1652 results about "Data hub" patented technology

The invention discloses an electric power measurement data management system based on cloud edge collaboration and an electric quantity distributed acquisition method, and relates to the technical field of electric power information acquisition and processing. The system comprises an intelligent electric meter terminal layer, an edge computing node layer, a regional collaborative gateway layer and a cloud data center. The intelligent terminal can dynamically adjust the sampling frequency according to the load change rate and the historical volatility, and identifies abnormity through an LSTM-ATT model; the edge nodes realize prediction caching based on Kalman filtering and RMSE judgment; the regional gateway executes dynamic time warping and consistency arbitration of topological weighting; and the cloud end completes digital twinborn modeling and exception evidence storage. According to the method, the real-time performance and accuracy of electric power data acquisition are improved, the communication bandwidth occupation is reduced, the system abnormity sensing capability and the operation stability are enhanced, and the method is suitable for an intelligent power distribution network and a distributed energy metering scene.

The invention discloses a heterogeneous resource scheduling method and device of a cloud data center, a medium and a product, and relates to the technical field of cloud computing, and the method comprises the following steps: receiving a scheduling task of an application instance containing source architecture resource information; screening the processing nodes according to the computing power demand of the application instance and the specification reference values of the processing nodes, and determining candidate nodes according to the screening result; constructing an application performance portrait of the application instance according to the performance data of the application instance; according to the application performance portrait and the scheduling task, equivalent resource configuration corresponding to deployment of the application instance on the candidate node is determined, a score is determined based on the equivalent resource configuration and the specification reference value, and a target node is determined based on the score; and modifying the resource request of the application instance based on the equivalent resource configuration to obtain a modified resource request, and deploying the application instance to the target node, so that the target node performs resource configuration based on the modified resource request to complete heterogeneous resource scheduling. And equivalence quantization and intelligent scheduling of heterogeneous resources are realized.

The invention discloses a power equipment fault diagnosis system and method based on edge cloud cooperation. The system comprises an edge end module and a cloud end module. The edge end module is deployed in a power equipment site, is embedded with a lightweight diagnosis model, collects power equipment operation state parameters in real time, executes localized preliminary fault identification and alarm judgment, extracts key characteristic quantities, and uploads a processing result to the cloud end module through a network communication protocol; the cloud module is deployed in a data center or a control platform, and performs complex model reasoning, cross-device historical data comparison, fault depth research and judgment and model updating and distribution according to the characteristic quantity data uploaded by the edge module; the edge end module and the cloud end module perform data interaction through an FTP, MQTT or 5G protocol, a model trained by the cloud end can be automatically distributed to an edge end to realize rapid deployment and switching, and equipment fault diagnosis is realized. According to the invention, through a task division cooperation and data interaction mechanism, an efficient and low-delay fault diagnosis process is realized.

The invention provides a dynamic wavelength allocation method based on DCI wavelength division II type equipment. The method comprises the following steps: acquiring an occupation state and a transmission quality index of each wavelength channel of a current network; establishing a wavelength channel quality evaluation model, and scoring each wavelength channel according to the transmission quality index of each wavelength channel of the network; according to the bandwidth requirement, the priority and the QoS requirement of the service request, generating a traffic characteristic matrix containing the delay sensitivity and the bandwidth requirement through a traffic prediction algorithm; based on a network state and the traffic characteristic matrix, through resource availability calculation and path quality evaluation, forming an allocable wavelength resource pool and a quality score thereof; for the allocatable wavelength resource pool, generating an optimal wavelength allocation scheme by adopting an improved heuristic algorithm and comprehensively considering the resource utilization rate, the signal quality and the service priority; and according to the performance feedback of the optimal wavelength allocation result and the network state change, continuous optimization of wavelength allocation is realized through a self-adaptive adjustment mechanism. The method can effectively deal with the dynamic flow change in the DCI network, improve the resource utilization rate, ensure the signal quality and meet the QoS requirement of the service, thereby providing an efficient and reliable wavelength allocation solution for the data center interconnection network.

