23 results about "Correlation factor" patented technology

This invention discloses a USV berthing and unberthing pose estimation method based on ship-shore LiDAR collaboration, comprising: acquiring shipborne and shore-based point clouds and shore-based fixed reference poses; performing odometer calculations on continuous shipborne point clouds to generate LiDAR odometer factors; constructing a global descriptor for the ship-shore point clouds and performing scene matching to generate candidate ship-shore point cloud pairs; extracting key points and calculating FPFH descriptors, solving for high-precision ship-shore relative poses through coarse / fine registration, obtaining the final ship-shore relative poses through asynchronous reconstruction and generating correlation factors; constructing a collaborative factor graph model containing odometer and ship-shore correlation factors; solving the model and minimizing the overall residuals to obtain a globally consistent optimal pose, thereby achieving berthing and unberthing control. This invention improves ship-shore data transmission efficiency and matching accuracy through global descriptor matching and FPFH feature compression; and eliminates the cumulative error of single-ship odometers by leveraging shore-based global pose constraints, achieving high-precision continuous pose estimation under long-term navigation and ensuring the robustness of berthing and unberthing navigation.

The invention discloses a wind power generator stator winding fault diagnosis method, and relates to the technical field of wind power generators. The method comprises the following steps: detecting winding end surface roughness, environment humidity and vibration data features which are combined into a first risk-induced feature set, and detecting surface roughness and vibration data features which are combined into a second risk-induced feature set at the joint of a straight line segment and the end; extracting a first correlation factor between historical risk correlation data and a historical coverage area value in the historical risk detection data, obtaining a second correlation factor to which the connection part of the straight line segment and the end part belongs according to the first correlation factor, and obtaining a first risk induction feature set according to the first correlation factor and the first risk induction feature set; and estimating an insulativity reduction risk index and a structural fatigue aggravation risk index to which the end part of the winding belongs currently, and estimating a risk temperature value of the connection part of the straight line segment and the end part after the temperature rise is generated currently according to the second correlation factor and the second risk-induced feature set. The risk diagnosis efficiency can be improved.

The present application relates to a kind of energy efficiency dynamic monitoring and evaluation technical field, propose a kind of ship overall technical energy efficiency dynamic monitoring and evaluation method and system, can eliminate environmental influence, realize the transparent, fair, dynamic evaluation of ship " pure technology " energy efficiency state. Including the following steps: S100: carry out quasi-steady state trend test: for the parameter susceptible to ship maneuvering, system uses sliding time window to combine linear regression slope test method to distinguish;S200: carry out environmental self-adaptive threshold adjustment: for the fluctuation screening of speed, shaft speed and other parameters, system introduces the correlation factor of sea state, no longer uses fixed standard deviation threshold;S300: carry out physical validity final screening: the data reserved after above-mentioned step, finally carries out physical boundary check;S400: through core calculation engine module, input cleaned data set, calculate output index and its confidence interval;S500: carry out fault root cause analysis;S600: complete evaluation.

The present application relates to the technical field of smart grid, disclose a kind of multi-factor associated return home degree winter power load pre-control method and system, method includes: the historical power consumption data obtained in advance is transformed to obtain power load data, and power load data is identified to obtain power consumption problem;The influencing factor is obtained by analyzing power consumption problem, and the strong correlation factor data matrix is obtained by analyzing influencing factor, and the correlation influence coefficient is obtained by analyzing strong correlation factor data matrix;The trained power load prediction model is obtained by iterative training to power load prediction model, and the current power consumption data obtained in advance is input into trained power load prediction model to obtain load prediction data;The red yellow green classification area is obtained by dividing power grid;Power load pre-control scheme is formulated, so as to carry out power consumption prevention and control management.The present application can solve the problem that power grid overload is difficult to prevent and control during return home degree winter and power consumption prediction is not accurate enough.

