82 results about "Distributed decision" patented technology

The invention discloses a source network load storage real-time scheduling method for MADRL-GAN collaborative optimization, and the method comprises the steps: firstly constructing a carbon pollution collaborative optimization model, and converting the model into a solvable convex problem through function linearization and mixed integer conversion; further mapping the model into a multi-agent reinforcement learning model, dividing agents according to an electrical coupling degree, and designing a state and action space containing a dual-Critic reward mechanism; thirdly, learning system uncertainty distribution by using a generative adversarial network, generating diversified scenes to train a reinforcement learning model, and obtaining a preliminary strategy; and finally, correcting the strategy through the generative adversarial network, generating a final scheduling action through a strategy mixing mechanism, and realizing distributed real-time optimal scheduling. According to the method, the problems of multi-target collaboration, high-uncertainty scheduling and real-time distributed decision making in a high-proportion renewable energy system are effectively solved.

The invention provides a traffic corridor signal cooperative control method based on single agent reinforcement learning, and relates to the technical field of traffic management and control, and the control method comprises the steps: obtaining the real-time traffic state data of a traffic corridor comprising a plurality of signal intersections; acquiring a current signal control scheme of the traffic corridor; constructing a state vector according to the real-time traffic state data and the current signal control scheme; inputting the state vector into a pre-trained single-agent reinforcement learning model to obtain a corresponding action vector; based on the action vectors, phase division of all the signal intersections is synchronously adjusted, and a new signal control scheme is generated; according to the invention, signal timing of all signal intersections in a traffic corridor is cooperatively controlled by adopting a centralized single-agent architecture, so that the problems of system complexity and training instability caused by local observation, distributed decision and communication coordination among agents in a multi-agent scheme are fundamentally avoided.

The invention relates to a cluster cooperation-oriented unmanned aerial vehicle dynamic task allocation and path planning method and device, and the method comprises the steps: modeling a monitoring region comprising an unmanned aerial vehicle cluster, a plurality of to-be-tracked targets and a specified moving target as a partially observable Markov decision process, and enabling each unmanned aerial vehicle to obtain local observation information based on relative parameters; then, each unmanned aerial vehicle outputs a flight orientation adjustment instruction through a strategy neural network, and the network is trained and optimized under the guidance of a value network through a multi-agent near-end strategy optimization algorithm; the unmanned aerial vehicle cluster executes a joint action and generates a comprehensive reward signal fusing global task rewards, individual behavior rewards and anti-collision punishment; and finally, driving the strategy network to synchronously complete implicit task allocation and collaborative path planning through continuous feedback of the reward signal. According to the invention, integrated cooperative solution of task allocation and path planning is realized, so that the unmanned aerial vehicle cluster has adaptive cooperative capability and high robustness under a distributed decision framework.

The invention belongs to the technical field of cloud side-end collaborative reasoning, and discloses a cloud side-end model reasoning joint optimization method under air-ground collaboration. And designing a network architecture modeling module, an inference performance modeling module, a delay modeling module and a joint optimization module. An air-ground cooperative reasoning system is constructed, a thinking chain prompt mechanism is introduced to perform modeling on reasoning accuracy, and unmanned aerial vehicle selection, language model selection, reasoning task unloading decision and unmanned aerial vehicle trajectory are jointly optimized to minimize the total cost of the system. A continuous convex approximation method is adopted to optimize the trajectory of the unmanned aerial vehicle, and a multi-agent reinforcement learning method is combined to carry out distributed decision making and centralized training, so that low-delay and high-precision collaborative reasoning service is realized. According to the method provided by the invention, communication, calculation and resource reasoning are effectively coordinated in a cloud edge-end coordination scene with dynamic change of user requests and various task types, the overall service quality and resource utilization efficiency of the system are remarkably improved, and the method is superior to other existing methods.

The invention relates to a virtual power plant-oriented data center resource scheduling method, equipment and medium, and the method comprises the steps: carrying out the multi-time scale division and dynamic characteristic decoupling of IT equipment, energy storage system and refrigeration system resources, and forming a decoupling resource model; dividing a high-collaboration resource cluster, and calibrating an elastic capacity boundary of the resource cluster; solving a multi-market collaborative optimization model in a global optimization layer at a first preset time scale according to the electric power information, the carbon information and the computing power demand information, and generating a day-ahead baseline strategy; carrying out distributed decision making by adopting a federal reinforcement learning framework at a local decision making layer based on the day-ahead baseline strategy and the real-time operation data at a second preset time scale, and generating a real-time regulation and control instruction; and when a trigger event is monitored, triggering the global optimization layer to re-plan the day-ahead baseline strategy, issuing a re-planning result to a local decision-making layer, and updating a real-time regulation and control instruction. Compared with the prior art, the method has the advantages of high reliability, collaboration, robustness and the like.

