1750 results about "Data segment" patented technology

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 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 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.

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 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.

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 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.

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.

The invention relates to the technical field of frequency converter anomaly detection, in particular to a hydraulic engineering frequency converter state detection method and system. The invention aims to improve the accuracy of abnormal data point detection in the frequency converter performance parameter time sequence data, and particularly focuses on the influence of environmental factors on performance parameters. Performance parameter time sequence data and environment parameter time sequence data are synchronously obtained, discrete wavelet transform is used for decomposing performance parameters in a multi-layer mode, and approximation coefficients are mainly analyzed to insight the operation state of the frequency converter. And after the suspected abnormal data segments are preliminarily screened, environment abnormal indexes are calculated in combination with environment parameters, and fluctuation degree values of the suspected abnormal data segments are analyzed. And based on the environmental anomaly index and the fluctuation degree value, adaptively adjusting the initial threshold value of each approximation coefficient to obtain an adaptive threshold value, and finally detecting abnormal data based on the adaptive threshold value. According to the method, abnormal data points caused by environmental factors can be effectively eliminated, and abnormal data points caused by abnormity of the frequency converter can be accurately identified.

The invention discloses a multi-source data fusion accounting method for a full-life-cycle carbon footprint, and belongs to the technical field of carbon footprint accounting, and the method specifically comprises the steps: inputting a purchase order number set of a target product, and automatically associating a logistics fuel consumption record, a production process energy consumption log and a supplier indirect emission declaration form according to the purchase order number; constructing a dynamic traceability tree taking a purchase order as a root node, and generating a trunk path through matching of an order material code and a workpiece batch number of a process energy consumption log; when the difference between the logistics real-time energy consumption data and the supplier declaration form exceeds a set threshold value, activating a dispute data branch reconstruction algorithm, and creating parallel branches to temporarily replace dispute data segments while keeping trunk continuity; finally, outputting a full-life-cycle carbon footprint data graph with dispute marks, and reserving original association paths of all data sources; according to the invention, reliable support is provided for accurately tracing the full-life-cycle carbon emission of the product and improving the carbon footprint accounting quality.

The invention discloses a financial sensitive data desensitization method and device, equipment and a storage medium, and relates to the technical field of data security, and the method comprises the steps: recognizing a sensitive data segment of to-be-desensitized financial data in an interface request, obtaining a sensitive data type and a target specific character position, and storing the sensitive data type and the target specific character position; determining to-be-encrypted data in the to-be-desensitized financial data by using a preset regular expression and based on the target specific character position; extracting a first preset number of bits of data from dynamic identification information corresponding to the to-be-desensitized financial data to obtain a first type of characters, and intercepting a second preset number of bits of data at the tail of the to-be-desensitized financial data to obtain a second type of characters; and combining and encoding the first type of characters and the second type of characters to obtain an initial character sequence, encrypting the to-be-encrypted data by using a target converted character sequence obtained by carrying out system conversion on the initial character sequence to obtain encrypted data, and desensitizing the to-be-desensitized financial data based on the encrypted data to obtain desensitized financial data. Therefore, the data desensitization efficiency can be improved.

The invention discloses a data quality treatment method and system based on an AI Agent, and belongs to the technical field of artificial intelligence and data treatment. An initial data semantic distribution map is constructed, cross-dimension correlation feature factors are extracted, a multi-scale quality anomaly sensitive factor matrix is constructed, time sequence evolution weights are embedded, and a dynamic feature evolution trajectory is formed; performing perception modeling on the evolution trajectory by using an AI Agent, generating a multi-level quality risk thermodynamic diagram, extracting a deviation dense region and constructing an anomaly propagation path set; in combination with upstream and downstream data links and task flow information, calculating a potential impact factor weight, constructing a causal traceability map, and injecting a correction strategy label for a key field node; the AI Agent autonomously selects an adaptive strategy combination according to the target data segment, and performs online intervention on the target data segment; according to the method, closed-loop treatment of the data quality problem from perception and judgment to intervention and feedback is realized, and the method has the advantages of self-adaption, high interpretability and the like.

