420 results about "Integer linear programming model" patented technology

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention relates to an electricity-carbon cooperative scheduling optimization method and device for a low-carbon park integrated energy system, and the method comprises the steps: carrying out the cooperative prediction of a multi-state parameter through employing a panoramic situation deduction model, and generating a panoramic dynamic situation scene set; establishing an electricity-carbon cooperative scheduling model considering a carbon transaction mechanism, and deeply embedding the real-time carbon cost into a target function to carry out Pareto optimization of economic cost and carbon emission cost; an electricity-carbon cooperative scheduling model is converted into a standard mixed integer linear programming model, a situation deduction-day-ahead optimization-rolling correction hierarchical calculation framework is adopted to decompose a cooperative scheduling optimization problem to different time scales for decision making, and a global optimization plan is made on the day-ahead layer based on a panoramic dynamic situation. Deviation is corrected on line through rolling optimization in the intraday layer; and the integrated energy system executes the corrected scheduling plan. Compared with the prior art, the method has the advantages that the consumption rate of renewable energy sources can be remarkably increased and carbon emission can be effectively reduced while the operation economy of the system is ensured.

The invention provides a distributed FA cooperative control method for edge computing nodes of a distribution network terminal, and the method comprises the steps: synchronously collecting high-frequency electrical and partial discharge signals through an edge computing terminal, carrying out the multi-dimensional processing, and forming a fault feature vector; each edge node monitors a fault in real time by using a lightweight algorithm, the fault is broadcasted to an adjacent node after being subjected to Hilbert-Huang transform feature enhancement, accurate positioning is realized by adopting multi-node cross validation, and edge cloud collaboration is linked during complex topology. And after the fault is positioned, starting dual verification, sending a tripping instruction through an encrypted GOOSE protocol, searching an optimal recovery path based on a mixed integer linear programming model, and preferentially utilizing a distributed power supply to reversely supply power. A control strategy is dynamically optimized through federated learning and an MADDPG algorithm, and a digital twinborn model is trained to improve adaptability. According to the invention, the problems of slow response, low positioning precision and the like of the traditional centralized FA are effectively solved, and the fault processing efficiency, reliability and intelligent level of the distribution network are remarkably improved.

The invention provides a power system economic dispatching method considering dynamic capacity increase of a line and a related device. The method comprises the following steps: establishing a target function taking the minimum unit operation cost of a power system as a target; determining constraint conditions of the target function according to the parameters of the power system, wherein the constraint conditions comprise unit constraint, network variable constraint, linearized alternating current power flow constraint and linearized line thermal stability constraint; the economic dispatching model of the power system is solved, an economic dispatching scheme of the power system is obtained, and the economic dispatching model of the power system comprises a target function and constraint conditions. According to the method, the temperature change of the line is tracked by using the linearized line thermal stability constraint, so that the transmission capacity of the line can be improved on the basis of meeting the thermal stability constraint, an economic dispatching strategy is optimized, and the economic dispatching efficiency is improved by linearizing the alternating current power flow constraint and the line thermal stability constraint. The power system economic dispatching model is constructed into a mixed integer linear programming model, so that the solving speed and efficiency of the model can be improved.

The invention relates to the technical field of power grid side large-scale wind and light and energy storage configuration, in particular to a wind and light storage capacity distribution robust optimization configuration method, device and equipment and a medium. Establishing a power grid side wind and light energy storage optimization configuration model considering inertia constraint; uncertain factors in the optimal configuration model are considered, and a probability distribution fuzzy set based on a mixed norm is constructed; establishing a two-stage distribution robust optimization configuration model based on data driving; converting the two-stage distribution robust optimization configuration model into a mixed integer linear programming model by using a linearization technology; and solving the mixed integer linear programming model by using a column and number generation algorithm to obtain an optimal configuration and operation scheme of each device. According to the method, the economical efficiency and load fluctuation can be balanced, and the problems of frequency instability and the like of a large-scale wind and light energy grid-connected power grid are avoided by considering inertia constraint.

