61 results about "Multi criteria decision" patented technology

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention discloses a fire alarm linkage system for fire engineering based on the Internet of Things. The operation process of the system specifically comprises the following steps: generating a dynamic risk probability distribution diagram based on a building three-dimensional point cloud model and multi-source data, and deploying a differential sensor array; environment multi-physical field parameters are collected in real time through a differential sensor array, and a multi-mode fire behavior sensing signal set is generated; building a fire situation credibility map: calling a multi-criterion decision module to calculate a linkage equipment weight distribution matrix based on the fire situation credibility map, and generating a multi-stage linkage control instruction set in combination with the building topological structure model and the personnel distribution thermodynamic diagram; and performing multi-cross-system cooperative execution and closed-loop optimization. The method has the following advantages and effects that the inherent limitation of a traditional system can be broken through by constructing an intelligent prevention and control framework combining risk quantification, situation deduction and decision closed loop, the personnel evacuation efficiency is effectively improved, and damage caused by major accidents such as detonation is reduced.

The invention discloses a catalyst determination method and device and a machine readable storage medium, and relates to the technical field of weather modification. The method comprises the steps that index values of test indexes of a plurality of different types of catalyst samples are obtained respectively, and the index values are the index values of the catalyst samples tested in a preset strong freezing rain simulation environment; the freezing rain key condition is reproduced through the simulation platform, and the deviation between a traditional laboratory and a real scene is overcome. Determining the weight of each test index based on all index values and a weight distribution algorithm; aiming at each type of catalyst samples, based on a multi-criterion decision algorithm and the index value and the weight of each test index, determining an evaluation score of the catalyst samples; and determining the catalyst sample with the highest evaluation score as a target catalyst. By scientifically and comprehensively evaluating the performance of different types of catalysts, a scientific basis is provided for selection and application of the catalysts, and the freezing rain disaster prevention and control cost is reduced.

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 discloses a rail transit multi-network integration method based on a digital trunking technology, relates to the technical field of communication, and aims to solve the problem that a traditional narrowband trunking system cannot meet the requirements of voice and data comprehensive bearing and vehicle-ground large-bandwidth service real-time transmission. The method comprises the following steps: constructing a time division duplex long-term evolution broadband digital trunking unified bearing platform; an intermediate media access control layer fusion architecture is deployed, and seamless mobility management of the heterogeneous network is realized through unified authentication and multi-link aggregation; an intelligent vertical switching algorithm based on prediction and multi-criterion decision is introduced, a bidirectional long-short-term memory network model is utilized to predict a network state trend, and an improved entropy weight method and an analytic hierarchy process are combined to dynamically select an optimal access network according to service requirements. According to the invention, the network operation efficiency and the user experience can be obviously improved, and high reliability and high service quality of the rail transit communication system are ensured.

The invention discloses a multi-scale full-period rainfall flood toughness evaluation method, equipment, a medium and a product, and relates to the field of rainfall flood toughness evaluation. The method comprises the following steps: firstly, according to a rainstorm flood action period and three spatial scales of drainage basin-city-drainage partition, constructing a multi-scale full-period rainfall flood toughness factor library; collecting and calculating each element data in the element layer in the whole cycle process of coping with the rainstorm flood effect under three spatial scales of drainage basin-city-drainage partition; screening out a final multi-scale full-period rainfall flood toughness element library based on the correlation between the elements; calculating the weight of each element in the final multi-scale full-period rainfall flood toughness element library by using an entropy weight method; on the basis of weight calculation, a VIKOR multi-criterion decision-making method is used to calculate rainfall flood toughness under each spatial scale, and a geographic detector is used to analyze elements with most significant influence from two aspects of the spatial scale and a rainstorm flood action stage, so that multi-scale full-period scientific, efficient and accurate evaluation of rainfall flood toughness is realized.

