83 results about "TOPSIS" patented technology

The invention provides an RIES multi-objective optimization planning method considering the uncertainty of source load prediction, and the method comprises the steps: building an RIES basic structure, generating an uncertainty scene set of a source load prediction error through a Latin hypercube sampling method, carrying out the reduction of the uncertainty scene set through a clustering technology, and carrying out the reduction of the uncertainty scene set. According to the method, total cost, carbon dioxide emission and system reliability are taken as objective functions, an RIES multi-objective optimization planning model considering source load prediction uncertainty is constructed, a multi-objective optimization function is solved by adopting a non-dominated sorting genetic algorithm NSGA-III, a Pareto leading set is obtained, and through a strategy of combining a fuzzy entropy theory and TOPSIS, a Pareto leading set is obtained. And selecting the optimal solution of the Pareto leading edge set to obtain a multi-target optimization planning result of each typical scene. The method can comprehensively consider the influence of uncertain source load prediction on planning, improves the reliability of the system and the utilization rate of renewable energy sources, reduces the investment and operation and maintenance cost of the system, and reduces the emission of carbon dioxide at the same time.

The invention discloses a deep learning algorithm comprehensive evaluation method and system based on game theory combination weighting-TOPSIS, and the method comprises the steps: constructing a deep learning algorithm evaluation index system, calculating different evaluation index values of each candidate algorithm, constructing an evaluation matrix, and carrying out the range standardization processing according to a positive or negative index type; obtaining importance weights of four groups of indexes based on an analytic hierarchy process, an optimal sequence diagram method, an entropy weight method and a CRITIC weight method; on the basis of the four groups of index importance weights, a final index importance weight is obtained based on a game theory combination weighting method; and according to the final index importance weight, in combination with a weighted TOPSIS method, obtaining a comprehensive evaluation and selection sorting result of all candidate algorithms. According to the method, the comprehensiveness and accuracy of evaluation are improved, and a decision maker can be better supported to make scientific judgment in the deep learning algorithm selection process.

The invention discloses a multi-objective optimization water resource allocation scheduling method, which combines an LSTM-Prophet hybrid prediction model with an attention mechanism, synchronously captures climate periodicity, policy regulation sensitivity and industrial development tendency characteristics in water demand prediction, and improves water demand prediction precision through a genetic algorithm. Through an improved NSGA-III algorithm and a PSO collaborative optimization mechanism, the convergence problem of a high-dimensional target space is effectively solved in combination with a chaotic mapping technology, and the multi-target optimization efficiency is improved by 40% in cooperation with a dynamic weight adjustment strategy of deep reinforcement learning. And multi-scenario simulation verification of a scheduling scheme is realized by utilizing deep coupling of a digital twin system and a three-dimensional GIS, and a real-time feedback correction mechanism of a Markov decision process is realized. A finally constructed entropy weight-TOPSIS multi-dimensional evaluation system is combined with AR visualization and intelligent contract traceability technologies, and a Pareto optimal solution giving consideration to social fair, economic benefits and ecological integrity is provided for decision makers.

The invention discloses an identification method and system based on multi-sensor information fusion. According to the method, firstly, a one-dimensional recognition framework and a two-dimensional recognition framework are constructed, and sensor detection results and reliability thereof are described respectively; grouping is carried out according to sensor feature information, calculation is simplified, and the information fusion difficulty is reduced. And then the reliability of the sensor is evaluated by adopting an AHP-TOPSIS method, and evidence is corrected. And finally, integrating each group of evidences by applying a D-S evidence theory fusion rule to generate a final fusion detection result. The method effectively fuses multi-sensor data, improves decision-making efficiency, and is suitable for intelligent identification tasks in a complex environment.

