143 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 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 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 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 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 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.

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 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.

The invention relates to the technical field of power grid load power prediction data set construction, and discloses a load power prediction data set construction method, system and device and a medium, and the method comprises the steps: obtaining historical load and environment characteristic data, carrying out the preprocessing, selecting an input characteristic through a gray correlation analysis method and a maximum mutual information coefficient method, and carrying out the prediction of a load power prediction data set; a load power prediction data set is constructed, and then a bidirectional long-short-term memory network is used for training and prediction, so that the problems of poor original data quality, insufficient feature engineering processing, unbalanced data distribution and the like in the construction of the current load power prediction data set can be solved. In the training and prediction stage, the prediction performance of the bidirectional long-short-term memory network model trained based on the original data set and the constructed data set is compared, and the improvement effect of the constructed data set on the prediction precision is verified. The method improves the performance of the prediction model, meets the requirements of actual application for the accuracy and reliability of load power prediction, and has remarkable practical value and application prospect.

The invention discloses a commercial air conditioner load dynamic influence factor prediction method and device. The method comprises the following steps: firstly, collecting historical data according to a power load four-level classification system, and constructing a historical impact factor time sequence; then decomposing the sequence into four components including a reference value, a medium and short term value, a short term value and a disturbance value through multi-scale decomposition; on the basis of the reference value sequence and the prediction day features, similar days are screened through grey correlation analysis, and a training data set is constructed; aiming at the characteristics of different time scale components, a plurality of machine learning models are adopted to train prediction sub-models respectively; and finally, fusing prediction results of the components by optimizing weights to generate a dynamic impact factor time sequence. According to the method, through a multi-scale decomposition and model cooperation mechanism, prediction precision and adaptability are effectively improved, and a reliable basis is provided for fine scheduling of a power system.

The present application relates to the technical field of material forming prediction, in particular to a real-time regulation and early warning method in a composite material forming process, comprising acquisition of unstable production composite material morphology images and determination of corresponding processes, analysis of process factors corresponding to each unstable production result by using a grey correlation analysis method, establishment of an early warning model based on a large amount of data, real-time acquisition and analysis of the morphology in actual production, and repeated adjustment of the corresponding process when abnormal production occurs. The model can automatically identify unstable production conditions in the composite material forming process, quickly locate the problem source according to the weight of the process factors, provide early warning information, so as to timely adjust the production parameters and ensure stable production of the composite material.

The application discloses a kind of hybrid power system health management method and system, belong to power system health management field.This application selects the characteristic signal of engine, battery and vehicle in hybrid power system to establish hybrid power system health degree;When hybrid power system fails, by obtaining characteristic signal and carrying out normalization processing, hybrid power system health degree based on grey correlation analysis method GRA is calculated in real time, and the misjudgment and the missed judgment of fault are reduced by the fault confirmation method based on counting;When hybrid power system fault is confirmed, through the power redistribution health management strategy under system fault based on LSTM-MPC, the change of hybrid power system speed, battery temperature and diesel engine exhaust temperature in limited time domain is predicted, and the predictive maintenance of hybrid power system health degree is carried out in combination with the change prediction result of part of the state of hybrid power system, so that hybrid power system can still operate with fault under the condition of meeting constraint.

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

The invention relates to a quantitative collaborative evaluation method and system for power measurement differences and a storage medium. The method comprises the following steps: acquiring quantitative index data and qualitative index data of power measurement; performing normalization processing on the quantitative index data; respectively calculating subjective weights and objective weights of all indexes based on an analytic hierarchy process and an entropy weight method, and carrying out weighted fusion to obtain subjective and objective combined weights; processing the qualitative index data based on fuzzy comprehensive evaluation, and combining subjective and objective combination weights to obtain quantitative scores of qualitative indexes; calculating a correlation coefficient of the normalized quantitative index data and a corresponding reference sequence based on grey correlation analysis, and calculating a comprehensive correlation degree of the quantitative index; and integrating the comprehensive correlation degree of the quantitative indexes and the quantitative scores of the qualitative indexes, and calculating to obtain a comprehensive difference degree. Compared with the prior art, through subjective and objective combination and qualitative and quantitative collaborative evaluation process, accurate and consistent quantitative output of the power measurement difference is provided.

