228 results about "Grey correlation analysis" patented technology

The invention discloses a laboratory safety state evaluation method and system based on multi-source data fusion, and relates to the field of laboratory safety management.The method comprises the steps that multi-source sensing data of a laboratory are collected and aligned to a unified time axis, and a laboratory sensing data matrix is constructed; grey correlation analysis is introduced, the risk correlation degree between data of each sensor and a preset risk mode is calculated, and identification of a potential risk mode is achieved; the reliability weight is dynamically determined according to the historical stability, baseline deviation and calibration period of the sensor so as to suppress data distortion caused by aging and drifting; and comprehensively calculating the risk correlation degree, the reliability weight and the risk severity coefficient to form weighted risk energy, and generating laboratory risk field energy through space-time coupling superposition so as to calculate a corresponding laboratory risk state level. Therefore, the dynamic quantification and grading judgment of the overall safety state of the laboratory are realized, and the risk identification capability of the complex laboratory environment is improved.

The invention relates to the field of wind power plants, in particular to a dynamic grey correlation analysis method and system for the health state of a wind power transmission chain. The method comprises the following steps: constructing a multi-source heterogeneous monitoring data set by collecting SCADA operation data, CMS vibration spectrum data and gearbox oil temperature data of a wind turbine generator transmission chain in real time; and performing sliding window division and time domain alignment on the data to generate a standardized feature sequence with time consistency. Based on a grey correlation analysis algorithm, calculating a dynamic correlation degree between each monitoring parameter and a preset fault feature sequence, and generating a correlation degree matrix; and carrying out weighted fusion on the correlation degree matrix by adopting a self-adaptive weight adjustment strategy, and constructing a transmission chain health state evaluation model. And according to a model output result, generating a real-time health degree score and fault probability distribution of the transmission chain component. And when the health degree score is lower than a dynamic threshold value, triggering a multi-level early warning mechanism and generating a maintenance priority list. And carrying out sliding window iteration updating on the evaluation model based on a newly added fault sample, optimizing a grey relational degree calculation parameter, and realizing dynamic self-adaption of health state evaluation.

The invention discloses a slope stability prediction method based on deformation monitoring, and relates to the field of slope monitoring, and the method comprises the steps: obtaining a time sequence deformation field of a target slope through employing an InSAR; generating a time sequence Dn through differential interference processing; collecting rainfall data; generating a time sequence representing regional rainfall conditions; grey correlation analysis is adopted, and the correlation degree of Dn and Pn is calculated; constructing a slope slip prediction model based on InSAR displacement and rainfall factors; if the displacement time sequence has an acceleration trend or fluctuation, marking the corresponding slope unit as an early warning analysis object; obtaining rainfall forecast data in a future period of time, and calculating a displacement predicted value di '(N + 1) of the marked side slope unit at the (N + 1) th time point in the future; and comparing the displacement predicted value di '(N + 1) of the (N + 1) th time point with a threshold value, and if di' (N + 1) exceeds the threshold value, giving a slippage early warning level. Aiming at low precision of a rainfall-induced slope slippage early warning model in the prior art, the precision is improved.

The invention provides a charging pile energy consumption scheduling method based on a load prediction model, and the method comprises the steps: carrying out the data collection and preprocessing, obtaining multi-source data, such as charging, equipment, environment and electricity price, from a charging station management system, and cleaning abnormal values; and then using grey correlation analysis to determine weights of factors influencing energy consumption, constructing a grey prediction model, and combining preprocessing and weight distribution data to predict future charging pile load demands. And the control center receives a load prediction result and vehicle reservation information, preliminarily plans a charging distribution scheme according to the real-time state of the charging pile, and optimizes scheduling by taking the minimization of the total charging cost and consideration of the user satisfaction and the stability of the power system as targets through a simulated annealing algorithm. During operation, an actual state and an external environment are continuously monitored, a charging plan is rapidly adjusted when deviation occurs, finally, vehicle charging bills of all departments are generated according to an execution scheme, and system performance indexes are regularly evaluated to realize continuous optimization. The method can effectively reduce energy consumption cost and guarantee efficient and stable operation of the charging station.

