28 results about "Multi criteria decision" patented technology

The invention relates to the technical field of production process intelligent regulation and control, in particular to an intelligent regulation and control method and system for an instant gelatin production process, and the method specifically comprises the following steps: firstly, collecting process parameters such as reaction kettle temperature, acid-base concentration and the like in a production line sensor and control system to form production batch data; then, an optimization model and an intelligent regulation and control strategy are constructed, a dynamic incidence matrix is constructed through time-varying mutual information entropy, multiple types of features are fused to generate a process state descriptor, a constraint dominating relation and a population initialization strategy are optimized, and a self-adaptive genetic manipulation adjustment mechanism and a state-guided search and constraint processing method are designed; iteratively executing an improved NSGA-II algorithm, obtaining an optimized Pareto solution set, and evaluating diversity; and finally, selecting an optimal solution through a multi-criterion decision and a dynamic regulation and control strategy, and converting the optimal solution into an executable process parameter set value. According to the method, the accuracy and the high efficiency of the production process can be improved by realizing intelligent optimization regulation and control of the process parameters.

The invention provides a spraying quality regulation and control method and system based on fuzzy comprehensive evaluation, and relates to the technical field of artificial intelligence and industrial detection.The method comprises the steps that a spraying surface image collected in real time is processed through an improved lightweight target detection model, the type of a spraying area is recognized, and visual feature indexes are extracted; inputting the indexes into a pre-constructed fuzzy comprehensive evaluation model, and outputting a'good ', 'general' or'bad 'comprehensive quality grade in combination with an evaluation standard of dynamic calibration and a weight determined by principal component analysis; and according to the evaluation result and the number of times of spraying of the current area, a control instruction of finishing spraying, marking supplementary spraying or re-spraying is generated and fed back to the robot to be executed, and closed-loop regulation and control are achieved. According to the method, deep learning and multi-criterion decision are fused, the accuracy and real-time performance of spraying quality evaluation are improved, the problems that a traditional method is low in efficiency and poor in adaptability are solved, and the spraying quality is effectively improved.

The invention relates to an AHP-CRITIC-VIKOR-based power distribution network distributed photovoltaic bearing capacity evaluation and improvement method, and the method comprises the steps: constructing a power distribution network distributed photovoltaic bearing capacity evaluation index system which comprises a power grid operation safety index and a power grid loss index; obtaining a standardized evaluation matrix; determining subjective weight and objective weight of each index according to the standardized evaluation matrix; obtaining a combined weight according to the subjective weight and the objective weight; according to the combined weight, a VIKOR multi-criterion decision-making method is adopted to evaluate the distributed photovoltaic bearing capacity of the power distribution network; and if the distributed photovoltaic bearing capacity of the power distribution network does not meet the requirement, solving the distributed photovoltaic bearing capacity improvement model, and outputting the distributed photovoltaic maximum accessible capacity and the optimal access scheme corresponding to the improvement measure. According to the invention, the bearing capacity of the system can be effectively enhanced.

The invention provides a data-driven forward material selection method and system for an automobile multi-material body, and relates to the technical field of automobile research and development, and the method comprises the steps: obtaining a material selection target constraint condition and candidate material data of the automobile body; on the basis of the candidate material data, vehicle candidate material schemes are generated, multiple data-driven vehicle target material selection models are called to select the vehicle candidate material schemes in sequence, and vehicle feasible material selection schemes meeting material selection target constraint conditions are obtained; a non-dominated sorting algorithm is called to screen the feasible material selection schemes of the whole automobile, and candidate recommendation schemes of automobile body material selection are obtained; and multi-criterion decision making is carried out on the candidate recommendation schemes to obtain a final recommendation scheme of automobile body material selection. The method is used for solving the technical problem of low material selection process efficiency caused by complex vehicle body materials, material selection constraints and numerous research and development requirements when forward material selection is carried out by depending on manual experience in the prior art in automobile research and development.

