6651results about "Time-division multiplex" patented technology

The invention discloses a vehicle-mounted Beidou positioning deviation self-calibration system fused with inertial navigation information, and relates to the technical field of navigation and positioning. The method is used for solving the problems of positioning deviation accumulation and signal failure in a complex environment. The system realizes space-time alignment of inertial navigation and Beidou observation data through timestamp interpolation and coordinate system conversion, and constructs a multi-source synchronous data stream. A difference value between an inertial navigation calculation speed and a Beidou Doppler speed is analyzed based on a vehicle incomplete constraint model, and a weight factor is generated in combination with a confidence threshold to suppress abnormal observation. A geometric matching error is calculated through a high-precision lane map curvature and a vehicle steering angle, and candidate tracks are screened to generate transverse calibration parameters. The noise covariance is dynamically adjusted through a tight coupling filtering algorithm, the mode is switched to a lane constraint calibration mode when a Beidou signal is interrupted, inertial navigation errors are compensated through error boundary constraint, and stable output is achieved. Multi-source data deep fusion and adaptive robust calibration are realized, and system robustness and positioning continuity are improved.

The invention relates to the technical field of communication scheduling, discloses a communication scheduling network management intelligent optimization system and method based on AI dynamic decision, and solves the problems of insufficient scheduling dynamics, closed loop deficiency and poor edge adaptation in the prior art. Comprising a multi-dimensional data fusion acquisition module, a dynamic AI decision engine module, a cross-domain collaborative scheduling module and an intelligent closed-loop feedback optimization module. The dynamic AI decision engine module evaluates business value and resource pressure based on an edge-center collaborative architecture, predicts transmission quality and quantifies strategy income, the cross-domain collaborative scheduling module realizes intra-domain resource slicing and inter-domain strategy negotiation and path optimization, the intelligent closed-loop feedback optimization module constructs a data closed loop to iteratively optimize model parameters, and the dynamic AI decision engine module performs multi-domain collaborative scheduling on the basis of the edge-center collaborative architecture. Intelligent scheduling and autonomous optimization of network resources are realized, and the real-time performance, the reliability and the resource utilization rate of a communication network are improved.

The invention discloses a construction engineering risk assessment method and system for multi-source anomaly monitoring, and particularly relates to the field of construction engineering risk assessment, comprising the following steps: analyzing equipment-to-component node mapping and return delay characteristics based on multi-source monitoring data received in an idle task period; establishing a cross-device time alignment model and a pseudo-synchronization section time migration trajectory, identifying a potential abnormal path set through a time delay drift trend, and providing a time feature basis for subsequent structure anomaly reasoning and risk assessment; deducing a node triggering sequence and a time causal chain based on the final pseudo-synchronization segment index matrix and channel path mapping; multi-source monitoring data is received in an idle task period, and a cross-device time alignment model is established, so that the problem of time sequence dislocation caused by mechanisms such as cache delay and packet loss re-sending is effectively identified, and historical abnormal data is prevented from being misjudged as a future risk from the source.

The embodiment of the invention provides an industrial real-time data transmission guarantee method and system based on a TSN. The method is applied to the field of industrial communication and intelligent scheduling, and comprises the steps of obtaining industrial field network node information, completing TSN topology deployment and time synchronization, extracting communication characteristics and historical load data of various task flows, constructing a unified time sequence, and inputting a prediction model to generate a load prediction result. And in combination with task parameters, a scheduling strategy list and a prediction result, inputting a TSN digital twin network model to carry out analogue simulation, evaluating performance indexes under multiple strategies, selecting an optimal scheduling strategy and issuing the optimal scheduling strategy to TSN switching equipment, thereby realizing accurate scheduling control configuration of various task flows. According to the scheme, intelligent evaluation and automatic optimization issuing of the scheduling strategy are realized, the real-time performance and the deterministic communication capability of an industrial network are remarkably improved, the resource utilization efficiency and the adaptability of the scheduling decision are enhanced, and the key task flow is ensured to be reliably transmitted on time.

