294 results about "Spatial clustering" patented technology

The invention provides an intelligent eddy current nondestructive testing method and system based on a flexible GMR sensor array, and relates to the technical field of nondestructive testing. According to the invention, a GMR sensor array and a micro excitation coil array are integrated on a flexible substrate, and synchronous acquisition under multi-frequency and multi-phase excitation is realized by establishing a probe-workpiece unified space coordinate system and a time synchronization reference; performing temperature drift compensation, attitude correction and gap normalization processing on the original response tensor to generate a corrected response tensor; the intelligent inversion model based on the prior constraint of the electromagnetic field jointly identifies the conductivity, the thickness and the crack parameters, and outputs a defect parameter map and a confidence map; and a detection report containing the position, the size, the depth and the confidence coefficient is generated through spatial clustering and connectivity analysis, so that high-sensitivity and high-reliability nondestructive detection under a complex curved surface structure is realized.

The invention relates to the technical field of urban planning, and particularly discloses an urban planning construction land management and control system based on big data, and the system comprises the steps: obtaining geological environment, landform, engineering construction and human activity data through multi-source data collection; multi-source data fusion analysis is adopted to construct a construction land comprehensive evaluation field including geological stability, engineering suitability and environmental bearing capacity; a development suitability partition is generated based on a density peak value spatial clustering method; updating the development suitability level by dynamically monitoring the verification process; according to the method, accurate analysis and early warning of the three-dimensional deformation field are realized, and scientificity and safety of urban planning in a complex geological environment are improved.

The invention relates to a fitness action error correction method and device for bone key point detection, equipment and a medium. The method comprises the following steps: acquiring video data in a multi-person fitness scene, and performing skeleton key point detection on each frame of image in the video data to obtain a multi-person skeleton key point sequence; performing spatial clustering processing on the multi-person skeleton key point sequence to obtain a plurality of individual skeleton key point sequences; according to the skeleton key point sequence of each individual, generating an action track of each individual; based on a preset standard action sequence, identifying an action matching result corresponding to each action track; according to the action matching result, the action deviation value of each individual is calculated, and action error correction feedback information is generated based on the action deviation values; the error correction feedback information is used for indicating each individual to adjust the fitness action. The method can adapt to complex scenes, solves the problem that key points of scenes of multiple persons are confused, and provides effective support for fitness guidance and the like.

The invention discloses a multi-modal fusion bank receipt intelligent processing system and method based on vision and NLP, and the method comprises the steps: receiving a bank receipt picture or a PDF document, and completing the text detection, direction correction and character recognition through a visual processing engine; a three-level receipt independent segmentation mechanism is applied, and independent receipt records are divided through spatial clustering analysis, semantic analysis, visual verification and cross-page association processing; jointly extracting text features and layout features of each receipt through a double-flow multi-modal fusion model, and fusing the text features and the layout features; field-level data extraction is executed through a field extraction engine, and financial data verification including account number, amount, date and abnormity quadruple verification is carried out; and generating structured JSON output to obtain a bank receipt processing result. According to the invention, the innovative five-layer processing architecture realizes high-precision analysis of the bank receipts through an independently researched and developed receipt independent segmentation engine, a vision-semantic fusion model and a financial data verification system.

The invention discloses a method and system for space-time evaluation and restoring force early warning of a regional ecological hydrological function, and belongs to the technical field of ecological hydrology and environment monitoring and management, and the method comprises the steps: obtaining a multi-source index capable of sustainably diagnosing the ecological hydrological function; performing standardization processing on the multi-source indexes by adopting a global space-time threshold method; performing comprehensive evaluation on the standardized index data by adopting a combined weighting method and a nonlinear comprehensive model to obtain a comprehensive index; based on the comprehensive index or the multi-source index, dynamic early warning of the regional ecological hydrological function stability is carried out by adopting a critical moderation theory; and performing spatial clustering according to the comprehensive index, and performing driving force attribution analysis on the functional evolution of each clustering region to generate a spatial differentiated collaborative recovery path. The method solves the problems that a traditional evaluation method neglects quality, lacks space-time comparability and cannot carry out dynamic early warning and precise countermeasure, and has the remarkable advantages of being more scientific in evaluation, more reliable in result, and capable of carrying out early warning and countermeasure.

