91 results about "Tabu search" patented technology

The invention relates to the technical field of heat management of energy storage systems, and discloses a heat management method of an intelligent distributed liquid cooling energy storage system. The method comprises the following steps: acquiring operation state parameters of a plurality of single batteries and optimizing timestamp synchronization; and processing the temperature data by adopting small-batch time series decomposition, and extracting change trend characteristics and abnormal hot spot positions. And based on the information, optimizing a heat exchange control rule base parameter set through a non-dominated sorting strategy, and generating optimized control parameters and priority rules. And in combination with inlet temperature and flow parameters of the liquid cooling system, a tabu search mechanism is utilized to decide a cooling liquid flow distribution scheme, and a branch control instruction is output. And finally, a flow constraint boundary is dynamically adjusted by adopting an approximate gradient projection technology, and a branch and bound strategy is fused to discretize and optimize a distribution path, so that the branch flow is finely regulated and controlled, and the thermal management precision and reliability of the energy storage system are improved. According to the method, the accuracy, adaptability and stability of thermal management of the liquid cooling energy storage system are improved.

The invention discloses an industrial and commercial energy storage EMS scheduling system based on multi-objective optimization, and relates to the field of intelligent control of energy storage systems. The method is used for solving the problems of battery life attenuation, electricity price fluctuation and insufficient load abrupt change adaptability in energy storage scheduling. The service life management module analyzes the ion migration state to generate a dynamic power safety envelope line as a layered optimization hard constraint; the feature extraction module constructs an electricity price association network, predicts photovoltaic / load in combination with quantile regression and extracts net load abrupt change features; the hierarchical optimization module compresses the discharge depth and activates load regulation in demand risk, and synchronously optimizes economy, life loss and power grid constraint through taboo search multi-objective evolution; the execution arbitration module monitors the distance between the power and the envelope line in real time, and when the distance is lower than a threshold value, rolling optimization is started to improve the life weight and feed back the lithium precipitation characteristic to drive the envelope line to update, and closed-loop protection is formed.

The invention discloses a power module multi-objective optimization method and device, and the method comprises the steps: determining a key structure parameter and a constraint range of a to-be-optimized power module, and constructing a multi-dimensional parameter space based on the key structure parameter and the constraint range; performing global uniform sampling on the multi-dimensional parameter space based on a Sobo sequence to generate a sample point set; performing electromagnetic-thermal coupling simulation based on the sample point set to obtain a target performance index, and constructing a training data set based on the target performance index and the sample point set; constructing an agent model of the MLP, and training the agent model based on the training data set to obtain a target optimization model; and constructing a multi-target optimization model based on the target performance index, solving the multi-target optimization model based on a tabu search algorithm, a fast non-dominated sorting genetic fusion algorithm and the target optimization model, and obtaining optimal key parameters of the to-be-optimized power module.

The invention relates to a power distribution network reconstruction method, system and device based on an improved PSO algorithm and a medium, and the method comprises the steps: carrying out the double-layer coding processing of a switching state and a particle swarm parameter, and generating an initial particle swarm meeting the radial constraint; performing classification processing in combination with real-time current data and historical fluctuation characteristics, dividing the power distribution network into a high-load key area, a medium-load transition area and a low-load elastic area, constructing a differential tabu search strategy, and dynamically adjusting tabu period parameters through regional importance weights and load rates; a dynamic inertia weight adjustment mechanism and differential tabu parameter collaborative optimization particle swarm optimization algorithm are adopted, and candidate reconstruction schemes meeting power grid operation constraints are screened; according to the method, the network loss, the voltage deviation and the load balancing indexes are quantified, an optimal scheme is output, a network topology reconstruction instruction is generated based on priority ranking, power distribution automation accurate regulation and control are achieved, and therefore the real-time performance, the economical efficiency and the operation reliability of power distribution network reconstruction are improved.

