423results about How to "Improve extraction accuracy" patented technology

The invention relates to the technical field of optical detection, in particular to a workpiece surface defect detection method and system based on the optical technology, and the method comprises the following steps: S1, generating a curvature model of a workpiece; s2, dynamically adjusting the irradiation angle and the light spot shape of a light source according to the curvature model in the S1; s3, adjusting the focal length of the detection equipment in real time; s4, imaging acquisition is carried out on the workpiece at the same time through multiple angles; s5, correcting image distortion caused by surface curvature change, and fusing the multi-angle images into a high-precision workpiece surface image; s6, defect features of the surface of the workpiece are extracted, defects are classified, positions are marked, and a defect detection report is generated; according to the invention, by dynamically adjusting the light source and the focal length and combining multi-view imaging and advanced image processing technologies, efficient and accurate defect detection is realized, and the product quality and the production efficiency are remarkably improved.

The invention relates to the technical field of infrared image recognition, and discloses a hot target extraction method and system based on infrared image recognition, and the system comprises a multi-sensor collection module, a data dimension reduction module, a data verification module, a segmentation optimization module, and a dynamic feedback module. Multi-modal data dimension reduction processing is achieved by collecting infrared images, visible light images and environment dynamic parameters and combining Laplacian feature mapping, and a target area is extracted through geometric verification and topological analysis. An energy optimization model and a distribution alignment method are adopted to complete accurate segmentation of a target area, and meanwhile, segmentation model parameters are adjusted in real time based on environment dynamic parameters, so that the system stability is ensured. According to the method, high-precision and robust extraction of the thermal target is realized in a complex dynamic environment, the problems of fuzzy target boundary, noise interference and insufficient dynamic environment adaptability in the prior art are effectively solved, and the method is suitable for the fields of military reconnaissance, fireproof monitoring, electric power inspection and the like.

The invention relates to the technical field of audio testing, and discloses an intelligent audio testing system frequency response analysis method, device and equipment, and the method comprises the steps: carrying out the audio parameter initialization of an intelligent audio testing system, and obtaining a testing parameter matrix and an excitation signal; performing dual-channel acquisition and time-frequency feature extraction to obtain a time-frequency feature data set; executing double-branch processing of time domain noise reduction and frequency domain enhancement to obtain a purified feature data set and a feature weighting matrix; performing segmented recursive calculation and feature aggregation to obtain a frequency response feature vector; decomposing the frequency response feature vector according to a test frequency band, and performing environment compensation and nonlinear calibration to obtain a standard frequency response data set; and inputting the standard frequency response data set into the deep neural network for feature recognition and mode classification to obtain a frequency response curve and a phase characteristic curve, thereby improving the test precision, the anti-interference capability, the automation degree and the adaptability of the intelligent audio test system.

The invention relates to an energy consumption optimization method and device for a server, equipment and a storage medium, and aims to reduce the energy consumption of the server in operation and improve the energy efficiency management level. According to the method, energy consumption of a server in different load states is monitored through a super-resolution data acquisition technology, and load feature embedded representation is generated in combination with a contrast learning network. And based on the embedded representations, performing energy consumption prediction by using a generative adversarial network, and generating a hardware operation parameter adjustment scheme through a Markov decision process and a particle swarm optimization algorithm. And finally, iterative optimization is performed on the scheduling strategy through distributed reinforcement learning, adaptive regulation and control are realized, a target energy consumption optimization scheme is generated, and the accuracy and dynamic response capability of server energy consumption management are remarkably improved.

The invention discloses a building facade structure line accurate extraction method based on laser point cloud feature depth, and is applied to the technical field of point cloud feature extraction. The method comprises the following steps: acquiring building point cloud data, preprocessing the acquired building point cloud data, and dividing the preprocessed building point cloud data into a training set and a test set; inputting the training set into a PCPNET network for training to obtain a PCPNET network training model, and extracting normal vector features of the test set through the PCPNET network training model; adopting a Gaussian weighting function to realize quadratic optimization of the normal vector features, and identifying edge feature points according to the difference of the normal vector features after quadratic optimization; and optimizing the edge feature points through an improved filtering algorithm, calculating normal vectors of the optimized edge feature points, and connecting the normal vectors into feature lines based on preset angle and distance thresholds. According to the method, the extraction precision and robustness of the normal vector features are remarkably improved, and a reliable data basis is provided for building facade structure analysis.