The invention provides a tunnel health monitoring method based on distributed optical fiber sensing, and relates to the technical field of tunnel engineering health monitoring, and the method comprises the steps: carrying out the risk assessment of a tunnel through employing an analytic hierarchy process, setting a differential optical fiber laying scheme according to an assessment result, and laying distributed optical fiber sensors on the inner surface of a tunnel lining; the method comprises the following steps: acquiring original strain data through Brillouin optical time domain analysis, and preprocessing by using a moving average filtering algorithm; building a finite element model based on actual physical data of the tunnel, inverting strain data into full-field node displacement, and obtaining a complete strain tensor of a finite element unit; performing unit-level strain state evaluation, time sequence trend risk evaluation and spatial clustering risk evaluation to obtain a comprehensive risk value; and setting a dynamic evaluation threshold interval, and completing tunnel accurate region health evaluation. Accurate monitoring and risk assessment of the tunnel health state are achieved, and the method can be widely applied to the field of safety monitoring of highway and railway tunnels.

The invention discloses a postoperative infection monitoring and early warning method and system, and relates to the technical field of clinical monitoring, and the method comprises the steps: collecting a postoperative infection multi-source alignment data set, building an individual reference model, dynamically extracting postoperative change features, building a behavioral physiological coupling model, recognizing abnormal interaction, and quantifying the infection risk. According to the method, multi-source data are fused, an individualized dynamic baseline model and a behavior-physiological coupling model are established, intelligent recognition and grading early warning of postoperative infection risks are achieved, and the method is high in practicability and easy to popularize. Model parameters are automatically corrected by utilizing a self-learning and feedback recharge mechanism, a closed-loop process of monitoring, prediction, intervention and optimization is formed, infection symptoms can be recognized in advance, manual dependence is reduced, the real-time performance and accuracy of postoperative infection prevention and control are improved, and intelligent decision support is provided for clinic.

The invention relates to the technical field of machine vision and wind power detection, and discloses a surface defect identification method and system for a rotor core of a wind driven generator, and the method comprises the steps: collecting an original image of the surface of the rotor core, and carrying out the gray mapping, and obtaining a single-channel gray image; carrying out frequency domain periodic texture suppression and local histogram equalization processing with contrast limitation on the single-channel grayscale image to obtain an enhanced feature image; constructing a background fitting model for the enhanced feature image and performing differential operation to obtain a background differential image; performing adaptive segmentation and morphological refining based on a texture direction according to the background difference image to obtain a refined defect connected domain; local gray level distribution is extracted, sub-pixel-level geometric feature calculation is carried out, and defect geometric attribute data are obtained; and spatial clustering and grading evaluation are carried out according to the data, and a final defect distribution map is determined. According to the method, periodic texture interference can be effectively suppressed, and sub-pixel-level precise positioning and intelligent grading of the tiny defects are realized.

The invention belongs to the technical field of bulk material scanning, and relates to a bulk material scanning method based on a laser radar sensor, which abandons a low-efficiency mode that all laser radar sensors continuously work at full power in the prior art, carries out global monitoring by setting a master scanning node, and sets a slave scanning node in a low-power-consumption mode. The method comprises the following steps: registering point cloud data collected by a main scanning node in adjacent scanning periods, generating a change heat map through voxelization differential calculation and spatial clustering, retrieving a high heat change region in the map, and aiming at the high heat change region, considering a geometrical relationship between a sensor and the region and multi-sensor visual angle complementarity. A sensor subset is selected from all slave scanning nodes to execute cooperative enhanced scanning, high-density local point clouds obtained through enhanced scanning and global point clouds of the master scanning nodes are fused, a bulk material pile three-dimensional surface model is generated, and dynamic on-demand scheduling and energy efficiency optimization of scanning resources are achieved.