The invention provides a laser remelting process parameter optimization method and system based on a multi-objective optimization algorithm. The method comprises the steps that the range of laser remelting process parameters on the surface of a cladding layer is limited; designing a process parameter combination by using an ELHS method, carrying out a laser remelting experiment by using different process parameter combinations on the premise of keeping the surface quality of the cladding layer consistent, and collecting performance index data after remelting; establishing a nonlinear mapping model between the process parameters and the performance indexes by using a PSO-XGBoost model; a Pareto optimal solution set is obtained through population and individual initialization, non-dominated sorting, congestion degree calculation, optimal solution calculation and global optimization of process parameters by adopting an MOEDO algorithm based on a nonlinear mapping model; and a CRITIC-TOPSIS decision system is utilized to carry out evaluation sorting on the Pareto optimal solution set, and an optimal process parameter combination is screened out. According to the method, the influence of the laser power, the scanning speed and the lap joint rate on the quality and performance of the laser remelting surface can be considered at the same time, and the limitation of traditional single process parameter optimization is broken through.

The invention discloses a multi-objective optimization method for improving the comprehensive performance of a hydrostatic guideway, and the method comprises the steps: 1, carrying out the theoretical analysis and calculation of the performance indexes of the hydrostatic guideway based on a hydrostatic bearing theory, obtaining a mathematical model of each performance index, and determining design parameters which affect the working performance of the hydrostatic guideway; step 2, on the basis of a Sobol sensitivity analysis method, obtaining sensitivity indexes of each design parameter on the comprehensive performance of the hydrostatic guideway, determining key design parameters with relatively great influence, and analyzing an influence rule of each key design parameter on the performance of the guideway; and step 3, based on an NSGA-II multi-objective optimization algorithm, constructing a hydrostatic guideway comprehensive performance multi-objective optimization model, and obtaining a Pareto optimal solution set. And step 4, based on an AHP-TOPSIS method, selecting an optimal optimization scheme of the hydrostatic guideway in a Pareto optimal solution set obtained through a targeted optimization algorithm.

The invention discloses a reusable launch vehicle maintenance logic decision method based on FMEA and TOPSIS methods, and the method comprises the steps: carrying out the structural decomposition analysis of a reusable launch vehicle system, and determining a device which may have a fault; fMEA analysis is carried out on equipment which may have faults, an FMEA table is compiled, fault modes, fault reasons and fault influences of all the equipment are clarified, FMEA analysis results are analyzed, evaluation indexes are constructed for all the equipment, and the importance degree of all the equipment is determined; according to the importance degree of the equipment, calculating the importance degree sorting ratio of the equipment, dividing the equipment which possibly breaks down into three classes, and determining an equipment maintenance mode for each class of equipment by adopting a corresponding RCM logic decision process; and an entropy weight TOPSIS method is adopted to determine an optimal maintenance mode, so that the provided maintenance decision achieves economic benefit maximization on the basis of meeting related requirements of maintainability and reliability, and industrialization and large-scale development of related technologies in the future is facilitated.

An interactive intuitionistic fuzzy group decision-making method containing time weight comprises the following steps: firstly solving the time weight lambda (t), then gathering comprehensive information of a single decision maker in different time periods through a formula # imgabs0 #, and then utilizing a dynamic multi-attribute TOPSIS method of the single decision maker to obtain a dynamic multi-attribute TOPSIS method of the single decision maker. And solving a comprehensive decision matrix # imgabs2 # of a plurality of decision makers through the relative closeness # imgabs1 # of the scheme for a single decision maker, and then using the TOPSIS method for the first time to realize scheme optimization of final group decision. The method is relatively simple in calculation and is a flexible method for interactive intuition fuzzy group decision making; however, in the prior art, the TOPSIS method is only adopted in the last step to calculate the decision information at a time, and the method solves the problems that according to the method, calculation is tedious and not flexible enough, and the actual use scene is poor.

