8793 results about "Queue" patented technology

The invention discloses a data sharing system and method for realizing multi-agent interaction, and belongs to the technical field of agents. According to the method, production command instructions are labeled, a large language model is trained, and task types are identified; disassembling the production command instruction into a subtask sequence; constructing a knowledge relationship graph through the graph database; establishing a unified data interface, and constructing a data sharing center; dividing agent types according to the power transmission and distribution service scene, and determining a function boundary of each agent; a multi-agent simulation environment is used for testing the cooperation efficiency of the subtasks, and the execution sequence of the subtask sequence is optimized; sub-tasks are dynamically allocated according to the intelligent agent load and the skill matching degree; the intelligent agent accesses the knowledge relation graph through a unified data interface, and real-time pushing of data change is achieved through a message queue; the decision of the large language model is optimized through cue word engineering, specific operation is executed by utilizing a tool library component, and the intelligent agent executes operation according to a subtask sequence.

Job clusters take time to instantiate. A computing system is provided comprising a plurality of non-interactive job clusters, a control database storing a task queue, and a controller. The controller instantiates one or more clusters of the plurality of non-interactive job clusters based on a size of the task queue and monitoring if the one or more clusters are successfully instantiated. Each of the one or more clusters, after successfully being instantiated by the controller, executes a dispatcher process that includes: querying the control database to identify an available task from the task queue; obtaining and processing the available task; and, after completion of the available task, further querying the control database prior to terminating.

The invention provides a computing system and method for GPU computing power scheduling, and the method comprises the following steps: 1, obtaining computing task data, and carrying out the information collection of each task through receiving a to-be-processed computing task queue; according to the invention, through task information collection and deep analysis, and GPU resource omnibearing monitoring and workload prediction, deep understanding of tasks and GPU resources is realized, highly accurate computing power distribution can be carried out, resource waste is avoided, and the resource utilization rate is improved; advanced technologies such as a knowledge graph, mixed integer programming and deep reinforcement learning are fused through the intelligent scheduling model, the fusion mode is innovative in the GPU computing power scheduling field, the limitation of singleness of a traditional scheduling model is overcome, the method can better adapt to a complex and changeable computing environment, the task execution efficiency is optimized, and the task scheduling efficiency is improved. The adaptability and competitiveness of the whole system in a complex environment are remarkably improved, and an excellent solution is provided for the GPU computing power scheduling problem in the field of high-performance computing.

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention relates to a computing power network resource scheduling method. The method comprises the following steps: acquiring floating point operation performance parameters and operation states of node processors and energy index data of data centers where the node processors are located, and calculating to generate a node list; constructing a global resource pool based on the list, and generating a resource distribution table containing the total calculation power of the region; obtaining calculation requirements and time delay constraints of the task queue, extracting feature vectors in combination with the resource distribution table, and generating a resource utilization rate table; obtaining network link flow data, predicting a link congestion probability through a long short-term memory network, and generating a flow control strategy table; and finally, updating resource pool network constraints according to the resource utilization rate table and the flow control strategy table, and remapping tasks by taking node effective computing power as a weight to generate a scheduling execution scheme. According to the method, accurate quantitative evaluation of the computing power resources is realized, the matching precision of tasks and the computing power resources is effectively improved, the resource utilization rate of the computing power network can be remarkably improved, and the overall scheduling efficiency and stability of the system are enhanced.

The invention discloses an industrial equipment communication method and system based on the industrial internet, and the method comprises the following steps: S1, collecting the multi-modal data of industrial equipment, and generating a preliminary multi-modal fusion feature; s2, performing event-driven analysis on the preliminary multi-modal fusion features to generate event sensing data; s3, constructing a semantic-driven communication protocol, and generating a semantic data queue with a priority identifier; s4, compressing the semantic data queue and carrying out time consistency calibration; s5, optimizing a communication frequency and bandwidth resource allocation strategy in combination with a mixed firefly optimization algorithm and a dynamic differential evolution algorithm; s6, constructing an industrial equipment communication topology model, and predicting path delay and bandwidth occupation indexes of the communication network in combination with the space-time multi-task network; and S7, constructing a zero-sum game model to analyze the potential confrontation relationship in the communication process, and outputting a secure communication scheme. According to the invention, semantic-driven communication, a hybrid optimization algorithm and the like are adopted, and intelligent communication of industrial equipment is realized.

