380 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 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 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 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 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 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 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 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 discloses a cooperative perception task unloading method based on vehicle-mounted edge calculation, and aims to improve the perception quality and calculation efficiency of an automatic driving system. The method optimizes a task unloading strategy through information value evaluation and redundancy perception suppression; the method comprises the following steps: firstly, predicting a vehicle motion track and estimating a region of interest (ROI) by using a Kalman filter, and calculating a VOI value of perceptual data so as to quantify the contribution degree of the data to a decision; secondly, redundant data are recognized by calculating the vehicle view overlapping degree, and transmission and calculation burdens of redundant sensing tasks are reduced; then, based on an integer linear programming (ILP) model, optimizing an unloading decision, maximizing perception benefits, and ensuring that tasks are distributed to an optimal computing node; a dynamic unloading scheduling strategy based on VOI is further adopted, and a task unloading path is dynamically adjusted according to the real-time evaluation result and the network condition; besides, a performance feedback and optimization mechanism is designed for the system, the unloading execution effect is continuously monitored, a strategy is adjusted according to historical data, and long-term stable operation is achieved.

The invention discloses a non-full-length beam steel bar material breaking optimization method based on a hybrid optimization algorithm, and aims to solve the problem that an existing non-full-length beam steel bar material breaking method cannot balance the steel bar utilization rate and the calculation efficiency. The method comprises the following steps: for required beam steel bars under the same beam steel bar diameter, dividing the required beam steel bars into two groups according to the length of the beam steel bars; for required beam steel bars with the length larger than 12 m, a cut excess material splicing method is adopted, an optimized mathematical model of steel bar excess materials is established, and the number of used raw material steel bars and the division length of the raw material steel bars are calculated and determined through a genetic algorithm; and for required beam steel bars with the length smaller than 12 m, directly cutting from a single raw material steel bar or combining and processing the raw material steel bars with different specifications, listing all cutting modes, calculating steel bar excess materials corresponding to the cutting modes, and minimizing the total steel bar excess materials by the integer linear programming model to obtain a steel bar broken material combination scheme.

The invention provides an energy storage power station joint market optimal regulation and control method and system, and is applied to the field of power system optimal scheduling. The method comprises the steps of constructing a double-layer optimization model, merging a lower-layer model into an upper-layer model according to the Carlo demand-Kuhn-Tuck condition, carrying out linear processing to form a mixed integer linear programming model, solving and obtaining an optimal strategy, and regulating and controlling energy storage. The income is improved.

The invention provides an air conditioning system energy efficiency optimization method based on load prediction, and the method comprises the steps: collecting air conditioning load related data, building a load prediction model which employs a support vector regression model, and training the support vector regression model through the air conditioning load related data; constructing a model function by adopting a mixed kernel function of a linear kernel and a Gaussian kernel in the support vector regression model, and obtaining an air conditioner load prediction result according to the load prediction model; an energy efficiency optimization model is constructed, the energy efficiency optimization model adopts a mixed integer linear programming model, the mixed integer linear programming model takes the total energy consumption minimization of the air conditioning system as a target function, energy consumption of various devices in the air conditioning system is considered, and an energy consumption function is established; the energy consumption function represents the energy consumption under the given control variable and the air conditioner load prediction result. The operation state and parameters of the air conditioning system are adjusted in real time according to the prediction load and the result of the optimization model, and energy-saving operation of the system is achieved.

The invention relates to a source-grid-load-storage integrated optimal configuration method of an enterprise power grid, and belongs to the technical field of power system planning of the enterprise power grid. The invention aims to realize source network load storage collaborative configuration through a multi-dimensional data processing and optimization model. The method comprises the following steps: generating a year-round wind and light output per unit value based on wind and light historical data, wind speed or illumination data; constructing annual typical load data of the enterprise through load curve generation, k-means clustering and random sampling; a mixed integer linear programming model is established in combination with constraints such as investment operation cost and energy balance; key indexes such as green electricity proportion, energy abandoning and electricity purchasing cost are calculated through the model; and performing scheme checking on the maximum / minimum load day and the maximum / minimum green power ratio day. According to the method, through data driving and optimization modeling, the green power consumption efficiency can be effectively improved, the wind curtailment and light curtailment amount is reduced, meanwhile, the investment and operation cost is optimized, and scientific support is provided for source network load storage integrated configuration of an enterprise power grid.