The invention relates to the technical field of aircraft simulation training, and discloses a multi-mode man-machine interaction aircraft simulation training system. A multi-modal feature extraction module of the system generates a multi-modal feature map including a flight environment situation map and an equipment operation health map through a hierarchical feature extraction network; the incremental analysis module divides an incremental data set, updates flight state prediction model parameters through an incremental clustering algorithm and outputs an incremental state prediction result; the parameter optimization layer initializes a tabu search population according to a prediction result, and iteratively optimizes parameters of an operation rule base through a dynamic efficiency evaluation matrix; the decision generation module fuses the optimized rule base parameters and the real-time multi-modal feature map, and generates a flight operation instruction sequence based on dynamic confidence score; and the execution optimization layer analyzes the instruction sequence, satisfies constraint conditions through a constraint projection algorithm, and completes instruction sequence integer programming in combination with the operation portrait deviation. The system improves the interaction accuracy and scene adaptability of simulation training.

The invention discloses an intelligent scheduling system for sports event organization and management, particularly relates to the technical field of sports event management, and solves the problems of resource mismatching and event interruption caused by rule stiffness, single conflict resolution dimension and emergency response lag of an existing scheduling system. Dynamic priority evaluation is driven through multi-source data real-time acquisition, a Pareto optimal solution set is generated in combination with three-dimensional conflict detection and audience influence factor quantitative analysis, efficient scheduling is realized by adopting a genetic-tabu search hybrid engine, and a hierarchical emergency response and block chain evidence storage mechanism is established. And the resource adaptability, the scheduling credibility and the emergency response efficiency are improved.

The invention discloses an intelligent main plan scheduling system oriented to the chemical industry, and the system comprises a data center layer which is used for aggregating order, equipment, materials, inventory and process constraint data; the MPS optimizer operates the tabu search module, the step-counting hill-climbing module and the flood algorithm module in sequence or in parallel in the same optimization period, and automatically switches algorithms under the following conditions: when the continuous iteration improvement rate is lower than a set threshold value, the tabu search is switched to step-counting hill-climbing, and the step-counting hill-climbing is switched to step-counting hill-climbing; when the local search step number is not improved, starting the flood algorithm to re-initialize the candidate solution; and the process scheduler is used for splitting the coarse-grained plan output by the MPS optimizer into minute-level process schedules, and transmitting the execution deviation back to the data center layer in real time through a bidirectional feedback loop so as to trigger incremental rearrangement. According to the intelligent main plan scheduling system for the chemical industry, through a hybrid algorithm dynamic switching mode, the thousand-level order rearrangement time is greatly shortened, and the plan response speed is remarkably improved.

The invention discloses an in-port container truck scheduling optimization system and method based on dynamic time matrix prediction, and the method comprises the steps: building a dynamic time matrix which introduces a bridge crane rhythm and a lane impedance factor through collection of TOS and vehicle monitoring data, and carrying out the feedback correction of the matrix through a task result; self-adaptive adjustment of a container truck resource pool threshold value is realized based on a task density function, and multi-target scheduling optimization is carried out by adopting a tabu search and discrete particle swarm interaction algorithm; and when the storage yard is congested or rhythm deviates, task priorities and key road marks of operation roads are considered, task sequence rearrangement is triggered, and algorithm parameters are automatically updated according to execution results. According to the system, a real-time closed loop of prediction-scheduling-feedback is realized, and the port operation efficiency and the resource utilization rate are improved.

