379 results about "Scheduling instructions" patented technology

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 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 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 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 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 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 a virtual power plant load regulation method and system based on a deep learning scheduling strategy, and relates to the technical field of power plant load regulation, and the method comprises the steps: obtaining multi-source heterogeneous data, and obtaining a feature tensor through preprocessing; the feature tensor is input into an LSTM-Transform hybrid network model, and a context code is output; inputting the context code into the time sequence convolutional network model, outputting an ultra-short-term prediction result, and further obtaining a short-term prediction result; constructing a multi-objective optimization model, and modeling constraint conditions; solving the multi-objective optimization model through a preset layered architecture to obtain an optimal scheduling strategy, and generating a scheduling instruction; and issuing the scheduling instruction to distributed resources in the virtual power plant, and executing and converting the scheduling instruction into an equipment action. The method solves the problems that in the prior art, the optimization target is single, the dynamic adaptive capacity is lacked, and the real-time fluctuation response speed of the power grid is limited to a certain extent.

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 task scheduling method, a task scheduling device, electronic equipment and a readable storage medium, and relates to the technical field of task scheduling, the method comprises the steps that a first copy instruction is scheduled through an instruction scheduling unit, and the first copy instruction is used for instructing a single copy engine to carry a target tensor; receiving the first copy instruction through the task splitting unit, splitting the target tensor indicated by the first copy instruction into a plurality of sub-tensors, generating a plurality of second copy instructions, and correspondingly scheduling the plurality of second copy instructions to the plurality of copy engines, all the copying engines complete tensor carrying of the target tensor together; wherein the second copying instruction is used for indicating the single copying engine to carry one sub-tensor obtained by splitting the target tensor. The method and the device have the effects of effectively improving the load balance between the replication engines and improving the parallel efficiency and the throughput at the same time.

The invention discloses a multi-power-grid cooperative regulation and control method and system based on multi-level resource aggregation decoupling, and belongs to the technical field of intelligent power grid operation control. The method comprises the following steps: determining a time-varying feasible boundary of a microgrid in a power regulation domain of a common connection point by acquiring multivariate state information of heterogeneous resources of the microgrid; determining a power bidirectional interaction boundary constraint between the micro-grid and the power distribution network through the common connection point, and constructing a micro-grid power distribution optimization model according to the power bidirectional interaction boundary constraint and the electric energy characteristics of the power distribution network; dividing resource adjustment levels corresponding to the heterogeneous resources to obtain a multi-level adjustment sequence; when the micro-grid power distribution optimization model outputs a scheduling instruction, the scheduling instruction is decomposed into optimal scheduling strategies corresponding to the levels according to the multi-level adjustment sequence, and the optimal scheduling strategies are issued to the levels in sequence, so that the adjustment instruction issued by the power distribution network is decomposed in sequence according to the adjustable characteristics of heterogeneous resources, and the optimal scheduling strategies are obtained. The resource adjustment efficiency is improved; and the response cost is optimized.

The invention relates to the technical field of computers, and discloses an equipment protocol adaptive analysis and instruction scheduling method for an intelligent central control system, and the method comprises the steps: obtaining an original communication data flow of heterogeneous equipment, and extracting a protocol feature fingerprint to recognize a protocol type; calling a corresponding pluggable protocol parser to generate a standardized instruction object; analyzing instruction semantics and dynamically calculating priorities in combination with a system load; and performing non-blocking scheduling and execution monitoring on the instruction based on a resource token bucket mechanism. The system comprises a data acquisition module, a protocol identification module, a self-adaptive analysis module, a semantic analysis module, a priority calculation module, an instruction sorting module, a token management module, a scheduling execution module, an execution monitoring module and a response generation module. According to the method, through protocol self-learning, dynamic priority quantification and multi-dimensional resource isolation scheduling, the response certainty, expansibility and operation stability of the system in a high-concurrency scene are remarkably improved.

