81 results about "Biobjective optimization" patented technology

The invention relates to the technical field of intelligent mechanical arm control, and discloses a mechanical arm track optimization method and system based on deep learning and a fuzzy algorithm, and the method comprises the steps: building a kinematic model of a mechanical arm, determining the working space of the mechanical arm, carrying out the high-density random sampling, and generating a three-dimensional point cloud picture of a reachable region at the tail end of the mechanical arm; constructing a path planning model, designing a state space and an action space, and constructing a reward function; time-impact double-target optimization is carried out on the tail end path point sequence, a smooth joint trajectory is constructed, and balance between the shortest trajectory execution time and the minimum joint impact is achieved on the premise that speed and acceleration constraints are met; and tracking control is carried out on the trajectory, external disturbance and unmodeled dynamics are estimated and compensated in real time, a parameter adaptive law is designed, and the trajectory tracking precision of the system in a complex environment is improved. The autonomy, the accuracy and the anti-interference capability of the hot-line work mechanical arm in a complex environment are improved.

The invention discloses a subway depot upper cover building vibration response prediction method based on deep learning, and the method comprises the steps: constructing a feature library containing vibration signals and working condition data, generating enhanced data through a mechanical model, and fusing the enhanced data into a data set; a mixed deep learning model embedded with physical prior is constructed, and training and dual-objective parameter optimization are carried out; a prediction result is output after working conditions of real-time data are recognized through the lightweight model; parameters are finely adjusted through regular incremental learning, and transfer learning adaptation is carried out when working conditions suddenly change; verifying precision and rationality, and adjusting the weight of a regular term or suggesting to add a sensor; according to the method, measured data sparseness is made up by enhancing data fusion; the double-branch architecture overcomes the deep nonlinear mapping problem, and physical constraints are prevented from violating physical rules; the incremental learning reduces the cost, and the transfer learning solves the time-varying vibration capture problem; precision is improved through closed-loop verification, accurate real-time prediction of vibration response is achieved, and safety and comfort of a building are guaranteed.

The invention provides an HC-MOGA-based agent task collaborative scheduling method in an industrial internet, and aims to solve the key problems of low scheduling efficiency, poor system robustness and the like caused by heterogeneous types of equipment (subsequent modeling is an agent), complex task dependence and non-uniform resource distribution in an industrial site. According to the method, a heterogeneous agent scheduling model with task identification, resource matching and dependent modeling capabilities is constructed for a multi-stage operation task cooperatively completed by a data acquisition agent and an operation type agent in an industrial internet environment. An HC-MOGA (heterogeneous constraint multi-objective genetic algorithm) is provided, multi-dimensional chromosome coding, a layered mapping mechanism, a dual-stage variation strategy and a dynamic constraint repair mechanism are introduced, and a dual-objective optimization model fusing work completion income and comprehensive resource consumption and potential loss is established. The method is suitable for industrial internet typical application scenes such as intelligent manufacturing, automatic production lines and multi-agent task collaborative management.

The invention discloses a beneficiation whole process optimization control system and method based on a large model, and relates to the technical field of beneficiation process optimizing.The beneficiation whole process optimization control method comprises the steps that a global prediction model is built based on unified feature embedding, and the global prediction model is distilled into an edge lightweight model with a conditional branch selection mechanism; carrying out hot start on the edge lightweight model in combination with historical experience obtained by knowledge graph retrieval, constructing a dual-objective optimization problem, and carrying out search by utilizing improved difference based on loss constraint to generate a candidate control strategy set; performing calibration and confidence screening on the candidate control strategy set under a multi-scene condition to generate a safety control instruction; in the execution process of the safety control instruction, data drift is monitored. According to the method, the prediction precision is ensured, and meanwhile, the real-time performance and the availability in a low-computing-power environment are considered.