The invention relates to a multi-disaster coupling tough power grid dynamic defense method and a storage medium, and the method comprises the steps: a multi-disaster coupling analysis module receives disaster data and power grid data in real time, carries out the grid processing, obtains a disaster coupling value H of each grid, and carries out the risk grade division of each grid; the dynamic toughness evaluation module calculates the node vulnerability index of each grid according to the disaster coupling value H of each grid and the real-time data of the power grid; the collaborative recovery decision module formulates an optimal recovery strategy for the execution terminal by adopting a mixed integer linear programming model MILP according to the vulnerability index of each grid node, the equipment importance grading information and the fault influence range information; and the execution terminal executes the instruction according to the optimal recovery strategy and feeds back the executed state data in real time. Compared with the prior art, the method provided by the invention solves the three major problems of vulnerability identification lagging, low recovery resource scheduling efficiency and difficulty in cross-department collaboration of the oversize urban power grid under composite extreme disasters.

The embodiment of the invention provides a fault response method and device for a power distribution network, equipment and a storage medium. According to the method, target fault information determined based on operation state data of the power distribution network is obtained, the target fault information comprises at least one target fault type of faults and a sub-region corresponding to the at least one target fault type, and then the sub-region corresponding to the at least one target fault type is determined according to each target fault type. And performing hierarchical planning processing on the sub-region corresponding to the target fault type based on a preset mixed integer linear programming model to obtain at least one initial fault recovery strategy corresponding to the target fault type, and finally determining a target fault recovery strategy according to the at least one initial fault recovery strategy, the target fault recovery strategy is used for guiding the user to carry out fault recovery on the sub-region corresponding to the target fault type. According to the method provided by the invention, the fault response to the power distribution network is realized, and the fault response speed and the fault recovery efficiency are improved.

The invention discloses a distributed heterogeneous flexible flow shop batch processing scheduling method and system, relates to the technical field of distributed production scheduling in the manufacturing industry, and aims to solve the problems that an existing scheduling method is not comprehensive in constraint consideration, poor in energy consumption optimization and low in algorithm efficiency. According to the method, a mixed integer linear programming model containing multiple constraints such as release time and sequence-related preparation time is constructed, a learning-assisted dual-objective co-evolution framework is established, and the maximum completion time and the total energy consumption are synchronously optimized by combining mixed initialization, global-local search collaboration, decision reinforcement learning operator selection and a collaborative energy-saving strategy. The release time, the sequence-related preparation time, the inter-stage transportation time and the batch processing scheduling are simultaneously considered in the distributed heterogeneous flexible flow shop scheduling for the first time, the established mixed integer linear programming model better fits the actual production scene, and the method fits the actual production scene, is good in energy consumption optimization effect and can be adapted to the non-ferrous metal metallurgy aluminum production process.

The invention discloses a vehicle and unmanned aerial vehicle cooperative distribution path planning method based on a temporary waiting mode, and the method comprises the steps: carrying out the preprocessing of a customer set, carrying out the clustering of the customer set served by a vehicle-mounted unmanned aerial vehicle based on the capacity of a truck, obtaining a clustering center and affiliated to-be-served customers, and obtaining an initial network; modifying the initial network, constructing a spatial expansion network, splitting each clustering center into an unmanned aerial vehicle virtual outflow node and a virtual inflow node, and introducing a corresponding virtual arc; establishing a mixed integer nonlinear programming model; performing objective function and soft time window constraint linearization processing on the mixed integer nonlinear programming model, and establishing an arc-based mixed integer linear programming model; and solving to obtain an optimal distribution scheme. According to the invention, complex constraints of multi-vehicle multi-unmanned aerial vehicle cooperative distribution can be systematically considered, distribution cost minimization and time window constraint satisfaction are realized, and a scientific and efficient scheduling planning scheme is provided for urban logistics and intelligent distribution.

The invention discloses an electric heating integrated energy system elastic operation method considering load demand optimization configuration. The method comprises the steps that a load demand model considering flexible energy consumption behaviors of a user under extreme conditions is established; establishing a micro-energy network island division model adopting virtual power flow; establishing a power distribution network and heat supply network operation model; with improvement of the multi-element load recovery amount and the thermal load node temperature level as the target, nonlinear variables in the micro-energy network island division model are subjected to linearization processing, and an integrated energy system elastic operation optimization model is established; and solving the mixed integer linear programming model to obtain a post-disaster "source-network-load" cooperative scheduling strategy of the electric heating integrated energy system under extreme conditions. By means of the optimized operation scheme of the comprehensive energy system under the extreme condition, the load side multi-energy complementary potential in the comprehensive energy system can be brought into full play, the multi-element load recovery amount and the user comfort level in the comprehensive energy system can be effectively improved, and the elasticity level of the comprehensive energy system is further improved.