3931 results about "Data synchronization" patented technology

The invention discloses a mechanical equipment state monitoring method and system based on multiple sensors, and the method comprises the five core steps: multi-modal data collection and preprocessing, dynamic feature fusion, adaptive threshold diagnosis, digital twin fault tracing and predictive maintenance decision. All-domain coverage of equipment is realized through a three-layer sensor network architecture, the problems of data synchronization and interference resistance are solved by utilizing a temperature and vibration integrated sensor, deep fusion and anomaly detection of multi-source data are realized in combination with an attention mechanism, a Gaussian mixture model, a three-dimensional convolutional neural network and the like, and finally a precise maintenance strategy is generated through digital twinning and reinforcement learning. The multi-sensor-based mechanical equipment state monitoring system comprises a sensor network layer, an edge computing layer, a cloud platform layer and a man-machine interaction layer, supports federated learning to protect data privacy, improves real-time diagnosis capability through edge-cloud collaboration, and enhances a reality interface to realize intelligent operation and maintenance interaction.

The invention relates to the technical field of multi-sensor fusion, in particular to a high-reliability multi-sensor fusion pump station monitoring system and an intelligent early warning control method, and the method comprises the steps: 1, carrying out the synchronous fusion processing of data based on the sampling frequency difference of multi-source heterogeneous sensors, so as to guarantee the consistency of time axes; 2, calculating the consistency difference of the data of each sensor according to the synchronized data, and dynamically shielding an abnormal data source to avoid false alarm interference; 3, real-time operation state modeling is carried out based on the data change trend after dynamic shielding; and 4, accurate early warning and decision optimization are realized. According to the high-reliability multi-sensor fusion pump station monitoring system and the intelligent early warning control method, data synchronous fusion processing is performed based on the sampling frequency difference of the multi-source heterogeneous sensors, so that the consistency of a time axis is ensured, and the problem of influence of asynchronous data on the state judgment accuracy is effectively solved.

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention relates to the technical field of information retrieval, and discloses a distributed storage and indexing method, which comprises the following steps of: dynamically fragmenting an original file through an improved consistent Hash algorithm to generate a plurality of data blocks with timestamps; constructing a three-dimensional Bloom filter index matrix containing timestamps, data types and content features for the data blocks with the timestamps, associating a B + tree local index with an inverted global index through a hierarchical index structure, and establishing an index update priority queue by adopting a two-channel synchronization mechanism, performing real-time increment synchronization on the hotspot index through a heartbeat mechanism, performing batch synchronization on the cold data layer index according to a cold data synchronization period, predicting a data distribution probability through a distributed query statistics probability table during query, and initiating multi-path query in parallel based on probability weight, and the query path is dynamically optimized according to the node group storage medium type and the inter-node network transmission delay, so that rapid distribution adjustment and access of distributed storage are realized.

The invention discloses a power distribution network distributed measurement synchronization method and device based on Beidou satellite time service, and belongs to the technical field of power distribution network measurement, and the method comprises the steps: obtaining a Beidou satellite signal, calibrating a local clock source, and generating a standard time reference; when it is continuously monitored that the Beidou satellite signal quality parameter is lower than a quality threshold value, clock working environment parameters of a local clock source are collected, and a short-term clock offset predicted value is generated in combination with a standard time reference; generating topology correction according to the topology connection relation of the power distribution network; and fusing the short-term clock offset predicted value and the topology correction quantity to generate a dynamic compensation time reference, performing time marking on the acquired electrical quantity measurement data of the power distribution network, and generating synchronous measurement data with a timestamp. According to the technical scheme, when the Beidou signal is lost, the local clock drift predicted value and the adjacent node topology correction amount are fused to generate the dynamic compensation time reference, so that the measurement data synchronization precision and robustness of the power distribution network in a complex environment can be remarkably improved.

