189 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 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 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 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 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.

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 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 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.

The present invention relates to load forecasting, and in particular to a power grid load forecasting method, which decomposes a power grid load forecasting problem into different levels, constructs a judgment matrix, and calculates the preliminary weight of each factor based on the judgment matrix; calculates the coefficient of variation of each factor, and uses the coefficient of variation to adjust the preliminary weight of each factor to obtain the weight coefficient of each factor; constructs a grey correlation matrix of each factor according to historical power grid load data, and calculates the correlation between each factor and the power grid load based on the grey correlation matrix of each factor; constructs a fuzzy relationship matrix of each factor based on the weight coefficient and correlation of each factor, and obtains an electric quantity prediction matrix based on the fuzzy relationship matrix of each factor; inputs the electric quantity prediction matrix into a pre-trained power grid load forecasting model to obtain a first power grid load forecasting result; the technical solution provided by the present invention can effectively overcome the defect of the prior art that it is difficult to accurately predict the power grid load.

The invention discloses a multi-dimensional quantitative analysis method for comprehensive efficiency of power grid construction, and the method comprises the steps: generating a time sequence operation scene set containing a plurality of typical days through employing a clustering algorithm based on a pre-constructed multi-dimensional quantitative analysis index system; determining the comprehensive weight of each quantitative analysis index by adopting a new game theory combination weighting method; calculating the score of each construction scheme in the static fitness index layer and the score of each construction scheme in the dynamic robustness index layer; a new TOPSIS and grey relational degree combined model is adopted to calculate the comprehensive efficiency index of each construction scheme; performing comparison and robustness test on the schemes, and outputting a quantitative analysis result; according to the method, a quantitative analysis index system covering six dimensions is constructed, all aspects of the novel power system are covered, and the quantitative analysis dimensions are comprehensive; according to the method, the index comprehensive weight is determined by adopting a new game theory combination weighting method, and the comprehensive efficiency index is synthesized by utilizing a new TOPSIS and grey relational degree combination model, so that the scientificity of quantitative analysis is improved, and refined and scientific comparison and selection of power grid construction schemes are realized.

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

The invention relates to the technical field of electric energy metering, in particular to an electric energy metering data reliability verification system and method based on fusion of a random forest and a deep belief network. Comprising the steps that all-around electric energy metering data and environment data are collected in real time, abnormal data are removed through 3 sigma rule screening, wavelet transformation and the self-adaptive filtering technology, time synchronization processing and normalization processing are conducted, and preprocessed data are obtained; key features are extracted from the preprocessed data, and the relation between the features is analyzed through a Pearson's correlation coefficient, a mutual information method and grey correlation; a random forest and deep belief network fusion model is constructed and trained, an input layer of the random forest and deep belief network fusion model receives multi-source heterogeneous data subjected to preprocessing and feature extraction, a measuring instrument error value is output at an output layer after calculation, and monitoring and early warning are carried out according to a preset threshold value. According to the invention, accurate capturing and real-time monitoring of the error of the measuring instrument are realized, and the problem of real-time error acquisition under the conditions of no power failure and no physical standard is successfully solved.

The application relates to a power data fusion method, comprising the following steps: acquiring time series data from multiple data sources respectively; calculating the similarity between each time series data to obtain a similarity matrix; accumulating the matrix elements in the same row or the same column in the similarity matrix to obtain the similarity accumulation value of each data source and other data sources; performing longitudinal parameter fusion on each data source with a similarity accumulation value higher than a first threshold value, comprising the following steps: performing data preprocessing on the time series data; calculating the grey correlation degree between each two time series data of the time series data after the data preprocessing; filtering out the time series data with a grey correlation degree lower than a third threshold value; performing data fusion on the time series data by using a BP neural network to obtain a first fusion result; and performing horizontal parameter fusion on each data source with a similarity accumulation value lower than the first threshold value.