The invention discloses an intelligent warehouse logistics real-time goods allocation optimization method and system, relates to the technical field of intelligent warehouse logistics, and discloses the intelligent warehouse logistics real-time goods allocation optimization method and system. By obtaining real-time warehousing data, constructing a warehousing dynamic state map, generating an initial allocation scheme, inputting the initial allocation scheme into a distributed decision-making network to carry out parallel collaborative evaluation optimization and issuing a global instruction, the problems of delay and conflict in peak hours of a traditional method are solved, and the warehousing operation efficiency and the system throughput can be improved.

The invention provides an intelligent terminal outdoor cooperation method and system based on an ant colony algorithm. The method comprises the following steps: acquiring outdoor navigation data, processing the outdoor navigation data into navigation data fragments, and distributing the navigation data fragments to a plurality of intelligent terminals; screening out a main relay node; when entering the target area, determining the intelligent terminal as main navigation equipment, and controlling the gas intelligent terminal to enter a low-power-consumption mode; summarizing the navigation data fragments stored in the intelligent terminals through the main relay node, forwarding the summarized navigation data fragments to the main navigation equipment, and splicing to generate complete navigation data of the target area; and the main navigation equipment obtains and generates navigation information and synchronizes the navigation information to each intelligent terminal through the main relay node. Through pheromone dynamic iteration of the ant colony algorithm and a distributed decision-making mechanism, efficient cooperation of multiple intelligent terminals on division storage and group collaborative splicing is realized, and on the premise of guaranteeing complete navigation data splicing precision and real-time navigation reliability, the overall energy consumption is remarkably reduced, and the equipment endurance is prolonged.

The application provides an intelligent unmanned system decision reliability evaluation and optimization method based on reinforcement learning, and belongs to the technical field of intelligent unmanned systems. The application adopts a multi-layer operation network model based on a coloring graph theory to represent individual autonomy and functional diversity, overcomes the limitation of a single-layer network, and provides a basis for decision analysis. An intelligent unmanned system distributed decision model based on an enhanced actor-critic architecture is adopted to improve the strategy performance and learning stability of individual decision. An intelligent unmanned system decision reliability evaluation model oriented to an operation loop is adopted. The model integrates the proposed reliability indexes into the individual learning process to improve the effectiveness of evaluation. The model successfully quantifies the influence of individual decision on system reliability. A decision reliability optimization model based on a cooperative multi-agent deep deterministic policy gradient algorithm is adopted. The proposed cooperative reward is integrated into the policy gradient optimization process to optimize the decision reliability.

The present application relates to the technical field of fluid machinery control, and discloses an intelligent vibration reduction control method and system for centrifugal pumps, the method comprising: obtaining frequency spectrum features from vibration signal data; when the frequency spectrum features exceed a threshold value, extracting mutation parameters, fusing pipeline system coupling data, and generating a multi-pump collaborative state vector; classifying according to the state vector, generating a distributed decision instruction sequence; fusing feedback information according to the instruction sequence, obtaining initial adaptive strategy parameters; adjusting operating parameters according to the initial parameters and calculating efficiency, and iteratively optimizing until the efficiency meets the standard, obtaining final adaptive strategy parameters; checking stability after deploying the final parameters, and if the check fails, returning to the cycle optimization. Through multi-pump collaboration and double closed-loop optimization, the present application can improve the stability, efficiency and reliability of the collaborative operation of the pump group.

The invention relates to the technical field of equipment collaboration. The invention discloses a distributed multi-device cooperation method, device and system based on OpenHarmony and a medium. The efficiency that multiple devices cooperatively process a target task can be improved. The method comprises the following steps: acquiring a target task and a physical position topological relation of a plurality of service devices; performing calculation processing on the target task and the physical position topological relation based on a distributed decision engine to obtain a task allocation matrix; and allocating all the first target sub-tasks in the task allocation matrix to a plurality of target service devices in the plurality of service devices, so that the plurality of target service devices cooperatively complete the target task.