The invention relates to a virtual power plant optimization operation method and system based on data center shared energy storage and load space-time migration, and the method comprises the steps: quantifying the electric energy utilization efficiency of a data center according to the power consumption of IT equipment, the power consumption of refrigeration equipment and the power consumption of other auxiliary equipment in a data center power consumption model; in the load space-time migration model, delay processing time calculation is carried out on batch processing loads, and the load migration amount across the data centers is calculated among the data centers; dynamically distributing the energy storage capacity of each data center in the shared energy storage model, and sharing the energy storage investment cost by adopting a Shapley value; in the double-layer optimization model, the upper-layer model generates an electricity price signal and a demand response instruction according to the wind and light output prediction data, the real-time electricity price of the power grid and the initial load demand of each data center, and transmits the electricity price signal and the demand response instruction to the lower-layer model; and the lower-layer model feeds back the obtained data center response and the electrical load to the upper-layer model. Compared with the prior art, the method has the advantages of high collaboration, high efficiency, high consumption and the like.

The invention discloses a cloud data center full life cycle monitoring and early warning system based on digital twinning, and the system comprises a multi-source collection and preprocessing module which is used for collecting and preprocessing multi-source data; the twinborn modeling and simulation module is used for constructing a digital twinborn body and completing virtual-real mapping and mechanism simulation; the time sequence modeling module is used for constructing a state space-Kalman enhancement time sequence evolution twinborn model and carrying out continuous time sequence modeling; the virtual-real residual error self-correction closed loop module is used for generating a virtual-real residual error and updating the digital twinborn body and time sequence evolution twinborn model; and the risk and linkage module is used for executing risk identification, grading and life cycle linkage according to the corrected time sequence characteristic representation. According to the invention, a state space-Kalman enhanced time sequence evolution twinborn method is adopted, virtual-real self-correction and full-life-cycle early warning of the cloud data center are realized, and the method has the advantages of high precision, strong robustness and self-optimization.

The invention particularly relates to an intelligent factory integrated operation management method based on multi-system integration, and relates to the technical field of intelligent factory operation management. Performing intelligent planning and robustness decision making; performing production execution and monitoring; and carrying out equipment full-life-cycle management. According to the method, the limitation of a traditional static plan is thoroughly broken through through a collaborative mechanism of multi-dimensional risk index linkage, dynamic time buffering and a plan chain; on one hand, the DTB performs real-time calculation based on a supply chain risk index, an equipment health index and a process stability index, approaches reference buffer to avoid resource waste when the risk is low, automatically expands to absorb disturbance when the risk is high, and ensures dynamic balance of plan efficiency and disturbance resistance; and on the other hand, the pre-generated plan chain is rapidly triggered when the disturbance exceeds the limit, global rearrangement is not needed, and continuous production can be guaranteed only through local adjustment.

Some embodiments provide a method for a first data compute node (DCN) operating in a public datacenter. The method receives an encryption rule from a centralized network controller. The method determines that the network encryption rule requires encryption of packets between second and third DCNs operating in the public datacenter. The method requests a first key from a secure key storage. Upon receipt of the first key, the method uses the first key and additional parameters to generate second and third keys. The method distributes the second key to the second DCN and the third key to the third DCN in the public datacenter.

A method for overcoming intermittent, temporary, or other fetching failures by using multiple attempts for retrieving a content from a web server to a client device is disclosed. The URL fetching may use direct or non-direct fetching schemes, or a combination thereof. The non-direct fetching method may use intermediate devices, such as proxy server, Data-Center proxy server, tunnel devices, or any combination thereof. Upon sensing a failure of a fetching action, the action is repeated using the same or different parameters or attributes, such as by using different intermediate devices, selected based on different parameters or attributes, such as different countries. The repetitions are limited to a pre-defined maximum number or attempts. The fetching attempts may be performed by the client device, by an intermediate device in a non-direct fetching scheme, or a combination thereof. Various fetching schemes may be used sequentially until the content is retrieved.

The invention relates to the technical field of block chain and smart home data management, discloses a smart home data contribution quantification and excitation method and system based on a block chain identifier, and aims to solve the data source problem of vertical large model training in the smart home industry and guarantee data security and personal privacy. The method comprises the steps of equipment identifier registration and binding, home local data processing and desensitization, data contribution degree quantitative evaluation, intelligent contract incentive execution and standardized data delivery and evidence storage. The system comprises a block chain identifier management module, a home edge data center, a data contribution quantitative evaluation module, an intelligent contract incentive engine and a large model training data delivery interface. According to the technical scheme, accurate tracing of data sources and clear definition of contribution subjects can be achieved, the family computing power is activated, the risk of data privacy disclosure is reduced, high-quality and scenarized credible data sources are provided, and marketization circulation and value conversion of smart home data elements are promoted.

