169 results about "Flexible scheduling" patented technology

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 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 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 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 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 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.

The invention provides a scene-based random optimization method for micro-grid energy and flexibility scheduling, and belongs to the technical field of micro-grid optimization scheduling. The method comprises the following specific steps: minimizing the expected cost of the micro-grid as a target; setting a power balance constraint in the micro-grid according to a relation among photovoltaic power generation capacity, power load power consumption, battery energy storage charging and discharging power and power loss in the micro-grid internal transmission and conversion process in the micro-grid; determining a flexible service constraint of the micro-grid according to the flexible service model and the battery energy storage model; predicting and updating by using a rolling time domain method, modeling uncertainty of the micro-grid by combining scene generation and scene reduction technologies, determining a day-ahead prediction error of the micro-grid, and constructing a random optimization model of energy and flexibility of the micro-grid; based on a flexible intra-day framework, solving a random optimization model of micro-grid energy and flexibility according to a scene at different time stages, and obtaining a flexible scheduling strategy; meanwhile, the stability and efficiency of the micro-grid are considered.

The invention relates to the field of intelligent manufacturing and production scheduling, and discloses a heater assembly line flexible scheduling method based on production data, and the method comprises the steps: S1, obtaining the production data of a heater assembly line and corresponding environment parameters; initializing an assembly line scheduling constraint condition; wherein the assembly line scheduling constraint condition is used for defining a processing time range, an equipment load threshold value and a material supply period reference value allowed by each process in the heater assembly process; s2, if the environmental parameters exceed a preset safety threshold, dynamically correcting the real-time production data according to the environmental parameters to obtain corrected real-time production data; wherein the environment parameters comprise temperature, humidity and air cleanliness, the scheduling effect evaluation model and the association rule mining algorithm are introduced, the production efficiency improvement rate, the equipment failure rate reduction degree, the material waste reduction amount and the like can be quantitatively evaluated, and the influence relation of the key equipment operation parameters on the scheduling effect is mined.

Embodiments of the present application relate to a task scheduling method and apparatus, an electronic device and a storage medium, applied to a resource pool having multiple tenants, where a task queue corresponding to each tenant is configured with a minimum resource limit and a maximum resource limit, a difference between the maximum resource limit and the minimum resource limit is an elastic resource limit of the task queue, and the maximum resource limit is an elastic resource limit of the task queue. Comprising the steps that priority information of a next to-be-executed task in a target task queue and current available resource information of the target task queue are obtained, the priority information comprises a high priority or a low priority, and the current available resource information comprises available resources in a minimum resource limit and available resources in an elastic resource limit; or no available resource exists in the maximum resource limit; and based on the priority information of the next to-be-executed task and the current available resource information of the target task queue, performing resource elastic scheduling on the next to-be-executed task. The task throughput and the resource utilization rate of the scheduling system can be improved in an elastic scheduling mode.

The invention discloses a cluster computing resource allocation method. The method supports three resource service forms of a virtual machine, a container cloud and a bare computer. Wherein the virtual machine resources are used for deploying system software with a UI (User Interface) and a C / S (Client / Server) architecture; the container cloud resource is used for deployment of system software of a B / S architecture and operation of a large test sample; the bare computer resources are used for specific function system software, Kafka middleware and / or database deployment; and service computing resource elastic scheduling and control are deployed to realize cluster computing resource allocation. According to the method, multiple resource service forms are flexibly adopted, and virtual cluster management and high-availability design are applied, so that test resource automatic deployment strategy generation and computing resource flexible scheduling and control can be realized, and high-performance, high-reliability, servitization and easy-to-use cluster computing resource allocation and deployment services are provided for users.

The invention relates to the technical field of data reasoning, and discloses a mining equipment intelligent manufacturing production scheduling optimization system and method.The system comprises a topological graph generation module, an initial scheduling Gantt chart generation module, a demand strength coefficient acquisition module, a flexible scheduling Gantt chart acquisition module and a final scheduling instruction set generation module; constructing a topological graph based on the process relation of the mining equipment; generating an initial scheduling Gantt chart by taking the part task set as a longitudinal axis and the corresponding general assembly line task sequence as a transverse axis; when a disturbance event occurs, an affected process dependence tree is extracted, task delay cost is evaluated, and a requirement strength coefficient for rescheduling in the manufacturing process is output; generating a flexible scheduling Gantt chart based on the process beat deviation and the equipment utilization rate in the synchronous adjustment manufacturing process; determining a final scheduling instruction set through process path dynamic correction and process optimization results; the efficiency of the mining equipment manufacturing and production process scheduling scheme can be improved.