The embodiment of the invention discloses an intelligent downhole operation state monitoring system and method, and relates to the technical field of monitoring data processing. The method comprises the following steps: acquiring multi-modal monitoring data for reflecting an underground operation state; performing fluctuation analysis on each piece of time sequence data in the multi-modal monitoring data to obtain a corresponding fluctuation analysis result; the correlation degree between the fluctuation analysis results corresponding to the different time series data is calculated based on a grey correlation analysis method, and fluctuation correlation information is obtained; performing information fusion on the fluctuation analysis result and corresponding time sequence data through a time window alignment mode to obtain target monitoring data; taking the fluctuation associated information as a weight parameter of an attention mechanism, performing multi-modal feature coding on the target monitoring data through a coding network, and outputting a corresponding operation state representation vector; and inputting the operation state representation vector into a decoding network to carry out downhole operation state anomaly analysis to obtain intelligent downhole operation state monitoring data.

The invention relates to the technical field of linear guide rail pair performance evaluation, and provides a linear guide rail pair comprehensive performance evaluation method, which constructs a multi-dimensional evaluation index system, and can more comprehensively reflect the guide rail performance compared with some methods with single indexes or incomplete indexes. In the aspect of weight determination, the weight is dynamically adjusted according to a precision sensitive factor, a speed demand factor, a load intensity factor and the like in different scenes by combining grey correlation analysis and an entropy weight method, so that the problem that subjective randomness possibly exists in the prior art or a fixed weight cannot adapt to scene change is avoided; the evaluation result better meets the actual working condition requirement; requirements of different scenes on guide rail performance can be accurately quantified through relevant parameter definitions and formulas applied to diversified scenes, powerful support is provided for accurate model selection and optimization of a linear guide rail pair in complex and diversified industrial environments, the overall performance and reliability of equipment are effectively improved, and the equipment failure risk caused by mismatching of the guide rail performance is reduced.

The invention relates to the technical field of underground space development, and provides a coastal bedrock type city underground space development geological suitability evaluation method comprising the following steps: analyzing key geological problems of land area and sea area underground space development, and extracting related geological factors; basic geological data are collected and classified, a geological suitability evaluation index system is constructed, and geological evaluation units are divided; subjective and objective weights are determined based on an analytic hierarchy process and an entropy weight method, and an optimal comprehensive weight is obtained through game theory combination weighting; modeling based on a grey correlation analysis method, and calculating and grading suitability scores of the evaluation units; and integrating evaluation results, and generating an underground space development geological suitability partition map of land-sea overall planning. According to the scheme, the underground space development pattern is optimized, comprehensive utilization of land and ocean resources is promoted, and safe, efficient and sustainable development and utilization of the coastal city underground space are powerfully supported.

The invention relates to the technical field of sports event content processing, in particular to a sports event content comprehensive processing method based on artificial intelligence, which comprises the following steps: generating a plurality of popularity events from sports event content data propagated by a network through a popularity word mining model based on improved BERTopic; constructing a preheating degree event weight and a popularity index weight for the plurality of popularity indexes corresponding to the plurality of popularity events through an expert evaluation method; calculating and adjusting the weight of the preheating degree event according to the time sequence data of the multiple heat indexes through a correlation analysis model based on grey correlation analysis; and the evaluation matrix, the time sequence popularity event weight and the popularity index weight are used to determine a comprehensive sports event attention event through a popularity event judgment model based on a fuzzy evaluation method. According to the method and the device, the hot events of the sports events can be timely and accurately determined, and the content of the sports events can be spread and popularized more in accordance with audience interests.

