533 results about "Outlier" patented technology

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) robust adaptive fusion method for high-precision three-dimensional surface deformation monitoring, which belongs to the technical field of space geodetic survey and remote sensing, and comprises the following steps of: constructing a time sequence deformation field based on a small baseline set time sequence InSAR processing technology, and resampling GNSS observation data to a grid consistent with the InSAR by using Kriging spatial interpolation; a Helmert variance component estimation method is adopted to adaptively estimate variance components of InSAR and GNSS data, and reasonable weight fixing of a multi-source observation value is achieved; iterative reweighted least square estimation is introduced, outlier influences in various observation data are dynamically restrained through a Tukey double-weight function, robust optimization of a fusion model is achieved, and therefore a high-precision three-dimensional deformation field is reconstructed. The method comprises the steps of Helmert weight fixing, IRLS robust, variance component updating and a closed-loop feedback mechanism of weight matrix optimization.

The present invention relates to the technical field of very-short-term photovoltaic power forecasting, and in particular to a very-short-term photovoltaic power forecasting method and system for real-time control, which intend to improve precision and real-time performance in photovoltaic power forecasting. The method comprises the following steps: performing normalization processing on meteorological data, and performing a feature correlation analysis; using a BP neural network to perform short-term photovoltaic power forecasting, inputting the meteorological data and historical output data, and outputting a short-term forecasting value with a resolution of 15 minutes; and performing spline interpolation and outlier removal on an upper-layer result of the BP neural network, and using same as a long short-term memory recurrent neural network input, so as to improve a temporal resolution of forecast data and obtain very-short-term photovoltaic power forecast data with a resolution of 1 minute. The method comprehensively considers meteorological factors and uses advanced neural network models and data processing techniques to achieve photovoltaic power forecasting on a very short temporal scale while ensuring forecasting precision, making the method suitable for the real-time control and optimized operation of photovoltaic power stations.

The invention is suitable for the technical field of artificial intelligence and human resources, and provides an intelligent AI interview system and method based on multi-model fusion, and the system comprises an AI resume matching diagnosis module, an AI post question setting official module and an AI interview evaluation report module. The AI post question setting official module is used for automatically generating interview questions matched with post heights according to the post recruitment information and supporting various self-defined configurations; the AI interview evaluation report module is used for generating objective post fitness evaluation and personalized improvement suggestions through a multi-model evaluation and abnormal value elimination algorithm based on interview question and answer conditions; according to the method, the problems of low efficiency, high subjectivity and the like of traditional recruitment interview are solved, resume matching, post question setting and interview evaluation full-process intelligentization are realized, evaluation standards can be dynamically adjusted, personalized questions and interpretable reports can be generated, the recruitment efficiency and evaluation accuracy are remarkably improved, and the recruitment cost is reduced.

The invention provides a distributed radar robust target positioning method based on outlier sparsity perception, and the method comprises the steps: carrying out the sparse modeling of outlier noise, enabling the robust target positioning to be expressed as a nonlinear least square problem of potential constraint, precisely representing the potential constraint through employing the convex function difference, and achieving the robust target positioning. A potential constraint nonlinear least square problem is equivalently remodeled into a function difference penalty nonlinear least square problem easy to process, finally, a PBCD-based efficient solving algorithm is proposed to solve the problem, and in the solving process, two sub-problems are solved through two algorithms based on main optimization-minimization and convex function difference respectively. Therefore, the accurate target position and the outlier noise are obtained. According to the method, through sparse modeling of the outlier noise and the PBCD algorithm, the outlier influence is effectively suppressed, the positioning precision is kept stable along with the increase of the number of the outliers or the increase of the upper bound of the amplitude of the outliers, the calculation complexity is low, and the positioning accuracy is high.

The invention belongs to the technical field of wind power prediction, and particularly relates to a single-fan ultra-short-term output prediction method based on mRMR and TabNet. The method comprises the steps of firstly obtaining historical operation data of a single-machine wind turbine generator; carrying out abnormal value identification and elimination on the original data through a random forest algorithm, and filling missing data by adopting linear interpolation; derived features are constructed on the basis; inputting all the candidate features into a maximum correlation and minimum redundancy feature screening module, and obtaining an optimal feature subset for ultra-short-term prediction according to the correlation between the features and output and the redundancy among the features; constructing a TabNet deep neural network prediction model by using the feature subset, and performing training by inputting single-machine operation features and taking historical ultra-short-term output as a supervision signal to obtain a single-fan ultra-short-term output prediction model; according to the method, the output prediction precision of the single wind turbine generator in a high-volatility scene can be effectively improved, and the interpretability and the feature utilization efficiency of the prediction model are improved.

