220 results about "Flexible scheduling" patented technology

Flexible scheduling, self-healing, high-utilization strong stability for cellular network communications are discussed. Cellular network communication settings and strategies are developed with communication interval, transmit power data rate, channel delay, spectrum occupancy, source coding, receiving sensitivity, channel coding, signal-to-noise ratio, packet loss rate, channel occupancy rate, reception frequencies frequency band size of data package transmission frequencies, data package structure, modulation scheme, information coding scheme, antenna configuration.

The invention relates to the technical field of intelligent scheduling, and discloses a multimodal transport intelligent scheduling optimization method, device and equipment and a storage medium, and the method comprises the steps: carrying out the modeling of a plurality of transport nodes and transport paths, and obtaining a network topology structure; performing time period division on the scheduling period based on the network topology structure to obtain a time window scheduling framework; inputting the historical freight data into a multi-layer feedforward neural network for training to obtain a demand predictor; dynamically adjusting a time window of the time window scheduling framework to obtain a dynamically adjusted time window; inputting the state space and the action space into an improved double-delay depth deterministic strategy gradient algorithm for training to obtain a strategy generator; and performing multimodal transport scheduling decision generation based on the demand predictor, the dynamically adjusted time window and the strategy generator to obtain a multimodal transport scheduling scheme, so that efficient and flexible scheduling decision generation is realized, and the operation efficiency of multimodal transport is improved.

The invention provides a flexible adjustable control method and device for a distributed photovoltaic system, and relates to the technical field of power network intelligent scheduling, and the method comprises the steps: obtaining multi-mode sensing time sequence data of a deployed photovoltaic array in a historical preset time period and weather forecast data corresponding to a future preset time period through an edge node; predicting the predicted photovoltaic generating capacity and predicted power supply load demand of the corresponding photovoltaic array based on the time sequence prediction module; predicting an initial scheduling parameter of the corresponding photovoltaic array based on the scheduling decision model; sending the predicted photovoltaic power generation amount, the predicted power supply load demand and the initial scheduling parameter to a core node, so that the core node updates a target scheduling parameter for each edge node; and receiving the corresponding target scheduling parameter from the core node, and regulating and controlling the corresponding photovoltaic array according to the target scheduling parameter. Therefore, the global optimization dynamic cooperative control method is implemented in the large-scale distributed photovoltaic system, and the reliability of global flexible scheduling is improved.

The invention belongs to the technical field of distributed computing, and relates to a distributed computing resource dynamic scheduling method and system suitable for a new energy scene. According to the method, the priorities of the calculation tasks are obtained by carrying out priority division on the calculation tasks according to the task characteristics. And a distributed computing node resource pool is established, so that centralized management and flexible scheduling of computing resources can be conveniently realized. Performance indexes of all nodes in the resource pool are collected and monitored, and reasonable allocation and efficient utilization of resources can be achieved. And scheduling the calculation tasks based on the priorities of the calculation tasks and the performance indexes of the nodes, generating a scheduling strategy, and ensuring that the tasks with high priorities preferentially obtain the calculation resources so as to meet the requirements on the task execution sequence in a new energy scene. And the scheduling strategy is adaptively optimized according to the performance index of each node, so that the utilization rate of resources and the stability of the system are improved. According to the method, the priority of the calculation task can be dynamically adjusted according to the characteristics of a new energy scene, and flexible scheduling of calculation resources is realized.

The invention belongs to the field of workshop dynamic scheduling, and provides a dynamic disturbance-oriented tire vulcanization workshop intelligent flexible scheduling method and system, and the method comprises the steps: building a production scheduling model with the minimization of the maximum completion time as an optimization target; obtaining an initial processing sequence and a selectable machine set of each workpiece process, and solving by taking the production scheduling model as a target function to obtain an optimal initial scheduling scheme; when a dynamic event occurs in the production process, detecting the type of the dynamic event and the occurrence time of the event, and extracting a completed process, a processing process and an unstarted process; and determining a to-be-scheduled process based on the influence of the dynamic event on the processing process and the unstarted process, retaining the process processing sequence of the optimal initial scheduling scheme, updating the optimal processing machine of the to-be-scheduled process, and generating a rescheduling scheme. The device only replaces a machine in an unprocessed process, is embedded into a cooling time buffer area between the same workpiece processes, solves the problem of insufficient cooling of the rubber material, and guarantees the quality and process stability.

