125 results about "Hybrid Scheduling" patented technology

A system for multi-stage planning of construction processes and resource allocation, consisting of: a central planning engine configured to receive input data, including architectural design models, structural constraints, procurement schedules, and historical performance indicators; a task decomposition processor that is operationally connected to the central planning engine and configured to generate a hierarchical construction task graph by decomposing macro-level construction milestones into mid-level and micro-level subtasks, with each subtask having time estimates, location identifiers, resource requirements, and mutual dependencies; a hybrid planning processing unit configured to resolve time and resource constraints across the entire task diagram; a resource coordination controller that is operationally connected to the central planning engine, wherein the resource coordination controller includes a real-time database of work units, machines and material stocks, each resource being tagged with attributes such as availability, usage history, operating status and spatial location; a multitude of distributed execution units distributed across the construction zones, each distributed execution unit comprising an embedded controller, sensor interfaces, task status processing logic, and communication circuitry, each distributed execution unit being configured to receive planning instructions from the central planning machine, execute localized control logic for task confirmation and resource activation, and transmit task execution data back to the central planning machine; an adaptive conflict resolution processing unit that is operationally connected to the central planning engine and configured to detect conflicts in task execution or resource conflicts, simulate alternative task-resource allocation scenarios using a real-time multi-agent model, and autonomously update the task graph with revised task sequences and resource allocations; and A dashboard for the construction process, configured to visualize task progress, deviations from the planned schedule, and resource efficiency metrics, with the dashboard also being able to receive manual override inputs or approve automated conflict resolution proposals generated by the adaptive conflict resolution module.

The invention relates to an intelligent computing power and storage scheduling method and system for a multi-service system, and belongs to the technical field of intelligent computing resource scheduling and storage management, and the method comprises the steps: collecting the real-time monitoring data of each node in the multi-service system, and carrying out the feature extraction; predicting a computing power demand value and a storage demand layering strategy in a future preset time window; dynamically correcting the prediction result, and generating a dynamic resource demand table and a cross-service priority weight matrix; generating a task allocation scheme through a hybrid scheduling algorithm; generating a storage data migration instruction through a storage scheduling engine; executing calculation node capacity expansion, task migration and storage data redistribution operation; and based on the executed latest state snapshot of the resource pool and the scheduling failure case library, updating a weight parameter of the time sequence prediction model and a scheduling strategy rule library to obtain an updated strategy version, and applying the updated strategy version to a next scheduling period. According to the invention, efficient scheduling of resources can be realized in a multi-service system environment.

The invention relates to a multi-objective optimization scheduling method and system for a multi-AGV flexible job shop. The method comprises the steps of collecting job shop layout data, AGV operation parameters and production task data, and constructing a scheduling system basic model; performing recurrent neural network training and physical constraint model integration to obtain a hybrid scheduling prediction model; subtask decomposition and path search processing are carried out, and a dual-objective optimization model is obtained through constraint linearization conversion; performing particle swarm optimization solution on the dual-objective optimization model, and obtaining a non-dominated solution set through two-stage iterative calculation of an adaptive weight strategy and constraint conversion; performing multi-dimensional evaluation calculation on the non-dominated solution set to obtain an optimal scheduling execution scheme; and performing model feedback updating on the hybrid scheduling prediction model to obtain a target scheduling prediction model. According to the invention, multi-target balance of task completion time and energy consumption is realized, and the real-time response capability and operation stability of the system are improved.

The invention relates to the technical field of smart community data management, in particular to a smart community metadata interaction method and system based on an edge computing framework, and the method comprises the steps: carrying out the dynamic storage of multi-dimensional data through a hierarchical rolling cache; generating a uniform resource vector through sequential progressive mapping; community semantic tags are added for the uniform resource vectors through extensible tag slots, connection is established according to association rules, and a resource semantic graph oriented to community services is constructed; receiving a resident task request, constructing a multi-factor weighted scoring engine based on the resource semantic graph, and generating a task data packet with a priority label; a two-channel hybrid scheduling network is constructed, a first channel outputs resource availability and node association degree, and a second channel outputs time sequence characteristics and load prediction parameters; and fusing dual-channel output to obtain task-node correlation and generate an optimal matching strategy. According to the method, task collaboration and resource adaptive management are realized through semantic resource modeling and hybrid scheduling.

