124 results about "Group scheduling" patented technology

The invention provides a computing card group scheduling method and device based on task perception and generation length prediction, and the method comprises the steps: sending a plurality of reasoning requests with user prompt words to a computing card group, packaging each reasoning request as a request object, and enabling the computing card group to have a plurality of intelligent computing cards, the video memories of all the intelligent computing cards form a shared video memory pool; adding the request object into a user request pool; performing generation length prediction on the user prompt word of the request object in the user request pool by adopting a predictor module to obtain a predicted generation length; creating a corresponding reasoning task and metadata for each request object in the user request pool, applying for a video memory block as required from the shared video memory pool according to the predicted generation length, obtaining a video memory block handle with a video memory allocation address and size, and writing the metadata into the video memory block handle; generating a task scheduling sequence according to the metadata of the reasoning task and the running state of each computing card; and reasoning tasks are selected from the task scheduling sequence in sequence.

The invention belongs to the crossing field of water conservancy project scheduling and risk management, and particularly discloses a drainage basin water engineering system scheduling risk reverse traceability diagnosis and regulation method and system, and the method comprises the steps: obtaining drainage basin water engineering system data; constructing a multi-target collaborative scheduling risk field of the watershed water engineering system based on the watershed water engineering system data; based on the multi-target collaborative scheduling risk field of the watershed water engineering system, performing reverse multi-dimensional probability flux tracking on the scheduling of the watershed water engineering system to obtain a risk contribution degree entropy weight; according to the risk contribution degree entropy weight, performing multi-level risk diagnosis on drainage basin water engineering system scheduling to obtain a diagnosis result; according to a diagnosis result, carrying out asymmetric game dynamic compensation decision making on drainage basin water engineering system scheduling to obtain an optimal decision making scheme; and regulating and controlling the drainage basin water engineering system scheduling by using the optimal decision scheme. According to the method, the cascade instability risk caused by reservoir group scheduling can be dynamically identified.

The invention discloses a regional fan virtual inertia support scheduling method considering a wind power plant group synergistic effect, and aims to solve the problems of non-uniform inertia distribution, response conflict and economy in wind power plant group scheduling. By introducing a hybrid multi-level cooperative control architecture, the inertia vacancy of the power grid is calculated in real time, the inertia demand of each wind power plant is allocated, and the scheduling strategy of the wind power plant group is optimized. And a time-varying inertia demand curve is generated by adopting a hybrid algorithm, the future inertia demand is accurately predicted, and the fan rotating speed recovery cost, the equipment loss and the wind curtailment amount are minimized in combination with a multi-target optimization model. A model predictive control framework and a Nash equilibrium theory are introduced, the game problem between wind power plants is solved, collaborative scheduling of a wind power plant group is optimized, and timeliness and stability of a scheduling instruction are ensured by embedding a time delay compensation algorithm. According to the method, the inertia supporting efficiency of the power grid can be remarkably improved, the equipment loss and the wind curtailment amount are reduced, the robustness of the system is improved, and the method has high application value and prospect.

The invention discloses a processor, a graphics card, equipment and a resource allocation method and device, and belongs to the technical field of computer resource management. The processor comprises a thread group assembling unit, a thread group scheduling execution unit and at least two processing units, the thread group assembling unit is used for assembling the tasks into one or more thread groups based on the attribute information of the tasks in a task assembling stage; and allocating computing resources for one or more thread groups with the thread group granularity, wherein each thread group is allocated with a processing unit; the thread group scheduling execution unit is used for scheduling one or more thread groups to the processing unit corresponding to each thread group for execution according to a unified scheduling rule, and the unified scheduling rule is a scheduling rule shared by different types of tasks; and the at least two processing units are used for executing the scheduled thread groups. And the computing resources are allocated according to the thread group granularity, so that the execution efficiency of the processor is improved.