The invention provides a power distribution area interconnection planning method and related equipment, and the method comprises the steps: obtaining the historical data of distributed new energy output and load power of a target area, and calculating the maximum net load rate of each low-voltage power distribution area to determine a heavy overload area; the method comprises the following steps of: interconnecting a heavy overload transformer area and a transformer area connected with new energy with other normal transformer areas, screening out interconnectable combinations of the transformer areas, and taking all original connecting lines of a medium-voltage distribution network as SOP to-be-selected lines; based on the interconnectable combination of the transformer area and SOP candidate lines, by constructing a target function and constraint conditions with the optimal economical efficiency as a target, obtaining a mixed integer linear programming model converted from a distribution transformer area interconnection stochastic programming model under multiple scenes and solving the mixed integer linear programming model, and obtaining a transformer area interconnection programming scheme and a medium-voltage distribution network SOP access condition; power mutual aid and capacity sharing between low-voltage distribution areas are achieved, local consumption of distributed new energy is promoted, and the heavy overload phenomenon of the areas is relieved.

The invention relates to the technical field of high-performance computing resource management, and discloses a computing resource allocation method for a distributed supercomputing center. The method comprises the following steps: on the basis of obtaining real-time computing task and supercomputing center resource data and uniformly quantifying, integrally predicting resource requirements of future tasks; constructing a mixed integer linear programming model with the minimization of the total operation cost as a single target, wherein the total operation cost is the sum of the energy cost, the carbon emission cost, the data transmission cost and the SLA default penalty cost; solving the model by taking the time-varying electricity price, the green energy ratio, the resource capacity and the network parameters of each center as constraint conditions to generate an optimal resource allocation scheme; and then, by dynamically monitoring the resource state and the task progress, the model is triggered to resolve when the resource utilization rate is detected to be unbalanced or default risks, so that self-adaptive adjustment is realized. According to the invention, global collaborative resource allocation across super computing centers is realized, and operation economy, environmental sustainability and service reliability are considered.

The invention discloses an auxiliary decision-making method and system for regional coal market purchase, and belongs to the technical field of coal market purchase decision-making, and the method comprises the steps: building model input parameters through collecting the data of a fire coal demand plan of a power plant, the maximum supply of a supplier, a coal quality index, a purchase price and a transportation cost; constructing a mixed integer linear programming model taking the minimization of the total purchase cost as a target function, and setting a demand satisfaction constraint, a supply capability constraint and a coal quality standard reaching constraint; solving the mixed integer linear programming model by adopting a two-stage approximation algorithm; and converting the obtained approximate optimal solution into a visual purchasing scheme chart and report, and supporting a user to dynamically adjust the purchasing scheme through an interactive interface to generate a final purchasing decision. According to the invention, on the basis of comprehensively considering the coal quality, the purchase cost, the transportation mode and the supplier capability, the coal purchase plan is optimized, the cost minimization is realized, and the overall quality of coal purchase is improved.

The invention discloses a laser cutting path intelligent optimization method and system based on deep learning, and relates to the field of laser cutting, and the method comprises the steps: employing wavelet transform and a feature pyramid network to carry out cross-scale feature fusion on multi-scale material thermal response data, and obtaining a multi-scale thermal response feature vector; inputting the multi-scale thermal response feature vector into the coupling model to obtain corrected temperature field data; based on the corrected temperature field data, a reinforcement learning framework is used for solving a self-adaptive adjustment cutting index; and based on the cutting indexes, determining an optimal cutting path by using a mixed integer linear programming model. The optimal cutting path is determined in combination with the coupling model and the double-delay depth deterministic strategy gradient algorithm, and the adaptability of laser cutting to batch differences, environment disturbance and working condition changes can be improved.

The embodiment of the invention relates to the technical field of chemical products, in particular to a scheduling optimization method, device and equipment for chemical materials, and the method comprises the steps: initializing a plurality of mark sequences corresponding to a plurality of chemical material production devices; calculating the daily output of each batch of chemical materials according to the production rate and the production duration of each batch in each brand sequence; according to the daily output and the physical property parameters of each batch of chemical materials, determining the loss index of a chemical material production device and the daily consumption and the utilization rate of the chemical materials; constructing a mixed integer linear programming model by taking maximization of the utilization rate and minimization of the loss index as targets and taking conditions that the daily consumption meets daily inventory constraints and the starting time of each batch meets time window switching constraints, the mixed integer linear programming model comprises a production rate linear term and a production time linear term which are used for representing the daily output of each batch of chemical materials; and optimizing the plurality of mark sequences according to the mixed integer linear programming model.

