673 results about "Scheduling instructions" patented technology

The invention belongs to the technical field of multi-target global dynamic production scheduling, and discloses a manufacturing resource dynamic production scheduling data comprehensive processing method, which comprises the following steps: acquiring multi-source heterogeneous data, constructing a field mapping table, constructing an equipment fingerprint database in combination with historical equipment performance data, dynamically adjusting confidence coefficient weight, and further judging an equipment state. Based on an equipment state judgment result, integrating operator report data and multi-source heterogeneous data through a preset rule base trigger event, and generating an abnormal event flow with a context tag; based on the abnormal event type and the equipment state judgment result, a recommendation scheme is constructed through a production scheduling knowledge graph, and a production scheduling instruction set is generated; according to the production scheduling instruction set, generating a local optimization scheme and a global optimization strategy, and forming a final execution scheme; calculating a resource utilization rate and a bottleneck process index through the 3D virtual workshop model, and generating a difference analysis report; according to the method, global resource scheduling and closed-loop iteration are realized, and the production efficiency and the system robustness are improved.

The invention discloses a cross-regional virtual power plant cooperative scheduling method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring real-time characteristic data such as space-time positions, output / demand prediction and the like of distributed energy resources and loads, and determining dynamic weights of characteristic dimensions based on a global optimization target and data of a current scheduling period; generating a dynamic resource cluster division instruction containing a member list and a coordination constraint condition according to the dynamic weight and the real-time data, and sending an initial cross-regional coordination scheduling instruction containing a net exchange power target value and the like and a compensation price signal to each dynamic resource cluster local agent; after aggregation response boundary information returned by the local agent is received, an instruction and a signal are updated, a target collaborative scheduling instruction is obtained and finally sent to each dynamic resource cluster for execution, and effective control over cross-regional virtual power plant resources is achieved. According to the method, the problem that the adaptability of the cross-regional virtual power plant collaborative scheduling instruction and the actual resource capacity is insufficient can be relieved.

The invention relates to the technical field of intelligent scheduling, in particular to an enterprise production real-time monitoring and intelligent scheduling system based on artificial intelligence, which comprises a multi-source heterogeneous data fusion unit, a priority resource coupling decision unit, a bottleneck prediction and tracing unit and a scheduling instruction generation unit, the multi-source heterogeneous data fusion unit collects multi-dimensional data such as equipment vibration, temperature, order delivery time and the like in real time and constructs a joint feature vector, and the priority resource coupling decision unit dynamically adjusts task priority and resource allocation through a dual-channel depth Q network to cope with order insertion tasks and equipment health degree fluctuation. The bottleneck prediction and tracing unit predicts production bottlenecks and traces root causes by using a process dependency graph, a multi-modal fusion model and a causal discovery algorithm, and supports preventive maintenance and dynamic scheduling, and the scheduling instruction generation unit synthesizes a preorder result to generate an adaptive scheduling instruction. And enterprise production equipment utilization rate and production efficiency are improved.

The invention discloses an industrial digital factory cooperative work method and a cloud platform, and the method comprises the steps: collecting factory multi-mode discrete source data, carrying out the cross-domain feature integration through a dynamic tensor fusion algorithm, and generating a global consistent factory situation fusion tensor; inputting the factory situation fusion tensor into a resource constrained collaborative decision engine, constructing a multi-device collaborative task optimization scheme based on a dynamic resource game model, and outputting a non-competitive multi-node collaborative scheduling instruction; performing abnormal situation recovery processing on the multi-node cooperative scheduling instruction, and outputting a continuous production control flow with enhanced stability; and inputting the continuous production control flow into a distributed edge collaborative architecture, dynamically configuring computing power resources based on an intelligent contract of a trusted execution environment, and generating a globally convergent factory collaborative control strategy set. By utilizing the embodiment of the invention, a high-robustness and self-adaptive cooperative working method can be provided for the industrial digital factory, the production efficiency is improved, and the energy consumption is reduced.

