80 results about "Intuitionistic fuzzy" patented technology

The invention provides a battery health state prediction method and system, and the method comprises the steps: obtaining a high-dimensional data set in a battery discharge process, and carrying out the normalization processing of the high-dimensional data set, and obtaining standardized data; analyzing the correlation between the standardized data and the battery capacity by using a Spearman correlation coefficient to obtain a target data set; inputting the target data set into a width auto-encoder, extracting feature nodes and enhanced nodes of the width auto-encoder, and obtaining intermediate feature representation; and inputting the intermediate feature representation into a random configuration network fused with an intuitionistic fuzzy mechanism, and generating a battery health state preliminary prediction result, thereby alleviating the technical problem of low accuracy of the existing battery health state prediction.

The invention relates to the technical field of education evaluation, and discloses a course teaching effect evaluation method, which comprises the steps of constructing a two-dimensional evaluation index system containing student ability and teacher teaching as a basic framework of evaluation; based on the two-dimensional evaluation index system, determining an initial weight by adopting an interval intuitionistic fuzzy analytic hierarchy process, and dynamically correcting the initial weight in combination with an entropy weight method to form a combined weight; collecting multi-source data corresponding to the two-dimensional evaluation index system through a triangular verification method, and preprocessing the multi-source data; combining the combined weight with the preprocessed multi-source data, and performing layered fuzzy comprehensive evaluation, thereby realizing quantitative evaluation and weak link diagnosis of the course teaching effect; according to the method, expert knowledge and objective data are fused, so that the evaluation weight is dynamically changed along with actual teaching data, and the problem that evaluation is separated from reality due to weight solidification is solved.

The invention discloses a fuzzy theory fused tongue diagnosis characteristic quantitative analysis method and system, and relates to the technical field of traditional Chinese medicine tongue diagnos.The method comprises the steps that a micro piezoelectric sensor array and a hyperspectral imaging module are integrated, tongue surface Young modulus distribution data and hyperspectral reflectivity data are synchronously collected, and a multi-mode tongue picture fusion data set is generated; further performing crack region segmentation and super-resolution reconstruction through a neural network model, outputting a crack network diagram and a tongue papilla form entropy, generating topological fingerprints in combination with continuous homology analysis, identifying a mechanical gradient mutation region and extracting a metabolic index, constructing a mechanical-metabolic associated feature set, and obtaining a mechanical-metabolic associated feature set; topological fingerprints and mechanical-metabolic characteristics are fused in a three-dimensional space-time coordinate system, a dynamic intuitionistic fuzzy decision field is constructed, finally, tongue picture pathology evolution risk indexes and a visual early warning report are output through a pathology prediction model, and the system provides quantifiable decision support for'prevention before illness' in traditional Chinese medicine.

A method performed by a computer system for monitoring diabetes and comprising: a model explainability phase, comprising steps of: - collecting specific foot data from at least one sensor disposed at least within a shoe insole of a user, - collecting a plurality of trained machine learning models configured for decision-making in diabetes diagnosis based on a plurality of collected data, - based on the plurality of trained machine learning models, generating a plurality of specific machine learning models, said specific machine learning models being specified for decision-making in diabetes diagnosis based on the specific foot data, and a model reliability assessment phase, comprising steps of: - evaluating a reliability of each of the specific machine learning model based on neutrosophic sets and intuitionistic fuzzy sets, - based on the result of the evaluation step, selecting the optimized machine learning model for monitoring diabetes based on the specific foot data.

The invention provides a method for evaluating an electricity-hydrogen comprehensive energy system. The method comprises the following steps of: constructing an evaluation standard according to a multi-dimensional comprehensive index; converting the multi-dimensional comprehensive index into a triangular intuitionistic fuzzy number, and performing normalization processing on the triangular intuitionistic fuzzy number; the subjective weight of the triangular intuitionistic fuzzy number is calculated through an optimal and worst method; the objective weight of the triangular intuitionistic fuzzy number is calculated through a CRITIC method; fusing the subjective weight and the objective weight through a game theory model to obtain a final weight; and based on the final weight, sorting the alternative schemes through a multi-attribute boundary approximation comparison method to obtain an evaluation result. According to the method, a comprehensive evaluation system based on fuzzy comprehensive evaluation is constructed, the evaluation index subjective and objective combination weighting method is constructed by introducing the game theory idea, the rationality and reliability of weight quantification are improved, and a reliable basis is provided for planning and decision making of a comprehensive energy system.

