3819 results about "Data loss" patented technology

The invention belongs to the technical field of earthquake disaster scene recognition, and discloses an earthquake disaster scene recognition method based on deep learning. The method comprises the following specific steps: S1, data acquisition and preprocessing; S1.1, multi-source heterogeneous data acquisition and establishment of a comprehensive database containing seismic waveform data, surface deformation data, building structure data, geographic information data and historical disaster record data; through fusion of a 3D convolutional network, a graph attention mechanism, a space-time LSTM and an adaptive cross-modal attention fusion technology, combined modeling of a seismic waveform space-time evolution law, an earth surface deformation space distribution characteristic, a building group topology vulnerability and disaster chain time sequence association is realized, the characterization capability of a complex nonlinear disaster mode is effectively improved, and the method has the advantages of high adaptability and high reliability. And disaster assessment response time is shortened to a sub-second level through mixed precision quantification and edge computing deployment, and high recognition accuracy is still kept in a scene with strong noise and data missing in combination with a multi-task classifier and a physical constraint verification mechanism.

Image series transformation for optimal compressibility is performed with neural upsampling and error resilience. A novel correlation network composed of convolutional layers for feature extraction that extract multi-dimensional features from the image and a channel-wise transformer with attention to capture complex inter-channel dependencies. An angle optimizer enhances compressibility of an image and an error resilience subsystem improves robustness against transmission errors and data loss. The error resilience subsystem applies forward error correction coding, data partitioning based on importance, and embeds error concealment hints. This hybrid approach addresses both local and global features, mitigates compression artifacts, improves image quality, and enhances data integrity during transmission. The correlation network incorporates error correction and concealment techniques during decoding. The model's outputs enable effective image reconstruction, achieving advanced compression while preserving information for accurate analysis.

The embodiment of the invention discloses a data distributed storage method and device, and the method comprises the steps: carrying out the fragmentation processing of to-be-stored data, and obtaining an initial fragmentation result; utilizing a reinforcement learning model to perform popularity prediction on the data fragments according to the historical access data to obtain the access popularity of the data fragments; determining the number of copies based on the access popularity and importance of the data fragments, generating fragment copies of the data fragments according to the number of the copies, and storing the data fragments and the fragment copies into storage nodes according to the access popularity and node load state scores; when the loads of the storage nodes are unbalanced, data migration is carried out on the data fragments stored in the storage nodes, and when the node health state of a certain storage node is abnormal or the stored data fragments are lost, data reconstruction is carried out on the data fragments stored in the storage node. According to the method, the problems of non-uniform access load, resource waste, data loss and the like in a distributed storage system can be relieved.

The invention belongs to the technical field of aviation flight, and particularly relates to a multi-source data fusion and dynamic adjustment algorithm and system suitable for a complex flight environment. Fusing multi-source data; path planning and dynamic adjustment; self-adaptive control and execution are carried out; and performing exception processing and redundancy compensation. According to the invention, on the basis of a multi-source data fusion and dynamic adjustment algorithm, various sensor data are fused and a flight strategy is dynamically adjusted in real time, so that the navigation capability and safety of an aircraft in a complex environment are remarkably improved. Compared with a traditional navigation system that a single sensor depends on. Through multi-source data fusion and a dynamic adjustment algorithm, the system significantly improves the autonomous navigation and control capability of an aircraft in a complex flight environment, and effectively solves the problem of uncertainty caused by data loss and environment change in the prior art.

The invention discloses a solar radiation space-time prediction method and system based on physical information constraint and a neural network, and the method comprises the steps: collecting multi-dimensional time sequence meteorological data, extracting high-dimensional time sequence dynamic features, converting geographic space data into a fuzzy set, and carrying out the defuzzification of the fuzzy set through an inference rule, thereby obtaining geographic space features; and a gating mechanism is adopted to realize deep fusion of the space-time features to generate high-dimensional space-time fusion features. In a model training stage, an energy conservation equation is introduced into an optimization process, a physical residual error is constructed by calculating a time derivative and a space derivative of a predicted value, a physical constraint total loss function is formed in combination with a data loss item, and model parameters are updated by using a gradient descent method. According to the method, the accuracy and reliability of a prediction result are remarkably improved while the calculation efficiency is ensured, and the method is particularly suitable for solar radiation prediction under complex meteorological conditions; according to the method, abnormal prediction caused by data noise can be effectively corrected, and a solution with physical rationality and data adaptability is provided for the fields of solar resource evaluation, photovoltaic power generation power prediction and the like.