The invention discloses a water energy accumulator and ground source heat pump combined control method, relates to the technical field of building environment and equipment engineering, accurately captures a time sequence change rule of a building cold load based on the load prediction capability of a time convolution network, overcomes the defect that a traditional method is insufficient in response to sudden fluctuation, and improves the control efficiency. The system can plan an energy storage strategy in advance; an electricity price-load change rate sensitivity index is introduced, and through an adaptive weight adjustment mechanism, economic operation and load tracking requirements are dynamically balanced, so that strategy stiffness caused by fixed priorities is avoided, and the utilization efficiency of time-of-use electricity price signals is remarkably improved; according to the joint optimization design of the mixed integer linear programming model, the equipment operation constraint and the cost target are comprehensively considered, the collaborative decision of the energy accumulator charging and discharging strategy and the heat pump frequency setting is realized, and the direct energy supply dependence in the midday high electricity price period is effectively reduced.

The invention provides an intelligent fire-fighting parallelization service function chain deployment method based on energy perception and time delay guarantee. The method comprises the steps that a traditional service function chain is converted into a parallelization service function chain; under the condition that the end-to-end delay requirement is ensured to be met, establishing a target function which aims at minimizing the energy consumption of deploying the parallel service function chain, and establishing an integer linear programming model according to the target function; and solving the integer linear programming model by adopting a heuristic algorithm considering edge node computing resources, link bandwidth resources, a node starting state and a link starting state to obtain an optimal deployment scheme. According to the method, the intelligent fire-fighting service request is abstracted into the service function chain, the service request processing problem is converted into the service function chain deployment problem, and the deployment strategy and the routing strategy of the parallel SFC are dynamically adjusted under the condition that the low-delay requirement is met, so that the average total energy consumption of SFC deployment is minimized, the SFC request acceptance rate is remarkably improved, and the service performance of the service function chain is improved. The method is suitable for intelligent fire-fighting services with high requirements on time delay sensitivity and reliability.

The invention belongs to a path planning method, and particularly relates to a search path planning method and device based on a graph neural network and electronic equipment. The method comprises the following steps: constructing a two-dimensional grid chart, wherein the two-dimensional grid chart is used for representing a search environment; a space-time mixed integer linear programming model is constructed based on a search task, and the target of the search task is to determine a search path, so that the probability of detecting a search object from the search environment within preset time is maximized; constructing a weighted directed hypergraph based on the space-time mixed integer linear programming model; and based on the weighted directed hypergraph, solving the space-time mixed integer linear programming model by adopting a branch and bound method to obtain an optimal solution, the optimal solution being used for representing the search path, and the branch and bound method determining a branch variable based on a graph neural network. According to the method, the dynamic distribution of the search object can be represented, and the efficiency and the accuracy of the search path are improved.

The invention discloses a plunger gas lift well group peak shifting well opening and closing scheduling optimization method. The method comprises the following steps: acquiring plunger gas lift well group field data; a plunger gas lift well group production operation scheduling collaborative optimization mixed integer linear programming model described based on discrete time, namely an MI LP model; a plunger gas lift well group production operation scheduling collaborative optimization M ILP model solving method based on a genetic algorithm; obtaining a plunger gas lift well group peak shifting scheduling feasibility scheme under different objective functions; and evaluating and optimizing the effect of the plunger gas lift well group peak shifting scheduling feasibility scheme. According to the method, an automatic optimization method is adopted, the intensity of manual decision making and possible errors are reduced, and the scheduling efficiency and accuracy are improved; the genetic algorithm is adopted for optimization solution, an approximate optimal solution can be obtained in a short time, and the method is suitable for the scheduling problem of large-scale well groups.

The invention discloses a reconfigurable flexible job shop scheduling optimization method with secondary clamping constraint, and relates to the technical field of intelligent manufacturing and production optimization. The method comprises the following steps of: 1) establishing a mixed integer linear programming model considering a reconfigurable flexible job shop scheduling problem of secondary clamping by taking minimization of maximum completion time and minimum number of chemical workers as targets; 2) designing a three-segment coding mode and a decoding mode corresponding to the mixed integer linear programming model based on process sorting, machine selection and worker selection; and 3) based on the three-segment coding mode and the decoding mode, adopting an improved multi-target genetic algorithm to solve an optimal scheduling scheme of the mixed integer linear programming model. According to the method, processing machine selection, auxiliary module selection, processing sequence sorting and secondary clamping worker selection of a manufacturing workshop can be considered at the same time, the workshop production efficiency is improved, and the method has the advantages of being good in model performance, small in result fluctuation and high in stability.