The sewage plant multi-energy utilization robust decision method, device, computer equipment and medium belong to the field of urban sewage plant comprehensive energy utilization and multi-energy complementary optimization. Among them, the method comprises the following steps: constructing a practical application-oriented sewage plant comprehensive energy utilization and multi-energy complementary optimization stochastic multi-criteria decision analysis framework for the robust decision of sewage plant comprehensive energy utilization and multi-energy complementary optimization under uncertain conditions; using game theory to aggregate and resolve the weight of each decision standard of sewage plant comprehensive energy utilization and multi-energy complementary optimization, and estimating the uncertainty of each decision standard of sewage plant comprehensive energy utilization and multi-energy complementary optimization; constructing a sewage plant comprehensive energy utilization and multi-energy complementary optimization stochastic multi-criteria decision acceptability analysis-gray correlation analysis model to quantitatively evaluate the uncertainty and decision-making risk of sewage plant comprehensive energy utilization and multi-energy complementary optimization multi-criteria decision; using significance analysis method to determine the influence of input parameter uncertainty of each decision standard of sewage plant comprehensive energy utilization and multi-energy complementary optimization on the decision scheme of sewage plant comprehensive energy utilization and multi-energy complementary optimization.

The application relates to the technical field of Internet of Things, and discloses a dynamic stable pairing method for sensors and gateways, which comprises the following steps: step 1: obtaining the relevant information of all the sensors to be paired and all the available gateways; calculating gateway evaluation data and sensor evaluation data; step 2: according to the gateway evaluation data, corresponding first weights, the sensor evaluation data and corresponding second weights, the evaluation scores of each sensor to different gateways and the evaluation scores of each gateway to different sensors are calculated through a multi-criteria decision model; step 3: constructing a preference list of each sensor and a preference list of each gateway; step 4: according to the preference list of each sensor and the preference list of each gateway, an improved delayed reception algorithm is adopted to generate a stable pairing scheme; and step 5: according to the stable pairing scheme, the sensors and the corresponding gateways establish network connection. Meanwhile, a dynamic stable pairing system for sensors and gateways is also disclosed.

The invention relates to the technical field of urban intelligent management and control, and provides an urban composite environment health risk assessment and intervention method and system, and the method comprises the steps: obtaining the geographic space data and ground observation data of an urban region; constructing a collaborative environmental stress index, and calculating an SESI predicted value; performing grid construction on the urban area to construct a graph structure, performing feature learning by using a GNN algorithm, predicting a node SESI value, and generating a collaborative stress map; constructing a comprehensive vulnerability evaluation model, calculating a vulnerability index, and identifying a high-vulnerability region; fuzzy comprehensive evaluation and TOPSIS methods are fused, intervention measures of the high-fragility area are sorted, and an intervention scheme is generated. According to the method, the nonlinear interaction modeling and the high-resolution graph neural network are fused to realize accurate recognition of the composite environment health risk, and the intervention strategy is optimized through the multi-criterion decision model, so that scientific regulation and dynamic optimization of the urban intervention scheme are realized.

The present application relates to the technical field of precast beam design optimization, and particularly relates to a precast beam design optimization method and system based on a hybrid element heuristic algorithm. The method comprises the following steps: processing original design parameter data through knowledge embedding to obtain geometric material comprehensive features; obtaining constraint compliance features by encoding and standardizing constraints, and obtaining construction schedule influence features by analyzing construction schedule data; based on a multi-criteria decision empowerment strategy, using the geometric material comprehensive features, the constraint compliance features and the construction schedule influence features, comprehensive priority features are obtained; using weighted knowledge fusion, manufacturability features and case analogy features are obtained to obtain manufacturing case heuristic features; combining performance approximation model features, the comprehensive priority features and the manufacturing case heuristic features, precast beam multi-objective design optimization is completed according to a hybrid element heuristic framework. The present application realizes the global optimization of the precast beam design scheme through phased feature extraction and multi-dimensional feature fusion.

The invention discloses a decision generation method and system for city updating multi-objective collaborative optimization and a medium. The decision generation method comprises the steps of S1, establishing a system boundary covering the full life cycle of building material production and transportation, building construction, operation maintenance, dismantling and recycling; s2, constructing a dynamic reference line carbon emission model, and calculating a reference line carbon emission Cbase; s3, establishing a project carbon footprint accounting model, and calculating the full life cycle carbon footprint Cproj of the project; s4, constructing an optimization model taking economic net present value maximization and carbon emission reduction maximization as double targets; s5, an improved NSGA-II algorithm is adopted to solve the optimization model, and a Pareto optimal solution set is output; and S6, selecting a recommendation scheme from the Pareto optimal solution set based on a multi-criterion decision-making method. The invention provides the scientific, efficient and transparent urban carbon updating-economy multi-objective collaborative optimization decision scheme generation method and system.