The invention discloses a thermoelectric coordination regulation system for a new and old battery mixing station, and relates to the technical field of thermoelectric coordination regulation, and the system comprises a data collection module which collects the surface contact temperature data of a battery, the instantaneous fluctuation data of the terminal voltage of the battery and the state of charge in the new and old battery mixing station in real time; the early warning analysis module is used for dynamically analyzing the battery surface contact temperature data and the battery end voltage instantaneous fluctuation data in the continuous sliding time window to obtain a fractal dimension standard exceeding early warning signal; the risk modeling module is used for constructing a three-dimensional target vector by adopting a multi-target cooperative computing framework based on the fractal dimension standard exceeding early warning signal, the battery surface contact temperature data, the battery end voltage instantaneous fluctuation data and the state of charge; the decision regulation and control module is used for performing multi-target decision on the three-dimensional target vector through a tabu search algorithm to generate three types of regulation and control instruction sets; the taboo search mechanism breaks through the dimension coupling constraint of the multi-objective decision, and achieves the dynamic optimization of the thermal-electric regulation and control strategy.

The invention relates to the technical field of power distribution network fault recovery, in particular to a power distribution network local load segmented transfer method based on line bearing capacity evaluation and a parallel tabu search algorithm. Obtaining a power distribution network coefficient, distributed photovoltaic and energy storage equipment output, a load demand and fault position information; calculating the power flow distribution of the power distribution network after the fault occurs and before transfer by adopting a forward-backward substitution method, defining a safety margin index, and screening out a transferable safety line set; a power distribution network load transfer model fusing high voltage priority, important user priority and loss load priority is constructed, and based on a transferable safety line set, a parallel tabu search algorithm is adopted to perform optimization in a safety solution space to obtain an optimal transfer scheme; and calculating a fault rate based on the load rate of the line after transfer, and marking a high-risk line exceeding a threshold value. According to the invention, an effective load transfer scheme can be provided immediately after the power distribution network has a fault, so that the power failure loss after the fault occurs is reduced, and the user satisfaction is improved.

The invention relates to the technical field of substation monitoring, and discloses an indoor substation high-voltage live display state identification method and system. The system comprises a data acquisition module for acquiring multi-modal state data of a display and optimizing an acquisition process; the state feature extraction module is used for performing incremental feature learning analysis on the real-time data; the identification model optimization module is used for optimizing model parameters and structures through a multi-target particle swarm; the state reasoning module is used for performing deep reasoning by using taboo search in combination with newly added data and an optimization model and converting the data into identification signals; the control output module is used for adjusting control constraints by using an approximate gradient method and optimizing output by combining a potential field method so as to generate a control instruction; and the state verification module verifies the state of the equipment through adaptive filtering and feeds parameters back to the related module so as to adjust the identification strategy. The system can comprehensively and accurately identify the state of the display, improves the adaptability and operation and maintenance safety, and adapts to the dynamic operation environment of a transformer substation.

The invention discloses a smart city-oriented multi-modal data transmission optimization method. The method comprises the following steps of collecting and preprocessing multi-modal original data generated in a city; performing joint feature extraction by using an improved CorrNet network and outputting an importance label; collecting operation data and carrying out regular analysis on the urban network to generate network state information; the multi-modal features, the importance labels and the network state information are associated and analyzed, and a coding control instruction is generated; performing differential compression and coding processing on the multi-modal data to generate a to-be-transmitted data queue; path calculation and resource allocation analysis are carried out, and a scheduling control instruction is generated and implemented through a tabu search method; and generating and uploading a transmission performance evaluation result based on the feedback index and the network state information. According to the method, multi-modal feature extraction, differential coding and intelligent scheduling technologies are fused, efficient and reliable transmission of urban data is realized, and the method has the advantages of high accuracy, controllable time delay and high resource utilization rate.

The invention relates to a distribution robust optimization-based library-crossing vehicle path planning method, which comprises the following steps of: obtaining statistical information of travel time based on road network data analysis of a distribution area; constructing a library-crossing vehicle path model under the uncertain travel time by taking the minimum comprehensive time cost as an objective function; a travel time uncertainty set is constructed based on a Wasserstein distance, and a library-crossing vehicle path distribution robust optimization model is established; and finally, solving the model by adopting a taboo search algorithm to obtain a robust optimal planning scheme of the over-warehouse distribution vehicle path. According to the method, distribution robust optimization is carried out on the warehouse-crossing distribution vehicle path, the feasibility and robustness of the warehouse-crossing vehicle distribution path scheme under the uncertain travel time are improved, and the method is of great significance in guaranteeing warehouse-crossing collaborative operation and achieving cost reduction and efficiency improvement of distribution.