The invention relates to the technical field of artificial intelligence and edge computing, in particular to an end-side cloud collaboration system based on a hybrid scheduling strategy. The end-side cloud cooperation system based on the hybrid scheduling strategy comprises a model segmentation module, a state sensing module, a reasoning scheduling module, a model compression and deployment module, an edge optimization engine and a communication synchronization module. According to the end-side cloud cooperation system based on the hybrid scheduling strategy, elastic deployment and dynamic reasoning of a complex model in a multilayer heterogeneous environment are supported, the system state can be sensed in real time, a scheduling path can be optimized, and the robustness of the system is improved; and in combination with model compression and edge operator optimization, the reasoning efficiency is remarkably improved, the communication load is reduced, and then low-delay cooperation between the terminal and the cloud is ensured.

The invention discloses a dual-stage resource reservation and scheduling system for vehicle networking sudden accident emergency calculation, and relates to the technical field of edge calculation and intelligent traffic. The system comprises a traffic situation sensing module, a risk prediction decision module, a resource pre-freezing execution module, a task grading analysis module and a hybrid scheduling optimization module. The system innovatively divides a traffic accident handling process into an early warning stage and a response stage: in the early warning stage, the system adjusts a resource reservation ratio of edge nodes based on traffic flow density and historical task load conditions; in a response stage, the system performs hierarchical scheduling according to task types and dependency relationships by using a mixed integer programming model and a priority perception strategy. Through an end-side cloud collaborative architecture and a resource reservation mechanism, the system can significantly improve the task processing efficiency under emergencies and reduce the response time delay of high-priority tasks, has good expansibility and practicability, and is suitable for various vehicle networking application scenes such as intelligent expressways and urban traffic.

The invention discloses a shared cache system applied to a real-time network terminal chip. The shared cache system comprises queue management, enqueue scheduling control, dequeue scheduling control, write bus control, read bus control, a data cache, a register module, an RXFIFO module and a TXFIFO module. According to the cache allocation strategy, the variable-length data is stored by using the fixed-length unit. The user can customize and configure the private space, the residual space is the shared space, and the storage utilization rate is high. The shared cache is mainly used for storing data packets sent by an external application layer through a host interface. And storing the received data packet, extracting frame information according to a network flow protocol, and directly discarding frames which do not meet the protocol. The shared cache is provided with a dynamic cache structure, the state of each queue can be updated in real time, frame loss is prevented, and the data stream processing capacity is improved. And meanwhile, complex hybrid scheduling of a real-time network is met. The whole shared cache is subjected to anti-radiation reinforcement, the scene requirement of space navigation application is met, and high reliability is achieved.

The invention belongs to the technical field of heterogeneous computing resource scheduling, and particularly relates to a heterogeneous computing resource unified scheduling method based on a multi-instruction-set architecture. Comprising a heterogeneous resource hybrid scheduling module, a task resource complexity analysis and load real-time detection module, a technical optimization module in a credential environment, a fault domain hidden danger monitoring and recovery module, a resource scheduling optimization and adjustment module and a historical performance analysis and energy consumption monitoring module. The tasks are reasonably distributed according to the dependence degree of the tasks on different computing resources, and resource idle waste is avoided; meanwhile, the task resource complexity analysis and load real-time detection module dynamically adjusts resource allocation, efficient execution of tasks is ensured, and the resource utilization rate and the task execution efficiency are improved.

The invention discloses a medical logistics robot task allocation system based on time division multiplexing and dynamic scheduling, which comprises a cloud platform, a plurality of robots and a cabin body replacement station, and is characterized in that the cloud platform adopts a conflict resolution strategy based on priority and time windows on the basis of task time division and class division allocation logic of a mixed AI scheduling algorithm; tasks are dynamically and intelligently distributed according to hospital business laws and real-time states of the robots; the robot communicates with the cloud platform through the communication equipment, various tasks issued by the cloud platform are executed by carrying various cabins, and the tasks comprise material distribution, cleaning, disinfection and polling; and the cabin body replacement station is used for providing cabin body replacement equipment and assisting the robot in replacing the cabin body. According to the invention, tasks can be dynamically and intelligently allocated according to hospital business rules and real-time states of the robots, so that the task processing efficiency and the resource utilization rate are remarkably improved, and the hospital logistics operation cost is reduced.