The embodiment of the invention provides an instruction processing method and a processor, and the method comprises the steps: obtaining a to-be-executed instruction, and determining a conflict type of the to-be-executed instruction; distributing a physical register for the to-be-executed instruction based on the conflict type; determining a dependency conflict condition and a resource conflict condition of the to-be-executed instruction, and determining an execution sequence of the to-be-executed instruction based on the dependency conflict condition and the resource conflict condition; determining a target execution unit based on the execution sequence, and sending the instruction to be executed to the target execution unit; and controlling the target execution unit to execute the to-be-executed instruction, and writing the to-be-executed instruction into the physical register when the to-be-executed instruction is executed, so that efficient out-of-order execution is realized through refined conflict detection, resource allocation and instruction scheduling, and the performance of the RISC-V processor can be remarkably improved. And the adaptability to complex applications is enhanced.

According to the instruction processing system, method and device provided by the invention, the instructions can be efficiently and smoothly distributed and run through the at least one processing module of the processor, and different instruction distribution logics are adopted based on different types of business processing instructions through two-stage instruction distribution, so that the service processing efficiency is improved. The business processing instructions are stored in the target storage area and then scheduled to the instruction execution module, so that quick distribution and hardware support of different types of business processing instructions are realized, the instruction distribution efficiency is greatly improved, complex calculation tasks can be flexibly and efficiently processed, instruction scheduling can be performed according to the priorities of the business processing instructions, and the service processing efficiency is improved. The key business operation is ensured to be responded and processed in time, and the overall performance and the operation efficiency of the database processor are effectively improved. The technical effect of flexibly and efficiently processing the database acceleration task is achieved.

The invention relates to the technical field of PC (Personal Computer) terminal AI (Artificial Intelligence) computing, and discloses a method and a system for dynamically scheduling generative AI heterogeneous computing resources of a PC terminal. The system comprises a resource state acquisition module, a scheduling graph generation module, a resource fluctuation entropy analysis module and a scheduling decision engine module. The resource state acquisition module captures running state parameters of a GPU kernel, a CPU thread and a memory block in real time, and generates a resource state feature tensor through normalization processing; the scheduling atlas generation module analyzes and computes the node connection topology, extracts the correlation between the devices, and constructs a multi-dimensional scheduling atlas; the resource fluctuation entropy analysis module separates the load feature vectors, calculates the entropy of each calculation unit and generates a heterogeneous resource entropy matrix; and the scheduling decision engine module jointly analyzes the atlas and the matrix, identifies bottleneck node resource competition characteristics, generates a dynamic scheduling instruction set, adapts to generative AI task requirements, and ensures efficient and stable operation of the task.

The invention discloses an adjustable load security access method for a virtual power plant, and relates to the technical field of power system automation, and the method comprises the following steps: in the operation process of a virtual power plant scheduling system, after an upper-layer scheduling center issues a unified adjustment instruction according to an operation demand, all access nodes are monitored in real time, and the adjustment instruction is sent to the upper-layer scheduling center; the method comprises the following steps: receiving a scheduling instruction from an access node, collecting response behavior data of the access node to the scheduling instruction, preprocessing the collected original response data of a single node, and carrying out unified coding and formatted storage on historical and current response behaviors according to node identification and scheduling time sequence information to construct a structured data set. According to the method, accurate identification and dynamic correction of the adjustable load frequency response offset risk in the virtual power plant are realized, the heterogeneous load perception and control capability is improved, frequency staggering resonance and scheduling failure caused by inconsistent response are avoided, the system cooperative adjustment stability and the load safety access reliability are enhanced, and the method has a good application prospect.

