54 results about "Hybrid Scheduling" patented technology

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 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 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 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 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 application is suitable for the technical field of electric power metering and prepayment system, and relates to a Token server heterogeneous security module mixed scheduling method and system, which comprises the following steps: setting an active module set of the system, m is the number of active modules, acquiring the current remaining Token quantity and concurrent processing capacity of each active module i, and acquiring the total number N of current Token generation requests to be processed; calculating the remaining Token proportion after the distribution of the active module i and the proportion of the concurrent capacity of the active module i in the comprehensive concurrent capacity of all modules; establishing an optimization model with and as a target function; solving the optimization model to obtain the Token request quantity of each module, completing the distribution of the Token generation request and updating the remaining Token quantity of each module. The application can greatly reduce the Token purchase quantity and the number of security module deployments, and greatly improve the economy of system configuration.

The present application relates to the technical field of data transmission, and specifically provides a data transmission method and system for solid waste sorting equipment, a terminal and a storage medium, comprising: adding basic classification marks and dynamic priority marks to data packets according to the function types of sorting data, and dividing the data into synchronous control flow, real-time event flow, process awareness flow and background management flow; a data source end acquires link state information before sending, and adaptively adjusts the dynamic priority of the real-time event flow; a network switch performs mixed scheduling transmission on the data packets based on time-aware shaping, credit shaping and preemption mechanism, wherein the preemption of low-priority queues is only allowed when the data packets of the highest-priority sub-queue carry the highest dynamic priority mark. Through the cooperative mechanism of basic classification and dynamic priority, the present application realizes fine classification transmission of heterogeneous data flow, solves the problem that key instructions are easily affected by network congestion, and effectively reduces transmission delay and jitter.

The present application relates to the technical field of database, in particular to an index updating method, product, medium and equipment. The index updating method comprises: triggering index page inspection by using a preset hybrid scheduling strategy, and identifying a to-be-merged page in an index file; judging whether a target page matching the to-be-merged page exists in the index file; if yes, acquiring a page lock of the to-be-merged page and the target page; merging the to-be-merged page and the target page, and releasing the page lock. The present application locks and releases by page granularity before and after page merging, ensures the atomicity and concurrent safety of the merging operation, avoids locking the whole table or the whole index, effectively suppresses the index file expansion without blocking normal read-write business, and improves the system availability.

The application discloses a vertical and plane transportation mixed scheduling system and method based on space-time coordination, relates to the technical field of intelligent scheduling, and comprises the following steps: obtaining new transportation equipment information, determining initial task allocation weights of the transportation equipment based on rated operation capacity of the transportation equipment; constructing a historical coordination efficiency set and a real-time coordination efficiency set; constructing a historical load characteristic curve; obtaining a current task allocation request quantity, matching a composite load value of the same request quantity in the historical load characteristic curve, calculating an average value as a standard operation load and determining a fluctuation range; combining the total number of the same type of equipment before and after expansion to calculate a theoretical load value; calculating a current actual load value; comparing the actual load value with the theoretical load value, and adjusting the task allocation weights when the actual load value exceeds the fluctuation range; and counting the total number of paired tasks of the vertical and plane equipment and the space-time coordination hit number, calculating a hit rate, and allocating the task weights based on the hit rate, so that the vertical and plane transportation mixed scheduling is realized.

The invention discloses an end-cloud collaborative flexible production line dynamic production scheduling method and system and a storage medium, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: carrying out the modular decomposition of a flexible production line, and dividing the production line into a rigid execution unit and an elastic adjustment unit; constructing a hierarchical control network comprising a plurality of edge computing ends, and establishing a two-way communication channel with the cloud decision center; establishing a real-time resource portrait window, forming a dynamically updated resource situation map, pushing real-time resource data to a cloud decision center, performing global production scheduling planning, generating a mixed production scheduling scheme, and distributing the mixed production scheduling scheme to corresponding computing nodes for execution. According to the invention, the technical problems of low production scheduling efficiency and difficulty in flexibly responding to emergencies due to the lack of real-time adaptive capacity to dynamic changes in the existing flexible production line are solved, and hierarchical control and real-time resource monitoring of end-cloud collaboration are achieved. And the production flexibility, the resource utilization rate and the overall efficiency of the flexible production line are improved.