The application discloses a mold control equipment operation state monitoring method and device and a medium, relates to the technical field of intelligent equipment monitoring, and comprises the following steps: carrying out denoising, normalization and outlier rejection processing on a mold operation data set to obtain clean operation data; performing correlation analysis and feature extraction on the clean operation data to generate a sound-vibration coupling mode vector set, and performing feature index calculation and mode deviation evaluation to obtain a sound-vibration state feature set; performing state coding on the sound-vibration state feature set to generate a mold operation state table, and performing health degree calculation and state evaluation to output a mold operation feature set; performing operation level judgment and trend analysis on the mold operation feature set to output an operation state result; and performing information arrangement and instruction generation on the operation state result to generate an operation state report. The mold operation state is quantitatively monitored with high precision.

The invention relates to the technical field of artificial intelligence, and particularly provides a power system data generation method based on outlier detection and a depth generation model. The method comprises the following steps: collecting original operation data of a power system, and carrying out outlier detection and elimination on the original operation data; after outliers are detected and removed, missing and removed data are complemented through cubic spline interpolation and moving average; according to the constructed confrontation auto-encoder model based on CAD-TCN-Adaptive Skip-LSTM, fitting is carried out on the complemented data, and a local time sequence and long-range dependence of the data are captured; and a new power system data sample is generated by adopting the fitted model so as to expand the data volume, and the method improves the integrity of the power system data.

The invention relates to the technical field of intelligent power grid monitoring, in particular to a measuring and testing method for an intelligent low-voltage distribution box line, which comprises the following steps of: acquiring and decomposing an original signal of a reconstructed line to form a characteristic image; steady-state and outlier feature clusters are autonomously emerged through unsupervised clustering, and anomaly recognition without a preset threshold value is achieved. And parameterizing the steady-state features into a dynamic template for real-time comparison and marking of feature deviation events. For a deviation event, an original signal is deeply deconstructed to extract energy distribution and phase synchronization distortion characteristics, and the characteristics, a switching event and a load record are subjected to time sequence alignment and coupling analysis, so that the logic relevance of an abnormal source is locked. Based on the relevance, the sensing device is driven to adaptively adjust a sampling strategy, a new round of verification measurement is started, and a closed loop from sensing, diagnosis to optimization tracking is formed. According to the method, unknown abnormity can be adaptively identified, and automatic analysis of abnormity reasons and closed-loop optimization of a monitoring strategy are realized.

The invention relates to the technical field of trajectory control, in particular to a multi-modal motion trajectory real-time tracking method and system for machine control, which performs weight redistribution through channel noise variance and amplitude difference, performs weighted stacking and continuous updating in combination with Kalman filtering, reduces state estimation variance and channel bias transfer, and improves the tracking accuracy of a multi-modal motion trajectory. Time consistency and amplitude consistency of fusion track state data are improved, an overshoot peak value is inhibited, a deviation attenuation process is shortened, abnormal samples in a deviation sequence are rejected through an isolated forest, interference of outliers on parameter updating is reduced, and speed difference and acceleration difference are compared for a correction control sequence, so that the accuracy of correction control is improved. The boundary detection rate and the determinacy of the stage number are improved, the energy change rate is screened through the speed difference and the curvature value of the adjacent stages, the transfer relation is determined, the time sequence coherence and the geometric continuity of stage switching are kept, and the time sequence stability and the stage connection coherence of the control instruction are kept in the multi-degree-of-freedom space.

The invention discloses a data fusion dynamic operation state evaluation method and device, and relates to the technical field of operation state evaluation methods, and the method comprises the steps: collecting original data from a plurality of sensors, monitoring equipment or an external system, and carrying out the normalization processing and abnormal value elimination operation of the collected original data, obtaining a preprocessed standardized data sequence; based on the preprocessed standardized data sequence, the dynamic confidence value of each data source at the current moment is calculated, the dynamic confidence value is comprehensively determined by three factors of historical consistency, environmental adaptability and data stability of the data sources, and a dynamic weight value corresponding to each data source is obtained; based on the dynamic weight value, performing weighted fusion operation on the standardized data sequence in a set time window, introducing a time decay factor to enhance the attention on recent data, and obtaining a fused state feature vector; and taking the fused state feature vector as an input.

