362 results about "Integer programming model" patented technology

The invention discloses a blasting design-oriented multi-source data fusion preprocessing method, and particularly relates to the technical field of blasting data scientific processing. Comprising the following steps: acquiring multi-source heterogeneous data of a blasting area through a multi-source sensor array and a geological modeling system, establishing a multi-modal data unified representation framework based on ontology, constructing a depth feature fusion network with physical constraints, developing a blasting parameter optimization-oriented mixed integer programming model, and establishing a blasting parameter optimization-oriented mixed integer programming model; integrating a multi-target genetic algorithm and an expert experience knowledge base to perform parameter optimization; according to the method, a multi-mode unified representation framework based on the ontology is constructed, millimeter-level space alignment and millisecond-level time synchronization of multi-source data are achieved through a space-time reference coordinate system and an improved DTW-B spline composite algorithm, the spatial resolution of blasting design is improved to the centimeter level, the time synchronization precision reaches the millisecond level, and the time synchronization efficiency is improved. Compared with a traditional method, the data utilization rate is increased by more than 40%, and a solid foundation is laid for fine blasting design.

The invention relates to the technical field of electric power spot transaction, and discloses an electric power spot transaction optimization system. The system comprises a data acquisition module, a dynamic pricing module, a supply and demand prediction module, a transaction optimization model construction module, a real-time scheduling module and the like. The data acquisition module acquires market data; the dynamic pricing module generates a dynamic electricity price strategy based on a deep reinforcement learning algorithm and a mixed integer programming model; the supply and demand prediction module predicts supply and demand through a federated learning framework aggregation model; the transaction optimization model construction module constructs an objective function and constraint conditions by combining the results; and the real-time scheduling module uses an improved algorithm to solve and generate an optimal scheduling scheme. In addition, the system also has the functions of risk control, abnormal transaction detection, intelligent contract management, performance evaluation and the like. The system can optimize electric power spot transaction, improve transaction efficiency, guarantee power grid stability, prevent and control market risks, and promote healthy development of the electric power market.

A neural network on-chip mapping method and apparatus based on a tabu search algorithm are provided. The method includes: constructing a tabu search table and using a heuristic-based iterative search process to select local computing cores of a network-on-chip as candidates, establishing an integer programming model and solving an optimal solution, continuously reducing an objective cost function of a mapping solution by loop iteration, and finally obtaining an approximately optimal deployment scheme.

The invention belongs to the technical field of computers, particularly relates to an AIGC paper-cut generation system and method based on culture gene multi-modal constraints, and solves the dual problems of'culture distortion 'and'physically unachievable' of a traditional AIGC generation result through mathematical fusion (such as a mixed integer programming model) of semantic, topology and process three-level constraints. The improved ControlNet architecture realizes directional fusion of culture gene coding vectors through a culture attention mechanism, and dynamically adjusts feature weights in a generation process, so that the generation precision of pattern details is improved to a submillimeter level; based on the combination of an interactive correction tool and an inverse diffusion inversion algorithm, the modification of a local pattern by a user can be quickly fed back to a system, and a'creativity-generation-optimization 'closed loop is realized.

The invention discloses a furniture production informatization management system based on data analysis, and relates to the technical field of production management, and the system comprises a production plan optimization module, a raw material cutting optimization module and a quality prediction center module. The production plan optimization module collects data by deploying an RFID sensing network, establishes a multi-dimensional evaluation matrix to determine order priority, optimizes a production plan by using a mixed integer programming model and an improved algorithm, and generates a production scheduling scheme after digital twinborn verification; the raw material cutting optimization module analyzes order data to construct a raw material model, optimizes component layout by applying a self-adaptive algorithm, and generates a cutting scheme with process constraint and an excess material map in combination with stress analysis; through an intelligent production management means, the production efficiency, the raw material utilization rate and the product quality stability are remarkably improved, and remarkable economic benefits are brought to enterprises.