The application provides a large model inference optimization method, system, medium, terminal and program product for Ascend processors. The method comprises: comparing the automatically detected version with a built-in compatibility table; if the versions do not match, automatically loading a runtime patch or calling a backup library; if the versions match, processing a user request based on a preset hybrid scheduling mechanism; dynamically selecting a MindIE inference framework or a vLLM inference framework based on a runtime adaptation layer according to the user request parameters; converting large model weights into low-bit weights and compensating based on an error compensation matrix; identifying subgraphs and fusing operators for a model graph according to the computing characteristics of the Ascend processor, and rearranging intermediate tensors according to the 512B memory access alignment rule of the Ascend processor; performing pipelined overlap processing on the matrix multiplication calculation of the current token and the full aggregation operation based on an asynchronous execution flow; and performing inference and generating an inference result according to the user request. The application can improve the efficiency and reliability of large model inference.

The invention provides an intelligent calculation center cloud scheduling system, and relates to the technical field of artificial intelligence and big data, and the system comprises a GPU cluster which is formed by the mixed deployment of at least a first type of GPU cards and a second type of GPU cards; and the scheduler is connected with the GPU cluster, a plurality of scheduling strategies are configured in the scheduler, and the scheduler is used for carrying out hybrid scheduling on the first type of GPU cards and the second type of GPU cards based on the scheduling strategies. The method has the beneficial effects that multi-strategy hybrid scheduling of different types of GPU cards is effectively realized, the use efficiency of computing power resources is improved, the operation cost is reduced, the operation efficiency of a system is improved, and the system risk is reduced.

The invention provides a long text-oriented analysis checking method, device and equipment and a storage medium. The checking efficiency and accuracy are remarkably improved through intelligent fragmentation and mixed scheduling. The core of the method is that according to semantic relevance among text fragments, fragments with reference or dependency are distributed to serial auditing nodes for deep and coherent context verification, independent fragments are distributed to parallel auditing nodes for concurrent processing, and therefore the problem of long text semantic segmentation is solved, and meanwhile the content of the text fragments is improved. And the auditing time is greatly shortened from the hour level to the minute level. Besides, the output behavior of the large language model is restrained by loading a predefined structured instruction set in the auditing node, so that'illusion 'and misjudgment generated when the model processes complex tables and nested terms are effectively inhibited, and the reliability of a checking result is remarkably improved. The method has flexibility and expandability, and is suitable for automatic and intelligent checking scenes of various professional long texts such as contracts and bid invitation files.

The application discloses a hybrid scheduling system and method based on clothing flexible production line rules and machine learning. The system contains rule algorithm module, machine learning algorithm module and coordination and decision module, through rule processing deterministic task, machine learning optimization dynamic scene, and using dynamic weight adjustment to realize collaborative scheduling. The method includes initialization, rule preliminary scheduling, machine learning optimization, coordination decision and production monitoring steps, introduces priority quantization model, equipment matching degree formula and dynamic weight algorithm, solves the problem of insufficient adaptability of traditional scheduling. The application combines rule algorithm and machine learning algorithm, realizes reasonable allocation of production task and optimization scheduling of equipment, improves production efficiency, resource utilization rate and product quality of clothing flexible production line, and reduces production cost.

The invention discloses an AGV scheduling method, scheduling equipment, AGV equipment and a storage medium, and the method comprises the steps: a scheduling equipment side creates a map information model based on the topological information of a to-be-scheduled region; under the condition that vehicle body registration messages sent by the multiple AGV devices are received and registration succeeds, a map information model is sent to the multiple AGV devices, and the map information model is used for indicating that the multiple AGV devices have unified attitude information in the to-be-dispatched area. The AGV equipment side receives the map information model sent by the scheduling equipment under the condition that the AGV equipment successfully registers to the scheduling equipment; and carrying out AGV scheduling in the to-be-scheduled area based on the map information model. Therefore, for the multiple brands of AGVs operating in the same area, the unified map information model is created, so that the hybrid scheduling of the multiple brands of AGVs can be compatible, and the operation efficiency of the AGVs is improved.