The invention relates to the technical field of water environment management, and discloses a simulation result optimization method and system of a lake and reservoir water quality model. Parameters and target variables which need to be calibrated are selected; setting prior distribution of each parameter, and generating m groups of parameter combinations through random sampling; obtaining m groups of simulation results according to the m groups of parameter combinations, and respectively calculating RMSE, NSE and MAE of each target variable in each group of simulation results; determining weights of RMSE, NSE and MAE of each target variable by adopting grey correlation analysis, and calculating a comprehensive index of each target variable; calculating a comprehensive score of each group of simulation results; and obtaining a final simulation result through a Bayesian average method according to the comprehensive score of the simulation result. According to the method, the uncertainty caused by single parameter combination can be reduced, the accuracy of a simulation result is improved, the robustness and generalization ability of the model are enhanced, and the simulation ability of the model to the lake and reservoir water quality can be improved.

The invention relates to the technical field of industrial park green low-carbon development, and discloses an industrial park green low-carbon multi-dimensional performance evaluation index system construction method. Setting evaluation dimensions of energy, resources, economy, environment, management and the like, determining a core index, and calculating a comprehensive weight based on objective and subjective weights; after multi-source data are collected and processed, a TOPSIS method and a grey correlation analysis method are adopted for comprehensive evaluation, and an industry comparison report is generated; establishing a real-time monitoring system to track, evaluate and display key data and indexes; meanwhile, an index system and a weight distribution mechanism are periodically optimized in combination with a machine learning technology, core indexes are screened through clustering analysis, variance contribution evaluation, correlation matrix construction and dynamic threshold adjustment, and dynamic adaptive optimization of the indexes and the weights is realized by utilizing a feature importance analysis and reinforcement learning algorithm. According to the invention, scientific evaluation and intelligent management of park green low-carbon performance are realized, and the accuracy, adaptability and continuous optimization capability of evaluation are improved.

The invention provides a motorized spindle dynamic thermal error modeling method based on time sequence analysis, and belongs to the technical field of machining. Comprising the following steps: acquiring all temperature rise data and all thermal error data of the motorized spindle under different working conditions; performing iterative clustering on all the temperature rise data by using an elbow rule to obtain a plurality of final clusters; performing grey correlation analysis on all clustering temperature rise data and all thermal error data in each final clustering cluster, sorting all correlation degrees in each obtained correlation degree set from large to small, and constructing a feature data set according to a sorting result; selecting an initial dynamic model, and sequentially performing order determination, parameter identification and verification on the initial dynamic model by utilizing all the feature data and all the thermal error data to obtain a final thermal error dynamic model; and performing thermal error prediction and thermal error compensation on the motorized spindle by using the final thermal error dynamic model. According to the invention, the precision and robustness of thermal error prediction of the motorized spindle can be improved.

The invention discloses a multi-metric fusion evaluation method for the inconsistency of parallel battery packs. The method comprises the steps of obtaining the average difference and the instantaneous maximum difference of dynamic performance parameters in the discharging process of multiple parallel battery packs; redundant features are removed by using a grey correlation analysis method, and evaluation indexes are simplified; setting an appropriate threshold value for each evaluation index, and calculating an inconsistency score shown by the battery pack through each evaluation index in the discharging process; an objective weight and a subjective weight are calculated by using a CRITIC method and an AHP method respectively, and then the subjective weight and the objective weight are fused through a geometric averaging method to obtain a comprehensive weight of each index in reflecting inconsistency; and finally, calculating the comprehensive inconsistency score of the battery pack according to the individual score of each evaluation index in combination with the corresponding weight, and dividing the comprehensive inconsistency score into four grades. According to the method, a multi-index and subjective and objective weight fused inconsistency quantitative evaluation system is constructed, and an accurate and reliable reference basis is provided for performance evaluation after the batteries are connected in parallel and grouped.

