2237 results about "Timestamping" patented technology

The invention discloses a power transmission line inspection unmanned aerial vehicle obstacle intelligent detection and obstacle avoidance method. The method comprises the steps that multi-source sensing data is acquired, and alignment is completed through calibration and timestamp matching; heterogeneous data preprocessing and feature enhancement; constructing a high-precision environment fusing a geometric structure and a semantic tag, mapping a two-dimensional target detection result output by the recognition network to a three-dimensional coordinate system through spatial transformation, and fusing the two-dimensional target detection result with a point cloud structure to construct a semantic occupation grid map; performing preliminary route planning according to a preset power grid topological structure and task coverage requirements, and generating a barrier-free flight path covering the whole inspection area; reinforcing learning of a dynamic obstacle avoidance strategy; track dynamic reconstruction and energy consumption optimization scheduling are carried out; the technical problems that an existing technical system has defects in the aspects of obstacle recognition accuracy, complex environment adaptability, data fusion capacity and obstacle avoidance strategy intelligence, and the requirements for high-reliability, low-energy-consumption and high-efficiency unmanned aerial vehicle power transmission line inspection are difficult to meet are solved.

The systems and methods disclosed herein monitor application (e.g., artificial intelligence (AI) model) operations using interactions between the application and a kernel. The systems and methods disclosed herein intercept, using a kernel interface, one or more function invocations transmitted from the application (e.g., an AI model without model modification). Event record(s) are generated for one or more functions to define process identifiers, resource interaction types, timestamps, and / or resource identifiers. Observed pattern(s) for the application are identified by comparing current event record(s) with previous record(s), and the identified observed pattern(s) are evaluated against reference pattern(s) to generate score(s). Data packet(s) that indicate observed pattern(s), corresponding score(s), and / or cryptographic digital fingerprint(s) of the one or more functions are generated. The data packet(s) are transmitted to distributed ledgers for immutable storage.

The invention relates to the technical field of edge computing, in particular to a safety production monitoring method based on edge collaboration, which comprises the following steps: collecting multi-node clock pulses, calculating adjacent time differences to form a pulse difference parameter set, carrying out statistical distribution, setting a dynamic tolerance boundary, and constructing an effective deviation parameter set; the method comprises the following steps of: generating a weighted smooth deviation result by combining clock stability and link delay weighted normalization, sorting monitoring data according to a timestamp to identify duplication abrupt change exception, constructing a cross-node exception time sequence by associating nodes and time information, and judging a safety state transition result of equipment. A time difference parameter is constructed through multi-node reference pulse arrival time, a dynamic deviation constraint is formed by combining distribution characteristics, clock stability and link measurement weighted smoothing are introduced, cross-node time reference consistency is kept, operation data unified time sequence rearrangement is carried out, a sudden change event reliably reappears in a time window, and an abnormal identifier is associated with a node identity. And forming a cross-node event evolution link.

The invention discloses a video stream adaptive low-delay real-time transmission method and system based on edge calculation. The method comprises the following steps: receiving a real-time video stream from a network camera, creating a pipeline queue, adding timestamp information for each video frame, and setting a queue protection mechanism; the coded video frames are taken out from the input queue, and the frames in the video are processed through hardware acceleration decoding; the resource use condition of the system is monitored in real time; executing a self-adaptive frame skipping decision according to a performance monitoring result; timestamp generation: dynamically calculating a timestamp interval according to an actual processing frame rate; receiving the decoded original video frame and the corresponding timestamp information, accelerating decoding by using hardware, and executing a video coding operation; and packaging and transmitting the coded video data, and providing a standard protocol interface to be connected with a client for playing. According to the scheme, stable low delay and relatively low resource occupation can be kept, and meanwhile, the video quality is remarkably improved.

The embodiment of the invention provides a power distribution network dynamic reconstruction method based on knowledge-guided large model enhanced fine tuning and related equipment, and belongs to the technical field of smart power grids and artificial intelligence. The method comprises the following steps: constructing a power distribution network dynamic knowledge graph, and providing structured knowledge guidance for model training; subgraph sampling is carried out based on the timestamp and converted into a fine tuning sample, and a training data set is generated; utilizing the data set to supervise, finely adjust and preheat the large language model; designing a multi-dimensional reward function of fusion format accuracy, economy and security based on mechanism knowledge in the knowledge graph and expert experience; a group relative strategy optimization mechanism is adopted to carry out reinforced fine tuning on the large language model, and the large language model is guided to output a safe, reliable and economical dynamic reconstruction strategy in interaction with the environment. According to the method, the problems of lack of training data, lack of physical knowledge guidance and insufficient decision reliability of a large language model in the power grid field are solved, and the intelligent level and decision quality of dynamic reconstruction of the power distribution network are remarkably improved.