The invention relates to the technical field of civil engineering tunnel structure safety monitoring and intelligent detection, in particular to a multi-modal fusion tunnel structure apparent disease identification and risk assessment system, which comprises an image acquisition module used for acquiring continuous images of the inner wall of a tunnel lining; a laser point cloud acquisition module; a structure sensor acquisition module; a data synchronization and preprocessing module; the multi-modal feature extraction module is used for performing depth feature extraction on the image, the point cloud and the sensor data; the heterogeneous feature fusion and disease identification module is used for fusing each modal feature and outputting a disease type identification result; and the risk assessment module is used for carrying out size estimation and parameterized expression on the identified diseases. The problems that in an existing tunnel inspection technology, the detection means is single, appearance and internal information cannot be considered, and the disease size is difficult to quantify automatically are solved.

The invention relates to the technical field of data information processing, in particular to an artificial intelligence-based index layer model construction analysis method and system, and the method comprises the following steps: 1, multi-source data synchronization and quality evaluation; 2, service context analysis and subject domain intelligent division are carried out, and service document and data table field semantics are analyzed; 3, index demand intelligent analysis and calculation logic generation: analyzing index demands input by a user through a natural language processing technology, matching service entities in the knowledge graph, generating initial calculation logic and optimizing execution efficiency; and 4, dynamically updating the index data and verifying the consistency, executing the optimized computational logic to generate the index data, tracking the blood relationship of the data, triggering dynamic updating, and setting a threshold value according to a historical fluctuation range to carry out abnormal alarm. Through automatic and intelligent processing, the probability of human intervention and human errors is reduced, and the efficiency and accuracy of data processing are improved.

The invention relates to the technical field of fault prediction, in particular to a centrifuge fault prediction system based on machine learning, which comprises a data channel synchronization module, a multi-dimensional feature extraction module, a state evolution index construction module, a trend aggregation trajectory recognition module and a fault section recognition module. According to the method, different types of data are synchronously aligned by a multi-channel signal segmentation processing mechanism based on a periodic state, a state evolution sequence is constructed in combination with a unified sampling structure based on a time scale, a state characteristic track is established through a multi-dimensional parameter set, a trend change index is constructed by means of a difference root-mean-square between adjacent states, and the state evolution sequence is analyzed. According to the method, the aggregation section is recognized and the trajectory deviation frequency is counted by utilizing continuous trend mutation, so that dynamic migration of the trajectory boundary and intelligent recognition of the fault section are realized, the boundary failure problem caused by static preset conditions is avoided, and the continuous prediction stability of long-period equipment and the application range under a non-standard working condition are effectively enhanced.

The embodiment of the invention relates to the technical field of tunnel environment monitoring, and particularly discloses a tunnel environment monitoring method and system based on multi-source data fusion. According to the embodiment of the invention, multi-source monitoring is carried out on the target tunnel, and data synchronization and preprocessing are carried out on the multi-source monitoring data; performing spatial-temporal feature extraction on the multi-source standard data, and performing unified feature mapping; performing multi-source fusion on the multi-modal feature sequence; performing environment classification identification on the fused feature data; and constructing an information visualization platform, and monitoring and displaying the abnormal event data and the plurality of environment identification data. Multi-source monitoring, data synchronization and pre-processing, spatial-temporal feature extraction, multi-source fusion and environment classification identification, construction of an information visualization platform for monitoring display, data fusion processing of multi-source monitoring and comprehensive perception of multi-source data can be performed, so that linkage coupling analysis can be performed on various data, and multi-source data can be acquired. The abnormity identification capability and the response capability are improved, and the false alarm rate is effectively reduced.

