2267 results about "Data consistency" patented technology

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a multi-source knowledge processing and querying method, which comprises the following steps: data acquisition, cleaning and standardization processing, and standardized database construction; extracting core concepts and association relationships, and generating knowledge elements; constructing a multi-dimensional knowledge graph based on knowledge elements, and establishing a semantic index to realize data semantic annotation and bidirectional mapping; and analyzing the query intention, extracting a query constraint condition, and executing association reasoning based on the multi-dimensional knowledge graph to generate a query result. According to the method, the standardized database of the multi-source heterogeneous data is constructed, so that the data consistency is improved; through multi-dimensional knowledge graph construction and semantic index establishment, the relevance and structural expression of data are enhanced, so that the relationship between knowledge elements is clear and traceable; and through association reasoning based on query constraint conditions, the query accuracy and efficiency are improved.

The invention relates to the technical field of cable operation state detection, and particularly discloses a multi-source data driven cable operation state comprehensive evaluation method, which comprises the following steps of S1, adopting a layered distributed sensing network architecture, and deploying three types of core sensors at key nodes of a cable, through space-time calibration of the multi-source heterogeneous sensor, data consistency is improved, fusion deviation is eliminated, the problem of data islands of a traditional system is solved, and a precise evaluation foundation is laid; noise suppression and dynamic correlation modeling are adopted, environmental interference is stripped, a vibration and displacement coupling relation is quantified, limitation of a single parameter is broken through, heterogeneous fault features are captured, and evaluation comprehensiveness and sensitivity are improved; a self-adaptive threshold mechanism is constructed based on environment weight and historical data, the bottleneck of a fixed threshold is broken through, an evaluation standard is corrected along with equipment aging and environment change, misjudgment is avoided, and diagnosis robustness in different scenes is enhanced.

The invention provides an MES-based power distribution cabinet quality inspection analysis and process regulation and control method and system, and the method comprises the steps: converting a quality inspection standard into an executable rule, generating a standardized quality inspection template, evaluating the quality of a single project and a batch, monitoring the mean value and fluctuation of a production process based on a statistical process control theory, and calculating a process capability index. A dynamic threshold value formula is combined to adjust a control limit in real time, process stability is evaluated, equipment data is standardized, production batches are associated, data consistency is verified and ensured, graded early warning is performed according to the deviation degree, a machine learning model is combined to realize predictive maintenance, process parameters are dynamically corrected, historical data are analyzed through machine learning, and the process quality is improved. According to the method, high-order feature quantification influence factors are extracted, abnormal parameter combination is detected in combination with an isolated forest, a process strategy library is updated, closed-loop optimization is formed, full-process automation from data acquisition, real-time analysis and parameter adaptive adjustment to predictive maintenance is realized, and quality inspection efficiency and process stability are improved.

The invention discloses a hybrid energy storage system optimization scheduling method based on AI intelligent regulation and control, and relates to the technical field of hybrid energy storage, and the method comprises the following steps: collecting real-time data, and carrying out the cross verification of data consistency through a multi-source data fusion technology; and identifying data abnormity caused by sensor faults, communication delay and environmental interference by combining adaptive threshold detection with a statistical analysis method, and eliminating abnormal data. According to the method, energy storage scheduling is optimized through data cleaning, time sequence prediction and reinforcement learning, and the intelligent management and dynamic adaptive capacity is improved. Multi-source data fusion and anomaly detection are adopted to ensure data accuracy, energy consumption is predicted by means of LSTM and Transform, and an energy storage strategy is optimized in advance. By combining reinforcement learning and system dynamic adjustment charging and discharging, the photovoltaic consumption rate is improved, the electricity purchasing cost is reduced, the SOC is intelligently controlled to be 40%-80%, and the service life of the battery is prolonged. Meanwhile, the system stability is improved through anomaly detection and correction, and the maintenance cost is reduced.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a database migration method, device, equipment and medium. And deploying a double-write control module of the application program layer to write the source database and the target database in parallel, and completing consistency verification through a data verification comparison task. And controlling a target database write-in channel according to a difference analysis result, closing a source database write-in channel in combination with a service takeover state, and completing a migration process from the source database to the target database. According to the method, full migration, application layer double writing, differential data verification and dynamic write-in channel control are combined, data consistency of the source database and the target database in the migration process is guaranteed, the source database and the target database are safely switched to after the service takeover capacity is verified, and the migration safety and continuity are improved.

