439 results about "Spatial clustering" patented technology

The invention relates to a water quality change trend rapid prediction method based on multi-source data fusion and physical constraint, and the method specifically comprises the following steps: 1, synchronously collecting spectral information, DO, COD, temperature, pH and other data at a key monitoring station, constructing a hydrodynamic water quality coupling equation, simulating the spatial-temporal dynamic distribution of water quality parameters, and calculating the water quality change trend; 2, outputting a water quality sensitive area through a hydrodynamic force-water quality model, screening sensor layout point positions in combination with 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 diagram by adopting a spatial interpolation method, 3, synchronously collecting spectral information according to key monitoring sites, analyzing main pollution sources, and adopting a principal component analysis and attention mechanism neural network; 4, based on real-time optical characteristic value-DO data, in combination with a spatial topology network, a water quality gradient and a cross-regional covariance, capturing water quality parameter spatial correlation among different sites, and determining the water quality parameter spatial correlation among different sites; an optical characteristic value-DO-COD dynamic prediction model is constructed; a COD predicted value is corrected by combining pollution traceability and spectral characteristics, and multi-source data is assimilated by adopting ensemble Kalman filtering, so that the model precision is improved.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a Beidou-based geological disaster intelligent monitoring and early warning method and system. Beidou high-precision monitoring equipment is deployed by selecting a geological disaster prone area, earth surface displacement, settlement and inclination deformation data are collected in real time, and a multi-modal database is constructed in combination with environmental parameters. And performing alignment and noise correction on the spatio-temporal data by adopting Kalman filtering and a weighted evidence theory, extracting short-term and long-term deformation characteristics by utilizing a DBSCAN spatial clustering algorithm, and realizing multi-scale abnormal change pattern recognition in combination with a GeoHash grid index. Dimensional differences are eliminated through Z-score standardization processing, a geological stability index and change rate model is established, a causal reasoning framework is further constructed based on a Bayesian network, and a risk prediction model is trained in combination with a space-time neural network. The system can dynamically adjust a monitoring period threshold value and automatically trigger graded early warning, and supports hidden danger rectification whole-process tracing and multi-level gridding management. According to the scheme, the limitation of traditional single-source monitoring is broken through, the full-chain prevention and control of geological disasters from deformation feature extraction, causal relationship modeling to dynamic risk prediction is realized, and the early warning timeliness and accuracy are remarkably improved.

The invention relates to the technical field of land resource monitoring, in particular to a land resource dynamic monitoring and early warning method and system based on multi-source remote sensing data fusion, and the method comprises the steps: employing an unmanned plane to periodically collect optical images, SAR echoes and LiDAR point clouds, constructing a ground three-dimensional digital model, and carrying out the land parcel division; performing fusion to form a multi-dimensional feature vector, establishing an LSTM land parcel feature evolution model, and predicting a change rate interval of each feature in a current period based on a historical sequence; constructing a time sequence difference change detection algorithm, calculating a land parcel change rate, and screening potential abnormal land parcels by taking a prediction interval as an anomaly judgment threshold value; a double-branch convolutional neural network is adopted to identify crop states, growth stages and construction violation behaviors, abnormity is judged and determined, and confidence is given; spatial clustering is carried out on determined abnormal land parcels, accurate boundaries are obtained in combination with a three-dimensional model, multi-level early warning information is generated, and the decision-making efficiency and response speed of land resource monitoring are improved.

The invention relates to the technical field of workpiece airtightness detection, in particular to a workpiece airtightness detection method and system based on multi-source data fusion, and the method comprises the steps: collecting the pressure fluctuation, temperature field distribution and vibration spectrum data of a workpiece through a sensor array, and executing a stepped pressure excitation process; the method comprises the following steps: dynamically configuring an inflation rate according to the heat-conducting property of a material, reversely compensating pressure based on temperature field data in a pressure maintaining stage to eliminate thermal deformation interference, and extracting a pressure attenuation rate and nonlinear characteristics, an area proportion of an abnormal low-temperature region and space aggregation of an energy abrupt change point in a vibration spectrum from compensated data; according to the method, multi-physical field features are fused, the detection precision and reliability are improved, and the method is suitable for the airtightness detection requirement in the high-precision manufacturing field.

