188 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 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 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 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 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.

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 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 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 relates to the technical field of satellite scheduling, in particular to a satellite-borne resource scheduling method in a multi-task coordination scene, which comprises the following steps: S1, constructing a single-satellite multi-task coordination scene; s2, judging the priority of a to-be-completed task of a satellite based on a multi-task cooperation scene, and analyzing evaluation indexes of different tasks and satellite-borne resources required by the evaluation indexes; and S3, designing a spaceborne resource scheduling algorithm based on spaceborne resources required by different tasks. According to the satellite-borne resource scheduling method in the multi-task collaborative scene, an evolution model based on a dynamic environment and a grey correlation analysis method are adopted, the diversity of satellite-borne resource types in the modeling process can be kept, meanwhile, the importance degrees of the tasks are ranked, and the task priorities are determined; and designing a corresponding spaceborne resource scheduling algorithm, and finally constructing a spaceborne resource scheduling method in a multi-task collaborative scene, so that reasonable allocation, efficient management and dynamic adjustment of spaceborne resources can be realized.

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 present invention provides a method, apparatus, device, and storage medium for sorting NOTAMs. The method obtains historical navigation data, wherein the historical navigation data includes historical abnormal flight data and historical NOTAM data; determines a parent sequence and a child sequence based on the historical navigation data; and obtains a NOTAM sorting benchmark value using a gray correlation analysis method, wherein the parent sequence includes historical abnormal flight data and the child sequence includes historical NOTAM data; then obtains the NOTAMs to be sorted, sorts the NOTAMs to be sorted based on the NOTAM sorting benchmark value, and obtains a NOTAM processing order. The method of the present invention differs from existing sorting methods based on effective time or receipt time. It identifies the degree of impact of different notification types on flights and associates them with flights on the same day, ensuring that important notifications are processed promptly and avoiding adverse effects on flight operations due to untimely processing.

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.