The application relates to the technical field of cloud computing, in particular to a computing resource scheduling method and device, equipment and a storage medium, the method comprises the following steps: constructing a self-defined type of resource, and constructing a corresponding controller according to the self-defined type of resource; when the category of the controller is a virtual machine, a container containing the virtual machine is constructed; according to the type corresponding to the self-defined type of resource, corresponding resource configuration is extracted, and the creation instruction of the container containing the virtual machine is rewritten, the virtual machine is scheduled, and thus a single physical computer can support mixed scheduling of virtual machines and containers simultaneously.

The invention discloses a CAN bus hybrid scheduling and fault-tolerant communication method and system for multi-module equipment, and the method comprises the steps: receiving and confirming an instruction of an upper computer in real time through a hardware interruption mechanism, and endowing the instruction with the highest priority through an event-driven task; serial polling communication is executed on a plurality of lower computer modules based on the state machine model, and different fault-tolerant strategy parameters including the maximum number of retry times and timeout are called according to instruction types; in a response receiving stage of the lower computer module, establishing an application layer session verification mechanism based on a two-factor session verification mechanism of an identifier and a data field; and under the condition that the lower computer module has no response, fault isolation is realized by adopting a limited number of retry and fault node skipping strategy, and system-level communication blockage is prevented. Compared with the prior art, the real-time performance, the certainty and the robustness of multi-device CAN bus communication are remarkably improved, and the method is suitable for the fields of power supply control, embedded systems and the like.

The invention provides a self-adaptive hybrid scheduling accelerator based on an FPGA (Field Programmable Gate Array) and an implementation method thereof. The accelerator comprises a control interface unit (CIU), a multi-level priority queue array (MPQA) and an adaptive scheduling core (ASH). The method aims at solving the problems of large task scheduling delay, slow interrupt response, system throughput bottleneck and the like caused by a traditional software scheduler in a heterogeneous multi-core SoC, a high-requirement embedded system and an edge computing node.

The application discloses a mixed flow scheduling method and device, and relates to the technical field of network scheduling. The method comprises the following steps: determining a shortest scheduling path of a mixed flow based on network node information and basic information of the mixed flow, wherein the mixed flow comprises at least a periodic flow and a continuous flow; generating an arrival curve of the continuous flow based on characteristic demand information of the continuous flow; determining port time slot data after the periodic flow is scheduled based on the shortest scheduling path of the periodic flow, and determining a port residual time slot service curve based on the port time slot data; determining a service curve of the continuous flow with different priorities based on the shortest scheduling path of the continuous flow, the arrival curve of the continuous flow and the port residual time slot service curve; determining time delay data of the continuous flow based on the service curve of the continuous flow; and scheduling the mixed flow based on the shortest scheduling path of the mixed flow when the time delay data of the continuous flow meets scheduling requirements. In the above manner, the mixed scheduling of the periodic flow and the continuous flow can be simultaneously supported.

The invention provides a resource hybrid scheduling system and method. The system comprises an API server, resource schedulers corresponding to at least two types of resources and a resource pool, the resource pool comprises a plurality of physical machines, each physical machine is provided with an agent of the physical machine, and the physical machines support deployment of the at least two types of resources; the API server is used for creating a resource instance based on a resource creation request of a user; the resource scheduler is used for discovering newly created resource instances of corresponding types, determining a target physical machine from the resource pool according to a resource use list of the resource pool and resource requirements of the newly created resource instances, and scheduling the newly created resource instances to the target physical machine; the agent of the target physical machine is used for discovering the newly created resource instance scheduled to the physical machine and creating the corresponding resource according to the resource demand. According to the resource scheduling method and device, the corresponding resource schedulers are matched for different types of resources, mixed deployment of multiple types of resources on the same physical machine is supported, and the same resource pool is shared.