Techniques are described for monitoring and analyzing input / output (I / O) messages for patterns indicative of ransomware attacks affecting computer systems of a cloud provider, and for performing various remediation actions to mitigate data loss once a potential ransomware attack is detected. The monitoring of I / O activity for such patterns is performed at least in part by I / O proxy devices coupled to computer systems of a cloud provider network, where an I / O proxy device is interposed in the I / O path between guest operating systems running on a computer system and storage devices to which I / O messages are destined. An I / O proxy device can analyze I / O messages for patterns indicative of potential ransomware attacks by monitoring for anomalous I / O patterns which may, e.g., be indicative of a malicious process attempting to encrypt or otherwise render in accessible a significant portion of one or more storage volumes as part of a ransomware attack.

The invention relates to the technical field of crop yield analysis, in particular to a crop yield prediction method based on a time sequence remote sensing image, and the method comprises the steps: obtaining an optical remote sensing image and a synthetic aperture radar image in a crop growth period, and constructing a time sequence image through complementary information fusion and under-cloud information recovery; the problems of cloud interference and data missing are solved, a multi-scale time regulation and control convolution module is used for processing a time sequence image, multi-scale time sequence features are extracted, feature fusion is carried out in combination with attention difference jump connection, and a rice distribution map is obtained; determining a rice planting area based on the rice distribution diagram, selecting an optimal assimilation time period in the rice planting area, and assimilating the leaf area index subjected to remote sensing inversion and a crop growth model simulation result to generate a simulated yield after assimilation; based on the multi-scale time sequence characteristics, a parameter dynamic response matrix related to crop growth model parameters is established, and residual error correction is performed on assimilated simulation yield, so that the rice yield prediction precision is improved.

Aspects of the disclosed technology include techniques and mechanisms for securing machine learning (ML) training pipelines and ML pipeline models using data loss prevention (DLP). A DLP ML engine may use different levels of detection to determine whether a command manipulates or disseminates sensitive data. The DLP ML engine may progress through the different levels of detection where each level of detection may engage different methods for determining whether the command is used for generative AI purposes. Based on determining a command is not used for generative AI purposes, the DLP ML engine may allow execution of the command. Alternatively, based on determining the command is used for generative AI purposes, the DLP ML engine may trigger preventive measures.

The invention discloses an electronic lead seal automatic detection method applied to logistics tracking, and relates to the technical field of Internet of Things safety monitoring. The problems of packaging data loss, tampering risk increase and real-time monitoring failure caused by wireless communication signal interference, transmission delay and state updating lag of an existing electronic lead seal are solved. The method comprises the following steps: fusing multi-physical field sensing data through a space-time correlation sampling algorithm, and constructing an anti-interference characteristic matrix; evaluating communication quality based on the dynamic probability network model, and triggering a multi-path fragmentation concurrent transmission strategy to avoid signal attenuation; a hybrid reasoning model is deployed at an edge end to screen key event data, and the transmission efficiency is optimized in combination with differential coding and an IEEE 1588 clock synchronization mechanism; the cloud end adopts a space-time diagram fusion analysis model to carry out cross-modal abnormal association scoring, corrects misjudgment and updates an edge model; according to the invention, the communication reliability, the real-time transmission efficiency and the anomaly detection precision of the lead sealing state data in a complex environment are obviously improved.

Systems and methods are provided for dynamically selecting, displaying, and synchronizing electronic forms within a conversational AI interface. A chat-based assistant determines the most contextually relevant form or form section using machine learning models and updates the graphical user interface in a split-screen layout. The system supports bidirectional synchronization, such that changes in form fields generate corresponding chat messages, and chat inputs populate structured form fields in real time. Users may pause and resume form workflows across sessions without data loss. The system further supports folder-based organization of structured inputs and document uploads, enabling hierarchical navigation and predictive workflow continuation. These improvements enhance form completion accuracy, user experience, and operational efficiency.