The invention relates to the technical field of communication, and discloses a communication state monitoring management method and system. The method comprises the following steps: acquiring a transmission data packet in a communication network in real time, extracting protocol header information and load content of the data packet, and generating an original monitoring data sequence containing a protocol type, a timestamp and load characteristics; and performing multi-dimensional correlation analysis on the original monitoring data sequence, identifying a logical relationship between protocol header information and load content, detecting an abnormal correlation mode, and generating an abnormal event mark set with a confidence score. And constructing an adaptive behavior model based on the original monitoring data sequence and the abnormal event mark set, and capturing normal behavior characteristics of the communication network by dynamically updating model parameters. Transmitting data packets collected in real time are input into the self-adaptive behavior model for deviation degree calculation, abnormal data packets exceeding a deviation threshold value are screened out, and historical abnormal event mark sets are associated to generate abnormal context records.

The invention relates to the technical field of artificial intelligence, in particular to a large language model compliance reasoning method and system based on domain knowledge graph constraint, and the method comprises the steps: collecting domain literature corpora, extracting an entity relationship triple, labeling a timestamp interval and confidence, and constructing a domain knowledge basic data layer; organizing the state change of the same entity at different time points into a directed evolution link, forming a knowledge evolution tracking graph, and generating a time sequence perception type domain knowledge graph; constructing a semantic alignment detection mechanism, and detecting a semantic drift region; selecting a current effective graph knowledge fragment according to a detection result, and embedding time sequence constraint into a model context through a dynamic knowledge injection channel to realize real-time knowledge calibration in a reasoning process; and establishing a compliance reasoning verification process, comparing the reasoning result with the time sequence constraint rules one by one, marking and correcting, and outputting a compliance conclusion conforming to the current specification.

The invention relates to a gas sensor environment anti-interference drift compensation method, system and equipment, and the method comprises the steps: collecting a mixed gas response signal in an environment, and obtaining an electromagnetic interference signal under the same timestamp; threshold truncation is carried out on spike pulses in the mixed gas response signals to obtain trend waveform signals caused by gas concentration changes, and denoising processing is carried out on the electromagnetic interference signals to obtain effective electromagnetic interference parameters; performing feature recognition on the effective electromagnetic interference parameters through a preset interference-response cooperative processing model to determine an interference mode and an interference intensity grade, synchronously analyzing the trend waveform signal to obtain a response feature curve of the target gas and the interference gas, and obtaining a drift compensation coefficient of the target gas; and carrying out collaborative optimization on the drift compensation coefficient through a preset interaction influence model to obtain a final compensation result so as to achieve the purpose of outputting a high-precision target gas concentration detection result.

The invention relates to the technical field of mechanical arms, in particular to a man-machine interaction system of an intelligent mechanical arm with a body. The system decomposes a long time sequence task into sub-tasks through a large language model, and introduces a task acquisition module, a task queue management module, a task re-planning module and a control execution module, uses visual detection to identify gestures and environment events to trigger temporary tasks, and maintains interruptible marks and safety anchor points based on priorities and interruption risks. And during interruption, the mechanical arm with the timestamp and the environment state are collected, during recovery, the states are compared, local re-planning is carried out, transition sub-tasks are automatically generated for failure sub-tasks, and the mechanical arm is controlled to execute after constraint verification. According to the system, on the premise that the structure consistency, the space consistency and the time consistency are guaranteed, safe interruption and efficient recovery in a long-time-sequence task can be achieved, and the autonomy, the real-time performance and the operation safety of the mechanical arm in a man-machine cooperation scene are remarkably improved.

The invention relates to the technical field of computers, and discloses a multi-mode audio and video synchronous processing method and system based on a distributed architecture. The method comprises the following steps: generating a unique logic sequence identifier consisting of a device code, a media type and a frame number for each frame of audio and video data; distributing the frame carrying the identifier to a distributed node for processing and adding a local timestamp; and the sink node reconstructs an original frame sequence according to the identifier, calculates a synchronous offset by combining high-precision clock calibration, and realizes dynamic reordering and output through a double-buffer structure and a self-adaptive drift compensation algorithm. The system comprises a source end acquisition module, an identifier generation module, a task scheduling module, a distributed processing cluster module, a convergence synchronization module and a synchronous output module. According to the scheme, the system effectively guarantees the audio and video synchronization precision in a high-concurrency heterogeneous environment, the synchronization error is controlled within 20 milliseconds, and the playing quality and the user experience of scenes such as live broadcast and cloud games are remarkably improved.