The invention discloses an IQ imbalance calibration method and system. The method comprises pre-calibration and fine calibration. During pre-calibration, firstly, M frequency points are selected in a sectional mode, narrow-band calibration is carried out, correlation factors of amplitude imbalance errors and phase imbalance errors of the multiple frequency points are obtained, interpolation fitting is carried out on zero frequency points, and frequency response characteristics in the whole bandwidth are obtained. On the basis, a solution equation is constructed to calculate a filter coefficient, and a filter is designed to compensate the IQ imbalance error. During fine calibration, based on the least square method, the compensation coefficient of the filter is calculated in a real-time and self-adaptive manner, whether iteration is updated or not is judged, iterative filtering is carried out to enhance the stability of the system, and the problem of calibration performance deterioration caused by environmental factors such as temperature change is effectively solved. Compared with traditional narrowband calibration, the calibration system combining pre-calibration and fine calibration has a calibration effect on receiver large-bandwidth IQ imbalance, the adaptive capacity of the calibration system to environment temperature changes is enhanced, and the calibration precision and robustness are improved.

The invention discloses a processing quality control method, a process quality cause searching method and a related device, which are used for improving the yield of products. The method comprises the steps of obtaining parameter information corresponding to a product; performing feature processing on the parameter information to obtain processed parameter information; inputting the processed parameter information into a pre-trained correlation factor analysis model to obtain accuracy information output by the correlation factor analysis model; obtaining the ranking of important factors influencing the product quality based on the accuracy information; and processing the processing equipment or jig corresponding to the product based on the ranking of the important factors. In the application, the parameter information of the product and the pre-trained correlation factor analysis model are adopted to determine the ranking of the important factors influencing the product quality, and the jigs are processed according to the ranking of the important factors, so that the jigs with lower ranking can be locked, the jigs with lower ranking are prevented from participating in the processing process, and the processing efficiency is improved. And thus, the yield of products is ensured.

The application relates to the technical field of data analysis, in particular to an ultralow-temperature refrigerator based on operation data analysis, which comprises the following steps: regarding a time period corresponding to temperature fluctuation as a fluctuation event, forming a fluctuation event sequence under multiple time periods; dividing the fluctuation event sequence according to the value in the operation period, dividing the event type corresponding to the fluctuation event, calculating the fluctuation time length proportion under each time section; defining the temperature state transition of multiple fluctuation events under each fluctuation time length proportion, combining the fluctuation events according to the multiple time periods, and solving the fluctuation event combination probability; taking the event attribute and temperature characteristics of the fluctuation event as input, performing correlation analysis on the fluctuation event, and obtaining the correlation factors of the combined fluctuation event; performing multi-target solving according to the correlation factors of the fluctuation event, solving the optimal control parameter combination, and taking the optimal control parameter combination as the output control strategy; and the efficiency of the refrigerator control and the accuracy of the temperature regulation are realized.

A TDR-based measuring device for measuring the dielectric constant (DC) of a medium (2), comprising: - a measuring probe (11) that can be brought into contact with the medium (2), - a signal generation unit (12) designed to couple an edge-based measurement signal (sHF) into the measuring probe (11) according to the TDR measurement principle, - an evaluation unit (13) designed to: ◯ receive the reflected edge-based measurement signal (rHF) after reflection in the measuring probe (11), ◯ determine a signal propagation time between coupling and reception of the respective edge according to the TDR measurement principle, and determine the dielectric constant (DC) based on the signal propagation time, characterized by: - ​​a filter unit (14) designed to: ◯ filter the emitted and / or the reflected edge-based measurement signal (sHF, rHF), and ◯ its filter type and / or to change at least one filter parameter (R, C), and an analysis unit (15) designed to◯ to determine a correlation factor between the edge shape of the transmitted and received measurement signal (sHF, rHF), an SWR ratio, or a maximum curvature of the received measurement signal (rHF), and ◯ to adjust the filter type and / or at least one filter parameter (R, C) of the filter unit (14) such that the correlation factor, the SWR ratio and / or the maximum curvature reaches a minimum value or converges to the minimum value.

The invention discloses a power grid side independent energy storage scheduling optimization method and system based on a knowledge graph, and relates to the technical field of energy storage scheduling. The method comprises the following steps: carrying out complete-period relation carding based on independent energy storage at a power grid side, extracting correlation factors of each period, fitting a correlation influence relation, and constructing a multi-scale knowledge graph; establishing a multi-scale benefit evaluation model; performing constraint configuration on the multi-scale benefit evaluation model, and constructing a benefit evaluation execution model; and taking the benefit evaluation execution model as a search engine, performing deep strategy search in the multi-scale knowledge graph to obtain an association factor strategy combination relationship meeting a search convergence target, and generating an energy storage scheduling strategy for performing independent energy storage scheduling control. The technical problem that in the prior art, power grid side independent energy storage scheduling depends on empirical rules, and consequently the scheduling strategy is difficult to dynamically adapt to the power grid operation requirement is solved, and the technical effects that the scheduling strategy is adaptively optimized along with the power grid state, and energy storage scheduling precision and collaboration are improved are achieved.

