167 results about "Group scheduling" patented technology

The invention discloses a reservoir group scheduling decision-making method considering multi-source uncertainty propagation and evolution tracking, relates to the technical field of reservoir scheduling, and is used for solving the problem of reservoir scheduling multi-target risk decision-making in a random environment. According to the method, a space-time two-dimensional risk deduction model is constructed, refined risk diagnosis and dynamic deduction are performed on a reservoir group scheduling process, an uncertainty propagation and evolution tracking mechanism is obtained, and the robustness and reliability of a reservoir scheduling decision in an uncertainty environment are effectively improved; and a preference demodulator FAHP-OWA which comprehensively considers subjective weight and preference conflicts is adopted to obtain a high-dimensional weight vector in the aspect of obtaining group preference information. Meanwhile, multiple rounds of interactive optimization and a weight distribution mechanism are combined, preference information of decision makers can be introduced step by step, the scheduling process is more flexible, decision groups are more harmonious, the decision efficiency and accuracy of the decision groups in an uncertain environment are improved, and accurate judgment of the advantages and disadvantages of the scheme is achieved.

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

The invention discloses a reservoir group scheduling interval multi-target optimization method under an uncertain condition, and belongs to the technical field of reservoir optimization scheduling. The method comprises the following steps: firstly, establishing an innovative cascade reservoir group interval multi-target scheduling model; secondly, providing a double-population dynamic coordination interval optimization method for solving the interval multi-target scheduling model, and further obtaining an optimal scheduling scheme of the reservoir group; and finally, outputting an optimal scheduling scheme set of the reservoir group. According to the optimization method, through organic combination of a chaos-order mapping initialization strategy, a dynamic adaptive mutual promotion updating strategy and an elite guidance optimization strategy, optimization is rapidly and efficiently carried out to obtain a group of reliable and accurate scheduling scheme sets considering runoff uncertainty; an effective decision-making technical means for solving the cascade reservoir group optimization multi-target complex engineering problem is provided for a scheduling decision maker, the intelligent level and comprehensive benefits of water resource management are improved, and the capacity of a water resource system for coping with complex environments and uncertain factors is further enhanced.

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

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

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

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

The invention discloses an intelligent water pump group scheduling system and method based on irrigation demand response, and aims to solve the problems of complex scheduling, heavy communication burden and uneven upstream and downstream water supply of a small watershed distributed pump station group. The method comprises the following steps: acquiring crop water demand, weather and soil data, and calculating irrigation demand; establishing a water pump operation and photovoltaic power generation output model; then, on the premise that the photovoltaic power generation utilization rate is maximum, water pump irrigation power is preliminarily distributed, and surplus or vacant electric power is treated; and finally, monitoring water volume, water level and photovoltaic power generation data in real time, and dynamically adjusting the pumping power of the water pump. Through intelligent decision making and dynamic adjustment, the irrigation requirement is met, the problem that downstream water supply is insufficient due to upstream excessive water pumping is effectively avoided, meanwhile, power use is optimized, the external power introduction cost is reduced, and dual optimization of irrigation and power use is achieved.

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

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

A distributed photovoltaic group scheduling and group control method belongs to the technical field of group scheduling and group control, and comprises the following steps of: improving an initial interval by acquiring a time-ground correlation improvement coefficient and an active coordination time-ground criticality coefficient, acquiring a time-ground relation among multivariate information through the time-ground correlation improvement coefficient to identify continuous maintenance on a time point, and performing active coordination on the initial interval and the initiative coordination time-ground criticality coefficient to obtain the initiative coordination time-ground criticality coefficient. However, an outlier output voltage value that fluctuates slowly; the sensitivity is configured according to part of maneuvering attributes through an active coordination time-ground criticality coefficient, so that a complicated outlier output voltage value mode is obtained more accurately; therefore, the complexity and variability in a distributed photovoltaic cluster scene can be efficiently processed, and the method can maintain an efficient outlier monitoring function in a distributed photovoltaic system with small dust concentration in the surrounding air or a distributed photovoltaic system with large dust concentration in the surrounding air. The monitoring sensitivity of subtle output voltage changes is improved, and the identification accuracy of a hidden distributed photovoltaic system which operates abnormally is enhanced.