A cascade reservoir optimization dynamic control method based on different scheduling objectives of flood season staging comprises the following steps: step 1, performing flood season staging on a cascade reservoir based on rainstorm flood characteristics, and establishing a pre / main flood season'flood control-power generation 'and post flood season'flood control-water storage' optimization scheduling model; step 2, constructing a cascade reservoir dynamic control domain; step 3, based on the flood season staging result in the step 1 and the cascade reservoir dynamic control domain constructed in the step 2, constructing an optimization model taking the maximum cascade generating capacity and the minimum flood control risk as targets; setting constraint conditions of the optimization model; and solving the optimization model through a multi-target intelligent optimization algorithm to obtain a dynamic scheduling scheme. According to the method, optimal scheduling methods such as flood season staging and'aggregation-decomposition 'are adopted, flood control risks of drainage basin reservoir group scheduling are fully considered, and efficient utilization of cascade reservoir water resources is realized by utilizing a cascade reservoir flood control capacity complementation mechanism.

The invention discloses a reservoir group multi-target game scheduling model based on space-time collaborative dimensionality reduction and a solving method thereof, and the method comprises the steps: taking the water level of each reservoir as a decision variable, calculating each interest related party and constraint condition of a reservoir scheduling system for reservoir group scheduling, and constructing a reservoir group multi-target game scheduling model; a long-medium-short term layered nesting mechanism is constructed, and month, day and hour scale reservoir group target water levels are obtained based on a reservoir group multi-target game scheduling model to generate month, day and hour scale dynamic constraint intervals; returning a daily scale based on a deviation feedback mechanism at an hour scale to carry out iterative optimization on the model; reservoir key indexes serve as the basis for judging the importance of reservoirs, high-importance reservoirs are reserved, and low-importance reservoirs are clustered into equivalent virtual reservoirs. The method has the advantages that the problem of dimensionality disaster caused by space-time dual-dimension coupling in large-scale reservoir group multi-target scheduling of a traditional scheduling model is solved, and the calculation complexity is reduced on the premise that the scheduling precision is guaranteed.

The invention provides a transformer area voltage out-of-limit autonomous system based on active-reactive comprehensive control, which comprises a group control and group scheduling master station, a power distribution intelligent gateway, a protocol converter and an inverter, and is characterized in that the group control and group scheduling master station communicates with the power distribution intelligent gateway in a 4G network mode, the power distribution intelligent gateway receives an instruction of the group control and group scheduling master station, and the protocol converter is connected with the inverter. The intelligent power distribution gateway is used for receiving operation information of the inverters and transmitting remote signaling and telemetering information to the group control and group dispatching master station at the same time, the intelligent power distribution gateway is communicated with the protocol converter through a broadband power line carrier HPLC, the intelligent power distribution gateway receives the operation information of the inverters and can decompose received active and reactive instructions of the intelligent power distribution gateway to the inverters at the same time, and the inverters are connected with the protocol converter. And the protocol converter communicates with the inverters through an RS-485 protocol, and the protocol converter is used for forwarding an adjustment instruction issued by the intelligent power distribution gateway to each inverter, collecting inverter data and uploading an execution result to the intelligent power distribution gateway at the same time.

The invention provides a foaming machine group production scheduling method under multi-target constraint. The method comprises the following steps: S1, initializing resource and task data; s2, generating an initial production scheduling scheme; s3, carrying out feasibility check and conflict repair; s4, multi-algorithm fusion optimization is carried out; s5, making a decision and outputting a production scheduling scheme; s6, online rearrangement and exception handling are carried out; and S7, monitoring and data feedback are executed. Through the full-process design of data-initial scheme-feasible scheme-optimization scheme-execution-feedback, the problems of many resource conflicts, difficult target balance, poor dynamic adaptation and the like in traditional production scheduling are solved, and efficient and intelligent production scheduling of the foaming machine group is realized.