The invention discloses an optimization control method for a power system, and relates to the technical field of power control, and the method comprises the steps: constructing a multi-energy state space, and building a layered optimization architecture based on the multi-energy state space; the decision-making layer generates a multi-energy balance scheduling instruction through a multi-target collaborative optimization algorithm to realize multi-target collaborative optimization of the electric heating gas system; the execution layer comprises a plurality of energy subsystem control modules, and collaborative strategy optimization is carried out through a distributed reinforcement learning algorithm; positioning a disturbance source of topology mutation through a causal reasoning algorithm, and generating a reconstruction instruction; training a control strategy of each energy subsystem by adopting a distributed learning framework, and executing a distributed cooperative control action according to the reconstruction instruction; the network connectivity is judged through the communication state of the energy subsystem, and if the communication is normal, collaborative decision making is carried out through a distributed learning framework; and if the communication is interrupted, switching to an edge node offline autonomous mode.

The invention relates to the technical field of industrial information processing of production logistics scheduling, in particular to an intelligent material scheduling decision-making method and system based on a large language model, and the method comprises the steps: obtaining historical scheduling data, a production plan text, real-time material state information, production rhythm information and production main data; constructing a time statistical model and analyzing the plan text to obtain a scheduling constraint set; generating a candidate transfer time window after consistency verification, and determining a target time window; and generating a timestamp sequence, aligning and correcting according to rhythms, and outputting a scheduling instruction sequence. Constraint conflicts and non-executable instructions can be reduced, and delivery and beat stability is improved.

The invention provides a loading storage circuit and a graphics processor, and relates to the technical field of graphics processing. The loading storage circuit comprises an instruction scheduling module, an address generation module and a data service module, and is provided with a buffer area module which comprises an operand buffer area and an effective data buffer area and is used for caching information required by address calculation and data access; the instruction scheduling module is used for collecting a data access instruction and outputting instruction information; the address generation module calculates a target access address according to the instruction information and the operand; and the data service module executes corresponding data loading or storage operation on the target cache unit. According to the scheme, the operands and the valid data are pre-cached, so that overflow and stagnation caused by inconsistent processing rhythms among modules can be avoided, and the parallel processing capability of an assembly line is improved; through the independent address calculation and data access process, the stability of the access time sequence and the data processing efficiency can be improved.

The invention relates to a multi-scheduling-instruction cooperative control method and device, computer equipment, a storage medium and a program product. The method comprises the steps of receiving current dispatching instructions issued by at least two power dispatching systems for a power plant, and obtaining current power grid state information of the power plant; if the current power grid state information satisfies a preset power grid state condition, determining a current operation mode of the power plant according to a current demand overlapping degree between current scheduling demand information in the at least two current scheduling instructions; when an updated scheduling instruction issued for the power plant is received, performing demand overlapping degree analysis on updated scheduling demand information carried in the updated scheduling instruction to obtain an updated demand overlapping degree; and updating the current operation mode according to the current demand overlapping degree and the updated demand overlapping degree to obtain an updated operation mode of the power plant, and obtaining a power dispatching result of the power plant based on power dispatching processing of the updated operation mode. By adopting the method, the switching efficiency of the power plant operation mode can be improved.

The invention relates to the technical field of computers, and discloses a multi-task processing-oriented Soc chip server computing power distribution method, which comprises the following steps of: acquiring execution primitive vectors of at least two tasks concurrently executed on an Soc chip; converting the execution primitive vector into an execution symbol sequence corresponding to each task; based on the execution symbol sequence, identifying an execution morpheme representing a task execution stage, and constructing a task grammar state machine, used for predicting a next morpheme, of each task; analyzing cross-task morpheme association between the execution morphemes of the concurrent tasks; and on the basis of the current morpheme of a certain task, the predicted next morpheme is associated with the cross-task morpheme, and a collaborative scheduling instruction is generated. By converting the execution primitive vector into the execution symbol sequence and identifying the execution morphemes based on the sequence to construct the task grammar state machine, the prediction of the next execution stage of the task is realized.

