431 results about "Grey correlation" patented technology

The invention discloses a method for comprehensively evaluating the electric energy quality by applying a TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) of a grey correlation coefficient matrix, which comprises the following steps of: taking electric energy quality data at all time intervals and standard data at all grades as an original decision matrix; determining subjective weights through an improved AHP (Analytic Hierarchy Process); determining objective weights through an entropy method; establishing a least square method decision model to obtain a comprehensive weight, so that the deviation of decision results under the subjective weights and the objective weights of all indexes is the least; carrying out standardization and weighted standardization on the decision matrix and obtaining the grey correction coefficient matrix by utilizing the grey theory; taking the grey correction coefficient matrix as a decision matrix of the TOPSIS to obtain the distance between positive ideal solutions and negative ideal solutions of all schemes and the relative closeness of the positive ideal solutions and the negative ideal solutions; and comparing the electric energy quality closeness at all the time intervals with the standard closeness at all the electric energy quality grades to finally obtain the electric energy quality grades at all the time intervals. With the adoption of the method, the electric energy quality evaluation result can be objectively and reasonably obtained under the situation of poor information, so that the practicability and the feasibility are stronger.

The invention discloses a method for evaluating a reservoir of a fractured well. The method comprises the following steps: S1, establishing an evaluation database M of the reservoir, wherein M is composed of a parameter set X, a sample set U and an index set Y; S2, dividing all the parameters into three kinds, and establishing a three-stage evaluation system including an index layer, an element layer and a target layer; S3, calculating the correlation between each parameter and open-flow capacity in the sample set U, and establishing a single-factor evaluation criterion set V; S4, calculating the influence weight W of each parameter to fracture improvement effect and the influence weight K of each kind of parameters to the fracture improvement effect in the sample set U by using a grey correlation method; S5, calculating and evaluating the membership degree R of each parameter in the object X in four different stages in the single-factor evaluation criterion set V by using normal-distribution membership functions; and S6, carrying out blurring operation on the weight W and the membership degree R, and grading quantifiably and comprehensively. The method for evaluating the reservoir of the fractured well, which is disclosed by the invention, has high objectivity and accuracy; a novel decision method is provided for selecting a fractured improvement well.

The invention discloses a comprehensive cigarette process quality evaluation system based on grey correlation analysis and a method thereof, aims at overcoming the disadvantage that the existing single process evaluation method can not comprehensively evaluate the overall process quality of the cigarette process, and changing the situation of fuzzy process capability evaluation of a whole line at present. The evaluation system and the method thereof help comprehensively evaluates the quality of a plurality of processes, and can effectively grasp comprehensive situation of the process quality so as to provide basis for the continuous improvement in the process quality, and provide assurance for quality improvement of cigarette products. The system and the method combine the single-process quality evaluation method and the grey correlation analysis method. The method comprises the following steps: firstly, computing process capability indexes of various process parameters; then, comprehensively evaluating a Cpk value by the grey correlation method; finally, obtaining a comprehensive evaluation result which is taken as a process quality rank of various evaluated objects. Production practice can be effectively directed by analyzing the Cpk and the process quality ranking result.

The invention discloses a high-voltage circuit interrupter fault diagnosis method based on grey correlation fuzzy clustering. A fuzzy similarity relation matrix is gained by means of a correlation coefficient method, and then a transitive closure operation is conducted. On the basis, clustering analysis is conducted, so that a class which a fault to be diagnosed is in can be gained. By searching for a fault which is similar to the fault to be diagnosed in the class, the component which produces the fault can be gained. The grey correlation analysis method is a powerful tool for resolving a fault diagnosis with little data and weak conditions, and has the advantages of being simple in modeling, little in needed data, and capable of gaining an accurate fault diagnosis under the condition that a confidence level is not very good, thereby providing a basis for reasonable recondition arrangement and safe operation. Large amount of human resource is saved and unnecessary waste is reduced. By adopting the high-voltage circuit interrupter fault diagnosis method based on grey correlation fuzzy clustering in the fault diagnosis of a high-voltage circuit interrupter, the work volume is reduced greatly. The high-voltage circuit interrupter fault diagnosis method based on grey correlation fuzzy clustering has a good application prospect.