The application discloses a logistics transportation and distribution scheduling method, an electronic device and a storage medium, and comprises the following steps: based on the position distribution information of a distribution warehouse, taking the distribution warehouse as a node, an n-ary tree corresponding to the distribution warehouse is constructed; based on the article information of the distribution warehouse and the demand order of a guarantee object, nodes in the n-ary tree are traversed to obtain a target warehouse meeting the demand order; based on a minimum spanning tree and a reverse topology algorithm, an optimized transportation route between the guarantee object and the target warehouse is determined; and based on a tabu search algorithm, the optimized transportation route is optimized to obtain a target transportation path, and the demand order of the guarantee object is transported and distributed according to the target transportation path. The technical scheme of the application solves the technical problems of the existing logistics transportation and distribution scheduling method, such as insufficient adaptability to changes, limitation of actual application of an algorithm, trade-off difficulty in multi-objective planning, inflexibility of a system, and obstacles in resource integration, and realizes providing a comprehensive and accurate transportation scheme.

The application provides a multiple layout splitting method, system, medium and program product. First, an initial splitting coloring operation is performed based on a saturation priority strategy to generate an initial layout splitting scheme. Then, the global exploration ability of a tabu search algorithm is utilized, and a preset target function is combined to perform multiple rounds of iteration update operations on an initial conflict node set to generate a multiple layout splitting optimization scheme. For ultimate conflict nodes that cannot be completely eliminated by the tabu search algorithm, a backtracking pruning algorithm is started to maximize the attempt to eliminate all ultimate conflicts to generate a final multiple layout splitting target scheme. The application combines the initial optimization ability of the saturation priority strategy, the global exploration ability of the tabu search algorithm, and the quality guarantee of the backtracking pruning algorithm to realize accurate splitting of multiple layouts, guarantee splitting quality and lithography yield, improve splitting efficiency, reduce computational complexity, and adapt to the needs of multiple layout splitting.

The invention discloses a steel coil specification optimization method and device, equipment and a medium, and relates to the technical field of quality control, and the method comprises the steps: preprocessing order data through width de-weighting and weight merging, generating a combination set satisfying a preset width and coil number range, determining the number of initial steel coils, and constructing an initial combination scheme group; the tabu search algorithm is used for optimizing the combination scheme, the steel coil width variance is made to be minimum, the unified steel coil specification is determined, the result is sent to the production control system to execute the longitudinal cutting and coil dividing operation, the accuracy and efficiency of steel coil specification optimization are improved, the inventory pressure is reduced, and high production efficiency and high resource utilization rate are achieved.

This invention relates to the field of equipment scheduling technology and discloses an intelligent scheduling method and system for ore-carrying equipment in underground mines. The method includes: processing multi-source data during the equipment scheduling process to obtain initial feature representations of each scheduling element; processing the initial feature representations to obtain multi-dimensional constraint data for mine scheduling and constructing an objective function for an optimization algorithm; encoding the decision variables corresponding to the scheduling scheme into particle position vectors based on the objective function, and calculating the comprehensive performance of each particle using a fitness function; generating a population by employing different initialization strategies to improve the quality of the initial solution; introducing a deep deterministic strategy gradient algorithm in the particle swarm optimization iteration to achieve adaptive control of core parameters, and using tabu search to avoid local optima; generating a specific execution plan based on the optimal scheduling scheme obtained through training to achieve intelligent scheduling. This invention solves the problem of low solution efficiency in existing equipment scheduling methods.