The invention discloses a multi-dimensional carbon flux monitoring method, and particularly relates to the technical field of carbon flux monitoring, and the method comprises the following steps: collecting multi-source heterogeneous sensor data, and carrying out spatial resolution unification, time synchronization alignment and data format standardization preprocessing to ensure data consistency; executing fusion validity detection, and correcting time synchronization deviation and data redundancy conflicts; according to an abnormal detection result, dynamically adjusting a fusion weight or determining a weight based on historical clustering, and combining spatial adjacency interpolation and time interpolation to realize data fusion; and finally, inputting a carbon flux inversion model, and outputting a high-precision carbon flux monitoring value to realize dynamic monitoring and prediction. According to the method, a dynamic fusion weight adjustment mechanism is adopted, the fusion weight of each data source is dynamically optimized, adaptive fusion of multi-source observation data is realized, the problems of data discontinuity and insufficient data coverage in the prior art are solved, and a multi-dimensional, full-coverage and high-resolution carbon flux fusion data set is ensured to be formed.

The invention belongs to the technical field of medical data processing, and discloses a medical data multi-source fusion verification and correction method and system, and the method comprises the steps: obtaining multi-source data; constructing a dynamic baseline model of the emergency department, the ICU and the common ward, responding to and verifying the abnormal condition of the patient data, and performing early warning; establishing a term library, carrying out multi-source fusion on the patient data obtained from multiple sources, and carrying out data correction; and generating a multi-source data quality evaluation table of different departments based on the whole-process data, and feeding back and updating the term library and fusing the correction weight based on the quality evaluation table. Through a real-time monitoring and intelligent early warning mechanism, quality problems in the data can be found and corrected in time, human errors are reduced, and the data management cost is greatly reduced. Through multi-source data fusion and consistency verification, the system can ensure seamless connection of data from different data sources, and cross-system and cross-platform data consistency and cooperative work are realized, so that the accuracy of medical decision and the reliability of data are improved.

The invention discloses a data acquisition method and system and a storage medium, and relates to the technical field of data acquisition and processing.According to the technical scheme, multiple sensor devices and data interfaces are integrated, multi-source data are acquired in real time, format standardization processing, time synchronization correction and spatial information alignment are carried out through a multi-mode fusion module, and the data acquisition efficiency is improved. Calculating to obtain a data consistency factor Tyhz and evaluating the data consistency factor Tyhz; when the data consistency factor Tyhz does not reach the standard, a data optimization module performs noise filtering and abnormal value elimination on a multi-source data set, and an intelligent analysis module calculates a risk prediction parameter Fcyz by using a convolutional neural network; the early warning evaluation module compares the Fcyz with a risk evaluation threshold Fth, calculates a risk early warning index Gyzs, compares the risk early warning index Gyzs with a risk early warning threshold E, and dynamically generates an early warning execution scheme, so that information pushing and emergency resource scheduling are realized, the problems of low multi-source data fusion efficiency and insufficient early warning precision are solved, and the safety of the system is improved. And the risk identification and emergency response capabilities of the urban emergency management system are effectively improved.

The invention relates to the technical field of engineering collaborative management, and discloses a BIM (Building Information Modeling)-based engineering full-life-cycle collaborative management method. The method comprises the following steps: acquiring building information model data and associated progress monitoring data of a full life cycle of a project, and providing a data basis for collaborative management. Based on a stage decomposition strategy, a single-stage dependency graph is constructed according to building information model data, and association logic of elements such as tasks and resources in each stage is clearly presented. Through a cross-stage dependency integration method, the single-stage dependency graph is integrated into a full-life-cycle dependency graph, and a cross-stage task connection and influence relationship is defined. In combination with data consistency verification and resource constraint review, conflict detection is performed according to a full life cycle dependency graph, and potential data conflicts and resource conflicts are identified in advance. And based on a progress fluctuation analysis mechanism, correcting the dynamic parameters according to a conflict detection result, and finally generating a full-life-cycle collaborative management report.