The invention provides a gas leakage time-space correlation early warning method and system based on multi-modal data fusion, and relates to the technical field of data processing, and the method comprises the steps: collecting real-time concentration data and a historical leakage accident database through an NB-IoT combustible gas sensor network disposed at a key node of a gas pipe network; according to the real-time concentration data and a historical leakage accident database, a UTM projection coordinate system is adopted to carry out registration on geographic space data, a three-dimensional space mapping model of a pipe network topological structure is established, timestamp alignment is carried out on all dynamic monitoring data streams through an NTP clock synchronization protocol, and a time-space correlation standardized data set is formed. According to the invention, full-chain intelligent management of the gas leakage risk from sensing, prediction to disposal is realized.

The invention discloses a cross-device clock correction method supporting accurate synchronization of meters, and aims to improve the data time consistency among multiple meter devices and meet the requirements of high-precision electric power measurement and event analysis. The method comprises the following steps: acquiring a network time protocol timestamp and a first sampling time sequence in a reference meter, generating a clock reference signal, and broadcasting the clock reference signal to a corrected meter; comparing the corrected meter with a local second sampling time sequence, and calculating a clock skew coarse value vector; further performing wavelet decomposition on the offset coarse value, extracting to form a drift feature vector, fusing the drift feature vector with a historical template, and calculating a unified correction parameter set by using a Bayesian recursion method; and generating a correction pulse sequence carrying a delay residual compensation code based on the parameter set, sending the correction pulse sequence to a corrected meter, calling an interpolation compensation function to construct a piecewise polynomial drift compensation table, and finally realizing dynamic correction and cross-device accurate synchronization of a local real-time clock.

The invention relates to the technical field of data processing, and discloses a PTP network performance monitoring method and system based on big data analysis, and a medium. The method comprises the following steps: acquiring PTP message data, equipment state data and RTP stream data; preprocessing the data and calculating a clock error; constructing a time sequence database and an analysis model; real-time monitoring and anomaly detection are executed; performing performance scoring and fault positioning by applying machine learning; and carrying out network optimization and predictive maintenance. According to the PTP network performance monitoring method based on big data analysis, global state monitoring, intelligent anomaly detection, accurate fault positioning and predictive maintenance of the PTP network are achieved, and therefore the high-precision time synchronization requirement of a broadcast and television IP making and broadcasting system is guaranteed.

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

The invention relates to a network real-time synchronization communication system, and relates to the technical field of computer network communication. The system comprises four core modules: a hardware optical IO synchronization module which integrates a multi-wavelength optical transceiver array and an anti-jitter circuit and provides a nanosecond global clock signal, and the synchronization precision is less than or equal to 15ns; the time sequence arrangement module is used for dividing a fixed time window and a dynamic buffer window based on a time sensitive network, supporting a priority preemption mechanism of the SRIO bus and realizing deterministic data transmission; the distributed RTDATA nodes replace a traditional single-board computer, a heterogeneous computing unit and an intelligent storage controller are integrated, and protocol stack processing delay is reduced through the zero copy technology; according to the dynamic multicast routing system, a safely isolated multicast tree is constructed as required, and link load distribution is optimized in combination with machine learning. Through collaborative design of hardware optical synchronization and dynamic data arrangement, the problem of real-time performance reduction caused by multi-node communication concurrence is solved.

The invention discloses an electronic lead seal automatic detection method applied to logistics tracking, and relates to the technical field of Internet of Things safety monitoring. The problems of packaging data loss, tampering risk increase and real-time monitoring failure caused by wireless communication signal interference, transmission delay and state updating lag of an existing electronic lead seal are solved. The method comprises the following steps: fusing multi-physical field sensing data through a space-time correlation sampling algorithm, and constructing an anti-interference characteristic matrix; evaluating communication quality based on the dynamic probability network model, and triggering a multi-path fragmentation concurrent transmission strategy to avoid signal attenuation; a hybrid reasoning model is deployed at an edge end to screen key event data, and the transmission efficiency is optimized in combination with differential coding and an IEEE 1588 clock synchronization mechanism; the cloud end adopts a space-time diagram fusion analysis model to carry out cross-modal abnormal association scoring, corrects misjudgment and updates an edge model; according to the invention, the communication reliability, the real-time transmission efficiency and the anomaly detection precision of the lead sealing state data in a complex environment are obviously improved.