The invention discloses a method for aggregating ocean buoy data, which comprises the following steps of: establishing a space-time correlation model according to the spatial distribution density of buoy clusters and the dynamic characteristics of an ocean environment, and adaptively dividing buoy groups with a data compression cooperative relationship; performing a data-related compression operation within the buoy group; constructing a transmission priority dynamic sorting engine, and calculating a dynamic weight coefficient; performing hierarchical packaging processing on the differential coding data, distributing enhanced forward error correction coding resources for high-priority data, and reserving redundant transmission time slots; the method comprises the following steps: receiving a hierarchical data packet through a low-orbit satellite and executing on-satellite preprocessing, reconstructing an original data stream by using a distributed decoder, and meanwhile, fusing multi-dimensional space-time related data to generate an aggregated data product; and finally, the aggregated data product is transmitted to a ground data center through a satellite downlink. According to the invention, uplink data volume is reduced, disaster monitoring timeliness is improved, ground station processing load is reduced, and system environment adaptability is enhanced.

The invention provides an intelligent optical cable performance prediction method and system for a data center, and relates to the technical field of data center performance analys.The intelligent optical cable performance prediction method comprises the steps that firstly, an optical cable microstructure disturbance quantization model is constructed, and an optical cable is divided into a plurality of virtual quantization units in the longitudinal direction; each unit packages a real-time stress-strain state parameter and a response sensitivity coefficient of the basic structure unit, inputting a Brillouin scattering spectrum frequency shift data flow collected in real time into the model for disturbance source analysis, generating a physical excitation decomposition sequence, driving a virtual quantization unit to perform state evolution iterative operation according to the physical excitation decomposition sequence, and obtaining a Brillouin scattering spectrum frequency shift data flow model; a stress-strain state evolution track set is obtained, a time-space correlation network of optical cable link disturbance propagation is constructed according to the stress-strain state evolution track set, network flow characteristic analysis is conducted on the time-space correlation network, an early warning node set is extracted, the future evolution trend of the early warning node set is calculated, and serialized optical cable performance abnormity early warning information is generated. The optical cable performance abnormity can be predicted in advance, and stable operation of the data center optical cable is guaranteed.

The invention discloses a hydraulic power plant equipment full life cycle health management system and method based on the Internet of Things, and relates to the technical field of hydraulic power plant equipment management, and the method comprises the steps: collecting operation parameters through a sensor network disposed on various types of equipment of a hydraulic power plant, and forming a data sequence after time synchronization and analog-to-digital conversion; the data sequence is subjected to noise reduction processing at the edge computing node, whether an abnormal state exists or not is judged on the basis of the data analyzed and processed by the lightweight anomaly detection model, the data is graded and marked according to a judgment result, and only statistical characteristics of abnormal data or non-abnormal data marked as high priorities are uploaded to a cloud data center; and the cloud data center identifies the type of the equipment component according to the uploaded data, calls the corresponding physical degradation evolution model, dynamically calculates the degradation degree of each component in combination with the real-time operation state, determines the weight relationship of each degradation dimension according to the service stage of the equipment, and generates a comprehensive health index.

The invention belongs to the technical field of computers, and particularly relates to a data center server resource allocation method and system based on dynamic load balancing, and the method comprises the steps: collecting a physical resource state, instance constraint and network flow through a multi-dimensional probe, constructing a dynamic weighted load scoring model, and introducing a migration penalty term to correct node load evaluation; solving maximum weight matching of the bipartite graph with constraint by combining a Hungary algorithm, and realizing global optimal mapping of the migration instance and a target node; a memory, a CPU and network resources are pre-configured before migration, a delay recovery mechanism is triggered after migration, and the service quality is guaranteed. The system comprises a resource acquisition module, a score generation module, a matching solution module, a pre-configuration module, a self-adaptive period adjustment module and the like. The cluster resource utilization rate is obviously improved by 28%, hotspots are reduced by 76%, the SLA default rate is reduced by 92%, meanwhile, energy is saved by 15%, and collaborative optimization of efficiency and service quality is achieved.