The invention relates to a method for water inrush prediction and seepage control for underwater-tunnel broken surrounding rocks. The method includes the steps of S1), exploring by adopting a geophysical exploration and advanced-level geological drilling method and performing tests; S2), establishing a saltation prediction model of analytic hierarchy grey correlation of water inrush of the surrounding rocks by adopting an analytic hierarchy grey correlation method; S3), establishing a three-dimensional porous continuous medium fluid-structure coupled finite element model of the underwater-tunnel broken surrounding rocks by adopting an orthogonal back-analysis method; S4), performing dynamic prediction and seepage control on water inflow of the broken surrounding rocks and performing intelligent fuzzy logic control and instability early-warning forecast on high-pressure water inrush of the broken surrounding rocks; S5), adopting comprehensive prevention and control measures. Compared with the prior art, the method has the advantages that instability of water inrush of the underwater-tunnel broken surrounding rocks under high water pressure can be predicted and subjected to economical, reasonable, safe and reliable comprehensive seepage control.

The present invention discloses a method and an apparatus of acquiring an inter-well communication relationship. The method comprises: acquiring a first production data set of a first oil well and a second production data set of a second oil well, wherein the first production data set and the second production data set are production data of the oil wells at different time points within the same time period; acquiring a grey correlation degree coefficient between the first production data set and the second production data based on a grey correlation analysis method; acquiring a dynamic time similarity value between the first production data set and the second production data set based on a dynamic time warping algorithm; and according to the grey correlation degree coefficient and the dynamic time similarity value, acquiring a communication coefficient for representing communication performance between the first oil well and the second oil well. The technical scheme solves a technical problem in the prior art that considering a delay causes determining of an inter-well communication relationship to be inaccurate, thereby improving accuracy of acquiring an inter-well communication relationship.

The invention discloses an energy efficiency combination evaluation method of a machine tool product manufacture system. The method comprises the following steps: firstly, establishing a three-layer energy efficiency management evaluation index system of the comprehensive evaluation of machine tool product manufacture from an energy resource angle; then, establishing a mathematical model of the dynamic evaluation of the energy efficiency of the machine tool product manufacture; and finally, combining an analytic hierarchy process, a grey correlation method and a fuzzy comprehensive evaluation method to evaluate the manufacture energy efficiency by aiming at the mathematical model. On the basis of the statistical data of energy utilization and historical and current production data, the energy consumption of each hierarchy of the manufacture system is comprehensively considered mainly by aiming at the collection of a wireless sensor technology, a network technology and the like, qualitative analysis and quantitative analysis are effectively combined, subjectivity and uncertainty in an enterprise energy efficiency evaluation process are favorably avoided, and the systematicness and the reasonability of an evaluation result are effectively guaranteed.

An objective of the invention is to provide a modulation signal identification method based on a complexity characteristic under a low signal-to-noise ratio condition. The method comprises the following steps that: discretization is carried out on an intercepted unknown communication signal to obtain a time signal sequence with a certain interval; the time signal sequence is recombined into characteristic vectors with various lengths according to a certain rule; multiple fractal dimension operation is carried out on the characteristic vectors to obtain a multiple fractal dimension characteristic of the communication signal is extracted; fine characteristics of different signals are extracted under a low signal-to-noise ratio condition; a grey correlation theory is utilized to carry out a correlation algorithm on an extracted unknown signal characteristic and a multiple fractal dimension characteristic of a known modulation type signal in a database, so that it is determined that the modulation type of the signal is a modulation type of a signal having a greatest correlation degree and thus classification identification of the communication modulation signal is realized. According to the invention, capability of detection and distinguishment of communication signals with different modulation types in the strong interference environment is realized, so that an objective of identification on modulation types of communication signals is achieved.