The invention relates to the technical field of water resource scheduling, in particular to a refined multi-dimensional collaborative scheduling optimization method and system for a cascade reservoir group. According to the method and the system, macroscopic prediction and real-time dynamic correction are combined, an intelligent gate actuator with local autonomous adjustment and fault processing capabilities is introduced, sensing, decision making, execution and feedback capabilities are dispersed to each gate unit, and a distributed intelligent scheduling architecture combining global collaborative optimization and local intelligent strain is formed. The invention provides a refined multi-dimensional collaborative scheduling optimization method and system for a cascade reservoir group, so as to solve or at least alleviate the problems of signal transmission lag, execution blind areas, insufficient system toughness and the like caused by centralized control in the existing cascade reservoir group scheduling method. Finer water volume scheduling, quicker emergency management and higher system operation reliability are realized, and the scheduling efficiency and the disaster prevention capability are improved.

The invention discloses a thread group scheduling method and device for a GPU (Graphics Processing Unit), the GPU and equipment. The method comprises the following steps of: 1) receiving a scheduling request of a thread group; 2) task type priority scheduling; according to a thread group weight value set by a user, obtaining execution priorities of the vertex thread group and the fragment thread group in the current scheduling period; 3) instruction type priority scheduling; pre-analyzing to-be-executed instructions of the thread group, and determining a priority sequence of schedulable instruction types; and 4) priority scheduling of the thread groups: selecting the thread group with the highest priority for scheduling according to the specified task type priority and instruction type priority in combination with the thread group generation time. The invention provides a thread group three-level scheduling strategy so as to improve the instruction throughput rate and the key task response speed of the GPU under the complex load.

The invention discloses a micro-grid group scheduling method and system based on hierarchical regulation and elastic load response, and belongs to the technical field of power system collaborative optimization and energy transaction, and the method comprises the steps: obtaining the power generation potential of a power generation side and the power utilization regulation potential of a demand side based on the operation constraint of the power generation side and the response constraint of the demand side; constructing a micro-grid group layered interactive coupling model based on the execution rule and the information interaction constraint; constructing a marketization transaction strategy model based on the transaction mode, the bidding mechanism and the income distribution mechanism; improving the particle swarm algorithm through model prediction control to obtain an optimized particle swarm algorithm; and based on the power generation of the power generation side, the power utilization regulation potential of the demand side, the micro-grid group interactive coupling model and the marketization transaction strategy model, obtaining an optimal scheduling scheme by using an optimized particle swarm algorithm. The technical problem that the feasibility of a scheduling scheme is difficult to improve in the prior art is solved.

The invention discloses a QMIX framework-based sluice group scheduling method. The method comprises the following steps of: constructing a simulation basin environment by utilizing a historical environment data set; constructing a multi-agent reinforcement learning method for cooperative sluices in the watershed based on a QMIX framework to realize dynamic cooperation and global optimization of the sluices in the watershed; designing a reinforcement learning single-agent DQN model of a single sluice, and supporting autonomous perception and decision of the single sluice; designing a multi-agent global reward function and a single-agent independent reward function, and balancing a group collaboration target and individual local requirements through a layered reward mechanism; a mixed experience playback mechanism based on high-priority experience similarity matching is introduced, and the problems that a traditional experience playback method is low in sample utilization efficiency and weak in key event generalization ability in a complex dynamic environment are solved. According to the method, the water gate intelligent agent can fully explore the scheduling action space in the compliance framework, and the robustness and the control accuracy of the water gate scheduling strategy under the QMIX framework are improved.

Disclosed in the present invention are an NL2SQL system capability improvement method and apparatus, a device and a medium. The method comprises: determining from a capability indicator library capability items required by a NL2SQL system to be improved in a target scenario; performing capability level assessment on the capability items on the basis of a preset scenario test question bank, so as to determine capability level details; determining capability improvement module matrices corresponding to the capability items on the basis of the capability level details; and, on the basis of task improvement categories to which capability improvement tasks in the capability improvement module matrices belong, selecting the optimal target capability improvement tasks to perform set grouping so as to obtain grouped target group combinations, and scheduling the target group combinations so as to perform capability awareness improvement on said NL2SQL system. By means of the technical solutions, the embodiments of the present invention can perform comprehensive and detailed quantitative measurement on NL2SQL systems, has an efficient group scheduling mode, and achieves high efficiency and refined improvement of capability awareness of NL2SQL systems when the improvement complexity is low.