The invention relates to the technical field of data management, in particular to a data management method and system for an industrial energy storage system. The method comprises the following steps: carrying out real-time energy storage data acquisition on an industrial energy storage unit, and generating corrected energy storage monitoring data; carrying out distributed redundancy transmission on the corrected energy storage monitoring data, and carrying out uploading data difference analysis to obtain monitoring uploading difference data; performing difference conflict processing on the redundant monitoring and uploading data through the monitoring and uploading difference data to generate monitoring fragment feature data; performing dynamic cache synchronization processing according to the monitoring fragment feature data to obtain monitoring cache state data; and performing management state comprehensive analysis on the monitored fragment feature data by monitoring the cache state data to generate a comprehensive data management index. According to the method, efficient, reliable and safe uploading management optimization of mass industrial energy storage monitoring data is realized through distributed intelligent caching, and the problems of inconsistent data uploading and even data loss are effectively solved.

The invention relates to a state evaluation method and device of a solid state disk, a storage medium and a program product, and belongs to the technical field of storage, and the method comprises the following steps: obtaining related parameter information of the solid state disk, and preprocessing the related parameter information to obtain a first target data feature; according to a joint probability distribution value between the historical merged data features and the health degree, determining a correlation degree between the historical merged data features and the health degree, and according to the correlation degree, screening out a second target data feature from the first target data features; determining a health score corresponding to the second target data feature based on a health score calculation function; and determining a state evaluation result of the solid state disk according to the health score corresponding to the second target data feature, and visually displaying the state evaluation result. According to the method, the accuracy of state evaluation of the solid state disk is improved, and the risk of data loss or server downtime caused by solid state disk faults is effectively reduced.

The invention discloses an injection mold part feature recognition method based on improved PointNet + +, and the method comprises the steps: obtaining standard injection molding part models of a plurality of injection mold parts, and carrying out the uniform sampling of point clouds, and constructing a point cloud set; performing label labeling based on RGB information on each point of the point cloud set according to different processing feature types, and then performing normalization, random scaling and random translation; constructing and training a classification label prediction model based on an improved PointNet + + network according to the processed point cloud set; the entity model of the injection mold part needing feature recognition is converted into point cloud data, the point cloud data are input into the trained classification label prediction model, semantic segmentation output of different machining features is obtained, the semantic segmentation output is matched with the entity model of the injection mold part needing feature recognition in a mapping mode, and feature recognition is achieved. According to the method, data loss and precision reduction are avoided, and the feature recognition efficiency is improved.

The invention relates to the field of excavation equipment obstacle avoidance based on image analysis, in particular to a coal mine excavation equipment obstacle avoidance method and system based on machine vision, and the method comprises the steps: obtaining the frequency domain characteristic parameters of the current electromagnetic noise during the operation of underground electromechanical equipment, and synchronously collecting the current original image frame output by a visual sensor; after a current de-noised image frame is obtained after dynamic de-noising processing, an obstacle contour feature point set is extracted through multi-scale edge detection, and an obstacle position confidence map is obtained according to space-time consistency analysis; obtaining point cloud coordinates of an image data loss area caused by electromagnetic interference, and mapping the point cloud coordinates to an image coordinate system to obtain a three-dimensional geometric complemented image; and constructing a dynamic occupation grid map of the underground environment according to the three-dimensional geometric complementation image and the obstacle position confidence map, and planning an obstacle avoidance path of the mining equipment according to the dynamic occupation grid map. And the obstacle avoidance capability of the mining equipment is improved, and safe and efficient coal mining operation is guaranteed.