The embodiment of the invention provides a model parameter compression method and device for a large language model, equipment and a storage medium. The method comprises the steps of obtaining verification data and inputting the verification data into a large language model to obtain an input activation tensor received by each network layer; obtaining the reasoning confusion degree of the large language model for reasoning the verification data, and taking the minimization of the reasoning confusion degree as an optimization target to carry out iterative cutting decision to obtain a cutting decision vector; performing outlier clipping processing on the input activation tensor of the network layer based on the clipping decision vector to obtain a target activation tensor; for each network layer, calculating an importance score of a model parameter based on the target activation tensor, and pruning the initial model parameter tensor in combination with the importance score to obtain an intermediate model parameter tensor; and performing quantization processing on the intermediate model parameter tensor of the network layer to obtain a target large language model. Therefore, the storage and calculation complexity of the large language model can be reduced while the performance of the large language model is maintained.

The invention relates to a fixed parameter optimization cubature Kalman filtering tracking method for a high-speed high-maneuvering target, belongs to the technical field of signal processing and target tracking, and aims to solve the problem of insufficient tracking performance caused by difficulty in parameter tuning and isolation of an improved mechanism when an existing method is used for coexistence of model mismatch, noise time variation and outlier interference. According to the scheme, a framework integrating off-line multi-parameter collaborative optimization and on-line multi-mechanism adaptive filtering is constructed; in the off-line stage, an optimal combination of key parameters such as covariance adjustment factors is determined through a grid search system; in the online stage, the combination is loaded, and a complete filtering process including innovation feedback type dynamic covariance adjustment, sliding window type noise estimation and outlier suppression and trace-related adaptive regularization is executed, so that an enhanced tracking method with active pre-judgment and closed-loop learning capabilities is formed. The method is mainly used for carrying out high-precision and high-robustness real-time state estimation on the high-speed high-maneuvering target.

The invention provides an automatic homework evaluation method and system based on multi-agent parallel voting, and the method comprises the steps: receiving a to-be-corrected target homework answer, and distributing a converted task to a parallel scoring agent cluster for processing; performing parallel scoring operation on the target homework answers, and respectively outputting respective correction scores and corresponding confidence indexes; collecting a correction result from each agent, and calculating to obtain a final correction score of the target job according to a preset dynamic weight distribution mechanism and an abnormal value elimination mechanism; and returning the calculated final correction score and an interpretable report including the scoring details of each scoring model to the terminal. According to the method, multiple scoring models are adopted for parallel scoring, and the voting aggregation technology is combined, so that the scoring variance of a single model is effectively reduced, and the overall accuracy is improved; and the model weight is dynamically adjusted according to the historical accuracy and the real-time confidence, so that the adaptability and robustness to different question types and different answers can be improved.

The invention provides a limited power compensation method and system based on multi-scale time convolution and a Transform network, and the method comprises the steps: S1, obtaining the historical data of a wind turbine generator SCADA, and constructing a high-quality feature data set through feature engineering and abnormal value processing; s2, respectively inputting the feature data set into a multi-scale TCN module and a Transform module, and extracting local time sequence features and global dependency features of different time granularities in parallel; s3, adaptively fusing the multi-scale features and the global features through an attention mechanism; and S4, predicting future available power by using the full-connection network. The method effectively solves the problems that a traditional statistical method has high requirements for data stability and a recurrent neural network is insufficient in long-range dependence modeling capability, and remarkably improves the accuracy and robustness of wind power prediction in a power limiting scene.

The invention provides a fine-grained sentiment analysis-oriented sentiment data automatic labeling method and system, and belongs to the field of artificial intelligence and sentiment analysis. According to the method, coarse-grained emotion pre-classification and confidence weighted fusion are carried out by extracting text, voice and visual features; fine-grained emotion recognition is realized by combining large language model reasoning and spectral clustering; optimizing a result by utilizing a conflict resolution mechanism, and generating a final label through multi-level voting integration; and finally, through multiple dimensions of multi-modal consistency, feature space outlier degree and conflict resolution decision effect, evaluating the credibility of the final label, and identifying a low-credibility sample. According to the method, progressive analysis from coarse granularity to fine granularity is realized, the problems of modal isomerism, information conflict and low labeling credibility are effectively solved, the manual labeling cost is reduced, and the accuracy and reliability of sentiment analysis are improved.