The invention discloses a dynamic aircraft stand redistribution method based on a passenger gathering degree and an electronic terminal, and the method comprises the steps: obtaining aircraft stand information and initial flight plan information of an airport, and obtaining flight delay information; according to the flight initial plan information, according to a flight first-arrival first-service rule, carrying out machine position distribution, and generating a machine position pre-distribution scheme; calculating a single-flight passenger gathering probability at a boarding gate; and setting parameters of a taboo search algorithm, taking the minimum passenger adjustment loss time after the aircraft position changes as a target function, inputting the aircraft position pre-distribution scheme and the flight delay information into the taboo search algorithm for solving, and generating an aircraft position redistribution scheme. According to the invention, extra adjustment time of passengers caused by change of the boarding gate can be reduced to ensure the satisfaction of the passengers; the tabu length is adjusted to be a variable which changes along with the frequency of a tabu object, and a neighborhood moving method is finely divided, so that the solving speed is improved, and rapid optimization is realized.

The invention discloses an intelligent vehicle path planning method, device and equipment for electronic waste recovery and a storage medium, and relates to the technical field of path planning, and the method comprises the steps: defining each recovery demand as a node demand and a side demand, and constructing a path optimization model based on each preset constraint condition and each demand; and determining a polar coordinate angle between the node required by each node and the target warehouse, sorting each node to obtain a sorting result, and performing path distribution on each node based on the sorting result by using a path optimization model to obtain a to-be-understood result, performing neighborhood search operation on the solution to be processed by using a tabu search algorithm and a simulated annealing algorithm to obtain a candidate solution set; and judging whether the to-be-processed profit of the current candidate solution is greater than the current profit maximum value, if not, determining a new current candidate solution, skipping to the step of judging the to-be-processed profit of the current candidate solution again, and setting the current candidate solution as a target solution so as to plan the vehicle path and improve the path planning efficiency.

The invention relates to a power grid material logistics layout and influence factor evaluation method, and belongs to the technical field of electric power material distribution optimization. According to the method, for a complex logistics scene of multiple parking lots, multiple tasks and multiple vehicle types, a unified network model including the parking lots, a distribution center and customer demand points is constructed, and dynamic planning and risk assessment of a distribution path are realized by integrating a time window punishment mechanism, multi-vehicle-type cost optimization and a multi-scene simulation technology. The method specifically comprises the following steps: coding a parking lot, a distribution center and a customer point into network nodes, and designing a path rule of closed-loop distribution after a vehicle starts from the parking lot and is loaded by the distribution center; time window deviation punishment cost is defined, and a total cost minimization target is established in combination with transportation cost and empty driving cost; generating a distribution scheme by adopting a hybrid optimization algorithm combining a genetic algorithm and tabu search; according to the invention, the logistics cost can be effectively reduced, the distribution timeliness is improved, the power grid material emergency response capability is enhanced, and scientific decision support is provided for power enterprises.

The invention discloses a power distribution network three-phase imbalance dynamic regulation and control method based on a hybrid intelligent optimization algorithm, and belongs to the field of power system automation. Establishing a dynamic regulation and control model taking minimization of three-phase current unbalance degree and minimization of switch action times as double targets; solving the model based on an improved genetic algorithm fusing an experience knowledge guidance mechanism, a Pareto optimal selection mechanism and a global and local collaborative search mechanism; and generating a commutation control instruction according to the obtained optimal commutation scheme, and issuing the commutation control instruction to a load commutation switch for execution. By introducing a gene pool to guide a breeding process, a multi-target selection mechanism based on non-dominated sorting and a local optimization strategy combined with tabu search, comprehensive improvement of the algorithm in the aspects of convergence speed, solution diversity and global optimality is realized.