The present disclosure relates to the technical field of optimization of charging facilities, and in particular, to a day-ahead coordinated optimization scheduling method for a truck mobile charging station. The method includes: constructing an optimization scheduling model framework, wherein the optimization scheduling model framework includes: an EV charging demand generation model and a TMCS spatial-temporal model, the EV model is used to determine the position and time of the TMCS charging demand, and the TMCS scheduling model is used to describe the spatial-temporal dynamic characteristics of TMCS operation and complete the coordinated optimization scheduling of TMCS between EV charging service and energy arbitrage; capturing a charging decision process of heterogeneous EV users by adopting MCS and a multinomial Logit model; establishing an extended graph model to describe the spatial-temporal dynamic characteristics of TMCS; and then expressing the coordinated scheduling model as a mixed integer linear programming model.

The invention discloses a building energy consumption control method, device and equipment and a storage medium, and relates to the technical field of building management. The method comprises the following steps: acquiring energy consumption associated data of a current building, and determining predicted energy consumption of the current building by using a preset neural network according to the energy consumption associated data; for different types of electric equipment in the current building, generating an energy-saving control strategy by using a mixed integer linear programming model according to the predicted energy consumption corresponding to the electric equipment and a preset constraint condition; and controlling the electric equipment according to the energy-saving control strategy and historical energy-saving control strategy feedback data. According to the technical scheme of the embodiment of the invention, the prediction accuracy is improved, the performability of the control strategy is improved, and a full-link intelligent energy-saving system with high performability is constructed.

The invention discloses an aeronautical composite material hot press molding productivity evaluation method considering interval grey number working hours, which comprises the following steps: determining the interval grey number working hours of each workpiece through historical production data and an uncertain working hour generation algorithm; on the basis, the maximum completion time is obtained as an evaluation index by a mixed integer linear programming model and a heuristic method with the purpose of minimizing the maximum completion time of a production task, and a productivity evaluation result is generated by comparing the maximum completion time with the available time of a machine. On the premise that constraint conditions are met, according to the method, the production capacity evaluation method aiming at the working procedure is designed by comprehensively considering the production characteristics of the aviation composite material hot-press forming working procedure aiming at the uncertain working hours in the actual production process, so that the production capacity is reflected more accurately; the productivity evaluation result provided by the method can provide guidance for the design of an actual production plan and the configuration of production resources, the production delay is reduced, and the production efficiency is improved.

The invention discloses a site-level CCUS dynamic source-sink matching optimization method for an oil field enterprise, relates to the technical field of large-scale deployment of carbon capture, utilization and storage of the oil field enterprise, and particularly relates to the site-level CCUS dynamic source-sink matching optimization method for the oil field enterprise. Comprising the following steps: integrating a carbon source end full life cycle technical economy evaluation system and a storage target area'geology-potential-economy 'three-dimensional grading model to form a dynamic database; on the basis of the dynamic database, a mixed integer linear programming model fusing source sink dynamic priority coefficients is constructed, three scenes of cost minimization, oil displacement income maximization and carbon sink subsidy excitation are set, and a constraint system is coupled; according to the method, carbon source technology economic evaluation and storage target area three-dimensional grading are fused, the specific injection-production cycle, policy incentive and pipe network constraint of an oil field are converted into time-varying weight coefficients, traditional static optimization limitation is broken through, and site-level source-sink dynamic accurate matching is achieved.

The invention discloses a family medical care path planning and scheduling method based on deep reinforcement learning, and the method comprises the steps: obtaining HHCRSP instance data, modeling the HHCRSP instance data into a mixed integer linear programming model, carrying out the problem decomposition and sorting, and obtaining a plurality of VRP sub-problems with the types of services needed by patients as the grouping basis and the service dependency as the solving sequence; modeling the solving process of each VRP sub-problem into a Markov decision process, and solving the constructed Markov decision process according to a solving sequence through a strategy network to obtain a service path scheme and a service timetable thereof; and integrating a service path scheme obtained by solving each VRP sub-problem with a service timetable to form a planning and scheduling scheme including family medical care paths of all patients. According to the method, through key technologies such as problem decomposition, a neural network parameterization strategy, service embedding and constraint perception mask, the technical problems of the HHCRSP in the aspects of expandability, real-time performance, generalization ability and complex constraint processing are systematically solved.