The invention relates to a supply chain risk assessment decision method and system. The method comprises the following steps: carrying out single-factor evaluation modality and multi-factor weighted evaluation modality on a risk original data set of each link of a supply chain, and creating a multi-dimensional risk quantification model; according to the multi-dimensional risk quantification model, performing multi-stage time scale evaluation on the risk original data set to obtain a multi-stage risk evaluation result; based on the multi-stage risk assessment result, modeling a supply chain network into a directed graph, and constructing an integer programming model for solving to obtain a risk-oriented path allocation scheme; and according to the risk-oriented path distribution scheme, risk index calculation and early warning grade division are carried out on the multi-stage warehousing system to obtain a collaborative early warning strategy of the multi-stage warehousing system. According to the invention, reasonable distribution of supply chain resources is realized, the congestion risk of logistics nodes is effectively relieved, and the operation efficiency of the whole supply chain is improved.

The invention relates to a power distribution network two-stage recovery method based on an electric vehicle and mobile energy storage, and belongs to the technical field of disaster prevention and reduction and emergency recovery of a power system. According to the technical scheme, the method is divided into two stages of recovery: stage 1 pre-disaster pre-configuration and stage 2 post-disaster collaborative recovery; establishing a power distribution network line vulnerability evaluation model, and generating a fault scene in extreme weather based on a wind speed-fault probability curve; constructing a charging station and maintenance station pre-configuration optimization model, and determining the optimal site selection in the traffic network by adopting integer nonlinear programming; establishing a post-disaster recovery mixed integer programming model, coordinating and dispatching the electric vehicle, the mobile energy storage system and the maintenance team, and realizing collaborative optimization of load recovery and line repair; and designing a traffic network and power distribution network coupling mechanism, quantifying a system toughness index and optimizing a resource scheduling strategy. Through multi-resource cooperative scheduling, the post-disaster recovery capability of the power distribution network is remarkably improved, and an innovative solution is provided for improving the toughness of the power distribution network in extreme weather.

The embodiment of the invention relates to the technical field of emergency material distribution, and provides a material distribution method and system, an electronic device and a medium, and the method comprises the steps: building a mixed integer programming model with a plurality of pieces of preset constraint information as constraint conditions according to a plurality of disaster points of a target region and isolation point and non-isolation point data of each disaster point, the mixed integer programming model is a multi-truck and multi-ground unmanned vehicle collaborative distribution model; solving the mixed integer programming model based on a multi-heuristic initialized adaptive particle swarm optimization algorithm by taking minimization of delivery time as a target; determining an optimal delivery path of the plurality of trucks and the plurality of ground unmanned vehicles based on a solving result of the mixed integer programming model; and based on the optimal distribution path, carrying out material distribution on a plurality of disaster-affected points of the target area and isolation points and non-isolation points of each disaster-affected point. Therefore, efficient path planning and resource optimization scheduling are realized.

The invention discloses a dual-stage resource reservation and scheduling system for vehicle networking sudden accident emergency calculation, and relates to the technical field of edge calculation and intelligent traffic. The system comprises a traffic situation sensing module, a risk prediction decision module, a resource pre-freezing execution module, a task grading analysis module and a hybrid scheduling optimization module. The system innovatively divides a traffic accident handling process into an early warning stage and a response stage: in the early warning stage, the system adjusts a resource reservation ratio of edge nodes based on traffic flow density and historical task load conditions; in a response stage, the system performs hierarchical scheduling according to task types and dependency relationships by using a mixed integer programming model and a priority perception strategy. Through an end-side cloud collaborative architecture and a resource reservation mechanism, the system can significantly improve the task processing efficiency under emergencies and reduce the response time delay of high-priority tasks, has good expansibility and practicability, and is suitable for various vehicle networking application scenes such as intelligent expressways and urban traffic.