The invention relates to the field of traffic signal control, in particular to a trunk line adaptive dynamic green wave traffic signal control method, which comprises the following steps of: establishing constraint conditions according to core parameters such as a fixed trunk line coordination public period, a coordination phase and a phase difference, and fusing single-intersection adaptive control and trunk line green wave coordination control; a corrected traffic state vector is generated based on real-time and historical entrance lane queuing data, and dynamic regulation and control of green wave bandwidth and green light duration of each phase are realized through stage sequence optimization driven by pressure, green time distribution optimization based on shared contribution degree, and distributed decision stage release sequence and green light duration of each intersection. Finally, the technical problems that in the prior art, green wave timing is fixed, and traffic flow randomness cannot be dynamically adapted are solved, traffic flow in the coordinated direction passes without stopping, the waiting time of traffic flow in the non-coordinated direction is shortened, and the overall passing efficiency of all intersections of the trunk line is greatly improved.

The invention discloses a virtual power plant multi-level architecture model resource collaboration method and system, and the method comprises the steps: carrying out the systematic modeling of distributed resources based on a resource heterogeneity compatibility criterion; a space-time decoupling mechanism of information interaction between hierarchies is adopted, and interaction of a multi-level framework is carried out; the distributed resource physical layer is used for performing unified abstract and standardized access on heterogeneous equipment so as to execute a perception-conversion-execution function; the regional aggregation agent layer executes dynamic aggregation and optimal scheduling of heterogeneous resources in a region through a distributed decision-making mechanism; the global coordination optimization layer executes cross-region and cross-time scale resource coordination optimization; the dynamic communication topology model performs inter-level interaction through an event triggering information updating mechanism and a communication delay compensation algorithm; through systematic modeling of the multi-dimensional fusion framework, the collaborative operation efficiency of different types of distributed resources is remarkably improved, and the system stability during large-scale heterogeneous resource access is ensured.

The application discloses a virtual power plant cooperative control method based on multi-agent layered reinforcement learning, and a layered multi-agent system (MAS) processes complex tasks through hierarchical cooperation and distributed decision among agents, and specifically comprises the following steps: S1, an attribute information of a single agent is acquired by a bottom execution layer, specific equipment is directly controlled, and an inverter output power is adjusted or a load transfer instruction is executed according to a middle management layer; S2, a middle management layer receives an instruction of a top coordination layer, a sub-group is mixedly divided according to a region or a resource type, the instruction of the top coordination layer is executed, and real-time data is fed back; S3, the top coordination layer serves as a global decision center, is responsible for power market strategy formulation and resource aggregation optimization, and finally outputs a structured decision instruction and dynamic feedback; after the virtual power plant (VPP) receives the structured decision instruction and dynamic feedback output by the top coordination layer, layered processing and dynamic optimization are carried out. The application can obviously reduce delay and reduce load.

The application discloses a kind of distributed decision-making methods based on multi-agent deep reinforcement learning, constructs the integrated scheduling problem model of satellite observation and data download based on decentralized partially observable Markov decision process, each satellite is regarded as intelligent agent with autonomous decision-making ability, each intelligent agent can make autonomous decision on observation task, determine the observation time of ground target and the download time of observation data, so that the total income in scheduling period is maximum, in online phase, the decision result of satellite cluster is obtained by satellite cluster scheduling network trained in real time according to observation task data, whether each satellite distributed decision-making each observation task is executed, execution time, the download time of observation data and download ground station are realized.The application can dynamically carry out satellite observation data download while carrying out satellite observation task planning, and significantly improve the observation efficiency of satellite cluster.

The invention relates to the technical field of package monitoring, in particular to an intelligent package monitoring system integrating intelligent sensing and controlled release. The intelligent edge decision-making module is constructed through the cloud server and the edge nodes, and the controlled release characteristic data is analyzed to obtain the food material controlled release demand information, so that the food material fresh-keeping effect is improved, the resource waste is reduced, and the personalized fresh-keeping demands of different types of food materials are met; then a packaging controlled release scheme is obtained through food material controlled release demand information and sent to a driving execution module, and real-time sensing and controlled release response delay caused by network fluctuation are avoided through distributed decision making; the controlled-release optimization unit receives the controlled-release dynamic tracking data, performs controlled-release optimization analysis on the controlled-release dynamic tracking data to obtain a controlled-release optimization strategy, then sends the controlled-release optimization strategy to the driving execution module, extracts abnormal event parameters of each edge node through the cloud server and performs deep analysis to obtain an edge control strategy; and controlled release execution abnormity is reduced.