The invention provides a user electricity consumption data analysis modeling method and system based on semi-supervised learning, and belongs to the technical field of power system data analysis and prediction. The problems of low data inspection accuracy, large characteristic correlation degree deviation and the like in the prior art are solved. The method comprises the following steps: on the basis of user electricity consumption characteristic data and climate characteristic data, constructing a semi-supervised learning driven electricity consumption data inspection and tracking feedback model by adopting a graph convolutional network; calculating the Pearson's correlation coefficient among the electricity consumption characteristics of different types of users, constructing a characteristic correlation model based on a deep belief network, and analyzing the nonlinear relationship among the user types, the user electricity consumption time sequence characteristics and the climate characteristics; based on a grey correlation analysis method, capturing distribution characteristics of electricity consumption changes; the method is suitable for power system resource optimization, and especially improves the operation efficiency of a power grid under climate variability.

The invention discloses an asphalt material anti-rutting performance prediction and design optimization method based on data driving, which comprises the following steps: sorting and preprocessing data obtained by a dynamic shear rheometer test of a rubber powder / SBS composite modified asphalt material with various contents in different aging states, and calculating the rutting resistance of the asphalt material by adopting a grey correlation analysis method; screening the data set based on a gray analysis result, and determining input and output of a prediction model; three machine learning models of GPR, LSBboost integration and ANN are established, the deformation resistance of the asphalt material is predicted, a better model is obtained through multi-index comparison, and the prediction result of the optimal model is explained based on SHAP; and based on the optimal prediction model, performing targeted design optimization on the formula of the asphalt material by using a Bayesian optimization algorithm, and simulating application requirements under different actual working conditions by limiting part of parameters in the formula. According to the invention, the test cost of the asphalt material in deformation resistance test and formula design is reduced.

The invention discloses a concrete permeability multi-parameter comprehensive detection system and an evaluation method. The system comprises a data acquisition module, a data processing module, a comprehensive evaluation module and a man-machine interaction module, and synchronous acquisition and calibration of multiple parameters such as electric flux, a chloride ion diffusion coefficient, water absorption and an air permeability coefficient are realized through a unified synchronous trigger mechanism and an environment compensation algorithm; carrying out intelligent processing on the data by utilizing an improved data cleaning, multi-level and multi-modal feature extraction and fusion strategy; and finally, through dynamic fusion of fuzzy comprehensive evaluation, grey correlation analysis and a machine learning classification model, outputting a quantitative grade evaluation result of the concrete permeability. The method can comprehensively and accurately evaluate the permeability of the concrete, remarkably improves the detection precision and efficiency, adapts to a complex environment and a long-term aging process, and provides a scientific basis for durability evaluation and maintenance of a concrete structure.

The invention relates to the technical field of new energy and energy saving, and provides a new energy-based park carbon emission carbon reduction amount prediction method and system, which fuse and use a comprehensive similar day selection method of grey correlation analysis-dynamic time warping-Euclidean distance-included angle cosine combination, completely integrate empirical mode decomposition adaptive noise, and improve the prediction accuracy of the carbon reduction amount of park carbon emission. A high-precision photovoltaic short-term power generation power combined prediction model is established, and the problems that a similar day selection method of a traditional photovoltaic output prediction model excessively depends on subjective experience judgment, the screening standard is single, photovoltaic power generation data contains noise and multi-scale characteristics and the like are solved. The difficulty of accurate prediction of output caused by intermittency and volatility of photovoltaic power generation is overcome, and accurate prediction of photovoltaic power generation capacity is realized; and on the other hand, the photovoltaic output prediction result is innovatively combined with the electric power carbon emission factor, accurate measurement and calculation of the carbon reduction amount of the park new energy project are achieved, and powerful support is provided for achieving the green and low-carbon development target of the park.

The invention discloses a drilling and blasting method construction tunnel back break prediction method based on a PSO-LSSVM model and related equipment. The method comprises the following steps that S1, parameters of drilling and blasting method construction tunnel back break influence factors are obtained; s2, performing data preprocessing on the parameters; s3, finally determining main parameters influenced by the back break of the tunnel by adopting a grey correlation analysis method; s4, establishing a PSO-LSSVM model by adopting an MATLAB simulation platform; s5, selecting a root-mean-square error as a fitness function, calculating the fitness value of each particle, updating the value of each particle through multiple iterations, comparing and updating the value of Vbest, and judging whether an end condition of PSO is met or not; s6, a PSO-LSSVM model is established according to the optimal parameter combination, prediction is performed by using the model in combination with the back break influence parameters of the specific tunnel blasting engineering, and a blasting effect prediction result is obtained; according to the method, the particle swarm optimization algorithm is adopted to iterate the two key parameters of the optimization model, the blasting effect of the drilling and blasting method can be predicted with high precision, and efficient construction of tunnel engineering is achieved.