The invention relates to the technical field of virtual power plant collaborative optimization, and discloses a virtual power plant multi-target carbon economy optimization method based on swarm intelligence, which comprises the following steps: S1, constructing a digital twinborn body of a virtual power plant, and synchronizing distributed energy output, load demand and carbon emission data of a physical VPP (virtual power plant) in real time; s2, establishing an energy-carbon economy dual-objective optimization model; s3, solving the dual-objective optimization model through an improved swarm intelligence algorithm, wherein the algorithm dynamically adjusts the weight of the economical efficiency and the weight of the carbon emission objective; s4, real-time data based on the digital twinborn body; and S5, outputting a Pareto optimal solution set. The total operation cost and the full life cycle carbon emission in the operation cycle of the virtual power plant are taken as double optimization targets, the target weight is adjusted through the dynamic carbon price sensitivity coefficient, carbon market signals are coupled, the decision-making deviation of high implicit carbon and operation pseudo-low carbon of equipment is avoided, and the multi-target decision-making scientificity of the virtual power plant under the carbon constraint is improved.

The invention discloses a tungsten-nickel-iron alloy strength and toughness dual-objective optimization method and system based on machine learning, and the method comprises the following steps: obtaining original data of the components, sintering process, processing process, heat treatment process, strength and toughness of a tungsten-nickel-iron alloy, and carrying out the integration and summarization, so as to obtain an initial data set; preprocessing the initial data set to obtain a processed data set; constructing a plurality of candidate models based on a machine learning algorithm, screening the plurality of candidate models, and training the screened optimal candidate model by using the processed data set to obtain a prediction model; performing strength and toughness prediction on the target tungsten-nickel-iron alloy by using the prediction model to obtain a prediction result; and on the basis of the prediction result, candidate alloys meeting the application requirements are screened out, a dual-objective optimization framework is constructed by using a Pareto optimal criterion to optimize the candidate alloys, and the alloy with the strength and toughness cooperatively optimized is obtained.

The invention discloses a fire-fighting smoke exhaust pipeline laying planning system based on an industrial cloud platform, and relates to the technical field of pipeline laying planning, and the system comprises a building information collection module which collects building data; the cloud edge cooperative computing module is used for constructing a smoke exhaust simulation model by using distributed computing resources of an industrial cloud platform and carrying out real-time parameter iteration through edge nodes; the pipeline path planning module is used for generating a plurality of candidate laying paths by using an improved algorithm and carrying out path optimization based on a pipeline resistance coefficient and a construction cost constraint condition; and the scheme verification and optimization module is used for carrying out smoke flow dynamics simulation verification on the optimal path scheme, identifying scheme defects and carrying out iterative optimization on path parameters through a genetic algorithm. According to the invention, through a cloud-edge collaborative intelligent computing architecture, a multi-dimensional constrained dual-target optimization mechanism and a dynamic adaptive path planning and verification system are fused, and collaborative improvement of fire-fighting smoke exhaust pipeline laying planning in accuracy, efficiency and compliance is realized.

The invention belongs to the technical field of concrete material design, and particularly relates to a data-driven concrete mix proportion multi-objective collaborative optimization method and system.The method comprises the steps that firstly, a concrete test data set is obtained, and the concrete test data set comprises concrete components, the curing age and the compressive strength; then, a BP neural network model is trained by utilizing the concrete test data set, and a multi-objective optimization model is constructed by taking cost minimization and carbon emission as objectives on the premise of preset compressive strength based on the trained BP neural network model; and finally, solving the multi-objective optimization model to obtain a Pareto optimal solution set, and outputting an optimal concrete mix proportion scheme under preset compressive strength. According to the method, concrete compressive strength constraint is set, only double-objective optimization needs to be considered, the calculation efficiency can be improved, meanwhile, the curing age of the concrete is taken into consideration during compressive strength prediction, and the accuracy of a compressive strength prediction result can be improved.

The invention relates to the technical field of control and adjustment, in particular to an energy storage configuration point-based power consumption cost calculation method, which comprises the following steps of: acquiring power supply data, renewable energy output fluctuation data and computing power load fluctuation data, and generating a coupling influence feature vector; splicing the electricity price features and the coupling features to form configuration optimization input features; the input features are processed through a dual-objective optimization model, and robustness configuration parameters and economical efficiency operation parameters are output; calculating the reserve cost compensation amount of the extreme scene based on the robustness parameter, and calculating the power purchase cost and maintenance cost of the power grid based on the economical efficiency parameter; and aggregating the cost component and outputting a power consumption cost calculation result. According to the method, the interactive influence of wind-light fluctuation and load mutation is quantified through coupling features, the target conflict between robustness and economy is coordinated by adopting the Pareto algorithm, the risk compensation amount is generated in combination with a pressure test, a feature updating mechanism is fed back in a closed loop mode, and the problem that the operation cost deviates from expectation due to quantization deviation of an existing model is solved.