The invention relates to a PLC-bridging gateway internet-of-things automatic identification method, system and device and a medium. The method comprises the following steps: sequentially sending handshake detection signals to a plurality of PLC devices and receiving response messages; forming a candidate handshake data combination set, and completing handshake matching through a priority algorithm and an adaptive polling mechanism; verifying the response frame, analyzing the equipment information, and confirming the active state and communication parameters of the PLC equipment; generating a drive binding strategy according to a matching result, and distributing a session identifier for the drive binding strategy; generating a session identifier of the target PLC equipment, and updating a session routing table in the bridging gateway; and translating the industrial data collected by the target PLC device into an Internet of Things protocol data packet and uploading the Internet of Things protocol data packet to a cloud data center. According to the invention, automatic identification and rapid adaptation of various interfaces and protocols can be realized; the communication stability and real-time performance are improved through dynamic scheduling and route updating; uniform data uploading is supported, and efficient deployment of cloud analysis and the industrial Internet of Things is facilitated.

Provide are a system and a method for data management based on an Industrial IIoT data center. The system includes an IIoT user platform, an IIoT service platform, an IIoT management platform, an IIoT sensor network platform, and an IIoT sensing and control platform. The IIoT management platform is configured to: determine a future acquisition parameter of a sensor network sub-platform based on a remaining storage space, a first retrieval feature, and a future retrieval feature; determine a pre-increment storage capacity based on the future acquisition parameter; determine a group to be deleted based on the pre-increment storage capacity, the remaining storage space, and a group retrieval feature and a group data volume corresponding to each of a plurality of data groups; generate a parameter update instruction based on the future acquisition parameter; and generate a data deletion instruction based on the group to be deleted.

The embodiment of the invention provides a power encryption database fine-grained access control method based on a quantum key, and relates to the technical field of quantum communication. The access control method comprises the following steps: performing identity authentication on a data user applying for data access; after the data user passes the identity verification, verifying the user attribute of the data user, and issuing a dynamic access token; after identity verification and attribute verification, establishing a QKD channel between the data center and the data user, and generating a quantum key; the data center defines an access strategy according to the user attribute corresponding to the plaintext; encrypting a plaintext and an access strategy through the generated quantum key to obtain a ciphertext; storing the ciphertext in a distributed database of the data center; verifying whether the user attribute of the data user is consistent with the access strategy in the ciphertext according to the access token; under the condition of consistency, the data center distributes the secret key to the data user through the QKD channel; and the data user decrypts the ciphertext according to the distributed key to obtain a plaintext.

The invention relates to a multi-data center elastic scheduling method, which comprises the following steps of: establishing a data center polymer (DCA) to integrate the space-time regulation potential of a dispersed data center, establishing a comprehensive model covering dynamic migration of a working load, power consumption regulation and control of a DVFS (Distributed Valve File System) and collaboration of an optical storage system, and adopting a double-layer elastic scheduling architecture, energy storage charging and discharging, photovoltaic output and other hyperopia actions are optimized to improve long-term decision robustness; and the lower layer solves short view actions such as server scheduling and load distribution in real time based on mixed integer linear programming to realize instant cost optimization, so that power-computing power collaborative scheduling optimization of the data center is realized. According to the method, flexible scheduling of time delay sensitive type and time delay tolerant type workloads is combined, the cooperative effect of an energy storage system and photovoltaic power generation is utilized, the power use efficiency and economical efficiency of the data center are optimized, and dynamic participation of multiple data centers in the power market is achieved.

The invention relates to the technical field of data center resource scheduling, and discloses an intelligent data center optimization method based on distributed GPU computing power scheduling. According to the method, historical scheduling records are quantitatively evaluated, historical strategies are distributed to experience groups of different levels according to scheduling effect indexes and strategy trust levels, and a structured strategy library is formed. And for a target performance level defined in the dynamic behavior model of the data center, calculating a serialized scheduling scheme for achieving a target, and selecting candidate strategies from the structured strategy library. And carrying out adaptive correction on the serialized scheduling scheme and the candidate strategy by adopting a fusion decision-making method to generate a final scheduling strategy. And executing the final scheduling strategy, generating a control command in combination with the real-time resource data to implement resource adjustment, and recording an actual performance result. By comparing the deviation between the actual performance result and the target performance level, the deviation source is diagnosed, the experience grouping content is revised, and the structured strategy library is optimized.