The invention provides a supply chain collaborative material management system and method, and relates to the technical field of material management, and the method comprises the steps: receiving a multi-enterprise heterogeneous material data flow, and constructing an industry knowledge graph based on the field-level semantic mapping of a dynamic ontology library; a standardized data table is generated; standardized data is injected into a distributed message queue for parallel processing, dynamic cache partitions are created according to material object labels, timestamp indexes are built in the dynamic cache partitions, a sliding window mechanism is adopted to scan data, and multi-strategy resolution is performed on conflict data in a window; performing conjoint analysis on the cleaned data to generate an enhanced analysis report, and activating a case transfer learning mechanism to update a model when the similarity between new data and historical cases exceeds a threshold value; constructing a material state transfer matrix for the abnormal data, simulating a correction operation in a digital twin environment, and triggering automatic correction; the problems of multi-enterprise heterogeneous data integration, dynamic data processing, intelligent analysis and the like are solved, and the collaboration efficiency and the management level of a supply chain are improved.

The invention discloses a multi-agent non-cooperative game driven high-concurrency task reasoning method and system, and the method comprises the steps: decomposing a heterogeneous task into standardized task units, and carrying out the quantitative modeling of the calculation resource demands of the task; constructing a task auction mechanism of the multi-agent non-cooperative game model, performing task allocation by adopting an anti-strategic auction rule and a fragmentation asynchronous protocol, and constraining resource declaration behaviors of the agents through a credit pledge mechanism; the real-time resource utilization rate of the system is monitored, the task priority is dynamically adjusted in combination with the task preemption historical state, a resource soft preemption strategy is triggered, and a compensation queue is established; a self-adaptive model splitting strategy is adopted to distribute reasoning tasks to end side equipment and edge computing nodes, cooperative execution is achieved through data compression and transmission, and task rescheduling is conducted according to feedback of an execution result; according to the method, the task execution efficiency, the resource utilization rate and the system stability can be remarkably improved in a high-concurrency scene.

The invention provides a business data mining method and system applied to intelligent port scheduling, and the method comprises the steps: firstly obtaining real-time operation data containing an equipment operation state, a transportation task queue and environment monitoring information during the loading and unloading operation of a target port, then carrying out the dynamic preprocessing of the real-time operation data, screening and marking conflict tasks according to a preset rule, and obtaining effective operation data; and then performing multi-dimensional feature coding on the effective data to generate an operation feature set containing equipment operation features, task distribution features and environment constraint features, and based on the operation feature set, generating a port equipment scheduling strategy meeting balance of operation efficiency and resource consumption through a pre-training model, covering an equipment start-stop sequence and the like. And finally, dynamically adjusting port equipment to execute tasks according to a scheduling strategy, and updating real-time operation data, thereby realizing efficient and reasonable scheduling of the intelligent port.

The invention discloses a real-time interactive digital human system supporting high concurrency and an implementation method thereof, and relates to the technical field of digital human interaction.The system comprises a model instance pool module used for loading the weight of a deep learning model to a shared memory area through the memory mapping technology; the multi-thread scheduling module is used for scheduling user requests by adopting a lock-free queue and a dynamic priority algorithm; the asynchronous pipeline processing module is composed of decoupled micro-services, and the modules are connected in series through asynchronous message queues; the client SDK is used for dynamically switching a rendering mode according to terminal hardware performance and network conditions; the elastic capacity expansion and contraction module is used for monitoring resource loads in real time and automatically adjusting the number of service instances; and the audio and video synchronization calibration module is used for ensuring that the synchronization error of the audio and video frames is lower than a preset threshold value through a timestamp alignment algorithm. According to the scheme, core challenges in a high-concurrency digital human interaction scene can be systematically solved, and innovative support is provided for large-scale real-time application.