The application discloses an intelligent warehouse logistics real-time goods location optimization method and system, relates to the technical field of intelligent warehouse logistics, and discloses the intelligent warehouse logistics real-time goods location optimization method and system. The disclosed intelligent warehouse logistics real-time goods location optimization method and system solve the delay and conflict problems of traditional methods in the peak period, can improve the warehouse operation efficiency and system throughput by acquiring warehouse real-time data, constructing a warehouse dynamic state atlas, generating an initial allocation scheme, inputting a distributed decision network for parallel collaborative evaluation optimization, and issuing a global instruction.

The invention provides an expressway electromechanical equipment dynamic self-healing operation and maintenance method based on digital twinning and distributed decision making, and belongs to the technical field of expressway operation and maintenance, and the method comprises the steps: collecting the data of electromechanical equipment, and constructing a multi-dimensional twinning model; the method comprises the steps of determining operation and maintenance response partitions in combination with equipment importance levels and historical fault time-influence sequences, simulating various fault scenes to generate a fault feature library, constructing a three-level distributed architecture applied to electromechanical equipment, and allocating decision permissions to the corresponding electromechanical equipment according to fault attribute vectors of the electromechanical equipment and the operation and maintenance response partitions. Based on decision nodes of different levels in the decision authority, analyzing the multi-dimensional twinborn model, the fault feature library and the traffic of the road section radiated by the corresponding electromechanical equipment, and determining an optimal self-healing scheme; and monitoring and recording the self-healing effect of the optimal self-healing scheme based on the multi-dimensional twinborn model so as to carry out reinforcement learning on the multi-dimensional twinborn model. Powerful guarantee is provided for efficient and safe operation of the expressway.

The invention provides an asynchronous duplex multi-mechanical-arm cooperation system and method.The system comprises an upper layer module, a middle layer module and a bottom layer module, and the upper layer module is used for receiving a manufacturing instruction issued by a cloud end and obtaining order information in the manufacturing instruction; the middle layer module is used for splitting the order information to obtain a plurality of sub-order tasks and distributing the plurality of sub-order tasks to corresponding task queues according to the type of an order object, and the plurality of task queues are in one-to-one correspondence with a plurality of mechanical arms; a mechanical arm execution instruction is generated according to the sub-order tasks in the task queue, and under the condition that the mechanical arm needs to occupy the public working space, the use permission of the public working space is obtained; and the bottom layer module is used for receiving a mechanical arm execution instruction corresponding to the mechanical arm, controlling the mechanical arm to complete manufacturing of the order object, and realizing efficient and robust multi-mechanical-arm cooperation through an asynchronous cooperation architecture in combination with distributed decision and dynamic task allocation.

The invention discloses a shore power interaction optimization synergistic method based on multiple subjects of a power grid, a port and a ship owner, and the method comprises the steps: building a power grid operation benefit model, building a port operation benefit model, building a ship owner operation benefit model, building a two-way master-slave game model, and solving the two-layer master-slave game model through employing a two-layer particle swarm algorithm. A decentralized efficient distributed decision-making method is realized, a power grid-port-ship owner three-party friendly interactive cooperation mechanism is established, efficient coupling of an energy flow and a value flow is realized by constructing a power grid, port and ship owner three-party benefit cooperation framework, and the flexibility and economical efficiency of system operation are remarkably improved. The method is suitable for being applied as a shore power interaction optimization and efficiency improvement method based on multiple subjects such as a power grid, a port and a ship owner.

This invention relates to a method and system for adaptive learning rate adjustment in dynamic unmanned aerial vehicle (UAV) swarms. The method includes: each UAV, through an environmental perception module, real-time monitoring and collection of environmental parameters and flight status data, transmitting this data to an adaptive learning module to calculate the adaptability of the current learning rate; adjusting the initial learning rate in real-time within a learning rate threshold range based on the adaptability of the current learning rate to obtain a target learning rate; and a central control unit dynamically adjusting flight mission data based on a distributed decision-making algorithm, deciding the flight path and actions of each UAV according to the target flight mission data. By real-time collection of environmental parameters and flight status data for adaptive learning rate adjustment, each UAV operates independently, while the central control unit performs collaborative optimization based on a distributed decision-making algorithm. This avoids the single point of failure problem of centralized control, improves robustness and flexibility, and ensures the efficient and stable operation of the UAV swarm in dynamic environments.