The invention belongs to the technical field of multi-source heterogeneous data fusion and intelligent decision, and discloses a multi-source heterogeneous information fusion and analysis method for an AI large model and data knitting, which comprises the following steps: accessing multi-source heterogeneous data through data knitting, calibrating time and space by taking a key event as an anchor point, generating time and space alignment data, and synchronously constructing a dynamic metadata network. Generating a metadata graph; constructing a dynamic hypergraph, and calling a modal exclusive coding cluster to generate a multi-modal fusion feature; edge-cloud collaborative reasoning is started, a collaborative reasoning intermediate result is generated, confidence data is output by the general large model to arbitrate cross-domain conflicts, and a final decision packet is generated; triggering two-way feedback closed-loop optimization, driving data source optimization and knowledge completion of a metadata graph by an AI large model, further dynamically scheduling the priority of a data stream, and encrypting and updating large model parameters; and iterating the metadata atlas, and feeding back to a link of constructing a dynamic metadata network and generating a multi-modal fusion feature.

The invention discloses a panoramic image real-time splicing algorithm and system based on multi-sensor fusion, and particularly relates to the technical field of panoramic image real-time splicing, and the algorithm comprises the following steps: constructing a structured fusion sequence based on multi-source images, postures and position information, optimizing a matching effect through high-density feature extraction and repeated texture recognition, and obtaining a multi-source image fusion sequence; a dynamic foreground and a static background are distinguished by using sparse optical flow so as to improve the visual angle estimation precision, pose fusion optimization is realized in combination with a multi-mode residual error, and the continuity and stability of a spliced image are improved through edge smoothing, brightness tuning and color correction; according to the method, the structured fusion sequence is constructed through multi-source data alignment, so that the data synchronization and splicing stability is improved; identifying repeated regions based on texture direction features, and optimizing feature matching accuracy; and through edge smoothing, brightness harmonizing and color consistency processing, the visual coherence and output quality of the panoramic image are enhanced.

The invention discloses a double-arm robot autonomous control system and method based on teleoperation and visual features. The system comprises a teleoperation acquisition module, a multi-view visual perception module, a data synchronization and demonstration acquisition module, a strategy model training module, an autonomous strategy execution module, a track deviation detection and takeover module and a hybrid control interface module. Human demonstration is completed through teleoperation, multi-modal information of images, tracks, clamping jaws and muscle activation is collected, perception features are fused by adopting multi-view space-time alignment and a cross-view attention mechanism, strategy learning is completed in combination with an end-to-end large model, and in the autonomous operation stage, the multi-view space-time alignment and the cross-view attention mechanism are combined with the end-to-end large model. The risk of current operation is predicted and evaluated through track deviation detection and collision probability, the autonomous control proportion is dynamically adjusted, manual intervention is allowed when necessary, the safety and stability of the whole system are improved, the robot control mode of seamless switching between man-machine cooperation and autonomous and teleoperation is achieved, and the method is suitable for object control tasks in complex and highly variable environments.

The invention relates to a multi-sensor fault feature fusion diagnosis method for electrical equipment, which comprises the following steps: synchronously acquiring operation data of the electrical equipment through a vibration sensor, a temperature sensor, a current sensor and an ultrasonic sensor, dynamically adjusting the sampling frequency according to the physical characteristics of each sensor, and the sampling rate of the temperature signal is not lower than 1Hz. Through a multi-source sensor data synchronous acquisition and time sequence alignment technology and a signal alignment method combining a dynamic time warping (DTW) algorithm and Hilbert-Huang transformation, the problem of time asynchronization of heterogeneous sensor data such as vibration and temperature is solved, so that the time alignment precision of multi-source data is improved, the feature extraction accuracy is improved, and the accuracy of feature extraction is improved. Through hierarchical feature extraction and graph convolutional network fusion, a feature incidence matrix based on mutual information is constructed, deep correlation between vibration signal TKEO features and cross-modal features such as current harmonics is mined by using GCN, the feature dimension is reduced, and the fault feature separability index is improved.