The invention discloses a building integrated energy system optimization scheduling method giving consideration to the comfort level of a light storage soft environment, and relates to the field of building energy system optimization scheduling, and the method comprises the steps: building a photovoltaic output uncertainty model based on Beta distribution according to typical daily solar radiation intensity data; secondly, constructing an energy storage model; thirdly, a comfort level quantitative model considering temperature regulation and control of the indoor air conditioner and the electric water heater is constructed, and the influence on the environmental comfort level is reflected laterally; and finally, according to the various formed models, constructing a building integrated energy system optimization scheduling model considering the light storage soft comfort degree, and performing solving based on a mixed integer linear programming method. The environment comfort level of the building integrated energy system can be quantified, the flexibility of the building integrated energy system is improved by introducing photovoltaic and energy storage equipment, flexible scheduling of load resources is facilitated, peak load shifting is better achieved, the operation cost of the building integrated energy system is reduced, and the operation efficiency of the building integrated energy system is improved.

The invention discloses a digital twinning system construction method and system based on flexible job shop scheduling, and relates to the technical field of digital twinning, and the method specifically comprises the steps: constructing a twinning model of a physical workshop according to the production data of the physical workshop; building a workshop scheduling discrete event simulation model based on the twinborn model of the physical workshop; based on an internal and external virtual-real interaction mechanism, virtual-real mapping interaction between the twin model of the physical workshop and the physical workshop and virtual-real mapping interaction between the twin model of the physical workshop and the workshop scheduling discrete event simulation model are realized, and a virtual workshop is generated; data generated in the interaction process is stored in a twin data pool in real time; a workshop service system is constructed based on a three-dimensional visual integration technology, so that a physical workshop and a virtual workshop carry out interactive feedback, and construction of a scheduling digital twin system is completed. According to the method, the self-adaptive capability of the system in the face of complex production tasks is enhanced, and flexible scheduling according to actual conditions is supported.

The invention provides a plug-in service processing method and a Spring Boot service dynamic aggregation integration system, and relates to the technical field of computer software, and the plug-in service processing method comprises the following steps: receiving a plug-in service directory, and executing initialization operation; traversing the plug-in service directory by using the plug-in management framework, and generating a plug-in service package and a plug-in resource list corresponding to the plug-in service; the plug-in service package is loaded through the plug-in management framework, so that a key component of the plug-in service package is registered to Servlet Context of a Servlet container of a main service; and instantiating the service starting class of the plug-in service through the plug-in management framework, and executing starting operation for the registered plug-in service by utilizing the instantiated service starting class, so that a key component of the plug-in service monitors and responds to an external HTTP request. According to the method, flexible scheduling and efficient utilization of resources are realized, the micro-service deployment and operation and maintenance cost can be reduced, and the resource use efficiency can be effectively improved.

The invention discloses an electricity-hydrogen coupling port comprehensive energy system flexible scheduling method considering port-ship two-way interaction. The method comprises the following steps: 1) establishing an objective function of an electricity-hydrogen coupling port comprehensive energy system flexible scheduling model; 2) establishing constraint conditions of a logistics system in the electricity-hydrogen coupling port comprehensive energy system, comprising an electric energy ship scheduling constraint considering port-ship bidirectional interaction, a hydrogen energy ship scheduling constraint considering port-ship bidirectional interaction, an electrically-driven storage yard crane scheduling constraint, an electrically-driven wharf crane scheduling constraint, an electric energy container truck scheduling constraint, a hydrogen energy container truck scheduling constraint, a logistics system logic constraint and a logistics system electricity-hydrogen power balance constraint. 3) establishing constraint conditions of an energy system in the electricity-hydrogen coupling port comprehensive energy system, wherein the constraint conditions comprise an energy system gas turbine constraint and an energy system electricity-hydrogen power balance constraint; and 4) solving the flexible scheduling model of the integrated energy system of the electricity-hydrogen coupling port, and obtaining a logistics system and energy system scheduling scheme.