The invention discloses an integrated power supply system and a scheduling method thereof, and belongs to the technical field of power supply systems, and the method comprises the steps: collecting the operation state data of a micro-grid in real time; establishing a storage battery stress factor table, identifying an effective charge-discharge cycle, calculating a life loss value, and converting the life loss value into life loss cost; the method comprises the following steps of: constructing an optimization model containing life loss cost by taking minimization of total operation cost as a target, and generating an optimal scheduling instruction set in each scheduling period by adopting a model prediction control framework and an improved particle swarm optimization algorithm. According to the invention, through real-time quantitative evaluation and optimal scheduling of the service life of the storage battery, the economy and reliability of the whole life cycle of the system are improved.

The invention discloses a multi-dimensional verification and exception backtracking method for a CTC system scheduling instruction. The method comprises the following steps: firstly, performing structured conversion on an instruction, endowing a global unique tracking identifier and complementing real-time context information; sequentially executing format, logic and equipment state three-layer progressive verification, completing logic verification by a configurable rule engine supporting hot update, and verifying the equipment state by adopting a hybrid retransmission mechanism with self-adaptive capability; after an instruction is issued, based on the tracking identifier, generating and associatively storing a structured log at each circulation node in real time, and constructing a full-link log chain; and when the instruction is abnormal, the full-link log is quickly retrieved according to the identifier, and a fault is accurately positioned and a report is generated by automatically checking the state of each node. According to the method, full-dimension safety verification, full-link state tracing and minute-level anomaly positioning of the instruction are realized, and the scheduling safety and the operation and maintenance efficiency are remarkably improved.

The invention relates to the technical field of mobile communication and Internet of Things, and discloses a heterogeneous network space multi-level mobile Internet of Things cloud system based on artificial intelligence, which comprises a data acquisition module for acquiring real-time state data of a mobile Internet of Things terminal and generating a standardized network-service joint situation vector; the endogenous interference sensing module is used for generating a network interference association diagram based on the vector; the service-network dynamic decoupling module is used for reversely reconstructing terminal service characteristics according to the association diagram and decoupling a key network state sequence; the business intention abstraction module constructs a heterogeneous network cooperation state target according to the sequence; the collaborative scheduling strategy generation module is used for generating a multi-dimensional collaborative scheduling instruction by using a collaborative decision model; the network resource self-growing module is used for performing state sequence evolution after the rehearsal instruction and constructing a compensation network plane; and the instruction distribution and verification module is used for executing the instruction of the fusion compensation logic and updating the association diagram. According to the method, the problems of heterogeneous network interference and scheduling lag of the mobile Internet of Things are effectively solved.

The invention discloses a computing power resource scheduling method and device, equipment and a storage medium. The method is applied to a same-domain scheduling agent. The method comprises the following steps: acquiring a first computing power state of each same-domain edge node in a same-domain edge node cluster; under the condition that it is determined that at least one same-domain edge node meeting the edge information synchronization condition exists according to the first computing power states, the same-domain edge nodes are marked as source nodes, and whether a target same-domain edge node exists in the same-domain edge node cluster or not is determined according to the first computing power states; if yes, migrating the computing power task on the source node to the target same-domain edge node; if the distributed computing power network does not exist, generating a cross-domain scheduling instruction and sending the cross-domain scheduling instruction to the cross-domain scheduling agent, so that the cross-domain scheduling agent performs computing power state synchronization and computing power task migration operation of cross-domain edge nodes, thereby improving the load balancing capability of the distributed computing power network, the computing power resource utilization rate and the real-time performance of computing power task scheduling; and thus, smooth execution of the computing power task is ensured.