The invention provides a multi-source heterogeneous distributed computing power fusion scheduling method and system, and the method comprises the steps: carrying out the multi-dimensional collection and preprocessing of heterogeneous scheduling source data, generating a scheduling joint data set, carrying out the key demand prediction processing of a task set based on a preset quantum channel topology, generating a key pre-distribution matrix, and carrying out the key pre-distribution matrix, and generating a real-time task queue according to a task set of the scheduling joint data set, and performing multi-target matching degree calculation processing on the key pre-distribution matrix and the real-time task queue based on a dynamic priority quantification model to generate a distribution decision matrix. Then adaptive key supplement and migration path optimization processing are performed on the allocation decision matrix to obtain a migration task set, and finally hidden node resource prediction and preset quantum channel topology updating are performed on the migration task set, so that the rationality of key allocation is improved, the problem of compatible conflicts of resource scheduling is solved, and the resource scheduling efficiency is improved. Therefore, the task migration success rate is improved.

The present invention relates to a data interaction method, device, equipment and storage medium of a multi-layer stacked memory, comprising the following steps: based on the access frequency clustering result, the target transmission data is mapped to a storage location to obtain a multi-layer storage allocation strategy table; based on the multi-layer storage allocation strategy table, the target transmission data is interactively planned to obtain an optimal interactive path plan; based on the optimal interactive path plan, the target transmission data is hierarchically migrated and scheduled to obtain a data migration execution queue; and the data migration execution queue is transmitted in parallel through an asynchronous pipeline transmission mechanism, which solves the technical problem that the existing data transmission strategies often lack in-depth analysis of the historical access characteristics of the storage hierarchy, resulting in unreasonable data distribution, and frequent cross-level data migration increases system delay and energy consumption.

The invention provides a multi-agent cooperative task planning method, system and device and a storage medium, and the method comprises the steps: constructing an asynchronous communication framework, responding to a task event through an event-driven model, and decoupling the production and consumption of the task event through a message buffering queue; performing condition matching on the task event based on a predefined behavior rule, if matching succeeds, commanding the intelligent agent to execute an action corresponding to the rule, otherwise, marking the task event as an unmatched task, and transmitting the unmatched task to an LLM language model; loading the historical process data of the unmatched task into the historical message context of the LLM language model, generating a task allocation decision in combination with the guide template, and optimizing the task allocation decision through an abnormal re-decision mechanism to obtain a decision result; and distributing a decision result to a corresponding agent for execution, and updating context data of the task event and a predefined behavior rule in real time. The method can significantly improve the reliability, efficiency and flexibility of task execution, and is suitable for various complex business scenes.

The invention discloses a computer system service optimization scheduling method for a photoelectric system, and particularly relates to the technical field of computer service optimization, which comprises the following steps of: sensing and extracting information such as task types and time constraints through task characteristics, and constructing a multi-dimensional resource model such as processing capability and idle degree by combining resource modeling; a weighting mechanism is used for evaluating task priorities and generating a scheduling queue, tasks are dynamically mapped to matched resources, a scheduling strategy is adjusted according to system loads and feedback, model parameters are optimized through service feedback, and efficient, self-adaptive and intelligent optimization of task scheduling of the photoelectric system is achieved; according to the method, a dynamic scheduling mechanism based on task characteristics and resource prediction is realized, and the response speed of the high-optimal task is improved; optimizing the resource utilization rate by adopting resource state modeling and prospective mapping; and reinforcement learning and a feedback closed loop are introduced, a sustainable optimization scheduling system is constructed, and the adaptability and stability of the system in a complex photoelectric task environment are enhanced.

The invention provides a cache information processing method and device of a port platform and a medium, and belongs to the technical field of port distributed information processing. An update request is received, and authentication and flow limiting processing are performed on the request; obtaining a cache key prefix and a data structure type, routing a cache key to a fragment node based on a consumption write-back event, executing a write-in operation in a distributed cache service, and setting survival time and multi-copy synchronization; delivering the message body to the message middleware and obtaining delivery confirmation; the messages are pulled from the queue according to the configured concurrency, deduplication verification is carried out on idempotent identifiers of the messages, and data persistence operation is executed after the verification message version is higher than the current version of the database; index collection points are implanted into all modules of the whole link, and the request delay, the cache hit rate and the message accumulation depth index are visually displayed. The reasonable distribution and optimization of system resources are realized; the consistency and integrity of the data are guaranteed, and the reliability of message transmission is improved at the same time.