The invention provides a supply chain risk mitigation strategy evaluation method based on a multi-criterion decision model, and the method comprises the steps: carrying out the cross analysis of a supplier number weight adjustment coefficient and a component technical parameter, obtaining a technical risk coefficient, and carrying out the analysis of component replaceability data, obtaining a technical path dependency degree and supply source limitation, calculating comprehensive risk scores of the key components, and determining a risk priority sequence; formulating a multi-supplier configuration scheme based on a vulnerability assessment result, assessing supplier distribution and component compatibility, calculating a component interchange rate and a supplier coverage range, obtaining a standardized replacement power according to the interchange rate and the coverage range, and if the standardized replacement power is lower than a threshold value, increasing a supplier number weight; and analyzing supply interruption response time and replacement switching efficiency based on the component interchange rate, the supplier coverage range and the quantity weight, monitoring the supplier capacity utilization rate, measuring and calculating the production maintaining time length and the capacity recovery speed, and obtaining risk relief parameters after dynamic adjustment.

The invention relates to the technical field of enterprise risk management and intelligent decision, and discloses an enterprise risk prediction and control method and system, and the method comprises the steps: collecting enterprise multi-source data, carrying out the preprocessing, constructing a risk prediction model of fusion modeling, and outputting a risk score; extracting an operation log behavior path based on the score, and generating a risk situation coding vector; similar events are matched in a historical event library, and the events are combined to re-calculate a response path; inputting the plurality of response paths into a strategy evolution sandbox for dynamic environment simulation, and outputting an evolution stability index; and performing path optimization through a multi-criterion decision method, updating an execution result and behavior codes to a historical mapping pool, and constructing a self-learning closed loop. The method has high interpretability, structural adaptability and strategy evolution capability, and improves the intelligent level of enterprise risk control.

The invention discloses a GIS-based offshore photovoltaic intelligent site selection method and equipment, and relates to the technical field of offshore photovoltaic site selection. Based on a multi-criterion decision model of an analytic hierarchy process, multiple factors (including solar irradiation intensity, water depth, wind speed, distance from a power grid and wave height) in offshore photovoltaic power station site selection are fully considered; according to the method, the overall life cycle key factors of the offshore photovoltaic project from construction, operation to safety are covered, the overall target of'selecting the optimal offshore photovoltaic site 'is disassembled into quantifiable criterion layers and then corresponds to specific alternative schemes, so that evaluation of each index serves the overall target, and coherence and pertinence of analysis logic are ensured.

The embodiment of the invention provides a training method of an esophageal squamous carcinoma risk prediction model, and an esophageal squamous carcinoma risk prediction method and system, and belongs to the technical field of medical treatment. According to the scheme, a training feature set is obtained, global importance analysis is carried out on the training feature set through a plurality of different feature selection algorithms, and an importance scoring result of each feature selection algorithm and consensus feature results of all the feature selection algorithms are obtained; performing screening processing according to each importance scoring result to obtain a feature subset corresponding to each feature selection algorithm; inputting each feature subset into a plurality of different classifier algorithms for training to obtain a corresponding basic model; a plurality of high-performance models are screened out from all the basic models through a multi-criterion decision classification fusion framework for weighted fusion, input features after model fusion are determined according to consensus feature results, an esophageal squamous cell carcinoma risk prediction model is obtained, the accuracy of esophageal squamous cell carcinoma risk prediction can be improved, and potential patients can be found in advance.

The invention relates to a micro-grid photovoltaic energy storage capacity optimization method based on double-layer multi-target collaborative decision, and the method comprises the following steps: 1, analyzing the structure and operation mode of a new energy micro-grid in a rural region, and constructing a capacity optimization configuration model of an optical storage system; step 2, optimizing an objective function by an inner layer and an outer layer; the inner-layer optimization target is to minimize the daily volatility of the complementary power generation system and minimize the daily peak-valley difference; 3, solving an optical storage capacity optimal configuration model: obtaining an optimal Pareto solution set of the target function by adopting a quantum particle swarm algorithm; 4, adopting an interactive multi-criterion decision based on compromise solution selection; 5, outputting a capacity configuration scheme with the optimal comprehensive efficiency; the method has the advantages that intermittency and peak regulation capacity are comprehensively considered, stable power supply is ensured, power supply reliability is improved, energy utilization efficiency is improved, system construction and operation cost is reduced, and economic benefits are improved.