A system and method for detecting anomalies in encrypted network traffic without decrypting encrypted payloads is disclosed. The invention comprises a hardware-accelerated pipeline including a packet acquisition unit configured to extract observable header attributes and timestamp values, a signal analysis unit implemented using digital signal processing circuitry to generate temporal and spectral characteristics of encrypted packet flows, and a morphometric extraction unit that produces encrypted-flow descriptors based on statistical dispersion, burst patterns, and ciphertext variability. A behavioral graph construction unit generates graph-structured representations of encrypted flows using incremental state-transition encoding. An artificial-intelligence inference unit employing a tensor-processing processor performs multi-stage anomaly analysis through temporal reconstruction, attention-based weighting of descriptors, and graph-structural deviation assessment to compute anomaly scores without decrypting any payload. A secure response control unit evaluates the scores against policy thresholds and performs mitigation actions while preserving confidentiality.

The invention relates to the technical field of industrial digital twinning, in particular to a digital twinning-oriented real-time data synchronization and consistency verification method, which comprises the following steps of: S1, acquiring multi-source heterogeneous data from a physical entity and a service system, and performing timestamp alignment based on a unified time reference to generate a standardized data stream; and S2, based on the standardized data stream, carrying out data cleaning, aggregation and preprocessing, verifying the integrity and time sequence of the data stream through a transmission verification mechanism, and outputting high-fidelity credible data. According to the method, a bidirectional consistency verification mechanism between a physical entity and a digital twinborn model is constructed, real-time deviation is quantified into a loss function, an online learning engine is driven to adaptively adjust physical parameters in the model, and a complete closed loop from data acquisition to parameter optimization is formed; the digital twinborn model can continuously adapt to dynamic conditions such as material aging and working condition change of a physical entity, and the tedious process that a traditional system depends on manual regular calibration is avoided.

The invention discloses a Beidou time service synchronous multi-source data fusion monitoring device, system and method, and aims to solve the core problems of low meteorological factor-ground surface settlement causal correlation analysis precision and poor early warning reliability caused by multi-source data time asynchronization due to sensor clock independence. The device directly drives each sensor in the multi-source data acquisition module to perform physical-level synchronous acquisition through a hardware synchronous trigger pulse generated by the Beidou time service module, and ensures that settlement, weather and positioning data have the same timestamp at the source; and time axis calibration and dynamic weight fusion based on real-time data quality are carried out through the preprocessing module. The system integrates the devices, a cloud analysis model and an early warning engine. According to the invention, a closed loop from hardware-level synchronous acquisition, space-time alignment to intelligent analysis is realized, a multi-source data synchronization error is reduced from a second level to a millisecond level, and the accuracy and timeliness of water and soil conservation monitoring are remarkably improved.

The invention discloses a dynamic calibration method and system for cross-terminal detection score offset, relates to the technical field of dynamic calibration of score consistency of a multi-terminal detection system, and is used for solving the problem that detection scores are incomparable due to inconsistent time bases among terminals and segmented drift of score scales. Generating an event record containing an original detection score and a quality mark by solidifying a terminal identifier and a field specification; the unified timestamp is corrected through four-timestamp time service, the aligned event groups are merged according to the aligned time windows, and the optimal shift amount is solved; dividing the offset sequence into segments, establishing order-preserving linear mapping in the segments, and performing interpolation connection on a transition region; and aperture self-certification and version updating verification are realized by combining the micro-correction list and the residual offset.

The invention discloses a wide-area time traceability credible authentication method in a power heterogeneous network environment, and belongs to the technical field of power system informatization and automation. Comprising the following steps: acquiring various high-precision time source information in a power heterogeneous network environment, and acquiring an initial time reference of the power system time synchronization device; generating uniform reference time, and embedding a digital watermark containing time source information and credibility thereof for each timestamp by using the digital watermark embedding module; controlling the power system time synchronization device to sequentially generate time transmission fingerprints at all levels of network nodes; constructing a unique time fingerprint chain covering the whole link; packaging the time fingerprint of each time transmission node and the traceability information thereof, and performing uplink storage; precise prediction and optimal control of time transmission errors are realized; if the overall cumulative time error exceeds a range of + / -10 [mu] s, the predictive correction is maintained until the error is controlled within + / -10 [mu] s.

