1724 results about "Data loss" patented technology

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.

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 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 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 relates to the technical field of geophysical exploration, in particular to a multichannel deep learning magnetotelluric inversion method based on physical information constraint. The method comprises the following steps: generating a synthetic data set containing a geoelectric model and forward modeling response thereof, and adding a noise simulation actual observation condition; constructing a hybrid network architecture combining Transform and U-Net, taking apparent resistivity and impedance phase as dual-channel input, extracting global features by using an encoder, gradually recovering spatial resolution through a decoder, and outputting an underground resistivity model; network training adopts a composite loss function fusing model loss and data loss, and an inversion process is constrained by introducing a magnetotelluric forward modeling physical rule, so that a result is ensured to fit observation data and conform to a physical mechanism; after training is completed, preprocessed actual measurement data are input into the model, and a resistivity image can be directly obtained. The method is used for geological structure identification and reservoir interpretation, and the inversion precision and reliability are effectively improved.

The invention relates to the technical field of image analysis, and discloses a multi-modal emotion recognition method and system based on hypergraph diffusion and evidence fusion, a terminal and a storage medium, and the method comprises the steps: carrying out the random shielding of a data set through a randomly generated mask, thereby simulating the random data missing condition, and carrying out inverse sampling on the preprocessed simulation data by using a trained conditional diffusion model to obtain a training set in which missing modals are complemented so as to train an emotion classification network, and finally carrying out emotion recognition. According to the method, through dual-channel evidence fusion, uncertainty is estimated at a feature source level and a discrimination level at the same time, so that adaptive evidence fusion is realized, the condition of performance reduction caused by modal loss is reduced, potential features of the lost modal are explicitly recovered in a feature space, and the accuracy of final emotion recognition is improved.

A method for implementing data loss prevention (DLP) includes: generating an asset lineage map from file system metadata; identifying, based on the asset lineage map, an input feature linked to the asset, a type of the asset, and a plurality of activities linked to the asset; obtaining a sensitivity score for the asset based on the input feature and the type of the asset; obtaining, based on the plurality of activities, a malicious score and a data loss score for the asset; determining a user level of a user; and initiating implementation of a first DLP policy for the user based on the user level, the malicious score, the data loss score, and the sensitivity score.

Systems and methods for protecting electronic communications are described. A cyber security appliance may be configured to calculate a topic shift score for a communication by comparing a first lexical profile derived from the communication to a historical lexical profile established for an associated user. This analysis may be performed without using a large language model. The system may also parse communications to extract sensitive data and content from attachments, performing behavioral modeling on the extracted data. Based on the analysis, an autonomous response module may take a variety of mitigation actions. Furthermore, a security mailbox assistant module may perform a secondary, in-depth analysis on user-submitted communications and generate a deterministic report. For outbound communications, a data loss prevention architecture may divert messages for in-line analysis and may include a fail-safe timeout mechanism to ensure service continuity.

The invention discloses a PINN-based limited space internal explosion damage parameter prediction method and system. The method comprises the steps of completing data acquisition and constructing a time sequence data set; model construction is completed, and the time sequence modeling capability is enhanced; determining a total loss function and adding boundary condition constraints to enable model prediction to accord with physical laws; firstly optimizing data loss, then introducing a physical residual error, and finally activating a boundary speed suppression loss item and adjusting a learning rate; in combination with a learning rate dynamic scheduling and early stop mechanism, the training efficiency and stability are improved through adaptive residual weighted balance data and physical constraint loss; and predicting parameters such as pressure, temperature and speed of each point in the limited space in multiple time frames by using the trained model, calculating impulse based on a pressure time history, evaluating personnel damage, and completing damage zoning in the limited space. And efficient modeling under a complex boundary condition and a limited space environment is completed.

The invention discloses a bridge structure health state intelligent sensing and early warning system, relates to the technical field of bridge monitoring, and provides the following scheme that the bridge structure health state intelligent sensing and early warning system comprises a sensing layer, a transmission layer, a data layer, an analysis layer and an application layer which are connected in sequence and work cooperatively: the sensing layer is used for comprehensively collecting health state parameters of a bridge structure; according to the method, the comprehensiveness of monitoring parameters is guaranteed, the effectiveness and transmission efficiency of collected data are improved, redundant data are prevented from occupying resources, deep mining is achieved, a more scientific basis is provided for health status assessment and disease diagnosis, misjudgment caused by single parameter analysis is reduced, the diagnosis precision is improved, and the method is suitable for popularization and application. The intelligent optimization and simulation evaluation of the maintenance scheme are realized, the limitation of the traditional empirical scheme is avoided, the maintenance cost is reduced, the construction period is shortened, the influence on traffic is reduced, the performability of the scheme is improved, the stable and safe transmission of monitoring data in a complex environment is ensured, and the data loss or leakage is avoided.

