2618 results about "Data segment" patented technology

A computing device can receive a first conversational input of one or more sequential conversational inputs via one or more user input devices. The first conversational input can be associated with a particular uniform resource locator (URL) address. The computing device can process the first conversational input using one or more natural language processing algorithms to determine one or more intents based on the first conversational input. The computing device can select one or more data segments from a data store based on the intents. The computing device can generate a command including the data segments. The computing device can generate a generative response by performing the command with a generative artificial intelligence algorithm. The computing device can provide a response to the first conversational input based on the generative response.

Systems and methods for decentralized data management across interoperable distributed platforms are disclosed. A computing system receives input data associated with a unique decentralized identifier (DID) representing an entity or event. The computing system segments the input data into encrypted data segments, each cryptographically linked to the DID, and distributes these encrypted segments across decentralized storage nodes according to a redundancy scheme. A cryptographic lineage record, including segment identifiers, timestamps, and hashes linked to the DID, is stored in a decentralized ledger. In response to authenticated access requests, the computing system reconstructs the input data by retrieving, decrypting, and cryptographically verifying the distributed data segments against the lineage record. Authorized entities access the reconstructed data through interfaces enforcing cryptographically secured access permissions defined within the decentralized ledger, providing enhanced security, provenance verification, and data resilience.

The invention provides a service data security protection method and system for digital enterprise management, and the method comprises the steps: obtaining an access request sequence initiated by a target user at a service operation terminal, generating a dynamic access control strategy vector according to a historical behavior track associated with an access operation identifier, and carrying out the dynamic access control strategy vector; analyzing the dynamic access control strategy vector through a strategy decoder, and generating a real-time access permission instruction; based on the real-time access permission instruction, performing homomorphic encryption mapping on a sensitive data segment in the request context information to generate a ciphertext transmission channel, and performing security parameter synchronization on the ciphertext transmission channel and the cross-department conjoint analysis model; and calling a federated learning framework to carry out multi-modal feature fusion on the real-time operation flow of the service operation terminal, generating an abnormal operation confidence score, triggering a cross-level interception protocol when the abnormal operation confidence score exceeds a dynamic threshold, and rolling back the current session state to a security baseline version. According to the invention, the accuracy and response timeliness of anomaly detection can be improved.

The invention relates to the technical field of data management, in particular to an industrial control system remote monitoring data synchronization method, which comprises the following steps of: 1, generating a dynamic synchronization time window based on an equipment state sudden change frequency and a network fluctuation degree; 2, dividing transmission priorities according to the influence degree of data on equipment security control; 3, quality characteristics of available transmission paths are evaluated, data of different priorities are transmitted in a fragmented mode according to a differentiation strategy, and data streams are recombined at a receiving end based on time sequence identification; 4, after network disconnection is recovered, synchronous consistency guarantee is achieved through data segment cascade hash verification and control instruction logic conflict detection; and 5, optimizing a time window generation algorithm and a path evaluation rule according to the historical synchronization effect data. Through accurate control, priority division, network adaptability enhancement and consistency guarantee of data transmission, an efficient and safe industrial control system remote monitoring data synchronization scheme with adaptive capability is provided.

The invention discloses a stationary pollution source data acquisition and calibration method and system, a medium and a program product, and the method comprises the steps: carrying out the segmentation processing of a plurality of concentration change data, and obtaining a plurality of data segments; determining the concentration change trend of various pollutants, and calculating a correlation coefficient matrix; the Euclidean distance value between the correlation coefficient matrix of the current data segment and each correlation coefficient matrix before sorting is calculated; when the Euclidean distance is greater than a preset threshold value, marking the corresponding data segment as an abnormal data segment; determining the pollutant as a target pollutant, and establishing a material balance equation; based on the material balance equation, generating a theoretical concentration change curve of the target pollutant; calculating a deviation value, and when the deviation value exceeds a preset dynamic error limit value, determining a sensor correction coefficient according to the process parameters; and carrying out calibration processing on the acquired data of the target pollutant according to the sensor correction coefficient. According to the invention, the accuracy of data acquisition and calibration of the stationary pollution source is improved, so that the influence on formulation and execution of pollution prevention and control measures is reduced.

