1000 results about "Data loss" patented technology

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

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

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

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

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

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

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

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

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

The invention discloses a dynamic self-adaptive NAND Flash reading method and system based on artificial intelligence, a medium and equipment, and relates to the technical field of solid-state storage, the method comprises the following steps: S1, initiating a reading request, reading page data by using default voltage, decoding the page data through an LDPC algorithm, constructing a feature vector and performing preprocessing, inputting the voltage into a pre-trained neural network model, and carrying out voltage prediction; s2, carrying out fine sampling on the predicted voltage, reconstructing accurate threshold voltage distribution, and calculating a log-likelihood ratio; s3, designing a shared bottom layer backbone network, enabling a neural network model to have independent output branches, predicting the voltage, and evaluating the long-term health state of the block; and S4, designing an online transfer learning mechanism, and enabling the neural network model to quickly adapt to a new environment by using a small amount of new data. According to the method, the delay is greatly reduced, the power consumption is remarkably saved, data loss is prevented through early warning of bad blocks, and the write amplification factor is reduced to 1.26 through dynamic read interference suppression.

The invention discloses an asynchronous processing method and device for business process data, and belongs to a distributed system data processing technology, and the method comprises the following steps: S101, receiving a business request message, and persistently storing the business request message in a main message queue; s102, acquiring a to-be-processed message from the main message queue, and locking an associated target data record before executing processing; s103, verifying data consistency based on the version number recorded by the target data, if the version number is matched, executing processing and updating the version number, and otherwise, discarding the message; s104, after successful processing, sending a confirmation instruction deletion message to the main message queue; through persistent storage of the main message queue, a version number verification mechanism and management of the dead message queue, the problem of data inconsistency caused by concurrent operation is effectively prevented, reliable transmission of messages is ensured, loss of service data is avoided, dead message processing efficiency is optimized, and the method has the advantages of ensuring data consistency, preventing message loss and improving system reliability.

The invention specifically relates to a multi-state sensing cable fault prediction method and system, and relates to the technical field of power equipment state monitoring and fault early warning, and the method comprises the steps: constructing a data fitting and physical constraint combined PINN model of a dual-drive network architecture; training the PINN model by using preset data until the PINN model converges; and on the basis of the trained PINN model, receiving real-time preprocessed data and then outputting an RUL predicted value. According to the invention, through multi-dimensional data acquisition processing and physical and data dual-drive PINN model design, the core problems that a traditional system is poor in data quality and the model is easy to violate physical rules are solved; pINN is embedded into an Arrhenius thermal aging equation, double loss calculation of data loss and physical loss is adopted, dynamic weight is matched, a model is forced to follow the law of temperature rise and life shortening, and RUL prediction precision and credibility are improved.

The invention discloses a dynamic self-adaptive precision switching method for a voice recognition NPU, and aims to solve the core problems that precision and energy efficiency of an end-side voice recognition NPU in multiple scenes cannot be considered at the same time, fixed-precision hardware resources are seriously wasted, software switching efficiency is low, and errors are accumulated. A hardware architecture of a 4 * 4 reconfigurable MAC subunit array and a switch matrix is constructed, and a task-driven adaptive scheduling mechanism, an operation period synchronous control technology (single-period hardware reconfiguration is realized by utilizing idle gap latch configuration) and a direction-sensitive error compensation algorithm (historical data interpolation extension or correction truncation) are combined; hardware-level dynamic lossless switching of the MAC unit between 8bit / 16bit / 32bit precision is realized, so that different voice tasks achieve the highest necessary computing power with the lowest necessary power consumption, no data is lost in the switching process, and finally, the energy efficiency rigidity of a fixed-precision framework is oppositely converted into the precision on-demand supply capability capable of being deployed in an engineering manner.

The invention discloses ground penetrating radar equipment based on an all-wheel-drive unmanned aerial vehicle and an underground geological three-dimensional map construction method, and relates to the technical field of three-dimensional map construction. The invention aims to solve the problems of terrain information perception distortion, difficulty in constructing a three-dimensional geologic model which is fused with terrain information and has space authenticity and the like caused by vehicle bumping and inclination in the prior art of integrating a ground penetrating radar into an intelligent unmanned vehicle platform for underground detection. According to the integrated structure design of the all-wheel-drive unmanned aerial vehicle integrated ground penetrating radar, the attitude of the whole unmanned aerial vehicle is kept stable when the unmanned aerial vehicle carries the radar for flight operation, data loss and noise introduction caused by attitude jitter of the unmanned aerial vehicle in the radar acquisition process are reduced, and a data foundation is laid for subsequent accurate three-dimensional geological map construction. According to the multi-sensor fusion method, the absolute precision of RTK, the high resolution of laser ranging and the dynamic response characteristic of an accelerometer are fully utilized, and the centimeter-level absolute position of the unmanned aerial vehicle, the height of the unmanned aerial vehicle to the ground and the absolute altitude of the ground surface below a scanning point are obtained. According to the invention, decoupling control of six degrees of freedom corresponding to the full-drive unmanned aerial vehicle is realized, so that the unmanned aerial vehicle can fly close to the rugged topography, and automatic and full-coverage scanning path flight is carried out with a constant ground clearance and a stable attitude.