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 discloses a metal ore body identification method based on remote sensing interpretation, relates to the technical field of mineral resource exploration, and aims to solve the problems of low identification precision, neglect of geological laws and poor generalization caused by dependence on single remote sensing data in the prior art. After standardization processing, spectrum and texture features are fused to generate a composite vector; extracting a multi-scale mineralization signal by using a convolutional neural network, and interpreting and constructing an ore control area in combination with a terrain gradient and a curvature; alteration zoning is identified through spectrum angle matching, and a favorable mineralization area is determined by combining lithology combination analysis; and dynamically weighting geological elements by adopting a deep learning fusion network, outputting an ore body position according to a comprehensive score, and finally optimizing a boundary through spatial clustering and morphological filtering. The method significantly improves the automation degree and reliability of ore body identification in a complex environment, and is suitable for metal mineral exploration target area delineation.

The invention discloses a target detection method and system based on a millimeter wave radar. The target detection method and system are used for realizing accurate detection, positioning and dynamic and static recognition of multiple targets under a complex background. According to the method, a distance-Doppler spectrogram is generated through the technical means of sliding window construction, spectral analysis, clutter suppression and the like, and candidate target points are detected by adopting an SO-CFAR algorithm. Then, determining a target position through high-resolution direction estimation and coordinate transformation, performing spatial clustering in combination with a density-based DBSCAN algorithm, and extracting a target geometric center and a bounding box; in the aspect of target tracking, Kalman filtering is used for predicting and updating the position and speed of the target, and a beam forming technology is used for enhancing a target signal, so that the target recognition stability is improved. And finally, the system performs robust dynamic and static state recognition on the target through a dynamic and static judgment module, so that high precision and robustness of the target detection process are ensured. The method can effectively cope with static background interference and dynamic target changes, and is suitable for target detection and tracking in a complex environment.

The invention discloses a material supply and resource recovery collaborative scheduling method and system, and belongs to the technical field of logistics scheduling, and the method comprises the steps: obtaining a material supply instruction and a garbage recovery instruction, carrying out the image recognition of a building garbage picture, obtaining the garbage attribute, and generating a demand instruction library; the method comprises the following steps: establishing a feature knowledge base, setting a spatial clustering method, carrying out spatial clustering and time window screening, carrying out geographic coordinate analysis and time sequence sorting on material demand points and recovery points, generating an initial task pool, setting a loading matching method, and generating a material supply and recovery loading schematic diagram according to building material and garbage attributes; a circulation path is planned, a path planning method is set, an ant colony algorithm is used for initial path planning, a monitoring feedback method is set, data are returned in real time, the running state of the vehicle is monitored, and an alarm is given out immediately once abnormity is found; and the sorted and regenerated aggregate data is synchronized to a building material database, a resource feedback method is set, a reverse transportation task is identified, and a material supply and resource recovery closed loop is formed.

The invention belongs to the technical field of trajectory planning, and discloses an urban road trajectory planning and control method and system, and the method comprises the steps: generating an obstacle set and a Frenet projection of a road physical boundary through employing a spatial clustering algorithm; utilizing an extreme value method and a self-adaptive smoothing algorithm to dynamically construct a feasible trajectory region trajectory, and synchronously performing interval constraint expansion on a future prediction trajectory of the dynamic obstacle; constructing a path optimization objective function with interval constraints, and solving an optimal path variable in real time by using a numerical optimization method; an expected speed and acceleration sequence of each sampling point is generated based on a space-time domain dynamic planning method, and a longitudinal motion curve is optimized in real time by using an objective function; and inputting the space path and the longitudinal speed sequence into an MPC system, and calculating an optimal front wheel steering angle and acceleration control instruction to realize vehicle trajectory tracking. The method can sense errors and environment changes in a self-adaptive mode, the response speed and robustness of dynamic traffic flow are improved, and multi-target collaborative optimization is achieved.

The invention discloses an unknown radar radiation source sorting method based on time sequence feature clustering. The unknown radar radiation source sorting method comprises the following steps: acquiring radar signal pulse description words of pulse signals; according to the radar signal pulse description word, performing spatial clustering on the pulse signal to obtain a spatial clustering result; for each spatial clustering result, according to the radar signal pulse description word of the pulse signal in each spatial clustering result, performing time feature clustering on the pulse signal in each spatial clustering result to obtain a time sequence clustering result; and analyzing a time parallel relationship and a time continuous relationship of the time sequence clustering results, and performing pulse group sequence blending on the time sequence clustering results to obtain a sorting result. On the basis of the existing clustering algorithm, the information of the pulse signal in the time dimension is introduced, the time sequence feature clustering of the pulse signal is realized by using the ST-DBSCAN algorithm thought and introducing the TOA-PA constraint interval, and the method has strong robustness for the complex electromagnetic environment.