The invention discloses a data and physical dual-drive crack propagation prediction method, which belongs to the field of petroleum engineering and comprises the following steps: step 1, constructing a real observation data set and a sampling data set; step 2, constructing a hybrid architecture fusing a Transform encoder and a graph attention network; 3, three independent and parallel decoders are constructed to map the shared features into the geometric dimensions and mechanical parameters of the cracks; 4, establishing a physical loss function based on linear elastic fracture mechanics and a material balance principle, and combining the physical loss function with a data loss function to construct a mixed loss function for model training; and 5, predicting the geometric dimension and mechanical parameters of the fracturing crack by using the trained model. According to the method, physical priori knowledge is embedded into a multi-task deep learning framework, and physical loss is embedded into a loss function, so that the precision and interpretability of model prediction are remarkably improved.

The invention belongs to the field of industrial thermal engineering and intelligent modeling, and discloses a heat exchanger heat exchange coefficient prediction method and system based on a physical information neural network. The method comprises the following steps: acquiring multi-dimensional operation data through a signal acquisition system, cleaning abnormal and blank values, standardizing, and segmenting into time sequence samples by adopting a sliding window method; a double-layer physical information long-short-term memory network is constructed, and a time sequence feature and a physical equation residual error are combined to generate a space-time fusion feature matrix. And a composite loss function including data loss, physical equation loss and physical consistency loss is designed, physical and data driving influences are balanced through hyper-parameter tuning, and accurate prediction of the heat exchange coefficient is achieved based on a gradient descent optimization model. The method combines field physical laws and data features, improves the reliability and physical interpretability of prediction, and is suitable for operation optimization of the heat exchanger of the desulfurization wastewater treatment system of the thermal power plant.

The invention relates to a multi-source perception-based mechanical arm operation data acquisition system and method, the system comprises an isomorphic mechanical arm operation module, a multi-view visual image acquisition module, an optical motion capture module and a shielding detection and data fusion module, vision, pose and force sense data are cooperatively acquired through multiple sensors, and time-space consistency is synchronously kept by adopting hardware. The system has a multi-dimensional occlusion detection mechanism and can dynamically switch data processing strategies according to the occlusion degree: visual data is preferentially adopted when no occlusion exists, multi-sensor information is fused when partial occlusion exists, and force feedback compensation is predicted and introduced based on a dynamic model when complete occlusion exists. Physical feedback and visual perception are creatively combined, the problem of data missing of a traditional single sensor data acquisition system under the shielding condition is solved, more comprehensive and more reliable data support is provided for mechanical arm algorithm training and performance optimization, and the method is particularly suitable for complex operation scenes with shielding.

The embodiment of the invention provides a cross-network data exchange method, electronic equipment, a storage medium and a program product, and the method comprises the steps: receiving transmission data, determining the identification information of the transmission data according to the service identification, timestamp and segment sequence number of the transmission data, and determining the verification value according to the performance requirement, the security requirement and the data integrity; the hash algorithm is obtained by performing hash operation on the segmented data; according to the identification information, judging whether the transmission data is duplicated data or not; judging whether the transmission data is complete data or not according to the verification value; whether the transmission data is lost data or not is judged according to the segment serial number, if yes, a retransmission instruction is sent to the sending end equipment, the data segments which are repeatedly transmitted can be accurately recognized through the unique identification information, redundant storage and processing are avoided, the data repetition problem is solved from the source, then the integrity of the data is verified according to the verification value, and the data transmission efficiency is improved. And the lost data end is judged according to the segment sequence number, so that the problem of data loss is solved.

