449 results about "Spatiotemporal correlation" patented technology

The invention belongs to the technical field of bridge diagnosis, and discloses a bridge crack intelligent diagnosis system based on multi-modal data fusion. By fusing multi-source data such as visual images, sound wave detection and vibration signals, comprehensive perception and characterization of crack features are realized; constructing a bridge crack characteristic spectrum diagram by adopting a cross-modal feature extraction and heterogeneous feature coding technology; generating a crack evolution situation map based on space-time correlation analysis and knowledge graph construction; the robustness of the system in a complex environment is improved through environmental adaptability feature enhancement and multi-scale characterization; constructing a bridge safety risk hypergraph in combination with multi-dimensional risk analysis and multi-agent collaborative diagnosis; analyzing and revealing a crack evolution mechanism by applying a causal relationship; and finally, through dynamic fusion and uncertainty quantification, a crack intelligent diagnosis comprehensive report is generated. According to the system, the limitation of traditional single-mode diagnosis is broken through, and dynamic prediction and accurate risk assessment of fracture evolution are realized.

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

The invention relates to the technical field of power equipment fault diagnosis, in particular to a power equipment fault intelligent diagnosis method and system based on deep learning. The method comprises the following steps: automatically learning high-dimensional space-time correlation features in original time series data through a deep feature extraction network, and generating feature vectors representing potential abnormal modes of equipment; performing adaptive weight distribution on the high-dimensional space-time correlation features by using an attention enhancement mechanism, and marking a fault sensitive area to form enhanced fault features; inputting the enhanced fault features into a multi-level classifier for joint fault mode recognition and severity evaluation, and outputting a diagnosis result tensor containing a fault type and confidence; and an equipment maintenance decision signal is triggered based on the diagnosis result tensor, and the feature extraction network and classifier parameters are iteratively optimized according to feedback data, so that the intelligent level of operation and maintenance of the power equipment can be comprehensively improved.

The invention discloses a method and system for managing expressway construction based on a BIM technology. The method comprises the following steps: acquiring multi-modal data in expressway construction; constructing an environment-load-response multi-modal data fusion risk matrix by using a space-time correlation model; constructing an AI prediction model by using bridge motion signals and humidity and CO2 concentration data in the multi-modal data; dynamic risk early warning and hierarchical response are realized; the bridge vibration signals and thermal infrared imager data are utilized to analyze and identify invisible faults in highway construction; constructing a fault and health management scheme based on dynamic risk early warning, hierarchical response and invisible faults; performing construction simulation, conflict elimination and extreme working condition deduction by using the digital twin environment of the BIM technology; and according to a deduction result, carrying out highway construction abnormal area management through VR visualization. According to the scheme of the invention, the safety early warning, fault prediction and health management levels of highway construction can be improved.

The invention discloses an urban water body pollution traceability system based on multi-source sensing data fusion, and the system comprises a data acquisition module which is used for deploying a multi-source water quality sensor to collect initial multi-source water body data, and carrying out the time-space unified alignment processing, and obtaining the time-space aligned multi-source time-space water body data; the pollution factor tracing module is used for constructing a pollution event deconstructor and a factor tracing reasoning engine based on a water network topological graph neural network on the basis of multi-source space-time water body data, and outputting pollution component vectors through pollution component decomposition driven by the pollution event deconstructor; inputting the pollution component vector into a tracing reason inference engine to carry out tracing reason space-time correlation to obtain a tracing reason pollution fusion map; and the traceability decision module is used for performing inversion through a reverse traceability algorithm based on the traceability pollution fusion map, calculating the probability that each upstream area is a pollution source, mapping the probability that each upstream area is the pollution source to a GIS platform, obtaining a pollution traceability confidence distribution map, and realizing accurate traceability of the urban water pollution source.

The invention discloses a road roller construction quality real-time monitoring system based on digital twinning, and relates to the technical field of road roller construction intelligent monitoring, and the road roller construction quality real-time monitoring system comprises a data acquisition module which uses a multi-modal fusion sensor network and edge calculation to comprehensively acquire and preprocess data; the digital twinborn model building module is used for modeling by combining physical-data dual drive with geological characteristics; the data transmission module is used for ensuring efficient and safe transmission by using a software defined network and a block chain; the real-time monitoring and analysis module is used for carrying out multi-scale space-time correlation analysis and generating virtual data; and the decision support module fuses deep reinforcement learning and a knowledge graph, supports man-machine cooperation decision, and provides intelligent suggestions for construction. According to the invention, the advantages are obvious, multi-modal acquisition and edge calculation ensure accurate and real-time data, a dual-drive model truly simulates construction, an advanced transmission technology ensures data safety, multi-scale analysis comprehensively evaluates quality, full-process coverage improves construction quality and management intelligence, and cost reduction and efficiency improvement are realized.