The invention discloses a retrieval enhancement generation reasoning acceleration system and method based on in-memory calculation. According to the system, a decentralized document attention decomposition mechanism is adopted, attention calculation of document key value pairs is distributed to a plurality of in-memory processing devices to be independently executed, and the data transmission amount is reduced through local aggregation. The system architecture comprises a plurality of in-memory computing PIM devices, and each device integrates a PIM unit and a controller side unit to jointly complete a computing task. The scheduler adopts a hybrid scheduling strategy combining static mapping and dynamic triggering, supports multiple parallel modes, and improves the resource utilization rate through an interlaced execution mechanism. According to the method, data transmission overhead and calculation delay in RAG reasoning are remarkably reduced, and the method is suitable for a language model reasoning scene with large-scale knowledge enhancement.

The application relates to the field of artificial intelligence and data processing technology, and discloses a PCB pricing and customer service integrated system and method based on artificial intelligence, aiming to solve the problems of low efficiency and dependence on manual work in PCB industry pricing, and the problem of the existing intelligent customer service and pricing system being fragmented and unable to handle complex process requirements. The method comprises the following steps: a user describes requirements in natural language through a dialogue interface; the system processes user input through a hybrid scheduling mechanism and extracts key process parameters to convert into structured data; when the parameters meet the conditions or the user issues an instruction, a pricing engine is called to calculate and feed back the results according to a preset model; and interaction data is recorded and associated with a user account. The system comprises the following modules: a pricing rule database, a core pricing engine, an AI interface gateway, an AI supplementary instruction library, natural language processing and scheduling, a user account and data record, and a unified user interface. Through the above technical scheme, the application can realize second-level automatic pricing of complex PCBs, improve efficiency and accuracy, and form a complete business closed loop of requirement acquisition, real-time pricing and data sedimentation.

The invention discloses a state estimation and bit rate allocation collaborative optimization method for a resource-constrained unmanned system, and the method comprises the steps: firstly building an unmanned system dynamics and sensor measurement model, introducing a quantization error model, determining the index relation between the quantization error model and a bit rate, and fusing bounded noise and quantization errors; then designing hybrid scheduling in static and dynamic segments, transmitting the two segments by adopting polling and event triggering protocols, and forming a complete measurement sequence by virtue of a scheduling matrix and a zero-order holder; designing a rolling time domain estimator, and constructing a cost function fusing multiple information to solve optimal estimation; and finally, constructing a bit rate allocation optimization problem by taking the cost function as a target, and solving by using a particle swarm algorithm to realize dynamic allocation under the limitation of the total bit rate. According to the scheme, high-precision, high-robustness and real-time state estimation of the unmanned system is realized under the condition that airborne computing resources and the total bit rate are strictly limited.

The invention relates to the technical field of computer networks, and discloses a hybrid scheduling system fusing flow completion time and delay constraint in a heterogeneous network, which comprises a centralized controller and a plurality of switches, and is characterized in that the centralized controller comprises a self-adaptive throughput sampling module, a hybrid cost target construction module and a minimum cost path distribution module; the invention also discloses a hybrid scheduling method for fusing the flow completion time and the delay constraint in the heterogeneous network, which is used for inputting and sampling the flow to be scheduled and calculating and distributing the hybrid cost. According to the method, the adaptive sampling method is adopted, the prediction of the FCT is much more accurate than that of a traditional method through intelligent identification and skipping of a TCP slow start stage, and a reliable data basis is provided for subsequent intelligent decision making. Two heterogeneous indexes of "throughput / FCT" and "delay / DDL", which seem to conflict, are unified to a computable cost dimension, a traditional heuristic rule is got rid of, and a quantitative optimization decision is made.

The invention discloses a scheduling method for a task waiting queue of a message queue under a Linux system. The method comprises the following steps: judging whether a scheduling strategy is expanded or not according to macro definition, adopting priority scheduling by default, and if expansion is started, supporting a priority strategy and a first-in first-out strategy; attributes are set according to application requirements when the message queue is created, and dynamic modification during operation is allowed; when a message sending / receiving task is blocked, inserting the task into a waiting queue according to a current strategy and a priority sequence or a first-in first-out rule; and when the message queue resource is available, waking up the first task execution operation of the queue according to a set strategy. Dynamic management of the task queue is achieved through the configurable scheduling strategy attributes, the real-time requirement and fairness are effectively balanced on the basis that an original communication mechanism of the message queue is guaranteed, the flexibility and efficiency of the system are improved, and the method is particularly suitable for an embedded system needing a mixed scheduling scene.