The invention discloses a time sequence data management method of an edge computing gateway, which relates to the technical field of edge computing and industrial Internet of Things, and comprises the following steps of: respectively recording a communication bandwidth occupancy rate, a buffer area residual rate and a scheduling thread occupancy rate of the edge computing gateway in a preset fixed time period; and constructing a resource use original data matrix covering all time points in the fixed time period. According to the method, by periodically monitoring the resource use state and fusing the high-priority task scheduling performance, the scheduling resource abnormal occupancy index is dynamically generated, and intelligent sensing and scheduling optimization of the edge computing gateway on the resource pressure are achieved. When the abnormal index is increased, the system automatically triggers buffer area redistribution and low-optimal task data compression, data writing and scheduling real-time performance of key tasks are guaranteed preferentially, the problems of task starvation and data loss are effectively avoided, and the stability and the response capability of the system in a high-pressure environment are improved.

The invention discloses an IVYA-PINN-based intelligent modeling method for process characteristics of a heavy duty gas turbine, which is used for realizing accurate modeling of the process characteristics of the heavy duty gas turbine. According to the method, on the basis of actual operation data of a certain heavy-duty gas turbine, operation characteristics of the heavy-duty gas turbine are analyzed, a physical differential equation of each component is obtained, and a physical loss item is provided for a physical information neural network. Through Spearman correlation analysis, input and output variables of a neural network are selected, a relationship between the input and output variables is established based on a PRform network, data loss and physical loss are used as a total loss function, and the network is guided to perform parameter updating. The weight coefficient of each loss item is optimized through the ivy algorithm, different loss parts are effectively balanced, it is guaranteed that the model meets the physical law, and meanwhile the interpretability and generalization ability of the model are enhanced. The invention provides a scheme and a thought for solving the problem of precise modeling of the process characteristics of the heavy duty gas turbine.

The invention provides a protocol-based Internet of Things central control host data communication method and system, and is applied to the field of communication data processing. By means of data updating timestamp detection, equipment updating frequency division, local data buffering and the like, the real-time data acquisition capability of the central control host on the multi-protocol Internet of Things equipment is improved, a layered acquisition strategy is adopted, the polling period is optimized for equipment with different frequencies, unnecessary requests are reduced, and the efficiency is improved. Meanwhile, data missing caused by long-time no response of low-frequency updating equipment is avoided, data updating abnormity can be intelligently detected, a data refreshing request is triggered, the polling period is shortened, acquisition of latest data is ensured, finally, data compensation is conducted through historical data trend analysis and missing estimation value calculation, and data integrity and continuity are improved.

Systems and methods for Data Loss Protection (DLP) utilizing distilled models include receiving a plurality of general data predictions from a teacher model; determining one or more strengths of the teacher model based on the received general data predictions; generating a synthetic dataset based on the one or more strengths of the teacher model; providing the synthetic dataset to the teacher model and receiving a plurality of synthetic data predictions from the teacher model based thereon; and performing knowledge distillation on a student model based on the synthetic data predictions received from the teacher model to produce a distilled model. The distilled model is then used in production for classifying inputs to a DLP system.

The invention provides an electromagnetic field prediction method and device and electronic equipment, and relates to the technical field of electromagnetic field solving. The method comprises the following steps: acquiring historical electromagnetic original data in a field-line coupling scene, preprocessing the historical electromagnetic original data, inputting the preprocessed historical electromagnetic original data into an LSTM-PINN model, and outputting a physical field quantity mapping result; wherein the physical field quantity comprises an electric field component and a magnetic field component; setting a weighted loss function, and performing optimization training on the LSTM-PINN model based on a physical field quantity mapping result and an error of a real physical field quantity corresponding to historical electromagnetic original data to obtain a trained electromagnetic field prediction model; wherein the weighted loss function comprises a data loss function, a physical residual loss function, an initial condition loss function and a boundary condition loss function; and inputting real-time electromagnetic original data into the trained electromagnetic field prediction model to obtain a spatio-temporal distribution electromagnetic field prediction result. The method can effectively extract the spatial distribution features and the time sequence features at the same time, and is suitable for a complex field-line coupling problem.