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 the technical field of defect detection, in particular to an inner package defect detection system which comprises an illumination control module, a boundary recognition module, a defect gathering module, a grade mapping module and a response linkage module. According to the method, through the convergence degree and Euclidean vector distribution of the boundary contour form, boundary offset distribution information is established, tiny offset or abnormal positions are accurately captured, aggregation extraction and spatial clustering of defect boundaries are achieved, abnormal clusters are screened through distance and number distribution parameters, and the defect detection accuracy is improved. And a defect level is mapped by combining a structure level and a functional risk factor, and differential station response operation is triggered according to level evaluation, so that on the basis of improving image quality, the component identification precision is guaranteed, the space representation capability of defect clustering is enhanced, and the grading judgment granularity of defect detection in multi-component assembly is enhanced. Dynamic linkage response control in the inner packaging process is achieved, and the accuracy of overall defect recognition and the timeliness of control decision making are improved.

The invention relates to the technical field of intelligent traffic and road safety monitoring, and discloses a 4D imaging millimeter wave radar-based slope deformation monitoring and identification method, which comprises the following steps of: acquiring a continuous point cloud; carrying out voxel modeling on the point cloud in a three-dimensional space; selecting three continuous frames of point clouds, and identifying mutant voxels by calculating the point number variation of each voxel in adjacent frames; performing spatial clustering on the mutant voxels to obtain an obstacle region; performing feature extraction on the obstacle area, wherein the extracted features comprise the height difference, the average reflection intensity and the centroid drift of the obstacle area; carrying out linear weighted fusion on the extracted features, and constructing a landslide mutation index; and judging whether an obstacle target exists in an obstacle area or not according to a preset condition, and performing risk grading identification according to the landslide sudden change index. According to the invention, all-weather, structured and high-reliability perception of major traffic potential safety hazards such as landslide / rockfall is realized, and a new basic capability is provided for a traffic safety system.

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 embodiment of the invention provides a road disease inspection method and device based on AI recognition, equipment and a storage medium, and is used for road maintenance. The method comprises the steps of performing multi-source data synchronous acquisition on a road surface to obtain an original image-positioning data set, performing dynamic interference suppression and image enhancement on the original image-positioning data set to obtain a stable enhanced image sequence, and performing disease target detection and classification in combination with a deep learning recognition model to obtain a disease target set, and performing multi-target space-time correlation and satellite positioning data fusion on the disease target set to obtain a stable target trajectory set, performing geometric coordinate conversion on the stable target trajectory set to obtain a road disease data set, and performing clustering analysis through a spatial clustering analysis algorithm to generate a road disease maintenance strategy report. Through the technical means of multi-source cooperation, deep learning, space-time fusion and the like, high-precision and intelligent detection of road diseases is realized, the road maintenance efficiency is improved, and the manpower and material resource cost is reduced.

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 relates to the technical field of fan blade fault diagnosis, and discloses a fan blade fault diagnosis system and method based on HHT and DBSCAN. Synchronously acquiring vibration signals acquired by a three-axis acceleration sensor and wind speed and rotating speed working condition data acquired by an SCADA (Supervisory Control and Data Acquisition) system; eMD empirical mode decomposition is carried out on the collected vibration signals, the vibration signals are decomposed into a limited number of IMF signals, and effective IMF signals are screened; generating a Hilbert spectrum through HHT, extracting multi-scale frequency band energy features, and fusing SCADA data to carry out working condition adaptive normalization on the features; and then, processing the normalized features by adopting a self-adaptive DBSCAN clustering algorithm based on K-distance map parameters, and realizing fault diagnosis by identifying an abnormal cluster with a sample proportion of less than 5% and combining with a frequency band energy deviation threshold. According to the method, the problems that a traditional method is not thorough in decomposition of non-stationary signals, depends on labeled samples and is insufficient in clustering robustness are solved, and unsupervised and high-precision blade state monitoring is achieved.

The invention discloses a road broken line automatic repair method based on unmanned aerial vehicle image segmentation and fitting reconstruction, and relates to a remote sensing image semantic processing and road connectivity recovery method. The method comprises the steps of firstly extracting a road category region in an image; skeletonizing the extracted road area to form a road trunk structure; introducing a multi-scale density clustering algorithm to perform multi-level spatial clustering on the breakpoints, and superposing clustering results to enhance breakpoint coverage; screening out breakpoint pairs conforming to broken line characteristics; for each pair of matched breakpoints, backtracking along the road direction to obtain a certain number of skeleton points, and reconstructing a road connection path; and restoring the repaired road in combination with the width information of the original road to form a complete connected road structure. The method can effectively improve the automatic detection and continuity recovery capability of the road broken line area in the remote sensing image, and is suitable for a road structure intelligent restoration task under a low-quality image condition and a complex road network scene.