The invention relates to a calculation method, a calculation system and equipment for the occurrence frequency of external water logging of a nuclear power plant and a readable storage medium. The method for calculating the occurrence frequency of external water logging of the nuclear power plant comprises the following steps: S1, determining an independent factor, a correlation factor and a deterministic factor according to the determined characteristics and correlation of external water logging factors of the nuclear power plant, wherein the correlation factor comprises a variable X and a second variable; s2, acquiring an edge distribution function of a variable X and a second variable; s3, fitting the joint distribution of the variable X and the second variable based on an edge distribution function to obtain a joint distribution function of the variable X and the second variable; s4, based on the joint distribution function, distribution of the combined water level is obtained through calculation; s5, typhoon serves as a core driving event of associated factors, and distribution of combined water levels under typhoon combination is obtained; and S6, obtaining the distribution of the total combined water level T based on the combined water level under the combination of the independent factors and the typhoon. The method can provide reliable basis for safety design of the nuclear power plant.

This invention provides a method, apparatus, medium, and computer device for video recommendation. The method includes: determining a set of associated videos and a set of unassociated videos for a target live stream; the target live stream being a user's historical live stream viewing history; for any video, determining a correlation factor between the video and the target live stream; determining the priority of the video based on the correlation factor and the weight of the target live stream; and recommending corresponding videos to the user based on the priority. Thus, even if a user does not have sufficient historical video data on the live streaming platform, the correlation factor between the video and the historical live stream can be determined by migrating the user's historical live stream viewing information to the video context, and then recommending videos to the user based on the weight of the historical live stream and the correlation factor. The correlation factor and weight can highlight the user's viewing interests, thereby ensuring the accuracy and precision of video recommendations.

The invention discloses a big data processing analysis platform for user portrait driving, and relates to the field of big data analysis, and the platform comprises the following steps: obtaining user data information corresponding to a corresponding user account according to a multi-source data interface, and setting an edge processing node to carry out the preprocessing of the user data information; constructing an account user portrait and a service user portrait for the user data information after preprocessing; obtaining historical user portraits, performing association analysis according to the historical user portraits and the data processing analysis process, obtaining driving association factors, and generating a driving association factor table; according to the constructed account user portrait and the business user portrait, obtaining a corresponding driving association factor based on a driving association factor table, and according to the obtained driving association factor, performing intelligent analysis processing according to a data processing analysis process; performing verification analysis on the intelligent analysis result according to the corresponding comprehensive feedback data, and performing correction optimization processing according to the verification analysis result; the accuracy of the data processing process is improved.

The application discloses an advertisement effect attribution analysis method and system based on multi-source data fusion, relates to the technical field of data acquisition and processing, and comprises the following steps: collecting multi-source advertisement data of an advertisement activity to be evaluated and preprocessing, to generate a standardized attribution data set; extracting an effect evaluation index and locating an index correlation factor; identifying statistical characteristics of the index correlation factor based on historical interaction data and the standardized attribution data set, to construct a factor level of the index correlation factor with respect to the effect evaluation index; performing screening, filtering and dimension reduction processing on the index correlation factor according to the factor level, to obtain a dimension-reduced correlation factor; calculating factor state data of the dimension-reduced correlation factor corresponding to the historical interaction data, and determining an attribution analysis model of the advertisement activity to be evaluated according to the factor state data; and determining a real-time attribution result of the advertisement activity to be evaluated by using the attribution analysis model according to real-time conversion data. The application meets the actual needs of fine and intelligent advertisement operation.