The invention discloses an isotope-based surface water pollution tracing method, which relates to the technical field of environment monitoring, and comprises the following steps: carrying out spatial aggregation analysis on an isotope residual distribution diagram, obtaining a time evolution characteristic table, executing space-time coding on the time evolution characteristic table, and obtaining an abnormal event sequence; analyzing the time continuity and the space change of the abnormal event sequence, outputting a pollution propagation characteristic table, and performing space-time similarity matching and node association on the pollution propagation characteristic table to generate a pollution propagation association matrix; and deducing a link direction of the pollution propagation incidence matrix, generating a pollution propagation path set, executing reverse tracking and regional assembly on the pollution propagation path set, and outputting a surface water pollution traceability report. According to the method, the isotope background model based on the space-time environment feature set is constructed, and the pollution propagation features are analyzed, so that chain reconstruction and dynamic tracking of the pollution event in time and space dimensions are realized.

The invention relates to the technical field of product design, in particular to a household appliance appearance defect optimization product design method based on image discrimination, which comprises the following steps: acquiring a household appliance appearance image, calculating a gradient direction, constructing a distribution matrix, analyzing consistency to generate a defect edge image, extracting high-frequency feature scores to identify defect areas, and clustering point cloud to output defect clusters. According to the method, by calculating the gradient direction numerical value of each pixel point and constructing the gradient direction distribution matrix, the processing capacity of the directivity difference of the household appliance surface texture is effectively improved, the defect point cloud is accurately divided through the density-based noise application spatial clustering algorithm, the defect point cloud is accurately divided, and the defect point cloud is accurately divided. According to the method, the space positioning accuracy of the defect area is improved, the precision of optimization design is ensured, curved surface reconstruction is performed in combination with the least square method, the appearance design of the household appliance is accurately corrected, and the production efficiency and the design quality are optimized.

The invention relates to the technical field of image processing, in particular to a lens edge detection method and system based on image generation, and the method comprises the following steps: collecting a lens image through an industrial camera, carrying out the weighted graying, extracting an edge coordinate through a Laplace operator, constructing an edge pixel coordinate set, and carrying out the image processing; screening an edge validity marking result by combining a gray difference threshold, calculating a phase difference between a gradient direction and light source incidence based on a Sobel operator, extracting pixels in a consistent direction, carrying out spatial clustering, generating a lens edge pixel cluster, and fitting a continuous curve to construct a complete lens edge contour. Noise and reflection interference are restrained through pixel neighborhood gray difference, effective pixels are screened in combination with a gradient direction and light source incident phase relation, edge direction consistency is enhanced, pixels are clustered according to spatial continuity and gradient intensity, contours are continuously fitted based on pixel cluster distribution, and lens edge integrity and geometric consistency under complex illumination are improved.

The invention discloses a single cell space transcriptome data analysis method, device and system, and a storage medium. The method comprises the following steps: acquiring single cell space transcriptome data; the spatial transcriptome data comprises a gene expression map and spatial site coordinates; constructing a graph structure considering gene expression similarity and spatial position continuity on the basis of the data, and performing data representation on a gene expression graph by using an adversarial auto-encoder; in combination with a graph neural network, robust potential characterization is learned, training loss is constructed through reconstruction of a gene expression map, and meanwhile, a clustering prediction result of spatial sites is obtained in combination with mcluster clustering. By adopting the technical scheme of the invention, the spatial clustering analysis with higher precision can be realized, and the key spatial functional region in the biological tissue can be identified.