The invention discloses a short-period load predicating model based on a temperature accumulation effect and a gray correlation degree. The method comprises the steps of 1, selecting meteorological indexes with relatively high correlation degree; 2, considering a highest temperature of a previous day of a to-be-predicated day and average temperature data in a predicating process; 3, selecting a data sample; 4, establishing an LSSVM predication model; and 5, performing load predicating by means of trained model. The short-period load predicating model can well improve similar day selection precision and load predicating precision. The short-period load predicating model has advantages of optimizing a model training process, realizing consideration of the temperature accumulation effect, effectively tracking influence of air temperature change to the load according to a method of selecting the similar day according to the gray correlation degree, reducing time length in training, remarkably improving load predicating precision, and verifying feasibility of the presented predicating model.

The invention relates to a comprehensive power grid planning scheme selection evaluation system based on combination of the analytic hierarchy process and the data envelopment analysis, and particularly belongs to the technical field of electric power system automation. The method is used for reasonably selecting a power grid planning construction scheme. By establishing a power grid planning multi-layer evaluation index system, the analytic hierarchy process and the data envelopment analysis are used for determining the weights of the indexes jointly, then, the degrees of correlation between all the schemes and the optimal scheme are calculated through a grey correlation degree, and the power grid planning scheme is evaluated. The comprehensive power grid planning scheme selection evaluation system based on combination of the analytic hierarchy process and the data envelopment analysis has very high fault tolerance, the defects that the analytic hierarchy process is too high in subjectivity and the data envelopment analysis cannot represent the preference of a decision maker can be effectively overcome, the distinction degree of the power grid planning schemes can be improved through the grey correlation degree, the advantages and disadvantages of the power grid planning schemes can be comprehensively balanced, and the system has wide application prospects in power grid planning.

The invention discloses a photovoltaic power generation power prediction method based on support vector machine regression, and the method comprises the steps: firstly, obtaining the historical outputdata and numerical weather forecast data of a target station; Screening out meteorological factors with high correlation from the meteorological factors; Secondly, preprocessing the historical data set, selecting appropriate input parameters, and performing data normalization to construct an input vector of a support vector machine; Calculating correlation degrees between the historical data setand four typical days day by day by using a grey correlation coefficient method; Clustering correlation degree calculation results so as to divide the historical data into four training sets accordingto weather types; Carrying out training modeling on the classified historical samples by adopting a support vector machine regression algorithm to obtain a prediction model; Determining the weather type of the to-be-predicted day through correlation calculation, and calling a corresponding prediction model; And finally, prediction day value weather forecast parameters are input, and a power prediction result is obtained based on a support vector machine regression algorithm and a prediction model.

The invention discloses a real-time state evaluation method for a wind turbine generator. The method comprises the steps of constructing a wind turbine generator state evaluation index system, establishing a cloud mapping relation between the index relative degradation degree and the state grade, determining the weight of each index in the index system, determining the membership degree of the evaluation index to each state grade, and evaluating the state. According to the method, various influence factors of the wind turbine generator and influence degrees of the factors are comprehensively considered; the method avoids the index weight precision deterioration caused by a single weight determination method, combines the advantages of an entropy weight method, an analytic hierarchy process, a grey correlation degree method and other weight calculation methods by adopting a cooperative game theory, and can reflect the real operation state of the wind turbine generator.

The invention discloses a network selection method based on grey relational analytic hierarchy. The network selection method comprises the following steps of (1) utilizing an analytic hierarchy, establishing a hierarchical order and constructing a judgment matrix (formula); (2) calculating a normalized weight omegaj; (3) utilizing an entropy weight method, constructing an evaluation matrix (formula) and normalizing the evaluation matrix; (4) solving an entropy Ej of a decision-making property, and calculating the entropy weight phij; (5) utilizing a grey relational method, constructing a global decision matrix (formula) and finding out a group of reference solutions (formula); and (6) calculating a grey relational grade (formula), carrying out grey multi-attribute judgment (formula) and selecting a network having a largest correlation coefficient with the reference network as the optimal network. According to the method, the best network which is most matched with the reference networkis selected according to the grey correlation method, the problem that the network decision-making property is not a network selection problem of a monotonous function can be effectively solved.