The invention belongs to but not limited to the technical field of text extraction, and particularly relates to an automatic construction method and system for a process industry text knowledge extraction dataset. Firstly, based on a word segmentation tool and a TF-IDF algorithm, an unlabeled text in the process industry field is converted into a large language model fine tuning dataset, and a large language model is subjected to fine tuning; the system has specific knowledge of the field; then, generating an initial knowledge extraction sample set with confidence through multi-stage exploration-utilization ratio adjustment based on the fine-tuned large language model; and finally, through a confidence-based large-scale sample set error detection and correction method, obtaining a sample data set which is finally used for domain knowledge extraction model training. Through the low-cost and high-efficiency industrial text knowledge extraction sample set construction and application method, the input-output ratio of resources is increased to the maximum extent, and the construction and application efficiency of an industrial safety knowledge extraction algorithm is improved.

The invention discloses a lithium battery health state evaluation method based on deep learning, and relates to the technical field of battery evaluation. Comprising the following steps: acquiring a Nyquist curve graph of a lithium battery through an electrochemical impedance spectroscopy test, and mapping the Nyquist curve graph with internal impedance characteristic parameters one by one to generate a sample image set; and establishing a deep learning network model based on the sample image set, and training and generating a prediction model by taking the Nyquist curve graph as input and the internal impedance characteristic parameters as labels. And testing the to-be-evaluated battery, and inputting the target Nyquist curve graph to predict internal impedance characteristic parameters. And calculating a capacity retention ratio index, a cycle efficiency degradation index and a shape expansion influence index in combination with the electric power characteristic parameters. The indexes are comprehensively analyzed to obtain a health state comprehensive coefficient, and the health state comprehensive coefficient is compared with a health threshold value to judge the health state of the battery. According to the method, the evaluation precision is improved through deep learning, and efficient and accurate health state evaluation is realized.

The invention discloses a PCB surface defect detection method and system, an electronic device and a medium, and relates to the technical field of defect detection, the connection condition caused by excessive tin soldering of a tin bridge or adjacent welding spots can be automatically identified by judging the connection state between the welding spots, an overall detection instruction is triggered, early detection is facilitated, connectivity defects are avoided, and the detection efficiency is improved. And the welding spot connection reliability is improved. The image is divided into a plurality of image blocks, the texture difference data of each image block is obtained, the texture abnormal state of the surface of the PCB can be finely analyzed by calculating the texture deviation coefficient, the subtle texture change in different areas can be identified by the block analysis method, and surface defects such as scratches and bubbles on the surface of the PCB can be accurately detected. And in combination with the texture deviation coefficient and the geometric features of the welding spots, unified normalization fitting is performed on the defect features to generate the defect recognition index, so that the defect features of different types can be captured, and the robustness and accuracy of the model can be improved.

The invention discloses a corn phenotype parameter extraction method based on point cloud data. The method comprises the following steps: step 1, constructing an experimental data set; step 2, corn point cloud segmentation; and step 3, corn phenotype parameter extraction. The method constructs a corn point cloud data set, deeply explores a segmentation technology of a corn population and organs thereof based on three-dimensional point cloud data, can realize complete extraction and segmentation of the population and a single corn plant, accurately extracts field corn phenotype parameters, is not only suitable for phenotype parameter extraction of a single corn plant, but also suitable for phenotype parameter extraction of a corn population, and improves the extraction efficiency of the corn population. The method shows high applicability and accuracy, provides an effective technical means for corn phenotype analysis, and can provide an automatic, efficient and low-cost solution for accurate measurement of crop individual phenotype information.

The invention discloses a high-resolution remote sensing image road extraction method and device, equipment and a storage medium, and relates to the technical field of remote sensing. The specific implementation scheme comprises the steps of obtaining a sample set; constructing a road extraction model, performing model training on the road extraction model through the sample set until a loss function is converged, and obtaining a target road extraction model; and performing road extraction on the input high-resolution remote sensing image through the target road extraction model to obtain a road image. According to the invention, the problem of low road extraction precision of a road network with a complex background in the prior art is solved, and the road extraction precision of the road network with the complex background is improved.