The application discloses a kind of power line loss analysis system and method based on big data, it is related to power line loss analysis technical field, including real-time acquisition power grid equipment operating state and environmental parameter, constructs power line loss dataset;Each power line loss data and line loss rate's rank correlation coefficient is calculated, and strong correlation factor of line loss is output to be weighted, and the real-time data of each line's strong correlation factor of line loss is analyzed by first line loss constraint, whether to meet constraint is judged, and second line loss constraint analysis command is sent out;The strong correlation factor of line loss of each line is analyzed by exponential smoothing residual error method, and second constraint analysis result is obtained, the first constraint analysis result and the second constraint analysis result are comprehensively, and third constraint analysis result is obtained, to third constraint analysis result is rendered line loss rate distribution in real time.Further analysis residual error, capture line aging, nonlinear factors such as load mutation superimposed influence on line loss, combined with multi-stage constraint analysis improves decision robustness.

The present application relates to the technical field of power system stability analysis, and is a low-frequency oscillation severity evaluation method and system for a new energy direct current sending system, which comprises the following steps: calculating correlation factors affecting the low-frequency oscillation severity of the new energy direct current sending system; determining the weights between the correlation factors based on stability simulation analysis; calculating a comprehensive evaluation index reflecting the low-frequency oscillation severity of the new energy direct current sending system based on the correlation factors and the weights between the correlation factors; and obtaining the threshold value of the comprehensive evaluation index based on data simulation driving. The present application can directly evaluate the possibility and severity of low-frequency oscillation of the system by using system steady-state operation state data such as new energy power generation output, direct current sending power and operating voltage, and compared with the traditional low-frequency oscillation evaluation method, the present application does not need to perform transient simulation or stability analysis each time, has small calculation amount and strong operability, and has guiding significance for evaluating the stability of the new energy direct current sending system.

The present invention relates to the technical field of risk level classification. Disclosed is a port disaster risk level classification method. On the basis of the prior art, the present invention further takes into consideration a comprehensive correlation coefficient among all consequences of each disaster type in a data-driven manner, so as to ensure that correlation factors between different consequences can be comprehensively taken into account; in addition, a first composite index and a second composite index of each disaster type are analyzed and calculated, and a third composite index is obtained on the basis of the first composite index and the second composite index, so as to ensure that complexity factors of the consequences of each disaster type are effectively taken into consideration; finally, obtained results are incorporated into an existing calculation formula for an expected loss value, so as to ensure that a more accurate, more comprehensive, and more scientific expected loss value is obtained; thus, even when subsequently determining risk levels of disaster types at the same coordinates, it is possible to effectively reduce the probability of occurrence of risk level misclassification to the greatest extent.

The invention relates to the technical field of energy financial prediction, in particular to a crude oil price trend prediction method based on multi-dimensional factor weighted fusion and deep learning, which comprises the following steps: collecting historical crude oil price data and multi-dimensional factor data related to crude oil price, and preprocessing to construct a data set; carrying out feature extraction on the multi-dimensional factors in the data set, and respectively extracting a time sequence feature, a lag feature and a cross feature of each factor; a deep learning model based on TensorFlow is constructed, and a data set is used for training; utilizing the trained deep learning model to predict the increase trend and increase percentage of the crude oil price in the future; and outputting a prediction result in the form of increase and decrease percentage, and displaying the price trend in the prediction period. According to the invention, through synchronous fusion and integration of a plurality of high correlation factors, the coverage rate of oil price driving factors is significantly improved, the prediction output is a relative fluctuation range, and the robustness of the model to long-period price structural change is improved.

The application relates to the technical field of runoff prediction, and discloses a factor dimension reduction and error correction method and system for runoff prediction, which can significantly improve the runoff prediction accuracy. The method comprises the following steps: calculating the correlation coefficient of each prediction factor in a data set and runoff data by using a Pearson correlation coefficient, and screening a strong correlation factor set; evaluating the prediction importance contribution of each factor in the strong correlation factor set by using an extreme gradient learning tree; constructing a runoff prediction model, and determining the final input dimension and runoff prediction value according to the principle that the larger the Nash coefficient is and the smaller the root mean square error is; decomposing the prediction residual by using ensemble empirical mode decomposition to obtain mode components and residual components; respectively predicting the mode components and the residual components by using an autoregressive model; superimposing the mode components and the residual components of the prediction residual, adding the mode components and the residual components to the original prediction value, and finally obtaining the sample prediction value after error correction.