The invention discloses a transfer robot cluster scheduling method, electronic equipment and a medium. The method comprises the steps that an ERP issues an order group to an enterprise warehouse management system WMS to query goods allocation storage information; order group order picking information is formed and issued to a WCS, and AGV group scheduling is carried out in combination with available AGV resources; based on the cost optimization model of the multiple transfer robots, considering two optimization objectives of minimum total operation cost of the multiple transfer robots and balanced operation cost of the multiple transfer robots, and solving an AGV (Automatic Guided Vehicle) carrying path according to a total utility optimal principle; the AGV group executes path planning and executes two walking modes of European style and Manhattan distance for carrying; and finishing the carrying task and updating the carrying task. According to the invention, the linear four-direction constraint of Manhattan distance is relieved, the robot is allowed to pass through the preset oblique channel to carry out load-carrying oblique running, and the energy consumed in the steering process of the robot is reduced; in addition, a waiting area is omitted, the storage area is saved, and the warehouse construction cost is reduced.

The invention discloses a group scheduling control method and system for a PCS energy storage system, relates to the technical field of group scheduling of energy storage systems, and solves the technical problems of insufficient SOC balance regulation and control accuracy and weak capacity proportion distribution fault-tolerant capability. The SOH correction coefficient is accurately calculated through the linear relation, it is ensured that the PCS with the high health degree and the large capacity preferentially undertakes the balancing task, the weak health battery is effectively protected, the cycle life of the battery is prolonged, multiple screening constraints of the available PCS are defined, and according to the size relation between the exceeding power and the total redundant power, a capacity proportion or redundant power proportion distribution strategy is adopted, so that the capacity of the PCS is effectively protected. Reasonable power distribution and no equipment overload risk are ensured, the power distribution deviation after the fault of a single PCS can be reduced, and the fault-tolerant response speed of the system is improved.

A UE determines a group identifier based at least in part on a timing advance (TA) associated with the UE, the group identifier associated with at least the UE and a second UE and receives a sidelink resource allocation associated with the group identifier. The UE transmits sidelink communication using the sidelink resource allocation. A network node associates a UE with a group identifier based on a timing advance (TA) associated with the UE and transmits a sidelink resource allocation associated with the group identifier, the group identifier associated with at least the UE and a second UE.

The embodiment of the invention provides a reservoir group multi-target scheduling method and system fusing an SHAP interpretability analysis mechanism. The method comprises the following steps: carrying out training data integration based on obtained historical operation data of a target basin reservoir group, carrying out reservoir group scheduling model training based on a DNN network structure, and carrying out multi-target iterative optimization on model structure parameters to obtain an optimal parameter set meeting multi-decision preference; based on the optimal parameter set, through an SHAP interpretability analysis mechanism, calculating to obtain a factor contribution matrix of each model input factor vector relative to model prediction output; performing statistical feature extraction based on the factor contribution matrix of each model input factor vector to obtain a corresponding factor feature vector; and performing rule mining based on the factor feature vector, and performing rule optimization based on weight and explanatory evaluation to obtain a structured scheduling rule set for real-time scheduling aided decision making. The implementation of the method can provide more explanatory decision support for multi-target scheduling of the reservoir group.