The application discloses a kind of power transformer health state determination methods, including obtaining the attribute data of power transformer;Attribute data is normalized and corresponding grey correlation coefficient is calculated;The weight value of attribute data information is calculated using analytic hierarchy process, and grey correlation degree value is calculated;The attribute of power transformer is divided into health grade and the priority of each health grade is determined by comparison;Health grade is associated with grey correlation degree value to construct fuzzy logic control rule;The health state determination of actual power transformer is carried out using fuzzy logic control rule.The application also discloses a kind of system for realizing the power transformer health state determination method.The application obtains and processes the data of power transformer, and predicts the state of power transformer based on analytic hierarchy process and grey correlation analysis scheme, so that the application can not only realize the health state determination of power transformer, but also has high reliability and good accuracy.

The invention relates to the technical field of resource evaluation, in particular to a three-dimensional visual dynamic evaluation method and system for deep brine resource quantity, and the method comprises the following steps: constructing a grid unit and a Kriging interpolation boundary through borehole logging data and geologic horizon three-dimensional coordinates, outputting a topological relation table, obtaining brine thickness data, and calculating volume change; performing sequence symbol detection and classification, extracting mineralization degree and sodium ion data, performing grey correlation analysis to output a coupling coefficient, performing rendering engine color gradient mapping and interpolation, and constructing a three-dimensional dynamic evaluation model. According to the method, fine quantification of underground brine resource structure and change, generation of volume sequence and trend classification, dynamic tracking of resource evolution, linkage of multiple physical and chemical indexes, enhancement of quantity and quality interpretation and improvement of control of distribution heterogeneity and dynamic process are realized by relying on a three-dimensional grid and interpolation technology; the evaluation scientificity, timeliness and intelligent level are remarkably improved, and resource regulation and control and sustainable utilization in a complex environment are effectively supported.

The invention provides an extreme weather power load prediction method and system, and the method comprises the steps: obtaining historical data, and determining a basic load in extreme weather according to the historical data; constructing a total load decomposition model, stripping initial estimation of a basic load and a random load in the total load according to the total load decomposition model, and obtaining a meteorological load mid-value through multiple regression fitting; according to the meteorological load median, screening core meteorological factors through improved grey correlation analysis, and combining stepwise regression screening and extreme value adaptation correction to obtain an accurate meteorological load; constructing a comprehensive predictive factor set according to the precise meteorological load; and inputting each prediction factor of the comprehensive prediction factor set into the trained improved BP neural network model, and outputting a power load prediction value in extreme weather, thereby effectively improving meteorological load separation precision and extreme weather load prediction precision.

The invention discloses a food detection risk assessment method and system based on artificial intelligence, and relates to the technical field of intelligent food detection, and the method comprises the steps: collecting food attribute data, carrying out the behavior modeling of dynamic microorganisms, and extracting the correlation characteristics of the behaviors of the microorganisms and environment variables; and a grey correlation analysis method is used to calculate correlation weights of the correlation features in different situations, a PCA method is used to optimize the correlation features and obtain a feature matrix, and a multi-model fusion method is combined with a meta-learning algorithm to construct a food risk assessment model and output a risk assessment result. Through combination of key feature weighted optimization and multi-model fusion prediction, the accuracy and stability of food microorganism risk assessment are improved, the adaptability of a food risk assessment model to a complex environment is enhanced, dynamic optimization of a risk assessment result is realized, and the problems of low assessment precision and poor universality of an existing method are solved.

The invention provides a trailing suction dredger loading yield real-time prediction method based on data mechanism dual drive, and belongs to the technical field of trailing suction dredger loading yield real-time prediction. The method comprises the steps that 1, actual measurement data of a trailing suction dredger are obtained; step 2, preprocessing the actually measured data to obtain preprocessed data; 3, performing feature selection on the preprocessed data by adopting a grey correlation analysis method to serve as key feature parameters; 4, constructing a self-adaptive deep learning framework, and predicting the loading yield by using the key characteristic parameters to obtain prediction results of the slurry density and the slurry flow velocity; and 5, based on the prediction result, global optimization is conducted on key control parameters of the drag suction dredger through a genetic algorithm, and an optimal operation strategy is generated. According to the method, the optimal operation strategy is generated by adopting a grey correlation analysis method, a self-adaptive deep learning framework and a genetic algorithm, and the dredging yield is maximized.