The invention provides an HPLC (High Performance Liquid Chromatography) station area cooperative communication method based on clock synchronization, which comprises the following steps: step 1, multi-dimensional data acquisition and broadcasting: each concentrator periodically acquires clock precision, stability and reliability data, and sends a beacon frame containing a timestamp and a health degree score through a power line carrier broadcasting channel; step 2, dynamically calculating the index distinction degree by using an entropy weight method, adaptively allocating weights, and electing an optimal main CCO in real time; step 3, dual-mode PID cooperative control: the main CCO adopts a dual-mode switching strategy of a dynamic PID algorithm and a chaotic fractional order PID algorithm; 4, monitoring a synchronization state in real time by constructing a clock check network, and optimizing a fault isolation strategy in combination with weighted scoring to realize that the state switching time of the master node and the slave node is less than or equal to 50ms; cooperative communication is realized on the basis of completing synchronization of the concentrators CCO in the whole network. Redundant message transmission is reduced through a dynamic parameter resynchronization mechanism, and the utilization rate of network resources is optimized.

The invention provides a multi-modal sensor time synchronization method for road monitoring, which comprises the following steps: acquiring sensor data of target road detection, and determining communication time delay; the compensation value is optimized in real time according to the actual network condition and the environment change, and an optimized data set is obtained; determining a network topology structure based on the complete optimization data set; according to the optimized network topology structure, determining a time synchronization signal sequence of each node relative to the time delay information in the network; time synchronization of all sensors for target road detection is optimized. According to the invention, by introducing strategies of dynamic environment modeling and adaptive network topology optimization, the defects of the traditional time synchronization technology are effectively overcome, and high-precision multi-modal data time synchronization can be realized in a complex dynamic environment.

The invention relates to the technical field of synchronization in data communication, and provides a PTP protocol-based chip-level hardware timestamp generation method and system, and the method comprises the steps: 1, receiving a synchronization message and a delay request message from a main clock, carrying out the evaluation of a physical layer signal, and recording a first hardware timestamp; 2, responding to the received delay request message, generating and sending a delay response message, acquiring bidirectional transmission delay data by exchanging the delay request and the delay response message, and calculating clock skew in combination with the first hardware timestamp and the bidirectional transmission delay data; 3, adjusting the local clock frequency according to the clock skew; 4, after the synchronization is completed, associating the second hardware timestamp with the corresponding data message, and storing the associated data in a buffer area in the chip; and 5, generating standardized timestamp information, and outputting the standardized timestamp information through a chip interface. And high-precision time synchronization and timestamp generation are realized.

The invention relates to the technical field of edge calculation application and partial discharge detection, in particular to a partial discharge multichannel signal real-time synchronous acquisition method based on edge calculation. According to the method, the functions of multi-channel signal acquisition, time synchronization, feature analysis, hierarchical storage and the like are integrated at edge nodes, asynchronous acquisition, unified timestamp marking and data synchronous fusion of multiple types of partial discharge signals are realized, and extraction of multi-dimensional feature parameters such as time domain, frequency domain and energy and intelligent event discrimination are locally completed. For abnormal signals, the system realizes classified storage and remote uploading after encryption and compression processing; and for normal signals, dynamic management is carried out through circular caching. According to the method, the accuracy and efficiency of multichannel signal synchronous acquisition are remarkably improved, the data safety and the system adaptive capacity are enhanced, and the method is suitable for real-time online monitoring and intelligent diagnosis of partial discharge of power equipment.

The invention relates to the technical field of environment monitoring, in particular to a forest fire monitoring method based on edge intelligence, which comprises the following steps: S1, deploying edge nodes and sensing equipment; s2, collecting data acquired by the sensing equipment in real time, and performing primary processing on the data; s3, fire risk intelligent identification and decision making are carried out through the edge nodes; s4, emergency response and event management; and S5, user interaction and feedback. The edge intelligence technology is introduced, the architecture of the forest fire monitoring system is optimized, and local processing and real-time analysis of data are realized, so that the response speed and the detection precision of fire monitoring are improved, and the method is suitable for further popularization and application.

