147 results about "Flow scheduling" patented technology

Disclosed in the present application are a service flow scheduling method, apparatus and device based on deep reinforcement learning, and a medium. The method comprises: acquiring target data, wherein the target data comprises a current target topology state, current target service flow information and current target link information of a time-sensitive network; and inputting the target data into a deep reinforcement learning model to obtain a scheduling scheme, so as to implement scheduling of service flows on the basis of the scheduling scheme, wherein the deep reinforcement learning model comprises an agent and an environment, the agent is configured to determine gate control orchestration of the scheduling scheme, and the environment is configured to determine slot orchestration of the scheduling scheme on the basis of the gate control orchestration.

The invention relates to a side cloud collaborative adaptive workflow scheduling method and system, and belongs to the technical field of distributed computing and artificial intelligence. The method comprises the following steps of: firstly, in a macroscopic candidate screening stage, reducing problem granularity through task clustering, and obtaining balance between utilization and exploration based on a weighted distance probabilistic preferential strategy; then, in a collaborative scheduling decision-making stage, a global network state diagram is constructed through a graph neural network, deep spatial features of nodes and neighborhoods of the nodes are extracted, context-aware state representation is formed, and a reinforcement learning agent makes an optimal collaborative decision in multiple options such as local execution, edge migration or cloud unloading according to the state representation; and finally, in a local adaptive optimization stage, performing fine-grained optimization after the task is issued, dynamically adjusting a scheduling frequency and a multi-target weight through an online learning mechanism, realizing balance between a task deadline and a resource utilization rate, and ensuring efficient and robust execution of a node level.

A routing and scheduling method based on multi-cycle CSQF mechanism and GDRL is provided. The routing and scheduling method includes the following steps: S1, initializing a cycle index detection mechanism, a queue mapping and a queue mapping constraint of a multi-cycle CSQF; S2, constructing a DFRLLS model; S3, optimizing the DFRLLS model based on GDRL; S4, off-line training a learning strategy of a GDRL model; S5, making a decision on-line based on a trained GDRL model. The routing and scheduling method based on multi-cycle CSQF mechanism and GDRL is adopted, and GCN network is used to extract topology information between networks. Compared with the method of only using reinforcement learning, the routing and scheduling method can achieve more flow scheduling, and its performance is also stable under complex network topology, multi-cycle CSQF can reduce the start-to-end delay of the flow compared to the CSQF.

The present disclosure relates to systems and methods for flow scheduling of machine learning workloads. The systems and methods use prior knowledge of the traffic patterns for machine learning workloads and the network topology to schedule the flows of the machine learning workloads. A centralized controller leverages the knowledge of the traffic patterns for the machine learning workloads to guide per-flow routing decisions for the machine learning workflows to schedule the flows across all available network paths to fully utilize network bandwidth capacity.

The invention discloses an active congestion avoiding method and system based on flow prediction, and relates to the technical field of network communication, and the method comprises the steps: collecting the network state information of a physical network in real time; dynamically synchronizing and mapping network state information of a physical network to a virtual mirror image by using a digital twinning technology; under the virtual mirror image, traffic prediction is carried out on the network state information based on a time domain convolutional network model, and a traffic prediction result is obtained; the traffic prediction result is transmitted into a scheduling strategy engine for dynamic analysis and evaluation, and an active traffic scheduling strategy for traffic prediction is determined; issuing the active flow scheduling strategy to a physical network for execution; and according to the latest network state information, an active flow scheduling strategy is dynamically adjusted to form a closed-loop adaptive system. According to the method, the virtual mirroring of the network state information is realized by using the digital twin technology, the traffic prediction result is obtained by using the time domain convolutional network model, and the traffic scheduling strategy is actively adjusted before traffic congestion occurs, so that congestion is effectively avoided.

The invention discloses a traffic scheduling method and electronic equipment, and relates to the technical field of traffic scheduling, and the method comprises the steps: responding to a condition that a to-be-sent data stream accords with a transport layer protocol and the traffic size of the to-be-sent data stream is greater than a preset threshold value, and generating a plurality of candidate port values used for identifying the to-be-sent data stream at a data sending end; creating a plurality of detection data packets, wherein source port fields of the detection data packets are set as different candidate port values; sending the detection data packets to a data receiving end, and obtaining performance indexes of candidate network paths corresponding to the plurality of detection data packets based on the response of the data receiving end; determining a target source port value from the plurality of candidate port values based on the performance index; according to the embodiment of the invention, the source port field of the data packet corresponding to the to-be-sent data stream is modified into the target source port value, and the modified data packet corresponding to the to-be-sent data stream is sent to the data receiving end, so that the technical problem of load imbalance of a multi-path network in related technologies is solved, and the reliability of traffic scheduling is improved.