The invention belongs to the technical field of machine learning and mode recognition, and particularly relates to an intuitionistic fuzzy double support vector machine classification method based on relative density and non-membership, which comprises the following steps: constructing and training an RD-IFTSVM model, and performing data classification through the trained RD-IFTSVM model. According to the method, a relative density membership function is provided, the proportion of local neighborhood heterogeneous samples to global similar density consistent samples is combined, misjudgment caused by the fact that a traditional method only depends on local neighborhoods is avoided, noise is restrained, and effective samples are reserved; optimization problems are respectively constructed for positive and negative classes, independent regularization parameters and slack variables are introduced, key protection of core samples is realized through membership weighting, and robustness of noise samples is improved; meanwhile, a double-hyperplane discrimination criterion based on a geometric distance is provided, and the sample attribution is determined by the minimum distance to the positive and negative kernel hyperplanes.

The invention discloses an infrared and visible light image multi-level fusion method and device based on fractional differential and an intuitive fuzzy set, and relates to the field of computer vision. The method comprises the following steps: respectively decomposing an infrared image and a visible light image based on an MDLatLRR decomposition method to obtain a corresponding basic component and a series of multi-scale detail components; fusing the detail components by using a rule based on an intuitionistic fuzzy set to obtain a detail fusion component; fusing the basic components by adopting a fractional order differential rule to obtain a basic fusion component; and obtaining a reconstructed image of the target object according to the detail fusion component and the basic fusion component. According to the method, the local texture richness is enhanced and the key feature extraction effect is improved through the rule of an intuitionistic fuzzy set, meanwhile, the overall feature of the image is effectively reserved through the global and memory features of fractional differential, and finally, the high-quality fusion effect is achieved through recombination of a detail fusion component and a basic fusion component.

The invention relates to the technical field of electric power information, and provides a frequency modulation market-oriented electric vehicle package design and parameter formulation method and system. The frequency modulation market-oriented electric vehicle package design and parameter formulation method comprises the following steps of: obtaining user historical data, and constructing a user preference fuzzy set of a user under an evaluation criterion according to the acceptance degree of the user on the evaluation criterion; based on all retail frequency modulation packages, calculating the conformity degree of the packages and the evaluation criteria, and constructing a package intuitionistic fuzzy set of each package under the evaluation criteria; matching the user with the package to obtain a similarity measurement matrix, and calculating the probability that the user selects a certain package to obtain a user package selection probability matrix; based on the user plan selection probability matrix, plan subsidy cost is determined; and constructing a target function based on the energy purchase cost, the package subsidy cost, the expected risk cost and the frequency modulation income, and solving an optimal scheme in combination with constraint conditions. And a user selection behavior can be simulated through the fuzzy set to formulate package parameters.

The invention belongs to the technical field of cloud computing networks, and discloses a computing power network workload spatio-temporal dynamic prediction method and device, equipment and a medium, and the method comprises the steps: determining a fuzzy set for identifying a workload mode through employing a clustering algorithm based on dynamic time warping according to a performance index during operation; determining a state transition conditional probability matrix of the computing power node, the heterogeneous hypergraph and the working load mode; adopting hypergraph convolution kernel learning to obtain a heterogeneous feature fusion model of workload and computing power nodes; processing the fuzzy set by adopting a fuzzy membership covariance, and further training by adopting a time convolution network of a dynamic receptive field to obtain a time sequence feature of a workload; and according to the heterogeneous feature fusion model and the time sequence features of the workload, carrying out space-time fusion training of cross entropy measurement probability and fuzzy uncertainty by adopting a probability intuitionistic fuzzy relation matrix to obtain a workload space-time dynamic prediction model. The method has the beneficial effect that the error rate of resource scheduling decision is reduced.

The invention provides a computing network infrastructure efficiency evaluation method, device, equipment and program product, and relates to the technical field of infrastructure evaluation. The method comprises the following steps: acquiring intuitionistic fuzzy scores of a plurality of evaluation experts on efficiency evaluation indexes of the computing network infrastructure; determining the intuitionistic fuzzy entropy of each evaluation expert and an initial weight matrix based on the intuitionistic fuzzy score; determining the confidence of each evaluation expert based on the intuitionistic fuzzy entropy; based on the confidence coefficient of each evaluation expert and the initial weight matrix, determining a corrected target weight matrix; and based on the value of the to-be-evaluated computing network infrastructure in each efficiency evaluation index and the corresponding weight, determining an efficiency evaluation result of the to-be-evaluated computing network infrastructure. The method is used for accurately evaluating the efficiency of the computing network infrastructure.