The invention relates to the technical field of optical storage direct-flexible scheduling, and discloses a method for establishing an energy storage scheduling model of a multi-node optical storage direct-flexible system. The method comprises the following steps: acquiring real-time operation data of each node of a system, including a photovoltaic output fluctuation curve, an energy storage charge state, a load demand time sequence, a power grid interaction power limit value and an environmental irradiance change characteristic, and performing multi-dimensional alignment processing on the real-time operation data to form a multi-dimensional operation data set; performing operation state mapping topology construction processing on the data set, and separating out target operation data and state indication data; based on the target operation data, in combination with scheduling priority configuration information of each node, generating a multi-node collaborative energy storage scheduling initial model containing an energy storage charging and discharging time sequence strategy and a power distribution scheme; and identifying node operation abnormal characteristics based on the state indication data, dynamically reconstructing the initial model according to the node operation abnormal characteristics, and outputting the updated energy storage scheduling model to guarantee stable and efficient operation of the system.

The invention provides a dynamic scheduling method for new energy station loads, which relates to the technical field of new energy power generation, and comprises the steps of collecting power generation data and equipment operation state data, calling power grid load demand data and power grid operation parameters, building a comprehensive prediction model according to station historical data in combination with a neural network algorithm, and carrying out dynamic scheduling on the new energy station loads. The comprehensive prediction model comprises a power generation power prediction model and a load demand prediction model, inputting the power generation data, the equipment operation state data, the power grid load demand data and the power grid operation parameters into the comprehensive prediction model, performing power generation capacity prediction and load demand prediction, and performing dynamic scheduling based on a power generation capacity prediction result and a load demand prediction result. The technical problems of power grid power imbalance and resource waste caused by the fact that load scheduling is difficult to adapt to the power generation characteristics of the new energy station in the prior art are solved. The technical effects of realizing flexible scheduling of the load of the new energy station and effectively balancing the generated power of the new energy station and the load demand of the power grid are achieved.

The invention discloses an SD-WAN-based Internet security supervision method and system, which realizes flexible traffic scheduling and unified management of security policies through an SD-WAN network architecture, formulates comprehensive security policies through an SD-WAN controller, effectively defends external threats, detects and responds to potential threats in real time by SD-WAN edge equipment, reduces attack risks, and improves the security of the security policies. Security events, traffic information and logs are reported and analyzed in a centralized mode, an administrator is helped to comprehensively grasp security conditions, the SD-WAN controller intelligently analyzes reported information, rapidly identifies threats and assesses risks, supports rapid decision making and strategy adjustment and ensures optimal network security, and the intelligent routing function of the SD-WAN controller adjusts traffic paths according to needs and optimizes performance. And if threats occur, a security path or encryption processing is automatically guided, so that the service continuity is guaranteed, the operation and maintenance efficiency is improved by a centralized management and automatic response mechanism, manual errors are reduced, the cost is reduced, the SD-WAN architecture is flexible, rapid deployment and adjustment are supported, and the method adapts to the change of service requirements.

The invention discloses a virtual power plant system integrating multiple resources and a flexible scheduling method of the virtual power plant system, and relates to the technical field of virtual power plant management. Analyzing photovoltaic fluctuation characteristics, energy storage response capability indexes and stability indexes of the load power data corresponding to various types of data, and performing normalization processing; establishing a resource interaction tensor based on the collected data, fusing the power grid frequency deviation and the line load, and constructing a collaborative optimization model containing time delay compensation; updating the power grid frequency and the line load rate with a fixed time window, dynamically solving the collaborative optimization model, and outputting a load regulation quantity and an energy storage target scheduling instruction; and obtaining the power grid impedance characteristics and load distribution parameters of the area to be loaded with the control instruction, analyzing the scene deviation degree of the area, and correcting the scheduling value based on the scene deviation degree. The scheduling precision is improved, and the complex power grid compatibility is improved.

The invention provides a marine oil spill emergency resource scheduling method and system based on an improved ant colony algorithm, and relates to the technical field of data processing.The method comprises the steps that on the basis of a modified path transition probability function, multi-target ant colony collaborative optimization is executed on a dynamic resource scheduling architecture, a population is initialized to construct a scheduling path solution, and the scheduling path solution is optimized; calculating a time consumption target and an ecological loss target, iteratively updating pheromone distribution until convergence, and generating a multi-agent non-dominated scheduling strategy; according to a multi-agent non-dominated scheduling strategy, calculating a resource scheduling failure condition probability and an ecological sensitive area damage expected value; and if the scheduling failure conditional probability exceeds a risk tolerance threshold value, an ecological damage expected value is taken as a nonlinear penalty weight, and an anti-disturbance rescheduling mechanism is triggered to generate a flexible scheduling strategy. According to the method, the timeliness and accuracy of emergency resource transportation are improved, and collaborative optimization of'emergency efficiency-ecological protection 'is realized.

