544 results about "Scheduling instructions" patented technology

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 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 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 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 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 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 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 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 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.

The invention belongs to the technical field of power system optimization scheduling, and particularly discloses a VPP real-time scheduling method based on cloud edge collaborative architecture and ADMM consistency optimization, a cloud operator uses a particle swarm algorithm to carry out global cost optimal calculation on real-time power deviation, and after an edge side transformer area group receives a scheduling instruction through a main node, the scheduling instruction is sent to the main node; the ADMM algorithm is adopted to realize the consistency iterative optimization of the cost increment rate of each transformer area, and dynamic coupling is carried out through default penalty constraint and an electric power spot market transaction strategy. According to the VPP real-time scheduling method based on the cloud edge collaborative architecture and the ADMM consistency optimization, the problem of real-time power mismatch caused by distributed resource prediction deviation and load abrupt change is effectively solved through a double-level collaborative mechanism, so that a virtual power plant meets the day-ahead contract response requirement, and the real-time scheduling efficiency of the virtual power plant is improved. Multiple deviation correction modes such as energy storage charging and discharging, resource regulation and control or market transaction can be flexibly selected according to real-time electricity price fluctuation, and finally the real-time scheduling target with the optimal operation cost is achieved.

The invention relates to the technical field of power grid dispatching, and discloses a dynamically optimized micro-grid multi-target collaborative dispatching method and system, and the method comprises the steps: collecting real-time operation data and historical operation data of a micro-grid, and carrying out the preprocessing of the real-time operation data and the historical operation data, and obtaining standardized data; defining multi-dimensional risk indexes according to the standardized data, and calculating a dynamic weight corresponding to each risk index; establishing a layered optimization architecture according to the dynamic weight, wherein the layered optimization architecture comprises a long-term optimization layer, a short-term correction layer and an equipment control layer; and solving the layered optimization architecture, and outputting an optimal scheduling strategy. According to the scheme, multi-target dynamic balance during micro-grid cooperative scheduling can be realized, and the anti-interference capability of the system for coping with power fluctuation and the scheduling instruction execution precision are improved.

The invention discloses a U.2 interface-based server intelligent integrated hierarchical storage system and method, relates to the technical field of computer server storage, and is used for solving the problem of optimization of multi-type SSD data access efficiency and storage resource utilization rate. Firstly, a multi-protocol parallel channel is established by redefining U.2 interface reserved pins, signal integrity parameters are monitored in real time and fused with equipment I / O access features, and an I / O feature sequence of physical layer state information is generated; then, based on a time sequence convolutional neural network, extracting access mode multi-scale features, calculating a data block access heat probability, mapping and determining a target storage hierarchy, and generating a scheduling instruction containing a source physical address, the target hierarchy and a migration priority; the migration execution module realizes zero-copy migration of the data block between the shared DRAM pool and the SSD according to the instruction; and the resource coordination module monitors system load in real time, dynamically adjusts migration concurrency and an I / O sampling strategy, and realizes self-adaptive optimization of storage performance and resource utilization.

The invention provides a code pattern data efficient processing system and method based on real-time compression and intelligent cache. The code pattern data efficient processing system comprises a code pattern processor, a compression module, a multi-level cache structure, a cache controller and a multi-channel interface. And the compression module is connected with the code pattern processor and is used for performing segmented compression processing on the pseudo-random code pattern data generated by the code pattern processor to obtain compressed data. The multi-level cache structure is connected with the compression module, and the cache controller is connected with the multi-level cache structure and the compression module and used for monitoring the compression state of the compression module and the occupation state of the multi-level cache structure in real time, dynamically adjusting a cache allocation strategy and sending a scheduling instruction to the multi-channel interface; and the multi-channel interface is connected with the cache controller and the multi-stage cache structure and is used for reading the compressed data from the multi-stage cache structure according to the scheduling instruction and executing multi-channel load balancing transmission. The problem that in the prior art, compression and caching are difficult to collaboratively optimize is effectively solved.

The invention relates to the technical field of medical informatization and intelligent scheduling, and particularly discloses a ward nursing process prediction and dynamic scheduling method and system.The ward nursing process prediction and dynamic scheduling method comprises the steps that firstly, a digital twinborn simulation environment synchronized with a physical ward in real time is established, and a nursing process is simulated through a multi-agent network; carrying out multi-time sequence deduction based on the environment, generating a prospective scheduling instruction set, and issuing and executing the prospective scheduling instruction set; calculating a prediction mismatch degree index by collecting actual execution data and comparing the actual execution data with a plurality of dimensions of an expected state; when the index exceeds a dynamic threshold value, a meta control layer is triggered to carry out self-adaptive adjustment on simulation environment parameters, and the self-adaptive adjustment comprises reconstruction of an agent behavior logic weight or switching of a scene deduction mode; and deducing and generating an updated instruction set again based on the adjusted environment to form closed-loop optimization.

The invention discloses an intelligent power grid dispatching instruction optimization system based on semantic analysis, relates to the technical field of power grid dispatching instruction optimization, realizes the conversion of dispatching instruction generation from black box output to white box reasoning, constructs a new intelligent dispatching normal form with deep integration of knowledge driving and data driving, and improves the intelligent dispatching efficiency. The automation level, decision reliability and operation safety of power grid dispatching are remarkably improved, and the method has outstanding innovativeness and wide engineering application prospects.

