943 results about "Outlier" patented technology

To significantly improve the prediction accuracy, response speed and management efficiency of large river bank slope disasters, an integrated bank slope monitoring method based on multi-parameter collaborative recognition is proposed. The solution includes step S1 of synchronously collecting data on bank slope displacement, pore water pressure, inclination angle, vibration frequency and environmental temperature and humidity to form an original monitoring dataset and construct a multi-parameter collaborative recognition network; step S2 of using a multi-modal data fusion algorithm to generate a fusion data matrix including spatiotemporal correlation features and perform spatiotemporal data alignment and outlier cleansing; step S3 of combining a geomechanical parameter library and a past disaster case library to output a risk level map and perform dynamic risk assessment model analysis; and step S4 of triggering a multi-level early warning mechanism and generating linked control commands including treatment suggestions to perform multi-level early warning and linked control.

The invention discloses a three-dimensional Gaussian sputtering scene reconstruction method based on structure perception refined Gaussian, and aims to solve the problems of Gaussian drift, edge blur, structure artifacts and the like of a reconstruction model due to the fact that sparse point cloud contains outliers, Gaussian morphology and normal are mismatched and a multi-dimensional optimization target is lacked in an existing three-dimensional Gaussian sputtering reconstruction method. A key frame is extracted by collecting target scene video data, sparse three-dimensional point clouds are reconstructed by using an SfM algorithm, a depth map and a normal map are generated through a Lotus model, three-dimensional Gaussian distribution is initialized after the sparse point clouds are filtered, a Gaussian covariance matrix is adjusted by using a normal consistency regular term, and the sparse point clouds are extracted. And after structure attribute analysis is carried out, a comprehensive scoring function is constructed to screen Gaussian points, and finally, a combined training framework including luminosity, normal consistency and structure continuity loss is adopted to optimize and generate a three-dimensional Gaussian scene model. The method is mainly applied to the field of three-dimensional reconstruction and multi-view rendering, and scene reconstruction precision and geometric consistency can be improved.

The invention relates to the technical field of intelligent water affairs, in particular to an intelligent water affairs dynamic monitoring system and method based on digital twinning, and aims to detect data drift in real time, calculate a standard deviation change rate to generate abnormal judgment, analyze model stability and drift rate synchronism to output deviation indexes, screen data abnormal items to form quality evaluation, and realize dynamic monitoring of the intelligent water affairs. And comparing node synchronization difference to trigger compensation correction, and generating a monitoring adjustment result through integrity verification. According to the invention, through comparison of the multi-cycle standard deviation change rate and the environmental parameters, sensor drift classification and confidence evaluation are realized, the reliability of the model is enhanced by constructing a composite attenuation coefficient, abnormal sampling is positioned by combining the data change rate and an outlier map, and data synchronization is guaranteed by adopting a difference matrix and state feedback. And version verification and repair records are introduced to realize closed-loop tracing.

The invention discloses a financial data risk control system and method based on big data, and relates to the technical field of financial science and technology, and the system comprises a multi-source data collection module which achieves cross-mechanism safety collection through federal learning; the data cleaning and preprocessing module is used for processing abnormal values and missing values by using an improved algorithm; the knowledge graph construction module is used for constructing a dynamic knowledge network based on an innovative algorithm; the risk assessment engine fuses various models to assess risks; the real-time monitoring and early warning module is used for realizing second-level response by utilizing multi-scale analysis; the decision support module is used for optimizing a strategy based on reinforcement learning; and the audit tracking module guarantees evidence storage and privacy through zero-knowledge proof, and all the modules cooperate to improve the risk control capability. According to the financial data risk control system and method, risks are accurately recognized, real-time monitoring and early warning are achieved, data security sharing is achieved, risk control strategies are dynamically optimized, risks and business development are balanced, the risk prevention and control capacity and economic benefits of financial institutions are improved, and data privacy and risk control transparency are guaranteed.