The invention discloses a data consistency guarantee system of a distributed storage system. The system comprises an experiment platform used for simulating internal faults of a transformer; dynamic parameters of oil flow pressure and flow velocity are collected in real time on the to-be-measured pipeline; acquiring pressure intensity, flow velocity and heavy gas action signals of a gas relay under different fault working conditions; extracting dynamic characteristics of pressure intensity and flow velocity; and establishing a data mapping relationship between the extracted dynamic characteristics and the heavy gas action duration, calculating a correlation coefficient and correlation degree between each characteristic index and the heavy gas action duration based on a primary algorithm, and setting an optimal setting value. The collected experimental data are analyzed through a grey correlation analysis method, and the parameters of the heavy gas action are evaluated according to the analysis result, so that the correlation of each parameter to the heavy gas action can be effectively identified. And a data consistency guarantee value of the distributed storage system is optimized according to an analysis result, so that the maloperation rate is reduced, and the response speed and the fault recognition capability of the relay are improved.

The invention discloses a reservoir dam monitoring and early warning method and system based on multi-source information fusion, and relates to the technical field of early warning and monitoring. The method comprises the following steps: acquiring seepage flow, displacement rate and pore water pressure data through a sensor array; generating standardized data through Kalman filtering noise reduction; extracting key monitoring parameters through grey correlation analysis; aligning a main frequency amplitude sequence with acceleration variation to generate a deformation feature vector; and when the risk reaches a threshold value, inputting logistic regression to generate a coupling early warning level and feeding back to the terminal. According to the method, through multi-source data Kalman filtering noise reduction standardization, grey correlation screening high correlation parameter construction features, Fourier transform extraction of dominant frequency and acceleration alignment, fuzzy evaluation fusion of dominant frequency attenuation and accumulated acceleration quantization deformation, logic regression based on mutation and fluctuation grading early warning, and space-time coupling, hidden danger is captured. Non-linear mapping breaks through empirical limitation, and multi-dimensional synchronization improves evaluation precision and reduces false alarm and missing alarm.

The application discloses a kind of milling surface topography distribution characteristics analysis methods of aviation structural parts, analysis vibration displacement signal distribution characteristics;Milling surface topography is obtained and the construction of milling surface topography feature distribution curve;Analysis vibration displacement signal distribution characteristics and the relevance of surface topography feature distribution characteristics, with milling surface topography curve time frequency as reference sequence.Overefficient milling experiment, obtain milling surface topography, extract milling transition surface residual feature curve, analyze the time-frequency characteristics of milling transition surface feature distribution curve.Using the relevance analysis between milling vibration displacement signal and surface topography feature, using grey correlation analysis method, assess the close degree of the influence of milling vibration on processing surface topography, and the correlation characteristics of vibration and processing surface topography characteristics are clear.

The invention discloses a high-proportion new energy short-circuit current prediction method based on a GWOGAC-IELMsin mixed model. The invention relates to a high-proportion new energy power grid short-circuit current intelligent prediction method. The method comprises the following steps: constructing a hybrid prediction model GWOGAC-IELMsin based on an improved grey wolf optimizer and an incremental extreme learning machine; fault transient signal features are rapidly extracted in a 0.2 ms time window through wavelet transform, a high-dimensional input vector containing a fault phase angle, a voltage and current instantaneous value and a change rate thereof, d / q axis current reflecting new energy power electronic equipment features and other features is constructed, and key features are screened through grey correlation analysis; optimizing an input weight and a hidden layer offset parameter of the incremental extreme learning machine by adopting an improved grey wolf optimization algorithm; and finally, dynamically adjusting the network structure through an incremental learning mechanism until the prediction precision requirement is met. The method effectively solves the three technical problems that in high-proportion new energy power grid short-circuit current prediction, a traditional model is insufficient in transient feature capture, an optimization algorithm is prone to local optimization, and prediction speed and precision are difficult to balance.

The invention discloses a low-voltage transformer state evaluation method based on a fuzzy grey fusion algorithm. The method comprises the steps of multi-dimensional data index collection and self-adaptive preprocessing; and based on the preprocessed data indexes, state evaluation of fuzzy grey fusion is carried out. During work, a self-adaptive preprocessing system is constructed, a sliding window is used for dynamically normalizing and adjusting a boundary to solve dimension and working condition drifting, and a time-varying weight model is constructed in combination with a load fluctuation rate and an environment deviation degree to improve evaluation sensitivity; an intelligent completion strategy based on a gray model is designed, and the data integrity is effectively improved; a fuzzy grey fusion evaluation framework is innovated, a semi-trapezoidal membership function is used for eliminating state boundary jump, a fuzzy membership degree and a dynamic weight are coupled through double-layer grey correlation analysis, evaluation continuity and accuracy are improved, and technical support is provided for fine operation and maintenance and risk early warning of the low-voltage transformer.