The invention provides an abnormal power failure diagnosis method and device, and relates to the technical field of power failure diagnosis, the device comprises an MCU, the MCU is connected with a selection switch through a line, and one end of the selection switch is provided with a capacitor. Hardware hidden dangers can be eliminated at the beginning of power-on through hardware self-inspection in the system pre-starting stage, and it is guaranteed that operation baselines of the whole delay power supply module and the MCU are correct; according to the following capacitor energy charging function, a super capacitor is connected in parallel to the 12V low-voltage power supply side, and the charging current and voltage slope are monitored in real time, so that even if the capacitor stably and safely stores enough energy, overcharge damage is prevented, and monitoring is interrupted by data heartbeat of an MCU and a BMS master controller and power-down cmd, it is ensured that the system can sense a power-off instruction in time at any time, and the power-off state of the system is ensured to be stable and reliable. And persistent storage of the key operation parameters is completed in an atomicity write-in mode, so that the data loss risk caused by communication timeout or software faults is greatly reduced.

The invention discloses a black pig breeding disease intelligent monitoring management method based on big data, and relates to the technical field of animal husbandry intelligent management and animal disease monitoring, and the method comprises the following steps: obtaining multi-dimensional data information of pigs in a black pig breeding scene in real time through a sensor network, a video monitoring system and a physiological information collection device; the multi-dimensional data collected in real time is preprocessed and standardized, and the original data quality and the data analysis effectiveness are improved. According to the invention, by introducing an adaptive adjustment mechanism, the problem of pathological data loss caused by excessive elimination of abnormal values in the prior art is solved, and accurate retention and dynamic tracking of early disease signals of pigs are realized. The method integrates multi-dimensional feature extraction and intelligent evaluation, has pathological trend perception and processing strategy adaptive adjustment capabilities, effectively improves the early recognition sensitivity and discrimination accuracy of a disease early warning system, and provides more scientific health management support for farms.

The invention relates to the technical field of computers, and discloses a data transmission method and electronic equipment, the method is suitable for a host, a plurality of data channels are arranged between the host and a graphics processor, and the host is deployed with buffer areas corresponding to the data channels. The method comprises the following steps: determining scheduling priorities of a plurality of data channels based on first communication data of the plurality of data channels and a target scheduling model; determining a target data channel in the plurality of data channels based on the scheduling priority; writing to-be-sent data in the shared cache space to obtain a target address of the to-be-sent data in the shared cache space; writing the target address into a target buffer area corresponding to the target data channel; wherein the graphics processor is used for reading the target address in the target buffer area through the target data channel, and reading and processing the to-be-sent data in the shared cache space according to the target address. The problem of data loss in a data interaction scene of the host and the graphics processor can be solved.

The invention relates to the technical field of data processing, and discloses a mine production-oriented multi-source data acquisition and edge calculation fusion device, which comprises a clock synchronization access module, a data governance control module, a flow pipeline backpressure module, an AI reasoning optimization module, a rule closed-loop control module, a message safety uplink module and a center training iteration module, according to the method, the problem of difficulty in data fusion of multi-source equipment is solved, and the consistency of heterogeneous data in time and semantic dimensions is ensured; the stability of an edge computing system under a complex working condition is improved, and data loss and processing delay are avoided; collaborative decision-making of the AI model and the rule engine is realized, and the risk of false report and missing report is reduced; the security and reliability of the data transmission process are ensured, and local autonomy when the network is abnormal is supported; a model automatic iteration mechanism is established, and the ability of the system to adapt to different working conditions is improved.

The invention discloses a cloud hard disk data backup and recovery method and device, electronic equipment and a storage medium, and relates to the technical field of cloud computing. A cloud hard disk is segmented into a plurality of segmentation blocks and a multi-thread parallel processing mode is adopted, a backup and recovery task of a large-capacity cloud hard disk is decomposed into a plurality of sub-tasks to be processed at the same time, and the data backup and recovery efficiency is improved. The operation time is greatly shortened, the number of threads in the thread pool is determined according to the number of the segmentation blocks and the current available computing resources, the utilization rate of the computing resources can be improved, cloud hard disk segmentation strategy parameters can be flexibly configured according to service requirements and cloud hard disk characteristics, data protection requirements in different scenes are met, and the user experience is improved. Consistency verification is carried out on the transmitted data, so that the integrity and consistency of the data in the backup and recovery process are ensured, the data loss risk is reduced, and the purposes of improving the backup and recovery efficiency of the cloud hard disk and enhancing the system reliability and the computing resource utilization rate are achieved.