The invention discloses a miniature virtual instrument information acquisition method, relates to the technical field of information acquisition, provides a multi-modal data acquisition and time alignment scheme for a resource-limited miniature virtual instrument environment, and comprises the following steps: 1, carrying out normalization processing on an image sequence, an audio sequence and a sensor value sequence, and extracting a reference event; 2, generating a global time reference and a multi-modal alignment parameter based on a distributed consistency algorithm and the initial correction amount; 3, performing interpolation correction on the abnormal data segments according to the alignment parameters, and remarkably improving the time sequence consistency; and step 4, dynamic monitoring and cyclic self-correction are carried out through a drift metric function, accurate synchronization of long-time operation is ensured, high precision and low resource occupation can be considered, and the method has significant robustness and adaptability, can be widely applied to industrial monitoring, intelligent terminals, the Internet of Things and the like, realizes deep fusion and reliable analysis of cross-modal data, and has good application prospects. And long-term time consistency can be maintained in multiple iterations.

Systems and methods for generating generative AI based solution are disclosed. A system receives a request for generating the generative AI (GenAI) based solution. The system classifies the received request into solution patterns and performs actions corresponding to at least one of the solution patterns. The system extracts a metadata from the received request and the actions based on the type of GenAI based solution to be generated. Further, the system segments the extracted metadata into data segments and generates a vector representation of the data segments. The system generates the GenAI based solution corresponding to the received request based on the generated vector representation. The system validates the GenAI based solution large language model (LLM). The system continuously updates the LLM and the vector-based machine learning model with the validated GenAI based solution and user feedback. The system outputs the GenAI based solution on a user interface.

The invention provides an intelligent decision-making method and system for a job progress based on big data, and the method comprises the steps: obtaining a real-time data stream of a job site, and carrying out the unified format processing of the real-time data stream; segmenting and cleaning the data in the unified format to generate cleaned data fragments; performing aggregation processing on the cleaned data fragment set to generate an aggregated data set, and generating an early warning signal and a scheduling instruction according to the aggregated data set; carrying out data packaging on the early warning signal and the scheduling instruction, and generating a preliminary scheduling scheme in combination with historical records; and dynamically comparing the preliminary scheduling scheme with the dynamic input parameter sequence, and if a deviation value is detected to exceed a preset threshold value, optimizing the scheduling scheme based on the deviation value to obtain a final optimized scheduling scheme. The scheduling strategy is continuously optimized through dynamic comparison and a feedback mechanism, intelligent management and continuous optimization of the production process are achieved, and the production efficiency and the resource utilization rate are improved.

The invention discloses a secure data transmission method in a cloud platform salary management system, which belongs to the technical field of cloud platform data transmission, and comprises the following steps: acquiring user post attributes and authority levels to generate a post authority matrix, and generating a segmentation encryption rule to dynamically segment salary data to generate an encrypted data segment group; generating an environment security score based on the characteristics of the current network transmission path, and verifying the environment security score with the encrypted data segment group; after data transmission is completed, implanting a two-dimensional verification watermark into the encrypted data segment group to generate a controlled data packet; and generating a protection intensity grade and a gradient response instruction based on the current operation step and the environmental safety score, and executing a dynamic data protection operation. According to the method, the security classified storage, the dynamic encryption transmission and the accurate tracing control of the salary data can be realized by adopting the intelligent verification mechanism based on the dynamic segmentation encryption of the post permission matrix and the environmental security score and the multi-level data control technology of the two-dimensional verification watermark.

The invention discloses an aquatic product processing production link temperature control method, which comprises the following steps: dynamically adjusting temperature control parameters in a processing link according to temperature quality related parameters, and if the temperature quality related parameter of a certain link displays that the influence weight of temperature fluctuation on a quality index is higher than a preset threshold value, judging that the temperature control parameters are not the temperature control parameters; if yes, the temperature tolerance range of the link is reduced, and an optimized temperature control parameter set is obtained; and constructing a problem tracing path according to the abnormal feedback data monitored in real time, automatically tracing back to the corresponding data segment in the integrated temperature information base aiming at the time point and the link label of the alarm record, and extracting the temperature change sequence before and after the occurrence of the abnormality to obtain a detailed abnormality tracing report.