The invention relates to the field of aerospace data transmission and monitoring, and discloses a carrier rocket power supply data transmission and monitoring method and device and a storage medium, and the method comprises the following steps: obtaining historical data for analyzing a network state, and predicting network transmission abnormality based on a long short-term memory network model; dynamically adjusting a threshold value through a sliding window, and triggering an abnormal response mechanism; a reliability enhancement mechanism is adopted, data transmission is optimized through a TCP exponential backoff algorithm and a UDP redundancy coding technology, and data loss and retransmission are reduced; a Q-Learning algorithm in reinforcement learning is utilized to dynamically adjust transmission parameters according to a network state, the transmission parameters comprise a window size, a retransmission interval and a data packet size, and self-adaptive optimization is realized. The method is suitable for reliable transmission of key data and instructions in launch tasks of carrier rockets, and can be widely applied to data monitoring of spaceflight launch, high-concurrency network transmission and complex scenes.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

The invention discloses a wireless communication network data secure transmission method and system, and relates to the technical field of secure transmission. During system operation, agricultural monitoring data are collected in real time through various sensor devices arranged in an agricultural environment, encryption processing is performed by using a symmetric encryption algorithm, and a dynamic key length adjustment mechanism is supported; a random number generator is integrated, unified management is performed on key generation, distribution, updating and destruction in a system, a timestamp or a random serial number is embedded into each data packet, the uniqueness of the timestamp or the serial number is verified at a receiving end, so that replay attacks are resisted, and the data are comprehensively analyzed and evaluated according to a set algorithm, so that the replay attack is prevented. The method comprises the following steps: acquiring a security data transmission performance index SDTP through calculation, generating a monitoring report, detecting abnormal conditions in a data transmission process, including channel quality reduction, data loss or verification failure, and providing a visual monitoring result.

The invention relates to the technical field of information security, in particular to a computing method and system for information security. The method comprises the following steps of: partitioning and fragmenting input data, and distributing the data to a plurality of storage nodes through randomization, distributed storage and Hash mapping to ensure the safety of data storage and the distribution flexibility; establishing an index table for all the fragments; regularly triggering a data confusion mechanism, and adjusting the storage position of data fragments through a pseudo-random function; the integrity of the data is detected by generating a check code and an erasure code, it is ensured that the data is not tampered or damaged in the transmission or storage process, and the data is recovered through a redundant copy when the data is lost; the identity verification and the data integrity when the user accesses the data are ensured through authority authentication; and monitoring storage nodes and data access behaviors in real time, detecting potential threats and taking dynamic adjustment measures. According to the invention, through a multi-level security policy, the security and fault-tolerant capability of data processing are effectively improved.

The invention relates to the technical field of parking management, in particular to an intelligent parking order tracing and auditing method and system, and the method comprises the steps: receiving order data from a plurality of parking platforms, carrying out the preprocessing of the order data, and inputting the preprocessed order data into a block chain platform for storage and management; performing automatic processing and auditing on the order data by using an intelligent contract, and forming an auditing log; a time-space Transform model is established, intelligent analysis is carried out on data in an audit log, finally encryption tracing processing is carried out on the audit log, a user carries out order query, data audit and report generation through a user interaction module, data loss, tampering and malicious destruction are effectively prevented, order processing efficiency and accuracy are improved, and user experience is improved. Manual intervention and errors are reduced, an administrator can conveniently check and verify the access history and operation record of order data at any time, and the safety and traceability of the data are ensured.

A system to analyse impact of data breaches on sensitive data is disclosed. The system includes a hardware processor and memory with program instructions for executing various modules. The data collection module retrieves and enriches impacted data from multiple repositories. The data identification module uses data loss prevention (DLP) and named entity recognition (NER) techniques, enhanced by large language models (LLMs), to accurately identify personal information. The identity deduplication module consolidates individual references using deterministic and probabilistic techniques, while the residency inference module applies machine learning and heuristic methods to determine residency based on various data sources. The analysis module assesses impacted data to identify relevant laws and estimate fines. The automation module streamlines response actions, including generating notifications and ensuring compliance. This system enhances breach response efficiency through integrated, automated analysis and actions.