The invention provides a rock-soil body multi-field coupling intelligent monitoring system comprising a multi-field sensing device configured to monitor parameters of a deformation field, a seepage field, a temperature field and a stress field of a rock-soil body; the edge calculation and transmission device is configured to perform preprocessing and real-time transmission on the multi-field sensing data; and the intelligent decision-making device is configured to analyze the preprocessed data based on a multi-field coupling model and generate disaster early warning information. According to the rock-soil body multi-field coupling monitoring system and method provided by the invention, synchronous monitoring of a deformation field, a seepage field, a temperature field and a stress field is realized through the multi-field sensing device; real-time data processing is performed in combination with an edge calculation and transmission device, and a dynamic early warning mechanism is established by using an intelligent decision-making device, so that the technical problems of multi-field data space-time correlation failure, insufficient multi-source data fusion precision and poor early warning mechanism adaptability are effectively solved, and the geological disaster early warning method has the remarkable advantages of improving the geological disaster early warning accuracy and timeliness.

The invention discloses an environment pollution detection data processing system and method based on the Internet of Things, relates to the technical field of data analysis and evidence tracing, and aims to solve the problems of data isomerism, time-space correlation analysis splitting and insufficient propagation path recognition precision in the prior art. According to the system and the method, multi-source information such as Internet of Things monitoring data and environment management data is collected, the time trend, the incidence relation and the spatial distribution characteristics of pollutants are extracted after preprocessing, an environment pollution data chain with time, space and logic three-dimensional fusion is constructed, then pollution sources are positioned, a propagation path and an influence range are analyzed, and a real-time environment pollution analysis result is obtained. And a result is visually presented. According to the invention, the precision and reliability of pollution traceability are improved, and a systematic solution is provided for precise treatment of environmental pollution.

The invention discloses a power supply equipment fault prediction method and device based on deep learning, and relates to the technical field of power system equipment fault prediction and deep learning application. The method comprises the following steps: acquiring a power grid topological structure, an equipment operation state, a historical fault record, a real-time equipment load and environmental condition data; forming a space-time correlation basic diagram according to the power grid topology and the equipment operation state, and calculating the correlation strength by using a diagram neural network; calculating fault time delay and determining a transmission path set by using a long short-term memory network in combination with association strength and historical fault records; fusing multiple data to calculate a cross-regional fault propagation probability, and generating a predicted fault path list; and the fault prediction output of the long-short-term memory network input is updated, and the real-time operation data verification optimization of the power grid is combined, so that accurate cross-regional cascade fault prediction is realized, and safe and stable operation of the power grid is ensured.

The invention relates to the field of concrete detection, and discloses a concrete structure health real-time monitoring system based on multi-source sensing fusion, and the system comprises a data collection unit which carries out the collection of the physical field data of a concrete structure through a sensor array, and obtains the multi-source sensing data; performing structure response difference analysis on the multi-source sensing data to obtain concrete structure response difference data; according to the response difference data of the concrete structure, performing spatial and temporal distribution density analysis on a microcrack propagation path to obtain microcrack propagation spatial and temporal distribution density data; a data evaluation unit; multi-scale damage evolution path simulation is carried out through the structural damage time-space correlation characteristic data to obtain multi-scale damage evolution path data, the sensor array is used for collecting multi-source sensing data, structural response difference analysis is carried out, tiny changes of a concrete structure can be accurately captured, potential structural problems can be found in time, and the construction efficiency is improved. And a high-quality data basis is provided for subsequent analysis.

The invention relates to the technical field of data transmission of the Internet of Things, in particular to a self-adaptive data compression and transmission method of a low-power-consumption wide-area Internet of Things. According to the method, a three-layer collaborative architecture comprising an equipment layer, a fog computing layer and a cloud computing layer is constructed, data preprocessing and feature analysis are performed on the equipment layer, and data types, entropy values and repeatability information are extracted; the fog calculation layer selects an optimal compression algorithm based on a multi-dimensional decision engine, and reduces redundancy through spatial-temporal correlation analysis and data aggregation; the cloud computing layer collects compression performance data, adopts reinforcement learning and federal learning to train a global optimization model, and dynamically issues strategy parameters; and the fog node adaptively adjusts a compression strategy in combination with the system state to realize the optimal balance between the compression ratio and the reconstruction precision. The method is suitable for field deployment environments with limited electric quantity and network, has the advantages of low power consumption, high efficiency, strong adaptability and the like, and can be widely applied to Internet of Things scenes such as remote monitoring, smart energy and the like.