The embodiment of the invention provides a beam report reporting method and a related device. The reporting method comprises the following steps: sending a first message comprising a beam report, first information and second information; and if the timing duration after the first message is sent is greater than or equal to the first duration or greater than or equal to the second duration, releasing the QCL. The first information in the first message is used for indicating the QCL of the reference signal in the beam report stored by the terminal, and the network equipment can determine the beam to which the QCL stored by the terminal belongs based on the first information so as to carry out adaptive scheduling, so that the adaptive beam scheduling capability is enhanced. The second information in the first message can indicate the effective duration of the QCL, and the network device further determines the effective storage duration of the QCL, thereby facilitating flexible scheduling. The terminal releases the QCL, so that invalid occupation of memory resources caused by the fact that the QCL does not work and is not released can be avoided.

The invention relates to the technical field of cloud computing, and discloses a cloud resource flexible scheduling optimization method based on reinforcement learning, and the method comprises the steps: obtaining the dynamic demand data of tasks in a cloud computing platform, including but not limited to the type, priority, historical resource consumption, current system load and other information of the tasks; carrying out modeling on the obtained task; carrying out cloud resource scheduling by adopting a reinforcement learning algorithm; and a scheduling strategy is optimized through a feedback mechanism, and multi-objective optimization scheduling is realized by updating a state-action value function. According to the method, the dynamic demand data of the tasks in the cloud computing platform are acquired, the state space and the action space of the tasks are defined, the cloud resource scheduling strategy is optimized through the reinforcement learning algorithm, and the task scheduling is dynamically adjusted through the multi-dimensional reward function, so that the task allocation can be intelligently optimized under the real-time task load and resource constraint, and the task scheduling efficiency is improved. The resource utilization efficiency is improved, the task response time is shortened, and the system energy consumption is reduced.

The invention discloses a space-time price-based computing power and electric power space-time collaborative flexible scheduling method and device, and relates to the technical field of electric power systems, and the method comprises the steps: obtaining the architecture and operation characteristics of a data center, pre-establishing an energy consumption model and a load scheduling model of the data center, and pre-establishing a computing power-electric power collaborative regulation double-layer optimization model, the calculation power-electric power coordinated regulation double-layer optimization model is based on an energy consumption model and a load scheduling model of the data center, takes the initiative of the data center as an independent operator and the influence of the load of the data center on the electricity price of the system into consideration, and is constructed by taking the data center as a decision-making main body; a lower-layer power grid direct current optimal power flow problem is converted into a KKT condition to serve as a constraint to be substituted into an upper-layer problem, a strong dual theorem and a Big-M method are utilized to linearize and solve the double-layer optimization model of computing power-power coordinated regulation, and a computing power-power space-time coordinated flexible scheduling result is obtained.

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

The invention discloses a glass factory load low-carbon regulation and control method considering capacity compensation and carbon quota, and aims to realize refined carbon emission control and flexible scheduling in the production process of a glass factory. By fusing multi-dimensional input of historical load power, load change rate, environment temperature, humidity, wind speed, electricity price, time and the like and utilizing an STFormer architecture to model a complex time dependency relationship, a kilowatt-hour electricity carbon emission factor in a future time period is predicted, and prior information is provided for low-carbon scheduling; with minimization of carbon emission and electric charge cost and maximization of capacity income as targets, a future load scheduling strategy is optimized under constraints of carbon quota, an electric power regulation range and equipment operation; and globally searching an optimal load control scheme through a quantum particle swarm algorithm, and generating a multi-scene-oriented dynamic regulation and control strategy under the constraints of a multi-target fitness function and a penalty mechanism. The method not only improves the precision and robustness of carbon factor prediction, but also realizes flexible scheduling of the glass factory load driven by carbon economy.

The invention relates to a flexible scheduling conveying line process control method and device and terminal equipment. The method comprises the steps that process machining data of a machining process where a tray in a machining production conveying line is located is obtained, and process line body configuration parameters of the machining process corresponding to a machined product are obtained from a warehouse management system; the procedure processing data and the procedure line body configuration parameters are matched and screened to obtain a plurality of line body paths, corresponding line body configuration data are obtained from each line body path, and the line body configuration data comprise priorities, cache bits and current remaining caches; sorting all the line body configuration data by adopting a cache priority principle to obtain a line body path with the minimum cache bit and the closest distance, and extracting target point data from the line body path; according to the target point data, the processing production conveying line is controlled to convey the tray to the next destination, and accurate control and distribution of products are achieved; the problem that trays of an existing workshop cannot be distributed in real time according to product information in the conveying line circulation process is solved.