The invention discloses a multi-source power distribution network energy-saving optimization scheduling method, system, equipment and storage medium based on big data processing, and relates to the technical field of power systems and big data processing. After power distribution network multi-source data is collected, through preprocessing and feature extraction, loads are predicted and classified through a long and short-term memory network; the load dynamic optimization module combines cost, network loss and photovoltaic consumption, generates a scheduling instruction through a mixed integer programming model, compares power loss before and after scheduling after CPLEX solution, and generates a difference thermodynamic diagram. According to the scheme, accurate load prediction and dynamic optimization scheduling of the multi-source power distribution network are realized through big data processing, the cost and energy efficiency are effectively improved, the consumption capability of photovoltaic energy is enhanced, the energy-saving effect is visually displayed through the power loss difference thermodynamic diagram before and after scheduling, and powerful support is provided for intelligent management of the power distribution network.

The invention discloses a full-equipment energy consumption optimization management system in a building, and relates to the technical field of intelligent control, and the system is provided with a multi-modal data collection module which collects building environment parameters and equipment operation data through a distributed sensor network, and generates a multi-modal sensing matrix; an environment state prediction module is set to construct a dynamic knowledge graph based on a multi-modal sensing matrix, a graph attention network is used, an energy flow matrix of equipment topology is output, micro-environment state prediction data is generated through prediction of a finite element solver, a scheduling scheme solving module is set, a mixed integer programming model is constructed based on the micro-environment state prediction data, and a scheduling scheme is set. And solving the mixed integer programming model to obtain a scheduling scheme of the energy consumption equipment, setting a control instruction optimization module, and driving the edge execution equipment to operate based on the scheduling scheme. The global scheduling optimization of the whole building energy consumption equipment is realized, the energy consumption is effectively reduced, and the environmental comfort is improved.

The invention relates to the technical field of smart power grids, and provides a power distribution network power failure probability analysis and resource scheduling optimization method based on a knowledge graph, which realizes minute-level node association weight updating through a streaming updating mechanism, and remarkably improves the power failure risk identification accuracy in extreme weather by combining with dynamic adjustment of a meteorological risk function. A topology / environment double-attention architecture is adopted to fuse multi-source features, so that the fusion efficiency of equipment node features and environment node features is improved; and a fault deduction model with dynamic weight constraint is introduced, so that the secondary fault early warning capability is enhanced. In a resource scheduling link, a mixed integer programming model of a carbon emission factor and island power balance constraint is integrated, and Pareto optimization of first-aid repair efficiency and an environmental protection target is realized. Through transfer learning, knowledge graph cross-region adaptation is realized, and the novel transformer area access error is controlled to be reduced in a distribution network reconstruction scene. The method supports minute-level topology reconstruction and second-level weight updating, has low delay and high expansion capability, and is suitable for provincial power grid real-time deployment.

A coordinated control method for an urban rail passenger flow, and an electronic device and a storage medium, which belong to the technical field of rail transit control. In order to realize passenger flow management and control without increasing the transport capacity, in the present application, a rail simulation system is used to deduce a predicted passenger flow of an urban rail line during rush hour, which involves data such as entry station IDs, exit station IDs, entry time periods and passenger flow volumes; performing division on the basis of different time periods t and stations s, so as to obtain a flow of passengers arriving at a station s and preparing to enter the station to take a train within the time period t; on the basis of historical passenger flow distribution data of subway operating companies, compiling statistics on passenger flows in various directions among historical passenger flows, and calculating the proportions of the passenger flows in the various directions among the historical passenger flows; constructing a mixed integer programming model for multi-station passenger flow coordinated control; and using a branch and bound algorithm to solve the mixed integer programming model for multi-station passenger flow coordinated control constructed in step S3, so as to obtain an optimal passenger entry flow scheme for stations. The present application realizes the effect of prioritizing the evacuation in a station with a large passenger flow.