The invention discloses a public institution kitchen energy data management system based on cloud data, particularly relates to the field of energy management, and comprises a multi-source heterogeneous data acquisition module, an edge intelligent preprocessing module, a cloud data lake storage module, a multi-dimensional analysis engine module, an intelligent decision center module and a visual interaction module. According to the method, space-time alignment of multi-source data is realized through protocol adaptive conversion and millisecond time synchronization technologies, a high-consistency analysis basis is constructed, real-time index calculation and adaptive model optimization are implemented by edge calculation and cloud dynamic modeling collaborative architecture, hidden energy loss is accurately identified, and the real-time performance of the system is improved. The intelligent decision center is combined with digital twinborn verification to generate a control strategy, a minute-level closed-loop response mechanism is formed, automatic adjustment and optimization of equipment parameters and intelligent distribution of maintenance work orders are achieved, manual intervention delay is eliminated, timeliness and execution accuracy of an energy strategy are guaranteed, and multi-system collaborative management efficiency is remarkably improved.

The invention relates to the field of electronic communication, and provides a multi-protocol dynamic switching method and system based on an FPGA. The method comprises the following steps: carrying out regional division on an FPGA chip through a layout planning tool to obtain a static logic region and a plurality of reconfigurable logic partitions; performing hanging processing on the current protocol module based on the protocol switching trigger signal, and writing running state data of the current protocol module into a state cache unit to obtain protocol context snapshot data; according to the target protocol type, bit stream loading is carried out on the corresponding reconfigurable logic partition through configuration of an access port, and a reconfiguration protocol module is obtained; and reading protocol context snapshot data, and performing state recovery on the reconstruction protocol module to obtain a target protocol data path. According to the invention, rapid protocol switching is realized, the utilization rate of hardware resources is improved, and the system deployment cost is reduced.

The invention discloses an equipment abnormity early warning system based on AI intelligent analysis, particularly relates to the field of equipment group early warning, and is used for solving the problem that an early warning strategy of an existing group early warning system is rigid. The method comprises the following steps: acquiring spatial deployment information of each equipment node in a collaborative awareness network through an adjacency association module, and constructing a spatial adjacency structure; the data acquisition module organizes response data in a historical monitoring period, and extracts a local collaborative response sequence subset according to an adjacent structure; the model training module constructs a local collaborative response prediction model based on the response sequence; the drift identification module identifies drift abnormal nodes with trend residual error increase in real-time response by using a model; the diffusion evaluation module evaluates the collaborative interference degree of the residual error gradient diffusion to the adjacent domain on the basis of the residual error gradient diffusion; and the early warning adjustment module dynamically adjusts the early warning participation weights of the drift node and the interference adjacent node based on the disturbance influence result, thereby realizing adaptive optimization of the equipment group early warning strategy.

The invention relates to the technical field of data processing, and particularly provides an operation intention recognition method, system and device based on multi-mode fusion and a medium, and the method comprises the steps: synchronously collecting interaction data of at least two modes of a user, the modes comprising at least two of gestures, voice and eye gaze; carrying out alignment processing on the interaction data, wherein the alignment processing comprises time synchronization and space mapping to a unified coordinate system; recognizing structured semantic information from each piece of aligned modal data, wherein the structured semantic information comprises a gesture type, a voice text and a fixation point coordinate; and based on a preset semantic rule and context memory, performing semantic association and anaphora resolution on the structured semantic information to obtain an operation intention. The method effectively overcomes the inherent defects of unnatural single-mode interaction, easy ambiguity and poor fault tolerance.