The invention provides a data association maintenance processing method and system based on file reference, and relates to the technical field of data association maintenance, which comprises the following steps: constructing a directed reference graph, identifying direct and indirect reference paths, and constructing a reference dependence intensity matrix in combination with a time sequence, an interaction amount and a reference propagation probability. And carrying out file clustering based on the matrix, and determining a core reference file set. And when the core file is updated, acquiring incremental update data by adopting data block level difference comparison, and merging the data blocks based on the content similarity and the semantic association degree. After the merged data blocks are segmented according to threshold values and semantic boundaries, updating is propagated to a target node through a multi-path updating propagation network, and the data consistency is ensured through version consistency check and a compensation updating mechanism. According to the method, data association between files can be effectively maintained, and the data updating efficiency and consistency are improved.

The invention, which relates to the technical field of payment security, discloses a payment-scene-oriented interaction intention identification and error correction system comprising an input analysis module, an intention simulation module, a dynamic decision module, a biological verification module, an audit evidence storage module, and a cross-scene knowledge migration module. According to the method, multi-modal data such as voice, texts, images and touch tracks are integrated, structured feature vectors are generated through a cross-modal attention network, the problem of incomplete single-modal coverage is solved, cross-modal data consistency verification is achieved based on a unified semantic tag system, and the reliability of input sources is graded by combining equipment fingerprints and geographic positions, so that the reliability of the input sources is improved. A high-risk transaction protection capability is enhanced, a generative adversarial network is utilized to construct a virtual attack sample library, attacks such as tampering with characters similar in shape and AI faking voiceprints are simulated, unknown threats are actively defended through cosine similarity matching, a user historical behavior statistical model is integrated, and known risks such as high-frequency small-amount transfer are passively intercepted. And a closed-loop incremental learning continuous optimization model is supported.

The invention relates to the technical field of data processing and privacy protection, in particular to a data desensitization and integrity verification method and system based on a differential privacy algorithm, and aims to realize collaborative optimization of data privacy protection and integrity guarantee. Determining a total privacy budget by calculating global sensitivity, a data leakage risk coefficient and independent and joint data privacy information amount, dynamically allocating the privacy budget for a single-task or multi-task scene, and injecting noise based on an allocation result to realize differential privacy desensitization; meanwhile, a verification voucher is generated by using a hash function, a digital signature and an alliance chain, and the data integrity is ensured through signature verification, data consistency verification and block chain verification. The system comprises a data application processing module, a global sensitivity calculation module, a desensitization core processing module, a verification voucher generation module and an integrity verification module, is suitable for government affairs, enterprises and other scenes with high requirements for data privacy and integrity, and effectively improves the safety and reliability of data processing.

The invention relates to the technical field of offshore area monitoring, in particular to an offshore area pollutant diffusion real-time tracking and traceability monitoring system. The method comprises the following steps: a data perception and fusion unit collects and preprocesses data, carries out space-time calibration and multi-source fusion, and outputs a standardized fusion space-time sequence; the pollutant diffusion simulation and prediction unit calculates a flow velocity field, simulates a diffusion process and predicts a trend; the pollutant traceability positioning unit tracks a pollution path and inverts pollution source parameters; the emergency decision support unit determines a risk level and generates an emergency plan. And through a space-time alignment algorithm and a multi-source fusion mechanism, the data consistency and reliability are improved, a high-quality data basis is provided for subsequent processing, and the monitoring accuracy is guaranteed.

The invention relates to the technical field of cloud archive management, and discloses an archive management system based on a cloud archive library, which comprises a data acquisition module, a data preprocessing module, a cross-format semantic retrieval module, an intelligent rule generation module, a distributed authority control module and a dynamic resource optimization module. According to the method, through construction of the multi-modal semantic vector space, unified retrieval of heterogeneous archive data such as texts, images and audios is realized, and the problem of low retrieval efficiency caused by format differences in the prior art is solved; an archive life cycle rule is dynamically generated in combination with a reinforcement learning algorithm, so that the accuracy and suitability of resource management are effectively improved; authority control is realized based on a block chain technology, and the security and consistency of data access are enhanced; through load prediction and resource scheduling optimization, the system energy consumption is obviously reduced, and the response capability of a high-concurrency scene is improved. The file security and the data consistency are enhanced, and the problem of low authority control efficiency in a multi-terminal access environment in the prior art is solved.

