142 results about "Tabu search" patented technology

The invention provides a multi-stage embedded control equipment state sensing and energy cascade scheduling system. Comprising a master control decision center module, a distributed edge embedded node module, an equipment full-dimension state sensing module, an energy dynamic optimization scheduling module, a fault prediction and self-healing control module, a cross-protocol communication interconnection module and a man-machine cooperative command module. According to the invention, through constructing a three-layer time domain control chain of edge node nanosecond-level signal processing, cloud second-level optimization scheduling and equipment hour-level strategy presetting, seamless cooperation of turbine bearing pedestal micro-vibration monitoring and a power grid peak regulation strategy is realized, and real-time wavelet noise reduction preprocessing of embedded nodes is combined with cloud LSTM life prediction. A taboo search algorithm is driven to dynamically reconstruct a power supply scheme, and the pain point of control response lag in a high-fluctuation scene is solved.

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 production scheduling method, a production scheduling system and electronic equipment, and belongs to the technical field of production management and scheduling. The production scheduling method comprises the steps that according to production requirements and a preset production correlation model, a fitness function and an initial production scheduling scheme set are determined, and the fitness function comprises at least two of the following production scheduling strategies: total delay time minimization, resource utilization rate maximization, production line load balancing and intermediate product minimization; at least two preset algorithms are fused, the initial production scheduling scheme set is optimized based on the fitness function, the optimal production scheduling scheme is obtained, and the preset algorithms comprise any one of a genetic algorithm, a simulated annealing algorithm and a tabu search algorithm. The method can solve the problems of insufficient processing of resource constraints, low production scheduling efficiency and incapability of effectively finding the optimal production scheduling scheme in related technologies.

The invention provides a large-scale constellation earth observation task planning method and device, and belongs to the technical field of satellite task planning. According to the embodiment of the invention, a large-scale constellation earth observation task problem is divided into a multi-satellite-cluster cluster allocation problem and a single-satellite-cluster cluster autonomous task planning problem, and the improved adaptive large neighborhood search algorithm fused with tabu search is adopted to allocate the large-scale constellation earth observation tasks to different satellite clusters. And then, multi-satellite task planning in a satellite cluster is realized by adopting a satellite cluster autonomous task planning algorithm based on an extended contract network mechanism. Therefore, the dimension reduction of the problem scale can be realized, the search space can be greatly reduced, the solving efficiency can be improved, the cooperative capability between satellites in the satellite cluster can be improved, and especially in the scene of satellite damage and interference, the anti-interference capability and cooperative completion efficiency of the task can be improved, and the robustness and reliability of task planning can be ensured.

The invention discloses a departure peak period-oriented flight driving and taking-off collaborative optimization method, which comprises the following steps of: firstly, accurately identifying an optimal release control rate based on historical flight data, secondly, improving the sliding time prediction accuracy by utilizing integrated learning, and then, establishing a departure flight collaborative scheduling model according to an actual operation rule so as to realize the departure flight collaborative scheduling. And establishing a conflict mechanism to set flight priorities. A solving algorithm adopts a hybrid algorithm combining a genetic algorithm and tabu search, effective combination of global search and local search is ensured, and falling into a local optimal trap is avoided. The scheduling scheme covers key indexes such as runway throughput and average taxiing time, the airport scene congestion condition is relieved, the operation efficiency and safety of an airport in the departure peak period are improved, flight delay is reduced, and the utilization rate of runway time slot resources is increased.

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 discloses a self-adaptive micro-service deployment resource optimization method for cloud edge-end cooperation. The method comprises the following steps: 1) modeling a system model; 2) modeling a communication model; 3) modeling a user demand processing delay module; 4) deploying resource consumption according to a micro-service type required by user demand processing, and modeling a micro-service deployment resource consumption model and a user demand processing resource consumption model; 5) performing formulation definition on the micro-service deployment resource optimization objective function to complete heterogeneous constraint high-quality characterization; 6) aiming at the difference dimension of the multi-objective optimization function, finishing normalization processing of different dimensions; and 7) solving a multi-objective optimization problem by adopting a tabu search method to realize effective utilization of resources in a deployment process.The invention belongs to the technical field of system resource optimization of the Internet of Things, and solves the problem that the Internet of Things in the prior art has defects in the aspects of application space demand coupling, decentralized coordination and service difference.

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 a distribution network engineering transformer area site selection planning method, system and device and a medium, and the method comprises the steps: processing the obtained power equipment information, load information and topological structure information around a target distribution network transformer area through a geographic information system technology, and generating visual transformer area information; acquiring all fire falling point data in the visual transformer area information, and processing the fire falling point data by using a gravity center algorithm to obtain transformer area load center coordinates corresponding to the visual transformer area information; inputting the load center coordinates into the trained region screening model to obtain a preliminary available plot region; inputting a knowledge graph constructed by map information in the preliminary available plot region into a graph neural network model to obtain a recommended available plot region; using an improved taboo search algorithm to determine a target recommended plot area from the recommended available plot areas to perform site selection and planning of the courts; according to the method, the distribution network planning area site selection efficiency can be remarkably improved.