The invention relates to the field of nuclear power simulation, in particular to an automatic analysis system and method for failure mode and influence analysis of a nuclear power plant. The method comprises the following steps: step 1, constructing a nuclear power equipment knowledge graph, and integrating equipment structures, functions, failure modes and historical data; 2, automatically identifying a potential failure mode based on a machine learning model, and associating an influence path of the potential failure mode; integrating the machine learning model with digital twinborn simulation software to realize failure deduction and verification in a simulation environment; 3, after failure classification and influence path analysis are carried out on the accessed real-time data, a multi-criterion decision algorithm is adopted to calculate the risk priority, and optimization suggestions are output. According to the method, the automatic identification of the failure mode, the dynamic reasoning of the influence path and the quantitative evaluation of the risk priority are realized by constructing an intelligent analysis framework combining the knowledge graph and the machine learning.

The invention discloses a method for an MCData server to determine a target function alias of an SDS request, which belongs to the technical field of mobile communication networks and comprises the following steps: S1, conflict detection: the MCData server receives an SDS data transmission request of which a target address is a function alias and queries a database, and when the function alias is detected to be bound to two or more user IDs, the step S2 is entered; s2, determining a candidate set; s3, multi-criterion decision making: applying a plurality of decision criteria including a user state criterion, a role priority criterion, a geographic proximity criterion and a historical response efficiency criterion according to a predefined sequence, and filtering, scoring and sorting the candidate user set; s4, determining a unique target; and S5, performing message routing. According to the method, a technical closed loop of conflict detection-multi-criterion decision-target determination-message routing is constructed, and functional alias analysis and multi-dimensional user state evaluation are deeply coupled, so that fundamental transformation from simple random selection to intelligent accurate routing is realized.

The invention discloses a CAVs special lane configuration optimization method and system. The method comprises the following steps: constructing a road network and vehicle simulation model; road condition data and surrounding population data of a to-be-configured traffic road section are collected, the population data are used for estimating origin-destination (OD) demands of all entrances and exits of the road section, and a vehicle driving route is generated according to the OD demands. Correspondingly setting different numbers of CAVs special lanes according to different differences of the permeability of the CAVs; based on the determined number of lanes, the specific position of each special lane is further accurately positioned in combination with the main line flow ratio; and formulating an adaptive lane management strategy by integrating two key factors, namely the CAVs permeability and the main line flow ratio. According to the method, a TOPSIS multi-criterion decision-making method is adopted, multi-dimensional evaluation and optimization are carried out on the determined number of special lanes, the positions of all the lanes and a lane management strategy, an optimal lane configuration scheme is finally determined, and scientific and feasible technical support is provided for a traffic management department to carry out CAVs special lane planning and construction.

This invention discloses a multi-objective optimization-based method for preoperative tibial prosthesis localization in ankle replacement surgery, primarily addressing the problems of poor accuracy and low reliability in existing ankle prosthesis localization methods. The method includes: 1) acquiring and processing medical CT images and prosthesis model data of the affected lower limb, completing prosthesis standard position calibration and establishing a local coordinate system; 2) segmenting bone tissue, extracting three-dimensional models of the tibia, fibula, and talus, and including HU bone mineral density values; 3) standardizing the skeletal model, labeling anatomical landmarks, and constructing an orthogonal standard coordinate system; 4) pre-calculating relevant data of the distal tibial articular surface; 5) generating initial candidate solutions through grid search and constraint filtering; 6) obtaining the Pareto front by combining the NSGA-II-CDP algorithm and constraint dominance principles; 7) recommending the optimal solution and generating a visual model through multi-stage compromise solution filtering and multi-criteria decision-making. This invention can unify anatomical orientation, improve computational efficiency, and achieve accurate and efficient localization.