The invention discloses a small target bird flock motion monitoring system, and belongs to the technical field of bird identification, and the system specifically comprises the steps: obtaining a millimeter wave radar echo signal, a laser radar point cloud data set and a visible light image of a target region; performing spatial clustering segmentation on the laser radar point cloud to generate target point cloud clusters, acquiring a three-dimensional convex hull vertex set for any target point cloud cluster, and mapping a centroid to a millimeter wave radar coordinate system; performing spectrum analysis on the radar echo signals based on the mapping coordinates to obtain Doppler features, and calculating the three-dimensional position of the spatial extremal region in combination with the three-dimensional convex hull vertex set; and mapping the three-dimensional position to a visible light image to generate a spatial constraint mask, extracting an image feature vector and a spatial feature vector, and finally inputting the two types of feature vectors into a classifier model to obtain a bird recognition result, thereby realizing the type recognition of the small target bird flock in a complex scene.

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 safety management, in particular to a city safety emergency management system design scheme based on a three-dimensional map, and the scheme comprises a facility space construction module, a risk identification module, a disaster type area identification module, a linkage early warning marking module and an avoidance path analysis module. According to the invention, risk scoring is carried out on facility node coordinates, function levels, the number of accommodated people and service radiuses in a city three-dimensional map, the risk distribution condition of each facility node is visually reflected, and the node risk levels are displayed in a three-dimensional manner by setting the identification map layer through column height, color marking and flicker frequency. Visual focusing and spatial clustering of risk nodes are realized, a commander is helped to quickly identify a high-risk area, response grading of a disaster situation interaction area is effectively refined by performing composite situation grade evaluation on a composite disaster type area and marking an early warning icon, an avoidance passage path is automatically screened and planned, and traffic congestion and secondary risks are avoided in real time.

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 a road advertisement putting method and system based on intelligent travel data, and relates to the technical field of intelligent traffic and precise advertisement putting, and the method comprises the steps: collecting intelligent travel data; the intelligent travel data comprises vehicle real-time trajectory data, vehicle attributes, a road network topological structure and environment data; inputting the standard trajectory data set into an improved ST-DBSCAN spatial clustering algorithm, performing spatial-temporal density clustering analysis to identify a high-frequency passage path and an associated vehicle set, and outputting a high-frequency path list; associating the high-frequency path list with an advertisement exposure log, and performing strong rule mining through an FP-Growth algorithm to generate an advertisement putting rule base; and based on the high-frequency path list and the advertisement putting rule base, combining the real-time track data of the vehicle, and generating a dynamic advertisement putting instruction. According to the invention, the accuracy, the automation level and the resource utilization efficiency of advertisement putting are integrally improved.

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 discloses a shallow sea area water depth inversion method based on a satellite-borne laser radar, and particularly relates to the technical field of single-photon water depth information extraction. The denoising method comprises the following steps: S1, data preprocessing: reading data, carrying out visual presentation, dividing photons into offshore photons, sea surface photons and undersea photons, and intercepting an effective photon region; s2, coarse denoising: denoising the area intercepted in the step S1 by adopting a DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm; s3, fine denoising: screening effective signals of sea surface photons by adopting a Gaussian fitting algorithm, removing sea photons, separating subsea photons from the sea surface photons, performing density analysis on the subsea photons, extracting a density peak point, and constructing a density trend curve of the subsea photons by adopting a cubic spline interpolation method, eliminating residual noise deviating from normal distribution according to the trend curve; and S4, performing water depth inversion: performing refraction correction on the subsea photons subjected to fine denoising, fusing the corrected subsea photons and the sea surface photons subjected to fine denoising, and generating a water depth distribution diagram of the shallow sea area.

The invention discloses a soil humidity inversion method and device, a medium and equipment, and relates to the technical field of microwave remote sensing inversion. According to the method, the total backscattering coefficient, the normalized vegetation index, the leaf area index and the average leaf inclination angle of the electromagnetic waves of the target area pixel by pixel are obtained in combination with microwave remote sensing data and atmospheric apparent reflectance data of the target area; according to the normalized vegetation index, the leaf area index and the average leaf inclination angle, spatial clustering is carried out on vegetation, spatial heterogeneity of crop types is captured, and then based on the constraint that the surface roughness parameters and the vegetation parameters of the same vegetation type are the same and the soil humidity in the same surface space window is the same, the spatial heterogeneity of crop types is obtained. Through two-stage iteration inversion of the intra-class scale and the ground surface space window scale, the time dynamic state is adapted, the parameter calibration function of the in-situ soil humidity data is replaced, and the model parameters and the soil humidity can be determined without the in-situ soil humidity data. According to the invention, the accuracy and reliability of the soil humidity inversion method are improved.

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.