The invention relates to the technical field of artificial intelligence and robots. According to the multi-modal perception and interaction method and device in the body environment, the method comprises the steps that environment entropy estimation processing is carried out through a dynamic weighting multi-modal feature fusion algorithm, and an environment entropy value representing the disorder degree of the environment is generated; performing cross-modal alignment processing to generate a fusion environment understanding map; performing dynamic decision processing through the task adaptive reinforcement learning model to generate an interaction instruction; driving an execution mechanism to execute the interaction action to obtain an execution result of the interaction action; carrying out dynamic adjustment processing on the weight of the environment entropy value to generate an updated environment entropy weight; and performing local knowledge node incremental updating processing on the meta-knowledge base to generate an optimized meta-knowledge base so as to solve the problems of insufficient consistency of cross-modal data and poor environmental understanding robustness caused by large distribution deviation between virtual features generated by a generation model in a noise or data missing scene and a real environment in related technologies.

The invention discloses a cargo transportation real-time path planning method based on artificial intelligence, and relates to the technical field of artificial intelligence and intelligent traffic, and the method comprises the following steps: collecting real-time data from a traffic monitoring system, a vehicle-mounted sensor, a road event platform and a weather information system in a transportation process, forming an original data set containing multi-source information; time synchronization and space alignment are carried out on source data in an original data set, and a standardized data frame is constructed to ensure the consistency of different source information in a space-time dimension. According to the method, the accuracy, the real-time performance and the intelligence of path planning are effectively improved through multi-source data consistency identification, weighted fusion and a dynamic re-planning mechanism. The system can timely identify data conflicts, dynamically adjust paths and enhance transportation safety and efficiency, has a self-learning capability, continuously optimizes path strategies and adapts to complex traffic environments.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a data-driven unmanned aerial vehicle flight control system real-time defect prediction system and method, and provides the following scheme that the system comprises a data acquisition module, a sensor verification module, a defect prediction module, a credibility analysis module and a flight control optimization module. And the sensor verification module identifies sensor data exceptions and distinguishes fault types through data consistency, time sequence exception detection and environment correlation analysis. And the defect prediction module analyzes the verified data by using machine learning and predicts potential faults. And the credibility analysis module verifies the prediction result and generates a credibility score. And the flight control optimization module selects an optimization control strategy according to the credibility score, adjusts flight parameters or executes safe operations such as return flight and landing. According to the method, the fault identification and prediction capability of the unmanned aerial vehicle in a complex environment is improved, and the flight safety and the system reliability are enhanced.

The invention provides a multi-mode sensor fusion algorithm for multi-signal processing and a system thereof, and relates to the technical field of sensor data processing, and the system sequentially generates standardized features through Sp1, generates a fusion feature set through Sp2, optimizes fusion parameters through Sp3, generates compensation features through Sp4, outputs perception information through Sp5, and outputs abnormal conditions such as sensor failure and overload calculation. Through feedback mechanism processing, a termination condition of perception information output or new task triggering, and breakthrough in feature retention and data consistency, a fusion result can still keep high precision in a high noise or complex scene, through failure detection and feature association degree priority ranking, other sensor data with high association degree are used for reconstruction, and a fusion result is obtained. According to the method, the accuracy of feature reconstruction is greatly improved, the high real-time performance of edge equipment and the computing power of the cloud are fully utilized by an edge-cloud cooperation mechanism, the resource utilization rate is optimized, and the stability and adaptability of the system in practical application are greatly improved.