The invention discloses a flexible manufacturing structured process multi-objective optimization method based on evolutionary computation, and belongs to the technical field of intelligent manufacturing and production scheduling. The method comprises the following steps: modeling a production problem, and taking the workload balance degree of each vehicle type as an independent objective function; performing parallel optimization on process allocation by adopting a multi-objective evolutionary algorithm to generate a Pareto optimal solution set; and visualizing the Pareto frontier through an interactive decision support module, and carrying out multi-criterion decision recommendation based on the user preference weight. According to the method, the defects that a traditional single-target optimization method cannot reflect a multi-target tradeoff relation and depends on prior weight setting are overcome, a series of high-quality scheduling schemes can be provided for multi-variety mixed-line production, and the flexibility, transparency and overall efficiency of a production system are remarkably improved.

The application discloses a wind-solar coupling hydrogen production project site selection method based on multi-criteria decision in a fuzzy environment, and is specifically implemented according to the following steps: step 1, obtaining influence factors in a wind-solar coupling hydrogen production project site selection process, and constructing a wind-solar coupling hydrogen production project site selection evaluation index system; step 2, constructing an evaluation model of site selection decision; step 3, calculating comprehensive weights of evaluation indexes; and step 4, adopting a multi-attribute boundary approximate region comparison method to sort the alternative sites, and obtaining an optimal decision. The application solves the problem of poor accuracy of the existing site selection method.

The invention discloses a narrow space exploration method based on multi-criterion decision and adaptive ray casting, and relates to the technical field of ecological restoration. The flooding time distribution of the river beach under the ultra-frequency flood scene is calculated through a Weibull formula; secondly, extracting information such as beach vegetation types and coverage based on a multi-source remote sensing image, and dividing suitable plant partitions in combination with flooding time; a habitat quality evaluation system is constructed from the dimensions of vegetation conditions, hydrological conditions, topographic features and the like, and habitat quality rating of each subarea is completed; finally, according to flooding time characteristics, plant suitability and habitat quality grades, ecological restoration partitions are proposed in a targeted mode. According to the method, accurate coupling of the flood hydrological process and the beach ecosystem is achieved, the problem that a traditional ecological restoration measure lacks a hydrological-ecological cooperation basis is solved, the scientificity and feasibility of a restoration scheme are improved, and the method is suitable for ecological restoration engineering around water areas such as river beaches, lake wetlands and the like in northern arid and semi-arid areas.

The invention relates to the technical field of intelligent cutting, and discloses a composite material layered cutting control system which comprises a multi-modal data analysis module, a multi-physical field simulation planning module, a real-time monitoring module, a core regulation and control module and an optimization execution module. Performing digital image processing on the interlayer interface characteristics of the composite material to obtain digital characteristic parameters; based on the digital characteristic parameters, multi-physics field simulation prediction is conducted on stress distribution and thermal conductivity in the cutting process, and a layered cutting path is obtained; based on the layered cutting path, multi-source data fusion is conducted on vibration characteristics and temperature distribution in the machining process of the composite material, and real-time working condition data is obtained; performing multi-criterion decision fusion on the real-time working condition data to obtain target process parameters; correcting and coding the target process parameters to obtain a cutting instruction of the composite material; the layered cutting control efficiency of the composite material can be improved.

The invention provides a progressive optimization design method for seismic toughness of a seismic isolation building. The method sequentially comprises the four stages of shock insulation layer mechanical parameter optimization, shock insulation support model optimization, non-structural component reinforcement optimization and multi-criterion decision making. Verification is carried out by taking a certain five-storey frame teaching building as an example, and the result shows that the method can remarkably reduce the function loss of the building under frequent, fortification and rare occurrence earthquakes, effectively considers the anti-seismic toughness and the economic investment, and has high engineering applicability and popularization value.

The application discloses a kind of electric power operation VR training operation process adaptability evaluation method and equipment, it is related to electric power operation training technical field, comprising: through multi-source perception equipment acquisition VR training multidimensional data of whole process and label processing;Multi-dimensional evaluation index system covering process compliance, scene adaptability, capability matching is constructed;Based on improved multi-criteria decision algorithm and fuzzy evaluation algorithm, a dynamic evaluation model is constructed, quantitative scoring is realized;Short board of adaptability is identified and evaluation report is generated;Linkage intelligent recommendation engine pushes targeted training content;Establish data iteration updating mechanism and continuously optimize model.The application also discloses corresponding evaluation equipment and computer readable storage medium, realizes the overall, accurate, dynamic evaluation of electric power operation VR training operation process, provides scientific support for individualized training, significantly improves training quality and efficiency, and creatively breaks through the limitations of traditional evaluation.