The invention belongs to the technical field of centrifugal pump design, and discloses a centrifugal pump impeller optimization design method based on characteristic contribution degree and multi-objective decision, which comprises the following steps: S1, obtaining an initial model and parameters of the initial model according to a geometric structure of a centrifugal pump, obtaining lift, efficiency and a minimum efficiency index, and constructing an optimization data set; s2, optimizing the data set, constructing a regression model, and screening core optimization variables; s3, sampling to generate a secondary data set according to the core optimization variable, and simulating to obtain a lift and a minimum efficiency index value; a decomposition-based multi-objective evolutionary algorithm is adopted to carry out multi-objective optimization, and a Pareto leading-edge solution set is generated; and S4, constructing an SHAP-TOPSIS decision method, and screening the Pareto frontier solution set obtained in the step S3 to obtain an optimal parameter combination. According to the method, Bayesian optimization, SHAP analysis and multi-objective decision are fused, efficient energy-saving design of the centrifugal pump is achieved, and a new thought is provided for intelligent optimization of complex fluid machinery.

The invention provides a CORS network data quality evaluation method based on entropy weight TOPSIS. The method comprises the following steps: S-1, forming an original matrix; s-2, standardizing the xij to obtain a standardized Zij (Zij); s-3, calculating the proportion of the index j corresponding to the object i in the column sum; s-4, calculating an entropy value of an index j corresponding to the object i; s-5, calculating a difference coefficient of the jth index; s-6, calculating the weight of the jth index; s-7, multiplying the weight point calculated in the step S6 by the standardized matrix to obtain a weighted standardized matrix; s-8, positive and negative ideal solutions are determined; s-9, calculating an Euclidean distance; s-10, finally calculating a comprehensive score Si of the base station in the ith CORS network; and S-11, judging the final advantages and disadvantages of all the reference stations of the current CORS station network according to the score. The GNSS and CORS data quality evaluation method combines observation data acquired by a CORS station network in real time or after the event and entropy weight TOPSIS with objectivity, comprehensiveness and high efficiency as core advantages, and provides quality evaluation for GNSS and CORS data.

The invention discloses a configuration optimization method based on low-demand and non-low-demand ship spare parts, and belongs to the technical field of water surface JT equipment guarantee, and the method comprises the steps: solving a low-demand ship spare part configuration optimization model through employing improved gray Markov prediction; a TOPSIS-fused multi-target particle swarm optimization algorithm is adopted to solve the non-low-demand ship spare part configuration optimization model; and performing analogue simulation on an optimal spare part configuration list obtained by solving the multi-target particle swarm optimization model by adopting Monte Carlo, performing a simulation experiment by utilizing the configuration model, and verifying the multi-target particle swarm optimization model. According to the method, the low-demand spare part configuration optimization model and the non-low-demand spare part configuration optimization model are fused, and a better ship spare part configuration optimization method is realized by virtue of respective advantages, so that continuous, safe and reliable operation of a ship is ensured. And the configuration optimization efficiency and accuracy are improved.

The invention provides a delivery intensity evaluation method based on an entropy weight-TOPSIS method. A deliveryman labor intensity evaluation system is established in six aspects of delivery duration, the number of delivery households, delivery mileage, the number of delivery boxes, a rural power grid customer proportion and a photographing signing proportion. Determining the value of the comprehensive evaluation index according to the actual delivery business index data; the weight of each evaluation index is determined by adopting an entropy weight method, and a multi-index weight basis is provided for comprehensive evaluation of the delivery intensity. According to a TOPSIS algorithm, a delivery strength comprehensive evaluation model is established, an optimal ideal solution and a worst ideal solution are calculated, then the distance from an index evaluation value vector to the ideal solution is calculated, and finally the relative closeness degree between the index evaluation value vector of each deliveryman and a negative ideal solution is calculated, and the relative closeness degree is used as a delivery strength comprehensive evaluation value. The method comprehensively considers the business actions and data in the actual delivery process, not only pays attention to the actual business data, but also considers other implicit indexes, and provides a scientific basis for comprehensively evaluating the labor intensity of the deliveryman.