The invention discloses a charging robot fault diagnosis method and system based on deep learning, and particularly relates to the technical field of robot fault diagnosis, and the method comprises the following steps: collecting multi-type sensing channel data in an operation process, and constructing a structured input sequence; calculating a local disturbance amplitude and trend difference to generate a weak anomaly confidence value, and judging whether subsequent analysis is triggered or not; if so, extracting a modal drift residual index and a micro-abnormal evolution trend index, and splicing to form a fusion feature vector; outputting a numerical coefficient of each channel through the trained deep learning model; adjusting the sampling frequency according to the numerical coefficient and the characteristic; according to the invention, through unifying a multi-channel data processing flow, constructing a trend-driven anomaly identification mechanism, and combining with deep learning model output, dynamic closed-loop regulation and control of sampling frequency are realized, data consistency, identification sensitivity and resource utilization efficiency of the charging robot fault diagnosis system are improved, and fault diagnosis accuracy is improved. And refined and adaptive monitoring of multi-channel states is realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a processing environment switching and recovery method, device, equipment and medium. The method comprises the steps that multi-source heterogeneous data in a main processing environment and a standby processing environment are acquired, and the system fault probability is obtained through multi-model collaborative prediction; a dynamic threshold value is generated in combination with a historical service period mode and a real-time service load, when the fault probability exceeds the threshold value, a switching strategy is generated based on the fault scene knowledge base and the service priority, and flow scheduling between the main processing environment and the standby processing environment is executed; and monitoring the business index of the standby processing environment during the scheduling period, and triggering the fusing rollback when the business index is lower than the health standard. According to the method, the fault identification precision is improved through multi-source data fusion and multi-model prediction, adaptive scheduling is realized in combination with a dynamic threshold and a switching strategy, and fusing rollback is triggered to guarantee high availability and data consistency, so that the continuity and stability of key services are enhanced.

The invention discloses an MES work order dynamic scheduling and resource self-adaption system based on real-time event triggering, and relates to the technical field of intelligent manufacturing and industrial automation, equipment states and material events are monitored in real time through interfaces such as a PLC and an OPCUA, event types and validity are analyzed through hardware filtering, feature code recognition and double verification, and the real-time event triggering is realized. Event data, MES batch information and equipment load data are integrated to generate standardized scheduling input, a preset strategy is matched, an optimal dispatching scheme is generated through a priority algorithm, material, equipment and logistics resource allocation and dispatching operation are completed, synchronization and MES interaction ensure that page logic is consistent, full-process automation of dynamic work order scheduling is achieved, and work order scheduling efficiency is improved. The equipment state and the material event are responded in real time, the production cooperation efficiency is improved by more than 30%, the resource adaptation accuracy rate reaches 95% or above, manual intervention is reduced, and the consistency of data and MES is guaranteed.

The invention discloses a market supervision data exchange platform migration and security enhancement method and system based on a credential environment, and relates to the technical field of network security. In the data migration process, the consistency and privacy protection of the data are ensured through Hash check and a multi-party security computing technology; a domestic commercial cryptographic algorithm and a quantum resistance encryption technology are introduced, so that the protection capability of the platform to existing security threats is effectively improved, and the resistance to future quantum computing threats is enhanced; an intrusion detection system and a virtual honeypot node are integrated, and a defense strategy is monitored and updated in real time in combination with a deep learning model, so that novel attack behaviors can be detected and coped with in real time; according to an intrusion detection alarm and a block chain audit result, a security policy and resource allocation are dynamically adjusted in combination with a machine learning technology, it is ensured that high-risk nodes are monitored in time, resource allocation is automatically optimized, and the resource utilization efficiency and security of the system are improved.

The invention provides an industrial data online migration method, medium and system of a productivity middle platform, and belongs to the technical field of electrical digital data processing.The method comprises the steps that firstly, historical access records of an industrial data source are collected, and a deep neural network model comprising a multi-head attention layer, a time sequence feature extraction layer and a residual connection layer is constructed; and the prediction of the data change rate is realized. And based on a prediction result, dynamically calculating a data change rate demarcation point by utilizing an optimization equation set, and dividing a data table into different change frequency categories. And then constructing a data dependency relationship matrix, and determining a migration sequence by adopting an improved topological sorting algorithm. Batch migration, incremental migration and real-time synchronization mechanisms are adopted for different types of data, and model updating is triggered through real-time monitoring, so that dynamic optimization of a migration strategy is ensured. Finally, data consistency verification is carried out, migration accuracy is guaranteed, and the technical problem that the online migration efficiency of industrial data is low due to real-time dynamic change of the data change rate is solved.

The invention provides an online document processing method and system based on a document processing model, and the method comprises the steps: obtaining an editing request of a user for a target data unit, generating a temporary locking identifier according to a user authority identifier and a request timestamp, limiting the modification operation of other users for the target data unit, and obtaining a conflict isolation result; constructing a data consistency module according to a conflict isolation result, extracting editing state information of the current data unit from the temporary locking identifier, and performing sequential recording on concurrent operation by adopting a distributed transaction processing technology; if it is detected that version conflicts exist in the sequenced records, a rollback mechanism is triggered, operation priorities are redistributed, and data versions after consistency verification are determined; and designing a hierarchical cache structure according to the data version after consistency verification, storing the high-frequency access data into a memory cache region, and storing the low-frequency access data into a disk cache region to obtain a hierarchical storage structure. The data consistency and security are good, and the storage and access efficiency is improved.