The invention provides an intelligent factory fault diagnosis method and system based on an AI prediction model, and the method comprises the steps: obtaining an equipment monitoring data flow of a target production line of an intelligent factory, carrying out the diagnosis feature construction processing of the equipment monitoring data flow, generating a state evolution feature and a component correlation feature, and carrying out the fault diagnosis of the target production line of the intelligent factory; and inputting the state evolution characteristics and the component association characteristics into a pre-trained fault prediction model for fault prediction, and generating diagnosis result data containing fault risk levels. And the potential fault type and the propagation characteristic information are identified according to the diagnosis result data, and finally the maintenance guidance data containing the fault positioning identifier are generated based on the potential fault type and the propagation characteristic information and are transmitted to the factory operation and maintenance system to trigger the fault intervention operation, so that the accuracy and the maintenance efficiency of intelligent factory fault diagnosis are effectively improved.

The invention provides a safety production detection data processing method and system based on digitization, and the method comprises the steps: obtaining safety detection data in a target scene, and receiving a detection instruction for the safety detection data, the safety detection data comprising a continuous time sequence data stream collected by a multi-dimensional sensor; performing semantic analysis on the detection instruction to generate a detection instruction feature containing a task type and an entity distribution relationship; performing cross-modal fusion processing on the security detection data through a multi-modal feature extraction model of joint training, and generating a security feature vector of each time sequence node; based on a dynamic matching result of the detection instruction feature and the security feature vector of each time sequence node, screening out a key data segment from the time sequence data stream; and calling a pre-trained security decision model to carry out abnormal mode identification and risk assessment on the key data segment, and generating a structured security report corresponding to the detection instruction. According to the invention, the accuracy and reliability of safety detection can be improved.

The invention relates to a remote education data processing system which comprises the following steps: under a remote teaching task, pre-defining a task intention and a data expectation point; a semantic timestamp and a task binding label are printed on each data fragment; mapping the collected confusion data including silence, eye movement drift and prediction into a unified learning semantic vector; a micro-expression + interactive behavior + time sequence decision path ternary modeling mode is introduced, and a potential cognitive intention corresponding to the feature combination is recognized; teaching context information is fused; constructing a cognitive state mapping model; reasoning a current cognitive state label from multi-modal sensing data; searching intervention track VS effect feedback data in a historical database; generating a predicted intervention behavior sequence by using a sequence modeling algorithm; a dynamic combination suggestion chain including light prompt, content reconstruction, personalized practice and tutoring invitation is adopted; and superposing the cognitive state sequences of all students into a group cognitive trajectory map.

The invention relates to the technical field of sensing data security, and discloses a sensing data chip-level dynamic key negotiation method, which comprises the following steps of: acquiring a unique hardware identifier and key parameters of a sensor node, and generating a dynamic key seed matrix; after acquisition is completed, randomly intercepting data segments, performing median filtering and normalization preprocessing, extracting local statistical features and global features to generate a data feature sequence, and splicing the data feature sequence to a seed matrix to obtain a dynamic key generation matrix; a dynamic negotiation key is generated through standardization and SM3 Hash algorithm encryption, and is stored in a cloud and node security unit; during verification, dual verification is realized through hash comparison and plaintext bit-by-bit matching; and setting an environment parameter exception triggering mechanism, and updating the key if accumulative exception exceeds the limit. According to the method, hardware and dynamic data features are fused, and the key security and adaptability are improved.

A system for automatically self-labeling a digital dataset includes a first sensor for generating a first data stream, a second sensor for collecting information to generate a second data stream, and a causal model manager (CMM). The CMM is configured to determine a first causal event from a first data segment of the first data stream, and a causal relation between the first causal event and a second data segment selected from the second data stream. The system further includes (a) an interactive time model for determining an interaction time between the first and second data segments, and (b) a self-labeling subsystem configured to derive a label from the second data segment, associate the first data segment with the derived label, form a self-labeled data pair from the associated first data segment and the derived label, and automatically annotate the self-labeled data pair with the interaction time.