The invention provides an entity relationship extraction method and device based on a transfer learning model, equipment and a medium, and belongs to the technical field of data processing. Original data are acquired, the original data are preprocessed and labeled, a training data set is generated, and then an entity relationship is constructed; utilizing a machine learning algorithm to construct an entity relationship processing task in the knowledge graph; constructing a transfer learning model, and carrying out transfer learning on knowledge in a source domain or a source task to a target domain or a target task based on the transfer learning model to initialize or assist training of a target domain model; a machine learning algorithm and a transfer learning model are fused, feature representation useful for a target domain can be extracted by utilizing rich annotation data and domain knowledge of a source domain, and the feature representation is applied to tasks of the target domain, so that useful features can be learned more quickly, and the efficiency of the target domain is improved. Therefore, the accuracy of tasks such as entity recognition and relation extraction is improved.

The invention discloses an unmanned aerial vehicle laser radar point cloud individual tree segmentation method based on collaborative information guidance. The individual tree segmentation method comprises the steps of obtaining an initial crown class; based on the uniqueness of point cloud distribution of the unmanned aerial vehicle laser radar in the forest region, geometric significance features are constructed, and accurate extraction of trunk candidate points and objectified detection of trunks are realized; dividing the initial crown class into under-segmentation, correct segmentation and over-segmentation; outputting the correctly segmented crown as a single tree segmentation result, and further refining the under-segmented class cluster; and optimizing the under-segmented crown by using the mutual reference information of the crown and the trunk. According to the method, the tree boundary refined segmentation capability is improved, and the extraction precision and reliability of the unmanned aerial vehicle laser radar point cloud single tree are effectively improved.

The invention discloses a remote sensing coastline extraction method and device, equipment and a medium, and relates to the technical field of deep learning, and the method comprises the steps: obtaining a target remote sensing image of a target region; inputting the target remote sensing image into the trained coastline extraction model to obtain a target coastline extraction result of the target area; the coastline extraction model is an improved DeeplabV < 3 + > model; according to the improved DeeplabV3 + model, a CBAM attention mechanism module is introduced in the process of outputting the backbone network of the original DeeplabV3 + model to a decoder, the CBAM attention mechanism module is introduced before up-sampling, and the coastline extraction precision is improved.

The invention discloses a power distribution network fault detection method and device based on a traveling wave signal, and the method comprises the following steps: obtaining a fault traveling wave signal of a power distribution network, and determining the fault position of the power distribution network; performing frequency decomposition on the fault traveling wave signal based on an optimization decomposition algorithm to obtain an instantaneous frequency; performing feature extraction on the instantaneous frequency based on a pre-constructed optimization fault detection model, and giving fault features of the power distribution network; and according to the fault characteristics of the power distribution network, giving a fault type corresponding to the fault traveling wave signal. According to the invention, the specific position and category of the power distribution network fault can be accurately determined, and the accuracy and efficiency of fault detection are improved.

The invention discloses a social network public opinion monitoring method and system based on artificial intelligence. The method comprises the following steps of obtaining public opinion data from a social network platform and performing data preprocessing; hot events are automatically extracted from the public opinion data, and a density peak clustering algorithm is utilized to identify a high-density area and determine the popularity of the hot events. A BERT model is adopted to carry out sentiment classification on a text, context features are extracted in combination with bidirectional LSTM, and sentiment tendency is output through a Softmax classifier. For a specific target event, a multi-head attention mechanism and a convolutional neural network are used for specific target emotion classification. And performing noise reduction and decomposition on the time sequence data, constructing a plurality of basic prediction models, generating a combined prediction model in combination with weighted average, and monitoring and predicting the public opinion development trend in real time. Accurate public opinion analysis and decision support are provided for governments and enterprises, public opinion risks are reduced, social stability is maintained, and social construction is promoted.

The invention discloses a method and device for extracting a centerline of a quasi-cylinder by using a point cloud, and the method comprises the steps: obtaining a quasi-circular target of which the centerline is to be extracted, and carrying out the three-dimensional point cloud scanning, and obtaining the three-dimensional point cloud data; point clouds of a target in the three-dimensional point cloud data are cut at equal intervals in the direction of the center line, and a plurality of annular point clouds with noise are formed; projecting the annular point cloud to a two-dimensional plane in a direction perpendicular to the center line; calculating distances from all point positions in the annular two-dimensional image to external points on the projection image; the probability of occurrence of each distance is calculated, and the position corresponding to the pixel when the probability is the maximum value is obtained as the plane center point of the annular point cloud corresponding to the two-dimensional image; associating z values of the annular point cloud during cutting in the direction of the center line to form three-dimensional coordinates of the center point; and connecting the three-dimensional coordinates of all the central points of the equally-spaced cutting annular point clouds to obtain the central line of the cylinder-like body. According to the invention, the extraction precision of the centerline of the quasi-cylinder and the extraction reliability of the centerline are improved.

