251 results about "Grey correlation" patented technology

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 power grid fault characteristic determination and positioning method, system, device and storage medium, and relates to the field of power grid fault processing, and the method comprises the steps: synchronously collecting the voltage, current, frequency and other electrical quantities and temperature, humidity and other environmental quantities of a power grid node through a multi-mode sensor, and forming an original time sequence data set; separating electrical quantity frequency domain fault features by using improved variational mode decomposition, and analyzing the correlation between the environmental quantity and the electrical quantity through a grey correlation degree to suppress noise; mapping the dynamic deviation of each order of derivative of the voltage / current into a spatial-temporal characteristic matrix, and constructing a distribution map in combination with power grid topology; based on the dynamic graph neural network, inputting node features and edge features, and establishing a fault-environment association model through feature propagation and a graph attention mechanism; and carrying out fine trimming on a positioning result by combining double-end traveling wave coarse positioning with an environment correlation degree correction coefficient, and verifying connectivity of a fault point and a power grid topology.

The invention discloses a method for optimizing pressing rivet quality based on grey correlation and a factor analysis method, and relates to the technical field of pressing rivet quality optimization. Firstly, a taguchi method is adopted, a press riveting quality test model with busbar design parameters and control parameters of an automatic squeeze riveter as design optimization variables and with push-out force and twisting force as responses is constructed, a grey correlation method is adopted, the correlation degree in all responses is analyzed, dimension reduction is conducted on multiple indexes in combination with a factor analysis method, weights of different indexes are obtained, and a press riveting quality test model is obtained. Reducing multiple indexes into a single index, then calculating a weighted grey correlation degree, carrying out comprehensive evaluation, finally obtaining an optimal parameter level combination, and analyzing a factor with the maximum influence degree on the indexes. According to the method, the factors which have the most influence on the pressing rivet quality can be quickly selected from the numerous parameters, and when the pressing rivet quality is evaluated by the push-out force and the twisting force in the pressing rivet quality evaluation indexes, the problems of information overlapping of multiple evaluation indexes and the like can be solved.

The invention relates to the technical field of green product analysis, and discloses a green product full life cycle analysis system based on big data. The system comprises a data acquisition module, a preprocessing module, an environmental impact assessment module, a resource consumption optimization module, an association modeling and result generation module and a visual interaction module. The system collects data of all stages of a full life cycle and related element data of environment, resources, carbon emission and the like, after preprocessing of abnormal value elimination, entropy weight method weighting and the like, environment influence is dynamically evaluated through a fuzzy membership function, resource consumption is subjected to multi-objective optimization by utilizing a genetic algorithm, and the resource utilization rate is improved. A key stage is determined by means of grey relational degree, an analysis result is generated in combination with a multi-target decision model, and finally the analysis result is visually presented in the form of a three-dimensional map and a dynamic thermodynamic diagram. The system realizes efficient analysis and comprehensive evaluation of full-life-cycle data of green products, and provides scientific support for product environment performance optimization and decision making.

The invention discloses a building hidden carbon emission prediction method based on machine learning, and the method comprises the following steps: S1, building construction activities are comprehensively considered, a building hidden carbon emission structure is qualitatively and quantitatively analyzed, and a main carbon emission source is selected as an output parameter of a target prediction model; s2, the system identifies influence factors of hidden carbon emission of the building, and grey correlation analysis is adopted to select influence factors with large correlation as input features of the target prediction model; s3, making a data collection table, and carrying out data collection for an actual construction project; s4, constructing a construction consumption prediction model by using a machine learning algorithm; s5, building hidden carbon emission prediction is completed in combination with the carbon emission factors; and S6, according to the methods of S1 to S5, using Python to develop a building hidden carbon emission prediction device. According to the method, the hidden carbon emission level of the building can be rapidly and conveniently estimated in the initial design stage of the building, and then low-carbon design optimization, scheme comparison and selection, decision management and the like are supported.

The invention relates to the technical field of electric power, and discloses a novel electric power system load prediction method, which comprises the following steps of: acquiring electric power and corresponding comprehensive energy system data in an area on site, performing feature matching classification, determining prediction indexes, expanding a prediction range, establishing a secondary prediction system, and performing GM (1, 2, 3, 4, 5, 6, 7, 8, 8, 9, 10) on the secondary prediction indexes, the method comprises: 1) performing grey prediction to obtain a predicted value sequence, and generating a grey prediction model; performing grey correlation degree analysis on the second-level prediction index and the first-level prediction index; constructing a Bayesian network structure; constructing a father node; sequentially setting the value domains of the father node and the child node; determining a probability value and probability distribution; and comparing a prediction result with an actual result for verification. According to the method, all factors which play a role in the future are included in the prediction model, the influence weight of each prediction index is accurately analyzed, and assistance is provided for future work key points of employees of electric power departments and related industries.