The invention provides a numerical control multi-objective optimization energy-saving method and system based on an improved wolf pack algorithm, and the method comprises the steps: constructing a mixed integer linear programming model of multi-objective flexible job shop scheduling, taking machine tool distribution, a machining sequence, tool distribution and a machining speed as decision variables, and taking total weighted tardiness and total energy consumption as optimization targets; the method comprises the following steps: constructing an MOGWO-ALNS algorithm for solving a mixed integer linear programming model based on an MOGWO algorithm and an adaptive large neighborhood search mechanism, constructing a three-stage collaborative optimization architecture by the MOGWO-ALNS algorithm through a dual-threshold trigger mechanism, solving the mixed integer linear programming model based on the MOGWO-ALNS algorithm, obtaining a Pareto optimal solution set of the mixed integer linear programming model, decoding the Pareto optimal solution set, and finally obtaining the mixed integer linear programming model. And generating a multi-target balanced energy-saving scheduling scheme. According to the method, collaborative optimization of the delivery date and the energy consumption in numerical control machining is achieved, the solving efficiency and the optimization effect are improved, and the method has good engineering application value.

The invention relates to the technical field of project supervision informatization, and provides a supervision multi-heterogeneous system cloud edge collaborative adaptation method, which comprises the following steps: collecting heterogeneous information such as edge equipment hardware architecture and an operating system to generate records; integrating supervision monitoring data and cloud instructions, classifying the data according to functions and updating frequencies, and marking high-frequency units; unifying a data format and adding Hash verification information; the cloud constructs a three-dimensional mapping table, and edge nodes are grouped in combination with high-frequency data; converting the private protocol of the edge device into a general protocol; by taking minimization of transmission quantity and energy consumption as a target, distributing tasks by using an integer linear programming model; dynamically adjusting the equipment energy consumption; and dynamically optimizing the configuration based on the interface indexes. The cloud edge interaction efficiency can be improved, the energy consumption is reduced, and the reliable operation of supervision business is ensured.

The invention discloses a heterogeneous resource collaborative scheduling method for training an AI inference service, which comprises the following steps of: establishing a mixed integer linear programming model MILP based on an AI inference collaborative heterogeneous resource scheduling problem trained by a distributed model, and maximizing the number of successfully sent AI inference sub-tasks by the final goal of the proposed MILP, the utilization rate of heterogeneous resources in the NG-RAN and the backlog of the AI reasoning service are minimized; constructing an edge computing resource request, computing processing capability, a task backlog threshold value of an AI reasoning sub-task on an edge server and a complaint rate threshold value of the AI reasoning sub-task as constraint conditions; the deployment of the DU-CU in the network architecture is standardized; adjusting wavelength allocation in the network; and the capacity requirement of the network is specified. According to the method, the number of successfully sent AI inference service subtasks can be maximized, and meanwhile, the heterogeneous resource occupancy rate and the AI inference service subtask backlog amount are minimized.

The invention discloses a service scheduling method and system for a photoelectric hybrid low earth orbit satellite network, and relates to the technical field of satellite communication and network resource management.The method comprises the steps that the photoelectric hybrid low earth orbit satellite network is constructed, and topological information, node information and a to-be-scheduled service set of the photoelectric hybrid low earth orbit satellite network are obtained; establishing a mixed integer linear programming model containing node selection constraint, service scheduling sequence constraint and routing constraint by taking the minimum weighted sum of the total service completion energy consumption and the total service completion time as a target; in order to solve the problem of high model complexity, a heuristic algorithm based on simulated annealing is designed, and efficient solution is carried out by iteratively optimizing a scheduling sequence and a routing path of a service; and finally, implementing service scheduling according to the obtained optimal scheduling scheme. According to the method, heterogeneous characteristics of the photoelectric nodes and link resource conflicts are fully considered, dynamic balance of energy consumption and time delay is achieved, network energy efficiency and business service quality are remarkably improved, and the method is suitable for efficient operation of large-scale low-orbit satellite constellations.

The invention discloses a space-time cooperative scheduling method for an intelligent sky rail and an AGV in an automatic container terminal, and the method comprises the steps: determining the scheduling constraint conditions of an SMV and the AGV based on an SMV and AGV dual-cycle strategy, and constructing a model and constraint conditions which take the minimization of the completion time of all tasks as a target; using an LBBD algorithm to decompose the mixed integer linear programming model into a main problem and a sub-problem, constructing three acceleration strategies based on SMV and AGV dual-cycle strategies, embedding acceleration cut into the main problem as a constraint condition, solving the main problem and the sub-problem under the constraint condition, and generating Benders cut; embedding the Benders into the main problem, and solving again to obtain a scheduling optimization result; the scheduling decision quality is fundamentally improved, a set of scientific and efficient SMV-AGV collaborative operation method is provided for an intelligent air rail system, the equipment utilization rate can be remarkably improved, the operation completion time can be shortened, the optimal collaborative scheduling scheme can be rapidly and accurately obtained in a large-scale task scene, and the unloaded driving cost, the energy consumption cost and the operation cost are synchronously reduced.