The invention provides a ship-based unified storage and management design data method, which comprises the following steps that: an operation end generates and sends an editing request with a node identifier, an editing type, a timestamp and a digital signature, and a WEB front end receives and verifies the legality of the request through an encryption channel; the storage service generates an intention lock based on the permission table and version number matching, and concurrent access control is achieved. Then, the WEB front end carries out content definition partitioning (CDC) and Hash fingerprint calculation on a design file stream, the storage service realizes partitioning duplicate removal and incremental storage according to a fingerprint matching result, and a Merkle tree is utilized to construct a file version integrity Hash root; on this basis, the system analyzes model differences, constructs a semantic difference chart, and calls a rule engine to perform automatic pre-check. According to the method, efficient storage, collaborative editing, security and credibility and hierarchical scheduling management of ship design data are realized, and the method has the technical advantages of high data consistency, high resource utilization rate, traceability auditing and the like.

The invention discloses a synchronization method and device for time synchronization of a network system, and relates to the technical field of computers, and the method comprises the following steps: when a master station sends a dynamic compensation synchronization frame, recording a compensation sending timestamp in a sensing identification field of a sending end; when the frame is received and transmitted from the station, the identification field is sensed at the port to determine a compensation receiving timestamp and a compensation transmitting timestamp. And the slave station identifies the dynamic compensation synchronization frame after reverse return, calculates dynamic compensation time information based on the compensation receiving timestamp and the compensation sending timestamp corresponding to the slave station, and reads and writes the dynamic compensation time information to a corresponding field. And the master station analyzes the returned dynamic compensation synchronization frame, aggregates the dynamic compensation time information of each slave station, and solves the dynamic compensation values of the master and slave stations in combination with the fixed link delay. And the master station corrects a local high-precision clock according to the data, and writes a compensation value into a local time correction register of the slave station to trigger a hardware counter to correct so as to complete synchronization of the master and slave clocks. By realizing dynamic residence separation, the problem of overlarge error is solved.

Systems, methods, and techniques are directed to detecting potential anomalous activity based on changes in a security graph. In an example, a security system receives a first snapshot of a graph representative of a tenant account of a network-based system corresponding to a first timestamp. The security system receives a second snapshot of the graph corresponding to a second timestamp. The security system determines a first change in the graph based on the first and second snapshots and a second change related to the first change. The security system detects a potential anomaly based on the first and second changes. Responsive to detecting a potential anomaly, the security system causes a mitigation step to be performed with respect to the tenant account. In a further example, the security system determines relationships between a sequence of changes satisfies a cumulative anomaly criterion.

The invention discloses a quick data traceability method and system based on multi-source log analysis, and relates to a data processing system or method specially suitable for administrative, commercial, financial, management, supervision or prediction.The method comprises the steps that firstly, the system responds to a service instruction to generate a transaction receipt containing a unique serial number and service attribute parameters; and establishing an association relationship between the two records and generating a to-be-confirmed record and a timestamp. And sending a service message containing the service attribute parameters to a receiving end. When an ownership right reverse search request of a receiving end is received, the system calculates an effective time interval based on current time and a preset time delay threshold value, and screens out a target to-be-confirmed record which is matched with a service attribute parameter and generates a timestamp in the interval. And finally, extracting the unique serial number from the target record, generating a confirmation receipt and sending the confirmation receipt to a receiving end system. The method is used for reducing the calculation overhead and false alarm rate of accurately positioning specific abnormal transactions in massive logs by a system, and improving the confidence and timeliness of an automatic traceability result.

The invention provides a clinical test data security sharing method and device based on a block chain. The method is applied to the technical field of data processing, and comprises the following steps: extracting feature dimensions corresponding to various types of data, and calculating corresponding data sensitivity coefficients; determining a data sensitivity level of the corresponding data category; extracting corresponding associated metadata; selecting a corresponding encryption algorithm to encrypt the data, and generating corresponding encrypted data and a data abstract; uploading the associated metadata, the encrypted data and the data abstract to a block chain node, constructing a clinical test data sharing account book, and recording a timestamp and a node signature of data operation; extracting identity authentication information and historical access records of visitors, and calculating access credibility; judging whether the visitor is allowed to acquire the decryption key or not; after the visitor obtains the encrypted data, the integrity in the data transmission process is verified based on the non-tampering property of the block chain. Therefore, the security of clinical test data sharing based on the block chain is improved.