The invention relates to the field of power system dispatching control, in particular to a virtual power plant collaborative optimization dispatching method, system and device based on multiple spatial-temporal scales and a storage medium. The method comprises the following steps: acquiring real-time supply and demand data of a multi-energy data source, constructing a dynamic operation data set by adopting distributed data acquisition, and performing time sequence analysis on the data set to extract a multi-energy fluctuation feature set; the fluctuation feature set constructs a network topology model in a spatial dimension, and a resource allocation weight of each energy node is determined through graph calculation to generate a resource allocation optimization scheme; when the real-time demand fluctuation exceeds a threshold value, a reinforcement learning algorithm is adopted to carry out optimization adjustment to obtain a real-time scheduling instruction set; in combination with real-time data of the electricity market, an optimized economic signal set is obtained through multi-objective optimization, and an equipment control instruction set is generated by adopting distributed control; and performing real-time monitoring by utilizing edge calculation according to the equipment control instruction set, and dynamically updating the scheduling instruction set through adaptive adjustment based on the system operation deviation to obtain a final resource optimization configuration scheme.

The invention relates to the technical field of energy storage scheduling, and discloses a distributed energy storage scheduling prediction method and system driven by intelligent environment monitoring data. The method comprises the following steps: acquiring environmental parameters and processing to obtain an environmental data set; decomposing the environment data in a multi-level manner to obtain feature vectors and uncertainty indexes; inputting a hybrid deep network to obtain demand prediction distribution; constructing a robust optimization model to obtain a scheduling instruction sequence; performing decomposition execution through a layered architecture to obtain a running track; and carrying out deep reinforcement learning iterative optimization to obtain an energy storage scheduling strategy. According to the energy storage scheduling prediction method, the communication delay influence is overcome, the self-optimization capability is achieved, and efficient cooperative operation of the distributed energy storage system based on environmental data driving is achieved.

The invention provides a robot control instruction analysis method and system fusing continuous instructions, and relates to the technical field of electric digital data processing, and the method comprises the steps: carrying out the quantitative evaluation of the instruction execution quality and resource consumption according to prediction data, and generating a quality score and a consumption estimation; constructing a multi-objective optimization framework based on the quality score and the consumption estimation, and determining a candidate path set; switching cost and execution time are calculated according to the candidate path set, and optimized path schemes meeting constraint conditions are screened; the instruction execution priority and the resource allocation proportion are adjusted in combination with the real-time environment data, and a scheduling strategy is generated. According to the method, environment changes and task quality are monitored in real time, instruction priorities and resource allocation are dynamically adjusted, when execution efficiency is lower than a threshold value, online updating of a prediction model is triggered, model parameters are finely adjusted to adapt to new conditions, and through continuous optimization and monitoring, the execution efficiency and task quality of an instruction sequence can be improved, and the execution efficiency is improved. And intelligent instruction scheduling and resource allocation are realized.

The invention relates to the technical field of power distribution network dispatching. By providing a power grid load dynamic prediction and optimal scheduling method, device, equipment and medium, the method comprises the following steps: performing multi-source heterogeneous fusion processing on meteorological parameters, historical load curves and new energy output data to generate a dynamic load prediction map; constructing a dynamic network model, and performing power flow distribution simulation processing based on the dynamic network model to obtain a stability margin calculation result and a preset safety threshold boundary; performing stage decomposition processing on the global scheduling target to generate a progressive scheduling stage sequence; performing matching processing on the response characteristics of the power generation equipment to generate a self-adaptive progressive scheduling instruction sequence; and executing an adaptive progressive scheduling instruction sequence, performing feedback processing on the real-time state of the power grid, and generating a dynamic adjustment instruction so as to realize multi-dimensional data association modeling, stability margin quantitative analysis and dynamic instruction optimization, thereby improving the load prediction precision and reducing fault diffusion and transient oscillation.

The invention relates to the technical field of port ship scheduling, and discloses a ship scheduling carrying method based on space-time constraint scheduling simulation and conflict optimization. Obtaining ship scheduling tasks and port space-time resource data, and generating structured data in a classified manner; constructing a multi-dimensional space-time constraint model, and determining an initial constraint condition; scheduling simulation is carried out on the basis, and the robustness of the scheme is improved by introducing real-time data; detecting conflicts and carrying out iterative optimization by adopting a multi-objective optimization algorithm; and synchronously correcting model parameters with real-time data, generating a scheduling instruction set, and pushing and executing the scheduling instruction set. And meanwhile, path planning is performed by using a deep reinforcement learning algorithm, and a visual interface is constructed to assist scheduling. The method can effectively integrate data, optimize a scheduling scheme, improve the utilization rate of port resources, enhance the ability to deal with emergencies, and realize efficient and intelligent management of port ship scheduling.