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 discloses a fruit quality comprehensive detection method based on machine vision and X-rays. The fruit quality comprehensive detection method is used for performing graded detection on appearance, internal defects and sugar degree of fruits. The appearance detection comprises the steps of constructing a fruit surface defect feature matrix, a fruit shape feature matrix and a color feature matrix, and inputting the fruit surface defect feature matrix into a BPNN network to realize fruit surface defect detection; inputting the fruit shape feature matrix into another BPNN network to realize fruit shape detection; inputting the color feature matrix into an SVM model to realize color detection; the internal defect detection comprises the steps of constructing a plurality of different internal defect detection models, and fusing results of the internal defect detection models based on a multi-criterion decision to obtain an internal defect detection result; the sugar degree detection comprises the steps of extracting a pulp area based on X rays, extracting morphological characteristics and gray statistical characteristics, predicting the sugar degree by utilizing a sugar degree prediction model, and grading the sugar degree according to a sugar degree prediction value. The method can more comprehensively reflect the comprehensive quality of the fruits.

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.

A power system planning evaluation method based on random multi-criterion decision comprises the following steps: 1) constructing a multi-dimensional index system, and obtaining a plurality of power system planning schemes; 2) performing initial weight assignment on three-level indexes in the multi-dimensional index system by adopting an AHP method; 3) determining positive and negative ideal solutions of each three-level index in the multi-dimensional index system based on the randomness of the power system planning scheme to the three-level index parameters; 4) constructing a nonlinear programming optimization problem; and 5) solving the nonlinear planning optimization problem to obtain the optimal weight of each three-level index, and selecting an optimal scheme from the plurality of power system planning schemes according to the optimal weight of each three-level index. According to the method, the volatility and randomness of new energy output can be fully considered, and the subjectivity of expert judgment and the objectivity of a mathematical algorithm can be combined, so that the applicable value of a planning evaluation result is improved, and more reliable decision support is provided for planning of a high-proportion renewable energy power system.

The invention provides a load spectrum time domain load extrapolation method and device, a medium, a program product and a terminal, and the method comprises the steps: obtaining an original random load spectrum, and generating a plurality of alternative threshold points; performing GPD fitting on each alternative threshold point to form a fitting result library; and the fitting result is checked, the fitting quality index of each threshold point is calculated, and the optimal threshold is automatically selected based on a multi-criterion decision-making method. A GPD fitting result corresponding to the optimal threshold value is utilized to randomly generate a plurality of extreme loads, and an extreme load simulation database is constructed; and then performing multiple rounds of random replacement on the load in the simulation database to obtain a plurality of load time domain extrapolation results, and determining a final time domain extrapolation result through extrapolation error evaluation. According to the method, threshold optimization is realized through multi-criterion decision, and the stability of GPD fitting and the precision of load time domain extrapolation are effectively improved by combining multiple rounds of random replacement and error evaluation. And the accuracy and the reliability of extreme load time domain extrapolation are obviously enhanced.

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 discloses a shield tunnel joint sealing gasket type selection method based on multi-criterion decision, which comprises the following steps of: selecting design water pressure, maximum joint opening amount, maximum assembly force and allowable maximum groove filling rate as design threshold values; the average contact stress under the maximum seam opening amount, the maximum seam opening amount compatible under the design water pressure, the closing compression force, the maximum groove filling rate and the sealing gasket sectional area are selected as performance evaluation indexes, different sealing gasket evaluation index values are obtained through tests, the marginal effect of the indexes is considered, the marginal values of the sealing gasket evaluation indexes are obtained, and the performance of the sealing gasket is evaluated. And determining objective weights of the evaluation indexes by adopting a CRITIC method, and obtaining the sealing gasket with the best comprehensive performance in the different sealing gaskets by adopting a TOPSIS method according to the marginal values and the objective weights. Scientific quantitative evaluation of the waterproof performance, the assembling performance and the economical efficiency of the sealing gasket is achieved through the multiple evaluation indexes, the model selection result is more scientific, reasonable and economical, and blindness of an existing design method is avoided.