In the DevOps process, testing teams use automated testcases to test a product in a regression testing running daily, which generate thousands of log files per run, in various distributed environment with different formats. Solutions to errors depend on SMEs and the impact of solution leads to rework of defect / errors and high degree of heterogeneity. This leads to huge bottleneck in automation of DevOps process leading to loss of productivity and agility. Present disclosure addresses the challenges by providing systems and methods that auto capture the log files of different formats efficiently in a scalable, extendible, and plug-able way. The system then mines and parses the log files based on given identifiers to standardize and de-duplicate to create unique error records with detail description including cause, position, module, timestamp, etc. The system predicts the solutions leveraging the database containing solutions and errors by using a modified Smooth Inverse Frequency technique.

The invention discloses a disaster multi-source data real-time fusion early warning system based on deep learning, which belongs to the technical field of disaster early warning and comprises a multi-source data space-time alignment module, a cross-modal semantic fusion module, a disaster evolution dynamic prediction module and a minute-level early warning generation module. According to the method, accurate alignment of multi-source data is realized through an adaptive space-time resampling algorithm, cross-modal deep semantic fusion is realized through a cross attention mechanism, a disaster evolution trend is predicted through a space-time diagram attention network, minute-level early warning response is realized through an incremental calculation strategy, and disaster early warning is realized. According to the method, precise space-time alignment of the multi-source heterogeneous data is realized through adaptive space-time resampling and timestamp synchronous correction, and the space-time alignment precision is improved.

The invention discloses a multi-dimensional time series data dynamic evolution analysis method and device, a medium and electronic equipment. The method comprises the steps that time series data are obtained and subjected to structured preprocessing; performing multi-dimensional quantization and attribute endowing on each piece of time series data, and constructing a three-dimensional data node containing a space coordinate, an initial weight and a timestamp; based on the set of the three-dimensional data nodes in a specific time window, constructing a three-dimensional data density field capable of representing data hotspot distribution and intensity in the time window; in the three-dimensional data density field, identifying a density peak region as a core data cluster, calculating the cluster mass according to the density and the coverage range of the core data cluster, and calculating the association strength among different core data clusters; and serializing a plurality of continuous time window data field state snapshots to form a dynamic evolution view capable of showing data hot spot generation and extinguishing and data cluster fusion and splitting processes. According to the invention, abstract multi-dimensional data is converted into a visual three-dimensional density field.

The invention discloses an SLAM-oriented multi-sensor space-time synchronization and online calibration method, and relates to the technical field of computer vision and robot perception. Comprising the steps that S100, initialization is carried out, and an initial pose and an external parameter transformation matrix initial value are obtained through coarse alignment; s200, data acquisition and preprocessing: acquiring IMU data, LiDAR data and Odom data in real time, and performing data filtering, distortion removal, time sequence correction and point cloud downsampling processing on the acquired data; and S300, performing time synchronization processing, estimating the time offset of the LiDAR and Odom data in real time, and performing unified correction to generate a unified timestamp. According to the method, an online time offset estimation and compensation mechanism with an IMU as a main clock is established, accurate synchronization of sensor data of different frequencies is achieved, and the problem of data alignment caused by sampling frequency difference and clock drift is effectively solved; and meanwhile, tight coupling fusion is realized by using an IMU pre-integration factor, a LiDAR residual factor and an Odom constraint factor, so that the space-time consistency of multi-sensor data is remarkably improved.

The invention discloses a wireless communication system and method based on an EtherCAT network. The system comprises a wireless gateway and a wireless slave station, the wireless gateway comprises an EtherCAT slave station module, a wireless communication module, a control module and an auxiliary function module. The control module carries out packaging conversion on an EtherCAT frame received by the EtherCAT slave station module and a wireless frame of the wireless communication module through hardware-level protocol conversion; the wireless communication module is an FHSS module or a WiFi module. The auxiliary function module comprises a timestamp module for recording time information and is used for assisting the control module to perform master-slave clock synchronization by adopting a multi-stage distributed clock synchronization optimization algorithm; the wireless slave station comprises an EtherCAT slave station module, a wireless communication module and a local control unit, wherein the EtherCAT slave station module is used for receiving the control instruction of the EtherCAT master station and is compatible with the wireless gateway; the wireless communication module is used for uploading a wireless frame packaged with an EtherCAT sub-message and has the same model as the wireless frame; and the local control unit is used for analyzing the control instruction of the EtherCAT master station to collect local data and packaging the local data into the EtherCAT sub-message. According to the invention, requirements of industrial automation on high-precision and high-reliability wireless communication can be met.