The invention relates to the technical field of meteorological numerical forecasting, and provides a WRF model dynamic tuning method based on a multi-dimensional parameter sensitivity test and dual-objective optimization. The objective of the invention is to solve the problems of insufficient prediction accuracy and limited service application caused by parameter fragmentation, low test efficiency, one-sided evaluation mechanism and computing resource waste in existing WRF model parameter tuning. An automatic sensitivity test is realized by constructing a multi-dimensional parameter system and developing a Python interface program, RMSE, MAE and CC indexes are fused to generate a sensitivity index SI with a weight, and key parameter combinations are screened. And a calculation efficiency factor is introduced to establish a prediction precision-calculation efficiency double-objective optimization model, a particle swarm optimization algorithm is adopted to solve a Pareto optimal solution, and dynamic balance of prediction precision and calculation efficiency in a complex scene is realized. The method covers the number of grids, spin-up time and various parameterization schemes, breaks through the limitation of traditional single parameter adjustment and optimization, and improves the prediction accuracy of extreme weather and complex terrain regions.

The invention discloses a knowledge-driven intelligent metasurface unit rapid design method based on double neural networks. Aiming at the problems that a traditional intelligent metasurface unit design process depends on a large amount of electromagnetic simulation and is complex in calculation and low in efficiency, the invention provides a dual-artificial neural network (ANN) proxy model optimization framework combined with physical knowledge guidance. The method comprises the following steps: firstly, respectively training two neural network models to predict the amplitude and phase response of a metasurface unit of a tuning element in different extreme working states; and then, according to the physical prior knowledge of monotonic change of tuning element parameter change on electromagnetic response, the maximum reflection loss and phase difference under any geometric parameter combination are quickly and accurately deduced, so that double-target optimization design assisted by the agent model is realized. By means of the method, the design efficiency and performance of the metasurface unit can be greatly improved, dependence on traditional full-wave electromagnetic simulation is remarkably reduced, and the effectiveness and superiority of the method are verified through simulation and experimental results.

The invention discloses a collaborative optimization method and system for classified disposal and green disassembly of power grid waste and old materials, and relates to the technical field of classified disassembly of materials, and the method comprises the following steps: obtaining multi-source feature data of to-be-disposed power grid waste and old materials; constructing a residual value prediction model based on the multi-source feature data, and outputting a component residual value mapping graph; analyzing the structure information of the target material, and generating a disassembly path decoupling graph in combination with the residual value mapping graph; establishing a dual-objective optimization scheduling model, and solving the scheduling model to generate a green disassembly operation sequence meeting a first constraint condition; and when the disassembling operation sequence is executed, collecting real-time feedback data, dynamically correcting residual value prediction model parameters and scheduling model constraint weights. According to the method, the disassembly path decoupling graph and the disassembly state transition graph are constructed, collaborative representation of the component value and the disassembly topology in the equipment is realized, and the problems of decoupling of residual value evaluation and path planning and lack of green evaluation in a traditional method are effectively solved.

The invention relates to the technical field of power-carbon collaboration, and discloses a block chain-based power-carbon collaboration data management and intelligent optimization method, system, device and medium, and the method comprises the steps: collecting power-carbon multi-source data in real time, and carrying out the data preprocessing; establishing a correlation model between electricity and carbon data so as to quantify a correlation relationship between unit power generation and carbon emission, and obtaining carbon emission reduction data; a double-objective optimization function is adopted to construct an electricity-carbon collaborative decision-making large model, and an optimal unit output scheme is output through deep learning; and generating a block chain hash identifier for each green electricity transaction, performing contract automatic verification, and if a performance risk is monitored, triggering early warning and sending an optimization suggestion. According to the invention, by constructing a technical system based on power-carbon collaborative data management and intelligent optimization, accurate conduction of carbon emission technical constraints, safety interaction of cross-system data, credible tracing of environmental rights and interests and dynamic prevention and control of risks are realized, and fusion and efficient collaborative operation of a power system and carbon emission management are promoted.

