551 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 electromagnetic pulses, and discloses an electromagnetic pulse data transmission method. The method comprises the following steps: collecting natural electromagnetic pulse signal data of a detection target area through a six-dimensional complementary electrode array structure, and executing Kalman filtering peak extraction and characteristic frequency locking to obtain frequency-optimized signal data; executing first-order and second-order gradient difference matrix calculation and space vector synthesis to obtain signal source position coordinate data and signal intensity data; performing spectral domain reconstruction and multilayer dynamic threshold analysis to obtain a spatial clustering abnormal data packet; and selecting an optimal transmission channel to transmit the spatial clustering abnormal data packet to a cloud server. According to the invention, the influence of an external interference source is effectively eliminated, accurate extraction and characteristic frequency locking of effective frequency band signals are realized, power frequency and harmonic interference thereof are effectively suppressed, the signal-to-noise ratio of the effective frequency band of the signals is improved, and the reliability and timeliness of data transmission in a severe field environment are ensured.

The invention discloses a mine safety risk prevention and control method and system based on multi-source data fusion, and particularly relates to the technical field of mine safety monitoring, and the method comprises the steps: obtaining surface topography, an underground geologic structure and real-time monitoring parameters through remote sensing images, drilling data and dynamic monitoring equipment, and constructing a mining area geologic database with consistent time and space; fusing geologic feature data, integrating earth surface and underground feature data by using a three-dimensional geologic modeling technology, and mapping dynamic monitoring parameters into a model to generate a dynamic geologic feature field; performing clustering analysis on the abnormal feature points based on a spatial clustering algorithm, and calibrating a potential safety hazard region; calculating a regional risk comprehensive index by combining the risk values of the dynamic features, the geological features and the spatial features; early warning is triggered in a graded mode according to the comprehensive index, prevention and control measures are put forward, and the evaluation model is dynamically updated; comprehensive support is provided for mine potential safety hazard area monitoring and safety management decision making, and the method is suitable for risk management scenes of complex mining areas.

The invention discloses a landslide susceptibility assessment method and system fusing feature interaction and dynamic weighting, and belongs to the field of geological disaster assessment, and the method comprises the steps: building an evaluation index system, and carrying out the factor classification; acquiring landslide and non-landslide point sample data, evaluating factor importance by adopting a random forest to obtain a preliminary weight, and preliminarily evaluating the susceptibility based on an I-RF model; detecting double-factor interaction by using a geographic detector, screening interaction factors for collaborative enhancement, and optimizing an expansion index system; retraining the random forest model, obtaining a new weight containing a single factor and an interaction factor, and constructing an I-RF optimization model; for spatial heterogeneity, sub-regions are divided by using spatial clustering, and the weight of each sub-region is dynamically corrected according to the sensitivity of a main control factor, so that self-adaptive adjustment is realized; and calculating susceptibility indexes of the sub-regions and the whole region, and grading to form spatialized and multi-level risk partitions. According to the method, the scientificity and accuracy of evaluation are effectively improved through feature interaction optimization and partition dynamic weighting.

The invention provides a new energy vehicle aggregation prevention and control system based on clustering identification and regional risk modeling, and aims to solve the potential safety hazards such as thermal runaway caused by high-density vehicle aggregation. The system comprises a state acquisition module, a clustering identification module, a regional risk modeling module, a risk grade determination module, a response control scheduling module and a thermal risk prediction module. The system collects running state data such as vehicle position, battery temperature and charge state, uses a density-based spatial clustering algorithm to identify clusters, calculates risk score values of the clusters, and implements current limiting and path guiding control according to risk levels. Meanwhile, the risk change trend of a future area is predicted through the trained long-short-term memory neural network model, and early warning and response are achieved in advance. The system is suitable for safety management scenes of new energy vehicles in high-density areas such as charging stations and underground garages.

The invention relates to the technical field of offshore data monitoring, and discloses an offshore data monitoring method based on a wireless communication system, and the method comprises the following steps: S1, data collection: obtaining offshore monitoring data of a target; s2, data preprocessing: performing format conversion, duplicate removal, time alignment and data interpolation processing on the data acquired by the data acquisition; s3, data fusion: matching and fusing the processed data based on a time interleaving algorithm and a spatial clustering algorithm; s4, performing target tracking, and performing target trajectory prediction by adopting a Kalman filtering method under the condition of target data missing; and S5, monitoring output, generating a fusion data set, and outputting dynamic information of the target. Through combination of deep learning target identification, LSTM network state prediction and an efficient wireless communication technology, more accurate marine target monitoring, stable real-time data transmission and efficient target state tracking are realized.