The embodiment of the invention provides a kafka storage and calculation separated data processing method and device, and the method comprises the steps: constructing a multi-layer data processing architecture which comprises four layers: network communication, task distribution, processing scheduling and storage. Cold and hot data separation is realized through hierarchical design of a log pre-writing area and a main storage area, and intelligent scheduling of system resources is realized in combination with a copy manager, a coordinator and a load predictor. An adaptive compression algorithm and a verification mechanism are adopted to ensure the data transmission efficiency and integrity, a message cache region is created to store hotspot data, and flexible access of cold and hot data is realized based on message sites. According to the method, the defects of the traditional technology in storage and calculation coupling, storage efficiency, load balancing and the like are effectively overcome, and the performance and expandability of a message queue system are remarkably improved.

The invention provides a complex task decomposition and dynamic optimization method and device based on a large language model, and relates to the technical field of artificial intelligence. A complex task decomposition and dynamic optimization method based on a large language model comprises the following steps: based on a pre-trained large language model, decomposing a to-be-executed task into a plurality of sub-tasks, and generating a task instruction queue for executing the plurality of sub-tasks; sequentially executing the subtasks in the task instruction queue, and recording information in the execution process to obtain task execution data; generating execution state feedback information according to the task execution data and a preset normal range; and generating a task decomposition strategy adjustment instruction according to the execution state feedback information, wherein the task decomposition strategy adjustment instruction is used for adjusting the task instruction queue and / or the execution main body of the subtask. According to the technical scheme of the embodiment of the invention, the task process is adjusted in real time in the task execution process, the task processing adaptability is enhanced, the change of a complex environment can be flexibly coped with, and the task completion success rate and the overall efficiency are improved.

The invention relates to the technical field of intelligent operation and maintenance and data processing of an electric power system, in particular to an electric power inspection system and method. The method comprises the following steps: collecting equipment state data, constructing a time sequence evolution characteristic of a three-order state transition tensor field modeling equipment space state, and identifying short-term high-risk equipment based on a weighted abnormal score and a dynamic threshold strategy; a historical state peak value is recorded through a state-time matrix fusion exponential decay model, a forgetting risk factor is calculated in combination with the current silencing degree, and equipment which is seriously abnormal but is silencing recently is actively rechecked; constructing a dynamic evolution diagram structure, optimizing an inspection path by adopting a class genetic perturbation mechanism, and scheduling multiple teams in parallel through task cluster division and queue matching; and establishing an equipment abnormity linkage propagation model to predict a cascade risk, and automatically matching an optimal disposal scheme in combination with a strategy template library. According to the method, closed-loop control from anomaly recognition, path optimization to strategy execution is formed, and the accuracy and timeliness of power operation and maintenance are improved.

The invention relates to a deterministic network congestion avoidance flow routing scheduling method, which belongs to the technical field of industrial internet, and comprises the following steps: S1, sensing network congestion based on IFIT flow detection and queue state, and calculating a congestion coefficient; s2, performing planning decision on a traffic routing path based on deep reinforcement learning to avoid congestion; and S3, sinking congestion coefficient calculation and strategy mapping calculation logic to switch hardware by using a P4 programmable data plane to realize localized closed-loop control. Through dynamic path optimization and localization execution, the throughput is remarkably improved, the time delay and jitter are reduced, the intelligent routing mechanism effectively balances the load, and a high-reliability dynamic scheduling solution is provided for a large-scale deterministic network in combination with the deterministic guarantee capability.

The invention provides a supercomputing resource intelligent allocation method and system based on AI load monitoring, and the method comprises the steps: firstly obtaining a real-time load data set of a plurality of computing nodes in a supercomputing system, including a resource occupancy rate index and a task queue state parameter, then carrying out the load feature extraction processing of the real-time load data set, generating resource occupancy fluctuation characteristics and task queue evolution characteristics, performing resource demand analysis on the load characteristic set by using a pre-constructed resource demand prediction model, predicting resource demands of each computing node in a subsequent time window, determining resource allocation priorities and migration strategy parameters based on prediction results, and performing resource allocation according to the resource allocation priorities and the migration strategy parameters. The resource dynamic allocation instruction is generated and sent to the target computing node, the resource reallocation operation is triggered, intelligent and efficient allocation of super computing system resources is achieved, the resource utilization rate and the task execution efficiency are improved, and the overall energy efficiency ratio and stability of the system are optimized.