The invention discloses a cloud data anomaly detection and safety response system based on artificial intelligence, relates to the technical field of data processing, and solves the problems that firstly, an incremental compression algorithm is difficult to store and preprocess multi-source heterogeneous data; secondly, it is difficult to fuse statistical analysis and a deep learning model, locate outliers and analyze abnormal semantics in unstructured data, and then it is difficult to effectively predict a potential attack path; then, on the premise that data security and traceability are guaranteed, correlation analysis of cross-node anomalies is difficult to achieve so as to identify distributed attacks; and finally, an attack and defense confrontation model is difficult to construct for safety response, and the safety response effect is difficult to evaluate. According to the method, cloud environment data are processed through an adaptive probe cluster and the like, multiple models are fused to generate anomaly detection features and predict attack paths, and response and evaluation are carried out through reinforcement learning and digital twinning by means of cooperative detection such as federated learning and the like.

The invention provides a power equipment intelligent inspection abnormity identification method based on multi-dimensional data fusion, and the method comprises the steps: obtaining historical operation data and real-time monitoring data, fusing the historical operation data and the real-time monitoring data through a time sequence alignment method, generating a unified data set containing voltage, current, temperature and load information, and obtaining multi-dimensional equipment operation features; aiming at the outlier feature vector, adopting an adaptive neural network to adjust a weight coefficient of the health state evaluation model, generating an optimized weight parameter, and determining an optimized model structure; and performing health state evaluation on the unified data set through the optimized model structure, generating a health index scoring result of the equipment in combination with load over-limit period counting and a temperature abnormal fluctuation range, and determining an evaluation basis for guiding local maintenance of the miniature equipment.

The invention relates to the technical field of atmospheric pollutant detection, and discloses a method and a system for detecting excessive emission of atmospheric pollutants. The method comprises the following steps: acquiring pollutant concentration data of multiple monitoring points in a target area to form an original data set; abnormal value detection and correction are carried out on the data set, sensor fault outliers are eliminated, and a preprocessed data set is obtained; extracting concentration change trend characteristics in a preset time window of each monitoring point, and constructing a spatial-temporal characteristic matrix; inputting the matrix into a pollutant diffusion model, calculating a transmission path and strength between monitoring points, and generating a regional transmission network; identifying a potential source region of abnormal fluctuation of pollutant concentration based on a network, and marking the potential source region as a candidate region to be checked; performing multi-scale concentration gradient analysis on the candidate area, and determining a key monitoring area; arranging mobile equipment in the key monitoring area, and collecting high-precision component data; and comparing the data with a standard emission source feature library, matching emission source types of which the similarity exceeds a threshold value, judging whether the emission exceeds the standard or not, and generating a detection report.

The invention relates to the technical field of equipment monitoring, in particular to an equipment corrosion evaluation and life prediction method and application, and the method comprises the following steps: deploying a sensor network in an easily-corroded area of coal chemical equipment, and collecting multi-dimensional data; carrying out abnormal value elimination, data compression, time synchronization and space-time alignment preprocessing on the collected multi-source data; image features are extracted through a convolutional neural network, processed data are analyzed through an LSTM-attention model, and a fuzzy comprehensive evaluation matrix is established to evaluate the corrosion level; a physical model based on the Faraday electrolysis law and a data driving model based on the Transform network are constructed, and the residual life is predicted through Bayesian network fusion output and Monte Carlo simulation. Through fusion of multi-source data and an intelligent algorithm, accurate evaluation of the corrosion state of the equipment and accurate prediction of the residual life are realized, and safe and efficient operation of the coal chemical equipment is guaranteed.

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.

An apparatus providing tensor core matrix multiplication and accumulation (MMA) with hardware-based statistics collection and outlier suppression is disclosed. The apparatus includes processor circuitry comprising at least one processor core comprising matrix multiplication circuitry to: execute a matrix multiplication operation on first input data from a first set of registers and on second input data from a second set of registers; collect, as part of executing the matrix multiplication operation via statistics collection hardware circuitry of the matrix multiplication circuitry, output statistics data corresponding to the matrix multiplication operation; and output the output statistics data along with a result of the matrix multiplication operation; and output statistics storage to store the output statistics data.