The invention discloses a wind power prediction model double-target countermeasure attack method, which belongs to the technical field of crossing of power system security and artificial intelligence, and comprises the following steps: S1, collecting original data and determining parameters; s2, training a time sequence diagram auto-encoder detection model; s3, constructing and training a wind power prediction model; s4, constructing a dual-objective optimization function; s5, iteratively generating a confrontation sample; and S6, adversarial sample injection and detection. According to the wind power prediction model dual-target countermeasure attack method, through dual-target weighting function and reconstruction loss item coefficient adjustment, joint adjustment and optimization of attack destructiveness and concealment are realized, the detection probability is reduced, and bias selection is supported; through the improved GAE structure, the time sequence dependence is captured, artificial feature design is avoided, and the accuracy of detecting the time sequence adversarial sample is improved.

The invention discloses an efficient multi-target unloading and scheduling optimization method for a large-scale directed acyclic graph (DAG) task, and belongs to the technical field of mobile edge computing (MEC) and industrial internet of things (IIOT). The method comprises the following steps: constructing a system model containing DAG task topology, delay energy consumption dual-objective optimization and resource competition constraint; a comprehensive multi-objective optimization solution scheme is adopted for solving, a dynamic probability coding mechanism is introduced to replace traditional individual representation, and the high-dimensional decision space search efficiency is improved; designing a cooperative target domain-based decomposition strategy to enhance convergence and diversity; and in combination with a serial active scheduling mechanism based on random weight, generating a task sequence meeting priority constraints. According to the method, the collaborative optimization problem of large-scale dependent tasks under delay, energy consumption and resource competition in an industrial Internet of Things environment is effectively solved, and the task execution efficiency and energy efficiency are remarkably improved.

The invention provides a robot delivery planning method based on full-life-cycle carbon emission evaluation and optimization. The robot delivery planning method comprises the following steps: S1, constructing a full-life-cycle total carbon emission model; s2, constructing a multi-objective optimization model: defining decision variables, constraint conditions and a dual-objective optimization model; s3, dynamically adjusting the weight of the multi-model optimization model, then solving the multi-target optimization model by adopting an NSGA-II algorithm, and obtaining a Pareto optimal solution set for a user to select a Pareto optimal solution therein; and S4, based on the selected robot deployment scheme, correcting the Pareto optimal solution set. According to the invention, economic delivery optimization of the robot is realized, and intelligent decision support is provided for low-carbon economic transformation.

The application discloses a kind of composite cemented filling material design method, and the application relates to the technical field of mine filling, comprising the following steps: determining optimization component and its optimization domain, and randomly generating multiple groups of design combinations, preparing filling material and filling to be filled sample to form composite filling body test piece;Sample test body is cured under simulated environment, with the degree of hydration heat reaction as index, dynamically adjust sampling interval and determine multiple sampling time;Coupling monitoring test is carried out using the detection method of pulse velocity and low-frequency dielectric spectrum combination, interface coupling degree parameter is extracted, and cementation coupling degree is calculated, and the termination time is determined based on its change rate;The structural strength at this time is obtained, and the preset strength is used as constraint, the cementation coupling degree maximization and the experimental time minimization are used as optimization goal, the double objective optimization function is set, and the optimal design combination is determined by optimization algorithm, which can significantly reduce engineering risk and prolong the service life of structure.

The invention discloses a reverse logistics planning system and method based on transfer learning, and the system comprises a reverse logistics planning module which is used for minimizing a dual-objective optimization problem and a constraint condition set of the total cost and the total disassembly processing time of a reverse logistics system, and obtaining a reverse logistics planning model; the optimization solving module is used for solving the reverse logistics planning model by adopting a two-stage multi-objective optimization algorithm to obtain a Pareto optimal solution set; and the transfer learning module is used for performing transfer learning on the Pareto optimal solution of the previous period to obtain the initial population when the significant change of the part demand is detected in the new period. According to the method, a two-stage multi-objective optimization algorithm combined with a transfer learning mechanism is utilized, and the problems of split optimization of disassembly line balance and vehicle path planning in reverse logistics and demand dynamic uncertainty are solved. The efficient balance between the total cost and the disassembly time of the system is realized, and the planning efficiency, the adaptive capability and the resource utilization rate are remarkably improved.