The invention relates to the technical field of industrial internet, in particular to a mine network operation and maintenance management system and method for coal mine development, and the system comprises a path passage evaluation module, an equipment state recognition module, a scheduling priority module, an operation path conflict evaluation module and a scheduling task issuing module. According to the method, a weighted time consumption model is constructed through task positions and path section passing difficulty parameters to realize path time consumption dynamic estimation and optimal passing path combination, and equipment state identification adopts operation parameter interval assignment and weighted summarization to realize multi-dimensional risk judgment. In task sorting, time consumption and risk levels are normalized to establish a combination interval to dynamically mark the emergency degree, the scheduling sequence is adjusted in combination with task point distribution, a path point set intersection is extracted in path conflict processing, the proportion of overlapped sections is calculated, and task positions are adjusted according to a threshold value to guarantee scheduling continuity; and task issuing uniformly generates a scheduling instruction set containing a path equipment state and a task type, so that the scheduling precision and the execution consistency are improved.

The invention discloses a heterogeneous computing power resource dynamic cooperative scheduling method and system and a computer device. The method comprises the steps of obtaining multi-dimensional feature information of a to-be-scheduled task, current state information of each node in a heterogeneous computing power resource pool and scheduling environment state information; training a time sequence model by adopting historical associated data, inputting the multi-dimensional feature information, the current state information and the scheduling environment state information into the trained model to obtain associated trend information in a future preset time window, and then combining the multi-dimensional feature information and the current state information of all the nodes to obtain the scheduling environment state information. A multi-objective optimization function fusing task delay, cost consumption and energy consumption is constructed, the function is optimized based on a preset strategy, a target decision strategy is obtained, a scheduling instruction set is generated after analysis, allocation information and target nodes are determined according to the scheduling instruction set, and tasks are issued. According to the method, dynamic collaborative scheduling of heterogeneous computing power resources can be realized, and the overall utilization efficiency of the resources is accurately, efficiently and effectively improved.

The invention discloses a source network load storage oriented regional collaborative scheduling method and system, and relates to the technical field related to power resource scheduling, and the method comprises the steps: traversing target region source network load storage to carry out real-time data collection, and constructing a source network load storage multi-dimensional data set to carry out multi-scale layered optimization; operation constraint conditions are set, limitation is carried out in combination with a power grid topological structure, a cooperative scheduling instruction set is determined, cooperative control feedback is carried out, and regional power scheduling feedback parameters are generated; and multi-channel dynamic correction is carried out, and a cooperative scheduling optimization strategy is generated to carry out energy balance cooperative scheduling on the source network load storage in the target area. The technical problems that in the prior art, intermittent fluctuation of new energy is difficult to consume, the collaboration of all links of source network load storage is poor, the dispatching flexibility of a power system is insufficient, and the resource configuration efficiency is low are solved. The technical effects of optimizing regional energy resource allocation and improving the clean energy consumption level, the power grid operation stability and the source grid load storage cooperative response capability are achieved.

The present application discloses a compute-in-memory chip, an instruction scheduling method, and a related apparatus. The compute-in-memory chip comprises an instruction memory, an instruction scheduler, and at least one compute-in-memory memory; each compute-in-memory memory comprises at least one storage array; the instruction memory is used for acquiring a first tensor instruction to be executed; and the instruction scheduler is used for scheduling, on the basis of the association relationship between the first tensor instruction and a second tensor instruction and the state of a target storage array needing to be operated for executing the first tensor instruction, the first tensor instruction to the compute-in-memory memory to which the target storage array belongs so that the compute-in-memory memory executes the first tensor instruction. According to embodiments of the present application, diversified compute-in-memory computing can be supported, efficient out-of-order execution scheduling of a tensor instruction set is achieved on the basis of the compute-in-memory chip, and the requirements of compute-in-memory technology for high concurrency and high throughput rate are met.