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 invention discloses an automatic three-dimensional reconstruction method and system based on power transmission corridor point cloud. The method comprises the following steps: acquiring data and exporting point cloud data; noise points, outliers and ground points are removed from the acquired point cloud data of the power transmission corridor, and down-sampling is performed on the point cloud with a large data volume; performing clustering segmentation on the preprocessed point cloud of the power transmission corridor, positioning the position of a tower, determining a point cloud range of a power line, and removing point clouds of other objects on the ground; aiming at the structural characteristics of a power line and an insulator in the power transmission line, extracting point clouds of the power line, the insulator and a tower by adopting a geometric constraint principal component analysis (PCA) algorithm; performing automatic geometric model reconstruction on the tower, insulator and power line point clouds according to different reconstruction rules to generate each module grid model; and transforming the grid model of each module, restoring the grid model to the original position of the point cloud, and generating a power transmission line digital twin model conforming to the real size. According to the method, automatic segmentation, classification and real-time reconstruction can be accurately carried out on the point cloud of the power transmission corridor.

The invention relates to the technical field of wind field simulation, and provides a wind field numerical simulation method based on multiple meteorological data sources. The objective of the invention is to solve the problems of large simulation error, rough terrain boundary processing and poor turbulence model parameter adaptability caused by non-uniform coverage of a single data source, insufficient precision and unscientific multi-source fusion. The method is characterized by comprising the following steps: step 1, constructing a CFD three-dimensional grid based on a geometric model of a target area; 2, collecting original data of a ground station, satellite remote sensing, numerical forecasting and the like; 3, performing standardized preprocessing (abnormal value elimination, missing interpolation, radiation / geometric correction and resampling), determining a multi-source fusion weight by combining historical data analysis, and generating comprehensive meteorological data by adopting a weighted average method; and 4, inputting the comprehensive data into a CFD-RANS model, dynamically adjusting turbulence parameters, accurately setting terrain boundary conditions, and obtaining a wind field space-time distribution result through numerical solution. According to the invention, through combination of multi-source data fusion and CFD simulation, the wind field simulation precision and stability are improved.

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 discloses a numerical simulation-based roadway surrounding rock deformation monitoring method and device. The method comprises the following steps of: constructing a three-dimensional geologic model of a roadway area; based on the three-dimensional geologic model, establishing a numerical model of heat conduction, stress action and seepage multi-physics field coupling, and based on elastic-plastic characteristics of rocks in the roadway area, simulating simulation data of surrounding rocks in the roadway area under the multi-physics field action by the numerical model; acquiring real-time monitoring data of various types of sensors arranged in a risk monitoring area of surrounding rock deformation corresponding to the simulation data; denoising, filtering and abnormal value processing are carried out on the real-time monitoring data, and then multi-source data fusion is carried out to calibrate the numerical model; and predicting the deformation trend of the surrounding rock based on the calibrated target numerical model and the real-time monitoring data. Therefore, by means of geological modeling, numerical model simulation, sensor arrangement optimization, numerical model optimization and surrounding rock deformation trend prediction, roadway surrounding rock monitoring precision is improved, and safe operation of a roadway is guaranteed.

The invention discloses a method and system for assimilating meteorological satellite hyperspectral error non-Gaussian distribution data based on quantum computing, and the method carries out the generalized quality control of meteorological satellite hyperspectral data in consideration of an outlier value on the observation brightness temperature and equivalent data of a satellite hyperspectral infrared detector channel. Comprising the steps of channel optimal selection considering quantum information entropy, deviation correction based on artificial intelligence generalized integration generation type deep learning and cloud detection based on a minimum residual method to remove cloud view field point data, so that quality control data is obtained. And performing quantum space solution on the quantum non-Gaussian increment four-dimensional variational assimilation model compatible with error Gaussian distribution and non-Gaussian distribution based on quality control data to obtain analysis field data, and solving the problem that the brightness temperature assimilation of the hyperspectral infrared channel with error non-Gaussian distribution (or obvious non-Gaussian characteristics) is limited in the prior art. Meanwhile, the method is high in solving speed, and the obtained analysis field data has good precision and calculation timeliness.