The invention relates to the technical field of point cloud data processing, and discloses a contour feature fine extraction method for workpiece scanning point cloud, which mainly comprises two parts of preliminary extraction and fine processing. In the preliminary extraction part, the point cloud curvature is used as an extraction index, and the calculation method is optimized. Introducing a moving least square optimization normal vector for calculation; normal information is globally considered, normal curvature calculation is optimized in the mode that a normal section circle is constructed through a normal vector, and then the calculation precision of the point cloud principal curvature is optimized. According to the fine extraction part, a spatial clustering algorithm is used for initial feature points, blocky features and linear features are separated while miscellaneous points are removed, and targeted classification processing is carried out according to feature states. For the block-shaped features, using a boundary extraction mode to extract boundary feature points; for linear features, a point cloud thinning mode is used to extract contours. According to the method, the calculation accuracy and stability of the point cloud principal curvature can be optimized, and the extraction precision of the workpiece contour features is improved.

The invention relates to the technical field of corrugated board detection, in particular to a corrugated board warping detection method and system based on multi-modal data fusion. The method comprises the following steps: acquiring three-dimensional point cloud data of a corrugated board; a paperboard contour main body point set is obtained through a preset spatial clustering algorithm; performing point cloud feature extraction to obtain a point cloud global feature vector; performing cubic polynomial fitting on the paperboard contour main body point set to obtain a standard image; performing image feature extraction on the standard image to obtain an image global feature vector; performing fusion processing on the point cloud global feature vector and the image global feature vector to obtain a multi-modal fusion feature vector; and inputting the multi-modal fusion feature vector into a pre-trained classifier, and outputting the warping category of the corrugated board. The obtained multi-modal fusion feature vector gives full play to the geometric accuracy of the three-dimensional point cloud data and the semantic richness advantage of the standard image, and the detection accuracy of the corrugated board warping is improved.

The invention relates to the technical field of building structure safety monitoring, in particular to a stress deformation monitoring method and system for a rib truss floor support plate. The method comprises the following steps: establishing a digital twinborn model, mapping sensor data to grid nodes on a plate surface, calculating a local curvature, and ensuring data reliability through temperature compensation and confidence interval verification; an abnormal sensitivity index ASI is constructed based on multi-modal data, strain, displacement, frequency and curvature deviation are fused and dynamically weighted, and an abnormal area is positioned in combination with spatial clustering; the ASI evolution trend is analyzed through sliding window regression, slope and intercept parameters are extracted, and a health evolution index HEI is generated to achieve four-level health grading early warning; and establishing an abnormal dynamic model to simulate a local abnormal propagation path, predicting the residual life and the health risk index, and guiding a maintenance decision. According to the invention, monitoring-early warning-decision closed-loop management is realized.

The invention discloses a wind power plant power prediction method and device based on space-time collaboration and a storage medium, and belongs to the technical field of deep learning, and the method comprises the steps: obtaining historical wind field data and geographic space features of a wind power plant, the wind field data comprising fan operation data and meteorological feature data in the same time period; combining the historical wind field data, the geographic features and the corresponding total power labels of the wind power plant into a sample set, and training a wind power plant power prediction model by using the sample set; wherein the wind power plant power prediction model extracts local and overall double-layer similarity based on historical wind power plant data, obtains a time period cluster with high similarity with meteorological feature data from historical fan operation data according to the double-layer similarity, and performs spatial clustering on the time period cluster based on historical geographic features; and inputting the to-be-measured wind field data and the to-be-measured geographic features into the trained wind power plant power prediction model to obtain the total power prediction value of the wind power plant, thereby improving the wind power prediction precision and efficiency of the complex terrain.

The invention relates to the field of slope monitoring and early warning, and particularly discloses a mining open-air slope radar online monitoring and early warning system which comprises a deformation monitoring module, a sliding area calibration module, a sliding source determination module and a landslide risk assessment module. The system obtains slope deformation data through a radar image sequence, screens effective pixels and constructs a deformation time sequence; identifying a sliding region by utilizing spatial clustering and de-duplication processing; judging a slip source area and a secondary slip area based on the slip starting time, the slip main direction and the potential influence area; and finally, calculating a landslide risk coefficient through dynamic weight fusion in combination with the deformation rate and the sliding surface expansion speed of the sliding source region, and realizing graded early warning. The method overcomes the problems of low risk area identification precision, difficulty in source judgment, single evaluation index and the like in the prior art, has the advantages of accurate identification, comprehensive evaluation, timely early warning and the like, and is suitable for strip mine slope safety monitoring and disaster prevention and control.