The invention discloses a multi-stage scheduling architecture control method based on a TSN network. Efficient management of network resources is realized through modular cooperation. The time synchronization calibration module periodically calibrates the difference of multiple clock sources by using an improved network time protocol and jitter monitoring to ensure the time unification of the whole network; the priority analysis module divides data streams according to a reference value, and determines time slot distribution of key and non-key streams; and the traffic peak period scheduling module dynamically adjusts resources by using a time triggering algorithm to ensure key data transmission. Meanwhile, the real-time path optimization module deals with delay exceeding based on a Dijkstra algorithm, the intelligent load balancing module is combined with a random forest algorithm to deal with an overload problem, and the rapid fault switching and isolation module realizes rapid fault response through redundant paths and topology analysis. And iteratively adjusting the parameters according to the key indexes. According to the method, full-process closed-loop control from time reference unification to dynamic resource scheduling and fault processing is realized, and the transmission stability and the resource utilization rate of the TSN network are effectively improved.

The invention discloses a test equipment data synchronization processing method and system, particularly relates to the technical field of data synchronization, and is used for solving the problem of insufficient adaptive optimization of data synchronization. The method comprises the following steps: constructing a port data association graph, optimizing a connection relationship between ports based on a time-varying adjacency matrix, extracting port data representation by using a graph neural network, optimizing a data synchronization strategy, analyzing influence factors of a data synchronization error based on a causal relationship model, identifying a propagation path of data deviation between the ports, and calculating a data drift amount. The method comprises the following steps: optimizing a synchronization window by using causal inference, correcting data drift, constructing a unified time scale, extracting modal information based on feature coding, modeling cross-modal data association by using a self-attention mechanism, optimizing feature matching between ports, improving the precision of data fusion, and realizing high-precision synchronous alignment and fusion of port data. And higher-quality data input is provided for data analysis and anomaly detection of test equipment.

The invention discloses a vehicle-mounted cross-protocol data time sequence cooperative processing method, which comprises the following steps: establishing a unified time reference, and providing an accurate space-time coordinate system for heterogeneous network nodes by electing a global master clock and constructing a clock relation model and a periodic correction mechanism; systematic cognition and management of communication resources are realized, and a verified protocol capability table is generated by automatically identifying protocol types and variants; constructing an automatic mapping system from demand to execution, performing formalized packaging on a target scene by introducing a unified intermediate model, generating a cross-protocol mapping rule based on the model and a protocol capability table, and then forming a global scheduling table in combination with a unified time reference; the cooperative communication of all nodes is finally realized by using the global scheduling table, and the end-to-end delay is constrained within the preset range, so that the problem that the end-to-end time sequence in the vehicle-mounted heterogeneous network is uncontrollable is solved, and the controllability and reliability of the time sequence of the vehicle-mounted network are improved.

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

The invention discloses a fault root cause positioning method and system for a server cluster, and relates to the technical field of network fault diagnosis. According to the method, nanosecond-level synchronous acquisition of micro-service call chains, container indexes, physical nodes and network data is realized through a precise time protocol, and a consistent data set is constructed through entity association and standardized processing; a service-resource topological graph is dynamically constructed, and an inter-service calling edge weight model is innovatively designed: a real-time load factor and a historical fault index attenuation sum processed by a Sigmoid function are fused, and the weight is periodically updated to accurately quantify the inter-node influence intensity; converting the topological graph into a Bayesian network; when a fault occurs, a three-level assembly line compression alarm is adopted, frequent item sets are mined through bitmap indexes and parallel FP-Growth, and strong causal association item sets are screened in combination with topological edge weights and KL divergence; strong causal alarm is taken as evidence, probabilistic root cause sorting is output through reverse random walk sampling, and high-precision positioning of complex distributed system faults is achieved.

The invention discloses a cloud mobile phone end-to-end performance tracking method and related equipment, and relates to the technical field of cloud computing, and the method comprises the steps: obtaining client touch event data and network event data, and generating a global unique identifier based on a preset Hash algorithm; obtaining server resource event data; performing timestamp calibration on the client touch event data and the server resource event data based on a hardware-level clock synchronization and software compensation algorithm to generate a synchronous timestamp; based on the synchronization timestamp, aligning the event sequences of the client and the server through a dynamic time warping algorithm to generate an aligned event sequence; performing causal probability calculation on the aligned event sequence based on a Bayesian network model, and determining a causal relationship weight between resource events; and performing root cause matching according to the causal relationship weight and a preset abnormal mode library, generating a root cause list with priority ranking, and triggering execution of a self-healing strategy.