The invention discloses a flow scheduling method and device, a medium and a program product, and relates to the technical field of heat dissipation, and the method comprises the steps: obtaining a video memory access frequency as a main parameter when a parallel computing function is executed, matching with other auxiliary parameters related to heat consumption, distributing a weight, determining a heat load scoring result, and then adjusting a cold plate micro-channel flow distribution coefficient, and determining an opening value of a flow control valve and sending a flow scheduling instruction to the liquid cooling system so as to dynamically adjust the cooling liquid flow of each cold plate micro-channel. In this way, accurate temperature control and heat dissipation of the video memory area of the computing device can be achieved, energy waste caused by unnecessary circulation of cooling liquid is avoided, the heat dissipation efficiency is remarkably improved, energy consumption is reduced, and the service life of the computing device is prolonged; in addition, the mode can autonomously complete full-flow operation such as video memory heat consumption monitoring, score calculation and flow adjustment, intelligent management of the heat dissipation process is achieved, manual operation errors and cost are reduced, and the stability and reliability of operation of computing equipment are guaranteed.

The invention relates to the technical field of artificial intelligence, intelligent gateways and finance, and provides a flow scheduling method, device and equipment for an intelligent gateway and a medium, and the method comprises the steps: obtaining resource monitoring data and API call chain data of each node in the intelligent gateway through a distributed service cluster grid; determining resource use trend prediction information and dynamic threshold information according to the resource monitoring data and a trained time sequence prediction model; in response to the fact that the resource use trend prediction information is abnormal, determining a fault propagation path according to the API call chain data and a pre-trained causal reasoning model; and determining a dynamic flow scheduling strategy through a multi-factor weight formula, and determining an adaptive fusing strategy according to the dynamic threshold information. Through the technical scheme, the problems of difficult fault positioning and scheduling lagging in a traditional distributed system are solved, and the stability, the anti-risk capability and the resource utilization efficiency of the intelligent gateway in a complex load scene are remarkably improved.

The embodiment of the invention provides a logistics scheduling system, and belongs to the technical field of logistics. The system comprises a task layer which is used for generating an order task; the feedback layer is used for collecting dynamic resource data related to execution of the order task in a logistics site; the decision-making layer is respectively in communication connection with the task layer and the feedback layer, and the decision-making layer is used for adjusting a path planning strategy according to the dynamic resource data and generating a task execution instruction according to the adjusted path planning strategy; and the execution layer is in communication connection with the decision-making layer, and the execution layer is used for scheduling corresponding transportation equipment to execute the order task according to the task execution instruction. According to the embodiment of the invention, the scheduling flexibility of the transportation equipment can be improved.

The invention discloses a multi-dimensional information-based adaptive gateway traffic scheduling method, which comprises the following steps of: acquiring server performance indexes, network delay, bandwidth and geographic position data in real time, and establishing a historical database; performance load weights are generated through weighted calculation of CPU, memory and disk indexes, and routing priorities are dynamically adjusted by adopting exponential function mapping; performing normalization processing on network bandwidth, delay and geographic distance, and calculating a service access weight to optimize a transmission path in combination with a step function; analyzing historical traffic data based on an LSTM model to predict a future trend, and generating a traffic prediction weight through a sigmoid function to realize pre-equalization; and combining the three types of weights, weighting to generate dynamic routing configuration, eliminating abnormal nodes in real time in combination with a health check mechanism, triggering high-load early warning and linking a predictive balancing strategy. According to the method, multi-dimensional index collaborative decision-making is realized, routing distribution is dynamically optimized, network delay is effectively reduced, and system throughput and stability in a high-concurrency scene are improved.