The invention provides a three-dimensional steel structure full-coverage path planning inspection method based on topology reconstruction, and relates to the technical field of industrial robots, and the method comprises the steps: obtaining a model file of a to-be-inspected three-dimensional steel structure; constructing an original vertex set and an original edge set according to the model file; performing adaptive clustering on each original vertex in the original vertex set through a density-based spatial clustering algorithm to obtain a cluster; taking the geometric center of the original vertex in each cluster as a topological node, and establishing a mapping relation between the original vertex and the topological node; carrying out topology reconstruction on the original edge set, and constructing an undirected weighted graph; stitching non-connected sub-graphs in the undirected weighted graph through a K-dimensional tree and a minimum spanning tree algorithm to obtain a fully connected graph; generating a full-coverage continuous path sequence through a Chinese postman algorithm; the full-coverage continuous path sequence is converted into a three-dimensional space coordinate sequence, and the three-dimensional steel structure to be inspected is inspected according to the three-dimensional space coordinate sequence.

The invention discloses an urban public facility layout optimization method and system based on multi-source space-time big data, and relates to the technical field of urban planning. Comprising the following steps: firstly, acquiring and fusing multi-source space-time big data such as mobile equipment signaling data and point-of-interest data; identifying and delimiting an urban public service three-level center system based on the fusion data through a spatial clustering and network analysis algorithm, and determining each center level and a spatial influence range; constructing a differentiated service demand quantification model for each level of service center; constructing a multi-stage collaborative layout optimization model, and completing facility hierarchical assignment and site selection optimization; evaluating the layout scheme in the simulation environment and outputting a grading evaluation report; through dynamic analysis and multi-level collaborative optimization driven by multi-source space-time big data, accurate matching of public facility layout and service requirements is achieved, and meanwhile the service coverage rate and cross-level connection efficiency are improved.

The invention provides a mine multi-disaster collaborative early warning method and system based on multi-model fusion, and relates to the field of data processing. According to the method, multi-source monitoring data of a deep stope, overlying surrounding rock and a large-scale goaf are obtained, a multi-source monitoring data set is formed through space registration and time alignment, and a three-dimensional digital twinborn body is constructed and subjected to inversion correction; deducing a stable evolution behavior in the twin body to obtain a virtual precursor trajectory set, and forming a physical prediction state and a statistical prediction state respectively by combining a physical prediction model and a statistical prediction model; carrying out difference mapping on the virtual track and a real observation track to construct a multi-dimensional difference feature, and executing spatial clustering to obtain a silent abnormal cluster; and a multi-disaster risk map is further constructed in combination with a multi-disaster coupling relationship, reasoning is fused, a silent catastrophe high-risk area is identified, and a collaborative early warning result is output according to space connectivity and time continuity. By implementing the technical scheme, the accuracy of cooperative early warning for multiple disasters of the mine can be improved.

The invention discloses a magnetic source target positioning method, which belongs to the technical field of geophysical exploration, is used for positioning a magnetic source target, and comprises the following steps: collecting and preprocessing plane grid magnetic anomaly data, constructing a simplified Euler deconvolution model under the constraint of derivative features, segmenting observation data by using a local sliding window, and resolving to generate an Euler solution point cloud. The method comprises the following steps: converting a selection problem of an equivalent scale factor into a one-dimensional global optimization problem, adaptively searching an optimal solution through a Bayesian optimization method, identifying a magnetic source high-probability solution point and a noise solution point by using a density-based spatial clustering method, introducing a density penalty term to strengthen a high-probability solution constraint, and constructing a final positioning target function. And outputting the optimal position solution of the magnetic source. According to the method, a traditional Euler deconvolution model is simplified, noise interference is effectively suppressed, the stability and robustness of the inversion process are improved, uncertainty caused by manual parameter selection is avoided, and the positioning precision is improved.