The invention belongs to the related technical field of intelligent water conservancy construction, and discloses a knowledge graph construction method and system based on a reservoir group scheduling boundary and a constraint library. The method comprises the following steps: S1, obtaining different hydraulic engineering characteristics, and scheduling boundaries and constraints of operation control parameters of a reservoir in different scheduling stages and different scenes; s2, constructing a water conservancy project feature sub-map, a scheduling stage sub-map and a scheduling scene sub-map; and S3, merging the same entity nodes in the hydraulic engineering feature sub-atlas, the scheduling stage sub-atlas and the scheduling scene sub-atlas to realize fusion of each sub-atlas, thereby obtaining the required knowledge atlas. According to the invention, unified visual display of scheduling boundaries and constraint conditions and intelligent setting of reservoir managers for the boundaries and the constraint conditions are realized.

The invention discloses a distributed photovoltaic power station group control and group scheduling control structure, and belongs to the field of photovoltaic module support. A distributed photovoltaic power station group control and group dispatching control structure comprises a supporting cabinet body and further comprises a supporting seat arranged in the supporting cabinet body in a sliding mode, and a plurality of photovoltaic modules are arranged on the supporting seat. The hollow pipe is slidably arranged on the supporting seat, and a sliding block is arranged at one end, outside the supporting seat, of the hollow pipe; the auxiliary box is slidably arranged on the supporting seat, the sliding block is slidably arranged in the auxiliary box, and a plug is arranged on the outer wall of the auxiliary box; a first circuit board and a second circuit board, the plug is plugged on the first circuit board, and the first circuit board is provided with a main switch; according to the invention, when an operator carries out maintenance, power can be automatically cut off, the photovoltaic module moves downwards, the operator can carry out maintenance operation more conveniently while electric shock is avoided, the maintenance difficulty is reduced, and after maintenance, the photovoltaic module can be automatically powered on and used while being reset, so that control and management are facilitated.

The invention discloses a distributed energy consumption optimization scheduling method and system for a heat pump group, and relates to the technical field of heat pump energy consumption optimization scheduling, and the method comprises the steps: dividing the heat pump group into a plurality of sub-regions, determining the optimal combination of heat pumps in each sub-region, and forming local optimization units of the sub-regions; collecting state data of each heat pump in each local optimization unit in real time, and generating a scheduling strategy of the heat pumps in the sub-regions by using a multi-target local optimization algorithm; establishing a cross-subarea information exchange and coordination mechanism, carrying out global optimization and dynamic correction on a scheduling strategy, and generating a final heat pump group scheduling scheme; and issuing the final heat pump group scheduling scheme to each local optimization unit, and adjusting the local optimization units based on real-time feedback to form a closed-loop iterative optimization mechanism. According to the method, distributed partition optimization is combined with cross-regional coordination and global dynamic correction, so that the problems of high calculation complexity, poor real-time performance and insufficient inter-regional energy utilization of centralized scheduling are solved.

The invention relates to a flexible interconnection micro-grid group optimization scheduling method. The method comprises the following steps: determining a coupling boundary constraint condition between two adjacent microgrids in a flexible interconnection microgrid group; according to the objective function, the optimization constraint condition and the coupling boundary constraint condition of the flexible interconnection micro-grid group, constructing an optimization scheduling model of the flexible interconnection micro-grid group; decomposing the optimal scheduling model into a plurality of sub-optimal scheduling models, and performing distributed solving processing on the sub-optimal scheduling models of two adjacent microgrids to obtain sub-optimal scheduling results corresponding to two connected microgrids; and obtaining an optimal scheduling result of the flexible interconnection micro-grid group according to the sub-optimal scheduling result of each micro-grid. By adopting the method, the group scheduling optimization efficiency and safety of the flexible interconnected micro-grid can be improved.