The invention relates to the technical field of cloud fusion application operation support platforms, and discloses a session flow intelligent scheduling method under a fusion communication architecture, which comprises the following steps: monitoring a session flow logic sequence number of a current node; constructing an asynchronous mirror instance at the target node, and obtaining a computing power comparison parameter; adjusting an asynchronous mirror instance instruction execution rate to execute serial number chasing; after the sequence number difference value enters a synchronous threshold value, processor assembly line branch prediction data is injected, and instruction prefetching sequence filling is driven; according to the instruction level feature injection and execution phase alignment method, hardware execution momentum imbalance in a heterogeneous computing power environment is eliminated through instruction level feature injection and execution phase alignment, and progress connection and logic consistency of session streams at a migration interface are guaranteed.

The invention relates to the technical field of computers, and discloses a process scheduling method based on a tenant isolation strategy, and the method comprises the steps: receiving a process scheduling request of a tenant, and carrying out the standardization processing and legality verification; querying a predefined tenant mapping table, and determining an internal unified tenant identifier corresponding to the flow scheduling request and associated single-tenant concurrent quota, tenant resource weight, isolation level and strategy rule; obtaining a single-tenant concurrent quota and a tenant resource weight, generating a tenant isolation strategy in combination with the calculated rate limiting parameter and the tenant correction priority, and performing admission judgment; and based on the judgment result, distributing the flow scheduling request to the corresponding tenant scheduling unit according to the local priority, selecting a target tenant scheduling unit according to the scheduling qualification value and the global scheduling priority, generating an execution instance and triggering the execution instance. Through differentiated services, resource monopoly is prevented, task hunger is eliminated, system overload is avoided, multi-dimensional mutual restriction is carried out, and isolation between tenants is achieved.

The invention discloses a multi-link aggregated cloud native network system and a flow scheduling method. The system comprises an application sensing layer, a virtual link layer and a physical link layer, the application sensing layer is connected with the virtual link layer and used for analyzing network traffic to be distributed to obtain features of the network traffic, and the features comprise traffic types, priorities and bandwidth requirements; the virtual link layer is connected with the physical link layer, and is used for detecting the states of the plurality of links to obtain the state information of the plurality of links, and determining the flow distribution strategy of the plurality of links based on the characteristics and the state information; and the physical link layer is used for carrying out flow distribution on the plurality of links based on the flow distribution strategy. According to the invention, the technical problem that the traditional cloud native network depends on single link transmission, so that network congestion is easy to occur when the service flow is increased sharply is solved.

The invention discloses a real-time signaling flow intelligent arrangement system based on queue optimization, and relates to the technical field of communication networks, and the system comprises a multi-dimensional feature perception and fusion module which is used for collecting and outputting various dynamic attribute parameters of signaling in real time; and the self-adaptive scheduling decision module is connected with the multi-dimensional feature perception and fusion module and is used for receiving the dynamic attribute parameters and generating a scheduling instruction. According to the real-time signaling flow intelligent arrangement system based on queue optimization, by introducing a double-loop intelligent architecture of prediction and decision verification, the perspectiveness and reliability of signaling flow scheduling are effectively improved. According to the technical scheme, the system can make decisions by integrating the multi-dimensional dynamic factors, and through double inspection of the rule base and micro-simulation, the reasonability of the scheduling strategy and the system stability are still kept in the face of complex burst traffic, and the end-to-end service quality is guaranteed.

The invention discloses a CQF mechanism train communication network scheduling method based on a time sensitive network, comprising the following steps: establishing a train communication network topology model based on a TSN, defining each performance parameter in the train communication network topology model, establishing a traffic scheduling model based on the train communication network topology model, and scheduling the traffic scheduling model based on the train communication network topology model. Defining performance parameters in a communication flow scheduling model, defining related constraints based on a flow scheduling process, constructing an overall flow of a JFRF algorithm, and establishing a flow scheduling model with a scheduling success rate as an optimization target on the basis of scheduling constraint conditions; the method comprises the following steps of: constructing a sequencing equation consisting of flow features and route features by combining flow, offset and route through the combination of the flow, the offset and the route, determining and selecting all flow scheduling priorities according to the sequencing equation, and selecting the maximum priority from all the scheduling priorities for scheduling. According to the invention, the process of traffic scheduling can be more efficiently planned, load balancing with a higher level can be realized, and scheduling resource allocation can be optimized. The problem that part of time trigger flows cannot be scheduled under the background that the data volume is increased in a blowout mode after train intelligentization can be effectively solved.