The invention relates to the technical field of crack detection, in particular to a porcelain insulator micro-crack infrared spectrum detection method and device capable of accurately sounding, and the method comprises the following steps: obtaining a temperature change rate sequence, screening a stable interval, establishing a thermal radiation synchronous identifier, generating a time index set, carrying out energy registration mapping based on the index set to form a registration structure body, and carrying out infrared spectrum detection on the registration structure body; analyzing the energy gradient to identify the crack boundary to generate a three-dimensional coordinate set, and reconstructing voxel to analyze the crack morphology to generate a three-dimensional mapping set. According to the method, a crack voxel structure in a three-dimensional space is constructed by constructing an infrared acquisition time index, screening a radiation state stable section, combining multi-channel energy peak-valley comparison and space coordinate mapping and by means of an energy gradient distribution analysis and space clustering recognition mechanism; the synchronous and accurate acquisition of key characteristics such as crack position, shape, direction and depth is realized, and the comprehensive capability of spatial positioning, depth identification and crack penetration trend discrimination of the porcelain insulator microcrack is obviously improved.

The invention relates to the technical field of intelligent driving, in particular to a road construction area map layer construction method and device, equipment, a storage medium and a product. According to the scheme, vehicle end road environment perception data uploaded by crowdsourcing vehicles are used for recognizing and extracting construction related elements, and spatial clustering is carried out on the construction related elements to obtain a recognition perception result of a construction area; meanwhile, road width abrupt change areas are detected in combination with road reference data, construction areas are determined through the spatial incidence relation of the road width abrupt change areas and related attribute parameters are calculated, and then a standardized construction layer of each construction area is constructed and accurately pushed. According to the scheme, through a dual verification mechanism of construction element space aggregation and road width mutation, single feature misjudgment is effectively eliminated, the construction area recognition accuracy is improved, the dynamic layer is generated through the standardized attribute parameter set, structural description and efficient transmission of construction information are achieved, and the construction efficiency is improved. And a real-time and reliable road construction situation sensing capability is provided for a terminal user.

The invention relates to the field of image processing, in particular to a small target aphid identification method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring a to-be-processed crop leaf image, and inputting the to-be-processed crop leaf image into a pre-trained small target aphid identification model; according to the model, an image is processed, and a detection frame set of an aphid target and a continuous density map reflecting the spatial aggregation intensity of the aphid target are synchronously output. The training process of the model comprises the steps that farmland scene images are collected, double-label collaborative labeling is carried out, and bounding box labels and continuous density map labels are generated; training a preset model by using the labeled data, wherein the model has multi-scale feature extraction and fusion capabilities; model parameters are optimized through a loss function, an identification model capable of completing target detection and density estimation at the same time is finally obtained, and the detection capacity of small target aphids under the complex background is improved.

The invention provides a power line laser radar point cloud extraction method based on hierarchical feature perception, and belongs to the field of point cloud data processing, and the method comprises the steps: obtaining original laser radar three-dimensional point cloud data of a power line, generating a digital elevation model, and constructing a three-dimensional density field; performing normalization processing on the three-dimensional point cloud density field, calculating a dynamic elevation offset, and performing filtering according to a dynamic elevation threshold curved surface to obtain a potential power line point cloud; acquiring a local point set in a neighborhood, calculating a local linearity feature, and performing threshold determination on the local point set to obtain a refined power line point cloud; performing density-based spatial clustering on the refined power line point cloud, and obtaining an independent power line point cloud through graph segmentation and aggregation optimization; and performing iterative optimization on the independent power line point cloud according to spatial continuity constraint to obtain a power line point set. According to the invention, high-precision and high-robustness extraction of the power line point cloud in a complex scene is realized.