The invention provides a selection optimization method of a temperature measurement point combination for positioning errors of a numerically-controlled machine tool under thermal effect. The selection optimization method is capable of identifying the influence of the temperature measurement point in each position on the positioning errors of the machine tool based on a grey correlation policy and a rough set theory. The selection optimization method comprises the following steps: k temperature sensors are mounted in special positions of the machine tool to measure the real-time temperature values, changing over time, of the machine tool during operation, and meanwhile, a laser interferometer is used for measuring positioning error values affected by temperatures; n sensitive temperature measurement point positions are screened out by use of the grey correlation policy; the positioning errors and the temperature data of the machine tool are preprocessed according to the principle of the rough set theory and a policy table is established; m feasible temperature point combinations are obtained by use of rough set reduction software; the optimal temperature measurement point combination of the machine tool is identified by virtue of comprehensive analysis. The selection optimization method of the temperature measurement point combination for the positioning errors of the numerically-controlled machine tool under the thermal effect is capable of solving the problem of excessive temperature measurement points or poor compensation model robustness in the positioning error compensation modeling process of the numerically-controlled machine tool.

The invention discloses a building short-term load prediction method based on an ARIMA-LSTM combination model, and the method comprises the steps: collecting influence factor data through a data collector, and carrying out the maximum and minimum normalization of the load data and all influence factor data, and obtaining a dimensionless data set; selecting key influence factors; calculating cosinesimilarity, and obtaining similar day sample data as a training set; inputting the similar day load training set into an ARIMA-LSTM combination model to obtain a load prediction result, wherein the influence factor data comprises load data, meteorological data and date type data; according to the method, when the training sample data for building load prediction is analyzed and screened, the similar day data sequence is selected by considering the meteorological factors and the grey correlation degree of the date type sequence, so that the prediction precision is effectively improved.

The invention provides a fault diagnosis method of capacitive equipment. The fault diagnosis method comprises the following steps of: utilizing a grey correlation analysis method to obtain a grey correlation degree between a dielectric loss factor tandelta of the capacitive equipment and an environmental factor and taking the obtained grey correlation degree as characteristic quantity to establish a matter-element model of the capacitive equipment to be diagnosed; and then, gaining the grey correlation degree between the matter-element model of the capacitive equipment to be diagnosed and each pre-established typical fault matter-element model by applying an extension theory; and finally, comparing the grey correlation degrees to judge that the equipment has the type of fault if the grey correlation degree between the matter-element model of the capacitive equipment to be diagnosed and the type of fault matter-element model is the highest. According to the invention, influences on diagnosed results by the environmental factor are effectively eliminated, not only various probable faults can be accurately diagnosed, but also the condition that a plurality of the faults simultaneously happen can be diagnosed. The method provided by the invention has the advantages of definite physical meaning and easiness of realizing programming.

The invention discloses a track correlation method based on a LHD gray correlation degree. The method comprises acquiring multiple target information scanned by multiple sensors, carrying out processing to obtain a target track sequence, computing a grey relational degree between target tracks acquired by multiple sensors, calculating a gray correlation matrix according to the grey relational degree, determining a grey relational threshold to obtain a track correlation matrix and determining track correlation according to the track correlation matrix. The track correlation method has a high target track precision, and fast and correctly associates with the target track in a dense cluttered environment. The method effectively improves the accuracy of the track association algorithm, has a small amount of calculation and can be well used in the engineering practice.