The invention relates to the technical field of construction resource scheduling, in particular to a water conservancy construction equipment scheduling method and system, and the method comprises the following steps: obtaining on-site point cloud and soil data to construct an environmental resistance field; calculating a dynamic work efficiency matrix by combining the equipment power parameters and the resistance field; generating an initial time sequence chain based on the work efficiency matrix, and calculating an overlapping volume of a three-dimensional space envelope body of the equipment; if there is overlap, the job time is adjusted according to the priority to generate a conflict-free scheduling instruction set. According to the method, the rated work efficiency parameters of different machine types in a specific operation area are corrected in real time by constructing the equipment passing resistance model fusing the terrain gradient and the soil moisture content, the problem that static scheduling cannot adapt to environmental changes is solved, and meanwhile space-time conflict detection logic based on the three-dimensional operation envelope surface is introduced; and when it is recognized that the multi-machine moving tracks are overlapped, avoiding or peak shifting instructions with priorities are automatically generated, and conflict-free collaborative operation of the equipment cluster in a complex water conservancy scene is achieved.

The invention relates to a reservoir dispatching management method and system based on digital twinning, and the method comprises the following steps: carrying out the real-time parameter collection of a target reservoir, and obtaining a reservoir characteristic parameter matrix; performing three-dimensional space modeling based on the reservoir characteristic parameter matrix and the reservoir hydrogeological environment in the target reservoir to obtain a reservoir digital twinborn model; performing drainage basin rainfall-runoff dynamic response analysis on the reservoir digital twin model to obtain a drainage basin hydrological response characteristic curve group; performing prospective water balance calculation on the watershed hydrological response characteristic curve group to obtain a multi-period reservoir flow prediction sequence; and formulating an optimal scheduling instruction set of the target reservoir based on the multi-period reservoir inflow prediction sequence, and regulating and controlling a reservoir gate control system in the target reservoir based on the optimal scheduling instruction set. And the method is difficult to adapt to hydro meteorological conditions and multi-target scheduling requirements which change in real time.

The invention discloses a multi-nozzle collaborative printing method, and particularly relates to the technical field of printing path planning. According to the method, a three-dimensional space mesh model is constructed by collecting structural feature information of a target printing area; calculating collaborative coverage parameters of the sub-regions according to the space coverage capability and the region curvature characteristics of the nozzle; constructing a priority map based on nozzle performance and task suitability, and performing task matching by adopting a minimum cross entropy objective function to generate an initial path allocation scheme; constructing a jet printing path conflict graph, extracting conflict feature points, performing local path reconstruction by using a graph convolutional neural network, and generating an optimized jet printing path; and finally, the multiple nozzles are driven to cooperatively execute a jet printing task through time synchronization and instruction scheduling. According to the invention, the conflict rate and waiting time of the nozzles can be obviously reduced, the path planning efficiency and the jet printing precision are improved, and the method has good engineering applicability.

The invention discloses a rope net ladder data production scheduling and traceability system based on big data storage, and particularly relates to the field of data scheduling and traceability, comprising the following steps: the system obtains total factor data of equipment, process, quality and the like through a data perception and digital acquisition module and standardizes the total factor data; the integrated storage and quantitative analysis module constructs functions such as efficiency evaluation and quality risk to quantify production indexes; the adaptive decision optimization and scheduling module generates an optimal scheduling scheme and a dynamic rescheduling instruction based on a multi-target comprehensive decision function; the full-link tracing and self-feedback optimization module realizes full-link bidirectional tracing and drives process parameter and model self-feedback optimization; the system realizes the intelligent control of the whole production process, balances the efficiency, quality, cost and delivery target, improves the resource utilization rate and production stability, and is suitable for the precise production scene of structural members such as rope net ladders.

The invention relates to the technical field of computing power scheduling, in particular to a dynamic computing power scheduling method and system based on load awareness, and the method comprises the steps: collecting multi-dimensional load data of a computing power node in real time; inputting the real-time and historical data into the load prediction model to obtain future load prediction data; calculating a node comprehensive load value by combining real-time and predicted data, and determining a load level of the node comprehensive load value; based on the load level and the task demand, generating and executing a scheduling instruction of task allocation, migration or resource scaling through a multi-objective optimization function; collecting scheduled actual operation data, and adaptively adjusting a prediction model and scheduling decision parameters according to the actual operation data and an expected deviation; according to the method, by fusing multi-dimensional perception, load prediction and multi-target optimization, the crossing from passive response to active planning is realized, the resource utilization rate is effectively improved, the performance of high-priority tasks is guaranteed, and the problem of resource allocation imbalance caused by scheduling lag in the prior art is solved.

The invention discloses an instruction checking method and device based on semantic matching, terminal equipment and a storage medium, and belongs to the technical field of instruction checking. The method comprises the steps that a current scheduling instruction and an original scheduling instruction are input into an instruction checking model, according to the method, the instruction checking model is used for respectively capturing semantic information of the current scheduling instruction and the original scheduling instruction, so that a plurality of different semantic embedding vectors are obtained, the semantic deviation degree of the current instruction and the original instruction is obtained by comparing the plurality of groups of semantic embedding vectors of the current instruction and the original instruction, and different checking prompt information is generated according to different deviation conditions; and a dispatcher is prompted to correct the instruction with semantic deviation in time. Therefore, the semantic level of the instruction can be deeply understood, the semantic difference between the instructions can be accurately recognized, the accuracy of instruction checking is improved, and the problem that the accuracy of instruction checking is low due to the fact that the semantics of the instruction cannot be deeply understood in the prior art can be solved.