The invention belongs to the technical field of data mining and knowledge discovery, and particularly relates to a rough entropy-based key pathogenic factor acquisition method, which comprises the following steps of: depicting disease factors by using an intuitionistic fuzzy information system, and establishing an effective granulation mechanism based on a dominant relationship in the intuitionistic fuzzy information system, enabling the information particles under the relation to conform to the granularity monotonicity; uncertainty measurement is carried out on the intuitionistic fuzzy information system based on rough entropy, and then the importance degree of pathogenic factors is described; all key pathogenic factors are obtained through the importance degrees of the pathogenic factors, so that the decision quality of medical diagnosis and the efficiency of knowledge discovery are improved. According to the method, redundant factors and data dimensions are reduced, computing resource consumption is reduced, and decision quality and knowledge discovery efficiency are effectively improved.

This invention provides a parameter-driven, intuitionistic fuzzy logic method for evaluating the suitability of mega-span cavern sites, relating to the field of geotechnical engineering evaluation. This invention, for the first time, constructs a dedicated tunneling design index system for mega-span caverns integrating "rock mass quality, environmental response, shape effect, and support status." It separates evaluation criteria indicators from engineering response indicators, establishing a mapping relationship between the two through numerical simulation, making the evaluation process more consistent with engineering cognitive principles. Intuitionistic fuzzy numbers are used to quantify the merits of evaluation index values, achieving an organic fusion of objective data and fuzzy expression. The final output is a single, quantifiable site suitability index, enabling intuitive comparison of multiple sites and multiple indicators, greatly facilitating engineering decision-making. By replacing expert scoring with a pre-set mapping function, subjective interference is completely eliminated, ensuring that the evaluation results are entirely determined by the input parameters. Different personnel can obtain consistent conclusions, greatly improving the repeatability and engineering applicability of the method.

This invention discloses a method, storage medium, and device for aquaculture water quality assessment based on intuitionistic fuzzy set distance measures. The method includes: constructing an expert comment set based on water quality datasets collected under aquaculture conditions, and converting the expert comment set into an intuitionistic fuzzy matrix for aquaculture water quality assessment; constructing a Gaussian kernel-based dynamic hesitancy assignment model based on the intuitionistic fuzzy matrix to determine the hesitancy assignment of intuitionistic fuzzy numbers; forming quadruples of the intuitionistic fuzzy numbers and their corresponding hesitancy assignments in the intuitionistic fuzzy matrix; and calculating the intuitionistic fuzzy set distance measures with the optimal and worst water quality based on the quadruples of the intuitionistic fuzzy numbers to assess the aquaculture water quality. This invention can significantly improve the accuracy of aquaculture water quality assessment and enhance the timeliness and effectiveness of aquaculture water quality early warning.

This invention relates to a multi-level early warning method, equipment, and medium for substation carbon emissions based on PSR theory and grey relational analysis. It constructs a three-layer early warning index system comprising economic, energy efficiency, and environmental layers based on PSR theory; calculates the optimal combined weight vector using an intuitionistic fuzzy IDEMATEL-entropy weighting method; and calculates the early warning index using grey relational analysis. It establishes multi-level early warning threshold intervals and performs graded early warnings based on the calculated early warning index. Compared with existing technologies, this invention deeply integrates PSR theory with the grey relational decision-making model and ensures the reliability of the early warning results through a sample separation degree verification mechanism.

The invention relates to the technical field of management evaluation, and particularly discloses an evaluation system for talent management, which comprises an information input module, an assessment and evaluation module, a comprehensive analysis module and a result feedback module, and is characterized in that the information input module is used for inputting understood personnel basic information and position information into the system; the assessment and evaluation module is used for performing personnel ability assessment and evaluation according to the input information content, and the comprehensive analysis module is used for performing comparative analysis on assessment and evaluation results to obtain a final evaluation result; according to the method, input information is used for analysis, a plurality of evaluation indexes are considered, the relationship among the evaluation indexes is quantified, an intuitionistic fuzzy entropy is used for calculating evaluation index weights, objective weights and subjective weights jointly form combined weights, and objective and comprehensive evaluation on personnel is realized in combination with quality evaluation, so that the influence of subjective factors on evaluation results is reduced, and the evaluation efficiency is improved. The objectivity and the accuracy of evaluation are improved, and the ability, the skill, the experience and the potential of the personnel can be known more comprehensively.