The invention relates to an improved DQN-based flexible job shop dynamic batch flow scheduling optimization method considering emergency order insertion and reworking, and belongs to the technical field of shop scheduling. The method comprises the following steps of: considering a flexible job shop dynamic batch flow scheduling multi-objective optimization mathematical model of emergency order insertion and reworking; based on a knowledge-driven batching method and a multi-agent DQN model solving method, a local optimal solution of a flexible job shop dynamic batch flow scheduling model problem considering workpiece batching is further explored. The problem that a traditional optimization method is poor in applicability and efficiency under dynamic event interference is solved, and flexible job shop dynamic batch flow optimization scheduling is achieved. According to the optimization method, the problem of flexible job shop dynamic batch flow scheduling optimization under the order insertion and workpiece reworking dynamic event can be efficiently solved, waste of resources such as energy, time and cost is effectively avoided, production interruption and maintenance cost are reduced, and a scheduling scheme with good completion time and order insertion workpiece delay time is obtained.

The invention discloses an online incremental flow scheduling method, device and equipment based on a time-sensitive test launch control network, which are suitable for solving the problem of mixed service flow resource allocation in a ground test launch control system network and improving the scheduling capability of the network on time-sensitive service flows and measurement service flows. The method comprises the following steps: carrying out service flow category division and attribute representation; network topology and resource detection; constructing a mixed service flow incremental transmission model; based on a mixed service flow incremental transmission model, establishing a common flow scheduling optimization target and constraint conditions of different types of service flows; configuring a Markov decision process according to the optimization target; executing an online incremental scheduling decision based on a traffic scheduling algorithm of deep reinforcement learning; according to the method and the device, the scheduling success rate of the periodic time-sensitive service flow and the measurement type service flow can be improved while the end-to-end transmission delay of the non-periodic burst time-sensitive service flow is optimized.

The invention discloses a flow scheduling method and device based on machine room multi-index real-time prediction. The method comprises the following steps: predicting network environment data by using a machine room performance prediction model to generate performance prediction data; performing index weighting calculation on the performance prediction data and the network environment data to obtain a comprehensive score of each machine room; determining a score difference value between the machine rooms according to the comprehensive score, determining a traffic distribution strategy, distributing traffic for each machine room according to the traffic distribution strategy, updating the global routing table to execute traffic scheduling, and obtaining a traffic scheduling execution result; and monitoring network environment data after flow scheduling based on a flow scheduling execution result, determining a prediction deviation value based on the monitored network environment data and the performance prediction data, and updating the machine room performance prediction model according to the prediction deviation value and the network environment data. According to the method and the device, the problems of lack of perspectiveness, unbalanced resource utilization and single decision index of multi-machine-room flow scheduling in the prior art are solved.

The invention discloses a service flow scheduling method, equipment and a medium. The method comprises the following steps: acquiring business flow scheduling instruction information; analyzing the service flow scheduling instruction information to obtain a service flow scheduling constraint condition corresponding to the scheduling target and an effective condition of the service flow scheduling constraint condition; obtaining queue state information of a service flow queue of each service flow scheduling level; constructing a service flow scheduling optimization model based on the service flow scheduling constraint condition and the queue state information under the condition that the effective condition is met, and solving the service flow scheduling optimization model to obtain a scheduling strategy of the first service flow; wherein the service flow scheduling hierarchy comprises a flow hierarchy, a queue hierarchy and a port hierarchy. According to the technical scheme, the problem that the multi-layer queue business flow scheduling effect needs to be improved at present is solved, the stability of business flow scheduling can be improved through collaborative scheduling management of the multi-layer business flow queues and an intelligent decision-making mechanism for dynamically adjusting parameters, and the business transmission service effect is guaranteed.

The invention relates to the technical field of flow scheduling, in particular to a deterministic network interconnection method and system oriented to distributed computing power nodes, and the method comprises the steps: obtaining resource information of the distributed computing power nodes and basic parameters of a deterministic network, and building a node-network basic data set based on the resource information and the basic parameters; and constructing a network topology structure model of the deterministic network based on the node-network basic data set, and determining connection relation features and computing power matching degree features among distributed computing power nodes in the network topology structure model to obtain a topology-computing power correlation graph. According to the method, the network topology, the computing power matching degree and the flow types are accurately calculated, connection and data transmission between the distributed computing power nodes are optimized, and particularly, proper time slots and path selections are distributed for different types of flows, so that network congestion and resource waste are avoided, and the overall transmission performance of the network is improved.