The invention discloses an intelligent temperature control production system for an environment-friendly asphalt cold patch additive, and relates to the technical field of asphalt cold patch additive production, and the system comprises a controller initial modeling module which is used for recording the currently set proportional-integral-differential controller parameters, and constructing a reference control feature vector used for representing the initial response behavior of a controller; and the thermal load response identification module is used for applying micro-amplitude step type temperature disturbance to the to-be-heated environment-friendly asphalt cold patch additive and extracting a thermal load characteristic vector representing the thermal response characteristic of the additive. According to the method, intelligent matching and full-time-domain response evaluation are realized by constructing control and thermal load feature vectors and combining cross-correlation and grey correlation analysis; a sliding spectrum entropy and self-adaptive parameter correction mechanism is introduced, a closed-loop regulation system is constructed, the suitability, stability and additive dispersing performance of a temperature control system are effectively improved, and the problems of control lag and fluctuation are solved.

The invention discloses an asphalt mixture high-temperature performance prediction and design optimization method based on data driving. The method comprises the steps of sorting and preprocessing asphalt mixture high-temperature performance data, adopting a grey correlation analysis method, screening a data set based on a grey analysis result, and determining input and output of a high-temperature performance prediction model. Building three machine learning models, namely ANN, SVM and GPR, predicting the high-temperature performance of the asphalt mixture, and carrying out visualization processing on the models; based on the high-temperature performance optimization prediction model, a Bayesian optimization algorithm is utilized to perform targeted design optimization on the mix proportion of the asphalt mixture, and application requirements under different actual working conditions are simulated by limiting part of parameters in the mix proportion. According to the method, reliable prediction information is provided, the test cost of the asphalt mixture in high-temperature performance test and mix proportion design is reduced, the material design and research and development period is shortened, and the digital and intelligent development of asphalt mixture performance prediction and optimization is promoted.

The invention relates to the technical field of cutter residual life prediction, and discloses a cutter residual life prediction method based on line angle attention and contrast drive aggregation, which comprises the following steps of: extracting six types of statistical characteristics including a mean value, a standard deviation, a median, an absolute maximum value, a root mean square and skewness by using original data of a multi-channel sensor in a cutter cutting process; a dual feature dimension reduction strategy of Pearson's correlation coefficient and grey correlation analysis is adopted, key features strongly related to the wear state are screened, and standardization processing is carried out; and constructing a deep learning architecture fusing line angle attention and contrast driving feature aggregation. According to the method, the model has higher recognition capability on the characteristic mode of numerical jump but consistent trend in the tool wear process, the problem that a traditional attention mechanism is prone to losing key time sequence association in the nonlinear degradation process is solved, and the modeling precision of non-stationary sensor data under the complex cutting working condition is remarkably improved.

The invention discloses a method for evaluating the development of a low-carbon city under multi-condition constraints, and the method comprises the steps: S1, building a carbon emission prediction model: employing a gray correlation analysis method, taking the carbon emission over the years as a reference sequence, taking each influence factor as a comparison sequence, selecting social and economic factors which affect the carbon emission as constraint conditions, and carrying out the prediction of the carbon emission; the correlation degree between the influence factors is measured, and a regional carbon emission prediction model is constructed; s2, constructing a regional low-carbon development chasing model, analyzing urban low-carbon planning influence factors, and predicting the carbon emission of the region through an IPAT model; s3, determining a carbon emission prediction function, performing ridge regression analysis through a variance expansion factor (VIF), fitting a ridge regression result to determine a ridge regression equation, and establishing an STIRPAT expansion model; and S4, analyzing a result calculated by the STIRPAT expansion model, and planning an urban low-carbon development route under a double-control target in combination with problems existing in the urban development process.