The invention relates to the technical field of video monitoring and early warning, in particular to a video monitoring intelligent early warning and communication method and system based on deep learning. The method comprises the following steps: acquiring a monitoring video of a target area, extracting an image frame based on the monitoring video, and carrying out noise reduction processing on the image frame to obtain a noise-reduced image frame; carrying out improved intuitionistic fuzzy C-means clustering segmentation processing on the denoised image frame to obtain an attention region of the image frame; extracting risk feature information based on the attention area of the image frame, obtaining a hidden danger risk information index in the target area, calculating the similarity between the risk feature information and the hidden danger risk information index, judging whether a hidden danger risk exists in the target area based on the similarity, and if yes, generating an early warning signal. The abnormal condition in video monitoring can be identified based on the deep learning model, the labor cost is reduced, and the monitoring real-time performance and the monitoring efficiency are improved.

The invention relates to a loader shoveling dynamic decision-making method based on multi-source data fusion and reinforcement learning, and belongs to the technical field of engineering machinery automation. According to the method, a material pile feature model is constructed through multi-source data such as a laser radar, a visual sensor and a pressure sensor, the material shoveling path and force are dynamically optimized through a reinforcement learning algorithm, and accurate control over the target material shoveling amount is achieved. According to the method, uncertainty of material pile characteristics is described by using an intuitionistic fuzzy set, a multi-dimensional evaluation result is fused in combination with an evidence theory, and an optimal decision strategy is autonomously learned by virtue of a deep Q network, so that the problem of insufficient shoveling quantity control precision of a traditional method under a dynamic working condition is effectively solved.

The present application provides a battery health status prediction method and system, including: obtaining a high-dimensional data set during the battery discharge process, and normalizing the high-dimensional data set to obtain standardized data; using the Spearman correlation coefficient to analyze the correlation between the standardized data and the battery capacity to obtain a target data set; inputting the target data set into a width autoencoder, extracting the feature nodes and enhancement nodes of the width autoencoder, and obtaining an intermediate feature representation; inputting the intermediate feature representation into a random configuration network that integrates an intuitionistic fuzzy mechanism to generate a preliminary prediction result of the battery health status, thereby alleviating the technical problem of low accuracy of existing battery health status prediction.

The invention discloses a helmet lock remote control method and system based on the Internet, and relates to the technical field of Internet of Things control and intelligent decision, and the method comprises the steps: achieving the construction of multi-dimensional risk features of a user state through the combination of a context information tuple and a cloud agent cooperation mechanism and intuitive fuzzy modeling; the sensitivity of the system to user behavior differences is improved, unification of risk dynamic assessment and probability prediction is achieved through combination of an L1 kernel estimation function and an intuitionistic fuzzy entropy weight calculation mechanism, the generalization performance of the model is improved, and through combination of a grey correlation analysis model and improved intuitionistic fuzzy distance measurement, the accuracy of the system is improved. According to the method, multi-dimensional similarity calculation and risk grade division among user states are realized, the relativity of risk judgment is achieved, a continuous function optimization method of triangular fuzzy weighted geometric averaging is combined with a credibility driven fusion mechanism, a dynamic fusion decision of risk probability and similarity information is realized, and the reliability of risk judgment is improved. And the decision stability of the system in a complex scene is improved.

The invention discloses a comprehensive evaluation method for an organic waste composting process and product quality. Comprising the following steps: constructing a layered index system covering a composting process and product quality; and performing interval intuition fuzzy evaluation on the first-level indexes through experts, determining an expert comprehensive weight coefficient, and obtaining a group comprehensive IVIFN opinion value of each first-level index according to the expert comprehensive weight coefficient. And determining the subjective weight of the first-level index based on the positive and negative ideal evaluation references. The method comprises the following steps: collecting a compost sample, detecting a measured value of a secondary index, calculating an objective weight of the secondary index by utilizing an entropy weight method, and obtaining a final weight by adopting a subjective and objective dynamic fusion method. And calculating a comprehensive quality index according to a weight system and actually measured data to realize scientific quantification and process optimization of compost quality. According to the method, expert cognition and data difference are comprehensively considered, the scientificity, objectivity and stability of evaluation are improved, and a reliable basis is provided for compost process control and product quality grading.