The invention relates to the technical field of public transportation systems, and particularly discloses a federated learning-driven computing power flexible scheduling method, which comprises the following steps: S1, dynamic computing power perception: monitoring a computing power index of federated learning participation equipment in real time, and generating a dynamic resource vector; s2, elastic fragment generation: according to the dynamic resource vector, splitting a global federal model into differentiated sub-model fragments; s3, adaptive scheduling: dynamically allocating sub-model fragments to matched equipment based on equipment computing power fluctuation and fragment calculation complexity; s4, federal training is executed, wherein the device carries out local training on the distributed sub-model fragments, and local gradient updating is generated; and S5, incremental aggregation updating. According to the method, through full-process optimization of dynamic perception, elastic fragmentation, intelligent scheduling and safe aggregation, efficient utilization of computing power resources, adaptive allocation of tasks and enhanced protection of data privacy in a federated learning scene are realized, and the performance and robustness of a distributed machine learning system are remarkably improved.

The invention discloses a bus resource dynamic scheduling method based on spatial graph convolution and near-end strategy optimization in the technical field of bus resource scheduling, and the method comprises the following specific steps: employing a weight graph and AggreGatE to carry out the modeling of a road network structure, and carrying out the sampling and aggregation operation through a weighted GraphSAGE model, according to the method, relevance and edge feature capture between nodes of a vehicle and a station are realized, and interaction with the environment is carried out by utilizing a multi-output strategy network architecture and an objective function of a reward mechanism through a reinforcement learning near-end strategy optimization algorithm; according to the invention, dynamic and multi-line departure interval adjustment and passenger travel route planning are realized, and a Dijkstra algorithm is utilized to calculate an initial route of a bus from a current position to a passenger starting station, and the like. The method adapts to the change of traffic conditions, and generates a more dynamic and flexible scheduling decision.

The invention discloses a cross-provincial flexible scheduling method and system for a pumped storage power station, and the method comprises the steps: monitoring the operation state of each unit of the pumped storage power station in a region, and displaying the basic data and operation conditions of each unit on a front-end web page, and formulating a pumped storage calling method according to the capacity demand of each small region in the whole large region for the pumped storage power station and the available capacity of the inter-provincial tie line, and finally realizing rear-end data transmission and development and display of a front-end data simulation platform by using a Django framework. The method has the advantages that the current situation that pumped storage is ignored under the background of vigorous development of new energy is solved, meanwhile, the blank that pumped storage participates in trans-provincial calling is filled up, the energy calling process is visually displayed through the computer technology, and the pumped storage calling frequency and efficiency are improved.

The application relates to a service scheduling and deployment method for Internet of Things edge computing node resources, which is realized based on an Internet of Things edge computing system. The system comprises a cloud data center, a convergence layer core network and a plurality of access layer switch networks connected in sequence. Each access layer switch network is in communication connection with a plurality of edge computing nodes, and each edge computing node is in communication connection with a plurality of data acquisition terminals. The application comprehensively considers the current running state of the edge computing system, network delay problems and running results after deployment, and performs service scheduling and deployment of Internet of Things edge computing node resources on each real-time data stream computing application. The application is suitable for the decision environment of the real-time computing task load and bandwidth dynamic change of the Internet of Things edge computing node, realizes global optimization from the system level, takes into account the single node load capacity, and realizes the optimal scheme and application planning and deployment decision suggestion of the long-period stable operation and real-time task flexible scheduling strategy.

The invention relates to the technical field of intelligent scheduling, in particular to a distributed energy access-oriented power grid flexible control and optimal scheduling system, which comprises a dynamic sensing module, a prediction correction module, a flexible scheduling module, a self-adaptive control module and a collaborative traceability module. In the scheme, the dynamic sensing module of the system can capture fine fluctuation of distributed energy at high frequency, and second-level acquisition is started once the output change rate exceeds a certain range, which is far higher than the response speed of a traditional system. By means of the real-time and accurate sensing capacity, the system can rapidly capture energy at the initial stage of energy change, precious time is won for follow-up prediction and scheduling, it is ensured that the system closely follows the energy dynamic state all the time, the control force on distributed energy is greatly improved, the system can respond to various sudden changes in time, and the continuity of energy supply is effectively guaranteed.