The invention discloses an inspection report generation method and device, equipment and a storage medium. The method comprises the following steps: determining abnormal inspection data and a report object of data center equipment; determining structured inspection data according to the abnormal inspection data, a preset equipment knowledge graph and a historical case library; determining a report constraint condition according to the report object and a preset report generation rule; and inputting the structured inspection data and the report constraint condition into a pre-trained large language model, and generating an inspection report oriented to the report object. According to the technical scheme, the preset equipment knowledge graph and the historical case library are constructed in advance, the fault linkage effect and the historical similar cases across the equipment are identified based on the inspection data, the inspection reports facing different report objects are dynamically generated, and the generation efficiency, the flexibility and the comprehensiveness of the inspection reports are improved.

A system and method for providing reliable, legally compliant access to geographically restricted ATSC television signals via user-owned devices hosted in a datacenter. The invention verifies the physical address of each applicant to ensure eligibility within the designated market area before installation. It eliminates the need for rooftop or indoor antennas, resolving common ATSC reception challenges, including interference, terrain obstruction, and lightning risks. Designed for ease of use by non-technical users, the system provides consistent high-quality reception through a managed datacenter infrastructure. The invention complies with legal boundaries set by the U.S. Supreme Court's ruling in ABC v. Aereo by ensuring individualized device ownership and avoiding centralized retransmission. An optional encrypted group addressing mode further reduces energy and hardware redundancy while enforcing regional access through a conditional access system with access limited to persons with validated addresses and members of the datacenter.

The invention relates to the technical field of data security of the Internet of Things, in particular to an edge data security protection system and method based on a security chip, and the system comprises edge equipment which is deployed at the edge of a network and is used for detecting or collecting original data; the security chip is embedded in or externally connected to the edge equipment, and an encryption engine and a security storage area are integrated in the security chip; the encryption engine is integrated with an encryption algorithm, and a secret key is stored in the secure storage area; the security agent software runs on a control system of the edge equipment and is in communication connection with the security chip, and the security agent software is configured with a security policy; wherein the security agent software is configured to receive original data, call an encryption engine of the security chip according to a security policy to encrypt the original data, generate protected data, and send the protected data to a cloud or a data center; or the original data is monitored, and safety measures are taken for the original data with risks.

A system for recommendation and optimization of information technology (IT) server resources is disclosed. The system includes a server comprising at least one processor configured to access input datasets associated with an IT workload, determine types and counts of server resources based on predefined criteria and a server consolidation configuration, and access a multi-server performance database. The processor utilizes a trained deep learning model to predict infrastructure requirements and generate recommendations for an optimal server configuration by balancing performance, power consumption, and resource utilization. The processor automatically allocates server resources from multiple manufacturers based on the recommendations and generates data for display on a user interface dashboard. The dashboard presents server utilization patterns, recommended configurations, and real-time performance metrics of allocated resources. The system enables intelligent consolidation and efficient server management within a datacenter environment.

The invention discloses an interface design and implementation method of a BMC (Baseboard Management Controller) remote management system based on Redfish. According to the method, a multi-module resource model conforming to Redfish specifications is constructed in a baseboard management controller (BMC), management modules including Systems, Chassis, Managers, Account Service, Session Service, TaskService, Update Service, FRU and the like are integrated, and a unified RESTful interface architecture is formed. The method supports standardized access to management functions such as a server system state, hardware assets, a user account, remote control and firmware upgrading, and meanwhile, a unified authentication mechanism and an asynchronous task mechanism are introduced, so that an efficient and safe out-of-band management process is realized. The system has the advantages of uniform interface, clear structure, safety, controllability, high expandability and the like, and is suitable for various data center and server remote management scenes.

The invention discloses a CXL protocol extension method, device and system based on the Ethernet. The method comprises the following steps of: carrying out long-distance expansion interconnection on a CXL (Compute Express Link) protocol by using Ethernet as a physical transmission medium; by bearing the CXL protocol on the Ethernet, the range of memory pooling is expanded from the rack level to the whole data center (kilometer level), and the potential of the CXL technology is thoroughly released; through protocol translation, label management and flow control realized by pure hardware, an operating system kernel and a software protocol stack are bypassed, so that the end-to-end memory access delay is far lower than that of any software-based network storage scheme, and a reliable request-response tracking and timeout retransmission mechanism is realized through a hardware label manager. By distinguishing the control flow and the data flow, differentiated QoS guarantee is provided for different types of memory flows, and the stability of the system under high load is ensured.