The invention discloses a vehicle and unmanned aerial vehicle cooperative path planning method considering three-dimensional loading constraint. The method comprises the following steps: respectively adopting two unloading modes of first-in and second-out and unloading and then loading. Wherein the cargo first-in and second-out mode means that only cargos located on the outermost layer of the vehicle can be distributed every time, and the cargo unloading allowing and then loading mode means that cargos located on the inner layer of the vehicle are allowed to be distributed; constructing a mixed integer linear programming model including decision variables, auxiliary variables, optimization targets and constraint conditions; and designing a mixed meta-heuristic solving method with high solving efficiency, and solving a loading scheme and a distribution scheme. The method is applied to the field of collaborative path planning, the loading and distribution processes of goods are considered at the same time, the common modes of first-in and second-out and unloading and then loading in the logistics distribution process are adopted, the main path of the distribution vehicle and the path of the unmanned aerial vehicle are optimized, the logistics distribution efficiency is remarkably improved, and meanwhile the requirements of customers are met.

The invention discloses a multi-vehicle-type electric shared vehicle scheduling method under a predetermined order, and the method comprises the following steps: constructing a mixed integer linear programming model according to the information of the predetermined order on the current day, and constructing a joint decision of vehicle delivery, vehicle type selection, order selection, charging planning and personnel scheduling by the model by taking system revenue maximization as an objective function; solving the mixed integer linear programming model by using a solver to generate a vehicle scheduling scheme and a scheduling personnel path planning scheme; according to the invention, the benefit of the shared travel platform in the whole operation process is obviously improved, and the operation cost is reduced.

The invention discloses a distributed photovoltaic low-carbon economic bearing capacity assessment method and system, and the method comprises the steps: building a distributed photovoltaic low-carbon economic bearing capacity determinacy assessment model considering the low-carbon economic benefits with the optimization of the low-carbon economic benefits generated by distributed photovoltaic reduction as a target; a distributed photovoltaic output uncertainty evaluation model is constructed by adopting the distribution robust chance constraint of the Wasserstein distance; and processing nonlinear constraints in the distributed photovoltaic output uncertainty evaluation model, solving the converted mixed integer linear programming model by using a Cplex solver, and outputting an evaluation result of the distributed photovoltaic low-carbon economic bearing capacity. Unification of economic benefits and environmental benefits can be promoted, the bearing capacity of the power distribution network is enhanced, voltage fluctuation and system stability are relieved, the Wasserstein fuzzy set and the distribution robust opportunity constraint are adopted to deal with photovoltaic uncertainty, the McCormick relaxation technology is introduced to simplify the nonlinear problem, and the decision accuracy and the model practicability are improved.

The invention provides a path determination method and device, a readable storage medium and self-moving cleaning equipment, and relates to the technical field of path planning. The path determination method of the self-moving cleaning equipment comprises the following steps: determining a plurality of path points corresponding to a to-be-cleaned area according to an area map of the to-be-cleaned area; and a target cleaning path is determined based on the mixed integer linear programming model and the multiple path points, the target cleaning path is a moving path when the self-moving cleaning equipment cleans the to-be-cleaned area, and the target cleaning path sequentially passes through each path point in the multiple path points.

The invention provides a flight ground service guarantee vehicle scheduling determination method and device, equipment and a medium, and the method comprises the steps: carrying out the problem solving of a mixed integer linear programming model of a target airport based on a greedy algorithm, and determining an initial solving result of the mixed integer linear programming model; if a GCN removal operator is selected based on a roulette algorithm, inputting feature information of an initial solving result into a graph neural network model, predicting a target vehicle node and removing an aircraft task associated with the target vehicle node; and processing the damaged initial solution result based on the repair operator and the simulated annealing algorithm criterion to obtain a new solution result, dynamically updating the performance score and the weight of the corresponding operator to enter the next iteration, and determining a target solution result of the mixed integer linear programming model until the number of iterations is reached. And the performance of the ALNS algorithm is accelerated and improved by using a GCN removal operator, so that efficient and reliable multi-garage vehicle collaborative scheduling is realized.