The invention discloses an aero-engine gas path sensor fault diagnosis method based on GCN and BiLSTM, and belongs to the field of aero-engine health management. In order to solve the problems that in the prior art, sensor faults and gas path system fault signals are difficult to distinguish, and time sequence modeling is insufficient, a mixed model architecture fusing a graph convolutional network and a bidirectional long-short-term memory network is provided. The method comprises the following steps: screening high-correlation sensors through grey correlation degree to construct multiple groups of input-output combinations; the method comprises the following steps: extracting spatial topological characteristics among sensors by using a GCN, capturing bidirectional time sequence dependence by combining BiLSTM, and establishing a parallel prediction model cluster; and setting a dynamic threshold based on a reconstruction residual 3 sigma criterion, and realizing fault code matching through a Boolean diagnosis dictionary. The method effectively solves the problem of similarity between the sensor fault and the gas path system fault signal, can accurately diagnose the sensor fault or the gas path system fault, and improves the fault detection and maintenance efficiency of the aero-engine.

The invention discloses a single-phase fault interval positioning method and device, electronic equipment and a storage medium, and belongs to the technical field of power distribution networks, and the method comprises the steps: obtaining three-phase current data in a fault sampling period; according to the three-phase current data, determining a fault phase line based on grey correlation analysis; extracting current data of a fault phase line in the three-phase current data, and further determining a fault feeder line according to the current data of the fault phase line based on transient zero-sequence current group amplitude comparison; and extracting current data of a fault feeder line in the three-phase current data, and further determining a fault interval according to the current data of the fault feeder line based on a K-means clustering algorithm. According to the method, fault phase selection is analyzed through the grey correlation degree, fault line selection is performed through amplitude comparison of the transient zero-sequence current group, and the fault interval is positioned through the K-means clustering algorithm, so that the accuracy of single-phase fault interval positioning can be effectively improved, different working environments can be adapted, and the safety and stability of the power distribution network are effectively improved.

The invention discloses an efficient gathering and catching method and system for fertilized eggs of yellow-fin tuna, and aims at culturing the yellow-fin tuna, a three-dimensional visual water area model is constructed, and monitoring points are set to monitor water body characteristics, fish school activities and fertilized egg distribution. Through multi-cycle monitoring and serialization of a water body flow field, fish school activities and fertilized egg distribution characteristics, a grey correlation method is adopted to analyze change correlation, water body-activity correlation data is formed, a fertilized egg damage prevention system is constructed, a culture area is monitored in real time, data is imported, the data is compared with the correlation data, fertilized egg distribution is predicted, and meanwhile, the fertilized egg damage prevention system is established. The breeding environment is regulated and controlled in real time, a gathering and catching scheme is generated, and breeding management is optimized. By means of the system and method, relevance data analysis and mining can be effectively conducted on fertilized egg distribution, the water body, fish school activities and other information, precise regulation and control over the water body are achieved, the number and distribution of the fertilized eggs tend to be expected, meanwhile, the gathering and catching efficiency of the fertilized eggs is improved, and the breeding benefits are further improved.

The present invention relates to the field of meteorological forecasting and artificial intelligence technology, and in particular to a grey correlation-based ice thickness prediction method, system, device and medium, comprising collecting multi-dimensional ice cover data, dynamically screening key meteorological features through grey correlation; constructing and dividing a cleaned and normalized data set; integrating a bidirectional gated recurrent unit with a multi-head self-attention mechanism to design a hybrid model; performing parameter optimization and testing verification based on a training set; eliminating redundant interference through dynamic feature screening, bidirectionally analyzing the long-term and short-term ice cover evolution laws, strengthening the focus on extreme meteorological event features with an attention mechanism, and realizing cross-climate zone migration deployment in combination with a parameter adaptive compensation module, thereby significantly improving prediction accuracy and early warning capabilities for sudden ice and snow conditions, and providing intelligent decision-making support for power grid disaster prevention.

The invention relates to the technical field of data processing, in particular to an edge computing method for mass data analysis. According to the method, when massive Web server access logs are processed, a log field structure, field complexity and data magnitude are combined, resource configuration requirements are defined, accurate estimation of threads, memories and IO calling is achieved, a behavior sequence is further constructed on the basis of response time delay, the completion rate and the interruption condition in server behavior characteristics, and the service life of the server is prolonged. A dynamic evaluation standard for scheduling capability is formed, a grey correlation scoring method between resource requirements and behavior capability is fused in candidate node screening, and sorting optimization is performed in combination with node real-time resource occupancy states, so that a task allocation path has better balance in the aspect of multi-dimensional resource utilization; and parallel processing is realized through log fragment mapping after task scheduling, and structured analysis output is formed through edge side summarization, so that the fitness of edge node scheduling and the precision of task assignment are improved.