The invention relates to a major water conservancy and hydropower engineering quality improvement and efficiency improvement multi-criterion decision-making method, and the method comprises the steps: analyzing quality improvement and efficiency improvement influence factors, and identifying the quality improvement and efficiency improvement influence factors of major water conservancy and hydropower engineering; establishing an evaluation index system, determining a driving target of quality improvement and efficiency improvement of the major water conservancy and hydropower engineering, and constructing a multi-criterion evaluation key index system of quality improvement and efficiency improvement of the major water conservancy and hydropower engineering; according to the method, the technical problem that the quality improvement and efficiency improvement decision of major water conservancy and hydropower engineering is complicated under the multi-element driving-multi-factor synergistic effect is solved, and the conversion of the quality improvement and efficiency improvement decision of the reservoir dam from a single decision to a multi-criterion decision is realized; scientific decision support is provided for quality and efficiency improvement and sustainable development of major water conservancy and hydropower engineering.

The invention relates to the technical field of training resource scheduling, in particular to a training resource management method. The method comprises the steps of firstly constructing a class missing influence analysis directed attribute graph, identifying influence clusters needing collaborative adjustment through a community discovery algorithm, then establishing a constraint satisfaction problem model for each influence cluster, generating a preliminary feasible scheme, and performing optimization by means of a teacher and site preference simulation multi-agent negotiation mechanism to obtain a class missing influence analysis result; and finally, carrying out multi-dimensional quantitative evaluation on the candidate schemes, namely constructing a coordinated dependency network to analyze execution complexity, calculating a failure risk probability by utilizing a Bayesian network, testing a space-time error-tolerant rate by adopting Monte Carlo simulation, comprehensively evaluating student commuting burden of site change, and selecting an optimal scheme through a multi-criterion decision algorithm. According to the method, the whole-process intelligent decision-making from conflict identification, scheme generation and optimization to risk assessment is realized, and the scheduling capability of a training institution for coping with a complex resource interruption event and the scheme reliability are effectively improved.

The invention relates to the technical field of Internet of Things, and discloses a dynamic and stable pairing method for sensors and gateways, which comprises the following steps of: 1, acquiring related information of all sensors to be paired and all available gateways; calculating to obtain gateway evaluation data and sensor evaluation data; 2, according to the gateway evaluation data, the corresponding first weight, the sensor evaluation data and the corresponding second weight, the evaluation score of each sensor for different gateways and the evaluation score of each gateway for different sensors are calculated through a multi-criterion decision model; step 3, constructing a preference list of each sensor and a preference list of each gateway; 4, according to the preference list of each sensor and the preference list of each gateway, adopting an improved delay receiving algorithm to generate a stable pairing scheme; and step 5, according to the stable pairing scheme, establishing network connection between the sensors and the corresponding gateways. Meanwhile, the invention also discloses a dynamic stable pairing system for the sensor and the gateway.

The invention discloses a multi-objective optimization method and system for an open thin-wall energy-absorbing stand column, and relates to the technical field of mechanical structure design, and the method comprises the steps: building an optimization model which aims at the minimization of peak compression force, the maximization of total energy absorption and the minimization of structural mass and comprises the minimum load threshold constraint; a high-precision agent model composed of a BP-Transform-LSTM auto-encoder hybrid neural network prediction model and a BP quality prediction model is constructed to replace expensive simulation to carry out millisecond-level performance evaluation; performing global optimization based on an NSGA-III algorithm, and applying punishment to a scheme violating constraints to obtain a Pareto non-dominated solution set; and finally, objectively screening out an optimal compromise design scheme from the solution set by adopting a multi-criterion decision strategy combining an entropy weight method and a TOPSIS method. Unification of optimization efficiency, prediction precision and engineering reliability is achieved, and the design quality and research and development speed of the energy absorption structure are remarkably improved.