The invention discloses a data quality dynamic evaluation analysis method and system based on fusion factors. The method comprises the following steps: constructing a multi-dimensional data quality factor set; constructing a dynamic evaluation window based on an aging attenuation function, and automatically adjusting an evaluation cycle of each quality factor according to a data life cycle stage; based on each data quality factor evaluation period, processing a multi-factor conflict problem in the evaluation period through an improved D-S evidence theory; constructing a hierarchical scoring model based on non-conflict factors, and obtaining a comprehensive quality score by adopting an improved entropy weight-TOPSIS fusion algorithm; and constructing an adaptive threshold triggering mechanism, and dynamically adjusting the weight in the data quality scoring model based on the historical scoring standard deviation. The method has the advantages that the data quality evaluation period is dynamically adjusted based on the aging attenuation function, the improved D-S evidence theory is utilized to fuse the multi-factor conflict evidence, and high-timeliness and high-precision dynamic evaluation of the data quality is achieved.

The embodiment of the invention provides a seat comfort evaluation method and system based on entropy weight method-TOPSIS, and the method comprises the steps: designing a seat comfort evaluation table, and collecting the test data of a tested person after a seat experience test; constructing a decision matrix based on evaluation objects and evaluation indexes in the test data; standardizing the decision matrix to obtain a standardized matrix, calculating the ratio of the evaluation object in the standardized matrix under the evaluation index, calculating an entropy value according to the ratio, and further calculating the weight of the evaluation index; normalizing the decision matrix to obtain a scoring matrix, and calculating an optimal value and a worst value of an evaluation index in the scoring matrix; and calculating an optimal value distance between the evaluation object and the optimal value and a worst value distance between the evaluation object and the worst value based on the weight, the optimal value and the worst value of the evaluation index, and calculating a comfort result of the evaluation object according to the optimal value distance and the worst value distance.

The invention belongs to the technical field of material type selection, and particularly discloses an improved TOPSIS material type selection method and system based on information entropy, and the method comprises the steps: calculating the information entropy of each attribute of a material, and obtaining a weight coefficient of each attribute; adopting a TOPSIS method, determining positive and negative ideal solutions based on the weight coefficient, calculating the distance between each to-be-selected material scheme of the structure and the fuzzy positive and negative ideal solutions, further obtaining a relative close degree, and determining an optimal material scheme according to the relative close degree; wherein the calculation method of the positive and negative ideal solutions comprises the following steps: constructing a weighted standardized matrix according to a weight coefficient, then calculating the standard deviation of each attribute according to the weighted standardized matrix, and introducing a triangular fuzzy number to construct and obtain the fuzzy positive and negative ideal solutions. The method effectively improves scientificity and reliability of material type selection, is suitable for optimal decision-making of multi-attribute materials under complex working conditions, and particularly has remarkable advantages in lightweight design in the fields of automotive engineering and the like.

The application provides a processing parameter optimization method based on a GRA-CRITIC-TOPSIS method and processing surface integrity multi-indexes, and belongs to the technical field of high-efficiency precision processing. In view of the problems that the influence of processing parameters on workpiece surface integrity multi-indexes is complex in difficult-to-machine material milling processing, and the expected results of various indexes are contradictory, the grey correlation analysis method (GRA), the CRITIC objective weighting method and the ideal point method (TOPSIS) are organically combined, and a more scientific and comprehensive multi-objective processing parameter comprehensive optimization model is constructed. The method introduces the CRITIC objective weighting, the weight calculation is more reasonable, the index information overlap can be effectively avoided, and the optimization result has higher engineering reliability.

The invention discloses an industrial software resource evaluation method and system based on an improved FAHP-TOPSIS method, and belongs to the technical field of industrial software resource evaluation. The method comprises the following steps: determining an evaluation index system of industrial software resources by using an AHP method; according to score comparison of a plurality of experts for the evaluation index system, obtaining a weight cognitive matrix of each expert for each industrial software resource evaluation index; and analyzing and determining the relative importance of the weight cognitive matrix endowed by each expert in the overall index system through a grey correlation analysis method, and forming a comprehensive weight value of each evaluation index. According to the method, the TOPSIS method is utilized, the industrial software resources are subjected to efficiency sorting and optimization according to the comprehensive weight values, judgment errors caused by professional background differences of experts are effectively avoided, the accuracy and reliability of evaluation are remarkably improved, complex evaluation indexes and interrelations are effectively processed, and the decision making process is made to be more reasonable.