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 ceramic firing performance prediction method based on a GA-BP neural network, and the method comprises the steps: data collection and preprocessing, gray correlation analysis and screening of key process parameters, construction of a GA-BP neural network model, model training and verification, prediction model testing, and firing performance index prediction. By means of unique intelligent algorithm fusion, the problems of high experience dependence, high experiment cost, long period, low efficiency, time consumption, energy consumption, high efficiency and the like of a method for judging the ceramic firing performance through artificial experience in actual ceramic firing production are effectively solved. The technical problem that multi-target collaborative optimization is difficult to realize at the same time in the prior art is solved, remarkable advantages are shown in the aspects of prediction precision, intelligent degree and the like, the requirements of intelligent, green and high-quality ceramic firing process optimization are met, and the method has wide application prospects and important practical value.

The invention provides an optimization design method for structural parameters of a heavy oil burner, which comprises the following steps of: firstly, simulating the atomization characteristics of heavy oil, then establishing a heavy oil burner simulation model and verifying the reliability of the model; on the basis, key structure parameters influencing the performance of the heavy oil burner are determined and serve as design variables for experimental design, all the schemes are simulated, and a neural network is trained through simulation results; next, a multi-objective optimization model of heavy oil burner structure parameters is established, optimization is carried out by utilizing a genetic algorithm, multi-objective decision of a Pareto solution set is carried out according to user preferences and a grey correlation method, and optimal structure parameters are determined; and finally, testing the operation performance of the heavy oil burner, re-optimizing if the requirements are not met, and otherwise, finishing the design of the heavy oil burner. Based on a numerical simulation method and a small amount of test data, the optimal structural parameters of the heavy oil burner can be determined, and development time and capital cost can be reduced.

The invention discloses a debris flow risk level prediction method and system based on a GRA-PCA-RBF neural network model, and the method comprises the steps: taking a debris flow gully in a debris flow liable region as a research object, collecting and analyzing the data and data of historical debris flow disasters, determining the risk and risk evaluation indexes of the debris flow disasters, and carrying out the prediction of the risk level of the debris flow disasters. Analyzing the correlation degree of the debris flow danger and the evaluation indexes by using a grey correlation degree method, and selecting key evaluation indexes from the correlation degree; carrying out dimension reduction processing on the key evaluation index data by utilizing a principal component analysis method, removing redundant information of the index data, and establishing an evaluation index system; and finally, training a training sample by using an RBF neural network model optimized by a genetic algorithm, establishing a debris flow risk level prediction model, and performing accuracy estimation on the constructed model. According to the invention, a reasonable and effective debris flow danger method and system are established in a complex and hard mountain area, and an efficient working system is provided for disaster prevention and reduction of infrastructure construction in the mountain area.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment method and device, electronic equipment and a storage medium. Preprocessing the monitoring data to obtain preprocessed monitoring data; and in combination with the Pearson correlation coefficient, mutual information and grey correlation degree between each piece of monitoring data and the effluent quality index, screening target features strongly correlated with the effluent quality from the preprocessed monitoring data. According to the method, through a multi-dimensional feature screening mechanism, key influence factors are accurately identified, and the adaptability of the model under different working conditions is remarkably improved; by constructing a comprehensive objective function fusing water quality, cost and robustness and embedding process constraints, multi-objective collaborative optimization of pollution reduction and carbon reduction is realized; an optimal control strategy is efficiently solved by adopting an improved intelligent algorithm, and the core problems of insufficient model generalization ability, mechanism and data disjunction and the like in sewage treatment are effectively solved.

The invention discloses an industrial automatic control system based on batch production, and relates to the technical field of industrial automatic control, a matrix construction module compares the importance of indexes in pairs to obtain a judgment matrix, the judgment matrix is subjected to normalization processing, and then the weight vector of each index is calculated; the strategy output module generates the grey correlation degree of each alternative control strategy according to the difference coefficient, then substitutes the grey correlation degree of each alternative control strategy and the weight vector of the index into a fusion algorithm so as to calculate the comprehensive score of each alternative control strategy, sorts all the alternative control strategies according to the comprehensive score, and finally outputs the sorted alternative control strategies. And selecting the alternative control strategy with the first sequence to be applied to the current production scene of the production line. The control system can carry out multi-target comprehensive evaluation under an uncertain condition, so that the model can adapt to different production scenes, comprehensive optimization of multiple indexes is realized, and an optimal control strategy is selected for use.