The invention relates to a computing power task scheduling method and device based on new energy consumption and a storage medium. The method comprises the steps that a residual power output prediction curve is established according to historical output power data of at least one new energy power generation node and other load data taking power from the new energy power generation node, and the residual power output prediction curve is used for indicating residual output power data, which can be used for scheduling, of each new energy power generation node in a future preset time period; obtaining a task scheduling strategy of at least one computing power task based on the residual power output prediction curve; and based on the task scheduling strategy, controlling the computing power resource to execute at least one computing power task. According to the embodiment of the invention, the structural limitation of the load rigidity of the computing power center can be broken through, the computing power center side is taken as a leading view angle, the flexible scheduling of the computing power task facing the distributed new energy local consumption scene is realized, the light abandoning rate and the peak regulation pressure of the power grid are reduced, and the energy efficiency level and the green computing power of the operation of the computing power center are remarkably improved.

The invention relates to a network resource flexible scheduling method, system and device and a medium. The method is applied to a backbone transmission network and data network intelligent management and control system. The method comprises the following steps: disassembling network resources into a plurality of atomic power units in a virtualization layer, marking dynamic attribute tags for the atomic power units to obtain an atomic power library, and generating a trigger event when a real-time performance index exceeds a preset threshold value; according to the trigger event, the atomic power library and the alarm knowledge graph, carrying out combination optimization on the atomic power units to generate a resource combination scheme, and calling a three-dimensional topology model to carry out pre-verification on the resource combination scheme; and according to the execution scheme, carrying out capacity expansion and shrinkage operation on the atomic power unit through the virtualization layer, adjusting a load balancing weight based on real-time flow distribution, and generating a resource allocation result. According to the method, dynamic cooperative scheduling of network resources is realized, and the service quality and the operation efficiency of a backbone transmission network and a data network are improved.

The invention belongs to the technical field of power dispatching. The invention provides a user side energy flexible scheduling management method and system. The method comprises the following steps: acquiring first electricity demand historical data and second electricity demand historical data of each user in a target area; performing prediction processing on the first electricity demand historical data by using a first prediction model to obtain a first predicted electricity demand; using a second prediction model to perform prediction processing on the second power consumption demand historical data to obtain a first power consumption demand fluctuation coefficient; and calculating a second predicted power consumption demand based on the first predicted power consumption demand and the first power consumption demand fluctuation coefficient, and determining an energy flexible scheduling management scheme according to the second predicted power consumption demand. According to the method, the historical electricity utilization rule of the user and the influence on fluctuation of the electricity price and the electricity supply and demand condition are considered at the same time, and the accuracy and rationality of the determined energy flexible scheduling management scheme can be remarkably improved.

The invention belongs to the technical field of micro-service scheduling, and discloses an APP micro-service intelligent arrangement and scheduling method and device based on a fast application, and a medium. The method comprises the following steps: firstly, performing feature extraction on equipment data to obtain feature data with high representativeness, secondly, creating an atomization service component and a service dependency relationship graph by splitting service instances, and then, on the basis of considering the dependency relationship among services, resource requirements, network bandwidth and service performance by adopting a multi-objective optimization algorithm, establishing an atomization service component and a service dependency relationship graph. And optimizing a scheduling decision, designing an alternative path and a service degradation scheme, establishing a service scheduling strategy, and finally realizing flexible scheduling of service instances based on the set service scheduling strategy. According to the method, the technical problems of insufficient resources, delay, service interruption and the like in a high-concurrency environment are effectively solved, and the performance and the reliability of the industrial APP in a complex environment are improved.

The invention belongs to the field of big data stream processing, and particularly relates to a Storm task scheduling method based on a multi-agent deep reinforcement learning model, which comprises the following steps of: simulating a Storm task scheduling environment, and reflecting dynamic changes of factors such as resource allocation, task scheduling and cluster load in an actual Storm cluster; according to the method, a near-end strategy optimization (PPO) algorithm based on an anti-fact strategy gradient (COMA) is adopted as a reinforcement learning model for training; a training stage centralization and execution stage decentralization method is adopted to ensure the real-time performance of Storm in actual operation. According to the method, maximization of the resource utilization rate and minimization of the resource use cost are taken as optimization objectives, and multi-agent deep reinforcement learning and task scheduling characteristics are combined, so that the Storm can obtain a more intelligent and flexible scheduling strategy when facing dynamic resources and workloads.