The invention discloses a multi-agent collaborative knowledge reasoning system based on a large language model, and relates to the technical field of intelligent manufacturing and artificial intelligence. Natural language output of a large language model is converted into rules, facts and ontology fragments which can be directly consumed by an inference engine through knowledge obtaining and compiling, continuous increment updating of cross-domain knowledge is achieved in cooperation with metadata with sources and timestamps, and the limitation that a traditional static knowledge base is difficult to cover dynamic faults is overcome; secondly, a blackboard and agenda mechanism is used as a cooperative carrier, intermediate assertions of intelligent agents such as vibration, circuits and logs are published and subscribed in a structured mode, and a conflict resolution and consistency verification module carries out unified judgment according to specificity, time freshness and source credibility; therefore, delay and uncertain accumulation caused by long-chain natural language dialogues are avoided in a strong real-time scene.

The invention provides an inter-device time synchronization method and device, and the method comprises the steps: carrying out a handshake operation with a to-be-synchronized device based on a pre-constructed communication channel in response to the triggering of a time synchronization operation, and recording the local end timestamp information and the synchronization end timestamp information of the handshake operation, and reading synchronization end timestamp information according to a feedback message sent by the to-be-synchronized device, determining a clock skew value of the to-be-synchronized device and a synchronization confidence coefficient of the time synchronization based on the local end timestamp information and the synchronization end timestamp information, and matching a time synchronization strategy according to the clock skew value and the synchronization confidence coefficient, so that the to-be-synchronized device performs time synchronization correction. And determining a synchronization interval duration according to the synchronization confidence and the clock skew value, so as to execute the next time synchronization operation according to the synchronization interval duration, the timestamp information is recorded through the handshake operation, the clock skew and the synchronization confidence coefficient are determined, the time synchronization strategy is matched, the synchronization interval is determined, and the problems that the synchronization precision is insufficient and the strategy is not flexible are solved.

The invention relates to the technical field of log detection, in particular to a real-time anomaly detection and tracing method and system for a cloud storage log, and the method comprises the following steps: calculating an authentication failure proportion based on a user identifier and an internet protocol address, constructing a proportion change sequence, comparing an abrupt change threshold, screening candidate sections, fitting a slope, and aggregating anomaly records; calculating a fluctuation difference sequence, extracting a causal fracture node, recombining a subsequence graph, extracting a cross-node record and correcting a timestamp, connecting a transmission edge based on the timestamp, and accounting time consumption to construct a traceability path. According to the method, the serious defect that a fixed threshold value cannot sense system load fluctuation is overcome by constructing a dynamic proportion change slope, a causal fracture node is established to drive log atlas recombination so as to break an information barrier of a cross-node distributed environment, and a median function is used for correcting time migration to eliminate disorder interference caused by clock asynchronization; and the accurate risk tracing is realized by connecting the correction timestamp with the access transmission edge.

The invention relates to the technical field of power distribution network power supply, and discloses a power distribution network power supply system based on time domain control, comprising a unified time service synchronization module used for establishing a unified time service link and keeping node time synchronization; the time domain acquisition and judgment module is used for acquiring a zero-crossover timestamp and voltage and current samples and determining candidate target moments and execution modes; the instruction generation and calculation module is used for calculating a zero-crossing time difference and an instantaneous voltage difference and generating a time domain action instruction set; the starting advance locking module is used for determining and locking starting advance; the instruction rechecking judgment module is used for rechecking the effectiveness of the instruction set; the action execution checking module is used for completing loop closing or loop opening at a candidate target moment and judging a border crossing state; and the time difference updating and correcting module is used for calculating a time deviation value and updating control parameters. According to the invention, high-precision time synchronization, dynamic self-calibration and action safety coordination control of the loop closing and opening process of the power distribution network are realized.

Systems, methods, and apparatuses for securing a communication network are provided. Transmitting a first signal including encoding a first timestamp, a first identifier, and a message authentication component. Obtaining a message signal comprising one or more service variables associated and an encrypted unique identifier. Authenticating the encrypted unique identifier based on a comparison of the encrypted unique identifier against a plurality of stored unique identifiers in a lookup table. Generating a second signal comprising an encoding of a subset of the service variables, the encrypted unique identifier, and a second timestamp. Transmitting the second signal to each remote receiver in a subset of the plurality of remote receivers.