The invention provides a development project cost evaluation method and device based on improved grey correlation analysis, and belongs to the technical field of data processing. The method comprises the following steps: determining pricing indexes and pricing index weights corresponding to science and technology projects by using an expert evaluation method and an analytic hierarchy process; an important pricing index corresponding to the development project cost is screened by using a grey correlation analysis method and each pricing index weight; and performing project cost calculation according to the important pricing indexes. In the process, factors with relatively low contribution degree are eliminated, so that the benefit of model calculation is increased, and the accuracy of cost calculation is improved. And in combination with expert evaluation and an analytic hierarchy process, the importance of pricing indexes is reasonably embodied. According to the method, a grey correlation analysis method and pricing index weights are utilized to screen out important indexes having significant influence on the project cost, and accurate cost calculation is carried out according to the important indexes, so that the evaluation efficiency and accuracy are improved.

The invention provides a tunnel portal slope stability evaluation method based on the combination of a left and right half cloud asymmetric cloud model and an Euclidean distance method, and the method comprises the following steps: S1, determining tunnel portal slope stability evaluation indexes and grading standards thereof, and constructing a tunnel portal slope stability evaluation index system; s2, determining an evaluation index weight by adopting a grey correlation analysis method; s3, determining the membership degree of each evaluation index under each grade by adopting a left and right half cloud asymmetric cloud model; s4, determining the membership degree of the tunnel portal slope corresponding to each grade; and S5, identifying the stability level of the tunnel portal slope by adopting an Euclidean distance method. According to the method, the advantage of the cloud model in the aspect of processing index uncertainty and the characteristic of the Euclidean distance method in the aspect of quantitatively judging the slope stability level are combined, the fuzziness and randomness of the indexes are effectively solved, subjective errors are avoided, and therefore the accuracy of the evaluation result is improved.

An LSTM-Attention-OOA photovoltaic power prediction method based on similar daily clustering comprises the following steps: firstly, integrating historical photovoltaic power data and meteorological monitoring data (including irradiance, temperature, cloud cover and the like), and analyzing and screening key meteorological characteristics by using grey relational degree; similar daily clustering analysis is carried out through K-means, and an optimal clustering number is determined by adopting an elbow rule. And for different weather clusters, similar days are selected by using cosine similarity. And an LSTM-Attention-OAA prediction model is constructed: an LSTM layer captures a time sequence dependency relationship, an Attention mechanism dynamically allocates each time step weight to highlight an important meteorological period, an OOA is introduced to automatically adjust and optimize hyper-parameters, and autonomous optimization of model performance is realized. According to the method, through a progressive architecture of'feature screening-data clustering-intelligent prediction ', the prediction precision under different weather conditions is remarkably improved, and the method is of great significance in improving stable operation of a power system.

The invention discloses an energy-saving optimization method and system for an industrial circulating water system, and relates to the technical field of energy-saving control. According to the method and system, a heat exchanger is divided into a high area and a low area according to the installation height, non-circulating water cooling equipment is adopted in the high area, and circulating water continues to be used for supplying water to the low area; environment and operation data are collected, and key features are identified based on grey correlation analysis; constructing an integrated learning model to predict the return water temperature of the cooling tower, and dynamically adjusting the weight of the sub-model based on the characteristic difference and the energy consumption anomaly index; the water pump, the fan and the water supply flow are automatically adjusted according to the deviation between the prediction result and the energy-saving target, and closed-loop optimization control is formed. The energy consumption and the water consumption are effectively reduced, the operation stability and the intelligent level of the system are improved, and the system is suitable for various industrial cooling scenes.