The invention relates to the technical field of flywheel energy storage system structure optimization, and discloses a flywheel energy storage system variable thickness flywheel profile optimization method, system, device and medium, and the method comprises the steps: building a dual-target optimization model, and taking the maximum unit mass energy storage density and the minimum maximum centrifugal stress as targets; the geometric boundary of the flywheel, the orthotropic property of the composite material and the lamination process constraint are considered, the reasonable and feasible design is ensured, and the failure states such as interlayer stripping or fiber fracture are reduced. And further parameterizing flywheel thickness distribution by adopting a third-order B-spline function, forming a design parameter space in combination with a fiber laying angle, setting a non-uniform node vector and an adjacent coefficient difference upper limit constraint, and ensuring radial smooth change of the thickness. An optimized data set is generated through finite element simulation based on a parameterized model, and a foundation is laid for physical information neural network training. The method provides a scientific, efficient and reliable design optimization scheme for the flywheel energy storage system, and assists energy transformation and sustainable development.

The invention provides a scheduling method for dual-objective optimization of a multi-agent flow shop under time-of-use electricity price, and relates to the technical field of intelligent manufacturing and production scheduling. According to the method, unified modeling is carried out on the total power cost TEC and the total customer completion time TCTA, and a dual-target mixed integer programming model is constructed; a double-node decision tree structure is constructed, and synchronous processing of task sorting and processing time interval distribution is achieved; analyzing the structural characteristics of the multi-agent flow shop under the constraint of time-of-use electricity price, designing a series of inequality constraints and multiple pruning rules, and compressing a search space on the premise of ensuring the integrity of a feasible solution; the method comprises the following steps of: performing dual-objective cooperative solution, embedding a branch and bound algorithm into an improved epsilon-constraint framework to perform iterative solution, accurately obtaining a Pareto frontier, and providing multiple optimal scheduling schemes balanced between cost and service quality for decision makers. According to the method, dual decisions of task sorting and cycle allocation can be realized, and the problems of decision model splitting, low solving algorithm efficiency and the like are solved.

The invention provides a cascade reservoir coordinated dispatching-based dry-period hydroelectric regulation wind and light capability improvement method, which comprises the following steps of: for a cascade reservoir, constructing a dual-objective optimization function by taking maximization of annual energy output of water, wind and light clean energy as a first optimization objective and maximization of dry-period output of the water, wind and light clean energy as a second optimization objective; based on the dual-objective optimization function and a preset constraint condition, establishing a medium-and-long-term scheduling model of the cascade reservoir; and determining an initial water level process of each cascade reservoir by adopting an equal flow method as a boundary water level condition of a scheduling initial time period, solving the medium-and-long-term scheduling model of the cascade reservoirs by adopting a step-by-step optimization algorithm, and outputting an optimal scheduling scheme combined with a cascade reservoir joint regulation strategy. According to the method, overall utilization of the regulation capacity of the cascade reservoir in the year can be achieved, wind and light absorption difficulty caused by limited hydropower output in the dry period is avoided, and the overall output stability and operation economy of the clean energy system are improved.

The invention discloses an active power distribution network flexibility improvement method and system based on intelligent soft switch optimization, and the method comprises the steps: firstly constructing a network flexibility evaluation framework, and then constructing a deterministic planning model of intelligent soft switch optimization configuration based on an obtained flexibility evaluation index. Based on the deterministic planning model, introducing a scene-based maximum and minimum regret value method, and constructing an intelligent soft switch optimal configuration robust planning model; and finally, solving and verifying the model. According to the method, the maximum permeation capacity range of renewable energy is defined as a flexibility index, and the uncertainty of a new energy high-permeation scene can be more efficiently adapted; the economy and the flexibility of the power distribution network are considered through dual-objective optimization; the robust method based on the scene is adopted for solving, all considered renewable energy output fluctuation scenes can be covered, it is ensured that the obtained scheme has feasibility under various fluctuation working conditions, and the operation stability and the anti-interference capacity of the power distribution network are remarkably improved.