The invention relates to the technical field of computer storage, in particular to an encryption method and related equipment. The encryption method comprises the following steps: generating a unique device identifier through a polymorphic ring structure, wherein the unique device identifier is used for generating a unique identifier and a key of the solid-state storage device; based on the generated device identifier, a multi-dimensional dynamic key is generated in combination with a multi-dimensional mixed function and is used for data encryption; and encrypting the segmented data by using a multi-dimensional dynamic key, and embedding a progressive vector check code into each data segment to enhance the security of the data segment level. According to the method, the defects of the existing solid-state storage equipment encryption technology in the aspects of key management, encryption mechanism, key irreversibility, real-time monitoring and the like are overcome, and all-around guarantee is provided for data storage security.

The invention relates to the technical field of textile machinery fault detection, and discloses a spinning machine fault detection method and system based on deep learning, and the method comprises the steps: collecting vibration, temperature and current signals through a plurality of sensors, constructing a data stream after principal component analysis and dimension reduction, employing a variational auto-encoder to reconstruct an error positioning abnormal data segment, and obtaining a spinning machine fault detection result. The method comprises the following steps: extracting local features of a multi-source signal in combination with a convolutional neural network, dynamically distributing feature weights by using an attention mechanism, screening time sequence key features through a forgetting gate, introducing an LSTM network to model a feature sequence dynamic relationship, completing fault category mapping based on a support vector machine, and triggering LSTM secondary classification calibration for samples with insufficient confidence. A closed-loop technical system of data dimension reduction, anomaly detection, feature optimization, time sequence modeling and classification verification is formed, and efficient analysis and accurate recognition of multi-modal fault features under complex working conditions are achieved.

The invention relates to the technical field of data processing, in particular to a data processing method and system for a flexible regulation and control terminal, and the method comprises the steps: determining a fuzzy entropy value of each voltage data point in voltage data according to a fuzzy entropy calculation method, and carrying out the abnormality judgment according to the fuzzy entropy value, determining a voltage data point window where the voltage data is located at the sampling moment; calculating a first stability degree according to the quartile distance of the voltage data in the voltage data point window and the number of extreme points; calculating a second stability degree according to the similarity of the voltage data segment and the current data segment in the voltage data point window; and according to the second stability degree, calculating to obtain the sub-window length in the voltage data point window. According to the scheme, the accuracy of fuzzy entropy calculation and the reliability of anomaly detection are remarkably improved.

The invention relates to the technical field of engineering project management, and discloses an engineering project progress intelligent monitoring method and system. The method comprises the following steps: acquiring progress parameters including a task completion state, resource consumption and the like in real time through a multi-source data acquisition terminal; inputting the parameters into a preset anomaly detection model to identify and mark abnormal data segments, wherein the model dynamically adjusts a threshold value according to historical data characteristics; inputting the marked parameters into a collaborative prediction model to generate a progress trend prediction result, and carrying out multi-dimensional fitting on the model according to a task dependency relationship and a resource allocation weight; and finally, classifying and integrating the prediction result and the current progress parameter by using a priority scheduling algorithm to form a dynamic monitoring data set for storage. According to the invention, intelligent monitoring of the progress of the engineering project is realized, the monitoring accuracy and timeliness are improved, project delay and cost increase can be effectively prevented, and the project management efficiency is improved.