The invention discloses a service flow scheduling method, equipment and a medium. The method comprises the following steps: acquiring business flow scheduling instruction information; analyzing the service flow scheduling instruction information to obtain a service flow scheduling constraint condition corresponding to the scheduling target and an effective condition of the service flow scheduling constraint condition; obtaining queue state information of a service flow queue of each service flow scheduling level; constructing a service flow scheduling optimization model based on the service flow scheduling constraint condition and the queue state information under the condition that the effective condition is met, and solving the service flow scheduling optimization model to obtain a scheduling strategy of the first service flow; wherein the service flow scheduling hierarchy comprises a flow hierarchy, a queue hierarchy and a port hierarchy. According to the technical scheme, the problem that the multi-layer queue business flow scheduling effect needs to be improved at present is solved, the stability of business flow scheduling can be improved through collaborative scheduling management of the multi-layer business flow queues and an intelligent decision-making mechanism for dynamically adjusting parameters, and the business transmission service effect is guaranteed.

The invention discloses a dynamic anti-disturbance coordinated scheduling method for a hybrid energy micro-grid, and aims to solve the problems of poor stability, high cost, low consumption rate and the like of the hybrid energy micro-grid caused by disturbance such as renewable energy fluctuation and load abrupt change. The method comprises the following steps: firstly, constructing a system model containing multiple units, and collecting and preprocessing real-time operation data; establishing a multi-target scheduling objective function, and solving an initial scheduling instruction by adopting an improved predictive control algorithm; and finally, generating a coordinated scheduling strategy by combining the disturbance information dynamic correction instruction. Meanwhile, a security constraint and scheduling effect evaluation link is set, and mechanisms such as multi-time scale scheduling and layered response are adopted. The method can significantly improve the anti-disturbance capability and stability of the system, reduces the operation cost by 15%, improves the renewable energy consumption rate by more than 8%, is adaptive to multiple scenes, guarantees the safe operation, and is suitable for various hybrid energy microgrids.

Provided in the present application is a scheduling method. In the method, during the determination of a target scheduling action, action modules are traversed one by one on the basis of the current states and an action priority list corresponding to the action modules, an output is provided with a target scheduling action as the granularity, and it is not necessary to traverse all possible scheduling sequences, compare the scheduling sequences, and then output an optimal scheduling sequence, thereby greatly reducing the depth of traversal and search, and thus facilitating a reduction in time consumed for determining the target scheduling action and an improvement in the timeliness of the scheduling method, and ameliorating the problem of the degree of matching between a scheduling instruction, which is output by means of the scheduling method, and the actual state of a semiconductor process device being relatively low due to a relatively long period of time being consumed. Moreover, in the scheduling method, traversing action modules one by one to determine a target scheduling action can not only be applied to the scheduling of action modules for transport, e.g., robots, but can also be applied to the scheduling of action modules, e.g., spraying mechanisms, thereby facilitating an improvement in the applicability of the scheduling method.

The invention discloses a multi-robot scheduling method and device for a warehousing system and a related medium, and the method comprises the steps: obtaining a goods allocation-robot association data set, carrying out the statistics of the number of warehousing materials, and carrying out the sorting, and obtaining warehousing goods allocation distribution data; analyzing a warehouse-out demand by using the warehouse-in goods allocation distribution data to obtain warehouse-out goods allocation distribution data; arranging waves based on the warehouse-out goods allocation distribution data to obtain a palletizing instruction set; extracting a to-be-cast tray task set according to the tray loading instruction set, sorting the to-be-cast tray task set, and generating a cast scheduling instruction set according to the priority; and carrying out platform access processing on the tray loading instruction set and the line casting instruction set, and outputting scheduling instruction data according to the tray state. According to the invention, the platform access processing is carried out on the calculated tray loading instruction set and the calculated line casting instruction set, and the scheduling instruction data is output according to the tray state, so that multiple operation robots can be scheduled and optimized uniformly, and the overall task completion time is reduced.

The invention provides a scheduling generation method, device and equipment based on a large model, belongs to the field of data processing, is applied to a task scheduling large model, the task scheduling large model comprises an attribution module and a scheduling module, and comprises the following steps: obtaining execution process data of a scheduling job; converting the execution process data into prompts; determining a root cause node of the abnormal data through the attribution module according to the prompt; and generating a scheduling instruction through the scheduling module according to the root cause node. Therefore, the timeliness and accuracy of fault detection and repair can be improved, and the labor cost is reduced.