Provided is a smart gas Internet of Things (IoT) system for gas safety management. The system includes a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas indoor device sensing network platform, and a smart gas indoor device object platform. The smart gas user platform is configured as a terminal device; the smart gas indoor device sensing network platform is configured as a plurality of groups of gateway servers or a plurality of groups of intelligent routers; the smart gas indoor device object platform includes a safety valve control device object sub-platform, the safety valve control device object sub-platform is configured as indoor gas devices and gas safety detection devices; and the smart gas safety management platform includes a smart gas indoor safety management sub-platform and a smart gas data center.

The invention belongs to the technical field of product production cycle management, and particularly relates to an electronic image-text file remote multi-bin collaborative storage method based on a PLM. Comprising the steps that a 1 + N hybrid storage architecture is constructed, one center bin is deployed in an enterprise headquarter data center and stores full document metadata and latest version files, and N area bins are deployed according to geographic areas and store cache copies and incremental update data of local high-frequency access documents; the remote multi-warehouse document increment synchronization is realized through an intelligent synchronization engine, and the intelligent synchronization engine adopts a content awareness increment synchronization algorithm and a dynamic synchronization strategy based on network quality; version management is realized through a version cooperative control module, and the version cooperative control module is used for establishing a global version number mapping mechanism and a conflict resolution decision tree; cross-warehouse process automation is realized through a cross-warehouse workflow engine, and the cross-warehouse workflow engine integrates a BPM process engine and a message queue; the method can adapt to a remote network environment, and the large file synchronization efficiency is improved.

The invention discloses a data processing method and device of a data center network, equipment, a medium and a product, and relates to the technical field of data processing, and the method comprises the steps: obtaining network topology information and a data forwarding table rule corresponding to each equipment subnet in the data center network; performing parallel coding on each piece of network topology information and each data forwarding table rule based on a network parallel binary decision diagram to obtain a data forwarding path structure between subnets on each piece of equipment, the network parallel binary decision diagram being generated based on a lockless index and hierarchical cache; based on the path connectivity corresponding to the data forwarding path structure, constructing a data forwarding graph corresponding to the subnet on each device; and verifying the data reachability between the subnets on each device according to the data forwarding graph, and generating a data reachability verification result. According to the technical scheme, the concurrency limitation is overcome, high concurrency can be achieved, and the data plane verification efficiency of the data center network is improved.

The invention discloses a river valley area bridge earthquake vulnerability intelligent evaluation and earthquake resistance optimization analysis method, and relates to the technical field of bridge structure earthquake resistance analysis. The method comprises the following steps: constructing a geological model of an area where a bridge is located and a three-dimensional structure model of the bridge; a plurality of monitoring points are arranged in a bridge and a river valley area where the bridge is located, and collected data are uploaded to a cloud data center through a wireless transmission subsystem; carrying out seismic oscillation time history analysis based on the digital twinborn body, and visualizing the weak part of the structure through a cloud picture by adopting an artificial wave and actual measurement wave mixed input mode; and generating a multi-objective optimization scheme according to the vulnerability curve. According to the method, seismic oscillation time history analysis is carried out based on the digital twin, an artificial wave and actually measured wave mixed input mode is adopted, a weak part of a structure is visualized through a cloud picture, a multi-target optimization scheme is generated according to monitoring data, the seismic monitoring efficiency and supervision strength of a bridge in a river valley area are improved, and a bridge reinforcement method is rapidly provided.

The invention belongs to the technical field of data center network and clock synchronization, and discloses a dynamic time synchronization path optimization method and system based on topology awareness. The invention provides a closed-loop framework of state awareness-path optimization-resource assurance-dynamic adjustment and optimization: in the first stage, network topology, link load and PTP message performance data are periodically acquired through an SDN controller, and a global state view is constructed; in the second stage, the optimal path is dynamically calculated for the PTP synchronous flow through a multi-constraint optimization algorithm with the purpose of minimizing transmission delay jitter, and deterministic transmission is achieved in combination with bandwidth reservation and a priority queue; and meanwhile, a seamless switching mechanism of building first and disassembling second is introduced to adapt to the network state change in real time. On the basis of multiplexing existing hardware, the time synchronization error is remarkably reduced, the transmission stability and the dynamic adaptability are improved, microsecond-level time synchronization can be efficiently achieved, and the method is suitable for high-precision service scenes.