The invention provides a UWB-based hybrid scheduling method, apparatus and device, and a medium. The invention discloses a scheduling method applied to a main anchor point in a UWB communication system, belongs to the technical field of UWB communication, and solves the technical problems of poor overall performance and low operation efficiency of a scheduling method in the prior art, and the method comprises the steps: receiving and analyzing time sequence scheduling information, so as to obtain a distance measurement time period, a radar time period and a data time period of the main anchor point in a hybrid scheduling process; performing distance detection in the distance measurement time period to obtain first sub distance measurement data, and performing radar detection in the radar time period to obtain first sub radar detection data when the time sequence scheduling information comprises a radar enable signal of the main anchor point; receiving second sub-ranging data and second sub-radar detection data in the data period, and determining target ranging data and target radar detection data; and sending the target ranging data and the target radar detection data. The overall performance and the operation efficiency of the system are improved.

The invention discloses an ROS2 hybrid scheduling actuator system. The system comprises a registry, two ready callback sets, two schedulers and two thread pools, the two dispatchers are respectively a real-time dispatcher and a fair dispatcher; the real-time scheduler is responsible for scheduling callback instances of the key chain; the fair scheduler is responsible for scheduling callback instances of the common chain; the two thread pools comprise a special thread and a shared thread, and the special thread specially runs a real-time scheduler; the shared thread not only can run a real-time scheduler, but also can run a fair scheduler; the shared thread comprises two execution modes: (1) real-time scheduler priority: the thread operates the real-time scheduler preferentially, and only when the priority queue of the real-time scheduler is empty, the fair scheduler operates; and (2) priority of the fair scheduler: firstly operating the fair scheduler by the thread, and operating the real-time scheduler when the vector set of the fair scheduler is empty.

The invention discloses a hospital multi-scene transportation robot hybrid scheduling method based on dynamic priority, and belongs to the technical field of robot control and scheduling. The method comprises the following steps: acquiring hospital dynamic environment data; receiving and analyzing the multi-mode instruction to form task parameters; preliminarily grading according to task parameters and preset rules, and dynamically adjusting task priorities in combination with a dynamic map and the current state of the robot; based on the adjusted task priority and robot type, distributing robots and generating an initial path considering distance and people flow density; the robot is driven to track along the path trajectory, and obstacle behaviors are predicted in real time and dynamically avoided; decomposing a transportation task into task chains, and when the electric quantity is lower than a threshold value, deciding whether to insert a charging task in combination with the priority; and after the task is completed, prompting and transmitting feedback data. According to the invention, dynamic priority, behavior prediction and hybrid scheduling are adopted, intelligent distribution and path optimization of transportation tasks are realized, and the efficiency and safety of internal logistics of hospitals are improved.

The invention discloses a hybrid scheduling system and method for application performance perception, and the system comprises an interference injection and monitoring module which is used for managing and regulating the whole data collection process, so as to obtain key performance indexes of applications on all nodes in a cluster; the performance model module is used for modeling and analyzing the performance of the application by using the collected monitoring data so as to predict the performance of the application under different resource configurations; the reinforcement learning scheduling module is used for automatically adjusting and optimizing a scheduling strategy according to an analysis result of the performance model module by utilizing a reinforcement learning method; the scheduling simulator module is used for simulating various scheduling scenes and application loads in a real environment and verifying and optimizing a scheduling strategy. According to the method, the optimal scheduling strategy can be automatically selected for scheduling under the hybrid scene, the performance and the resource utilization rate of the application can be improved, the QoS of the application is effectively guaranteed, and the overall operation efficiency and stability of the cluster are improved.

The invention provides a data processing device for implementing a hybrid scheduling scheme, the hybrid scheduling scheme is used for scheduling data messages in a network, the data processing device comprises one or more processors, the one or more processors are used for processing the data messages, each data message belongs to one category, and the one or more processors are used for processing the data messages. Each category has a predetermined first transmission priority associated with each category; receiving a data message of data to be transmitted for one or more categories in the plurality of categories; and calculating a second indication of the transmission priority of each of the plurality of categories, and scheduling the plurality of categories of data messages for transmission according to the first transmission priority and the calculated second indication of the transmission priority.

The invention discloses a distributed real-time target detection tracking method and system based on dynamic perception and adaptive regulation and control, and belongs to the technical field of intelligent monitoring and computer vision. The method comprises the following steps: firstly, calculating the weight of each camera in real time by using the time, position and map information of an event and camera network topology, generating an optimal search path by predicting a target motion track, and decomposing a global tracking task into multiple levels of subtasks; then, according to the task complexity and the node resource state, adopting a hybrid scheduling strategy to realize short-term container elastic expansion and contraction and long-term resource prediction, and ensuring efficient and stable resource scheduling; then, through multi-modal feature fusion and spatio-temporal joint modeling, cross-camera video data alignment, target association and continuous tracking are realized; and finally, performing multi-source data fusion and risk reasoning by using the large model, and outputting graded early warning and three-dimensional visual decision information. According to the method, the tracking accuracy can be improved, and resource utilization optimization and real-time response are realized.