The application relates to the technical field of aircraft simulation training and discloses a multi-modal man-machine interaction aircraft simulation training system. A multi-modal feature extraction module of the system generates a multi-modal feature map containing a flight environment situation graph and a device operation health graph through a hierarchical feature extraction network; an incremental analysis module divides an incremental data set, updates flight state prediction model parameters through an incremental clustering algorithm, and outputs an incremental state prediction result; a parameter optimization layer initializes a tabu search population according to the prediction result, iteratively optimizes operation rule library parameters through a dynamic performance evaluation matrix, a decision generation module fuses the optimized rule library parameters and real-time multi-modal feature maps, generates a flight operation instruction sequence based on a dynamic confidence score, an execution optimization layer analyzes the instruction sequence, satisfies constraint conditions through a constraint projection algorithm, and completes integer programming of the instruction sequence in combination with operation image deviation. The system improves the interaction accuracy and scene adaptability of simulation training.

The application discloses a satellite slant beam inter-beam interference avoidance method and system based on cost flow and dynamic graph coloring, and accurately models the interference range of slant beams in a multi-satellite satellite hop-beam system, considers the influence of the slant beams when modeling the anti-beam inter-beam interference hop-beam pattern design problem, divides the dynamic graph coloring problem into two sub-problems of satellite-wave position matching and hop-beam time slot allocation which are coupled with each other, pre-solves the satellite-wave position matching problem by using a minimum cost maximum flow algorithm, obtains a satellite-wave position matching initial solution which maximizes the sum of satellite elevation angles of all wave positions in the system under the constraint of the maximum number of simultaneously opened beams of the satellite, and on the basis, combines a tabu search algorithm and a graph coloring algorithm to jointly optimize the two sub-problems, so as to further improve the inter-beam interference avoidance performance of the same hop-beam time slot opened beam. The application can obtain a multi-satellite hop-beam scheme with minimum inter-beam interference, and the algorithm has low complexity and is convenient for actual system implementation.

The application discloses a 3D parallel line layout solving method considering adaptive material loading and unloading points and obstacles, relates to the technical field of facility layout, and mainly comprises the following steps: establishing a target function, initializing parameters, generating an initial population, converting infeasible solutions in the initial population into feasible solutions, optimizing the initial population, removing duplicate solutions in the initial population and generating feasible solutions to fill the initial population, dividing the initial population into elite solutions and non-elite solutions, performing a tabu search operation on the elite solutions, performing a genetic operation on the non-elite solutions, combining the solutions after the two operations into a new population, outputting an optimal sequence and decision variables of the model according to the number of iterations, and solving the accurate coordinates of the material loading and unloading points on each facility and the final target value of the model by using the optimal sequence and the decision variables. The application considers adaptive material loading and unloading points and obstacle interference factors in three-dimensional space, and the obtained model is closer to the real situation, which is helpful for formulating more scientific facility planning and material handling strategies.

The application discloses a kind of unmanned aerial vehicle quantity allocation and spectrum resource optimization method, device, equipment and medium, it is related to unmanned aerial vehicle communication technical field, the method includes obtaining the number of fixed target distributed on the study ground area and the location of each fixed target;According to the above fixed target information and target optimization function, the optimal individual is determined using genetic tabu search algorithm;Target optimization function is the function that minimizes the total energy consumption of multi-unmanned aerial vehicle system under the restriction of the capacity of unmanned aerial vehicle battery, optimizes the number of unmanned aerial vehicles, the reconnaissance order of each unmanned aerial vehicle and spectrum use time;Genetic tabu search algorithm is the algorithm obtained by improving genetic algorithm using tabu search algorithm;Individual includes the number of unmanned aerial vehicles and the corresponding reconnaissance order of each unmanned aerial vehicle.The energy consumption of completing cooperative reconnaissance task can be further reduced by the application.