The invention relates to an inversion determination method for rainfall type landslide shear strength parameters, and belongs to the technical field of slope engineering stability evaluation. The method comprises the following steps that 1, the basic characteristic value of the cohesive force cj and the internal friction angle (shown in the specification) of the side slope rock-soil body is determined; 2, the basic characteristic value of the side slope rock-soil body is obtained, 2, monitoring a slope monitoring point cumulative displacement value Si and rainfall intensity Qi; Step 3, determining the side slope underground water level Hi under different rainfall conditions at the ti moment; Step 4, determining a slope stability coefficient Fij under the basic characteristic parameter condition of all the c,(shown in the specification); 5, determining a slope stability coefficient F0i displacement equivalent monitoring variable Lambda i; 6, determining the correlation degree Lambda 0ibetween the slope stability coefficient F0i and the displacement equivalent monitoring variable Lambda i; And 7, determining the cohesive force cj and the internal friction angle (shown in the specification) of the side slope according to the grey correlation degree. According to the method, the calculation result is accurate, and the overall shear strength of the side slope can be better reflected. and moreover, the operation steps are easy to implement, and the calculation cost is lower than that of other determination methods.

The invention discloses a short-term load prediction method for optimizing SVM based on MWOA algorithm in an electric power system. The method comprises the steps of firstly obtaining the historical load data and weather data of an electric power system, and carrying out the preprocessing of the obtained data; secondly, constructing a similar day set through grey relational degree analysis, and generating a training sample and a test sample; establishing a multi-input single-output support vector machine model (SVM) for feature learning, and searching an optimal kernel parameter p and a regularization parameter C of the vector machine model by adopting an improved whale algorithm (MWOA) in the training process; and finally, inputting the test sample into the prediction model to obtain a short-term load prediction result of the power system. The load prediction model algorithm provided by the invention is high in convergence rate, has relatively good balanced traversal, is not liable tofall into a local extreme value, and effectively obtains a relatively high-precision short-term load prediction result of the power system.

The invention relates to a short-term power prediction method for a photovoltaic power generation system. The method comprises the steps of re-dividing weather types: according to the factors that whether the variables of the natural environment are continuous and stable or not and the averaged values of the variables, dividing weather types in the generalized point of view; determining a similar day of a to-be-predicted day: determining a generalized weather type corresponding to the to-be-predicted day according to weather factors, selecting a historical day similar to the to-be-predicted day according to the grey correlation method, adopting the photovoltaic power output of the historical day as a model input, and selecting daily weather feature vectors; defining the degree of similarity between the to-be-predicted day and the above particular historical day; establishing a photovoltaic power generation short-term power prediction model based on a support vector machine, and optimizing the parameters of the prediction model according to the genetic algorithm. The above precision method is high in precision, and fast in convergence.

The invention discloses a fault diagnosis method by combining correlation analysis and data fusion, and is used for solving the technical problem of low fault diagnosis result precision of the existing fault diagnosis method. The fault diagnosis method has the technical scheme that equipment information from different measuring sources is extracted; multi-source information and same-source information corresponding to the existing fault mode are subjected to correlation analysis; the obtained correlation degree is used as a correlation coefficient between the existing fault mode and the multi-source data measured in a fault to be diagnosed; the information of a plurality of sources is synthesized by a data fusion method; the reliability, belonging to the existing fault mode, of the fault to be diagnosed is calculated; the fault mode corresponding to the maximum reliability is selected from the reliability. The method of replacing the correlation coefficient in the multi-source data fusion process by the grey correlation degree between the single physical quantity in the process of the fault to be diagnosed and the same physical quantity in the existing fault type is adopted, so that the single physical quantity and the multi-information fusion processes are effectively linked, and the precision of the fault diagnosis result is improved.

The invention relates to a short-term power load forecasting method which is characterized by comprising the steps of: selecting a similar day set; constructing a RBF (Radial Basis Function) neural network forecasting model optimized by a bat algorithm; forecasting a power load on a forecasting day by utilizing the RBF neural network forecasting model optimized by the bat algorithm; and the like.According to the invention, on the basis of conventional gray correlation analysis, a similar day with a higher similarity is selected by adopting a distance similarity and shape proximity correlatedcomprehensive gray correlation degree, the defect that a conventional gray correlation analysis method only considers a geometric similarity degree among data sequences, but ignores a number proximitydegree when selecting the similar day is made up, and forecasting accuracy is improved; a weight of a RBF neural network is optimized by utilizing the bat algorithm, so that the defect that the RBF neural network is easy to fall into local optimization can be overcome, a convergence rate of the integral network is improved and computation efficiency of the integral network is improved; and the short-term power load forecasting method has the advantages of scientificity, reasonability, high applicability, good effect and the like.