The invention discloses a grape wine brewing tank real-time monitoring method and system based on artificial intelligence, and relates to the technical field of grape wine brewing equipment. The method comprises the following steps: constructing a database storing the total sugar content of a wine sample at each time stage in the wine brewing process and near infrared spectrum data of the sample, extracting characteristic parameters of the near infrared spectrum data of the wine sample, and performing grey correlation analysis on the total sugar content and the characteristic parameter data of the wine sample; obtaining a grey correlation degree between each characteristic parameter and the total sugar content, setting a grey correlation degree threshold, screening out a master control characteristic parameter, and establishing a quantitative relation between the master control characteristic parameter of the wine sample and the total sugar content by using a BP neural network; and inputting the main control characteristic parameters of the near infrared spectrum data of the wine sample in the brewing tank to be monitored, which are acquired in real time, into the established quantitative relation to obtain the total sugar content predicted value of the wine sample in the brewing tank to be monitored.

The invention discloses a water quality prediction method based on fine tuning large language model knowledge driving, and belongs to the crossing field of artificial intelligence technology and environmental discipline. According to the method, a large language model is finely adjusted according to a predefined prompt template through professional knowledge in the water environment field, a knowledge extraction model suitable for the water environment field is formed, and a quintuple is extracted from a text to construct a water environment knowledge graph. Performing graph embedding learning on entities in the knowledge graph by adopting a GloVe model and a residual image attention network, and calculating correlation among water quality indexes through cosine similarity to obtain a knowledge domain correlation weight matrix; meanwhile, the Pearson's correlation coefficient, the weighted grey correlation degree analysis and the dynamic time warping correlation similarity coefficient are used for respectively calculating linear and nonlinear correlation among the water quality indexes, and a data field correlation weight matrix is obtained; and finally, constructing a KG-DW-CLA water quality prediction model jointly driven by knowledge and data to perform water quality prediction. According to the method, the problems that a traditional model is insufficient in domain knowledge utilization and single in data correlation modeling are solved, the accuracy and scientificity of water quality prediction are improved, and the method has high generalization ability and application value.

The invention provides a power cable state evaluation method and system based on an AHP-Critic method and improved TOPSIS grey correlation analysis, and the method comprises the steps: obtaining the index data of a to-be-evaluated cable based on a predetermined index affecting the state of the power cable, carrying out the corresponding preprocessing, and building a power cable state evaluation system; respectively calculating subjective and objective weights of the indexes based on an analytic hierarchy process and a Critic method, obtaining a combined weight through a game theory method, and determining a system health level; based on FCE, namely a fuzzy comprehensive evaluation method, a membership function is determined, a fuzzy evaluation matrix is calculated, and fuzzy comprehensive evaluation is carried out; and on the basis of improved TOPSIS grey correlation analysis, calculating a weighted mahalanobis distance and a comprehensive grey correlation degree between the state of the to-be-evaluated cable and the positive and negative ideal states, and combining the weighted mahalanobis distance and the comprehensive grey correlation degree to obtain the state of the to-be-evaluated cable so as to complete cable state evaluation.

The invention provides an offshore low-permeability reservoir classification evaluation optimization method based on machine learning, and relates to the technical field of offshore low-permeability reservoir classification, and the method comprises the following steps: S1, integrating core experiment data, logging curves and production dynamic records of an offshore low-permeability reservoir, extracting a multi-dimensional feature set, removing abnormal data points, and complementing missing parameters; s2, determining an initial feature weight based on grey correlation analysis, introducing a production dynamic feedback mechanism to adjust a parameter contribution degree, and constructing a composite evaluation factor Z containing a sorting coefficient correction term; s3, training subsets are divided according to the permeability, a corresponding regularization constraint intensity training model is adopted, kernel function bandwidth is automatically matched, and a loss function fault-tolerant threshold value is dynamically adjusted; and S4, deploying the model after cross validation, regularly updating a weight coefficient after production, and triggering model retraining through residual monitoring. The method improves the accuracy and reliability of the classification evaluation of the offshore low-permeability reservoir, adapts to the dynamic change of the reservoir, and has a good application prospect.