The invention relates to a method for removing a mountain shadow in an optical remote sensing image. The removing method comprises the following steps: determining a shadow region and a non-shadow region in the optical remote sensing image according to spectral data of different wavebands; acquiring first spectral data of various ground objects in a non-shadow area, and establishing a non-shadow area spectrum library; according to the similarity between each second spectrum data in the shadow area and the first spectrum data in the non-shadow area spectrum library, obtaining reference spectrum data in the non-shadow area spectrum library, and respectively generating new spectrum data corresponding to the second spectrum data of each pixel in the shadow area; and generating a new shadow area according to the new spectral data, and merging to obtain a shadow-free remote sensing image. The shadow-free remote sensing image is obtained by removing the shadow, so that the image is clearer and brighter, the target recognition capability is enhanced, the information extraction precision is improved, and the application of the remote sensing data is more accurate and reliable.

The invention relates to an end-to-end cross-country ground feature prediction method based on suspension stroke decoupling, belongs to the technical field of data prediction, and solves the problem of inaccurate ground feature prediction caused by low accuracy of extracted features in the prior art. Comprising the following steps: acquiring and preprocessing environment information and vehicle body response information, splicing multi-frame point cloud data in the environment information by using an SLAM method, and projecting the multi-frame point cloud data into a BEV map; extracting a plurality of statistics of the vehicle body response information as vehicle body response characteristics, wherein the suspension stroke in the vehicle body response information is the suspension stroke of each suspension point after stroke decoupling; constructing a sample set according to the preprocessed camera mask image, BEV map and vehicle body response characteristics in the environment information, and training a multi-branch fusion classification network; and acquiring a camera mask image, a BEV map and a vehicle body response characteristic at the current moment, transmitting the camera mask image, the BEV map and the vehicle body response characteristic into the trained multi-branch fusion prediction network, and predicting a road material type and a bumping degree grade at the next moment. And accurate prediction of off-road ground features at future moments is realized.

The invention discloses a surface feature information extraction method based on a hyperspectral and high-resolution remote sensing image, and relates to the technical field of remote sensing. The method comprises the following steps: acquiring a hyperspectral and high-resolution remote sensing image, and preprocessing the hyperspectral and high-resolution remote sensing image; cutting and fusing the preprocessed image to obtain a fused image; performing spectral analysis and wave band screening on the fused image to obtain first image data; performing image enhancement processing on the first image data to obtain a plurality of grayscale images; performing wave band synthesis on the plurality of grayscale images, and performing grid analysis and wave band screening to obtain second image data; performing threshold segmentation on the second image data, and performing grid surface turning to obtain a first vector; obtaining DEM data in a preset elevation range of the research area, and performing grid surface turning to obtain a second vector; and cutting the first vector according to the second vector to obtain a distribution vector result. According to the method provided by the embodiment of the invention, the extraction precision and reliability can be effectively improved.

The invention relates to the technical field of remote sensing image intelligent interpretation, in particular to a remote sensing image building extraction method and system based on a depth feature fusion network, and the method comprises the steps: firstly extracting the depth information of a remote sensing image through a depth information extraction module; fusing the depth information and the corresponding remote sensing image by using a depth feature fusion module to obtain a fused feature map; and finally, inputting the fused feature map into a U-Net network to carry out building extraction of the remote sensing image. According to the invention, the building extraction accuracy of the model is improved through depth information extraction and depth feature fusion; and extra multi-source registered remote sensing image data is not needed, so that the limitation of a traditional multi-source data fusion strategy is solved, and the extra multi-source registered remote sensing image data usually needs a large amount of time and energy to obtain.