The application discloses a kind of based on data-driven asphalt mixture high-temperature performance prediction and design optimization method, including collating and pre-processing asphalt mixture high-temperature performance data, using grey correlation analysis method, based on grey scale analysis result on data set is filtered, determine the input and output of high-temperature performance prediction model;Build ANN, SVM, GPR three kinds of machine learning models, realize the prediction of asphalt mixture high-temperature performance, and the model is visualized processing;Based on high-temperature performance optimization prediction model, the mix proportion of asphalt mixture is designed and optimized using bayesian optimization algorithm, and the application demand under different actual working conditions is simulated by limiting part of parameters in mix proportion.The present application provides reliable prediction information, reduces the test cost of asphalt mixture in high-temperature performance test and mix proportion design, shortens the material design and research and development cycle, promotes the digitalization and intelligent development of asphalt mixture performance prediction and optimization.

The invention discloses a metal mine soft rock roadway mechanical tunneling excavation method which comprises the following steps: step 1, dividing a roadway section into six areas, and designing eight different excavation sequence schemes; 2, lithology, compressive strength, cohesion and internal friction angle parameters of roadway section rock are determined, and a three-dimensional numerical model is established; 3, numerical simulation is conducted on surrounding rock responses under different excavation sequences; 4, calculating the ratio of the plastic zone area to the maximum deformation of each scheme, and preferably selecting four schemes with relatively large stability evaluation coefficients; 5, constructing a reference sequence and a comparison sequence, and calculating a correlation coefficient and a correlation degree; and 6, the excavation scheme with the minimum correlation degree is the optimal excavation sequence scheme. According to the method, a metal mine soft rock roadway mechanical tunneling excavation sequence method based on numerical simulation and grey correlation analysis is optimized, the problems that a traditional method is low in efficiency, poor in forming and large in surrounding rock disturbance are solved, and safe and efficient tunneling of the soft rock stratum is achieved.

The present application relates to the technical field of intelligent decision-making, in particular to an engineering change intelligent identification and influence analysis method and system. In the present application, by extracting the physical connection information of the field control equipment and associating the operating parameters and the logical constraint parameters, a field device interconnection topology model with signal flow direction as the connection line is constructed, the discrete engineering equipment is converted into a digital map with logical handshake relationship, the parameter modification behavior is monitored in real time and the change source equipment is locked, the history trend consistency and real-time availability of the redundant signal channel are verified by using the grey correlation analysis algorithm and the effectiveness threshold, when it is confirmed that it has effective backup, it is automatically determined as a blocking node, the input signal change is simulated and calculated according to the PID control algorithm combined with the loop gain coefficient, whether the fluctuation occurs physical dissipation is confirmed by comparing the absolute value of the difference of the theoretical output signal and the signal control dead zone threshold, and the effective fluctuation which can actually drive the downstream equipment action is accurately identified.

The invention provides a method for predicting the friction performance of a titanium alloy surface coating based on image features. The method comprises the following steps: preparing an electroplating solution; the Ti6Al7Nb titanium substrate is subjected to pretreatment; a titanium substrate is used as a cathode, a copper rod is used as an anode, and the copper and nano-hydroxyapatite composite coating is obtained through a jet electrodeposition process; collecting a coating surface image, and obtaining a coating surface friction coefficient through a friction test; extracting image features and carrying out normalization processing on test data; obtaining the most significant factors influencing the friction coefficient of the coating according to a grey correlation analysis method; and establishing a prediction model of the average friction coefficient with respect to image features based on a multiple regression algorithm, and performing reliability analysis. According to the method, the surface friction coefficient can be effectively predicted through the surface image features on the premise that the integrity of the titanium alloy coating is not damaged.

The embodiment of the invention discloses a power system evaluation method and device, equipment and a storage medium, and the method comprises the steps: obtaining the original data of the operation of a power system, and carrying out the time alignment processing and preprocessing of the original data; constructing a multi-dimensional evaluation index system from a security dimension, a flexibility dimension and a green low-carbon dimension based on the processed data; performing dimensionless processing on the numerical value of each evaluation index in the multi-dimensional evaluation index system to obtain corresponding dimensionless data; determining the weight coefficient of each evaluation index based on the combination of an analytic hierarchy process and an objective weighting method; and based on the weight coefficient and the dimensionless data of each evaluation index, carrying out comprehensive evaluation on the power system by adopting a grey correlation analysis method and a superior and inferior solution distance method. Therefore, the system state can be accurately and comprehensively judged, the accuracy of the evaluation result can be improved, the regional adaptability can be improved, and the evaluation accuracy in complex scene or multi-scene application can be improved.