A system and method for comprehensive data loss prevention and compliance management designed to identify and prevent cybersecurity attacks on modern, highly-interconnected networks, to identify attacks before data loss occurs, using a combination of human level, device level, system level, and organizational level monitoring and protection.

The invention discloses a high-order direct-current micro-grid system multi-loss synchronous convergence modeling method based on an improved physical information neural network. The method comprises the steps that S1, a physical information neural network model is initialized; s2, calculating physical loss and updating parameters of the physical information neural network model; s3, reflecting parameters of the physical information neural network model around physical optimization result points; s4, calculating data loss by using the reflected parameters and updating parameters of the physical information neural network model; s5, using the parameters obtained in S2 and S4 to update the parameters of the physical information neural network model through the iterative operation of Douglas-Rachford splitting; and S6, iterating the parameter updating process to solve the physical information neural network model. Different from a traditional physical information neural network which is difficult to converge for high-order system training, the method can realize synchronous convergence of physical loss and data loss in high-order direct-current micro-grid system fitting, and has important significance for mastering an internal operation rule of a direct-current micro-grid system in real time and establishing a physically interpretable data driving model.

The invention relates to the technical field of data management, and discloses a dynamic partitioning and priority scheduling method based on time sequence data, which comprises the following steps: collecting time sequence characteristics of an industrial data source through an edge gateway and carrying out structured storage in Redis, the time sequence characteristics comprising collection frequency, historical peak time period and service priority; load indexes are calculated based on time sequence characteristics, and a distributed message system is driven to execute dynamic partition adjustment of pre-judgment type capacity expansion, hot spot precise splitting and low-peak intelligent capacity reduction; and full-link data priority scheduling is realized through production end route isolation, Broker end resource inclination and consumption end thread pool isolation. According to the invention, the dynamic adaptation of the distributed message system resource and the industrial time series data load is realized, the real-time performance of high-quality data is ensured, the integrity of data transmission and the system adaptability are improved, and the problems of poor load fluctuation adaptation, high-quality data delay and easy data loss due to faults in the prior art are effectively solved.

The invention relates to the technical field of three-dimensional scanning, and particularly discloses a three-dimensional scanning efficiency and precision balancing method and system based on a self-adaptive sampling strategy. The method comprises the following steps: acquiring prior data and scanning state data of a target object, fusing an initial three-dimensional point cloud, a multispectral reflection image and reflection intensity information, accurately extracting and correcting surface curvature distribution characteristics, generating a local geometric feature vector sequence by combining geometric and optical characteristics, quantifying a scanning priority, marking a scanning path based on the sequence, and obtaining a target three-dimensional point cloud image. A scanning strategy matrix is constructed by combining equipment geometric positioning precision, maximum scanning speed and time constraint, differential scanning parameter matching is realized, a local compensation sub-path is dynamically generated through real-time quality monitoring during scanning, a main path and a supplementary scanning path are spliced, and an output result is output, so that scanning efficiency and precision are pointedly balanced, and the scanning precision is improved. Local data missing or quality reduction is avoided, the accuracy of the key area is guaranteed, and the overall scanning efficiency is improved.

A system and method of reducing data loss in a system utilizing a bus protocol that does not support flow control is disclosed. The peripheral device utilizes a spill buffer which is used to capture any data sent by the host before the peripheral device is able to properly configure the DMA controller. Additionally, the peripheral device includes a recovery routine, which is a software program that parses the spill buffer and extracts any headers or payloads that are contained therein. Using the spill buffer and recovery routine, the baud rate of the bus interface may be increases without incurring any increase in data loss.

The invention discloses an adaptive weighted physical information neural network-based monomer voltage prediction method. The method comprises the steps of constructing a sample set according to real vehicle battery multi-dimensional time domain data; establishing a physical branch output monomer voltage physical prediction vector based on an equivalent circuit model; constructing data branches based on a graph attention mechanism to extract node time sequence features to obtain a data-driven prediction vector; a residual error is calculated, a compensation module generates a correction amount, and the correction amount is superposed to a physical prediction vector to obtain a final prediction result; and constructing a multi-step loss function of physical loss and data loss, and introducing an adaptive weighting mechanism to dynamically adjust loss weight iteration optimization parameters. According to the method, the precision and stability of single voltage prediction under a complex operation condition are effectively improved, and the adaptability and generalization performance of the model are enhanced.