The invention relates to the field of data classification, and discloses an automatic review generation system and method based on artificial intelligence, and the system comprises a retrieval type construction module, a literature screening module, a scale evaluation module, a data extraction and information summarization module, an output and application module, and a system maintenance and update module. Wherein the retrieval formula construction module is used for receiving keywords and retrieval conditions input by a user, generating a retrieval formula and retrieving related literatures according to the retrieval formula; the literature screening module is used for carrying out quality evaluation and screening on the retrieved literatures; the scale evaluation module is used for reading and extracting key data in the screened literature and carrying out scale evaluation; the data extraction and information summarization module is used for summarizing the extracted information and generating a report; the output and application module outputs the generated report; and the system maintenance and updating module is used for carrying out system maintenance and updating. According to the method, the AI automatic construction retrieval formula is realized, the standard is proposed, and the retrieval efficiency and accuracy are improved.

The invention discloses a permanent magnet generator air gap eccentricity fault identification method and system, relates to the technical field of permanent magnet generators, and uses wavelet transform to extract time-frequency features and generate multi-band feature vectors, so that the system can more sensitively identify subtle features of air gap thickness deviation. And the extraction and analysis precision of characteristic signals of different frequency bands is effectively improved. And secondly, analyzing the air gap thickness deviation condition based on the feature vector, constructing an eccentricity degree index, and quantifying the eccentricity degree, so that the severity of the air gap eccentricity fault of the permanent magnet generator can be intuitively judged. And when the eccentricity degree exceeds a set threshold value, the system automatically sends a fault type checking instruction, so that the fault detection process is more intelligent. Afterwards, abnormal changes of all positions are analyzed through infrared thermal data and temperature gradients, air gap foreign matter faults possibly causing air gap eccentricity are further recognized, and the limitation of single signal analysis in traditional detection is made up.

The invention discloses a method and a system for rapidly extracting morphological indexes of an erosion gully, and belongs to the technical field of water and soil conservation. The method comprises the following steps: S1, watershed segmentation; s2, identifying an erosion gully object; comprising the following steps: performing spectral feature analysis based on an orthoimage to obtain a first principal component; selecting a texture feature variable and a topographic feature variable; performing multi-scale image segmentation on the first principal component based on the texture feature variable; identifying whether each object is an erosion gully according to the topographic feature variables; and correcting the identification result to obtain a final erosion gully object identification result. S3, channel line extraction is carried out on the erosion gully; extracting a river network water system through hydrological analysis, correcting the river network water system, and taking the corrected river network water system as a channel line of the erosion gully; and S4, extracting the morphological index of the erosion gully according to the erosion gully object identification result and the gully line. The method can effectively improve the extraction precision of the morphological characteristics of the erosion gully, and breaks through the problem of poor extraction precision of the erosion gully.

The invention proposes a small sample image generation method based on semantic mask reconstruction and category semantic learning, and relates to the technical field of image processing, and the method comprises the steps: S1, constructing a network model comprising a generator and a discriminator based on a LoFGAN network; s2, extracting depth features of the input image by using a generator, and fusing the depth features of the input image into depth semantic features through local feature fusion; s3, semantic mask reconstruction is carried out on the depth semantic features, and a corresponding image is generated through a decoder; s4, sending a real image and a corresponding image generated by the decoder into a discriminator to train the network model; s5, inputting the small sample image into the trained network model to obtain a generated image similar to the input image; according to the method, the extraction precision of the semantic information can be effectively enhanced, the understanding ability of the generation model for a complex semantic scene is improved, and the fidelity and definition of the image are improved.

The invention provides a non-power-cut test method for mutual inductance parameters of a power transmission line under excitation of different-frequency signals and a related device, and solves the problems of difficulty in accompanying stop and high safety risk of adjacent lines. The method comprises the following steps: selecting one circuit in the same corridor for power failure, connecting the head end with an induced electricity suppression device, and performing three-phase short-circuit grounding at the tail end; injecting a different-frequency sinusoidal current of 45-55Hz into the power failure line; the synchronous measurement device collects head and tail voltage and current of a running line based on a satellite synchronous clock; extracting pilot frequency signals through two-stage notch filtering and Fourier transform; and different-frequency zero-sequence self-impedance and mutual impedance are calculated and converted into power-frequency impedance. According to the invention, accurate test without power failure can be realized, and safety and power supply reliability are guaranteed.