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 relates to the field of data prediction, and provides a method and a system for acquiring a cutting sequence of panel-type customized furniture based on a hybrid strategy. The method comprises the following steps: grouping furniture plates in a plurality of orders to be processed according to plate designs and colors to obtain a plurality of design and color groups; based on the number of the to-be-processed orders associated with the pattern groups and the occurrence frequency of the cross-pattern to-be-processed orders, performing initial arrangement on each pattern group to obtain an initial pattern group sequence; in each design and color group, according to the order coverage rate and the position sensitivity of each furniture plate, performing initial arrangement on each furniture plate to obtain a plate sequence in an initial group; dynamically optimizing the position of each flower color group in the initial flower color group sequence by adopting an adjacency exchange method, and performing secondary optimization on an adjacency exchange result; dynamically optimizing the position of each furniture plate in the plate sequence in the initial group on the basis of an adjacent exchange method through a tabu search algorithm; and building a furniture plate cutting sequence, and starting a production process of the plurality of orders to be processed.

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 discloses a technological parameter optimization method for electric pulse rock breaking. The method comprises the steps that electric tree characteristic parameters under different technological parameters are obtained; establishing a process parameter prediction model by taking the process parameters as input and the electrical tree characteristic parameters as output; based on a process parameter prediction model, an improved NSGA-2 multi-objective optimization algorithm is utilized to optimize process parameters, and the process comprises the following steps: providing a selection, intersection and variation process fusing a tabu search thought, namely, in a filial generation population generation process, a process of generating a filial generation population is provided; a solution with better fitness than a parent and a suboptimal solution within the number limit of flouting criteria serve as offspring solutions and are added into a taboo table, repeated acquisition is avoided, the local search ability of the algorithm is enhanced, a crowding degree value calculation method is improved based on the super-lattice thought to complete offspring population selection and construction, and the global search ability of the algorithm is improved. According to the method, the technological parameters are optimized through the improved NSGA-2 multi-objective optimization algorithm, the optimal technological parameter combination can be obtained, and the rock breaking efficiency is improved.

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 an improved particle swarm optimization algorithm-based unequal-area facility layout method with interference. The method belongs to the field of intelligent optimization algorithms, and comprises the following main steps: S1, constructing a mathematical model with an interference unequal area facility layout problem; s2, initializing a population, and calculating an objective function value; s3, selecting a global optimal particle by using a target space division method; s4, executing heuristic mutation operation by using an adaptive gradient method to improve the search precision; and S5, using a tabu search rule and a neighborhood transformation rule to prevent the algorithm from being caught in a local optimal solution trap too early. Compared with the existing method, the method has the following main advantages: (1) the influence caused by a physical interference object is considered in the unequal-area facility layout problem, so that the applicability of the problem is enhanced, and the method can be more suitable for actual application scenes such as factory workshop transportation scenes; and (2) a tabu search rule is introduced into the multi-target particle swarm optimization algorithm, so that the convergence speed and the global search capability of the algorithm are improved.

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 discloses an emergency material vehicle cargo matching and stowage optimization method and device based on two-stage mixed taboo search and a medium, and the method comprises the steps: building a cargo stowage and vehicle path comprehensive transportation model, and the composition factors in the comprehensive transportation model comprise loading space, loading capacity and road section traffic capacity; inputting an emergency material transportation task set in the planned time period, wherein information contained in each transportation task comprises a starting place, a destination place, a cargo quantity, a cargo volume and a cargo weight of each transportation task; constructing an alternative path set by adopting a genetic algorithm according to the comprehensive transportation model and the emergency material transportation task; and in consideration of transport tasks, the number of vehicles and transport capacity constraint conditions, a tabu search algorithm is adopted to solve an emergency material transport optimization scheduling problem, and an optimal scheduling scheme is selected from the alternative path set. The cost is reduced, the logistics efficiency is improved, and a feasible solution is provided for the cargo transportation stowage problem.

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 present invention belongs to the technical field of AGV, and provides an AGV transportation method and a robot. The method includes: obtaining a task list and the initial positions of each AGV car; using tabu search to optimize the task execution order to minimize the weighted cost of the last task completion time, average completion time, and number of stops; constructing a spatio-temporal graph representing time and spatial positions of nodes for each AGV car according to the optimized order; using a time enhancement algorithm to plan the paths of each AGV car on the spatio-temporal graph, regarding the paths of other AGV cars as time obstacles, and using a path cost function; allowing the AGV cars to wait in place during path planning to avoid collisions, and updating the paths of each AGV car in real time; smoothing the planned discrete paths with cubic Bezier curves to generate a continuous and controllable driving trajectory; finally, controlling each AGV car to execute tasks along the trajectory. The present invention can solve the problems of low task scheduling efficiency and frequent path conflicts of multiple AGV cars in the prior art.

The invention discloses a marine multi-platform helicopter delivery task planning method, and relates to the technical field of marine aircraft task planning, and the method comprises the steps: obtaining the task parameter information of a to-be-planned task entity; according to the task parameter information, constructing a task planning model by taking minimization of total task completion time as a target; and solving the task planning model by adopting a tabu search algorithm to obtain a task planning scheme. According to the method, by constructing the task planning model and adopting optimization methods such as a tabu search algorithm, offshore multi-platform helicopter resources can be effectively integrated, cross-platform collaborative planning is realized, the reasonability and efficiency of delivery task planning are remarkably improved, the total task completion time is shortened, and the offshore delivery efficiency is improved.

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 present application relates to the technical field of UAV path planning, and provides a method for double-layer optimized UAV path planning and related devices. The method includes: obtaining the positions of the base station of the target UAV and the positions of a plurality of inspection target points; constructing a path optimization function of the target UAV based on the position of the base station and the positions of all inspection target points; using a genetic algorithm and a tabu search algorithm to solve the path optimization function to obtain an optimal path planning scheme for the target UAV; and controlling the target UAV to inspect all inspection target points according to the optimal path planning scheme. The method of the present application can improve the accuracy of UAV path planning.