The invention relates to the technical field of atmospheric dust pollution prevention and environmental protection, and discloses a multi-objective optimization layout method for dust monitoring points of a bulk cargo port, which comprises the following steps: processing historical meteorological data and port geographic information, and calculating the comprehensive evaluation concentration of each candidate grid point in combination with a pollutant diffusion model; constructing a multi-target nonlinear mixed integer programming model taking maximization of environment sensitive point coverage and minimization of concentration interpolation deviation as target functions, wherein the model comprehensively considers multiple reality constraints; solving the model by adopting an improved genetic algorithm to obtain an optimal solution set capable of forming tradeoff among different targets; and carrying out quantitative evaluation on the solution set through preset monitoring area coverage rate and monitoring accuracy indexes, and determining an optimal monitoring point position layout scheme. According to the invention, a scientific and quantitative optimized layout scheme can be provided for bulk cargo port dust monitoring, and the reliability and spatial representativeness of a monitoring network are improved.

The invention discloses a container port berth and storage yard allocation combined planning operation method and system based on a cluster strategy, and the method comprises the steps: collecting the information of ships, container trucks and storage yards in a target time period, including the planned arrival time of the ships, the number and types of containers carried by the ships, the stockpiling state of the storage yards, and the planned arrival time of outer container trucks; the collected information is input into a pre-constructed multi-target mixed integer programming model, and an optimal programming strategy is calculated and comprises berth plans of all ships in a specified time period and loading and unloading operation plans of container batches; wherein the pre-established multi-target mixed integer programming model comprises three target functions, and the targets are the shortest ship berthing deviation expected turnover time, the minimum container transportation total distance between a berth and a storage yard container area and the minimum workload imbalance of the storage yard container area respectively; and executing ship and storage yard operation according to the optimal planning strategy. The overall operation efficiency of the wharf can be improved.

The invention discloses a high-temperature environment multimodal transport method and system based on composite heat insulation and active temperature control. The method comprises the steps that a multimodal transport capacity grid model simulating high-temperature transport cooperation is constructed; determining model condition hypothesis, and defining decision variables, parameters and state variables of the model; high-temperature special items and safety penalty are included in the total cost, a high-temperature associated total cost model is constructed, and a multi-scene cost calculation formula is deduced; with the purpose of minimizing the total cost, a mixed integer programming model including path connection and flow conservation, temperature constraint, refrigeration / heat dissipation energy and power constraint, node refrigeration / sunshade capacity constraint, battery and key component temperature safety constraint, butt joint / node capacity constraint and time window constraint is constructed; solving the model by adopting an improved hybrid immunity-particle swarm dual-optimization algorithm, and outputting an optimal transportation path, transportation mode, transfer node and high-temperature scene exclusive scheduling parameter; and collaborative optimization of high-temperature transportation safety, energy efficiency and economy can be realized.

The invention discloses a power grid topology toughness enhancement method based on pre-disaster prediction of a graph neural network. The method comprises the following steps: constructing a power grid graph structure taking power grid equipment as nodes; predicting a node damage probability through a graph attention network, and defining a high-risk node set; according to the high-risk nodes and the propagation paths thereof, a mixed integer programming model is constructed and solved, and a pre-disaster optimal power grid structure adjustment strategy is generated; in combination with the strategy, a resource scheduling scheme is generated by using a deep reinforcement learning algorithm; calculating a power grid toughness core index after simulation operation, and if the index is lower than a preset threshold value, optimizing a structure adjustment strategy; and uploading the optimized strategy and scheduling scheme to a scheduling platform to complete pre-disaster active defense deployment. According to the method, collaborative linkage of pre-disaster risk prediction, topology reconstruction and resource deployment is realized, a differentiated pre-disaster defense scheme is automatically generated, post-disaster first-aid repair is converted into pre-disaster deployment, and the pre-disaster prevention and control response efficiency is remarkably improved.