The invention discloses a model used for evaluating the health state of electrical equipment, relates to the field of the electrical technology, can accurately evaluate the operation health state of the electrical equipment, and is convenient to carry out operations including reasonable use and maintenance, fault prediction, power distribution management and the like on the electrical equipment. The model used for evaluating the health state of the electrical equipment comprises an evaluation index module, a comment module, a fuzzy assessment module and a grey correlation module, wherein the evaluation index module comprises a plurality of state quantity indexes which affect the health state of the electrical equipment and the weight of each state quantity index; the comment module comprises a comment set and is used for assessing the health state of the electrical equipment; the fuzzy assessment module comprises a membership function established on the basis of the comment set, and is used for combining with the weight of each state quantity index through the membership function to carry out fuzzy assessment on the health state of the electrical equipment to obtain a fuzzy assessment conclusion; and the grey correlation module comprises a clear assessment standard established on the basis of the comment set, and is used for carrying out clear assessment processing on the fuzzy assessment conclusion to obtain an accurate health state evaluation result.

The invention discloses a health safety state assessment device used for an electrical car. The health safety state assessment device comprises a signal acquisition and transmission system, a signal processing system and an execution system, wherein the signal acquisition and transmission system comprises a data acquisition module and a signal conversion module; an output end of the data acquisition module is connected with an input end of the signal conversion module; an output end of the signal conversion module is connected with an input end of the signal processing system; an output end of the signal processing module is connected with the execution system; the signal processing system comprises a micro processor; a software system in the micro processor comprises an expert module, a grey correlation module and a storage module; and a TOPSIS optimal seeking method and a pink correlation degree method are set in the grey correlation module. The health safety state assessment device can solve the technical problems that safety level of an existing electric car storage battery cannot be monitored and accessed very well.

The invention discloses a design method of a multi-dimension attribute data oriented multi-layered clustering fusion mechanism. The method comprises the following steps: 1) converting a data set into a matrix form, and preprocessing data; 2) according to data index attribute characteristics, extracting an optimal reference standard, and carrying out normalization processing on the data; 3) calculating a grey correlation degree, generating a similar matrix of the grey correlation degree, and then, carrying out grey correlation degree clustering to obtain a primary clustering result; 4) according to the primary clustering result in the step 3), adopting a rough set theory to establish a decision table system; 5) calculating an attribute significance information entropy of the decision system for each clustering member; 6) setting a weight for each clustering member; and 7) according to the calculated weight, adopting a probability method to calculate a probability of each data object in each class level to which the data object belongs, selecting the class level where the data object belongs to when the probability is highest to serve as the class level to which the data object belongs to, and obtaining a final clustering fusion result.

Disclosed is an enterprise credit evaluation method based on gray fuzzy, which includes the following steps: initializing multi-dimension time series data; dividing credit evaluation grade standards, and confirming various credit evaluation indexes; mapping values of various credit evaluation indexes to a certain value interval by utilizing simple mathematical function transformation in a same credit index system; confirming reference sequences and comparison sequences, calculating gray correlation coefficient, and calculating gray correlation degree to obtain gray incidence matrix composed of credit evaluation values; and converting the gray incidence matrix into fuzzy similar matrix, subjecting the fuzzy similar matrix to square self-synthesis method to be converted into fuzzy equivalent matrix, selecting a confidence level value lambda belonging to a range from 0 to 1, and calculating lambda stage matrix of the fuzzy equivalent matrix. When the rij is less than or equal to the lambda, a sample xi and a sample zj can be combined into a same class and the obtained classification is an equivalent classification on the lambda level, accordingly different evaluation results are achieved. The enterprise credit evaluation method has the advantages of reducing the calculating complexity, having good timeliness, and effectively improving the reliability.