The invention provides a disaster site-oriented multi-unmanned aerial vehicle rapid three-dimensional reconstruction and decision-making auxiliary method and related equipment, and belongs to the technical field of emergency surveying and mapping and three-dimensional reconstruction. The method comprises the steps that multiple unmanned aerial vehicles are used for cooperatively collecting image data of a disaster site, and uncertainty indexes used for representing the data missing degree are constructed; taking the uncertainty index as input, selecting an optimal viewpoint to generate a supplementary collection task and executing the supplementary collection task based on the contribution degree of candidate viewpoints to reduction of uncertainty and the comprehensive cost of executing the viewpoints, and obtaining an image data set with coverage integrity; screening a key frame set for three-dimensional reconstruction from the image data set; performing motion recovery structure and space partitioning based on the key frame set, and fusing depth priori and semantic information to obtain an initialized point cloud; and based on a three-dimensional Gaussian sputtering model, extracting structured elements, constructing a risk cost map, and obtaining a rescue route and a disaster situation decision report. According to the invention, emergency command and rescue actions can be supported.

The invention discloses a steel structure digital modeling method and system, and the method comprises the steps: obtaining high-precision point cloud data of a steel structure member through a laser scanning technology, carrying out the noise filtering and point cloud segmentation of the point cloud data, automatically recognizing the geometric features of the steel structure member through a geometric feature recognition technology, and distributing a unique identifier, the method comprises the following steps: collecting non-geometric attribute data of a steel structure component from a plurality of heterogeneous information sources, fusing the non-geometric attribute data through priority ranking and a conflict resolution mechanism, performing semantic reasoning completion based on component types, function positioning and engineering specifications, performing strict consistency verification on the completed non-geometric attribute information, and outputting the non-geometric attribute information in an industry general format; according to the method, comprehensive, efficient and accurate digital modeling of the existing steel structure component is realized, the core problems of data missing, information isolation, much manual intervention, low efficiency, error accumulation and the like in the prior art are effectively solved, and a solid and reliable digital foundation is provided for transformation, maintenance and management of steel structure engineering; and the method has remarkable engineering application value and economic benefit.

The invention discloses an unmanned aerial vehicle task scheduling method and device, electronic equipment, a medium and a product. The method comprises the following steps: acquiring all to-be-executed tasks in the remote control system; uniformly registering the to-be-executed tasks into a pre-constructed task table; wherein the unified scheduling entry of the task table is a preset main scheduling function; triggering a new task scheduling cycle according to the minimum time beat of the system; preferentially executing the currently triggered target key event according to the function hook triggering condition of the key event; and after the target key event is processed, according to the main scheduling function and the scheduling frequency registered in the task table, the remaining non-core common tasks in the task table are executed in sequence. The method can solve the problems that a key radio frequency event is not responded in time and data is easy to lose.

The invention discloses a deep learning inversion AOD-assisted atmospheric correction-based method and device and a medium, and relates to the field of atmospheric remote sensing and deep learning cross technologies, and the method comprises the steps: constructing a multi-type training sample set fusing actual measurement and physical simulation data, and carrying out mass screening and layering processing to obtain a multi-type training sample set; obtaining a pre-training sample, a fine tuning sample and an extreme scene sample matched with the satellite remote sensing data; establishing a deep learning network model taking data loss and inverse operator constraint loss weighted fusion as a total loss function, and adopting a cross-satellite transfer learning strategy to sequentially complete pre-training, satellite exclusive fine tuning and extreme scene enhancement training to obtain a trained aerosol optical thickness inversion model; radiometric calibration, cloud detection, geometric correction preprocessing and input model reasoning calculation are carried out on satellite remote sensing original data, post-processing and quality grading are carried out on an output result, and an aerosol optical thickness inversion result used for assisting atmospheric correction is obtained.

A system detects changes in model context protocol (“MCP”) processes, and performs a remedial action. A user interface (“UI”) is generated that comprises a selections for identifying restricted actions, permissions for those restricted actions, triggers based on the restricted actions, and remedial actions that correspond to the triggers. The restricted actions can be based on a change to a resource profile of the MCP server. They can also apply to resource commands. The triggers can be specific keywords or change thresholds. The UI selections can be used to generate a management profile. Using the management profile, the system can automate remedial actions for MCP servers, protecting enterprises from data loss and other security issues.