The invention discloses a fine-grained flow scheduling priority marking system for a hardware unloading network, which comprises a packet analysis module for extracting quintuple flow identification information from a data packet and supporting multi-layer protocol field extraction, and a flow table module for maintaining the real-time state of the current active data flow by adopting a double-hash table structure and supporting parallel search and update, the priority judgment module is used for allocating scheduling priorities to current messages through configurable boundary matching rules on the basis of state information returned by a flow table, the message recombination and marking module is used for encoding priority labels into DSCP fields of an IPv4 message header, and the whole system adopts a full-assembly-line hardware architecture and can be ensured to continuously run at a linear speed under a 100Gbps network load. Dynamic packet-by-packet scheduling control is supported, a scheduling priority based on a real-time flow state can be given to each data packet, and fine scheduling of network resources is realized.

The invention belongs to the field of GPU computing performance testing, and provides a GPU flow scheduling performance measurement method and device.The method comprises the steps that in a pre-established basic execution environment, N null-kernel functions are submitted to a reference flow in sequence, and the reference flow executes the received null-kernel functions and measures the reference execution duration; alternately submitting the N null kernel functions to the first test flow and the second test flow, introducing a cross-flow synchronization event, and measuring the total execution duration; and according to the reference execution duration and the total execution duration, calculating to obtain synchronization time consumption between the streams. According to the method, the influence of a tracking tool and the like on the performance of the measured object is eliminated, the method has obvious advantages in accuracy and stability, high-precision and low-interference GPU scheduling performance measurement is realized, and a quantitative basis is provided for performance optimization of a system during GPU driving and operation.

The invention provides an intelligent logistics scheduling system and method based on multi-source information fusion, and relates to the technical field of logistics scheduling, and the method comprises the steps: determining the cooperative adaptation degree of a scheduling resource to a logistics task through the execution state characteristics of the logistics task and a scheduling strategy; dividing the transportation resources into a plurality of resource allocation units, determining a resource cooperation relationship between the resource allocation units, and determining the scheduling cost of each resource allocation unit for the logistics task according to the resource cooperation relationship and the scheduling period constraint condition of the logistics network; performing fuzzy evaluation on a scheduling adaptation relationship between resource allocation and task requirements in the logistics network through all the scheduling costs and the collaborative adaptation degrees, and determining a scheduling optimization index based on fuzzy logic; and carrying out load balancing scheduling on the logistics tasks of the resource allocation units based on the scheduling optimization indexes. According to the method, the resource cooperation relationship and the scheduling adaptation degree in the complex logistics scheduling are comprehensively evaluated through the fuzzy logic, and the load balancing of the logistics scheduling can be realized.

This invention relates to the field of distributed computing technology, and in particular to a unified registration and scheduling system for AI agent capabilities in edge-cloud collaboration, comprising: a dynamic behavior mapping module for capturing client interaction behavior features and mapping them to behavior control topology, performing path adjustment when deviations occur, and registering the control behavior to a unified MCP capability center; a semantic feature compression module for dimensionality reduction and compression of the original multimodal data stream to generate low-dimensional semantic feature stubs; an asynchronous state alignment module for converting feature stubs into asynchronous stateless message streams and reconstructing context mirror data on the cloud side; an inference stream scheduling module for generating inference stream scheduling strategies based on multi-dimensional states; a thought chain flow control module for dynamically truncating the streaming output of the large model according to the strategy and extracting core control segments and confidence levels; and a residual collaborative execution module for sending the core control segments to the edge for residual logic completion, driving the topology to execute control behaviors.

The invention discloses a network dynamic flow scheduling method, device, equipment and product, and relates to the technical field of network scheduling, and the method comprises the steps: monitoring the change of target parameters of the equipment in real time through obtaining the state data and historical data of each piece of equipment in real time, and analyzing the behavior and flow trend of the equipment. The method comprises the following steps: calculating a Q value of each device through a Q learning algorithm to obtain an optimal Q value of each device under a current task, calculating an optimal path and a time slot allocation result of each device under the current task according to the optimal Q value, and scheduling a network dynamic flow according to the optimal path and the time slot allocation result to ensure that the path is shortest and the resource allocation is most reasonable, thereby improving the network transmission efficiency. And the risks of time delay and network congestion are reduced. The dynamic flow scheduling mechanism can flexibly adjust the flow load of each path in the network according to the real-time data and the historical trend, thereby ensuring the efficient utilization of network resources, and improving the certainty of equipment in the dynamic network.