The invention discloses a quality management and control system for fabricated decoration construction, and the system comprises a sensing layer which constructs a multi-modal data collection network, carries out the three-dimensional real-time data synchronous collection through combining logistics API docking and an OCR recognition system, and constructs a construction process digital twin bottom plate; in the edge calculation layer, an edge node carries out lightweight processing on the original data; the cognitive layer is used for calling a Prolog rule through a process knowledge graph engine to reasone the feature snapshots, carrying out defect instant diagnosis and dynamic constraint propagation, outputting a root cause path with probability weight through three-stage verification, and quantifying intervention influence; the decision-making layer is used for constructing a dynamic prediction model based on a bidirectional LSTM and an attention mechanism, automatically activating a compensation mode when key interference is detected in combination with an anti-fact memory bank and a case-based reasoning compensator, and generating an alternative scheme of optimal cost / optimal construction period / comprehensive balance through a multi-target optimizer; and in the application layer, a Unity engine is utilized to develop the digital twinborn billboard.

The invention discloses a high-precision laser positioning unpacking path planning system, and belongs to the technical field of robot automatic control and industrial automation. The system comprises a data synchronization module used for multi-sensor hardware synchronization and data alignment; the high-precision positioning module is used for outputting a precise pose based on environment skeleton characteristics and sliding window optimization; the semantic map construction module is used for fusing vision and laser data to generate a dynamic semantic grid map; the global path planning module is used for planning a smooth path in the skeleton channel by utilizing a mixed potential field improved A * algorithm; the motion control module is used for realizing closed-loop motion control and safety monitoring through model prediction and tracking; and the operation execution module is used for finishing millimeter-level precise stopping of an operation point by adopting visual servo and triggering unpacking operation. According to the method, the positioning robustness under dynamic shielding is improved through the environmental skeleton features, the safety and the high efficiency of the path are ensured by utilizing semantic understanding and intelligent planning, and the full-process automation from navigation to precise operation is realized.

The invention discloses a resource awareness and task migration method and system for industrial edge nodes. The method comprises the following steps: constructing an edge node resource dynamic monitoring system, and sensing, calculating, storing and network resource states in real time; establishing a node health degree evaluation model, and predicting a potential fault risk; designing an intelligent task migration decision-making mechanism based on a resource state; the guarantee of data consistency and service continuity in the task migration process is realized; and constructing a distributed task scheduling optimization framework. The system comprises a resource monitoring module, a health assessment module, a migration decision module, a data synchronization module and a scheduling optimization module. According to the method, the problem of unstable task execution caused by dynamic resource change in an industrial edge computing environment is solved, and intelligent resource management and efficient task migration of the edge nodes are realized.

The invention discloses a non-aligned spatio-temporal data driven multi-sensor fusion method and system, and the method comprises the steps: building a matching measurement function through collecting radar point cloud density and visual image texture, and constructing a synchronous gradient field through employing a space depth change parameter and a visual motion contour, and determining a data synchronization standard; detecting a time sequence abnormal accumulation area based on a synchronization standard, and identifying an abnormal starting point and propagation time delay through a backtracking track to form an asynchronous sensor coordinate table; iMU angle increment and sensor response delay are collected, and sampling imbalance information is generated through time domain matching of an attitude switching signal and a data collection inflection point; performing association mapping on the asynchronous coordinate table and the sampling imbalance information to generate a feature offset, determining a matching core area, and creating a hierarchical calibration sequence; performing internal and external source difference identification analysis on the calibration sequence to obtain an offset transfer speed to form an offset coupling factor; and a fusion enhancement region is determined based on the offset coupling factor, and high-precision multi-source data fusion is realized through cascade analysis and gain coefficient coding.

The invention relates to the technical field of offshore wind turbine inspection, and discloses an offshore wind turbine inspection monitoring system based on digital twinning. A twinborn data synchronization module of the system collects fan structure state data and environmental hydrological data in real time through a multi-source sensor cluster, and constructs an offshore fan digital twinborn body; the routing inspection path dynamic generation module analyzes a fan fragile area and risk distribution based on the digital twinborn body, and generates a multi-unmanned aerial vehicle cooperative routing inspection path in combination with real-time marine weather forecast; the unmanned aerial vehicle cooperative control module schedules an unmanned aerial vehicle group to execute a task according to the cooperative inspection path, and dynamically responds to real-time data updated by the twin data synchronization module; and the real-time abnormity identification module fuses and analyzes unmanned aerial vehicle returned data, identifies fan defects, outputs abnormal information, calculates relative space deviation, generates an attitude control instruction, and sends the attitude control instruction to the unmanned aerial vehicle cooperative control module to realize unmanned aerial vehicle attitude real-time adjustment.