The invention provides a vector core module of an artificial intelligence chip and an operation method of the vector core module, which are used for improving the efficiency of the vector core module in an application situation that a vector core is a host and a tensor core module is a slave. The vector core module comprises a vector core instruction execution pipeline, a tensor core instruction processing pipeline and an instruction scheduling unit. An instruction scheduling unit performs instruction classification to distinguish vector core instructions and tensor core instructions from a thread bundle. In response to the thread bundle including a vector core instruction, the instruction scheduling unit sends the vector core instruction to a vector core instruction execution pipeline. The vector core instruction execution pipeline executes the vector core instruction and stores an execution result in the memory module. In response to the thread bundle including a tensor core instruction, the instruction scheduling unit sends the tensor core instruction to a tensor core instruction processing pipeline. The tensor core instruction processing pipeline processes the tensor core instruction and sends a processing result to the tensor core module.

The invention discloses a server energy-saving system and method based on a lag compensation type dynamic thermal impedance model, and relates to the field of server energy saving.By collecting operation data of a server hardware bottom layer in real time, a multi-dimensional thermal-computing force coupling time sequence atlas is constructed, and based on the multi-dimensional thermal-computing force coupling time sequence atlas, a dynamic thermal impedance model is established. Calculating a heat conduction time constant of hardware in a current environment; establishing a lag compensation type dynamic thermal impedance model based on the heat conduction time constant; constructing a thermodynamic phase space according to operation data; analyzing a noise reduction evolution trajectory in combination with the lag compensation type dynamic thermal impedance model; the method comprises the steps of generating a weighted thermal resonance state vector, activating an adaptive gain MPC predictive damping scheduling strategy based on the weighted thermal resonance state vector, constructing and solving a dynamic weighted multi-objective optimization function, generating an optimal damping scheduling instruction, and thoroughly solving the fan asthma phenomenon that the fan PWM duty ratio fluctuates substantially in a sine mode under the constant computing power load.

The invention discloses an instruction scheduling method, device and system, a product and a medium, and relates to the technical field of processor design. The invention provides an instruction scheduling method aiming at the problem that the dynamic change in the instruction transmitting time cannot be accurately scheduled by the existing out-of-order scheduling strategy. The historical delay information of each instruction is stored through the delay cache, and when the instruction execution delay corresponding to the instruction is dynamic delay, the dynamic delay is predicted through the historical delay information stored in the delay cache, so that instruction scheduling is performed. According to the method, the dynamic delay of which the delay time cannot be predetermined is included in the consideration range of instruction scheduling based on the dynamic delay prediction mechanism. On the basis of the method, for dynamic change scenes in instruction transmission time such as memory access delay and cache miss, the optimal instruction transmission opportunity can be accurately predicted, so that the effect of instruction scheduling on performance improvement of a processor is further improved.

The invention discloses a layered optimization regulation and control method and system for electricity-hydrogen coupling in a multi-energy complementary system, and relates to the technical field of multi-energy complementary system optimization regulation and control, and the method comprises the following steps: obtaining the operation data of the multi-energy complementary system, and carrying out the aggregation of the operation data based on the correlation of an optimization target, and forming a layered optimization data set; constructing a corresponding state space model based on the hierarchical optimization data set, defining an action space of each level, and designing a multi-target reward function; inputting the state space model, the action space and the multi-target reward function into a reinforcement learning agent for training to obtain a strategy model; based on the strategy model and the multi-target reward function, collaborative optimization is carried out on the strategies of all levels to generate a global optimization strategy; inputting the global optimization strategy and the state space model into a prediction optimization module, and performing rolling optimization to generate an optimization scheduling scheme; and according to the optimized scheduling scheme, generating a scheduling instruction after security constraint inspection, issuing the scheduling instruction to a system for execution, and updating the strategy model based on operation feedback.