The invention discloses a multi-branch pipe flow uniformity optimization method based on valve equivalent resistance model mapping, which comprises the following steps of: 1, replacing a real valve with an equivalent resistance area, performing CFD (Computational Fluid Dynamics) simulation on a single valve and a single pipe, and establishing a mapping table of the opening degree, the viscous resistance of the equivalent resistance area and the inertial resistance; 2, establishing a multi-branch-pipe CFD model, querying a mapping table for the opening degrees of all valves, randomly generating an inertia resistance coefficient and a viscous resistance coefficient, constructing a dual-objective optimization function by taking the virtual opening degree of each branch pipe as a design variable, then calling CFD to solve all the branch pipes, and returning the dual-objective optimization function, so as to obtain a multi-branch-pipe CFD model; the flow unevenness of each branch pipe and the most unfavorable loop resistance in the system are output, multi-objective optimization and global optimization are carried out by adopting an NSGA-II algorithm, and a Pareto leading solution set of an optimal valve opening combination is output for a user; and 3, the flow of the multiple branch pipes is leveled through the optimal valve opening degree combination. The method has the advantages of modeling simplification, high calculation efficiency, short optimization period, strong adaptability and good stability.

A flexible thin-wall crossed roller bearing multi-target parameter optimization method relates to the technical field of bearing design, and comprises the following steps: 1, establishing a parameterized system model containing a shaft system, a thin-wall crossed roller bearing and a box body assembly relation, the bearing model being determined by geometric and assembly parameters; 2, carrying out finite element modeling on the inner / outer ring of the bearing, and carrying out polycondensation on the degree of freedom of an interface to obtain an equivalent stiffness matrix; 3, inputting working conditions and material parameters, and setting a design variable feasible region and constraints; 4, coupling a mapping matrix to solve load distribution, calculating the maximum contact stress and the life damage rate, and constructing a dual-objective optimization model; 5, performing test design to obtain response data, and performing multi-target algorithm iterative optimization to obtain a Pareto optimal solution set; and 6, screening the solution set, and outputting a structure parameter combination meeting the requirement. The method can solve the problems that in the prior art, it is difficult to consider calculation efficiency and ferrule flexibility effect precision, and it is difficult to conduct rapid iterative optimization on roller load distribution and life / contact stress indexes.

The application provides a dual-target optimization decision method for a slag cooling process, and belongs to the technical field of industrial production process optimization. The method collects historical slag cooling data and performs preprocessing, and uses a multiple linear regression model to predict the temperature and cooling time after cooling. A dynamic weighted objective function is further constructed, and a hybrid algorithm (a combination of genetic algorithm, particle swarm algorithm and reinforcement learning algorithm) is used to solve the optimal control parameters. Finally, the optimal control parameters obtained are integrated into a real-time control system, and the control parameters are dynamically adjusted according to the real-time monitoring data to realize dual-target optimization of the cooling time and the furnace temperature fluctuation. The application adopts the above-mentioned dual-target optimization decision method for the slag cooling process, which can effectively improve the efficiency and stability of the slag cooling process, reduce energy consumption, and has significant economic and environmental benefits.

The invention discloses an AI-driven thermal power plant combustion stability-economic benefit double-target intelligent coal blending method, and relates to the technical field of thermal power plant coal blending optimizing.The method comprises the steps that a coal-wrapped type combustion fingerprint feature database is constructed; inputting the combustion fingerprint feature data of the candidate coal blending scheme into a boiler combustion risk prediction model based on a weighted support vector machine to obtain a corresponding combustion risk probability value; the coal blending proportion is used as an optimization variable, a dual-objective optimization function is constructed, a penalty function is adopted to perform relaxation processing on a preset constraint condition, and the dual-objective optimization function comprises an economical efficiency function taking fuel purchase cost minimization as a target and a safety function taking combustion risk probability value minimization as a target; carrying out Pareto optimization on the economy function and the safety function based on a multi-objective evolutionary algorithm, and outputting a Pareto optimal coal blending scheme set; and executing the coal blending scheme. Therefore, collaborative optimization of boiler combustion safety and fuel purchase economy is realized.