The invention relates to a low-delay edge reasoning deployment method for power terminal equipment. The method comprises the following steps: acquiring data under a unified time reference to form an operation data sequence; inputting the operation data sequence into a pre-trained reasoning model to generate a reasoning parameter set adaptive to the local operation capability; performing reasoning calculation based on the reasoning parameter set to obtain a load identification result and an electric quantity prediction result; determining a communication scheduling strategy according to the reasoning result, wherein the communication scheduling strategy comprises communication priority information, sending time sequence information and target receiving node information; a communication instruction is generated according to the communication scheduling strategy, and data content is sent to a target receiving node according to the communication instruction within a limited time, so that rapid data synchronization and dynamic response control based on load state change are realized; according to the invention, intelligent data processing and rapid communication scheduling can be locally realized at the power terminal equipment, the system response time delay is reduced, and the processing efficiency of a load change event is improved.

The invention discloses a non-power-grid sporadic material emergency purchase response system considering risk priority. The system comprises a sporadic material demand trend pre-judgment module, a risk perception purchase adaptation module, a risk income balance optimization module, an emergency purchase scheduling module, a non-power-grid material response module and a module cooperative control module. According to the system, data such as historical purchase and consumption rate are integrated through a demand trend pre-judgment module to output a demand prediction result, risk and performance parameters are coupled through a risk perception purchase adaptation module to generate an adaptation scheme, and an optimal purchase strategy is calculated through a risk income balance optimization module. A scheduling instruction is generated through an emergency purchase scheduling module according to the risk priority, a non-power-grid material response module executes inventory checking, transportation planning and other operations, and a module cooperation control module achieves whole-process data synchronization and parameter dynamic adjustment. According to the system, the decision-making scientificity and response efficiency of non-power-grid sporadic material emergency purchase are improved, and emergency purchase burstiness and complexity requirements are met.

The invention relates to the technical field of bearing fault diagnosis, in particular to a multi-bearing fault positioning and diagnosing method, which comprises the following steps of: acquiring bearing vibration signals of a plurality of sensing points, extracting peak time information to complete synchronous calibration, extracting high-frequency band energy and constructing a feature vector, screening deviation features and clustering and grouping, and identifying an energy abnormal channel, positioning a bearing position, extracting time domain and frequency domain features, comparing fault modes, and outputting a fault type diagnosis result. According to the method, the consistency of multi-channel data synchronization is realized by extracting the peak point of the vibration signal and calibrating the time offset, the high-frequency energy change rate is utilized to match the weight coefficient, the detection sensitivity of tiny fault features is enhanced, and the separability of a composite fault state is improved by combining feature relative deviation rate screening and density trend clustering. A frequency section energy proportion change recognition channel is adopted, accurate positioning of a fault bearing is achieved, and accurate judgment of a fault type is achieved in combination with a multi-feature comparison mode.

The invention discloses an L-shaped conveyor belt path dynamic planning and congestion prediction system, which relates to the technical field of automatic control, and constructs a conveying path node diagram by collecting material weight, speed and spacing information of each node on a conveying path, and calculates a congestion index of each path section; the method comprises the following steps: combining a light-mass material jumping sensitivity model, identifying a high-risk node and marking the high-risk node as a congestion prediction area, constructing a dynamic optimization model based on path risk distribution and a material flow direction, generating and evaluating a plurality of path adjustment strategies, executing an optimal strategy according to priority, and realizing path speed adjustment, standby path switching and feeding rhythm control; through a feedback mechanism, a strategy effect is evaluated in real time, a path state is updated, operation data is synchronized to a historical database, a learnable conveying operation file is formed, the stability and the intelligent response capability of a conveying system can be effectively improved, and the method is suitable for high-speed sorting and automatic packaging scenes.