The invention relates to the technical field of cloud edge collaborative computing power scheduling, and discloses a heterogeneous computing power scheduling optimization method based on a cloud edge collaborative architecture. The method comprises the following steps: acquiring real-time computing power state data of all available computing nodes in the cloud edge collaborative architecture; performing heterogeneous type division on the computing nodes according to the real-time computing power state data to generate a three-layer computing power resource pool containing cloud computing nodes, edge computing nodes and terminal computing nodes; extracting task calculation features for the current to-be-scheduled task set, wherein the features comprise calculation intensity, data dependence and real-time requirements; constructing an initial task allocation scheme based on the matching relationship between the task calculation features and the three-layer computing power resource pool; iteratively optimizing the initial scheme by adopting a dynamic load balancing strategy to generate a final task scheduling instruction; the instructions are distributed to the corresponding computing nodes to be executed, and computing power state changes in the task execution process are continuously monitored.

The embodiment of the invention discloses a dynamic computing power scheduling method and device based on deep learning and a medium, belongs to the technical field of cloud computing, and solves the problem that cloud computing resources are seriously wasted due to the fact that a cloud computing scheduling method in the prior art is prone to unbalanced resource allocation. Comprising the following steps: preprocessing acquired cloud computing data to generate a structured feature vector; wherein the cloud computing data at least comprises cloud node state data, computing task attributes and environment data; through a multi-head attention mechanism, task resource space-time correlation features corresponding to the structured feature vectors are extracted; the task resource space-time correlation features are input into a weight dynamic prediction module and a resource matching degree scoring module at the same time so as to carry out parallel processing of weight prediction and matching degree scoring calculation; and solving a multi-objective optimization function corresponding to computing power scheduling based on a parallel processing result, and generating a computing power scheduling instruction based on a solving result.

The invention provides an SIMT-based neural network processor and a task execution method thereof, and the method comprises the steps that a general processor queries a state register of a coprocessor, and the state register stores the resource condition of the coprocessor; the universal processor completes splitting from a thread block to a thread bundle according to the resource condition, and the universal processor forwards a thread bundle instruction to a thread bundle distributor of the coprocessor; and the thread beam distributor decodes instructions in the thread beams in sequence and schedules the instructions to instruction queues of different calculation cores, and the thread beams sequentially perform thread beam scheduling, instruction emission and instruction execution according to the sequence, so that all calculations of a neural network task are completed in parallel, and a running result of the neural network task is obtained. According to the method, a general processor for task splitting is introduced in front of the thread beam scheduler or a specific compiler is directly used, so that dynamic scheduling of the threads can be realized, and a scheme for dynamically expanding the number of the threads according to data precision becomes a feasible architecture option.

The invention discloses a power grid operation ticket intelligent generation method and system based on a safety knowledge base, and relates to the technical field of power grid operation ticket generation, and the method comprises the following steps: obtaining power grid topological data and historical operation ticket data; receiving a dispatching instruction ticket, and analyzing the topological data of the power grid based on a labyrinth path algorithm to obtain a primary step; according to the historical operation ticket data, a hidden operation rule is obtained through time, space, logic and round dimension correlation analysis; obtaining a power grid equipment state, a single-step operation duration and an anti-error rule base, constructing a state set by combining a hidden operation rule and environmental data, and constructing an action set according to preset secondary steps; constructing a reward function, constructing a secondary model, performing training according to the state set and the action set, and outputting an optimal secondary step; and combining the primary step and the optimal secondary step to generate an operation ticket and update the first data. The method is used for solving the problems that a traditional operation order generation system depends on a path of a dominant rule and anti-error checking lags.

The invention discloses a block chain-based scheduling instruction trusted storage system, which relates to the technical field of block chains, and comprises a block chain network layer, an instruction acquisition module, an instruction verification module, a cross-chain storage module, a trusted tracing interface, an instruction life cycle management smart contract and a quantum security module, the block chain network layer comprises a plurality of block chain service units deployed at power dispatching nodes, the instruction acquisition module is used for acquiring a dispatching instruction data set and an associated digital signature generated by a dispatching terminal, and the instruction verification module is configured with a compliance verification rule base and multi-level verification logic. And the cross-chain storage module realizes a sub-chain storage architecture of scheduling instruction metadata and content data. According to the method, a double-chain architecture in which a metadata chain and a content chain are separated is adopted, and an improved Merkletree cross-chain verification technology is combined, so that on the premise of ensuring data privacy, cross-regional instruction synchronization delay is reduced, and meanwhile, fine-grained access control of sensitive instruction content is realized.