The invention relates to the technical field of pavement construction, and particularly discloses an intelligent spreading and pressing machine group scheduling system and method for highway pavement construction, and the system comprises a road modeling and dividing module which is used for automatically planning an optimal construction path according to road information, and dividing the construction path into a plurality of regions; the compaction degrees in different areas in the construction process are estimated and calculated through the data calculation unit in combination with diversified data, the reliability and accuracy of the compaction degree calculation result can be improved, and then the compaction degrees in the different areas in the construction process are compared with the preset compaction degree through the data analysis unit; according to the method, whether the compaction effects of different areas are qualified or not can be accurately judged, and when the compaction effect of a certain area is judged to be unqualified, an intelligent scheduling adjustment scheme is formulated in time through the intelligent scheduling module according to the compaction degree information of the area, so that the subsequent compaction quality of the area is ensured, and the continuity and high efficiency of the construction process are ensured.

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

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

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

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

Methods, systems, and devices for wireless communication are described. A wireless device may obtain group control information in a group common physical downlink shared channel (PDSCH), determine resources for communicating on a shared channel based on the group control information, and communicate on the resources of the shared channel. A base station may configure a user equipment (UE) group comprising at least one UE, transmit, in a group common physical downlink shared channel (PDSCH), group control information for scheduling the at least one UE for communicating on a shared channel, and communicate on the shared channel with the at least one UE.

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

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

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

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

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

Provided in the embodiments of the present description are a scheduling method and system, a computing device and a computer-readable storage medium. The scheduling method comprises: using a virtual runtime reference value corresponding to a group scheduling entity where any scheduling entity is located to update kernel virtual runtime corresponding to said any scheduling entity; searching a run queue of a first logical processor for a first ancestor of a first scheduling entity, and searching a run queue of a second logical processor for a second ancestor of a second scheduling entity, a search condition comprising: the scheduling levels of the first ancestor and the second ancestor being the same; and comparing the kernel virtual runtime corresponding to the first ancestor with the kernel virtual runtime corresponding to the second ancestor, so as to determine the priorities of the first scheduling entity and the second scheduling entity.

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

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

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

In an approach, a processor indexes system documents of a computer system using a text search engine, resulting in a search index, where the computer system is described by a set of system parameters with values stored in the system documents. A processor provides a set of scheduling rules for allocating computing resources of the computer system, each scheduling rule comprising a search query referencing one or more of the system parameters. A processor executes search queries by the text search engine against the search index, resulting in scored system documents and associated search queries. A processor ranks at least part of the set of scheduling rules using scores of the associated search queries. A processor selects scheduling rules in accordance with the ranking. A processor executes the selected scheduling rules for scheduling a workload on the computer system.

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

The invention relates to the technical field of power system scheduling, and provides a microgrid group scheduling method and system based on multiple resources, and the method comprises the steps: constructing a centralized task scheduling center based on a microgrid group; basic resource parameters of the micro-grid group are collected, and resource models are constructed for different resources of the micro-grid group; constructing a resource scheduling model based on the centralized task scheduling center and the resource model; solving the resource scheduling model in the resource scheduling model by taking minimization of the operation cost of the micro-grid group and maximization of the profit of the centralized task scheduling center as targets to obtain a micro-grid group scheduling scheme, and scheduling the micro-grid group through the centralized task scheduling center based on the micro-grid group scheduling scheme. According to the method, the coordination and the utilization efficiency of different resources in the micro-grid group are remarkably improved.

The invention relates to the technical field of intelligent manufacturing workshops, in particular to an optimization method for solving group scheduling of a distributed heterogeneous flow shop, which comprises the following steps of: determining that minimization of product assembly completion time is taken as a problem solving target, and initializing parameters; initializing a solution; generating a probability random number, and judging and selecting to execute product and group operation or product and workpiece operation; and updating the optimal solution, judging whether termination time is reached or not, if yes, ending iteration, and outputting the current optimal solution. According to the application of the method, the problem of group scheduling of the distributed heterogeneous flow shop with the product assembly stage can be effectively solved, and the production efficiency of a manufacturing enterprise is improved by optimizing scheduling.