The invention provides a method, a device and a product for realizing intelligent scheduling and cooperative work of multiple unmanned aerial vehicles. The method comprises the following steps: step S1, receiving a task to be processed in real time and acquiring a real-time operation state of each unmanned aerial vehicle in an unmanned aerial vehicle group; s2, performing intelligent dynamic allocation on the tasks by using a pre-established task value scoring model, an unmanned aerial vehicle task adaptation degree model and a global multi-task dynamic allocation optimization model; the global multi-task dynamic allocation optimization model is a mixed integer programming model established by taking maximization of the total task income as a target under a predetermined constraint condition; and S3, after task allocation is completed, a pre-constructed three-dimensional grid airway model G (V, E) is used to plan an airway for each unmanned aerial vehicle executing the task. According to the technical scheme, the mixed integer programming algorithm and the particle swarm optimization are combined, and the global task allocation optimization model is constructed, so that task allocation can be efficiently solved, and various actual constraints can be met.

The invention relates to the technical field of cross-border logistics, in particular to an artificial intelligence algorithm for cross-border overseas warehouse location storage and picking path optimization, which comprises the following steps: S1, obtaining order details, commodity characteristics and warehouse environment data, and monitoring warehouse states and cargo storage conditions in real time; s2, data preprocessing is carried out, features are extracted, clustering analysis is carried out, and association rules are mined; s3, performing rule matching, constructing a mixed integer programming model, performing constraint processing, and outputting a commodity storage strategy; and S4, converting the storage strategy into a warehousing operation instruction, and executing the warehousing operation instruction. The method has the beneficial effects that the accuracy of goods classified storage and the space utilization rate of the warehouse location can be improved, and the storage cost of the warehouse location is reduced.

The invention discloses a multi-access-considered vehicle-unmanned aerial vehicle distribution path optimization method, relates to the technical field of vehicle and unmanned aerial vehicle collaborative distribution optimization, and aims to solve a path planning problem caused by complex constraints and multi-target optimization in urban logistics distribution. According to the method, firstly, multiple constraint conditions of vehicle-unmanned aerial vehicle cooperative distribution are analyzed, energy consumption cost and carbon emission factors are fused into a target function, an integer programming model with the minimum time and transportation cost as double targets is constructed, and carbon emission cost is considered in the transportation cost; secondly, solving a vehicle path and an initial solution by adopting an ant colony algorithm; and finally, a large-field search algorithm is used to solve the problem of vehicle-machine cooperation path optimization. According to the method, the vehicle and unmanned aerial vehicle cooperative distribution logistics efficiency is effectively improved, the total cost is reduced, and powerful support is provided for development of green logistics.

The invention provides a truck-unmanned aerial vehicle cooperative dynamic scheduling method and system in emergency logistics, and relates to the technical field of unmanned aerial vehicle intelligent scheduling. Constructing a truck-unmanned aerial vehicle cooperative distribution network, wherein each truck is equipped with a cooperative unit comprising a large unmanned aerial vehicle and a small unmanned aerial vehicle; the network comprises warehouses, hub points and demand points; establishing a mixed integer programming model by taking minimization of the total operation cost as a target and taking electric quantity management, service distribution, a time window and collaborative feasibility as constraints; and solving the model by adopting a randomized greedy algorithm, and synchronously deciding a truck path and an unmanned aerial vehicle task in single iteration to obtain a truck path scheme, an unmanned aerial vehicle service distribution scheme and a dynamic electric quantity scheduling scheme. Therefore, the cost optimization and dynamic coordination of emergency logistics scheduling are realized, and the efficiency and feasibility of emergency distribution are effectively improved.

Disclosed are a coordinated control method for urban rail transit passenger flow, an electronic device, and a storage medium. The method includes: predicting passenger flow in peak hours of urban rail lines using a simulation deduction function in a rail simulation system, including station entry ID, station exit ID, station entry time period, and passenger number data; obtaining passenger flow of passengers arriving at stations s and preparing to board during time periods t by dividing according to the different time periods t and the stations s; counting passenger flow in each direction of historical passenger flow, and calculating a proportion of the passenger flow in each direction of the historical passenger flow; constructing a mixed integer programming model of multi-station passenger flow coordinated control; and obtaining an optimal station entry passenger flow scheme by solving the mixed integer programming model of multi-station passenger flow coordinated control.