The application provides a kind of multi-microgrid collaborative economic dispatching method based on blockchain, comprising: based on microgrid group multi-group architecture model, construct microgrid group dispatching architecture model based on blockchain;Based on objective function submodel, constraint condition submodel and incremental cost submodel, an economic dispatching model is constructed;Wherein, economic dispatching model includes: microgrid layer economic dispatching model and microgrid group layer economic dispatching model;Based on microgrid group dispatching architecture model, intra-group scheduling is carried out on microgrid layer economic dispatching model, and inter-group scheduling is carried out on microgrid group layer economic dispatching model, to obtain multi-microgrid collaborative economic dispatching result.The application constructs microgrid group system distributed dispatching architecture, designs system economic dispatching model with comprehensive economic and environmental protection targets, and proposes a kind of intra-group optimization and inter-group optimization economic dispatching method based on blockchain.In the case of ensuring good expansibility, the economic efficiency and fault tolerance of microgrid group are improved.

The application discloses a multi-service traffic grouping scheduling method for a P4 switch processor core, mainly comprising a feature extraction method based on a statistical interval and a Sketch, comprehensive data packet time correlation and traffic dynamic change, an optimized Bloom filter conflict detection mechanism, improved adaptability and accuracy of the algorithm in a limited resource environment; a decision tree model is combined with scheduling, a decision tree is reconstructed to adapt to a P4 switch operation mode and data processing demand, high-precision service flow classification under line-speed operation is ensured; a grouping scheduling method AD-PIFO is designed based on variable priority, data packet forwarding priority is dynamically adjusted, different service QoS demand is met, and the overall performance and efficiency of the switch are improved. The method is outstanding in high-priority traffic throughput and low-latency performance, and the medium-low-priority traffic throughput is better than that of similar algorithms, is stable and effective under different load conditions, has reasonable additional time overhead of data packet transmission, and is widely used in SDN network optimization data packet scheduling scenes.

The present invention discloses a reservoir group optimization scheduling method and system, equipment and storage medium based on IMOSSA, which belongs to the field of water conservancy and hydropower. The scheduling method of the present invention includes: using the entropy weight method to screen the global optimal solution, based on the reference point strategy to perform individual screening and using the binomial crossover method to perform the finder individual update, and determining an improved multi-objective sparrow algorithm IMOSSA for solving multi-objective optimization problems; and collecting and collating the basic information of the reservoir group, generalizing the reservoir group system, and determining the hydraulic relationship between the reservoirs; and analyzing the tasks of the reservoir group, and constructing a multi-objective joint optimization scheduling mathematical model for the reservoir group; executing IMOSSA to solve the multi-objective joint optimization scheduling model of the reservoir group to obtain the Pareto non-inferior solution set, and determining the reservoir group scheduling operation plan. The reservoir group optimization scheduling method based on IMOSSA provided by the present invention can achieve global optimization, improve calculation efficiency, and meet the requirements of autonomous selection of reservoir scheduling plans.

The present invention discloses an intelligent water pump group scheduling system and method based on irrigation demand response, which aims to solve the problems of complex scheduling of distributed pump station groups in small watersheds, heavy communication burden and uneven water supply in upstream and downstream. First, the crop water demand, meteorological and soil data are obtained to calculate the irrigation demand. Then, a water pump operation and photovoltaic power generation output model is established. Then, under the premise of maximum photovoltaic power generation utilization, the water pump irrigation power is preliminarily allocated, and surplus or shortage electricity is processed. Finally, the water volume, water level and photovoltaic power generation data are monitored in real time to dynamically adjust the water pump pumping power. Through intelligent decision-making and dynamic adjustment, the present invention not only meets the irrigation demand, but also effectively avoids the problem of insufficient water supply in the downstream caused by excessive pumping in the upstream. At the same time, it optimizes the use of electricity, reduces the cost of introducing external electricity, and realizes the dual optimization of irrigation and electricity use.