The invention discloses an actuator sealing performance test method and system, particularly relates to the technical field of industrial equipment sealing performance detection, and is used for solving the problem of positioning failure caused by signal aliasing in a multi-leakage scene in an existing method. Pressure attenuation frequency spectrum characteristics and leakage impact frequency spectrum peaks are extracted respectively by pressurizing a sealed cavity and synchronously collecting dynamic pressure and vibration signals; calculating a path loss value based on the amplitude gradient and the propagation time difference, and screening candidate leakage nodes; constructing a space grid model to solve three-dimensional coordinates of the leakage source; after the coordinates are projected to the surface of the structure model, pressure modal coherence and vibration impact energy are coupled and analyzed to generate a detection map; and finally, outputting a multi-leakage-point identification graph through spatial clustering and intensity grading. Accurate separation and spatial positioning of multiple leakage signals are achieved, and the reliability of complex sealing structure detection is remarkably improved.

The invention provides a humanoid robot inertial navigation and vision fusion positioning method, device and equipment. The method comprises the following steps: firstly, pre-evaluating the quality of IMU inertial data and visual SLAM data to generate a data credibility identifier; establishing a causal relationship chain between the data sources through time sequence correlation analysis; constructing a multi-strategy superposition state based on data credibility, and determining an adaptive fusion strategy through collapse judgment; constructing a data evolution path by using a causal relationship, and generating an expected behavior feature and a causal deviation feature; the fusion parameters are optimized according to the deviation characteristics, and a plurality of fusion positioning estimations with different weights are generated; and through space aggregation analysis and confidence weight calculation, selecting optimal positioning estimation and outputting a high-precision fusion positioning result. According to the method, deep understanding of sensor data, intelligent selection of fusion strategies and robust output of positioning results are realized, and the positioning performance of the humanoid robot in a complex environment 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 risk analysis, in particular to a safety engineering comprehensive management system which comprises a geological parameter dynamic monitoring module, a risk threshold early warning module, a stability weight evaluation module and a risk comprehensive management and control module. According to the method, lithology data are extracted through a drilling profile to calculate the layer thickness sudden change rate and the strength attenuation rate, a lithology abnormal area is positioned through a variable coefficient method, lithology sudden change marks are analyzed through spatial clustering, a dynamic trend is captured through sliding window mean value comparison, and gradient amplitude and slope direction dispersion are calculated through a digital elevation model. Fusing the abnormal point density to generate a terrain instability index; establishing a multi-dimensional quantitative evaluation framework; carrying out fuzzy comprehensive evaluation and cross validation on the instability index and an engineering parameter trend; generating a dynamic priority sequence, carrying out stake number interval matching to construct a risk measure table, carrying out analytic hierarchy process dynamic resource allocation support drainage scheme, and carrying out real-time monitoring and prediction closed-loop iteration. And adaptively adjusting the control strategy.

The invention relates to the technical field of e-commerce supervision and prediction, in particular to a cross-border e-commerce retail risk supervision method and system. Comprising the following steps: acquiring cross-border e-commerce multi-source e-commerce data in real time by using the Internet of Things and an API interface; identifying heterogeneous risk factors by using a deep belief migration network; constructing a risk evolution model and generating a dynamic graph based on the spatial state transition type Markov convolutional network; adopting an abnormal subspace clustering algorithm to carry out self-adaptive clustering and real-time grading early warning on the risk modes; and finally, combining a reinforcement learning algorithm, an intelligent decision and a dynamic optimization risk intervention strategy with resource allocation, and outputting a whole-process risk management and control scheme. The intelligent, automatic and precise levels of cross-border e-commerce retail risk monitoring, early warning and response are improved, risk disposal lag and resource waste are effectively reduced, and the industry supervision safety guarantee capability is enhanced.

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 geological disaster early warning method based on remote sensing monitoring, and relates to the technical field of remote sensing monitoring, and the method comprises the following steps: obtaining a multi-temporal remote sensing image of a target monitoring area, constructing a standard deviation deformation field, and obtaining the standard deviation deformation field; performing regional sliding window analysis according to the standard deviation deformation field to obtain a disturbance image; performing boundary extraction according to the disturbance image to obtain a regional mask pattern; performing time sequence fitting according to the regional mask pattern to obtain a displacement rate image; performing stability analysis according to the displacement rate image to obtain a critical coefficient graph; and performing spatial clustering according to the critical coefficient graph to obtain a disaster early warning unit graph. According to the method, the response of the continuous boundary region is effectively enhanced, the influence of isolated noise points is reduced, the false alarm rate is reduced, the noise immunity is improved, the disturbance cluster and the background weak disturbance region can be accurately separated, the region recognition capability is improved, and the adaptability is enhanced.