The invention discloses a resource allocation method and device of a server, a storage medium and electronic equipment, and relates to the technical field of computers.The method comprises the steps that firstly, hardware state data of the server, task queue data of a data processing task corresponding to the server and timestamp data of the data processing task are obtained; inputting the hardware state data, the task queue data and the timestamp data into a target resource prediction model, and predicting a resource utilization rate corresponding to a data processing task executed by the server within a preset time period; based on the resource utilization rate, generating a resource allocation strategy corresponding to the server; and executing real-time dynamic allocation of the computing resources of the server according to the resource allocation strategy. Compared with the prior art, the resource allocation can be flexibly adjusted according to the actual load change, the resource utilization rate is remarkably improved, the resource waste is reduced, the task execution efficiency and the service quality are guaranteed, and the intelligent management level and the operation efficiency of the system are improved.

The invention discloses an extension equipment function configuration method and device, computer equipment and a storage medium, and relates to the technical field of data processing. According to the method, a hardware and firmware collaborative dynamic configuration framework is constructed, and a receiving queue is adopted at an extension equipment end to trigger a central processing unit firmware processing mechanism; in combination with virtualization design of a shadow configuration space and a shadow register, dynamic reconstruction of configuration parameters and an address mapping strategy during operation is realized; a host end supports configuration space access, base address register remapping and memory data transmission interaction through a transaction generation and routing mechanism driven by an event type. Hardware solidification limitation of traditional extension equipment is broken through through software and hardware decoupling design, the problems that function extension capacity is insufficient, resource dynamic allocation is difficult and protocol updating and virtualization scenes cannot be adapted are solved, and the technical effects of improving configuration reconstruction efficiency and address space utilization rate and supporting virtual function creation are achieved. And the adaptability of the equipment in cloud computing and edge computing environments is enhanced.

The invention provides a supercomputing center computing task dynamic scheduling method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a current to-be-processed computing task request data set of a supercomputing center, and extracting a task feature set of each computing task; calling a pre-trained task scheduling strategy decision model to analyze the task feature set, generating a real-time scheduling priority parameter and a resource allocation constraint condition set of the calculation task, and calculating the task feature set based on the real-time scheduling priority parameter and the resource allocation constraint condition set of the calculation task; according to the method, the dynamic resource allocation instruction set of the computing nodes of the super-computing center is generated, the computing nodes are controlled to execute the task scheduling operation, intelligent and efficient scheduling of the computing tasks of the super-computing center is achieved by updating the resource occupancy state of the computing nodes and the task queue execution progress in real time, and the resource utilization rate and the task execution efficiency are remarkably improved.

The invention relates to a multi-type industrial equipment PLC control method and device based on a hardware abstraction layer, and the method comprises the steps: constructing the hardware abstraction layer comprising a core layer and an adaptation layer; the core layer is provided with a standardized interface set, the adaptation layer is provided with a special adaptation module, and a control protocol is converted through a protocol analysis engine. And generating a unified instruction set based on the standardized interface set, and establishing a data interaction channel supporting priority scheduling. And identifying the newly-added equipment, matching the equipment configuration library, and generating a newly-added adapter drive if the equipment configuration library fails. And acquiring a device state by using a data interaction channel, and constructing a cooperative control logic to realize linkage. Equipment load and task queues are monitored in real time, and dynamic task scheduling and grading fault processing are carried out. And when an adapter configuration updating instruction is detected, the adaptive layer thermally loads the updated drive, and unified, intelligent, efficient and stable PLC control of multiple types of industrial equipment is realized through cooperation of the core layer and the adaptive layer.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a natural language driven table processing method, device, equipment and medium. And generating and executing an operation action by utilizing the reinforcement learning strategy network, generating a reward signal according to execution result data and user feedback information, storing the state vector representation, the target table operation action and the reward signal into an experience playback queue, and updating network parameters of the reinforcement learning strategy network based on historical data in the experience playback queue. According to the method, the state vector is constructed through the reinforcement learning strategy network in combination with the natural language instruction and the table context, and the operation strategy is continuously optimized according to the operation result and the user feedback, so that intelligent understanding and action planning of the spreadsheet operation intention are realized, and the data processing efficiency and the interaction intelligence level of non-professional users are improved.