The invention discloses a high-time-resolution flow field testing method and system based on sparse moment measured values and an intelligent power system, and belongs to the field of flow field testing and data reconstruction in bridge wind engineering. According to the method, overall low-frequency and local high-frequency sparse flow field data are obtained through a four-pulse fixed-frequency variable-frequency laser system and a four-exposure high-speed imaging PIV sampling system; a 32-dimensional nonlinear modal coefficient is extracted through a multi-scale convolution flow field sparse feature extraction model, then an unsampled time step coefficient is predicted through an LSTM intelligent power system model, and finally the unsampled time step coefficient is input into a multi-scale convolution auto-encoder to reconstruct a high-time-resolution flow field. The method does not need to depend on a pressure sequence, reduces the hardware and data processing cost through sparse sampling, accurately captures the flow field dynamics characteristics through intelligent modeling, solves the problems of expensive high-frequency hardware, loss of low-frequency reconstruction data and difficulty in model training in a traditional PIV test, and is suitable for flow field dynamics research and engineering optimization.

The invention discloses an AI-based distributed big data intelligent storage management method, and relates to the technical field of storage management. The method comprises the steps of generating an initial feature matrix by obtaining observation data of all storage devices in a distributed storage cluster, and inputting the initial feature matrix into a pre-training life prediction model to obtain residual life; equipment with the service life lower than a first safety threshold value is judged as high-risk equipment, and data of the high-risk equipment is included in a to-be-migrated source data set; the devices with the service life higher than a second safety threshold value are included into a to-be-migrated target device set; and generating a migration plan according to the source and target data sets and executing migration. By predicting the residual life of the equipment, advanced identification and active data migration of the high-risk equipment are realized, the data loss risk is reduced, and the utilization rate of the health equipment and the overall cluster storage resource efficiency are improved.

The invention belongs to the technical field of aero-engine performance testing, particularly relates to a physical constraint-based aero-engine state prediction model construction method and system, and aims to solve the problems of high calculation complexity and poor data quality of an existing physical information model. The method comprises the following steps: acquiring historical operation data of the aero-engine and a physical constraint rule set of engineering simplification; predicting performance parameters by adopting a deep learning model; constructing a total loss function formed by weighting a data loss item and a physical loss item to train the model; wherein the physical loss item is generated based on the deviation degree of the predicted performance parameter and the engineering simplified physical constraint rule set, and is used for replacing the complex partial differential equation constraint. According to the method, the engineering simplified physical rule is introduced, so that the calculation overhead of model training is remarkably reduced, the model is effectively guided to learn the characteristics conforming to the physical rule, and the accuracy and generalization ability of the prediction model are remarkably improved under the condition of limited data.

Data loss prevention systems and methods in an enterprise data management and monitoring system may intercept a request to a network service, e.g., a service using an artificial intelligence and / or machine learning model. The systems and methods may represent contents of the request via one or more vector embeddings, which may be compared to vector embeddings corresponding to respective ones of a plurality of sensitive data elements in the enterprise. The data loss prevention system and methods may apply various data sensitivity policies based on determinations of whether sensitive data of the enterprise is included in the request to the network service, e.g., by blocking or redacting the request to prevent exposure of the sensitive data to the network service.

The invention discloses a GIS equipment shell vibration fault identification method and system based on a physical information neural network, and relates to the technical field of GIS fault diagnosis, and the method comprises the steps: collecting a vibration acceleration signal of the surface of a GIS shell; establishing a simplified physical model of the vibration system, and determining a control equation, initial conditions and boundary conditions of the vibration system; based on the HFT-MPINN or the RL-PINN, constructing a parameter inversion model; fusing physical constraint loss, experimental data loss and multi-parameter coupling loss of the vibration system to construct a composite loss function; training a parameter inversion model by using the vibration acceleration signal to obtain key physical parameters of the vibration system; time-frequency domain analysis and finite element simulation verification are combined, and the mechanical fault type and degree of the GIS equipment shell are recognized. According to the method, the modeling precision, the noise robustness and the multi-working-condition adaptability of the high-frequency vibration signals are improved, and reliable technical support is provided for intelligent fault diagnosis of GIS equipment.