The invention discloses a passenger motion sickness prediction method based on multi-modal information fusion, and belongs to the technical field of vehicle comfort control. The model comprises a data acquisition system, a data preprocessing module and a multi-modal fusion deep learning model based on CNN + BiLSTM + Attention. The data acquisition system acquires electroencephalogram, myoelectricity, galvanic skin, electrocardio physiological signals and three-axis acceleration data; the data preprocessing module carries out filtering, feature extraction and data synchronization on the signals; the deep learning model carries out fusion processing on physiological signals and motion parameters through a modal specific feature extraction layer, a time sequence feature extraction layer, an attention mechanism layer, a feature fusion layer and a prediction layer, and motion sickness risk prediction is achieved. The model further comprises a personalized adaptation mechanism, the motion sickness risk can be predicted 30-60 seconds in advance, and linkage with a vehicle comfort control system is achieved through a grading early warning mechanism. The invention further provides a vehicle-mounted motion sickness early warning system, a training method and a lightweight method based on the model, the riding comfort can be effectively improved, and the risk of motion sickness is reduced.

The invention discloses a material supply and resource recovery collaborative scheduling method and system, and belongs to the technical field of logistics scheduling, and the method comprises the steps: obtaining a material supply instruction and a garbage recovery instruction, carrying out the image recognition of a building garbage picture, obtaining the garbage attribute, and generating a demand instruction library; the method comprises the following steps: establishing a feature knowledge base, setting a spatial clustering method, carrying out spatial clustering and time window screening, carrying out geographic coordinate analysis and time sequence sorting on material demand points and recovery points, generating an initial task pool, setting a loading matching method, and generating a material supply and recovery loading schematic diagram according to building material and garbage attributes; a circulation path is planned, a path planning method is set, an ant colony algorithm is used for initial path planning, a monitoring feedback method is set, data are returned in real time, the running state of the vehicle is monitored, and an alarm is given out immediately once abnormity is found; and the sorted and regenerated aggregate data is synchronized to a building material database, a resource feedback method is set, a reverse transportation task is identified, and a material supply and resource recovery closed loop is formed.

The invention relates to the technical field of cultural transmission content auditing, and discloses a cultural transmission content auditing method, which comprises an auditing system and comprises the following operation steps: S1, a preparation stage: initializing and configuring a module; the method comprises the following steps: S101, dynamic strategy engine deployment: accessing a federal learning model, configuring a cross-regional data synchronization rule (for example, dividing data cooperative training groups according to cultural transmission coverage regions), initializing a dynamic risk feature library, importing features such as basic sensitive words and cultural taboo symbols, and associating'cultural sensitivity parameters' of a cultural dimension quantizer; according to the cultural transmission content auditing method, system sustainability is guaranteed through a process closed loop, auditing accuracy is improved through technology fusion, cultural transmission complexity is dynamically adapted and coped with, the requirement for compliance auditing of the cultural transmission content can be met, protection of cultural diversity and innovativeness can be considered, and the method is suitable for popularization and application. And the method is suitable for auditing challenges of emerging scenes such as short videos, cross-culture transmission and meta universe.

The invention provides an automatic assembling method and system for an inductor magnetic core. Wherein continuous images of the surface of the magnetic core are captured through multi-band rapid scanning, and geometric deformation characteristics are extracted; meanwhile, a distributed optical fiber sensing array is adopted to collect pressure data of the contact surface, and a real-time distribution diagram is generated through a three-dimensional field reconstruction algorithm. And then carrying out space coordinate alignment on the edge contour offset of geometric deformation and the pressure gradient, and constructing a comprehensive quality index. And based on the dynamic deviation of the index, a contact angle compensation value and an assembly speed optimization value are solved in real time, and a parameter adjustment instruction is generated. And finally, through parallel calculation of instruction data, a pressure tolerance threshold value and a deformation compensation parameter are synchronously updated, dynamic balance of deformation correction and pressure control in the assembly process is realized, and a self-adaptive closed-loop precision control system is formed. According to the technical scheme, through multi-dimensional sensing data fusion and real-time parameter adjustment, dynamic matching of deformation and stress states in the magnetic core assembling process is achieved.