The invention proposes a steel slag asphalt interface adhesion work prediction method based on a physical neural network, and relates to the technical field of deep learning, and the method comprises the steps: obtaining chemical component parameters and environmental impact factors of a steel slag asphalt mixture interface sample; creating an original neural network prediction model based on a diffusion item, a reaction source item, a multi-mechanism dissipation item and a state variable corresponding to spatial-temporal evolution of the adhesion work; inputting the chemical component parameters and the environmental impact factors into an original neural network prediction model, and generating interface adhesion work prediction parameters in combination with physical constraint terms; training the original neural network prediction model by using the interface adhesion work prediction parameters and a gradient descent algorithm to obtain a target neural network prediction model; the physical constraint items comprise data loss, PDE residual error loss, state equation loss and boundary condition loss; and inputting the chemical component parameters and the environmental impact factors of a to-be-tested material into the target neural network prediction model to obtain target prediction parameters.

The invention provides a Redis cluster communication authentication method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of computers. The Redis cluster communication authentication method comprises the following steps: receiving a second cluster communication message from a second node; performing protocol version prepositive verification of trunking communication on the second trunking communication message; if the pre-verification is passed, reading a second cluster identity identifier in the second cluster communication message; performing consistency verification on the second cluster identity identifier and a first cluster identity identifier of the first node; and if the second cluster identity identifier is inconsistent with the first cluster identity identifier, terminating processing of the second cluster communication message. According to the technical scheme, the risk that different clusters are fused by mistake due to Gossip messages can be prevented on the basis of identity label consistency verification, and then the consequences that data are lost, fragment read-write is unavailable and the like are prevented.

The invention discloses a video data transmission method and system based on an FPGA, and relates to data transmission. The method comprises the steps that a video data stream is acquired, and a time sequence signal of the video data stream is analyzed; writing the parallel data into an external memory to form a frame buffer area; packaging the read video data into a data packet meeting a USB3.2 transmission protocol; packaging the data packet based on a USB3.2 link layer protocol; and converting the USB3.2 protocol transmission frame into a differential signal, and transmitting video data in the USB3.2 protocol transmission frame according to the differential signal. In view of data loss caused by mismatching of a video input rate and a USB output rate in the prior art, by establishing an annular buffer structure and adopting a dynamic pointer management and waterline threshold monitoring mechanism, rate adaptive matching of a video data stream is realized, and continuous and stable transmission of video data is ensured.

The invention provides a GPU (Graphics Processing Unit) hardware health prediction and active maintenance method and system, and belongs to the technical field of computing hardware maintenance and fault prediction. Based on the GPU health state data, a health degree comprehensive score of the GPU is calculated by using a preset health degree evaluation model, and a key health index in a future set time period is predicted by using a time sequence model. Calculating the fault probability of a preset type of fault risk by using a preset fault risk model based on the health degree comprehensive score of the GPU and / or the predicted key health index; and comparing the calculated fault risk probability with a preset threshold value, and executing a main preset maintenance action when the fault risk probability exceeds the preset threshold value. Through multi-source data acquisition and time sequence prediction, the potential fault risk of the GPU is found in advance, the maintenance action is actively executed, task interruption and data loss are reduced, and the reliability and availability of a GPU cluster are improved.

The invention discloses a formation collapse pressure prediction method based on a feature marking physical information neural network, and relates to the technical field of oil exploration and development. The method comprises the following steps: collecting multi-scale data of logging, earthquakes, cores and the like, and constructing a physical constraint framework in combination with a well track stress field model; a data enhancement module combining expert knowledge and a Beta distribution mixed strategy is utilized to carry out interpolation on missing values and generate an enhanced data set conforming to geological laws; a multi-head self-attention encoder comprising a feature word segmentation device and a Transform is constructed, original parameters are converted into embedded vectors, and deep interaction features are mined; a physical consistency measurement function based on a collapse pressure mechanism is introduced, and a total loss function containing data loss and physical loss is constructed for joint optimization. According to the method, the problem of geological sample scarcity is effectively solved, it is ensured that the prediction result meets the rock mechanics equilibrium condition, and the prediction precision and robustness of the complex formation collapse pressure are remarkably improved.

The invention relates to the technical field of data synchronization, in particular to an incremental synchronization and verification method and system for an access control authorization list, and the method comprises the steps: initializing a terminal and a server; the server side obtains the list change data to progressively increase the version number, and pushes the version number to the terminal equipment; the terminal initiates an incremental request to the server according to the notification message and obtains a server response incremental data block; the terminal performs multi-factor data verification on the incremental data block; performing cyclic synchronization on the incremental data; the terminal performs full-amount consistency verification on the incremental data; server real-time message notification and long connection transmission are adopted to replace a traditional polling mechanism, and the millisecond-level response speed is achieved; the rpcx protocol is matched with a proto binary format to perform data transmission, so that the protocol header overhead is reduced, and the serialization efficiency is improved; a multi-factor data verification mechanism is introduced, list number verification, incremental version number continuity verification and full-text hash verification are combined, a multi-level data consistency guarantee system is constructed, and the problems of data missing and wrong sending are avoided.