The invention discloses a distributed server load balancing system based on multi-modal data fusion. The distributed server load balancing system comprises a multi-modal sensing layer, a fusion analysis layer, an intelligent decision-making layer and an elastic execution layer, the multi-modal sensing layer collects multi-source heterogeneous data; the fusion analysis layer is used for mining an association relationship between data of different dimensions; meanwhile, converting multi-source data into a unified feature vector, and outputting a dynamic load balancing strategy; the intelligent decision-making layer generates an executable load balancing strategy according to the fusion analysis result; and the elastic execution layer converts the strategy generated by the intelligent decision-making layer into an actual action, and deals with load change through an elastic mechanism. According to the method, server performance, network topology and request semantic data are fused, a unified feature tensor is generated by using a cross-modal attention mechanism, and load association is accurately quantified; on the basis of cooperation of a global mixed integer programming model and a local lightweight DQN agent, load migration is completed in a very short time, and the response speed is increased.

The invention relates to the field of business management, in particular to a production and manufacturing resource scheduling-oriented mixed integer programming optimization system, which comprises six modules: a data acquisition module acquires industrial big data streams; the data mapping module processes the data flow and constructs a real-time data model; the dynamic modeling module is used for automatically generating a mixed integer programming model according to a built-in steel production process model library based on a real-time data model; the model solving module generates a resource scheduling scheme through an optimization engine; the instruction analysis module analyzes a specific management and operation instruction; and the scheduling recalculation module starts a new round of mixed integer programming modeling and solving according to the increment information. According to the system, information such as resources is summarized through a mixed integer programming method, a specific production instruction is generated through a solver, a new round of modeling and solving are started when an accident occurs, rapid and accurate response to resource scheduling is achieved, and the response efficiency of resource scheduling and the reliability of the system are improved.

The invention discloses a dynamic scheduling method based on a dynamic priority queue and space-time conflict prediction, and relates to the field of conflict prediction and coordinated scheduling. The method comprises the following steps: acquiring information data of a target airport; based on a multi-service desk queuing model, according to airport flight resource data, determining a time-slot-divided take-off and landing queue sequence; based on the conflict prediction model, potential conflict thermodynamic diagram information is predicted according to radar and airport topological data; the conflict prediction model is obtained by training a space-time diagram convolutional network obtained by time sequence modeling based on combination of a diagram convolutional network and a gating cycle unit according to historical radar and airport topological data; determining a scheduling scheme by adopting a mixed integer programming model according to the take-off and landing queue sequence and the potential conflict thermodynamic diagram information; the scheduling scheme is used for scheduling flights in an airport. According to the method and the device, the conflict and collision risk between flights can be effectively reduced while the operation efficiency is improved, and scheduling optimization is realized.

The invention provides a scene port entering and leaving aircraft joint optimization scheduling method. The method comprises the following steps: aiming at aircrafts which take off and land on a plurality of parallel runways in an airport, establishing a mixed integer programming model for a scene port entering and leaving aircraft IASP; setting a target function and a constraint condition of an IASTP problem by taking minimization of the total delay duration of the port entering and leaving aircrafts as a target; and based on the constraint conditions, solving a target function of the IASTP problem by adopting a heuristic algorithm based on VNS, and obtaining a comprehensive scheduling scheme of runway and sliding path distribution, take-off and landing sequence and time of the port entering and leaving aircraft. According to the method, actual operation constraints of various scenes are considered, runway distribution and sorting, taxiing path selection and taxiway scheduling of the port entering and leaving aircrafts, and a deducing sorting scheduling scheme of the port leaving aircrafts are planned and optimized as a whole, the take-off and landing delay time of the port entering and leaving aircrafts is shortened as much as possible, the hub airport operation efficiency is improved, and the airport landing time is shortened. And decision support is provided for efficient scheduling of aircrafts.