The invention discloses a multi-scale fatigue crack propagation prediction method and system based on a physical information neural network, and aims to improve the prediction precision of a crack propagation rate. The method comprises the following steps: S1, acquiring crack lengths and cycle cycles of a metal material under different scales by using a microscope; s2, calculating an initial small crack propagation rate based on the acquired data, performing fitting by adopting a Newman-Wu model, constructing an initial crack propagation physical model, further combining experimental data to identify physical characteristics influencing propagation behaviors, introducing a PSC-LC turning point to divide small crack and long crack stages, and constructing a crack propagation rate model fusing multiple characteristics; s3, preprocessing the expansion rate data; and S4, constructing a physical loss function in combination with a crack propagation rule in an experiment, fusing the physical loss function with data loss to form a total loss function, and training the neural network model to realize accurate prediction of the multi-scale fatigue crack propagation behavior of the metal material.

The invention discloses a low-altitude three-dimensional wind field inversion method and system based on a single wind measurement laser radar, relates to the technical field of radar detection, and solves the technical problems that a traditional wind measurement laser radar cannot directly obtain a velocity vector, data missing of a key area is caused by a blocking effect, and detection nodes are not uniform. The method specifically comprises the following steps: S1, executing a multi-elevation-angle body scanning detection mode and a wind profile detection mode by using a wind measurement laser radar, and obtaining radial wind speed data of a target area; s2, quality control is carried out on the radial wind speed, isolated abnormal points are eliminated, and isolated data missing points are filled; s3, carrying out blocking correction on a single scanning surface by using a smooth spline interpolation method; s4, after the radial wind speed is corrected based on the wind profile, an initial background three-dimensional wind field is obtained; and high-precision wind field inversion of a wind field in a three-dimensional space can be realized by using the radial wind speed of multi-layer body scanning of a single wind measurement laser radar.

The invention discloses a graph convolution recurrent neural network road traffic flow prediction method considering data missing, and relates to the technical field of intelligent traffic, and the method comprises the steps: data collection and preprocessing; constructing node features based on a Gaussian mixture model; designing a graph convolution layer considering data loss; establishing a space-time dynamic model and optimizing the space-time dynamic model; predicting a future traffic state; establishing a model, and continuously optimizing model hyper-parameters through training by using a stochastic gradient descent algorithm to obtain a traffic state prediction scheme; according to the method, the problem of data missing caused by sparse sensor deployment, data loss, hardware faults and other factors can be solved, meanwhile, the spatial-temporal correlation of traffic data can be effectively captured, and the accuracy and reliability of traffic state prediction are effectively improved.

The invention discloses an online monitoring system and method based on the use state of monitoring equipment, and particularly relates to the technical field of equipment monitoring. According to the method, whether the recovery performance of the RAID level is abnormal or not is judged by writing and reading data in an RAID array, the performance of a storage server is evaluated and divided according to the cache hit rate fluctuation condition of a read-write request in a cache in a storage system and the capacity of a storage device for processing an uncompleted I / O request, and a data writing strategy is optimized; when the system runs under a high load, video data are distributed to a plurality of storage nodes by configuring the storage system to realize load balancing and dynamically store the number of the nodes, so that the performance and the reliability of the storage system are remarkably improved, the load of single storage equipment is reduced, the service life of the equipment is prolonged, and the write-in delay and the data loss are reduced; the stable operation of the system under high load is ensured, and the maintenance cost and the downtime are reduced.

The invention discloses a solid-state storage hard disk data recovery method and system, and relates to the technical field of data processing, the method comprises the following steps: carrying out difference analysis on historical log state characteristics and health state characteristics stored in a solid-state hard disk, and identifying a data loss event of the solid-state hard disk based on a difference analysis result; constructing a data recovery mechanism based on the data loss abnormal event, and performing recovery processing on lost data of the solid state disk based on the data recovery mechanism; and verifying and evaluating the recovered data, and optimizing the data recovery mechanism according to a verification and evaluation result. According to the method, differentiated technical means such as metadata restoration, backtracking reasoning and data engraving are combined, classification positioning, cause recognition and structured restoration of lost data are achieved, and the pertinence, accuracy and automation degree of data restoration are effectively improved.