The invention discloses a judging method for the sensibility of a post-earthquake debris flow gully and application of the judging method. The method comprises the following steps: determining a possibility comprehensive judging value P of all slumped masses in a researching region under any grading condition of six evaluation factors; integrating previous grading conditions according to the size of the value P to obtain a special sensibility grading region of the six evaluation factors and establishing a corresponding scoring standard; then, acquiring score values of the gully on the six evaluation factors in reference to the scoring standard and according to the distribution condition of the single gully on the six evaluation factors; and finally, determining an evaluation factor weight with combination of a grey correlation method and establishing a single gully debris flow sensibility judging model to carry out debris flow prediction. Compared with the prior art, the judging method sufficiently considers region characteristics of the researching region of the single gully, avoids the error of a manually divided sensibility grading region of the evaluation factors of single gully debris flow, and can be used for performing specific-grade division on the evaluation factors rapidly and accurately so as to realize the effective judgment on the sensibility of the single gully debris flow.

The invention discloses a power grid planning risk evaluation system and method based on a grey correlation degree TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). The power grid program risk evaluation system comprises a power grid planning data acquisition module, a power grid planning scheme input module and a controller, wherein the power grid planning data acquisition module and the power grid planning scheme input module are independently connected with the controller; and the controller comprises a power grid risk evaluation index matrix generation module, a power grid risk evaluation index matrix solving module, a grey correlation degree matrix construction module and a power grid planning scheme risk sorting module. The invention has the beneficial effects that the power grid planning risk evaluation system introduces the controller which can process power grid planning data and sort power planning schemes to carry out optimal sorting on power grid enterprise risk indexes, a power grid planning scheme can be evaluated on the whole, potential risks in a power grid planning process can be effectively prevented and solved, power grid planning operation cost is saved, and power grid reliability is improved.

The invention relates to an electric energy quality prediction method based on similar days and improved LSTM. The method comprises the following steps: 1) collecting electric energy quality steady-state index data of a certain monitoring point in a certain region within a period of time and meteorological data of the region; 2) performing feature dimension reduction on the meteorological data byadopting a kernel principal component analysis method to obtain similar day feature vectors; 3) calculating meteorological factor matching coefficients of the historical day and the to-be-predicted day by adopting a grey correlation algorithm, and determining a similar day set; 4) selecting power quality historical data similar to the day to be predicted and similar day set data as a training sample set of the LSTM neural network, and optimizing LSTM neural network parameters by adopting a method of combining a Dropout algorithm and an Adam adaptive learning rate optimization algorithm, and taking the similar day feature vector as a model input variable to obtain a prediction result of the electric energy quality of each moment of the to-be-predicted day. Compared with the prior art, the method has the advantages of avoiding training overfitting, avoiding information interference and information repetition, improving prediction accuracy and the like.

The invention provides a multi-feature based multi-array track correlation method. The method includes performing track correlation processing on the target azimuth information obtained by different arrays after data is normalized by adopting a grey correlation algorithm so that a target azimuth track correlation degree can be obtained; performing track correlation processing on target beam outputenergy information obtained by the different arrays after energy compensation by adopting the grey correlation algorithm so that a target energy track correlation degree can be obtained; performing line spectrum extraction on target DEMON spectrums obtained by the different arrays to obtain the envelope spectrum frequency information of a target, performing difference comparison, and taking the minimum value of absolute values in each frequency difference as the comparison value of multi-array frequency so that the track correlation degree of DEMON spectrum information can be obtained; performing fusion on the above correlation degrees through a Dempster combination rule so that a multi-array joint correlation degree can be obtained; and performing selection on correlation tracks according to the joint correlation degree. Compared with pure azimuth track correlation methods, the method can optimize correlation quality and enhance correct correlation rates.