The invention discloses a cross-platform equipment alarm information acquisition method, and particularly relates to the field of information acquisition, the method adapts to different platform equipment through a standardized interface layer, the interface layer comprises an equipment connection protocol library and a unified data conversion unit, and an original log of the equipment is analyzed in real time based on a preset alarm rule template; extracting key information and generating an alarm event object; converting the alarm key information into an alarm event in a unified format, and adding equipment identification metadata; alarm events are asynchronously transmitted to a central processing server by utilizing a high-reliability message queue, and the transmission reliability is guaranteed by adopting various mechanisms; performing duplicate removal, priority classification and persistent storage on the alarm events in the central server, and ensuring data consistency by adopting a hierarchical storage architecture; the whole processing link is provided with perfect monitoring indexes, and automatic capacity expansion and contraction are achieved.

The invention relates to the technical field of natural language processing and database query, and particularly discloses a large model Text-to-SQL conditional clause improvement method and device based on fuzzy recall field value enhancement, which are used for solving the problem of SQL query errors caused by inaccurate field values input by a user. According to the main scheme, the method comprises the following steps: extracting and recognizing date, field name and field value keywords in user input through a large model entity; 2) dynamically acquiring related field values from a database by combining a multi-stage recall mechanism of ElasticSearch (ES) fuzzy matching and vector semantic retrieval, and improving the recall accuracy through threshold screening; (3) organizing recall field values into structured contexts, injecting the structured contexts into a large model cue word template, and guiding the structured contexts to generate SQL condition clauses (such as WHERE clauses) containing accurate field values; and (4) changing a real-time synchronous database into an ES and vector database to ensure data consistency.

Techniques are described for efficient replication and maintaining snapshot data consistency during file storage replication between file systems in different cloud infrastructure regions. In certain embodiments, provenance IDs are used to efficiently identify a starting point (e.g., a base snapshot) for a cross-region replication process, conserve cloud resources while reducing network and IO traffic.

The invention discloses a method, a product and equipment for sharing a file system by multiple hosts and a storage medium, and relates to the technical field of computers, which comprises the following steps: configuring a shared memory device as a character device, directly mapping to a user mode virtual address space, bypassing a kernel page cache, and directly reading and writing a physical address of the shared memory. A shared memory is divided into a super block area, a metadata area, a log area and a data area during formatting, all hosts perform unified operation through the shared metadata area, stand-alone cache interference is avoided, a hidden metadata file is created to map a non-data area during mounting, a log item is scanned to reconstruct a directory file structure, and the mounting efficiency is improved. All hosts generate a directory file structure completely consistent with the shared memory through log replay, the problem of data consistency caused by a cache strategy in a traditional multi-host file system is solved, and the effects that multiple hosts share a unified memory view, operation is real-time and synchronous, and data access delay is reduced are achieved.

The invention discloses a cross-host memory sharing method, system and device and a medium, the memory sharing system comprises a master node and a plurality of slave nodes, the memory sharing method is applied to the master node, and the method comprises the steps that a memory application request of a target slave node is received; applying for a target memory in a target memory sharing area according to a memory size requirement corresponding to the request, feeding back memory information of the target memory to a target slave node, so that the target slave node judges a corresponding memory type according to the memory information, and if a process corresponding to the target slave node executes a memory mapping method, sending the target memory to the target slave node. If yes, the target memory can be mapped to the proceeding virtual address space according to the target type and the offset corresponding to the target memory, so that the process can use the distributed target memory. Therefore, it can be guaranteed that when one node writes the memory, other nodes cannot synchronously write the memory, and then the data consistency of the shared memory accessed by multiple hosts is guaranteed.

Techniques are described for efficient replication and maintaining snapshot data consistency during file storage replication between file systems in different cloud infrastructure regions. In certain embodiments, snapshot creation and deletion requests that occur during cross-region replications may be temporarily withheld until appropriate times to execute such requests safely, depending on the timing relationship between such requests and cross-region replication cycles.