The invention discloses a virtual power plant dual-target aggregation method and system, and relates to the technical field of electric power automation. The method comprises the steps that virtual power plant operation data are collected and preprocessed; and constructing an economic objective function and a low-carbon objective function based on the preprocessed data. And performing dual-objective optimization solution on the constructed economic objective function and the low-carbon objective function by adopting a red fox optimization algorithm, and generating a Pareto optimal solution set under the condition of meeting the operation constraint of each controllable device. And determining the constraint boundary of the motion space of the deep reinforcement learning model based on the Pareto optimal solution set. And constructing a state space based on the preprocessed data, inputting the deep reinforcement learning model, and outputting a normalized scheduling instruction. And converting the output normalized scheduling instruction into a physical power control instruction of each controllable device, and monitoring the total power of the power distribution network in real time. And if the total power of the power distribution network exceeds a preset total power threshold value, starting an overload optimization process dynamic adjustment scheduling strategy.

The application discloses a power grid waste material classification disposal and green disassembly collaborative optimization method and system, relates to the technical field of material classification disassembly, and comprises the following steps: obtaining multi-source characteristic data of power grid waste materials to be disposed; constructing a residual value prediction model based on the multi-source characteristic data, and outputting a component residual value mapping atlas; analyzing structure information of target materials, combining the residual value mapping atlas to generate a disassembly path decoupling graph; establishing a double-objective optimization scheduling model, solving the scheduling model, and generating a green disassembly operation sequence meeting a first constraint condition; collecting real-time feedback data when the disassembly operation sequence is executed, and dynamically correcting residual value prediction model parameters and scheduling model constraint weights. The application constructs a disassembly path decoupling graph and a disassembly state transition graph, realizes collaborative representation of component values in equipment and disassembly topologies, and effectively solves the problems of decoupling of residual value evaluation and path planning and lack of greenness evaluation in traditional methods.

This invention relates to the field of industrial process management and optimization scheduling technology, and particularly to a method for tracking and optimizing carbon emission flows in a multi-product co-production process in the fluorochemical industry. First, a directed graph model of the product chain with process units as vertices is constructed. A multi-dimensional carbon emission flow calculation model is established, including direct carbon flows from fuel combustion, indirect carbon flows from purchased electricity and heat, direct carbon flows from the process itself, and carbon flows from the emission of fluorinated greenhouse gases. A dynamic evolution algorithm for node carbon potential and an iterative algorithm for reverse tracing of branch carbon flows are employed to achieve accurate tracking of spatiotemporally coupled carbon flows throughout the entire process. Using the time-varying carbon intensity coefficient as the core parameter, a dual-objective optimization scheduling model is established to minimize both total system carbon emissions and overall operating costs. An adaptive penalty mechanism with carbon constraints is introduced to solve for the optimal scheduling scheme, constructing a rolling optimization closed-loop control link for the production process. This invention effectively solves the problems of calculating carbon flows from the emission of fluorinated gases and allocating carbon flows among multiple products in the fluorochemical industry, achieving a synergistic improvement in carbon emission reduction and economic benefits.

The invention discloses an ecological hydrological benign relationship maintenance threshold defining method and system, and relates to the technical field of ecological hydrology, and the method comprises the steps: obtaining multi-source data of a target drainage basin, and selecting a core decision variable; constructing a dual-objective optimization model comprising a hydrological objective function and an ecological objective function based on the core decision variable; constructing a plurality of constraint conditions based on the multi-source data; based on all constraint conditions, a multi-objective evolutionary algorithm is adopted to solve the dual-objective optimization model, and a Pareto optimal solution set is obtained; and determining a weight based on an expert scoring method, and selecting an optimal threshold value from the Pareto optimal solution set based on an ideal point method and a comprehensive satisfaction index. And collaborative optimization of ecological protection and economic development is realized, so that the stability and practicability of the threshold value under the climate change background are ensured.