comprises the steps that a query request of a medical worker is responded, and a first analysis instruction is generated in combination with the role of the medical worker, the emergency degree of a diagnosis and treatment scene, historical behaviors and patient real-time data; and calling a rendering engine to generate an adaptive visual interface according to the terminal type and the like. And then receiving a second analysis instruction with a larger analysis amount, evaluating the data demand complexity of the second analysis instruction, and after identifying a data gap with the first instruction, calling the knowledge base and the interface supplementary data to generate second medical query information. And then identifying the difference between the first analysis instruction and the second analysis instruction, generating a recommendation card based on historical interaction, performing split-screen comparison or progressive replacement display, recording, feeding back and updating the user preference model, further adjusting a subsequent first analysis instruction generation strategy, and circularly responding to a new request. The whole medical query requirement can be accurately met, the information display suitability and the interaction efficiency are improved, the user experience is optimized, and efficient diagnosis and treatment are assisted.

The invention provides an automatic PoC report retrieval method pointing to the same vulnerability. The method comprises the following steps: firstly, for a plurality of PoC reports, converting report texts into semantic vectors with fixed dimensions by using a semantic embedding model, and constructing a local vector knowledge base to complete semantic conversion of multi-source data; then, candidate reports strongly associated with the PoC report input by the user are retrieved from a local knowledge base through a Cosine similarity algorithm, and a candidate set is generated; if the candidate set is empty, key information is extracted by using an LLM Agent, external API supplementary data is called, and the knowledge base and the candidate set are updated; finally, feature information is extracted from text semantics and code structure dimensions by means of LLM Agent in combination with Prompt engineering, Prompt analysis is judged through relevance, and a report list pointing to the same vulnerability as a given PoC report is output. Through semantic vector matching and multi-dimensional feature comparison technologies, security personnel are helped to reduce the manual comparison cost, multi-source PoC information is rapidly integrated, and the accuracy and efficiency of vulnerability availability evaluation are improved.

The invention relates to the field of artificial intelligence, can be applied to business system platforms of finance, medical health and the like, and discloses a classification method, device, equipment and medium based on cross-modal and dynamic distillation, and the method comprises the steps: collecting annotated image samples and unannotated text samples, and carrying out cross-modal bidirectional generation type enhancement processing and cross-modal sharing coding; performing teacher-student model cooperative training of dynamic distillation weight through annotated image samples and an extended data set; modal difference analysis is carried out according to the feature vectors of the unified semantic space, and distribution difference analysis is carried out on generated data and real data; and calculating a discriminant loss result according to each item of data so as to update model parameters, and obtaining a trained multi-modal classification model for classification processing. Small sample data distribution is effectively expanded through cross-modal generation of supplementary data and sharing of codes, the model convergence speed is increased through a dynamic knowledge distillation mechanism, the training efficiency is improved while the training effect is ensured, and the prediction classification accuracy is improved.

The invention provides a transformer-based insect detection method and system, electronic equipment and a storage medium, and aims to solve the problems of fixed receptive field and complex design of target detection, the method comprises the steps of inputting an image into a convolutional neural network to extract image features, and for data of which complete features cannot be extracted, generating supplementary data by adopting a denoising diffusion probability model; performing feature compression; spatial position information is added to the input feature map through position coding, and the spatial position information and the input feature map are added according to bits; flattening the added feature map, feeding the flattened feature map to a Transform encoder, and outputting a feature map with the same size; constructing N object queries; feeding the object query and encoder output feature map to a decoder to generate feature output; feature outputs of the decoder are mapped into two outputs using two fully connected layers for classification and location regression, respectively. According to the invention, the precision and robustness of insect detection can be improved.