The invention relates to the technical field of information processing, in particular to a hard mountain side slope digital twinborn monitoring method and system based on a cloud platform. The method comprises the following steps: obtaining real-time monitoring data of a slope in a hard mountain area, and carrying out data preprocessing to obtain standardized slope dynamic monitoring data; performing data compression and feature extraction on the standardized slope dynamic monitoring data, and transmitting a feature extraction result to a cloud platform to obtain cloud slope key information data; the method comprises the following steps: constructing a digital twinborn model by utilizing cloud side slope key information data based on a cloud platform to obtain a preliminary side slope digital twinborn model; through construction and optimization of the digital twinborn model, stability evaluation and risk early warning, the precision and real-time performance of slope monitoring in the hard mountainous area are remarkably improved, and a scientific basis and an executable long-term scheme are provided for slope treatment.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

The invention discloses an RFSOC-based FAST core array distributed collaborative observation and data fusion method and system, and the method comprises the steps: S1, system initialization: a master node RFSOC generates a global clock, achieves the phase synchronization of multiple board cards through an SYSREF differential signal, and completes the calibration of a three-stage clock tree; s2, signal acquisition and preprocessing: directly sampling a 3-8GHz radio frequency signal through an ADC (Analog to Digital Converter), and performing digital down-conversion to obtain a baseband signal; s3, intelligent resource scheduling: identifying a signal type based on an ESN (Echo State Neural Network), and dynamically allocating FPGA logic resources through an improved ant colony algorithm; s4, heterogeneous calculation acceleration: executing a 128-channel digital beam forming pipeline on the FPGA; s5, cross-domain data fusion; s6, collaborative observation planning; and S7, outputting data. The method is suitable for multi-beam synthesis, cross-region joint observation and mass data real-time processing scenes, and provides key technical support for solving the frontier scientific problems such as rapid radio storm origin and black hole activity monitoring.

The invention discloses an electronic jacquard machine control method and system based on multi-channel collaboration, and the method comprises the steps: calculating a delay compensation value of each channel in real time according to the hardware performance difference and signal transmission delay data of each channel, and dynamically adjusting the time sequence offset of a control instruction of an electronic jacquard machine by using the delay compensation value, generating a multi-channel cooperative control signal; according to input fabric pattern data, decomposing the fabric pattern data to generate a layered instruction set, and binding the layered instruction set to a multi-channel cooperative control signal to form a time-space synchronization instruction set; according to channel operation state data acquired in real time, identifying an abnormal channel and a fault type thereof, and generating a reconstruction instruction set after fault immunization; and dynamically allocating computing resources and storage bandwidth according to the reconstruction instruction set, generating an optimization execution instruction set, and issuing the optimization execution instruction set to each channel for execution in real time. By utilizing the embodiment of the invention, the control capability of the electronic jacquard machine and the fabric production efficiency can be improved, and the intelligent development of the textile industry is promoted.

The present invention discloses a method, device, equipment, medium and product for clock synchronization between clusters. The method is applied to a slave clock synchronization system in each cluster, and the slave clock synchronization system includes: a clock synchronization module and a calculation module; the clock synchronization module obtains the timestamp of the high-precision time synchronization protocol PTP message between the master clock server and the master clock server, and sends the timestamp to the calculation module; wherein the PTP message includes at least: a synchronization message, a follow-up message, a delay request message and a delay response message; the calculation module determines the corresponding master-slave clock deviation and transmission delay according to the received timestamp, and sends the clock synchronization signal determined according to the master-slave clock deviation, transmission delay and the local slave clock to the clock synchronization module; the clock synchronization module generates the corresponding slave clock second pulse according to the clock synchronization signal sent by the calculation module to complete the slave clock correction. This scheme can realize accurate clock synchronization between computing clusters in a multi-computing cluster system.