The application relates to the technical field of intelligent computing centers, in particular to a traffic scheduling method and device and related equipment. The method is applied to a controller, and the method comprises the following steps: acquiring network topology information and host ARP information of a whole network, wherein the network topology information of the whole network comprises link relationships and interface information of outgoing and incoming interfaces included in each link; defining initial cost values of each link included in the network topology information of the whole network, wherein the size of the initial cost value of a link is inversely proportional to the maximum bandwidth value of the link; determining source and destination information of a to-be-trained task, and determining a target path for transmitting traffic of the to-be-trained task based on the source and destination information of the to-be-trained task, host ARP information and cost values of each link included in the network topology information of the whole network, wherein the target path comprises target links, and the cost values of the target links are less than or equal to preset values.

The invention discloses a warehouse logistics scheduling method and system based on cloud computing, and relates to the field of warehouse logistics management, and the method comprises the steps: collecting to-be-processed cargo information and available robot state information in a warehouse management system in real time based on a cloud scheduling platform; carrying out the clustering of the to-be-processed goods, carrying out the calculation and evaluation of a clustering result, and obtaining an optimal task partition division scheme; obtaining the shortest collision-free path from each available robot to each task partition, and calculating the path length of the shortest collision-free path; calculating path suitability, load suitability and electric quantity sufficiency between each available robot and each task partition; determining an optimal allocation scheme between the available robots and the task partitions based on the three features; and scheduling the available robots based on the optimal allocation scheme and monitoring the operation states of the robots in real time. According to the invention, real-time, efficient and balanced global integrated scheduling can be carried out on logistics robots and order tasks, and the collaborative operation capability of a robot cluster is improved.

The invention discloses a multi-link flow scheduling method and device based on convergence and shunting, and a medium, and relates to the technical field of flow scheduling, and the method comprises the steps: collecting access messages, merging flow clusters, carrying out the statistics of the total business volume and the message number of the flow clusters, generating a business observation column and the average message length, executing link light detection and interface counting, and generating a link observation column; generating a normalized service volume sequence and a service discrimination threshold, obtaining a service phase sequence, generating a bearing state quantity and a link discrimination threshold, and obtaining a link phase sequence; generating a phase conflict value sequence and a time limit carrying quantity sequence, forming a candidate link set, solving a synchronization completion moment, and generating a link service quantity; according to the candidate link set and the link traffic, in combination with the average message length and the message number, the final number of continuous message blocks is generated, the continuous message blocks are segmented, and shunt sending is executed, so that link selection and service distribution are more targeted, and the coordination, the performability and the stability of multi-link traffic scheduling are improved.

For each respective flow over a network initiated by an application layer, a controller determines a respective path taken by the respective flow from respective source host to respective destination host, transmits respective probe packets along the respective path while taking timestamps at the respective source host and the respective destination host, and determines a respective one-way delay for the respective path based on the timestamps. The controller determines utilization for each path, determines determining an optimal usage across each path, and schedules transmission of data for each respective flow based on the optimal usage.

This disclosure provides a day-ahead and intraday collaborative robust optimization scheduling method for hydrogen-containing multi-energy microgrids, relating to the field of microgrid integrated optimization technology. The specific implementation involves: constructing a multi-stage robust optimization scheduling model incorporating uncertain variables and unexpected mechanisms; performing day-ahead offline training on the multi-stage robust optimization scheduling model to obtain a day-ahead stage unit combination scheduling strategy for handling worst-case scenarios and an intraday multi-stage robust optimization scheduling model; generating control commands based on the day-ahead stage unit combination scheduling strategy for handling worst-case scenarios to control the operating status of the unit combination components during the day-ahead stage; applying the intraday multi-stage robust optimization scheduling model online during the day to obtain energy flow scheduling strategies for each stage of the day; and generating control commands based on the energy flow scheduling strategies for each stage of the day to control the operating status of the energy flow components for each stage of the day. This disclosed technical solution can improve the feasibility and robustness of the intraday scheduling strategies for hydrogen-containing multi-energy microgrids.