This invention discloses a multi-dimensional monitoring system and method for engineering project progress based on information technology, applicable to the monitoring field. The system includes: an initial construction plan construction module, a data acquisition and environmental monitoring module, a data analysis and plan optimization module, a construction progress prediction module, and a risk management module. By collecting multi-dimensional data from the construction site in real time and applying an intuitionistic fuzzy inference algorithm, this invention can dynamically adjust the construction plan to cope with constantly changing site conditions and external environmental factors. Utilizing the intuitionistic fuzzy inference algorithm to analyze the needs and bottlenecks of the construction project, combined with construction logic and physical constraints, allows for more precise resource allocation. A fuzzy logic controller handles fuzzy and uncertain situations, ensuring the project complies with all relevant building and safety standards. Through continuous monitoring and timely adjustments, project quality and safety are guaranteed.

The application provides a control method and device for a power converter of an energy storage system, a coupling relationship between active power and reactive power is analyzed through a power outer loop control model based on a virtual synchronous machine and a power coupling coefficient matrix, PI parameters of a reactive voltage compensation channel are adjusted based on the coupling relationship and a fundamental component extracted, a voltage reference value output by a PI controller after correction is obtained, a current value in a prediction time domain is predicted through a model-free predictive control method based on a bias format dynamic linearization in a current inner loop, and the voltage reference value and the current value are optimized in combination with an intuitionistic fuzzy model and a rough decision model, so that a target voltage reference value and a target current value generated after optimization are used for regulation and control. Therefore, through fusion of power decoupling, model-free predictive control, fundamental component extraction, and the intuitionistic fuzzy model and the rough decision model, adaptability, robustness and power quality of the power converter in a complex power grid environment are improved.

This invention discloses a method, equipment, and storage medium for selecting favorable areas of low-permeability carbonate gas reservoirs based on intuitionistic fuzzy multi-attribute boundary approximation region comparison (MABAC). Specifically, it includes the following steps: S1, screening key indicators of carbonate gas reservoirs for favorable area selection; S2, determining attribute weights based on the Brown-Warshall Method (BWM); S3, establishing a favorable area selection model for low-permeability carbonate gas reservoirs using the intuitionistic fuzzy multi-attribute boundary approximation region comparison method (MABAC); S4, calculating conditional probabilities, and then ranking the decision objectives included in the positive, boundary, and negative domains to obtain the favorable area selection results. This invention can simultaneously consider the influence of multiple factors on different schemes and grasp the priority relationship between factors.

The invention belongs to the technical field of power anomaly detection, and particularly relates to a power Internet of Things anomaly detection method and system based on granular ball intuitionistic fuzzy modeling, and the method comprises the steps: constructing a plurality of training granular ball intuitionistic fuzzy models corresponding to known category labels according to known normal data and abnormal data, and forming a training granular ball intuitionistic fuzzy set; constructing a plurality of to-be-detected granular ball intuitionistic fuzzy model sets of unknown categories by taking the training granular ball intuitionistic fuzzy set as a reference; calculating the fuzzy distance between each to-be-tested granular ball intuitionistic fuzzy model and all training granular ball intuitionistic fuzzy models; endowing a known category label corresponding to the training particle ball intuitionistic fuzzy model with the minimum fuzzy distance with corresponding unknown category data; judging whether the known category label endowed by the unknown category data is an abnormal label or not; and if yes, determining that the unknown category data is abnormal data, and generating structured alarm information. According to the method, the detection precision is remarkably improved, the calculation efficiency is high, and high interpretability is achieved.

The application discloses a cross-border network anomaly detection method based on intuitionistic fuzzy set and double-path learning, and belongs to the technical field of network security. The application aims at the problems of unstable feature form and poor cross-domain generalization ability caused by ignoring cognitive uncertainty in the existing method, proposes an IRU uncertainty evaluation paradigm, decomposes feature evaluation into three dimensions of evidence strength, mixed degree and form stability, and maps them to the intuitionistic fuzzy set domain; designs a risk-averse robust credibility score RTS to screen credible elite features; and constructs a double-path fidelity training architecture, which constrains the model decision logic to be anchored on the credible features through a fidelity loss function. The application realizes the synchronous improvement of detection performance and feature selection stability on multiple cross-border intrusion data sets, is especially suitable for cross-border network environments with strong resistance and significant distribution offset, and has the ability of rapid cold start deployment.