The invention discloses a computer network security monitoring system and method. The system comprises a quantum encryption traffic acquisition module, a biological recognition feature extraction module, a block chain detection traceability analysis module and a visual management module. The quantum encryption flow collection module generates and distributes a secure quantum key through a quantum key distribution unit, encrypts a collected data packet through an encryption transmission unit, and generates a flow data segment through a flow collection subunit and a data cache statistical analysis subunit. The biological recognition feature extraction module verifies the identity of a user through a biological recognition unit, and extracts a multi-dimensional feature vector by using a feature extraction subunit. And the block chain detection traceability analysis module performs detection and traceability analysis on the abnormal traffic through the anomaly detection subunit and the traceability analysis subunit, and stores a result in the block chain storage unit. The visual management module provides visual network state display and user identity verification functions through a visual display subunit and an identity verification and access control subunit. According to the invention, quantum encryption, biological recognition and block chain technologies are combined, so that the accuracy, security and traceability of network security monitoring are improved.

The present disclosure is directed toward systems, methods, and non-transitory computer readable media that provide a contextual query answering system that trains and implements a unique machine learning architecture to generate accurate domain-specific contextual responses. For example, the disclosed systems receive a contextual query indicating a software context of a computer application within a software-specific domain. The disclosed systems utilize a context retrieval model to generate query embeddings from the contextual query and data segment embeddings from data segments of stored digital documents. Further, the context retrieval model determines relevant digital documents from among the stored digital documents based on comparing the query embeddings and the data segment embeddings. The disclosed systems provide the relevant digital documents to a response generator model to generate a contextual response within the software-specific domain.

Embodiments are directed to tiered data store with persistent layers. A write tier in the file system for storing in a file system. A value for a performance metric that corresponds to write requests to the file system may be predicted based on characteristics of the write requests such that the performance metric may be determined based on a plurality of interactions with the write tier. The predicted value that exceeds a threshold value of the performance metric may be employed to cause performance of further actions, including: queuing a portion of the write requests in a memory buffer based on the predicted value and the threshold value; combining the queued portion of the write requests into s; storing the data segments in the write tier such that a measured value of the performance metric may be less than the threshold value.

Systems, computer program products, and methods are described herein for systems and methods for identifying electronic accounts based on near real time machine learning in an electronic network. The present disclosure is configured to: identify at least one data storage component associated with an at least one application; determine at least one current requirement of the data; receive at least one data segment from the data storage component; collect at least one current element from at least one data segment; reformat and aggregate the at least one current element and link the at least one current element to a unique entity identifier; apply a trained machine learning model to the at least one current element; generate a new data segment; and generate a propensity score based on at least one of unique entity identifier, at least one current element, or at least one new data segment.

The invention provides an electronic book file secondary encryption method based on MD5 encryption and an asynchronous request, and relates to the field of data processing. Comprising the following steps: firstly, acquiring a current timestamp, a network state, a user request attribute and a system load state through a data acquisition module, and then performing MD5 preliminary encryption on an electronic book file to generate a first layer of ciphertext; segmenting the first-layer ciphertext and transmitting the segmented first-layer ciphertext to an encryption module through an asynchronous request; a self-adaptive distributed load balancing mechanism is adopted to monitor the system state through an encryption module, resources are dynamically allocated, and tasks are scheduled; in combination with intelligent priority scheduling, determining the priority according to the attributes of the data segments; generating a dynamic key based on a timestamp, a network state and the like to encrypt the data segment in real time; and finally, sorting, checking and combining the encrypted data segments into a complete ciphertext file.

The invention relates to the technical field of state monitoring, in particular to an inner package production data real-time monitoring and processing system which comprises a running state acquisition module, a performance deviation evaluation module, an abnormal behavior recognition module, a task scheduling optimization module and a load balancing optimization module. According to the invention, through a cooperative acquisition mode of operation data such as temperature fluctuation, pressure change and flow velocity stability, real-time perception of dynamic collection and high-frequency change of the operation state is realized, and by means of a cross discrimination strategy of distribution uniformity and response time, non-representative data segments are eliminated, and the accuracy of performance abnormity identification is improved. Through time coupling analysis of gradient tracks and offset, abnormal behavior fragments are locked in advance, forward recognition of trend instability is achieved, intervention windows are dynamically screened, actual effect and synchronization of response are ensured, a real-time matching mechanism based on loads and demands is adopted, task priority and processing channel distribution are optimized, and intervention efficiency and scheduling adaptability are improved.