The invention relates to the technical field of equipment maintenance scheduling, in particular to an equipment fault repair management system, which comprises a dependency identification and modeling module, a fault state analysis module, a task priority evaluation module, a work order scheduling generation module and a resource scheduling execution module. According to the method, a multi-level dependency topology between devices is constructed through a directed graph traversal algorithm, a trigger relation between physical connection and an operation process is quantitatively analyzed, a classification marking model is established in combination with dynamic parameters such as a performance degradation rate, a fusion influence range and a dependency factor are calculated through scalar superposition, and a priority scoring matrix is dynamically generated. A task queue structure is optimized through a sorting algorithm, a data-driven maintenance decision mechanism is formed, the fault positioning precision is improved, response delay caused by manual intervention is reduced, key node equipment maintenance lag is avoided, the resource configuration efficiency is optimized, the collaboration of fault processing and a production system is strengthened, and formulation of a preventive maintenance strategy is supported.

The invention discloses a remote centralized control distributed data processing and analysis method based on a JMS, and the method comprises the steps: achieving the message routing and resource scheduling through the real-time monitoring of the state of a system, reinforcement of learning decision, deep learning optimization and multi-dimensional message distribution; dynamically adjusting message flow rate and queue length based on real-time network conditions, node load and flow fluctuation; through edge node intelligent task scheduling, distributed collaboration of calculation and storage is realized; adaptive compression algorithm selection is carried out by using deep learning, and the consumption of message transmission bandwidth is reduced; non-tampering storage of messages is realized through a block chain technology; based on a container arrangement technology, dynamic expansion and contraction of computing resources are realized; and dynamic resource scheduling and optimization decision are realized by utilizing reinforcement learning and multi-dimensional monitoring data. According to the invention, the bottleneck problem of the traditional message transmission system is overcome, and the system can efficiently and reliably process mass data in a high-concurrency and high-throughput scene.

The invention provides a resource scheduling control method and system for a big data server, and the method comprises the steps: constructing a multi-dimensional resource portrait module, collecting the CPU, memory, network, storage I / O load and task queue length of each node in real time, and predicting a resource demand trend through a time sequence algorithm; extracting characteristics such as calculation intensity, data dependence, memory requirements, network transmission quantity and the like; adjusting the weight coefficients of the resource utilization rate, the task completion time and the energy consumption efficiency according to the system load and the historical effect; establishing a bipartite graph model by taking a resource trend as a node feature and a task vector as an edge feature, and calculating a matching score through graph convolution and a multi-objective optimization function; the scheduling scheme is synchronized by adopting a consistency algorithm; automatic rollback and reallocation are carried out when resources are detected to be insufficient; and optimizing a weight coefficient and a network parameter through reinforcement learning. Through the method, the system resource utilization rate can be improved, the task execution efficiency is improved, the overall scheduling effect stability is improved, and the system fault recovery time is shortened.

The invention discloses a power equipment data acquisition and processing system, which comprises a multi-protocol compatible acquisition module, an intelligent network detection module, a system service performance monitoring module and a high-concurrency processing module, the multi-protocol compatible acquisition module is responsible for adaptation and data acquisition of different power equipment protocols and normalization processing of the acquired data; the intelligent network detection module monitors a network link state, diagnoses a network fault reason and pushes fault information in real time; the system service performance monitoring module collects key performance indexes of a server, displays performance data through a visual interface, and triggers an alarm when the indexes are abnormal; the high-concurrency processing module compresses the collected data, realizes efficient pushing and priority processing of the data through a message queue, and guarantees the data processing efficiency in a high-concurrency scene; comprehensive monitoring of power equipment communication and system performance is realized, and data acquisition efficiency and system reliability are improved.

The invention discloses a dynamic scheduling method and device for mass call requests and a medium, and relates to the technical field of information management, and the method comprises the steps: receiving mass call request information through an information sending device, carrying out the language processing to generate an analysis data set, and obtaining the request types through deep learning classification; performing multi-objective optimization on the analysis data set to generate a dynamic call queue; constructing a time sequence prediction model based on historical records, predicting a call peak, carrying out resource scheduling analysis, and formulating a scheduling strategy; and generating a feedback result after executing the scheduling strategy, synchronizing the feedback result to the call center, and updating the strategy according to response information to realize intelligent dynamic scheduling. The technical problem that in the prior art, the resource scheduling efficiency of the call center in the mass request processing process is low is solved, and the technical effect of optimizing the resource scheduling efficiency of the call center is achieved.