The invention relates to the technical field of application of a neural network to interval prediction of regional power system load, and provides a method for reasonably and accurately predicting and estimating power load which is a key element of a novel power system, so that safe and stable operation of the power system is ensured, and efficient supply and demand balance is realized. According to the interval prediction method based on an adaptive cooperative myxomycete algorithm and parallel prediction, firstly, an improved grey correlation degree analysis IGRA is adopted to screen climate, holiday and festival factors, and factors having high non-linear correlation with a load sequence are identified; then, a neural network model of time convolutional neural network-deep learning is built; carrying out parameter optimization on the TCN-Transformer model by adopting an adaptive cooperative mucus algorithm (ACSMA); and utilizing the trained TCN-Transform to carry out interval prediction on the load of the regional power system. The method is mainly applied to power system load prediction management occasions.

The invention relates to load prediction, in particular to a power grid load prediction method, which comprises the following steps of: decomposing a power grid load prediction problem into different levels, constructing a judgment matrix, and calculating a preliminary weight of each factor based on the judgment matrix; calculating a variation coefficient of each factor, and adjusting the initial weight of each factor by using the variation coefficient to obtain a weight coefficient of each factor; constructing a grey incidence matrix of each factor according to the historical data of the power grid load, and calculating the correlation degree between each factor and the power grid load based on the grey incidence matrix of each factor; combining the weight coefficient and the correlation degree of each factor to construct a fuzzy relation matrix of each factor, and obtaining an electric quantity prediction matrix based on the fuzzy relation matrix of each factor; inputting the electric quantity prediction matrix into a pre-trained power grid load prediction model to obtain a first power grid load prediction result; according to the technical scheme provided by the invention, the defect that the power grid load is difficult to accurately predict in the prior art can be effectively overcome.

The invention relates to a multi-element reliability test data dynamic weight optimization method, which comprises the following steps of: inputting data, including accelerated life test data, fault excitation data, a mean value of historical failure and a standard deviation; calculating entropy values and dynamic weights of various types of data according to a formula; according to the dynamic weight optimization method, weight distribution is performed on existing test data related to various reliability faults through a multi-source data weight distribution method fusing an entropy weight method and grey correlation analysis, so that a verification result is more credible. According to the entropy-gray combined calculation technology, the theory of information entropy and the theory of gray relational degree are integrated, and test data are adjusted through the product term of the information entropy and the gray relational degree to verify the distribution weight. According to the full-automatic weight distribution system, a real-time weight updating mechanism without manual intervention is achieved by updating an input sequence of data.

The invention discloses a low-permeability reservoir geological sweet spot identification method based on grey correlation and analytic hierarchy process. The method comprises the following steps: S1, collecting geological parameter data of a low-permeability reservoir; s2, analyzing the geological parameter data obtained in the step S1 by applying a grey correlation algorithm, determining the correlation degree of each geological parameter, and sorting the geological parameters according to the correlation degree of the geological parameters; s3, analyzing and determining a judgment matrix by using an analytic hierarchy process, and calculating the weight of each index; s4, counting the weight obtained in the step S3, establishing a geological dessert comprehensive evaluation model, and calculating a geological sweetness indication factor through the geological dessert comprehensive evaluation model; and S5, according to the geological sweetness indication factor obtained in the step S4, determining reservoir geological sweet spot classification in combination with a geological sweet spot division table, and the like. Through the grey correlation algorithm and the analytic hierarchy process, geological sweet spot identification of the low-permeability reservoir is realized, and a basis is provided for subsequent remaining oil potential tapping scheme evaluation and comprehensive scheme adjustment.

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 a compact sandstone reservoir diagenetic phase quantitative characterization method which is characterized by comprising the following specific steps: step 1, determining diagenetic phase evaluation parameters and obtaining a parameter sequence; step 2, determining a correlation coefficient of the parameter sequence obtained in the step 1 by applying a grey correlation method, and calculating a correlation degree; step 3, constructing a multivariate comprehensive classification coefficient Feci; 4, dividing lithogenous phase types according to the multivariate comprehensive classification coefficient obtained in the step 3; and 5, performing logging characteristic analysis on the types of the diagenetic facies divided in the step 4, selecting a sensitive logging curve, and obtaining a discrimination function by using a Fisher discrimination method so as to realize quantitative identification of the diagenetic facies. According to the method, evaluation parameters are high in credibility and objective, so that lithogenous phase judgment can be more accurate.