The present invention relates to the field of bridge technology, and in particular to a method for evaluating the operating status of an in-service bridge monitoring system, the method comprising the following steps: solving the indicator weight according to the mutual influence degree between each evaluation indicator; establishing the evaluation criteria for each evaluation indicator and the operating status of the in-service bridge monitoring system, and evaluating and obtaining the specific score of each evaluation indicator; establishing the membership function and non-membership function of the evaluation indicator for the operating status of the in-service bridge monitoring system, and then evaluating the operating status of the in-service bridge monitoring system according to the indicator weight and the specific score. The present invention uses a triangular intuition network analysis method to divide the relative weights of the evaluation indicators, and uses a fuzzy comprehensive evaluation method integrating triangular intuition fuzzy numbers to evaluate the operating status of the in-service bridge monitoring system. In the evaluation process, the inevitable deviation between subjective opinions and actual conditions is considered, and a complete evaluation system for the in-service bridge monitoring system is established.

The application relates to a mobile phone product recommendation method based on comparison of question and answer text and comparison of video, a first comprehensive intuitionistic fuzzy number of a comparison question corresponding to an attribute is obtained based on a first emotion scoring agent; a mean value of the first comprehensive intuitionistic fuzzy numbers of all comparison questions corresponding to the attribute is calculated to obtain a first fuzzy number of a mobile phone product under the corresponding attribute; complete video content of an evaluation comparison video corresponding to the mobile phone product is obtained, and video text information of the complete video content is extracted; the video text information is split into multiple text parts according to the attribute, a second comprehensive intuitionistic fuzzy number of a text part corresponding to the attribute is obtained based on a second emotion scoring agent; the second comprehensive intuitionistic fuzzy numbers of all text parts corresponding to the attribute are aggregated to obtain a second fuzzy number of the mobile phone product under the corresponding attribute; a recommendation score of the mobile phone product is calculated based on the first fuzzy numbers and the second fuzzy numbers under all attributes, and a mobile phone product with the highest recommendation score is taken as a recommended product.

The invention discloses a ground wire state evaluation method and system based on an improved network hierarchy method, and aims at solving the problems that in a traditional evaluation method, indexes are numerous, data are disordered, and expert decision ambiguity is ignored. According to the system, key indexes are selected by constructing a ground wire original parameter system and combining industrial standards; key evaluation indexes are extracted by using a PCA dimension reduction technology, and data redundancy is reduced; an intuitive fuzzy number improved network analytic hierarchy process (ANP) is introduced, and a scientific and reasonable evaluation model is constructed. The system can accurately evaluate the state of the ground wire, provides a scientific basis for the operation and maintenance decision of a power system, and improves the evaluation efficiency and accuracy.

The present invention discloses a dynamic decision-making method for solving the complexity of attribute weight and time weight, comprising: extracting decision information of each target fighter according to the battlefield environment; converting the attribute information of the target fighter into the decision information of each target fighter at different time t k The interval intuitionistic fuzzy matrix is used to obtain the decision matrix #imgabs0#. The deviation maximization method is used to obtain the attribute weight w(t) of each target fighter; the data aggregation operator IVIFPWGA is used to aggregate the different time t k The elements #imgabs2# in the decision matrix #imgabs1# are aggregated to obtain the T of each enemy fighter. i At different times t k The comprehensive interval intuitive fuzzy value #imgabs3# is obtained by the superior and inferior solution distance method TOPSIS to obtain the time weight vector λ(t); the data aggregation operator DIVIFPWGA is used to obtain the T of each enemy fighter i At each moment t k The comprehensive interval intuitive fuzzy value #imgabs4# obtains the score function #imgabs6# and the accuracy function #imgabs7# of the comprehensive interval intuitive fuzzy value #imgabs5#, sorts the score function #imgabs8#, and obtains the decision result. This method improves the reliability of the decision result, effectively reduces the time complexity of the algorithm, and can more accurately assess the threat of air combat targets.

The invention discloses a residential building energy consumption prediction method based on a multi-attribute intuition fuzzy decision. The method comprises the following steps: firstly, constructing a residential building model; determining reference attributes based on an expansion plan behavior theory and a structural equation model; then energy consumption behavior preference data in different seasons are collected; the core lies in that a dynamic event chain is cyclically executed in an annual energy consumption simulation process through a Python control script leading EnergyPlus energy consumption simulation engine: according to a current season type, if the current season is a decision season, random sampling and multi-attribute intuitionistic fuzzy decision are carried out to trigger equipment, and if the current season is a transition season, the equipment is selected according to a preset rule; after the equipment is started, the running duration is randomly generated by a survival function, and energy consumption is recorded after simulation is finished; the method has the advantages that through a multi-attribute intuition fuzzy decision and a seasonal mixing strategy, the defects of human behavior cognition uncertainty, multi-attribute tradeoff and one-sided description of a decision mechanism are fundamentally solved, and the accuracy and reliability of residential building energy consumption prediction are remarkably improved.