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 relates to a building engineering progress intelligent monitoring method. According to the method, a building space is divided into area units for fine management, a progress deviation analysis result is generated in combination with a construction plan and field data collected in real time, a deviation value is dynamically mapped to a three-dimensional building model to realize visual monitoring, and meanwhile, resource allocation data is associated to perform spatial clustering and optimization adjustment, so that the construction efficiency is improved. The problem that in a traditional method, progress monitoring and resource allocation are disjointed in the spatial dimension is solved, accurate correlation analysis and closed-loop optimization of the progress and resource dynamics of the constructional engineering are achieved, and the real-time performance and decision-making scientificity of construction management are remarkably improved.

The invention relates to the technical field of geographic information data, in particular to a GIS-based CRM customer ecosphere dynamic visual management system, which comprises the following steps: acquiring geographical location information, transaction data, customer relationship data and time sequence data of customers; constructing a customer ecosphere space model to analyze the geographic position information and the customer relationship data, obtaining space association strength and business association strength among customers, and generating a dynamic evolution matrix; performing real-time spatial clustering analysis on the transaction data, and identifying a client distribution hotspot area; based on the time sequence data and the geographic position information of the customer, constructing a customer space-time trajectory model, analyzing a space-time behavior pattern of the customer, and obtaining a space-time trajectory prediction result including a customer periodic behavior pattern and a space activity trend; and carrying out three-dimensional rendering on the spatial association strength, the business association strength, the customer distribution hotspot area and the spatio-temporal trajectory prediction result to obtain a three-dimensional visual interface.

The invention relates to the technical field of hydraulic ring geological survey, in particular to a hydraulic ring geological survey system and method based on GPS positioning, and the method comprises the following steps: obtaining the three-dimensional coordinate data of a survey area through a GPS positioning device, generating an initial geological positioning point set, inputting the initial geological positioning point set into a three-dimensional modeling system, generating an initial three-dimensional geological model, and carrying out the three-dimensional modeling of the initial three-dimensional geological model. Dynamically correcting the initial three-dimensional geologic model through an environment compensation algorithm to generate a corrected geologic model, dividing a geologic abnormal region through a spatial clustering algorithm based on a rock stratum permeability coefficient abnormal gradient value in the corrected geologic model, comparing the geologic abnormal region with an engineering parameter threshold library to generate a visual investigation report, and performing the visual investigation on the geologic abnormal region. An internet of things monitoring node network is established, displacement monitoring is carried out on a geological abnormal area, if a monitoring value exceeds a preset threshold value, an early warning signal is triggered, and the method can be widely applied to the fields of hydraulic ring geological survey, engineering construction and disaster early warning and has remarkable technical advantages.

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 discloses a multi-target partition optimization method and system for a multi-water-source large water supply pipe network. The method comprises the steps of setting optimization basic parameters, identifying a main pipe, constructing a partition scheme, constructing a multi-target function, constructing partition constraint conditions and optimizing a model solving method. According to the multi-target water supply pipe network zoning method, pressure and metering management can be carried out conveniently, and meanwhile transformation economy is considered. The zoning method takes reduction of pipe network management difficulty as a core, pipe network operation transformation economy, zoning internal pressure uniformity and pipe network water quality safety are taken into consideration, a large water supply pipe network transmission and distribution separation pattern is constructed to clarify a pipeline transmission and distribution function, a candidate zoning scheme set is formed in combination with a spatial clustering algorithm, and a large water supply pipe network is obtained. And finally, comparing all optimization targets in the candidate schemes, and carrying out iterative updating by adopting a non-dominated sorting genetic algorithm to obtain a Pareto leading edge of the zoning scheme, so as to realize refined leakage control of the large-scale water supply pipe network.

The invention belongs to the technical field of electric digital data processing, and more specifically relates to an agricultural machinery behavior identification method fusing interpolation enhancement and depth spatial-temporal feature modeling. The method comprises the following steps: acquiring GNSS trajectory data of an agricultural machine and preprocessing the trajectory data; performing data enhancement on the road points in the data set by adopting a local interpolation method based on DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering; designing a set of spatial distribution feature extraction method; performing feature extraction on the trajectory data after data enhancement by combining motion feature extraction and spatial distribution feature extraction; introducing a variational auto-encoder VAE to carry out potential feature extraction on the initial features; then, a ResBiLSTM network is adopted to carry out space-time modeling; and finally, an end-to-end classification decision is realized through a linear classifier, so that intelligent identification of the operation behavior of the agricultural machine is realized. According to the method, two key problems in the existing track identification technology are solved, namely, data distribution is seriously unbalanced, and the spatial distribution characteristics of track points are often neglected.

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.