The invention discloses a medium-and-long-term power flow load energy storage balanced scheduling method and system for a power distribution network. The method comprises the steps of collecting real-time data of the power distribution network; substituting the real-time data into a pre-constructed load prediction model to obtain a load prediction value; substituting the load prediction value into a pre-constructed scheduling model, and solving the scheduling model by adopting a tabu search method to obtain a scheduling strategy; and performing balanced scheduling based on the scheduling strategy. Wherein the scheduling model is constructed by taking minimization of a load tracking error, minimization of system loss and maximization of energy storage efficiency as targets and limiting conditions. The tabu search algorithm is adopted to solve the scheduling model, an approximate optimal solution can be found in a complex solution space, and the energy storage efficiency of the system after scheduling is improved.

The application discloses a retail cabinet inventory path decision optimization method and equipment, comprising the following steps: (1) establishing an inventory path optimization model of a retail cabinet, wherein the inventory path optimization model takes the sum of inventory cost, shortage cost and vehicle path cost in a current planning period as a target function, and takes replenishment constraints, maximum inventory level constraints, vehicle capacity constraints, retail cabinet flow balance constraints, vehicle closed loop constraints and elimination sub-loop constraints as constraint conditions; (2) obtaining inventory path related information; (3) according to the inventory path related information, a tabu search method is used to solve the inventory path optimization model, and optimized inventory decisions and path decisions are obtained. The application simultaneously considers replenishment decisions and path planning decisions when making decisions, and is more efficient.

The application provides a multiple layout splitting method, system, medium and program product. First, an initial splitting coloring operation is performed based on a saturation priority strategy to generate an initial layout splitting scheme. Then, the global exploration ability of a tabu search algorithm is utilized, and a preset target function is combined to perform multiple rounds of iteration update operations on an initial conflict node set to generate a multiple layout splitting optimization scheme. For ultimate conflict nodes that cannot be completely eliminated by the tabu search algorithm, a backtracking pruning algorithm is started to maximize the attempt to eliminate all ultimate conflicts to generate a final multiple layout splitting target scheme. The application combines the initial optimization ability of the saturation priority strategy, the global exploration ability of the tabu search algorithm, and the quality guarantee of the backtracking pruning algorithm to accurately split the multiple layout, guarantee the splitting quality and the lithography yield, improve the splitting efficiency, reduce the calculation complexity, and adapt to the demand for multiple layout splitting.

The application provides an improved tabu search topology optimization method of a marine wind farm power collection system, which is based on the positions of wind turbines to intelligently preliminarily group and sort, to form an initial connection scheme, to connect all the wind turbines in each group with the minimum cost, to calculate the group cable cost, to check whether the lines cross, to impose a penalty in the total cost if the lines cross, to ensure that the scheme is feasible, to compare the current scheme with the historical best, to update the record and intelligently exchange the wind turbine groups to explore a new scheme, to repeat the optimization process until the number of iterations is satisfied, and to finally output a globally optimal topology scheme. The application has the advantages of better optimization effect, significantly improved economy, higher calculation efficiency, stronger engineering practicability, better algorithm robustness, better scheme reliability, high design automation degree and strong auxiliary decision-making capacity.

The invention particularly relates to a time-triggered service scheduling method and system based on flow feature grouping. The method comprises the following steps: firstly, constructing a network scheduling model and acquiring cycle and frame length parameters of all TT services to be scheduled; then, a multi-target conflict awareness routing algorithm is adopted to plan a service route; secondly, extracting the period, frame length and routing characteristics of the service to construct a comprehensive distance matrix, and performing characteristic grouping on the service by using K-means clustering; then determining an intra-group scheduling sequence in each group according to a period, a frame length and a conflict index rule; and finally, optimizing the packet scheduling sequence through tabu search to generate a global scheduling table. According to the method, the scheduling problem scale is reduced through flow feature grouping, the TT service scheduling with high scheduling success rate, high calculation efficiency and strong adaptability in a large-scale complex scene is realized by combining heuristic sorting and tabu search optimization, and the problems of high calculation complexity, need of pre-training, poor flexibility and slow convergence in the prior art are solved.