The invention discloses an elevator group competition scheduling method fusing ST-GCN and Transform and a simulation system, and belongs to the field of intelligent building traffic control. According to the method, firstly, an elevator system is constructed into a space-time diagram, passenger flow space-time characteristics are extracted through ST-GCN, and then the complex dependency relationship between the overall state of an elevator and passenger requests is captured through a self-attention mechanism of Transform. On the basis, a competition scheduling mechanism is adopted, each elevator is used as an intelligent agent to carry out assessment competition on requests of passengers based on a space-time dependency relationship, a central scheduler preferentially allocates tasks, and effective combination of centralized learning and distributed decision making is realized. A matched simulation system can simulate various typical passenger flow scenes, and support is provided for algorithm training and verification. According to the method, time-space correlation and time sequence dependence can be accurately captured, fine scheduling is achieved in combination with hierarchical decision, the elevator scheduling efficiency is remarkably improved, and the dynamic requirements of actual application scenes such as office buildings are effectively met.

The present application relates to the technical field of unmanned aerial vehicle cluster energy management, and discloses an autonomous inspection and charging nest system based on unmanned aerial vehicle ad hoc network, comprising: each unmanned aerial vehicle in the cluster calculates and broadcasts a freely disposable time dynamic scalar in real time, and when the scalar of a certain unmanned aerial vehicle is lower than a threshold value, a task and energy opportunity package are broadcasted, wireless energy supplement and task takeover are executed by a neighboring unmanned aerial vehicle with the minimum response time cost, and the present application drives distributed decision mechanism by energy potential difference, so that cluster energy dynamically flows along the path with the minimum resistance like liquid, energy consumption caused by fixed return paths in traditional inspection is avoided, and reliable energy cooperative transmission can still be maintained in complex environments by combining double-channel communication arbitration and mechanical locking units.

The invention discloses a multi-target switching optimization control system for standby energy storage of a distribution network. The multi-target switching optimization control system comprises a data acquisition unit, a multi-target optimization module, a distributed decision module, a digital twinborn pre-decision module and an execution control unit, the method relates to the technical field of distribution network standby energy storage switching, and comprises the following steps: constructing a multi-objective optimization model covering reliability, economy, new energy consumption, carbon cost, user experience and energy storage loss by collecting real-time operation data; the distributed decision-making module generates candidate strategies of regional collaboration based on the optimization instruction; the digital twin module performs simulation verification and screens an optimal strategy; and the execution control unit issues an instruction and performs closed-loop feedback to form a'sensing-decision-execution-feedback 'closed loop, so that safe, efficient and intelligent energy storage switching control is realized.

This invention discloses a multi-node collaborative, cloud-decentralized edge autonomous perception and decision-making system and method, comprising: multiple edge perception and decision-making nodes forming a decentralized autonomous cluster through an industrial mesh self-organizing network; each node includes a data acquisition unit, a computing and perception unit, a status monitoring unit, a communication unit, a collaborative decision-making unit, a storage unit, and a microcontroller unit. The status monitoring unit collects resource status and calculates a health score; the computing and perception unit extracts lightweight anomaly labels and feature vectors; the collaborative decision-making unit performs master node election based on the health score, fuses the received external anomaly labels and feature vectors with the local node's feature vector and health score, matches an anomaly propagation rule matrix, and outputs an action when the joint triggering condition is met. This invention achieves autonomous organization, collaborative perception, and distributed decision-making of the node cluster during cloud interruptions, featuring low overhead, high fault tolerance, and low communication burden.