The present invention discloses a method, a system, a device and a medium for system architecture trade-off analysis, which relates to the field of aerospace system architecture design. The method includes: obtaining decision items and alternative items of the rocket first-stage and second-stage separation system architecture to be trade-off analyzed; performing formal representation on the decision items and the alternative items to obtain a morphological matrix; the morphological matrix includes a plurality of alternative architectures; determining alternative architectures that meet the constraints among the plurality of alternative architectures according to the morphological matrix; the constraints include option mutual exclusion and option association; using a multi-objective optimization search method to determine alternative architectures on the Pareto front among the alternative architectures that meet the constraints; using a weight sensitivity analysis technique to determine the weight values of the system indicators of each alternative architecture on the Pareto front, and using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to determine the final system architecture. The present invention improves the efficiency of system architecture trade-off analysis.

This invention provides a general optimization method and system for water supply scheduling of parallel reservoir groups. By acquiring system operation data of the parallel reservoir group and its water supply network, a multi-level, multi-attribute generalized directed graph model is constructed. Based on this model, the dynamic operating state of the water resource network is dynamically generalized, and multiple objective functions and constraints are dynamically configured. Simultaneously, scheduling decision variables are identified and the degree of constraint violation is quantified, forming a generalized optimization problem definition. Different optimization algorithms are supported, and the optimization problem is solved in multiple cycles through a scheduling decision evaluation module. The TOPSIS method is used to select representative solutions, obtaining the optimal release decision for each reservoir in each scheduling cycle. Finally, the performance of the scheduling scheme is evaluated based on simulation results, and a detailed operation report is generated. This invention can effectively improve the scientific and economic efficiency of water supply scheduling of parallel reservoir groups, providing important technical support for regional water resource optimization scheduling and refined management.

The present invention relates to a variable heat capacity heat exchange network optimization design method that takes both economic and environmental impacts into consideration. The method extracts the physical property data of the stream temperature, convective heat transfer film coefficient and variable heat capacity. The genetic algorithm is used to generate a binary variable z indicating whether a stream heat exchanger exists. i,j,k ; Establish a multi-objective optimization model for variable heat capacity heat exchange network that minimizes both annual total cost and environmental impact; Use simple linear weighting method to decompose the model into several single-objective optimization sub-models; Solve the sub-models to obtain the optimal z of each sub-model i,j,k ; The ε-constraint method is used to solve the newly created single-objective optimization problem under different weights to obtain the Pareto frontier solution; the Pareto frontier solution is non-dominated sorted to obtain the final Pareto frontier solution of the model; the TOPSIS method is used to obtain the optimal solution of the model from the final Pareto frontier solution of the model, and the optimal solution is used as the trade-off solution of the variable heat capacity heat exchange network that considers both economic and environmental impacts.

The invention relates to a transducer index evaluation method based on TOPSIS multi-objective optimization, and the method comprises the following steps: S1, carrying out the response test of a transducer according to an optimization variable in an anechoic pool, and obtaining test data; s2, processing the test data based on a TOPSIS multi-objective optimization algorithm to obtain an optimization result; and S3, based on the optimization result, determining the optimal installation position of the transducer in the fairing, and completing transducer index evaluation. According to the invention, through combination of the multi-dimensional test and the multi-objective optimization algorithm, global optimal selection of the installation position of the transducer is realized, and the problems of dependence on experience and incomplete optimization in the prior art are solved.

The invention discloses a ship type multi-objective optimization decision-making method based on AHP and TOPSIS, and relates to the technical field of multi-objective optimization. Selecting a mother type ship, and taking wave-making resistance, heaving amplitude and pitching angle as targets of ship type hydrodynamic performance evaluation; carrying out numerical forecasting on wave-making resistance, heaving amplitude and pitching angle target values of the mother ship; setting a ship type position to be modified, a design variable of ship type transformation and a constraint range of the variable; generating a sample ship type similar to the mother type ship; calculating wave-making resistance, heaving amplitude and pitching angle target value of each sample ship type; and taking the target value as an optimization design objective function, combining AHP and TOPSIS as an objective function evaluation method, and taking a non-dominated sorting genetic algorithm NSGA-II as a multi-objective optimization algorithm to search in a ship type space to obtain a ship type corresponding to a multi-objective optimization optimal solution. According to the method, the ship type optimization design efficiency is improved.