The invention discloses a method and a system for enhancing the robustness of a multi-source data fusion algorithm, and aims to improve the adaptability to noise and outliers in a multi-sensor data fusion process. According to the method, an adaptive noise mechanism and an outlier detection mechanism are introduced, so that the influence of abnormal observation data is effectively suppressed, and the fusion precision and the system stability are improved. The method comprises the following steps: firstly, a system carries out preliminary state prediction by initializing a target state, a covariance matrix and observation noise; then, the mahalanobis distance of each observation data is calculated based on chi-square test through an outlier detection mechanism, if the observation data deviates greatly, the observation data is judged as an outlier, and the influence of the observation noise matrix on a fusion result is reduced by increasing the observation noise matrix of the observation data; then, an adaptive noise adjustment method is adopted, adaptive factors are calculated in real time according to observation data of each sensor, and observation noise is dynamically adjusted to adapt to changes of the noise level; and finally, state estimation and covariance updating are carried out in combination with fusion algorithms such as Kalman filtering, and data fusion and system feedback are completed. According to the scheme, noise interference and abnormal values in multi-source data can be effectively dealt with, the robustness and the real-time response capability of the system are improved, and the method has wide application prospects.

The invention discloses a feature recognition and path planning method for point cloud three-dimensional-two-dimensional-three-dimensional conversion of a curved-surface workpiece, and the method comprises the steps: obtaining three-dimensional point cloud data of the curved-surface workpiece, and carrying out the preprocessing of the three-dimensional point cloud data; projecting and converting the preprocessed three-dimensional point cloud data into two-dimensional point cloud data by adopting a slice projection method, and forming a fitting curve based on the two-dimensional point cloud data; identifying and obtaining a two-dimensional inflection point set meeting the requirement by a curve inflection point detection method based on an angle integral window; converting the two-dimensional inflection point set back to a three-dimensional coordinate space; adopting a two-stage fitting strategy to identify and obtain an optimal fitting plane from the three-dimensional inflection point set; and a plane cutting method is adopted to obtain a planned path for actual machining. According to the method, a complicated point cloud feature extraction problem is converted into a two-dimensional analysis problem with higher calculation efficiency, so that the algorithm complexity is reduced; the problem of low feature extraction accuracy in a noise environment is effectively solved; the robustness under the condition that the outliers exist is enhanced; an intermediate link of CAD modeling in a traditional method is avoided, and accumulative errors are reduced.

The invention discloses a soil water content abnormal data detection method and device and electronic equipment, and relates to the field of agricultural data abnormal value detection. The method comprises the following steps: determining a soil water content change rate and a time-frequency characterization diagram according to actual soil water content data collected by a sensor; according to the soil water content change rate, the standard soil water content data and the rainfall data, determining water content time sequence change characteristics through a multi-scale time sequence residual error convolutional neural network; according to the time-frequency representation diagram, determining water content frequency domain spatial characteristics through frequency dynamic convolution; fusing the water content time sequence change feature and the water content frequency domain spatial feature through an attention mechanism to obtain a fused feature, and performing feature mapping and classification operation on the fused feature through a full connection layer to obtain a soil water content abnormal data identification result. According to the technical scheme, the recognition precision of the abnormal data of the soil water content collected by the sensor is improved.

The invention discloses a lake water quality prediction method and system based on a hybrid neural network, and a computer readable storage medium, and belongs to the field of environmental science engineering and deep learning. The method comprises the following steps: screening original water quality data, removing abnormal values, performing linear interpolation, dividing a training set and a test set, decomposing a sequence by using VMD, optimizing VMD parameters by using PSO, reconstructing a new sequence with noise removed, and finally performing prediction by using LSTM-KAN. Through verification of total phosphorus concentration data of four sections of the Dian Lake, comparison with LSTM, VMD-LSTM, VMD-LSTM-KAN and LSTM-KAN models is carried out, and a correlation coefficient (), a mean absolute error (MAE) and a root-mean-square error (RMSE) are selected to evaluate precision. The result shows that the PVLK model has the best performance in single-step and multi-step prediction, the total phosphorus concentration prediction of each section can be kept at 0.75 in 10-step prediction with the step length of 4 hours, the applicability to time sequence data containing abnormal values and high sampling frequency is good, and efficient prediction of lake water quality is effectively promoted.