The invention discloses a scheduling instruction real-time monitoring system for streaming data processing, which relates to the technical field of streaming data processing and comprises a data access module, an instruction analysis module, a dynamic scheduling strategy module, a real-time monitoring module, a scheduling execution module, a visual monitoring interface, an exception handling module and an extension module. The data access module is used for receiving a multi-source heterogeneous data stream in real time and carrying out protocol analysis and priority marking on input data, and the instruction analysis module is connected with the data access module and is used for extracting a scheduling instruction in the data stream and converting the scheduling instruction into executable operation; and the dynamic scheduling strategy module generates a dynamic scheduling strategy based on the real-time data traffic, the system load and a preset rule. Through the dynamic scheduling strategy module and real-time monitoring feedback, the system can automatically adjust resource allocation according to real-time load prediction, and dynamic balance of high resource utilization rate and low delay is achieved.

The invention discloses a micro-grid group power cooperative scheduling method and device, and relates to the technical field of data encryption, and the method comprises the steps: constructing a space-time coordinate frame on a regional master control node, and obtaining a configuration file through importing a power grid historical operation data package and an inverter parameter; calculating an impedance value suitable for the current power grid state according to the frequency deviation, the cost change and the voltage fluctuation of the power grid, and constructing an impedance matrix containing an adjustment instruction; inputting the impedance matrix into a quantum-eagle swarm algorithm to perform global search to generate an optimized scheduling plan curve, making a preliminary scheduling plan according to the scheduling plan curve, performing local optimization adjustment based on the power grid operation deviation monitored in real time, outputting a three-level scheduling instruction set, and performing scheduling on the power grid operation deviation. Performing feasibility verification and confirmation on all the scheduling instructions to generate an execution instruction packet; the edge gateway receives the execution instruction packet and converts the execution instruction packet into a control instruction; and inputting the control instruction into the multi-physics field simulation model, and outputting a simulation result data set.

The invention provides a computing resource scheduling method and device, electronic equipment and a storage medium, and the method comprises the steps that a computing task request issued by a user is received, and the computing task request comprises a computing unit mask parameter; a scheduling instruction is generated according to the calculation task request, the scheduling instruction is sent to a command processing module, and the scheduling instruction comprises the calculation unit mask parameters; and analyzing the calculation unit mask parameter in the scheduling instruction by a command processing module, and distributing a calculation task to a target calculation unit set according to an analysis result. According to the method and the device, static binding of masks and task flows / queues in a traditional scheme is replaced by dynamic integration of the mask parameters and the scheduling instructions, the situation that independent task flows / queues are created for each mask combination is avoided, and memory occupation and system management overhead can be remarkably reduced.

The invention discloses a power grid operation order intelligent anti-error generation method and system based on a source compatibility principle, and relates to the technical field of electric power automation, and the method comprises the following steps: extracting first data from a scheduling instruction, the first data comprising an operation object and an equipment initial state; constructing a dynamic power grid model based on the first data, and performing source attribute labeling on each device in the model; performing analysis through a source compatibility rule engine in combination with the marked dynamic power grid model and a predefined source compatibility constraint criterion, and generating an operation framework containing a source matching requirement; searching and planning an operation sequence meeting a source matching requirement based on the operation framework, and converting the operation sequence into a specific operation item; and performing source compatibility verification on the specific operation item, optimizing the operation item according to a verification result, and generating a final operation ticket. The method is used for solving the problems that a traditional anti-misoperation system is insufficient in real-time matching of a power supply and a load, incomplete in operation sequence verification and lack of intelligent support in operation order generation.