The invention relates to a flexible load prediction method based on an optimized grey correlation model, which belongs to the technical field of optimized operation of a power system and comprises the following steps of: checking and correcting defects of load data; modeling a grey correlation model; correcting a grey correlation model parameter alpha; verifying the model precision according to the deviation; and carrying out multi-load prediction based on associated parameters. According to the method, the model parameter alpha of the grey correlation model is optimized, the grade ratio sigma (0) (k) falls into a feasible region, the outside of the grey correlation model is optimized, the concept of the correlation degree of a prediction curve and an actual curve is put forward, a multi-load classification prediction scheme is put forward, periodic changes and multi-factor influences of different loads are solved, and the prediction precision is improved.

The invention relates to an ultra-short-term photovoltaic power prediction method, in particular to an ultra-short-term photovoltaic power prediction two-stage GRU optimization method considering main meteorological factors and waveform classification, and aims to improve the photovoltaic power prediction precision and reduce the prediction time complexity. In the first stage, in order to improve the prediction precision and efficiency of a gated loop unit (GRU), an input data optimization method is provided, main meteorological factors based on a factor analysis method (FA) are extracted, similar modes considering photovoltaic power generation power fluctuation characteristics based on a fuzzy C-clustering algorithm (FCM) are clustered, and the prediction precision and efficiency of the GRU are improved. The maximum similarity pattern recognition based on grey correlation degree analysis and cosine similarity is combined; in the second stage, a GRU optimization method based on an improved sparrow search algorithm (ISSA) is provided, structural parameters of the GRU are optimized in the training stage of the GRU, and the prediction precision is further improved.

The invention discloses a power grid equipment investment comprehensive benefit evaluation method and system considering functions and cost. The method comprises the following steps: establishing a province-city-county power grid equipment investment comprehensive benefit evaluation index system library; determining each index data, and generating a comprehensive benefit evaluation index database; calculating subjective and objective weights of power grid equipment investment comprehensive benefit evaluation by adopting an analytic hierarchy process and an entropy weight method, and generating a comprehensive benefit evaluation index weight library; calculating a comprehensive weight of power grid equipment investment comprehensive benefit evaluation based on a least square method, and adding the comprehensive weight to a comprehensive benefit evaluation index weight library; calculating the power grid equipment investment comprehensive benefit correlation degree by adopting a grey correlation degree method, and generating a correlation degree sorting library; constructing a comprehensive benefit index trace tracking diagnosis model, and generating a comprehensive benefit evaluation index deviation contribution library; and according to the correlation degree sorting library and the comprehensive benefit evaluation index deviation contribution library, completing the comprehensive benefit evaluation of the power grid equipment investment. A method and a platform are provided for power grid enterprises to evaluate equipment benefits, and the fund use efficiency is improved.

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 relates to a multi-attribute sudden disaster emergency aid decision-making method based on a knowledge graph. The method comprises the following steps: constructing the knowledge graph; the method comprises the following steps: representing all historical sudden disaster emergency cases in the form of a knowledge graph, and respectively giving different initial decision matrices by decision makers according to the comprehensive situation of the historical sudden disaster emergency cases; based on the initial decision matrix, obtaining a normalized comprehensive influence matrix through a TSK model; taking the normalized comprehensive influence matrix corresponding to each decision maker as a decision matrix given by each decision maker for the target case, gathering the decision matrixes by considering a louvain algorithm of the comprehensive influence matrix, and determining a generalized Shapley value of fuzzy measurement; and calculating a grey correlation degree between the target case and a historical sudden disaster emergency case based on a generalized Shapley value of fuzzy measurement, determining historical similar cases based on the grey correlation degree, and carrying out emergency processing on the target case according to processing measures in the historical similar cases.

The invention discloses a network security situation awareness method based on multi-source heterogeneous data, and belongs to the technical field of network security, and the method comprises the steps: configuring a plurality of groups of independent data sources, so as to collect multi-source heterogeneous monitoring data; carrying out range transformation dimensionless preprocessing on the acquired data according to the reference sequence, and calculating a grey correlation degree by adopting an adaptive weight mechanism to obtain a quality evaluation reference value; establishing an evidence weight distribution mechanism based on the quality evaluation reference value, and constructing a recognition framework to calculate a basic probability assignment so as to fuse and / or redistribute the data; and performing stratified sampling on the data source according to the quality score to obtain a situation evolution trend by constructing regression prediction of the feature vector. Through the scheme of the invention, the accuracy and stability of network security situation prediction can be improved.