The application is directed to the problem of fault-tolerant formation control of fuzzy multi-leader UAV system. A fault-tolerant formation control method based on reduced-order observer is proposed, including: using T-S fuzzy model to model the multi-leader UAV system; transforming the original system into a reduced-order form, and then deriving an augmented form; designing a reduced-order fault observer based on the intermediate variable for the reduced-order augmented system to estimate the state of the follower UAV, process fault and system uncertainty; using the relative state information to design a tracking fault-tolerant formation controller, taking the estimation of process fault and system uncertainty as compensation term; designing adaptive parameters for the reduced-order fault observer and the tracking fault-tolerant formation controller; verifying the performance of the reduced-order fault observer and the tracking fault-tolerant formation controller; the application can more prominently deal with the nonlinear and uncertain control problem, while reducing the calculation amount and improving the efficiency of distributed decision-making.

The application provides a bionic group cooperative combat environment noise intelligent countermeasure method and system, relates to the technical field of electronic countermeasure, and comprises the following steps: collecting multi-dimensional noise signals, performing time-frequency domain decomposition to obtain a feature matrix, constructing a bionic group cooperative model containing multiple cooperative units, establishing a local countermeasure strategy and forming a global countermeasure knowledge base, mapping the countermeasure strategy and a target area space to generate a scheme set, generating adaptive countermeasure instructions through a distributed decision mechanism, implementing noise suppression, and performing effect evaluation and optimization. The application realizes accurate identification and adaptive suppression of combat environment noise, and improves the electronic countermeasure capability.

The invention discloses a digital twinning-based LCP cable manufacturing full-process data management and control system, and particularly relates to the technical field of data management and control, and the system comprises the steps: a process coupling multimode sensing module synchronously collects macroscopic process time sequence data and microscopic response signals, and achieves the "one-object-one-code" tracing of a product unit through a readable and writable RFID tag; the dynamic causal graph construction and updating module fuses the data to construct a dynamic causal graph, and accurately identifies and quantifies a key causal path influencing the product quality; the collaborative optimization digital twinborn body module constructs a virtual simulation environment through a mixed model, and process decision prospective evaluation and causal model adaptive calibration are completed; the distributed decision control module generates a local optimization instruction and a global strategy and executes closed-loop control of the manufacturing process; according to the system, full-process data communication and intelligent management and control are achieved, the product percent of pass and production efficiency are remarkably improved, energy consumption is reduced, and technical support is provided for high-precision LCP cable manufacturing.

The invention discloses a tunnel scheduling command system and method based on digital twinning and distributed decision, and belongs to the technical field of tunnel construction man-vehicle scheduling, and the system comprises a digital twinning engine module which is used for constructing a three-dimensional digital twinning model of a tunnel space; the data collaborative governance module is used for carrying out association analysis on historical scheduling decision data recorded in a distributed account book and converting an analysis result into a material scheduling instruction through an intelligent contract; the fusion positioning system is used for uploading positioning data of personnel and equipment in the tunnel to the edge computing cluster in real time; the meeting algorithm module is used for acquiring real-time traffic flow data and a prediction avoidance threshold value from the digital twin engine module, generating a decentralized passing strategy by combining adjacent vehicle positioning information provided by a fusion positioning system, and sending the decentralized passing strategy to the digital twin engine module; and the strategy execution result is issued to the transportation equipment after being subjected to authority verification through an intelligent contract of the data collaborative governance module. According to the invention, the safety and efficiency of tunnel scheduling are improved.

A constraint-driven trust state orchestration system intercepts proposed actions at execution boundaries prior to execution, deterministically evaluates trust states against constraints, enforces execution exclusively based on cryptographically verifiable qualification outcomes, and immutably records all decisions and outcomes to ensure consistency, auditability, reduced rollback costs, and improved system integrity across distributed decision networks.

The invention provides an isolation evaluation method and device for a distributed storage system, equipment and a medium. In the technical scheme provided by the invention, a centralized isolation evaluation center is introduced and a cluster steady-state sensing mechanism is combined; isolation evaluation logics originally dispersed on OSDs are collected to an isolation evaluation center elected by a state monitoring assembly to be processed in a unified mode, all isolation requests can be executed after being subjected to serialization evaluation through the isolation evaluation center, and when the isolation evaluation center judges that the distributed storage system is in an unstable state according to the state of a global PG fragment, all the isolation requests can be executed; according to the method, whether the redundancy of each isolated PG still meets the lowest availability requirement of the redundancy strategy adopted by the PG to which the PG fragment belongs is strictly evaluated, the situation that part of PGs cannot normally provide read-write response due to cross-fault-domain and multi-point concurrent isolation caused by multi-node decentralized decision is avoided, and the data security and service continuity of the system are improved.