This invention belongs to the field of coal mine power safety and intelligent risk management technology, and discloses a comprehensive risk rating method for geological disaster hazards in coal mine power supply lines based on fuzzy logic and multi-criteria decision-making. The method first collects multi-dimensional indicator data and completes hierarchical downgrading processing to construct a four-dimensional, three-layer geological disaster risk indicator system; an initial risk score is obtained through hierarchical fuzzy reasoning, and a geological disaster recoverability time correction function is introduced to correct the risk score; the score is calibrated through the fusion of subjective and objective weights and a conflict self-checking mechanism, and the entire process of risk rating update is triggered based on a preset dynamic threshold. This invention solves the problems of premature resolution of fuzzy information, lack of information conflict self-checking, and failure to consider recovery time in existing technologies. It retains the advantages of fuzzy reasoning uncertainty, avoids rule combination explosion, can proactively identify weight conflicts and circuit breakers for error correction, improves the robustness of risk assessment, and accurately matches the needs of coal mine power supply line repair resource allocation and safety management.

A verifiable policy simulation method includes: providing a verification model; collecting related data; simulating with the related data to build an evaluation index; providing a plurality of preliminary expert questionnaires with the evaluation index to obtain a preliminary expert result; building an ANP hierarchical structure according to the preliminary expert result; providing a plurality of ANP expert questionnaires to obtain an ANP expert result; calculating a weight of the ANP expert result to obtain a weighted value of model; calculating the ANP expert result to confirm an index of system dynamics model; producing a causal loop diagram according to the ANP expert result and the index of system dynamics model; inputting the index of system dynamics model and the causal loop diagram to the verification model to obtain a model verification data; comparing the ANP expert result with the model verification data to obtain a verification result of simulated comparison.

This application discloses a method and related equipment for predicting the functional failure probability of shield tunnels based on multiple variables. The method includes: constructing an integrated damage-function index system covering multiple scales and quantifying the mapping relationship between damage and function; optimizing the probabilistic seismic demand model using interval estimation methods and selecting the best vibration intensity index by combining fuzzy multi-criteria decision-making; introducing the Copula function to accurately characterize the nonlinear correlation between multidimensional damage / function parameters; and finally establishing a multivariate vulnerability analysis model that can output the failure probability of each function of the tunnel and the overall failure probability of the system. This provides reliable technical support for the seismic design, post-earthquake assessment, and operation and maintenance decisions of shield tunnels. It can accurately assess the seismic risk of shield tunnels under non-uniform liquefaction development, improve the reliability and robustness of the results, and can be widely applied in the field of computer technology.

This invention provides a comparative analysis system and method for domestic and imported welding materials, characterized by scientific accuracy and strong adaptability. By establishing a complete performance evaluation index system and combining a multi-criteria decision-making algorithm and an adaptive learning mechanism, it can provide accurate material comparison analysis results for different welding requirements. The system uses the entropy weight method to objectively determine index weights and combines it with the TOPSIS algorithm for comprehensive scoring, effectively avoiding interference from subjective factors. The introduced adaptive learning function can continuously optimize the weight allocation strategy based on user feedback, enabling the system to continuously improve. Multi-dimensional visualization output makes the comparison results more intuitive and clear. Compared with traditional methods, this invention not only improves the scientificity and accuracy of material selection but also significantly enhances work efficiency, making it particularly suitable for manufacturing fields with high welding quality requirements.

The invention discloses a multi-criterion optimized virtual power plant ordered clustering and resource mutual aid method and system, and relates to the technical field of information technology and energy system cross fusion. The method comprises the following steps: collecting multi-source heterogeneous operation data of a virtual power plant, and preprocessing the multi-source heterogeneous operation data; and based on the preprocessed data, an ordered clustering algorithm is adopted for analysis, time periods with similar operation characteristics are classified, and a corresponding virtual power plant operation mode is generated. And constructing a multi-index evaluation system, performing comprehensive evaluation and sorting on all the virtual power plant operation modes by adopting a PROMETHEE multi-criterion decision-making method, and selecting the virtual power plant operation mode with the highest net flow value as an optimal scheduling scheme. And executing a resource mutual aid decision based on the optimal scheduling scheme of each virtual power plant.