The invention discloses an enterprise digital asset comprehensive management method and system, and relates to the technical field of computers, risk indexes and correlation factors are aligned in a unified time in a report period, multivariate time series data are formed and input, subsequent analysis is carried out on the same caliber and the same time axis, and the risk indexes and the correlation factors are analyzed in real time. Interpretation deviation and repeated arrangement cost caused by cross-system caliber difference are reduced; time sequence causal discovery and lagging influence expression are included in the same calculation framework, a causal structure containing a driving factor and an influence path is output, risk changes can be attributed and organized in a path mode, and therefore discrete events and index fluctuation are included in an interpretable structured link; in addition, resampling stable selection is introduced to form a stable edge set, so that the generation of the causal graph is converted from single solution to structure screening with consistent frequency, the probability that an accidental edge enters an interpretation link is reduced, and the stability and consistency of cross-batch and cross-scene analysis results are improved.

The invention relates to the technical field of energy efficiency dynamic monitoring and evaluation, and provides a ship overall technology energy efficiency dynamic monitoring and evaluation method and system, which can eliminate environmental influence and realize transparent, fair and dynamic evaluation of a ship'pure technology 'energy efficiency state. Comprising the following steps: S100, carrying out quasi-steady-state trend inspection: for parameters which are easily influenced by ship manipulation, a system adopts a sliding time window to be combined with a linear regression slope inspection method to carry out discrimination; s200, environment self-adaption threshold value adjustment is carried out, fluctuation screening is carried out on parameters such as the navigational speed and the shaft rotating speed, a sea condition correlation factor is introduced into the system, and a fixed standard deviation threshold value is not used any more; s300, performing physical validity final screening: performing physical boundary inspection on the data retained in the above steps; s400, inputting the cleaned data set through a core calculation engine module, and calculating an output index and a confidence interval thereof; s500, fault root cause analysis is carried out; and S600, completing the evaluation.

The embodiment of the invention relates to the technical field of data processing, in particular to a correlation factor prediction model training method, a data completion method and equipment. The method mainly comprises the following steps: acquiring existing manufacturer sales volume data, terminal sales volume data and economic data; obtaining an association factor set according to the manufacturer sales volume data and the terminal sales volume data; and inputting the association factor set, the manufacturer sales volume data and the economic data as training samples into a preset initial model for training to obtain an association factor prediction model. Wherein the manufacturer sales volume data and the terminal sales volume data are automobile sales volume data counted based on different data sources, and the association factors in the association factor set are used for representing the proportional relation of the manufacturer sales volume data and the terminal sales volume data corresponding to a single statistical period. By adopting the method, the accuracy of missing data complementation can be improved.

The invention belongs to the field of soft-current electrics, and particularly relates to a method and system for reducing blurring of a moving image of a display. According to the method, related electrical parameters and time sequence parameters are collected, corresponding coefficients are configured, a correlation factor and a matching numerical value are obtained through relaxation modeling coupling operation, time sequence linkage progressive operation and backlight closed-loop feedback operation, and a driving control instruction is generated to control hardware to execute adjustment. The system correspondingly realizes the method. The problem that multi-factor linkage fuzziness cannot be restrained through single optimization in the prior art is solved, effective fuzziness restraining is achieved through linkage operation logic, the display definition is improved, and the method is simple in structure, capable of being implemented and suitable for various displayer driving scenes.

The present application relates to a kind of pressure original guide high with height prediction method and system, specifically to the technical field of coal mine safety production.The method includes obtaining sample data;Sample data is divided into training sample and test sample;The correlation between height and the correlation factor value sequence is calculated using grey relational analysis method;According to the correlation degree, the correlation factor is filtered to obtain the filtered correlation factor;The weight of each filtered correlation factor is calculated using entropy weight method;According to the correlation factor value sequence corresponding to each filtered correlation factor, the standardized filtered correlation factor value sequence is obtained;The value of each index is calculated according to the standardized filtered correlation factor value sequence and weight;Based on PCA-PSO-SVR, the value of each index and test sample, the original guide high height prediction model is obtained.The present application can overcome the shortcomings of empirical formula and neural network in small sample prediction, and the result has high accuracy.