The invention discloses an elastic scheduling method and system for a power grid based on a dynamic security domain, and relates to the technical field of elastic scheduling methods, and the method comprises the steps: determining the boundary of a non-key constraint based on an elastic adjustment factor, a power grid operation event corresponding to the power grid, and a plurality of constraints; the corresponding optimization event is determined according to the dynamic security domain, the boundary of the non-critical constraint and the real-time working data of the power grid, and the elastic scheduling system of the power grid is determined based on the optimization event, the real-time load data of the power grid and the corresponding real-time power supply data, so that the accuracy of the elastic scheduling system of the power grid is improved; and the elastic scheduling effect of the power grid is further improved. Therefore, the plurality of sub elastic scheduling projects are determined according to the identification of the elastic scheduling mode, the power optimization event of the power grid is determined based on the project content of each sub elastic scheduling project, the corresponding project position and the real-time load data of the power grid, and the accuracy of the online optimization logic of the power grid is ensured.

The invention provides a flexible load hierarchical polymerization optimization regulation and control method. Flexible load operation data is acquired and divided into adjustable and non-adjustable loads, and a classification foundation is laid for subsequent hierarchical aggregation; three-level aggregation of an equipment layer, a region layer and a system layer is executed on two types of loads, and scattered resources are integrated into a hierarchical structure, so that the complexity of directly regulating and controlling massive scattered loads is remarkably reduced; a regulation and control model is established based on a hierarchical aggregation result, and an optimization algorithm is adopted to solve a strategy, so that collaborative optimization from local equipment to a global system is realized. According to the method, the contradiction between resource dispersity and regulation and control real-time performance is solved through a layering mechanism, the transmission efficiency and execution precision of regulation and control instructions are improved, meanwhile, the communication burden and the calculation cost are reduced through an aggregation model, and an efficient framework is provided for flexible dispatching of power grid flexible loads.

The embodiment of the invention discloses a photovoltaic hydrogen production-oriented multi-target flexible scheduling control system and a control method thereof, and belongs to the technical field of control. The system comprises a data acquisition and communication unit, a prediction and modeling unit, a flexible scheduling optimization unit and a real-time control and execution unit. The data acquisition and communication unit obtains real-time data from a plurality of key nodes, and provides basic support. And the prediction and modeling unit constructs a photovoltaic power generation prediction model and an electrolytic cell dynamic mechanism model by using the data so as to accurately describe the photovoltaic power generation characteristics and the running state of the electrolytic cell. And the flexible scheduling optimization unit formulates a multi-target optimization scheduling strategy in combination with data output by the prediction model and the health condition of the electrolytic cell, so that efficient, economical and safe operation is ensured. And the real-time control and execution unit accurately controls the electrolytic cell cluster according to the scheduling strategy. The collaborative optimization of the photovoltaic power generation volatility and the water electrolysis hydrogen production system is realized, and the overall performance of the system is improved.

The invention belongs to the technical field of automobile manufacturing, and particularly discloses an intelligent flexible production scheduling method and system for automobile orders, and the method comprises the following steps: collecting order demands of a user; according to the order delivery time requirement of a user, reversely deducing production plans of a coating workshop and a welding workshop by taking the general assembly workshop as a starting point to obtain a production plan of a production line; according to the demand within a period of sales, in combination with the material arrival rhythm, the workshop capacity and the sales demand successive date factor, daily production vehicles are discharged, and according to the selected daily production vehicle distribution result and the final assembly lean production proportion requirement, constraint conditions are set, and coating production sorting and scheduling calculation are carried out; and on the basis of the scheduling, a welding vehicle queue plan is generated in combination with a coating workshop production plan in a user order demand. By adopting the technical scheme, production scheduling is carried out by using a production scheduling reverse pushing mode based on the production characteristics of the automobile orders, flexible production scheduling of the automobile orders is realized, and the efficiency is improved.