The invention provides a graph card-based hybrid scheduling rendering method and system, and the method comprises the steps: dividing a frame buffer area, binding the divided frame buffer area with a task, completing the rendering of each card through a hybrid task scheduling strategy, and finally synthesizing and outputting a complete graph; a frame buffer area of a rendering graph is divided into a plurality of graph cards according to display criticality, each graph card is bound with an independent rendering task and a video memory area, a unique task division and resource allocation mode is formed, and the mode is different from a traditional overall rendering mode; and in combination with a hybrid scheduling strategy of high-priority task preemptive scheduling and dynamic card task parallel rendering, the problems of rendering task priority conflict and resource utilization are effectively solved.

The invention discloses a remote sensing rapid diffusion super-resolution reconstruction method based on adaptive scene perception, belongs to the technical field of mode recognition, and is used for super-resolution reconstruction of remote sensing images. The method comprises the following steps: preprocessing a high-resolution image, and calculating a residual error between the high-resolution image and a bicubic up-sampling result as a training target of a diffusion model; performing wavelet transformation on the bicubic up-sampling image through a frequency domain enhancement module, extracting frequency domain features, and fusing the frequency domain features with original spatial domain features to serve as condition input of a model; inputting the fused features into a conditional diffusion model, carrying out adaptive rapid sampling by adopting a linear-cosine hybrid scheduling strategy, and completing high-quality reconstruction within 20 steps; the feature expression capability is enhanced through an overlapping and crossing attention module integrated in the U-Net denoising network; and finally, fusing the residual error reconstruction module and the bicubic up-sampling image to obtain a final super-resolution reconstruction result. Experiments carried out on two common data sets (Potsdam and Toronto) show that the method of the invention significantly improves the sampling efficiency while maintaining the reconstruction quality, and is superior to an existing remote sensing image super-resolution method.

The invention discloses a one-cloud multi-core heterogeneous resource hybrid scheduling method, which comprises the following steps of: calculating a node resource availability score RAS and a task type affinity score TAS, carrying out comprehensive scoring on nodes in combination with a weight factor so as to realize intelligent scheduling of Pod, and collecting and analyzing node resource data in real time by utilizing a Promeheus open source monitoring solution, so as to realize real-time scheduling of the Pod. By predicting the resource pressure index RP I and the resource pressure trend index RT I and optimizing the scheduling decision, the method is suitable for the cloud computing environment, and particularly, when complex and diversified computing tasks are processed, the resource management efficiency and the cluster performance can be remarkably improved, and efficient and reliable cloud computing services are provided for users.

The invention relates to the field of container and virtual machine mixed scheduling, in particular to a container and virtual machine mixed scheduling method and device based on a domestic AI server, and the method comprises the steps: judging whether the server is used for operating a virtual machine or a container, if the server is used for operating the virtual machine, operating the virtual machine and executing corresponding operation, and if the server is used for operating the container, executing corresponding operation; if yes, the container is operated, corresponding operation is executed, a Volcano scheduling framework is integrated, and a Gang scheduling strategy and a BinPack scheduling strategy are defined; and a resource pooling management and dynamic adjustment mechanism is adopted, so that the resource utilization rate and the task execution efficiency are improved. According to the method, the container and the virtual machine can share and efficiently utilize the Ascend-910B resources, collaborative operation of multiple types of tasks is achieved, deep fusion and efficient collaboration of the container, the virtual machine and the domestic AI resources are achieved, and the scheduling intelligence level and the resource utilization efficiency of the domestic AI infrastructure are improved.

The invention belongs to the field of computer hardware resource management, and discloses an intelligent management method for computer hardware resources. Comprising the steps that task data of a user and comprehensive monitoring data of a computer at the same time point are collected, and a structure association chart is constructed; establishing a resource pool, distributing the task of each user into the resource pool according to the structure association chart, and dynamically updating the tasks in the resource pool; adaptively updating the task priority and the time slice length of each user in the resource pool by using a hybrid scheduling algorithm, and dynamically queuing the tasks of the users; tasks of all the users are executed according to the dynamic queues, and efficient and high-quality intelligent management of computer hardware resources is achieved through comprehensive processing and a hybrid algorithm.

