306 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 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 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 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 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 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 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.

The invention belongs to the technical field of hybrid pumped storage power station dispatching, and particularly discloses a multi-time-scale nested hybrid pumped storage power station dispatching method and device, and the method comprises the steps: constructing a multi-objective function with the maximum power station income and the minimum power grid residual load peak-valley difference as the objective; based on the multi-objective function, sequentially carrying out optimal scheduling on the hybrid pumped storage power station according to a plurality of time scales from large to small to obtain a system model; converting the system model into a mixed integer linear programming model; and solving the mixed integer linear programming model to obtain a scheduling result. According to the method, the economic benefit of power station operation and the load regulation capability of a power grid can be remarkably improved, the complexity of nonlinear model solution is effectively reduced, and the scheduling efficiency and accuracy are improved.

The invention discloses a method and a system for solving cascade workshop multi-scene robust scheduling under a multi-objective framework, and the method comprises the following steps: constructing a mixed integer linear programming model considering the uncertainty of processing time, a robust scheduling problem in multiple scenes is converted into a multi-target problem taking the maximum completion time in each typical disturbance scene as an optimization target; designing a scene-driven heuristic construction method to generate a high-quality initial solution set; iterative search of a scheduling solution is carried out under an evolutionary greedy optimization framework based on scene decomposition, adaptive parameter control, subgroup division, local reconstruction and a variation guide strategy are fused, and the search capability and adaptability of an algorithm are improved; and finally, screening a Pareto optimal solution through an aggregation function and a niche mechanism. According to the method, multi-scene scheduling performance balance optimization is realized in a PCB manufacturing cascade production system, the method has the advantages of high modeling precision, high search efficiency, excellent solution set quality and the like, and the robustness and practicability of a scheduling scheme under a multi-scene disturbance condition are remarkably improved.

The invention relates to the technical field of production scheduling and intelligent manufacturing, and provides an assembly line scheduling optimization method considering generalized priority constraints, which comprises the following steps: constructing an integer linear programming model, performing generalized priority relation enhancement and propagation on an input instance of the integer linear programming model, and improving the capacity of a workstation; calculating a simple lower bound based on task processing time, a lower bound based on the longest path of the priority graph and a lower bound based on a maximum flow algorithm, and taking the maximum value of the three as a final lower bound; defining an action space, defining a state space to represent the change of a current solution on an objective function value and a distribution balance degree relative to a global optimal solution and a local optimal solution, and designing a reward function to adaptively select a neighborhood operation; constructing an initial scheme based on the final lower bound and a Q-learning dynamic selection module, and performing batch movement iteration through neighborhood operation adaptively selected by the Q-learning dynamic selection module; and outputting an assembly line scheduling scheme meeting the generalized priority constraint based on a result of the batch movement iteration local search.

The invention provides a numerical control multi-target energy-saving optimization method and system and a storage medium, and the method comprises the steps: constructing a mixed integer linear programming model for multi-target 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; a single-layer real number coding method is adopted, machine tool allocation, the machining sequence and the machining speed are coded in a one-dimensional real number sequence in a unified mode, an initial population is generated, the mixed integer linear programming model is solved through a multi-target optimization algorithm, and an algorithm optimal solution of the mixed integer linear programming model is obtained; decoding the optimal solution of the algorithm, distributing tools for each process, and generating a production plan table; and calculating the total weighted tardiness and the total energy consumption to obtain an algorithm energy-saving optimization scheduling scheme. According to the method, the dual-objective mixed integer linear programming model is constructed, and the innovative coding and decoding strategy and the dynamic optimization mechanism are combined, so that collaborative optimization of the production efficiency and the energy consumption is realized.

The invention is suitable for the field of power distribution network post-disaster reconstruction, and discloses a power distribution network post-disaster rapid reconstruction method, system and device based on radial constraint and a medium, and the method comprises the steps: obtaining post-disaster power distribution network data, and constructing a post-disaster reconstruction model; based on the post-disaster reconstruction model, establishing an optimization objective function and various constraint conditions; establishing a mixed integer linear programming model based on the optimization objective function and a plurality of constraint conditions, judging whether the set constraint conditions are met by combining a particle swarm optimization algorithm, and solving an optimization problem of the mixed integer linear programming model to obtain a final decision result; and obtaining a switch operation sequence based on the decision result, calculating a recovery result of each power supply unit, and obtaining a reconstruction decision scheme of the post-disaster power distribution network, thereby improving reconstruction calculation efficiency and engineering applicability, and realizing rapid recovery of the key load and stable operation of the post-disaster power distribution network.

The invention discloses a cascade hydropower decoupling optimization complementary scheduling method under multi-source uncertainty requirements, and belongs to the field of hydropower scheduling. According to the method, a new decoupling scheduling normal form of'interval commitment-autonomous response 'is provided for multi-source uncertainty requirements of new energy output fluctuation, power spot market clearing power fluctuation and the like faced by a power system, so that the problems of response lag, low decision-making efficiency and limited adjustment capability existing in a traditional'plan reporting-negotiation adjustment' scheduling mode are solved. The method comprises the following steps: firstly, establishing a cascade hydropower decoupling scheduling framework, enabling each power station to autonomously calculate and report a dynamic adjustable electric quantity interval based on own complex hydraulic constraint, and constructing a cascade hydropower decoupling optimization complementary scheduling model under a multi-source uncertainty demand on this basis; therefore, the regulation capability boundary of the water and electricity in the multi-source uncertainty environment can be accurately quantified. And finally, converting an original problem into a mixed integer linear programming model through a two-stage solving algorithm combining linear reconstruction and a strong duality theory to realize efficient solving.

The invention discloses an offshore wind power plant intensive current collection system planning method based on cable parallel laying quantification. The method comprises the following steps: acquiring node coordinates and engineering parameters of a wind power plant; an initial solution is obtained through clustering partition, candidate cables are screened, and the cross relation of the candidate cables is identified; a mixed integer linear programming model with the cable parallel laying length as a quantitative index is constructed, a target function of the mixed integer linear programming model fuses cable construction cost, full life cycle power loss cost and intensive excitation terms for parallel laying, and engineering constraints such as parallel laying logic, power conservation, node degree and crossing prevention are integrated; model solution and iterative optimization are carried out by setting and dynamically adjusting intensive excitation coefficients, finally, a reliable layout scheme which meets the intensive sea use policy and is better in total cost is automatically generated, and the defects that a traditional method depends on manpower, efficiency is low, and economy and intensive targets are difficult to plan as a whole are effectively overcome.

The invention belongs to the technical field of deterministic transmission, and particularly relates to a deterministic transmission method for a time division multiplexing passive optical network in computing power-power cooperative communication network networking, which comprises the following steps of: acquiring and identifying all time delay sensitive flows accessed to a TDM-PON (Time Division Multiplexing-Passive Optical Network), and pre-classifying periodic flows; clustering the similar periodic flow to form a shaping flow, and distributing a shaping window; calculating the least common multiple of all shaping flow periods as a super period; in a super cycle, an integer linear programming model is adopted to complete initial optimization time window distribution; carrying out secondary sorting and conflict elimination on the overlapped transmission windows, and scheduling the shaping stream by using the transmission windows after secondary optimization; the non-periodic flow is scheduled by using the idle time slot in the over-period; and continuously multiplexing a super-cycle scheduling scheme to complete a deterministic transmission process. According to the invention, high scheduling success rate, millisecond-level deterministic time delay and microsecond-level jitter control of large-scale periodic services are realized.