The invention discloses a multi-frequency multi-mode signal multi-path error weakening method, and relates to the technical field of precision positioning, and the method comprises the steps: constructing a multi-frequency multi-mode non-combination precision single-point positioning observation equation, separating multi-path errors in pseudo-range and phase observation values through the equation, and forming residual data; performing quality control on the residual error, and eliminating an abnormal value; feature extraction and judgment are carried out based on residual data, wherein time repeatability features, spatial distribution features and trend complexity features are extracted; selecting a correction model according to a feature judgment result, wherein the correction model comprises a fixed star daily filtering model, a multi-path hemispherical graph model and a trend surface analysis multi-path hemispherical graph model; and generating a multi-path error correction amount through a selected model, correcting an original observation value, and outputting a positioning result. The method solves the problem that the positioning precision is reduced due to multi-path errors, can effectively weaken the multi-path errors, improves the positioning precision and stability, adapts to a complex environment, and reduces data processing redundancy.

The invention provides a model and data driven low-orbit navigation enhanced satellite clock error forecasting method and equipment, and the method comprises the steps: carrying out the preprocessing of original clock error data of a low-orbit satellite: converting clock error time domain data into frequency domain data through time-frequency conversion, and employing a quartile method to recognize and eliminate frequency domain outliers and corresponding time domain abnormal values; constructing a frequency domain energy model and extracting periodic terms, including performing power spectral density analysis on the preprocessed clock error frequency domain data, and adaptively extracting significant periodic components of the low earth orbit satellite clock error through a threshold value; establishing a polynomial low earth orbit satellite clock error forecasting model considering periodic term correction and forecasting a clock error sequence to obtain a corresponding fitting residual error sequence and a clock error forecasting value; and normalizing the fitting residual error sequence, inputting the fitting residual error sequence into the gating circulation unit neural network, training and forecasting by adopting a window sliding input mode, and outputting a residual error prediction result. According to the method, the interpretability of the forecasting result can be effectively improved while the low earth orbit satellite clock error forecasting precision and stability are improved.

A system, method, and computer-program product includes receiving a machine-generated alert indicating a data outlier for a physical asset; obtaining, from a computer database, a dataset including observed data for the physical asset within a predefined temporal window of the machine-generated alert; generating, via a description generator, a contextual description of the data outlier based at least on the observed data for the physical asset; searching an embedding-based knowledgebase for a subset of embedding representations of the plurality of embedding representations within a similarity threshold of an embedding representation of the contextual description; obtaining, from the computer database, a subset of the plurality of reference artifacts that correspond to the subset of embedding representations; and generating, via a large language model, a resolution suggestion for resolving the data outlier based at least on the subset of reference artifacts obtained from the computer database.

The invention discloses an electric energy meter detection assembly line fault diagnosis and prediction method based on multi-modal time sequence analysis. The method comprises the steps of collecting multi-modal data including electric energy meter visual data, time sequence sensor data and text log data in real time; performing cross-modal fusion of time sequence alignment on the multi-modal data to generate joint feature representation; performing joint optimization of fault diagnosis and prediction; performing fault diagnosis based on the joint feature representation, and outputting a current fault type and probability; predicting a future fault probability based on the equipment state continuous evolution model; in response to batch conduction characteristics which are output by the prediction model and reach a preset abnormal value, triggering recalculation of the associated modal data; correcting an initial condition of the equipment state continuous evolution model according to the fault type obtained through re-calculation; and dynamically adjusting a diagnosis decision threshold according to the prediction confidence output by the corrected equipment state continuous evolution model.