Backup and recovery of multi-party computation (MPC) security data utilized to secure digital assets and transactions thereof can enhance user confidence and user experience in digital asset transactions. Example MPC security data can include cryptographic keys and key shares utilized with a N×M MPC signature and validation framework. A computing device participating in generation of MPC secure data can retain a segment of the MPC secure data and can encrypt and store an encrypted segment at a second device. A recovery service or recovery application at the second device can facilitate recovery of the MPC secure data segment at the computing device, or at an additional device not involved in generation of the MPC secure data. The recovery service or application can facilitate recovery of the MPC secure data segment even in the event the computing device is lost.

A correlation-aware adaptive codebook compaction system for multi-modal data compression that preserves cross-modal relationships while providing enhanced reconstruction quality. The system analyzes temporal and spatial relationships between different data modalities to generate correlation maps that guide compression decisions. A virtual management layer performs stream characterization and adaptive routing, while a processing pipeline implements primary codebook compression with mismatch handling for novel data blocks. High-entropy data segments receive pre-compression processing before codebook compression. Sequential registration data is processed through matrix factorization and dedicated matrix codebooks. The system continuously monitors data distribution characteristics and automatically retrains codebooks when drift thresholds are exceeded. A neural upsampling subsystem uses correlation information to guide cross-modal enhancement processes through modality-specific networks and attention mechanisms. The unified output includes compressed data streams, correlation maps, synchronization metadata, neural model parameters, and updated codebooks, enabling synchronized reconstruction with preserved cross-modal relationships and enhanced quality through correlation-guided neural upsampling.

The invention provides an unstructured data compression method and device, equipment and a storage medium, and the method comprises the steps: carrying out the feature extraction and clustering processing of a to-be-compressed unstructured data set, and obtaining a data segment clustering group; performing semantic region segmentation processing on each data fragment in the data fragment clustering group to obtain a semantic important region and a semantic non-important region; performing differential coding processing on the semantic non-important regions in the same clustering group to obtain differential coding data; and performing high-quality compression processing on the semantic important regions in the same clustering group, and performing merging processing on the compressed important semantic data and the difference coding data to obtain an unstructured data compression result. According to the method, semantic region segmentation and differential compression processing are performed on the unstructured data, so that the data compression efficiency can be effectively improved on the premise of ensuring the quality of important information, and an efficient solution is provided for storage and transmission of large-scale unstructured data.

The invention relates to the technical field of data transmission, and particularly discloses a block chain-based data security storage and verification method, which comprises the following steps: collecting biological characteristics, generating a first dynamic key, the first dynamic key being used for encrypting first data, and updating during each access to ensure the key timeliness; dividing the first data into a plurality of segments according to logic, generating segmented hash values, and storing the segmented hash values on a chain; dynamically loading the corresponding fragment according to the user permission, and realizing anonymous verification through a homomorphic encryption technology; based on the Internet of Things technology, the first positioning parameter and the first state parameter are combined, the access permission is dynamically adjusted, and a self-destruction mark is set to avoid the overtime access risk; and in the storage process of the data fragments, dynamic security scoring and path adjustment are performed through distributed storage. According to the method, the static problem in data storage and access control is solved, and a more flexible and reliable security guarantee can be provided in a dynamic access scene.

The invention discloses a magnetotelluric signal-noise separation method based on an artificial neural network. The magnetotelluric signal-noise separation method comprises the following steps: constructing massive sample libraries conforming to magnetotelluric weak signals and strong interference characteristics; inputting the sample library into a one-dimensional convolutional neural network for training to obtain a corresponding training set and a test set, and defining the network and related training parameters to obtain a training model; performing signal-noise identification on the simulated and actually measured magnetotelluric data by using the obtained mathematical model; noise suppression is only carried out on signals identified as strong interference by using a wavelet soft threshold method; and combining the electromagnetic signal segment after strong interference suppression with the weak electromagnetic signal segment to obtain a reconstructed magnetotelluric signal. According to the method, data segments of strong interference and weak electromagnetic signals can be identified in a more adaptive and high-precision mode, more useful magnetotelluric signals are reserved, and geoelectricity information of a measuring point is better reflected.