The invention discloses a metering box abnormal power consumption behavior diagnosis method and system based on edge calculation, and belongs to the technical field of power monitoring, and the method comprises the steps: collecting the voltage, current and time mark data of a metering box in real time through an edge end, synchronously obtaining the physical state information of a box door state and the like, generating a space-time feature matrix through an electric parameter space-time coupling analysis method, and carrying out the calculation of the time-space feature matrix; inputting an electric parameter feature mask pruning algorithm to obtain an optimized feature set, triggering an edge-sensing sleep wake-up linkage device when current abnormity is monitored, and collecting magnetic field data; the edge end calculates a total-branch electric energy difference value, combines the magnetic field data and a box door state, and constructs an electricity consumption abnormity classification determination tree through a metering error-electricity stealing behavior coupling diagnosis method to distinguish electricity stealing behaviors; an intermittent abnormal trajectory splicing algorithm is adopted to process fragmented abnormal data to generate an electricity stealing trajectory, and the electricity stealing trajectory is fed back to an electric parameter space-time coupling analysis method to dynamically update the space-time feature weight; according to the invention, the abnormity diagnosis precision and real-time performance are improved, and the energy consumption is reduced.

The invention provides an mbse-based system full life cycle management method and system, and relates to the technical field of data synchronization, and the method comprises the steps: obtaining to-be-synchronized data of a source system, constructing a semantic graph structure, generating a mapping strategy based on implicit association between semantic matching learning systems, executing transactional data transmission, and constructing a time sequence traceability tree to track change propagation. And adaptive reconstruction is carried out when a constraint conflict is detected. According to the method, the problem of data synchronization among heterogeneous systems is solved, and the model consistency maintenance capability and the adaptive conflict processing efficiency are improved.

The invention relates to the technical field of data management, in particular to a safety management method for financial data synchronization, which comprises the following steps: acquiring an original financial data stream, and performing data fragmentation processing on the original financial data stream to obtain fragmented data packets and data fingerprints; performing storage node mapping on the redundant coded data packet according to the storage network to obtain a storage node address chain; and performing abnormal access behavior identification on the storage node address chain according to the security threshold model to obtain a security policy instruction, and deploying the financial scheduling model to a cloud edge collaboration platform to realize security management of financial data synchronization. According to the method, data fragmentation processing, encryption and identity binding are performed on the original financial data, so that the confidentiality of the data can be effectively protected, the data is prevented from being accessed or tampered by an unauthorized third party in a transmission process, and the encrypted data packet ensures secure transmission of the data.

The invention discloses a space-air-ground integrated ecological environment monitoring data fusion method and system, and the method comprises the steps: obtaining the original data of heterogeneous multi-source environment monitoring in real time, carrying out the preprocessing, and carrying out the time-space data synchronization of the preprocessed data; performing data fusion on the synchronized data; the data fusion comprises the following steps: calculating initial fusion data by adopting a weighted average fusion method based on space-time weight to obtain an initial fusion result; acquiring a data quality factor, a space-time synchronization factor and a sensor stability factor, and determining an initial weight of each data source according to the data quality factor, the space-time synchronization factor and the sensor stability factor; dynamically adjusting the weight in real time through a Bayesian optimization strategy; and carrying out dynamic optimization on the data after real-time fusion based on an adaptive Kalman filtering algorithm. According to the method, the problem that a traditional data processing method is insufficient in multi-source heterogeneous data fusion capability is solved, and the utilization efficiency and value of the data are improved.