The small sample anti-migration prediction method suitable for the metallurgical process end point component under the zero expansion characteristic comprises the steps that smelting report data of a large sample steel grade and a small sample steel grade in the metallurgical process are collected to serve as source domain data and target domain data; adopting median to fill and restore abnormal values existing in the report data; dividing the source domain data and the target domain data into continuous feature variables and classification feature variables; inputting the continuous feature data and the classification feature data of the source domain data into a TabNet coding and decoding self-supervising network for self-supervising training, and inputting the target domain data into the trained self-supervising network for feature extraction and reconstruction; introducing an adversarial network, and gradually aligning feature distribution of a source domain and a target domain; and inputting the high-dimensional reconstruction features processed by the TabNet coding and decoding self-supervised network in the source domain into the deep table network model for training, and inputting the small sample steel grade features aligned by the adversarial network distribution into the trained deep table network model for transfer learning.

The invention discloses a robust adaptive fusion filtering astronomical attitude determination method and system, and belongs to the technical field of radio navigation. The method comprises the following steps: constructing an integrated navigation model of a starlight and inertial integrated navigation system based on Lie group description; obtaining an observation residual error and an observation residual error covariance of a corresponding mode; robust and adaptive mode parallel sub-filtering is carried out, and posterior states and covariances in the two modes are updated; updating the mode probability; and fusing the updated posterior state with the covariance to obtain fusion output of parallel sub-filtering, taking the fusion output of the parallel sub-filtering as final estimation of the starlight and inertial integrated navigation system based on Lie group description, and outputting an astronomical attitude determination result containing an azimuth angle, a pitch angle and a roll angle. The high-precision astronomical attitude determination method has strong adaptability, strong robustness and accuracy when coping with complex noise and measuring outliers, and can realize high-precision astronomical attitude determination of starlight and inertial integrated navigation in a complex dynamic environment.

The invention relates to the technical field of thermal power generating unit monitoring and control, in particular to a thermal power generating unit working condition division method based on dynamic principal component time series clustering, which comprises the following steps of: S1, acquiring time series data of key operation parameters of a thermal power generating unit, removing abnormal values in the data, filling missing values and normalizing the data; s2, setting a sliding time window parameter; s3, dynamic principal component extraction and steady-state discrimination index calculation are carried out; s4, carrying out transition state re-division and boundary determination; and S5, working condition similarity calculation and clustering. Compared with the prior art, the method has the advantages of high division precision, high dynamic adaptability, high calculation efficiency and high universality.

The invention discloses a multi-source wave velocity structure fusion method, which comprises the following steps: acquiring original data sources including a large-range wave velocity structure data set covering the range of an engineering structure, an earth surface geological lithology distribution diagram and engineering site drilling exploration information; for the earth surface geological lithology distribution map, classifying rocks through image recognition, and setting a wave velocity based on rock categories; for the remaining original data sources, outliers are removed through data cleaning and standardization processing so as to determine corresponding wave velocities; according to the wave velocity determined based on engineering site drilling exploration information, a transition area is additionally arranged around a drilling point to convert the one-dimensional wave velocity into the three-dimensional wave velocity; performing data enhancement on the three-dimensional wave velocity corresponding to each original data source through an interpolation method; and carrying out weight fusion on various wave velocities after data enhancement to obtain a fused wave velocity structure. According to the method, the modeling precision and efficiency of the wave velocity structure are improved, so that the wave velocity structure can be matched with broadband seismic oscillation numerical simulation of the required calculation frequency.