The invention relates to the technical field of code pre-configuration, in particular to a demand-oriented code pre-configuration method, system and equipment and a storage medium. Matching a plurality of performance function number sequences from the code function library according to the function requirements of the target scene; performing function demand sample frequent mining on the target scene to obtain a function demand input parameter and a function demand output identifier set; inputting the function demand input parameters into a plurality of function number sequences to obtain a plurality of function demand prediction outputs; traversing the plurality of function demand prediction outputs for outlier analysis to obtain a plurality of output prediction outlier factors; traversing the plurality of performance function number sequences to carry out calculation power demand prediction to obtain a plurality of expected calculation power; and based on the plurality of expected computing power and the plurality of output prediction outlier factors, sorting the plurality of performance function number sequences to obtain a target performance function number sequence, and executing code pre-configuration. The code development efficiency is improved, and the code practicability is enhanced.

The invention discloses a thermal insulation color steel sandwich composite board product quality detection system based on digital twinning, and particularly relates to the field of quality control. The system comprises a composite board full-dimension three-dimensional digital model construction module, a production digital twinborn body creation module, a total-factor real-time data acquisition module, a data preprocessing module, a twinborn data driving and model calibration module, a quality intelligent analysis decision module and an optimization execution module. The model construction module constructs a reference model and embeds a process standard and a quality threshold value; the twin creation module builds a virtual production line, standardizes a data interface and assigns an ID to a plate; the data acquisition module acquires real-time data; the preprocessing module removes abnormal values, standardizes data and associates the data; the calibration module updates the twin body, calculates the deviation and calibrates the model; the analysis module realizes compliance judgment, defect analysis and quality prediction; and the optimization module generates and executes an instruction, so that the problems of low efficiency, data splitting and no closed loop of traditional detection are solved, and the quality control precision and efficiency are improved.

The invention relates to the technical field of intelligent regulation and control, and discloses a data-driven workshop production equipment flow intelligent regulation and control method. Comprising the steps of S1, equipment state data acquisition in a data acquisition stage, S2, data cleaning abnormal value processing in a data preprocessing stage, S3, equipment health degree analysis model in a data analysis and modeling stage, S4, equipment scheduling decision in an intelligent regulation and control decision stage, and S5, regulation and control effect feedback and effect evaluation index production efficiency in an iteration stage. According to the data-driven workshop production equipment flow intelligent regulation and control method, the data acquisition and processing efficiency is improved, the integrity of equipment state data is improved to 98% through multi-dimensional sensor deployment (vibration 100Hz sampling and temperature 30s / time), the time consumed for automatic correction of an abnormal value is shortened from 30min to 1min, and the efficiency of automatic correction of the abnormal value is improved. And after data standardization, the multi-dimensional fusion analysis efficiency is improved by 4 times, and a foundation is laid for accurate regulation and control.

The invention provides a side slope radar point cloud data slice abnormal value complementing method based on space-time fusion, which comprises the following steps: acquiring point cloud data of a plurality of time slices obtained by scanning of a side slope radar, and mapping the point cloud data into a three-dimensional point cloud; identifying a piece of abnormal regions of the three-dimensional point cloud through statistical filtering and a density-based clustering method; constructing a point cloud processing model based on PointNet + +, generating masked data and a mask index by adopting a mask strategy, and masking off pieces of point clouds; spatial features are extracted in different regions, static space weight matrixes generated by longitude and latitude and time difference global features are fused, deformation values of mask regions are predicted through a full-connection network, and model parameters are optimized; and inputting the trained model into latitude and longitude coordinates of an abnormal region of actual missing point cloud data, performing abnormal value completion, outputting a completed deformation value, and calculating a completion precision index. According to the method, the completion effect is good, all abnormal values of multiple time slices can be completed at a time, and the calculation efficiency is improved.

Systems and methods for detecting patterns in data from a time-series and for detecting outliers in network data in an unsupervised manner are provided. In one implementation, a method includes the steps of obtaining network data from a network to be monitored and creating a window from the obtained network data. The method also includes the step of detecting outliers of the obtained data with respect to the window using an unsupervised deep learning process (e.g., using a Generalized Adversarial Network (GAN) learning technique and / or a Bidirectional GAN (BiGAN) learning technique) for enabling the learning of a data distribution. The unsupervised process, for example, does not require manual intervention.