A method is provided that includes: obtaining first urban data of a first sample urban region; inputting the first urban data into a multi-modal foundation model to obtain respective predicted vector representations of a plurality of first data segments; obtaining a plurality of general-purpose foundation models that are pre-trained; for each general-purpose foundation model: generating a vector representation label of a first data segment of a corresponding data modality by using the general-purpose foundation model; and determining a knowledge distillation loss of the general-purpose foundation model based on the vector representation label and a predicted vector representation of the first data segment; and adjusting parameters of the multi-modal foundation model based on at least respective knowledge distillation losses of the plurality of general-purpose foundation models.

A method is provided. The method includes accumulating a set of data blocks for a data segment in a first type of flash memory and determining whether a size of the data segment exceeds a threshold value as the set of blocks is accumulated for the data segment. The method includes identifying a second type of flash memory to store the set of data blocks and storing the set of data blocks in the second type of flash memory.

The invention relates to the field of data processing, in particular to an intelligent data analysis method and system, and the method comprises the steps: constructing a curve of data changing with time; decomposing the curve into three components of a trend item, a season item and a residual item, dividing the curve into a plurality of divided data segments by combining the characteristics of the trend item and the season item, and respectively calculating the abnormal degree of the trend item, the abnormal degree of the season item and the abnormal degree of the residual item corresponding to each divided data segment; and calculating the comprehensive abnormal degree of each divided data segment, performing correction by using the abnormal degree of the residual term to obtain the corrected comprehensive abnormal degree of each divided data segment, and when the abnormal degree of each divided data segment is greater than or equal to a preset threshold value, triggering an alarm mechanism. According to the method, the dominant effect of the main anomaly on the data anomaly degree is considered, and the important effect of the residual term in anomaly detection is also given, so that the accuracy and reliability of data evaluation are improved.

A monitoring system can generate compressed storage system monitoring data segments using monitoring data obtained from a storage system. The monitoring system can obtain storage system monitoring data and generate a segment by applying the storage system monitoring data to a machine learning model trained to segment the storage system monitoring data. The monitoring system can generate a compressed segment by applying a specified compression technique to the segment. In response to user query, the user query specifying a portion of the storage system monitoring data; the monitoring system can perform at least one of: reconstructing and providing the portion using the compressed segment; or providing the compressed segment for reconstruction of the portion.

The invention discloses a resting electroencephalogram quality evaluation method and system based on double-branch comparative learning, and the method comprises the steps: collecting an original EEG signal X, carrying out the data preprocessing and data enhancement, generating two different enhanced views, transmitting the two different enhanced views to a double-branch encoder in parallel, respectively extracting a time domain waveform and a time-frequency domain rhythm feature, and carrying out the deep fusion, the fused features X1 and X2 are sent to a projection head, and through a self-supervised contrast learning mechanism, the network weight is optimized by using contrast loss; a small amount of labeled fine-tuning data sets including X and corresponding labels y are adopted, after flowing through a pre-trained double-branch encoder, the fine-tuning data sets are directly sent to a classification head connected with the back of the double-branch encoder so as to output a prediction result of an input data segment, and a mixed loss function including classification loss and comparison loss is adopted for training in the optimization process. According to the invention, a complete online real-time quality control system is established, and end-to-end real-time closed loop from data acquisition to quality evaluation is realized.