The application relates to the technical field of high-intensity focused ultrasound treatment, and discloses an energy mode switching and treatment path planning method and system based on distance constraints, which comprises the following steps: segmenting and three-dimensionally reconstructing medical image data to obtain a three-dimensional model; generating a grid about a treatment point in a tumor area of the three-dimensional model, and binding a corresponding lesion physical property label to each treatment point; calculating the shortest distance from each treatment point to a key tissue to form an enhanced treatment point set; inputting the enhanced treatment point set into a preset decision model to obtain a final treatment point set; grouping the treatment points in the final treatment point set based on energy mode labels; and performing spatial clustering on the treatment points in each group to form multiple treatment clusters and generate a complete treatment path sequence. The application reduces process loss caused by mode switching, and makes the treatment process more systematic and controllable.

This invention proposes a method for extracting power line lidar point clouds based on hierarchical feature perception, belonging to the field of point cloud data processing. The method includes: acquiring raw 3D point cloud data of power lines from lidar, generating a digital elevation model, and constructing a 3D density field; normalizing the 3D point cloud density field, calculating dynamic elevation offset, and filtering based on a dynamic elevation threshold surface to obtain potential power line point clouds; acquiring local point sets within the neighborhood, calculating local linearity features, and thresholding the local point sets to obtain refined power line point clouds; performing density-based spatial clustering on the refined power line point clouds, and obtaining independent power line point clouds through graph segmentation and aggregation optimization; and iteratively optimizing the independent power line point clouds based on spatial continuity constraints to obtain power line point sets. This invention achieves high-precision and robust extraction of power line point clouds in complex scenarios.

The application relates to a water quality change trend rapid prediction method based on multi-source data fusion and physical constraints, and specifically comprises the following steps: step 1: synchronously collecting data such as spectrum information, DO, COD, temperature and pH at key monitoring sites, constructing a water dynamics-water quality coupling equation, and simulating the space-time dynamic distribution of water quality parameters; step 2: outputting a water quality sensitive area through a water dynamics-water quality model, screening sensor layout points by combining information entropy evaluation and spatial clustering, realizing low-cost water quality sensor network deployment through a multi-objective optimization algorithm, and calculating a water body global water quality distribution map by adopting a spatial interpolation method; step 3: analyzing main pollution sources according to the synchronous collection of spectrum information at key monitoring sites, and adopting principal component analysis and an attention mechanism neural network; step 4: based on real-time optical characteristic value-DO data, combining a spatial topological network, a water quality gradient and a cross-region covariance to capture the spatial correlation of water quality parameters between different points, and constructing an optical characteristic value-DO-COD dynamic prediction model; combining pollution tracing and spectrum characteristic correction to correct the COD prediction value, adopting ensemble Kalman filtering to assimilate multi-source data, and improving the model accuracy.

The invention discloses a power grid harmonic wave and inter-harmonic wave detection method and device based on distributed multi-frequency measurement data, equipment and a medium. A sine wave method comprises the following steps: detecting harmonic wave and inter-harmonic wave components existing at each sine wave measurement point based on a random matrix theory; determining the frequency and amplitude of each harmonic wave and inter-harmonic wave of each sine wave measurement point by using a rotation invariant technology; clustering the frequency of each harmonic wave and inter-harmonic wave on each measurement point by using a density-based noise application space clustering method to obtain the target frequency of each frequency cluster; and determining harmonics and inter-harmonics of the power grid according to the target frequency and the amplitude. Each harmonic and inter-harmonic component existing on a measurement point is detected through a random matrix theory and a rotation invariant technology, accurate frequencies and amplitudes of the harmonic and the inter-harmonic are obtained, and more accurate frequencies of the harmonic and the inter-harmonic of a power grid are obtained by fusing data of a plurality of distributed measurement points through a clustering method. Therefore, the detection capability of harmonic waves and inter-harmonic waves of a power grid is improved.