The invention discloses a PINN-based numerical control machine tool spindle Z-direction thermal error compensation method, and belongs to the technical field of numerical control machine tool thermal error compensation. S2, performing hyper-parameter optimization; and S3, model verification and error compensation: applying the established PINN thermal error prediction model to a machine tool of the same type as the tested machine tool for thermal error prediction. According to the numerical control machine tool spindle Z-direction thermal error compensation method based on the PINN, the core is that a spindle axial thermal deformation elongation physical formula is converted into a physical loss item through a PINN model, the physical loss item and a data loss item jointly form a total loss function, and compared with the problem that a traditional pure data driving model (such as a BP neural network) is prone to being limited by sample distribution, the numerical control machine tool spindle Z-direction thermal error compensation method based on the PINN is more accurate. According to the method, model output is restrained through a physical mechanism, even under the unseen rotating speed and temperature working conditions, stable thermal error prediction precision can be kept, model failure caused by working condition changes is avoided, and the generalization ability of the model in different machining scenes is greatly improved.

The invention provides a database migration method, device and equipment based on AI assistance and a medium, and belongs to the technical field of data migration. The method comprises the steps that a migration prediction result is generated based on a pre-trained migration knowledge graph model according to feature information of a source database and a target database; based on the minimum downtime, the data loss amount and the migration cost as optimization targets, an optimal migration strategy is generated according to the migration prediction result, the data playback state is monitored while the data migration task is started to be executed according to the optimal migration strategy, and migration parameters of the data migration task are adjusted according to the monitoring result; and according to a monitoring result, predicting an optimal switching opportunity and evaluating a rollback risk, and according to an evaluation result, adjusting an execution state of the data migration task. The database migration process is intelligent, self-adaptive and high in reliability, and migration efficiency and success rate are remarkably improved.

The invention relates to the technical field of satellite-ground laser communication, and discloses a banyan-shaped network communication method and system based on multi-beam multipath transmission and space diversity synthesis aperture reception. The method comprises the following steps: dividing a light beam into a plurality of beams at a satellite-based terminal through beam forming to point to a plurality of ground stations; multiple ground stations receive data in parallel and then gather the data to a data center for time-space synchronization; and the data center adopts a multi-branch neural network to intelligently fuse the synchronized data so as to reconstruct a signal. According to the invention, by constructing the satellite-to-ground collaborative architecture, signal interruption and data loss caused by atmospheric turbulence are effectively suppressed, the limitation of high-error-code communication is broken through, and remote, high-speed and high-reliability satellite-to-ground communication is realized.

The application discloses a block data archiving method and device for a blockchain and a medium. The method comprises the following steps: determining a block start number and a block end number corresponding to to-be-archived block data, constructing a to-be-executed archiving application through a private key of a to-be-archived blockchain node corresponding to the to-be-archived block data; publishing the to-be-executed archiving application on a chain, auditing the to-be-executed archiving application by an archiving smart contract in the blockchain, and publishing an archiving audit result; in the case that the audit is passed, packaging the to-be-archived block data by the to-be-archived blockchain node to generate a to-be-archived block data file and uploading the to-be-archived block data file to a preset storage area; after receiving a message notification of successful uploading, constructing a successful archiving notification based on an archiving application transaction Hash corresponding to the to-be-executed archiving application and a storage address of the to-be-archived block data file contained in the message notification, and publishing the successful archiving notification. The application avoids the risk of data loss or leakage and ensures that the archiving behavior is traceable.

The invention discloses a water regimen data acquisition dual-machine redundancy method and system, and the system comprises a main machine and a standby machine which are directly connected through an independent heartbeat interface. The main machine periodically sends heartbeat messages and local network configuration information to the standby machine through a heartbeat interface; and the standby machine monitors heartbeat in real time, judges that the main machine is abnormal once not receiving the heartbeat within a preset time, automatically takes over the IP address of the main machine, actively sends an address resolution protocol message according to pre-stored network configuration information so as to quickly update a forwarding table of the network equipment, immediately switches the state of the standby machine to be the main state, and synchronizes the state to a database. According to the method, automatic sub-second fault switching is achieved, the risk of key data loss is greatly reduced, continuity and integrity of water regimen telemetering data are guaranteed, meanwhile, the operation load of a background database is remarkably reduced through a main and standby mode, and the reliability of water regimen telemetering is improved. The problems that the load of the database is too heavy and automatic seamless switching of network channel data acquisition cannot be realized are solved.