The invention relates to an aeration fan predictive maintenance method, system and equipment based on multi-modal perception and adaptive learning and a medium. The method comprises the following steps: generating a time sequence data set through synchronous acquisition and combined noise reduction processing of a sensor group; generating a multi-dimensional feature vector through time-frequency feature spectrum characterization and interpretability contribution analysis in combination with dynamic weight distribution coupled by environmental factors; on the basis of the multi-dimensional feature vectors, real-time anomaly detection is carried out at the edge end through a lightweight model, and abnormal data fragments are uploaded to the cloud end; and performing cross-sensor bidirectional reasoning on abnormal data fragments through a reasoning model deployed at the cloud, reconstructing a sensor topological graph, intelligently triggering elastic incremental learning, cooperatively processing equipment degradation trend analysis, multi-source evidence fusion and space calibration, and outputting a life prediction result and a fault thermodynamic diagram. According to the method, the core pain points of high early fault omission ratio, insufficient model robustness and the like are solved, and cost reduction, efficiency improvement and equipment life prolonging are realized while the diagnosis precision is maintained.

The invention discloses a time-frequency synchronization tracking and judging method based on GFSK (Gaussian Frequency Shift Keying). The method comprises the following steps: S1, positioning the initial position of a synchronization word by detecting a lead code; s2, extracting a fixed synchronization word, completing frame synchronization based on correlation matching, and positioning a starting point of an effective data segment; s3, a range cycle counter collects a central sampling point and two adjacent sampling points to form a candidate sampling point sequence; s4, dynamically and finely adjusting the range cycle counter to form a time sequence calibration sampling point sequence; s5, performing phase difference calculation on the time sequence calibration sampling point sequence, estimating continuous frequency offset and generating a compensation value; s6, applying the compensation value to construct a decision feedback equalizer with delay front and back terms; s7, performing interference suppression on the compensation sampling point sequence by using a decision feedback equalizer with delay front and back items, and outputting a soft decision value; and S8, executing three times of judgment, constructing a sliding reference sequence, and outputting a final bit judgment result. According to the method, the problem of poor GFSK demodulation robustness under Doppler disturbance is solved.

A storage system performs garbage collection, with data compression, in storage memory. The system obtains hash results from data segments. The system determines similarity of content of data segments, based on the hash results. The system performs data compression of live data of two or more data segments that have similarity of content meeting a similarity threshold. The system writes the compressed live data of the two or more data segments into the storage memory.

The invention discloses an intelligent calculation center power supply flexible scheduling system and method, and relates to the technical field of electric power energy management, and the method comprises the steps: building a communication connection between a scheduling switch system and a remote control center; collecting real-time load power data, and determining an elastic scheduling load amount by setting a safety margin coefficient; based on the real-time load power data, load power prediction is carried out by adopting a long-short-term memory network model, and load abnormity is detected in real time; extracting a static data segment and a dynamic data segment, and determining a power supply regulation rate by calculating a ratio of static power to a dynamic change rate; defining multi-objective optimization, and generating an optimization scheduling strategy based on the real-time load power, the predicted load power and the static and dynamic data segments; and calculating a capacity adjustment amount, generating a specific scheduling instruction in combination with an optimized scheduling strategy, and executing a corresponding capacity adjustment operation. According to the invention, through load prediction, anomaly detection and a multi-objective optimization algorithm, the overall performance and operation reliability are improved.

The invention relates to the technical field of data flow control, in particular to a heterogeneous data flow synchronous control method and system.The method comprises the following steps that multi-source data segment receiving time is obtained, a time interval sequence is constructed, an interval difference value and a synchronous difference quantity are calculated, the sensitivity level is judged, a channel throughput change is extracted, a descending section is marked, and a priority index is generated; and mapping the high-sensitivity stream to a first-stage or second-stage output path to sort and distribute key stream segments, judging that serial numbers are continuously combined or independently output, and generating intersection and sequence information. According to the method, the time interval sequence is constructed based on the receiving time of the multiple data sources, the interval deviation is quantified, the labels are divided through the synchronization difference quantity, the sensitive level structure is established, differential management of the synchronization precision requirement of the heterogeneous data streams is achieved, the response efficiency and the alignment precision of the high-sensitivity data segments are improved in the data distribution process, and the data distribution efficiency is improved. And meanwhile, the competition of a low-sensitivity section for output resources is reduced, and the synchronous time sequence coordination capability and the channel bandwidth utilization rate in a multi-source heterogeneous environment are improved.