The application discloses an intelligent link stress testing method and device supporting flexible scheduling. When a calling request is received, each calling link corresponding to the calling request is dynamically filtered from preset calling chain information, and stress testing task distribution is performed according to a dynamic priority task scheduling algorithm and each calling link. The stress testing task at least includes a local stress testing task and an edge computing stress testing task. Under the condition of system load balancing, the stress testing task is executed, and in the process of executing the stress testing task, performance data of the stress testing task is monitored in real time, and stress configuration parameters are dynamically adjusted according to the performance data, so as to complete the process of full-link stress testing.

The invention discloses an optimal scheduling method for a virtual power plant, and relates to the technical field of power regulation, and the method comprises the steps: obtaining multi-modal data of a detection region, calculating a regional load fluctuation index and a resource mobility index, and generating a dynamic feature data set; performing real-time clustering division on the power grid nodes through a clustering algorithm based on the dynamic feature data set to generate a dynamic sub-region set; if the sub-region feature fluctuation exceeds a boundary stability threshold value, triggering boundary redivision; according to the dynamic sub-region set, comparing a building power consumption predicted value with the available load of the charging pile in real time to calculate a demand difference, and generating an elastic scheduling instruction through multi-objective optimization; uncertain factors are monitored in real time, a risk index is calculated, and the priority and the resource allocation strategy of the elastic scheduling instruction are dynamically adjusted based on the risk index; and generating a risk avoidance scheduling scheme through a robust optimization algorithm.

The application provides a flexible scheduling group and a scheduling method. The scheduling group comprises a manufacturing execution system, a logistics execution system, an intelligent group control system, a logistics intelligent trolley scheduling system and a process intelligent trolley scheduling system. The intelligent group control system receives a production plan issued by the manufacturing execution system, receives inventory material information of the logistics execution system, receives vehicle position information of the logistics intelligent trolley scheduling system and the process intelligent trolley scheduling system respectively, and formulates scheduling tasks of the process intelligent trolley and the logistics intelligent trolley in real time. Through deep cooperation of the intelligent group control system with the manufacturing execution system, the logistics execution system and other systems, overall management of the whole production process is realized. Through real-time sensing of data of environmental changes and / or data of demand fluctuations by the intelligent group control system, and through on-demand pulling, the coordination and stability of line operation are improved through cooperation with multiple systems.

The invention discloses a flexible video processing system based on a domestic chip platform, and relates to the technical field of video flexible processing, and the flexible video processing system comprises a construction video frame construction module, a construction video flexible analysis module, a construction video flexible scheduling module, an early warning terminal and a database. The method comprises the following steps: analyzing a video processing framework of construction monitoring corresponding to power construction, constructing a flexible processing video of construction monitoring corresponding to current power construction, evaluating the constructed video, analyzing whether the constructed video is adaptive, analyzing an unadaptive scheduling management mechanism, monitoring the scheduling management mechanism when the scheduling management mechanism is carried out, and carrying out real-time monitoring on the scheduling management mechanism when the scheduling management mechanism is carried out. Whether a scheduling management mechanism of flexible video processing corresponding to power construction monitoring is effective is analyzed, so that video data processing is more flexible, the video processing efficiency and quality are improved, and the performance requirement of extreme video processing is intelligently met.

This invention discloses a method and system for optimizing game resources in a virtual game. The invention relates to the technical field of virtual games. It loads the current load of the virtual game based on a target resource demand sequence and constructs the resource distribution of the virtual game. Then, it performs topological clustering based on the content to be loaded in the virtual game, generating a resource topology map containing multiple game resource clusters, thus improving the accuracy of the resource topology map. The node dependencies in the resource topology map are marked, and flexible scheduling is performed based on the resource content allocated to each resource cluster, thereby outputting a multi-level flexible scheduling strategy. The high-speed video memory area and low-speed memory area of ​​the virtual game are optimized along this flexible scheduling strategy. Resource optimization events of the virtual game are determined based on the smooth constraints of the virtual game. Furthermore, resource anomaly signals of the virtual game are used to determine optimization configuration measures for the high-speed video memory area and low-speed memory area in terms of resource dimensions, improving the accuracy of the optimization configuration measures.