The invention discloses a multi-dimensional electric power market operation effect evaluation method and system, and relates to the technical field of electric power market operation effect evaluation, and the method comprises the steps: constructing a multi-dimensional index system covering the core operation dimension of an electric power market, and according to a combined weighting model composed of a fusion analytic hierarchy process and an entropy weight method, carrying out the evaluation of the operation effect of the electric power market; subjective experience and objective data are combined; and calculating the distance between each province and an ideal solution according to the combined data by using a TOPSIS model to realize comprehensive sorting, and processing uncertainty through fuzzy logic and Bayesian reasoning to complete evaluation of the multi-dimensional power market operation effect. According to the method, subjective experience and objective data are fused through combined empowerment, inter-provincial multi-index comprehensive sorting is realized by means of TOPSIS, risk causal reasoning is performed by using a fuzzy Bayesian network (FBN), a complementary closed loop of'macroscopic sorting-microscopic diagnosis' is formed, and the blank in the field of provincial power market dynamic evaluation and risk traceability in the prior art is filled.

The invention provides a logistics industry green innovation performance spatio-temporal heterogeneity analysis method, which comprises the following steps of: 1, constructing a multi-dimensional index system, and calculating a logistics industry green innovation performance comprehensive index through a combined weighting method and a TOPSIS method; 2, coupling the weight matrixes of the geography, economy and logistics networks, and constructing a spatial weight matrix; step 3, analyzing a space driving mechanism of logistics industry green innovation performance by using an improved space Durbin model (SDM), and decomposing space effects into four classes of instant / accumulation and direct / indirect; and 4, constructing a space Markov chain model by taking a space lag factor as a condition, and realizing dynamic prediction and club convergence detection of a logistics industry green innovation performance state. According to the method, thoughts and schemes are provided for solving the problem that static evaluation and dynamic prediction are separated in traditional research, an evaluation-mechanism-prediction framework is formed, and accurate support is provided for formulating regional logistics policies.

The invention relates to an equipment transformation priority determination method and system based on an improved Sougee optimization algorithm, and the method comprises the steps: firstly constructing an evaluation index system used for reflecting the equipment transformation priority, then calculating a comprehensive weight, and carrying out the optimization based on a multi-objective optimization improved Sougee optimization algorithm to obtain an optimal comprehensive weight; and finally, a TOPSIS method based on DBSCAN-AGNES double clustering redefinition is adopted to calculate and obtain the closeness degree and sort the closeness degree so as to determine the transformation priority of the old production equipment of the power grid. According to the method, subjective and objective weights are calculated in combination with the AHP and the improved CRITIC method, the dynamic crowding speed and the global-local search mechanism are introduced to improve the South goose optimization algorithm, intelligent optimization of the weights is achieved, weight distribution is more accurate and reasonable, meanwhile, based on DBSCAN-AGNES dual clustering, the problem that a traditional TOPSIS method is interfered by abnormal values and noise is avoided, and the method is suitable for large-scale popularization and application. And the robustness and reliability of the evaluation result are improved, so that the equipment transformation priority can be determined more scientifically and accurately.

The invention provides a comprehensive evaluation method for a rice variety based on CRITIC-TOPSIS (Clustered Regularly Interspaced Interspaced apart Clusters-TOPSIS Comprising the following steps: step 1, obtaining related data of a tested variety; step 2, carrying out normalization processing on the data; step 3, calculating an index fluctuation degree and a conflict degree; step 4, calculating an index information bearing capacity; 8, calculating the Euclidean distance between each variety and the positive and negative ideal solutions; and 9, calculating the relative pasting degree of each variety and the ideal solutions. According to the method, human interference factors are eliminated, the index weight which contributes little to the evaluation result is reduced, and the objectivity and accuracy of evaluation are improved.