The invention discloses a multi-source navigation system adaptive fusion method, program, equipment and storage medium capable of eliminating the influence of a measuring line, and relates to the field of unmanned surface vehicle multi-source sensor integrated navigation fusion. Multi-source data fusion is realized by constructing a 15-dimensional unified state model of INS, DVL, GPS and USBL in combination with a federated filtering architecture, a standardized observation forecast residual processing technology is put forward to eliminate dimensional differences, a feedforward vector information distribution factor is designed to dynamically adjust the weight of a sub-filter, a feedback vector factor constructs an exponential decay smoothing mechanism to cope with a measurement outlier, and the multi-source data fusion is realized. Introducing a dynamic characteristic value screening mechanism to evaluate the precision of the sensor in real time and rejecting a low-confidence unit; according to the method, the problem of error accumulation caused by fixed weight distribution of traditional federated filtering is solved, the influence of dimensional difference on weight distribution in an information fusion process is eliminated, and the robustness and precision stability of a multi-source navigation system in a complex underwater environment are improved.

The invention relates to a power consumption prediction method, and belongs to the technical field of power analysis. The method mainly solves the problem that an existing prediction model is difficult to effectively capture complex multi-scale time sequence characteristics in an electricity consumption sequence, and consequently prediction precision is insufficient. According to the technical scheme, the method comprises the steps of obtaining historical power consumption related data; performing data cleaning, missing value filling and abnormal value processing to construct a standardized sample set; a neural network model fusing a one-dimensional convolution layer, a gating circulation unit layer and an attention mechanism layer is constructed, local features are extracted through the convolution layer, long-term dependence is captured through the circulation layer, and key information weighted focusing is achieved through the attention layer; a standardized sample training model is adopted, and parameters are optimized by minimizing prediction errors; and finally, realizing accurate prediction of future electricity consumption by using the completely trained model. The method mainly improves the accuracy of power consumption prediction.

The application discloses a kind of unmanned aerial vehicle laser radar point cloud data denoising and outlier detection method, it is related to point cloud data denoising technical field, comprising the following steps: based on unmanned aerial vehicle laser radar acquisition target's point cloud data, and pre-division processing is carried out to point cloud data, obtain first point cloud data;According to first point cloud data, radius acquisition processing is carried out, and obtains denoising radius data;Based on first point cloud data, corresponding final point number threshold value is obtained, and according to denoising radius data, first point cloud denoising processing is carried out, and second point cloud data is obtained;Based on second point cloud data, point cloud outlier detection processing is carried out, and third point cloud data is obtained;The application is used to solve the problem that when current point cloud data denoising technology is based on radius filtering method to laser radar point cloud data denoising, cannot automatically set the best radius range and quantity threshold according to point cloud density, accurately denoising the problem of point cloud data of uneven density.

The present disclosure relates to a data verification correction method and device, medium, electronic equipment and program product, and relates to the field of data processing. The method comprises: obtaining actual energy consumption data in the process of coal mining; inputting target coal production into a data relationship model to output reference energy consumption data corresponding to the target coal production through the data relationship model; the data relationship model is a data relationship model of coal production and energy consumption data; determining whether the actual energy consumption data is an outlier according to the deviation value of the actual energy consumption data and the reference energy consumption data; in the case that the actual energy consumption data is not an outlier, determining that the actual energy consumption data is valid data; in the case that the actual energy consumption data is an outlier, replacing the actual energy consumption data with the reference energy consumption data. Through verification and correction of energy consumption data in the process of coal mining, accurate data basis can be provided for carbon emission accounting, and the accuracy of carbon emission accounting can be improved.

The systems and methods described herein can identify meaningful relationships between variables, such as particular investments or general asset classes. Unlike conventional correlation analysis, these systems and methods provide an improved technique of co-movement analysis that implements a threshold to eliminate data “noise” and then discretizes the remaining observations to normalize any outliers. Such co-movement analysis has numerous advantages over known techniques for characterizing relationships between variables.

The application discloses a kind of financial data full-link monitoring method and system, mainly related to financial data processing technical field. Including the following steps: collecting financial data from financial business system;The financial data collected is cleaned, including: data format conversion, missing value processing and outlier processing;According to the correlation rule of pre-setting, the financial data of different sources is associated, and the financial data full-link graph is constructed;Based on the financial data full-link graph constructed, the financial data is monitored in real time;According to the risk early warning rule of pre-setting, the abnormal data monitored is risk early warning, and generates early warning report.The beneficial effects of the application are that it realizes the full-link monitoring of financial data by constructing the financial data full-link graph, breaks the data island, and improves the data transparency.