The invention discloses an FFU chemical filter life cycle prediction method and system, and the method comprises the following steps: collecting multi-dimensional dynamic parameters of an FFU chemical filter in real time through an edge sensor, the multi-dimensional dynamic parameters including environmental parameters, operation parameters and performance indexes; carrying out missing value filling and abnormal value filtering on the collected data; on the basis of the processed data, time sequence derivative features are constructed, and trans-periodic feature association is extracted by using Transform coding; the extracted features are trained by using a Transform-GBDT hybrid model, a residual life prediction result is output, a Transform layer captures a time sequence dependency relationship, and a GBDT layer analyzes a feature interaction effect; and optimizing the hybrid model through hyper-parameter search. According to the invention, the problems of low prediction precision and poor real-time performance of the life cycle of the FFU chemical filter in an industrial Internet of Things environment are solved.

The invention particularly relates to a hydrogeological analysis system and method based on big data, and relates to the technical field of hydrogeological analysis, and the method comprises the steps: placing a new model and an old model in an A / B test stage, and carrying out the parallel processing of real-time data; performing automatic judgment according to a preset performance index, and deciding whether to upgrade the candidate model to a new production model; and applying the model selected after decision making to hydrogeological analysis to solve the problem of long-term prediction precision under dynamic change of the hydrogeological system. According to the method, dynamic threshold monitoring, K-S outlier sample examination, sub-model optimization and A / B test smooth iteration processes are constructed, and a mechanism and machine learning coupling model and parallel calculation are combined, so that the model drift identification delay is reduced, the prediction precision is improved, scenes such as underground water over-mining prevention and control and pollution emergency are effectively supported, and the prediction accuracy is improved. And an accurate and efficient scientific decision basis is provided for water resource management.

A system identifying outlier data and generating corrective action, wherein the system includes at least a processor and a memory communicatively connected to the at least a processor and containing instructions. The instructions may configure the at least a processor to obtain a dataset, apply a clustering model to the dataset to generate a set of clusters based on the inherent relationships between the data points, determine the distance metric for each data point relative to its corresponding cluster centroid, define an outlier threshold based on the distance metrics of the data points, identify the data points that exceed the outlier threshold as one or more outliers, classify the one or more outliers across one or more axes, and output a report of the one or more identified outliers, wherein the report suggests corrective actions and insights for each of the one or more identified outliers.

The invention relates to the technical field of image recognition, and discloses a data abnormal value elimination method and system for fire monitoring. The method comprises the following steps: acquiring an original video stream of a fire monitoring area; extracting an image frame at the current time from the original video stream; extracting a foreground motion area of the image frame from the image frame; extracting fire features of the foreground motion area; identifying a fire area from the foreground motion area according to the fire features of the foreground motion area; calculating a feature mutation trend of each pixel in the fire area; according to the feature mutation trend of each pixel in the fire area, identifying interference pixels from the fire area; and carrying out elimination processing on the interference pixels. The method makes remarkable progress in the aspects of reducing the false alarm rate, improving the adaptability to complex scenes, retaining key feature dynamic information and the like, and provides effective technical support for reliable operation of a fire monitoring system.

The invention discloses a short temporary rainfall forecasting method based on a CAMS-Unet model. The method comprises the steps that original radar echo image data are acquired and preprocessed; a CAMS-Unet short temporary rainfall forecast model is constructed; based on the target radar echo image data, training optimization is carried out on the CAMS-Unet short temporary rainfall forecasting model, and a target CAMS-Unet short temporary rainfall forecasting model is obtained; and obtaining a to-be-predicted radar echo image, inputting the to-be-predicted radar echo image into the target CAMS-Unet short temporary rainfall forecasting model, and outputting to obtain a predicted radar echo image. According to the method, the radar echo image is preprocessed through three stages of data set screening, abnormal value and vacancy value processing, denoising and data enhancement processing; by constructing a CAMS-Unet model comprising a global feature extraction module CAMM and a local feature extraction module MSFM, the problems of multi-scale modeling imbalance and insufficient space-time isomerism capture are solved.