The present invention belongs to the cross - field of water conservancy project scheduling and risk management, and specifically discloses a method and system for reverse tracing diagnosis and regulation of the scheduling risk of the basin water project system, including: obtaining basin water project system data; constructing a multi - objective collaborative scheduling risk field of the basin water project system based on the basin water project system data; performing reverse multi - dimensional probability flux tracking on the scheduling of the basin water project system based on the multi - objective collaborative scheduling risk field of the basin water project system to obtain the risk contribution degree entropy weight; performing multi - level risk diagnosis on the scheduling of the basin water project system according to the risk contribution degree entropy weight to obtain a diagnosis result; performing an asymmetric game dynamic compensation decision on the scheduling of the basin water project system according to the diagnosis result to obtain an optimal decision - making scheme; and using the optimal decision - making scheme to regulate the scheduling of the basin water project system. The present invention can dynamically identify the cascading instability risks caused by the reservoir group scheduling.

The application discloses a kind of cascade reservoir group scheduling method and system for clean energy consumption, first, obtain the long sequence of the relative error of clean energy each period output and its mean, variance, according to which the output scene set is generated by using clean energy prediction output;Second, a large-scale scene set is reduced to several typical output scenes using clustering algorithm, and then the photovoltaic output process and its occurrence probability of different scenes are obtained;Finally, construct the applicable cascade reservoir group joint scheduling model, and then solve to obtain reasonable and feasible scheduling scheme using efficient optimization algorithm.The method disclosed by the application can quickly respond to the randomness and uncertainty characteristics of the clean energy output process, effectively handle the coupling operation constraints of cascade power stations, significantly improve the model calculation efficiency and scheduling scheme quality, and is suitable for the cascade reservoir group joint scheduling problem for clean energy consumption.

The embodiment of the invention provides a scheduling method of a working group, a graphics processor and electronic equipment, the scheduling method of the working group is applied to an arbitration module of the graphics processor, the graphics processor further comprises at least two execution modules, and the scheduling method comprises the following steps: aiming at each execution module, executing the execution modules; determining a first score of the execution module based on the resource request of the working group and the residual resource information of the execution module; under the condition that the first score of each execution module is smaller than a first score threshold value, a target execution module is determined from the at least two execution modules based on the first waiting duration of each execution module, and the first waiting duration of the execution module is the duration needed by the prediction execution module to release the target resource information; the target resource information is determined based on the resource request of the working group and the residual resource information of the execution module; and scheduling the working group to the target execution module.

A GPU cluster-oriented deep learning training task scheduling system comprises an offline performance characterization module and an online task scheduling deployment module. The offline performance characterization module respectively predicts the similarity of different training tasks to determine the hardware index set to be collected for a newly submitted task and the performance degradation degree, i.e., the performance interference degree, generated after mixed deployment of two training tasks, and collects the calculation time of the training task under different batch sizes in the exclusive GPU mode to fit the batch size-calculation time relationship. The online task scheduling deployment module takes out a task to be scheduled from a task queue and generates the GPU affinity and batch size distribution strategy of the task to be scheduled according to the batch size-calculation time relationship and the performance interference degree, and divides the training task submitted by a user into appropriate GPUs according to the GPU number specified by the user, i.e., the GPUs with the minimum performance interference after mixed deployment, to minimize the average job completion time and improve the cluster resource utilization. The problem of maximizing the overall system throughput and improving the cluster resource utilization under the condition that part of the training tasks cannot be fully utilized by the GPU resources alone and need to comply with the group scheduling principle in the GPU cluster is completely solved.

The application provides a pump station group scheduling system and method based on digital twinning, and relates to the technical field of pump station scheduling.The pump station group digital twinning model is constructed according to the bearing temperature curve, the flow curve and the pump cabin pressure curve when the rated power works in history, the solving condition I, the solving condition II and the solving condition III are set according to the task quantity and the task time, the scheduling quantity that each pump station should obtain is solved in MATLAB, the scheduling quantity is simulated in the pump station group digital twinning model, the simulated running state is obtained, the running state score is constructed, the health state score is constructed according to the data of the pump station, the ability of each pump station to complete the task is judged by analyzing the order of the running state score and the health state score, if there is a situation that cannot be completed, the scheduling quantity is redistributed, the failure caused by the overload operation of the pump station is avoided, and the smooth progress of the scheduling task is ensured.