The application discloses an online customer service user incoming line queuing distribution method and system, which comprises user state capturing, mixed scheduling strategy, incentive mechanism design, scheduling model training and distribution exception response. The application belongs to the field of intelligent scheduling, and specifically relates to an online customer service user incoming line queuing distribution method and system. The scheme is based on user cutting-in weight, customer service matching recommendation degree and service time length buffer adjustment value, fine-tuned outside the rule framework, avoids the abruptness of hard cutting-in, realizes fine distribution with high skill matching degree and load balance based on a dynamic matching-load balance mechanism, avoids taking an aggressive strategy for extremely shortening overtime waiting through segmented design of waiting time incentive, guarantees efficient use of resources through load imbalance degree to build load balance punishment, and reduces user loss caused by distribution failure based on a ladder type exception response, and further improves distribution effect.

The embodiment of the application provides a GPU computing power scheduling method, device, equipment, medium and program product, relates to the technical field of computing power scheduling, and the method comprises the following steps: according to real-time task demand, dividing the computing unit and the display memory resource of a GPU (Graphics Processing Unit) into a plurality of dynamically adjustable partitions; calculating the priority weight of each task; in combination with preemptive scheduling and cooperative scheduling, implementing resource preemption on a first priority task, and performing time-sharing multiplexing on a non-preempted task; performing dynamic voltage and frequency scaling (DVFS) and task merging strategy; through dynamic resource partitioning, dynamic priority evaluation, hybrid scheduling strategy and energy efficiency optimization algorithm, the GPU resource utilization and energy efficiency ratio in a multi-task scenario are significantly improved, and the energy consumption is reduced in combination with the DVFS and task merging strategy.

The invention discloses a hybrid scheduling network performance evaluation method and device based on network calculation. The method comprises the following steps: acquiring a network traffic information set and a traffic transmission network information set; processing the network traffic information set and the traffic transmission network information set to obtain a blocking effect value caused by the CQF traffic to the non-CQF traffic; and processing the blocking effect value caused by the CQF traffic to the non-CQF traffic and the traffic transmission network information set to obtain the worst end-to-end delay of other traffic under the interference of the CQF traffic under the hybrid scheduling architecture.

The invention discloses an LAES-CCHP integrated energy system hybrid scheduling optimization method considering low-carbon and multi-energy cooperation, and relates to the technical field of integrated energy system optimization scheduling, and the method comprises the steps: taking the maximum cycle efficiency of a liquid air energy storage system as a target, combining the energy storage and release and start-stop constraint conditions of the liquid air energy storage system, and optimizing the optimal scheduling of the liquid air energy storage system; constructing a liquid air energy storage system optimization model; constructing a combined cooling heating and power system optimization model by taking the minimum power system operation cost and the maximum renewable energy utilization rate as targets; generating a hybrid scheduling optimization model of the integrated energy system based on a cooperative relationship between the liquid air energy storage system optimization model and the combined cooling heating and power system optimization model; and solving the hybrid scheduling optimization model, and realizing hybrid scheduling optimization of the integrated energy system through an optimal scheduling scheme. The heat exchanger is used for recycling heat conduction oil and exhaust waste heat to achieve the heat supply function of the system, and the gradient utilization rate of compression heat is remarkably increased.

This invention proposes a hybrid scheduling method for airport shuttle buses that considers driver behavior uncertainty, comprising: Step 1, collection and preprocessing of operational information, including flight dynamics, driver information, and shuttle bus information; Step 2, generating a candidate task-driver set for each service flight number based on flight dynamics and driver information; the candidate task-driver set includes task entries for each driver; Step 3, solving for driver uncertainty indices, including task acceptance probability and short-window response reliability index; wherein, the task acceptance probability is used for ranking in Step 4; the short-window response reliability index is used for unified automatic allocation in Step 4; Step 4, generating scheduling instructions based on the hybrid scheduling mechanism. This invention improves the on-time completion rate and service stability of airport shuttle buses.