The invention belongs to the technical field of big data processing and resource management, and discloses a resource optimization-oriented big data scheduling method and device, equipment and a storage medium. The method comprises the following steps: synchronizing metadata of an off-line production task from an existing data processing system through a preset interface, and carrying out sorting and resource allocation on a task queue by adopting a hybrid scheduling algorithm according to a task priority, a predicted execution time length and a task dependency relationship, so as to generate a task scheduling sequence and a resource allocation scheme, then starting and executing the task, monitoring the task execution state and the resource use condition in real time in the execution process, and dynamically adjusting the task execution sequence to realize bidirectional synchronization of production data and scheduling information without additionally designing an external data acquisition component; the method completely depends on off-line production data acquired by an existing data processing system of a semiconductor enterprise, realizes efficient and reasonable allocation of resources, and remarkably improves the scheduling efficiency and the overall performance of the system.

The invention discloses a large machine tool production process-oriented real-time hybrid scheduling method, which comprises the following steps of: reading a current state st of a workshop after a machine tool completes a processing procedure of a current processing part in the workshop, and selecting an action at under the current state st based on a decision network; based on a path rule corresponding to the action at, performing path decision for the current process of each part in the buffer area, selecting a suitable machine tool, forming a to-be-processed workpiece set of all the machine tools in a workshop, and extracting the to-be-processed workpiece set of the current decision-making machine tool; determining a next machined part Js for the decision-making machine tool based on a sorting rule corresponding to the action at, selecting the part Js from the buffer area and sending the part Js into a corresponding procedure of the decision-making machine tool for machining, and deleting a to-be-machined procedure set of all machine tools when the part Js is selected and sent into the decision-making machine tool for machining; the path decision and the sorting decision are mutually combined, the simultaneous decision also provides regret and re-decision opportunities for the path decision, the mechanism is introduced into the decision process of the DRL, and the flexible workshop dynamic scheduling problem can be solved through a decision network.

The invention provides a multi-task cooperative scheduling method and system for a training and pushing all-in-one machine, and the method comprises the steps: carrying out the resource preemptive scheduling of a real-time reasoning task based on the delay tolerance of the real-time reasoning task, and monitoring a video memory state of a stream processor in a program execution container in real time after the scheduling is completed; determining a credible interval of model training video memory allocation according to the video memory state and the resource demand characteristics of the to-be-allocated task; performing redundancy adjustment on the resource demand of the model training task through the delay tolerance of the execution task and the training priority of the model training task to obtain the collaborative cost of the resource scheduling demand of the model training task; and when the collaboration cost of the resource scheduling demand is in the credible interval, carrying out collaboration scheduling on the model training task based on the credible interval allocated by the video memory. Based on the scheme, the mixed scheduling of the reasoning task and the training task in the training and pushing all-in-one machine can be realized.

The invention relates to the technical field of artificial intelligence and distributed computing, in particular to an AI service distributed deployment method and device, equipment and a storage medium. According to the AI service distributed deployment method disclosed by the invention, access to diversified AI services is unified and simplified through a single interface, and optimal allocation of heterogeneous resources, high-concurrency request processing, low-delay response and fine-grained authority control are realized through a hybrid scheduling mechanism, multi-stage security authentication and remote model localization calling; the AI service deployment efficiency, the resource utilization rate and the safety are remarkably improved, and the method is suitable for deployment, integration, management and unified service providing scenes of various artificial intelligence applications.

The invention provides a multi-core heterogeneous architecture chip operating system, an inter-core communication method, system and device and a medium, and relates to the technical field of computers. The multi-core heterogeneous architecture chip operating system comprises a main control core and at least one heterogeneous core, a self-adaptive instruction conversion channel is arranged between the main control core and the heterogeneous core, the self-adaptive instruction conversion channel is constructed according to an instruction set of the main control core and an instruction set of the heterogeneous core, and the self-adaptive instruction conversion channel is used for performing instruction conversion in an inter-core communication process. A task allocation unit is arranged in the main control core, the task allocation unit is used for acquiring operation characteristic data of the heterogeneous cores to construct a resource allocation matrix, performing task allocation on the plurality of heterogeneous cores according to the resource allocation matrix, and issuing a task allocation instruction through a self-adaptive instruction conversion channel, and the operation characteristic data comprises load data. According to the invention, through instruction conversion of the adaptive instruction conversion channel, hybrid scheduling of different architectures can be realized, the utilization rate of heterogeneous cores is improved, and the load balancing deviation is reduced.