The invention relates to a battery module thermal runaway key node identification method based on a graph theory and a multi-criterion decision. The method comprises the following steps: S1) constructing a weighted thermal network model of a battery module; s2) calculating a multi-centrality index of each node in the network; s3) constructing a decision matrix based on the centrality indexes of all nodes; s4) normalizing the decision matrix and determining an index weight; and S5) calculating comprehensive scores of the nodes by adopting a TOPSIS method, sorting the comprehensive scores, and outputting a thermal runaway protection priority list. Under the condition that the space structure and thermal connection parameters of the battery module are known, the method can accurately and quickly identify the key monomer in the thermal runaway spreading process, and provides a decision basis for precise thermal safety protection of the battery module and optimized layout of sensors.

The application discloses a kind of ship design multi-criteria decision-making method selection method and system. By generalizing the characteristics of general MCDM problem and defining the characteristics of common MCDM method, and establishing the matching mapping relationship between the characteristics of general MCDM problem in problem domain and the characteristics of common MCDM method in method domain, a generalized MCDM method preliminary selection rule is generated, and a preliminary applicable MCDM method / set under a specific decision situation is obtained. On this basis, a generalized MCDM method applicability criterion and the corresponding evaluation model are established. Following the principle of combining qualitative and quantitative analysis, the actual application effect of the selected MCDM method is compared comprehensively, and the optimal MCDM method under the specific decision situation is selected accordingly. The two-step ship design MCDM method selection strategy and system of preliminary selection and optimization can effectively ensure the applicability of the preliminary selected MCDM method and the superiority of the optimized MCDM method.

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 invention discloses an electric energy quality evaluation method based on combined weighting and a multi-criterion compromise solution sorting method, relates to the technical field of power grid electric energy quality evaluation, and solves the problems that in the prior art, weight distribution depends on artificial experience, results are not accurate, and a multi-dimensional decision mechanism is lacked. The subjective weight is calculated through the three-scale AHP method, the objective weight is generated through the improved entropy weight method fusing the entropy weight and the variance weight, and the subjective weight and the objective weight are fused based on the Lagrange multiplier method to obtain the comprehensive weight; and calculating the utility, the regret degree and the comprehensive value of each node by adopting a VIKOR method, and sorting the nodes in an ascending order according to the comprehensive values to realize node grading. According to the method, evaluation can be completed only through single-time data input, manual intervention or multi-round calculation is not needed, the subjective deviation is remarkably reduced through the established multi-criterion decision model, collaborative quantification of the overall performance of the electric energy quality and the risk short board is achieved, and meanwhile the evaluation efficiency and reliability are improved.

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.

This invention discloses a method and system for evaluating the multi-performance of connected vehicle (CAV) platooning. The method includes: constructing CAV platooning control models based on different car-following strategies to adapt to different traffic conditions and platooning performance requirements; establishing a multi-criteria decision-making evaluation system by setting different weights for different criteria to meet the diverse performance requirements of CAV platooning; applying fuzzy log-least squares (FLLS) to determine the weights of each performance criterion for different decision-makers, enabling the system to fully reflect the priority of different decision-makers in comparing different criteria; and constructing a GRA-TOPSIS method by combining grey relational analysis (GRA) and ideal solution similarity ranking method (TOPSIS) to calculate a comprehensive evaluation index for evaluating the multi-performance of CAV platooning, thereby ranking the evaluation results under different strategies. This invention can provide decision support for decision-makers to select the optimal CAV platooning strategy in complex traffic environments.

The invention discloses a breast cancer risk prediction method and system based on multi-modal information fusion, and the method comprises the steps: constructing a deep learning feature channel, a histogram feature channel and a texture omics feature channel through the comprehensive utilization of a two-dimensional mammary gland molybdenum target image, a three-dimensional digital mammary gland cross-sectional image and clinical information, and obtaining a plurality of imaging prediction probabilities. The two-dimensional mammary gland molybdenum target image and the three-dimensional digital mammary gland cross-sectional image are subjected to intra-modal information integration through a multi-channel fusion strategy, clinical information is modeled through a Logistic regression model, and three types of prediction probabilities are output. And finally, dynamically calculating the weight of each modal according to evaluation indexes such as accuracy, AUC, sensitivity and specificity by adopting a weighted fusion method based on multi-criterion decision, and realizing unified fusion prediction of multi-modal information. The accuracy and robustness of breast cancer screening are improved.

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.