The invention discloses a relay protection defect diagnosis method and device based on a dynamic knowledge graph, and belongs to the field of relay protection defect diagnos.The method comprises the steps that a first data set in an electric power system is collected in real time, standardization processing is conducted on the first data set, and a second data set is generated; the second data set is input into a MacBERT-BiLSTM-CRF model for entity extraction, and a plurality of entities are obtained; updating graph structure data according to the second data set and each entity, and inputting the graph structure data into a GNN model for relationship mining to obtain a plurality of entity relationships; updating a knowledge graph according to each entity and each entity relationship, performing rule reasoning based on the knowledge graph and real-time data of the target relay protection device, and performing probabilistic reasoning through a Bayesian network after a first diagnosis result is obtained to obtain a second diagnosis result; based on the second diagnosis result and the real-time data, grey correlation degree analysis is conducted, a consistency evaluation result is obtained, and when the consistency evaluation result is larger than a first threshold value, a diagnosis report is generated and output.

A method and storage medium for identifying schedulable flexible resources based on the grey correlation theory according to the present invention include establishing a power grid topological network structure according to the scale of the distribution network in the analysis area, selecting various schedulable flexible resources in the distribution network, and establishing a mathematical model; setting a system objective function and constraint functions, and generating an optimal unit output combination for realizing the coordinated optimization of robustness and economy; randomly generating configuration data of the schedulable flexible resources, substituting them into the objective function to calculate various costs and forming multiple groups of data; calculating the correlation degrees of multi-scenario data by using the grey correlation theory based on the entropy weight method to obtain the weighted comprehensive correlation degrees of each schedulable flexible resource; and the sorting result of the magnitudes of the weighted comprehensive correlation degrees is the influence degree of the schedulable flexible resources on the power grid planning. The present invention overcomes the problem of identifying the influence of schedulable flexible resources in the current power system planning, and improves the accuracy and efficiency of power system planning decisions.

The application provides a force-vibration-noise monitoring and prediction system for wet milling, which comprises an externally-mounted cutting fluid transmission system, a numerical control machine tool and workpiece system, a sound pressure and vibration measurement system, and a three-way force measurement system, can monitor and obtain three-way milling force signals, three-way vibration signals and milling sound pressure signals under given milling parameters. Based on experimental data and grey correlation theory, the correlation degrees of force, vibration and noise about milling parameters during wet milling are better than those during dry milling, which shows that the system has a certain optimization effect on the cutting state. Based on the response surface analysis method, the prediction models of force, vibration and noise about milling parameters and the comprehensive model of noise about force-vibration-milling parameters are established. The results show that the noise comprehensive prediction model established based on milling force-vibration is more reliable, and the correlation between milling force, vibration and milling noise is higher. The application can provide scientific guidance for the monitoring and prediction of the milling processing state.

The invention belongs to the technical field of virtual power plant operation evaluation, and particularly relates to a virtual power plant operation comprehensive evaluation method and system.The method comprises the steps that a three-level multi-dimensional operation risk evaluation index system is constructed, and three types of weight vectors are obtained through an analytic hierarchy process, an entropy weight method and an inter-index correlation and conflict objective weighting method; carrying out collaborative correction through a non-cooperative game model to obtain an optimal comprehensive weight vector; a standard cloud model of each risk level is constructed based on an extension cloud model, an actual evaluation cloud model is constructed in combination with the score of a professional scoring system and an optimal weight, the score weight of the professional scoring system is determined through a grey correlation degree, an evaluation value is corrected, the actual cloud model is updated, and finally the digital characteristics of the actual and standard cloud models are compared. And quantitatively judging the operation risk level of the virtual power plant. According to the method, the comprehensiveness, reliability and scientificity of evaluation are improved, and support is provided for risk prevention and control of the virtual power plant.

The invention discloses a long-acting augmented injection process effect evaluation method which comprises the following specific steps: step 1, acquiring the original porosity, post-acidification porosity, original permeability and post-acidification permeability of a water injection well, the radius of a shaft and the radius of an oil reservoir water injection unit according to geological data, and calculating the porosity change amplitude and the permeability change amplitude; 2, the water absorption index before acidification and the water absorption index after acidification of the water injection well are measured, the injection increasing multiple and the skin coefficient after acidification are calculated, and the pressure drop index IPI of the water injection well and the effective day number of acidification measures are determined; step 3, performing dimensionless processing on the parameters obtained in the step 1 and the step 2; 4, calculating a grey correlation coefficient, and calculating correlation degree; and 5, calculating a weight coefficient and a decision factor. The method solves the problems that in the prior art, an augmented injection process effect quantitative evaluation method is complex, and evaluation parameters are difficult to obtain.