The invention provides a domestic large model edge cloud collaborative management platform based on a quantum-classical hybrid architecture and an adaptive optimization method, and the platform comprises a quantum heuristic computing power optimization engine which carries out the heterogeneous computing power scheduling based on a quantum annealing algorithm, and enables a large model task to be divided into a quantum bit mapping problem capable of parallel computing, hybrid scheduling of classical calculation and quantum calculation is completed; the edge end dynamic knowledge distillation architecture is used for collecting real-time data through edge equipment, dynamically extracting cloud large model knowledge and compressing the cloud large model knowledge into a deployable edge small model; reverse synchronization of an incremental learning mechanism is set; the multi-modal data sovereignty management mechanism is used for realizing verifiable auditing of data use compliance in a model training process; the management platform realizes secure data input, completes cloud large model training and quantum acceleration, and establishes a bidirectional knowledge flow closed loop. According to the method, large model computing power efficiency and training bottlenecks are overcome, the problems of real-time performance, privacy and knowledge islands are solved, and it is guaranteed that data are safe and compliant.

The invention relates to the technical field of vehicle-mounted computing, in particular to a dynamic energy efficiency management and emergency redundancy processing device and method for vehicle-mounted heterogeneous computing, and the device comprises a reconfigurable computing array which is used for carrying out dynamic partitioning and resource scheduling on an FPGA (Field Programmable Gate Array) logic unit; the multi-protocol converged communication panel is used for receiving a plurality of sensor data input into the reconfigurable computing array and carrying out mixed scheduling on the plurality of sensor data; the AI resource scheduling engine is used for redistributing computing power in the reconfigurable computing array according to the biological characteristics and a pre-constructed reinforcement learning dynamic energy efficiency model; and the three-level emergency redundant equipment is used for switching a main power supply, activating a preset optical calculation standby channel and starting V2X point cloud reconstruction when a main calculation unit in the reconfigurable calculation array breaks down so as to release nuclear explosion calculation power. Therefore, the problems of dynamic energy efficiency deficiency, redundancy mechanism insufficiency, multi-sensor conflict and the like of an existing vehicle-mounted computing system are solved.

The present application provides a GPU pass-through and resource hybrid scheduling method and system, and a chip. The method comprises: S1, acquiring resource information of GPUs of nodes, and storing the resource information in a scheduling buffer; S2, on the basis of hybrid quantization computation, acquiring computing power data and total display memory demand information of the GPUs of the nodes; S3, on the basis of the total display memory demand information of the GPUs of the nodes, performing first screening on the nodes to obtain a first screening result; S4, on the basis of a full permutation combination function and a pruning function, acquiring a simplified two-dimensional full permutation array; and S5, on the basis of the first screening result, performing a second screening operation on the nodes to obtain a preferred node result.

The embodiment of the invention provides a GPU computing power scheduling method and device, equipment, a medium and a program product, and relates to the technical field of computing power scheduling, and the method comprises the steps: dividing a computing unit and video memory resources of a GPU into a plurality of dynamically adjustable partitions according to a real-time task demand; calculating the priority weight of each task; preemptive scheduling and cooperative scheduling are combined, resource preemption is carried out on the first priority task, and time division multiplexing is carried out on the non-preemptive task; executing a dynamic voltage frequency adjustment (DVFS) and task merging strategy; through dynamic resource partitioning, dynamic priority evaluation, a hybrid scheduling strategy and an energy efficiency optimization algorithm, the GPU resource utilization rate and the energy efficiency ratio in a multi-task scene are remarkably improved, and meanwhile, energy consumption is reduced by combining a DVFS and a task merging strategy.

The invention relates to a mixed scheduling method based on a containerized mixed key system, and the method specifically comprises the following steps: configuring a first-stage scheduling strategy in two-stage real-time scheduling in a scheduling blueprint, and building a main time frame and a container time frame; setting an execution sequence and execution time of each real-time container in each main time frame; configuring a second-level scheduling strategy in second-level real-time scheduling; configuring the number of time slices which can be occupied by each non-real-time task after the non-real-time task is scheduled; creating a container and starting the container, and operating the system according to the scheduling blueprint; and when the real-time container, the real-time task, the non-real-time container or the non-real-time task pauses or quits, the system automatically adjusts the corresponding execution sequence and execution time. The problem that the minimum response time of the real-time task cannot be guaranteed by an existing hierarchical scheduling strategy for the real-time container is solved, it is guaranteed that the response of the real-time task can be more timely, and the actual requirements of different key tasks are met.