The invention provides an intelligent traffic scheduling method based on multiple modes, and the method comprises the following steps: 1, obtaining a traffic data flow, and constructing a standardized space-time tensor X; step 2, preprocessing the standardized space-time tensor X to obtain an alignment matrix Dalign; 3, constructing a mixed integer programming model for the intelligent traffic scheduling problem, and solving to obtain a static optimal solution scheduling matrix according to the alignment matrix Dalign; and 4, constructing a deep reinforcement learning model, carrying out real-time fine adjustment on the static optimal solution scheduling matrix, obtaining a final intelligent traffic scheduling scheme, and completing intelligent traffic scheduling based on multiple modes.

The invention relates to a low-voltage distribution network topology identification method based on voltage-current correlation feature mining, and the method comprises the steps: carrying out the time synchronization processing of user-side multi-source electric parameter data after obtaining the user-side multi-source electric parameter data, and forming a unified electric parameter data set; after further preprocessing the integrated electric parameter data, extracting a voltage feature sequence based on the preprocessed point parameter data; the method comprises the following steps: establishing a user group, calculating voltage curve similarity among users, establishing a feature matrix and performing clustering analysis to preliminarily identify the user group, then establishing a mixed integer programming model by combining a Kirchhoff's current law, optimizing a phase-line-user relationship, and finally outputting a topological structure result. The problems that the low-voltage power distribution network topological structure is insufficient in recognition precision, poor in data consistency and low in dynamic adaptive capacity are solved, so that efficient and reliable network topology recognition is achieved, and monitoring and fault management of the power distribution network are effectively supported.

The invention discloses a topological communication coverage path planning method and system of an unmanned ship cluster for wide-area target search and detection, and belongs to the technical field of multi-agent collaborative path planning. According to the method, turning frequency optimization is converted into a minimum rectangular coverage problem, an integer programming model is established, a layered relaxation integer programming acceleration strategy based on maximum extension candidates is proposed, and the bottleneck of model expansion and calculation efficiency caused by candidate solution space explosion in a large-scale scene is broken through; then, constructing a rectangular topological connected graph, designing an improved spectral clustering algorithm fusing load balancing constraint and subgraph connectivity guarantee, realizing efficient and balanced distribution of regions in a complex environment, and ensuring internal connectivity of subregions; and finally, providing a dynamic pruning optimization method for the minimum corner spanning tree based on strong and weak connection characteristics, iteratively evaluating the corner cost and removing redundant edges, and finally generating a coverage path spanning tree with the minimum corner cost, thereby providing an accurate and efficient full-coverage operation path for scenes such as regional exploration and environment monitoring.

The invention discloses a multi-unmanned aerial vehicle distribution and bus charging combined path optimization method, which aims at minimizing task total time, constructs a mixed integer programming model based on a space-time network, and comprehensively considers unmanned aerial vehicle electric quantity constraint, demand point full coverage, bus time window and charging pile number limitation. An original model is decoupled to limit a main problem and a sub-problem by adopting branch pricing and a Dantzigzag-Wolfe decomposition theory, the main problem deals with demand coverage and charging resource allocation, and modeling is a set coverage problem; for single-machine path generation, the latter is modeled as a resource-constrained and replenishable shortest path problem with a time window dependent feature. A multi-unmanned aerial vehicle initial solution is constructed through a random generation method, a dual variable is iteratively solved after a main problem is initialized, a sub-problem is solved based on a multi-label algorithm, and global optimal solution search is realized in combination with a column generation mechanism and a branch strategy. According to the invention, through joint optimization of the departure time and path planning of the unmanned aerial vehicle, the collaborative optimization problem of distribution and charging is accurately solved.