The invention discloses an infrared space adjacent target super-resolution sub-pixel positioning method, which relates to the technical field of image processing, and is characterized by comprising the following steps: S1, obtaining an area containing a space adjacent target in an infrared image; s2, constructing an infrared space adjacent target VOC format data set; s3, the improved YOLOv5 neural network is trained; s4, inputting the infrared image into the trained improved YOLOv5 network, outputting the number of targets in the CSOs, and completing super-resolution processing; and S5, taking the number of the targets in the step 4) as input, and obtaining the sub-pixel centroid position of each target on the image plane by using an improved FCM clustering algorithm. According to the method, a small target detection layer and a convolution attention module are added in a training network, so that the training network is more sensitive to point target feature extraction, and the target number estimation effect of CSOs is improved; and an FCM clustering algorithm is combined with Lanczos3 interpolation and particle distribution, so that precise centroid positioning of the sub-pixels of the CSOs is realized, and prior information is provided for follow-up processing such as tracking and three-dimensional positioning.

The invention discloses a lithium iron phosphate battery multi-class fault diagnosis method based on deep learning, and belongs to the field of lithium battery fault diagnosis. The sparrow search algorithm optimized by the eagle-Cauchy variant is used for self-adaptive updating of variational mode decomposition parameters, and the lightweight fault diagnosis framework fusing the convolutional neural network and Transform is applied to fault diagnosis, so that local and global features in battery time sequence data can be effectively captured; complex modes and interdependence existing in fault features are skillfully captured, diagnosis and detection of various tiny faults of the lithium battery are facilitated, the recognition capability of the tiny faults is enhanced, fault information can be timely and accurately recognized, and fault misdiagnosis is reduced.

The embodiment of the invention provides a radiation information extraction method and device based on subatomic characteristics, and the method comprises the steps: determining a remote sensing satellite image of an exploratory area, carrying out the preprocessing operation of the remote sensing satellite image of the exploratory area, determining a corresponding remote sensing satellite image of a target substance, and carrying out the development drying operation of the remote sensing satellite image of the target substance, determining a corresponding target substance exposure image negative film, performing filtering operation on a negative film image after printing operation according to a preset optical filter, determining a corresponding to-be-detected substance anomaly, and performing receiving operation on the to-be-detected substance anomaly according to a preset information carrier. And performing radiation operation on the information carrier subjected to the receiving operation according to the set sub-atomic field generator and the ultraviolet radiation source, determining the corresponding abnormal radiation image of the to-be-detected substance, and determining the corresponding radiation information of the to-be-detected substance according to the abnormal radiation image of the to-be-detected substance, so that the efficiency and the accuracy of radiation information extraction can be improved.

The embodiment of the invention provides a conductor generation method and device based on point cloud data, equipment and a storage medium, and relates to the field of three-dimensional modeling, and the method comprises the steps: obtaining the point cloud data, carrying out the classification processing of the point cloud data based on a weak supervised learning framework, obtaining the conductor point cloud data and the tower point cloud data, and obtaining the conductor point cloud data and the tower point cloud data; performing clipping processing on the conductor point cloud data according to the tower point cloud data to obtain clipped conductor point cloud data, performing classification processing on the clipped conductor point cloud data based on a DBSCAN clustering algorithm to obtain a training sample, and training a random forest model according to the training sample to obtain a conductor fitting model, the lead coordinate points are generated according to the tower point cloud data, the lead coordinate points are input into the lead fitting model, the lead list is output, the lead path in the point cloud data is automatically identified and extracted through the lead fitting model, the lead path extraction speed and accuracy are improved, and the lead path generation efficiency is improved.

The invention provides an earth fluid effective angular momentum time sequence reconstruction method and device. The earth fluid effective angular momentum time sequence reconstruction method comprises the steps that earth fluid effective angular momentum is obtained, an original earth fluid effective angular momentum time sequence is obtained through linear interpolation, spectral analysis is conducted through fast Fourier transform, and an inherent periodic term is obtained; setting a lag window length based on an annual swing period and a Marcher swing period, performing singular value decomposition grouping and reconstruction, and obtaining a period time sequence, a trend time sequence and a residual time sequence; performing spectral analysis on the periodic time sequence, the trend time sequence and the residual time sequence obtained by decomposition through fast Fourier transform, judging whether the spectral characteristics are the same as those obtained by spectral analysis or not, and if not, continuing to decompose the periodic term; otherwise, reconstructing the earth fluid effective angular momentum time sequence. According to the method, a singular spectrum analysis scheme for setting a stable lag window is provided, signal integrity can be ensured, signal aliasing is avoided, and the extraction precision of quasi-periodic signals is improved; meanwhile, the data fusion consistency is ensured, and the earth fluid effective angular momentum reconstruction precision is improved.