The invention discloses an improved FMEA method combining component importance and fuzzy-entropy weight-TOPSIS. The method comprises the following steps: S1, constructing a system component index original decision matrix; s2, carrying out data normalization and forward processing; s3, calculating the importance weight of the system component; and S4, calculating a system fault mode priority sequence by applying an improved FMEA method. According to the method, component importance is introduced as a constraint, a triangular fuzzy number is applied to replace accurate values of risk factors O, S and D, the influence of subjectivity of FMEA team member group decision-making on evaluation values of the risk factors O, S and D is reduced, in addition, interaction and influence among different risk factors are fully considered in the improved method, and the accuracy of the FMEA team member group decision-making method is improved. The weight among the risk factors O, S and D is calculated by applying an entropy weight method, and the accuracy of system fault mode risk priority ranking is improved.

The invention discloses an internal income comprehensive distribution method, system and device capable of adjusting load after large-scale aggregation and a storage medium, and relates to the technical field of energy optimization scheduling and energy management, and the method comprises the steps: constructing a comprehensive contribution index system based on safety, economy and risk indexes; outputting the comprehensive contribution degree of each load resource according to the comprehensive contribution degree index system by adopting a comprehensive evaluation method fusing TOPSIS, grey correlation and an entropy weight method; creating an energy-auxiliary service market joint income model, and quantifying the value contribution of the adjustable load polymer; and according to the comprehensive contribution degree of each load resource and the energy-auxiliary service market joint income model, performing internal income distribution by using a double-layer settlement framework of fixed protocol electricity price and contribution degree assessment. According to the method, a comprehensive contribution degree model based on safety, economy and risk indexes is constructed, TOPSIS grey correlation and an entropy weight method are fused, and accurate quantification and fair distribution of load resource values are achieved.

The invention provides a friction damper multi-target parameter intelligent optimization and decision-making system and a friction damper multi-target parameter intelligent optimization and decision-making method. The system comprises a data input module, a hyper-parameter automatic configuration module, a high-performance CNN proxy model construction module, a Pareto solution set search module, an entropy weight TOPSIS decision module and a result output module. The invention further relates to a method of the friction damper multi-target parameter intelligent optimization and decision making system. According to the method of the friction damper multi-target parameter intelligent optimization and decision-making system, a high-performance CNN proxy model is automatically constructed by adopting Bayesian optimization, and the problems of multi-target optimization and scheme optimization of complex parameters of the friction damper are efficiently and objectively solved in combination with NSGA-II multi-target optimization and an entropy weight TOPSIS intelligent decision-making mechanism, so that the method has the advantages that the method is simple and convenient, and the efficiency is high. And the optimization efficiency and the decision reliability are obviously improved.

The invention provides a thermal storage performance optimization method and device based on solid-state hydrogen storage, and relates to the technical field of solid-state hydrogen storage, and the method comprises the steps: constructing a solid-state hydrogen storage device finite element model containing heat-mass-momentum transmission multi-physics field coupling; analyzing the influence level of the to-be-optimized parameters of the spiral heat exchange tube on the heat storage performance by adopting a Taguchi method; and generating a Pareto frontier solution set through a non-dominant sorting second-generation genetic algorithm, constructing a multi-target evaluation matrix based on weighted grey relational degree analysis, and performing secondary screening by adopting an approximate ideal solution sorting method to determine an optimal parameter combination. According to the method, a multi-physics field coupling simulation model is constructed, a two-stage optimization architecture of a non-dominant sorting second-generation genetic algorithm and an approximate ideal solution sorting method is adopted, and collaborative optimization of spiral pipe parameters and fluid dynamic parameters is achieved. Key factors are pre-screened through a Taguchi method, a three-dimensional evaluation matrix is established in combination with grey correlation analysis, and balanced optimization of reaction scores and outlet temperature is ensured.