The invention discloses a heavy truck charging health degree evaluation method based on an optimized graph convolutional network, and particularly relates to the technical field of electric power operation and maintenance. Multi-source heterogeneous operation data of the charging pile are collected, and unified time window segmentation is carried out; constructing a dynamic multi-graph structure fusing space, electrical and behavior coupling relations; extracting a node local state evolution trend and a neighbor influence factor, and generating a joint feature vector; inputting the features under multiple time windows into a multi-scale attention enhanced graph convolutional network to obtain joint potential health degree representation; calculating a health score based on the potential representation, identifying a high-risk node, and constructing a potential failure propagation path map; generating a maintenance priority table in combination with the centrality index and the degradation rate, and outputting an optimized inspection path; according to the invention, time-space correlation modeling and intelligent maintenance path planning of the health state of the charging pile are realized, and the method has the advantages of high accuracy and strong applicability.

The invention provides an information extraction processing method and system applied to a data sharing service, and the method comprises the steps: obtaining a historical interaction text set from a cross-department business data sharing system, carrying out the entity relation network construction of the historical interaction text set, generating a multi-dimensional entity relation network comprising entity nodes, relation edges and attribute tags, and carrying out the data sharing service through the multi-dimensional entity relation network. Performing semantic topological graph construction based on the multi-dimensional entity relationship network to obtain a semantic topological graph containing entity association strength and a semantic evolution path; calling a preset zero-knowledge proof protocol to perform access control vector generation on the semantic topological graph to obtain an access control vector containing a topological weight threshold and a node access permission; and performing joint processing on the access control vector and the semantic topological graph through a space-time correlation analysis model to generate a business event template containing a standardized field and a correlation index. According to the invention, the accuracy, security and scene adaptability of information extraction in the data sharing service can be improved.

Disclosed in the present invention is a precipitation runoff simulation method based on a multi-station multivariate weather generator. A multivariate first-order autoregressive model is used to generate precipitation and temperature series, effectively simulating interannual variation attributes of the precipitation and temperature series; the multivariate multi-station weather generator can generate a hydrometeorological element simulation field having inter-station, inter-variable, and inter-time-series correlations, effectively reflecting comprehensive features of hydrometeorological processes and improving the runoff simulation effect. If a simulation result of the weather generator is poor, introducing an Empirical Copula post-processing method further improves the spatiotemporal correlation of the meteorological element simulation field and provides more reliable input data for a hydrological model.

The invention relates to the technical field of intelligent traffic, in particular to a dynamic road network collaborative capacity expansion decision-making system based on federated learning-digital twinning, which comprises the following steps of: calibrating pulse type and periodic type data flow weights through a dynamic weight distributor, and generating a normalized feature vector; fusing the normalized feature vectors of all the domains through a privacy protection aggregation engine to obtain a passenger flow pressure distribution prediction matrix; the reconstruction module is used for constructing a digital twinborn body based on the prediction matrix and initializing the digital twinborn body; and based on the initialized twinborn environment, recognizing and sensing a missing region through a blind area data reconstructor, and fusing historical features and real-time data streams of adjacent nodes by adopting a space-time correlation algorithm to reconstruct a complete road network state. According to the method, the dynamic road network collaborative expansion decision system is constructed by fusing federated learning and digital twinning technologies, and the expansion decision efficiency of the traffic road network is improved.

The invention relates to the field of teaching monitoring and recognition, and particularly discloses a practical training monitoring system based on machine vision and deep learning, and the system comprises a data collection module which comprises an image collection unit, a sound collection unit, and a multi-type sensor unit, and is used for synchronously obtaining the image data, sound data, and environment parameter data of a practical training scene; the data processing module is used for preprocessing the multi-modal data acquired by the data acquisition module, realizing multi-modal data fusion through feature alignment and a space-time association algorithm, and generating structured practical training data; the deep learning analysis module adopts an improved convolutional neural network model and is used for performing real-time analysis on the fused structured practical training data and identifying personnel actions, equipment states and environment changes in a practical training scene; by adopting the technical scheme of the invention, the defects of dimension limitation of a single sensor and environment robustness of traditional image processing can be broken through, and accurate pre-judgment of practical training safety risks and objective evaluation of learning effects are realized.

The invention discloses a water-wind-light combined intelligent control system based on multi-energy coordinated control, and belongs to the technical field of multi-energy coordinated control, and the system comprises the steps: firstly collecting environment and operation parameters of wind power, photovoltaic and hydroelectric equipment, and carrying out the preprocessing of the environment and operation parameters to form a structured data set with a space-time label and a dynamic sampling identifier; performing space-time correlation analysis on the data set, constructing a wind power fluctuation and hydropower regulation margin real-time response matrix and a photovoltaic output attenuation and hydropower storage capacity compensation time sequence correlation model, and quantifying the multi-energy dynamic complementation degree and grading the data credibility through a two-dimensional coupling algorithm; then combining with a historical meteorological scene association rule base, constructing a water-wind-light combined power supply capability prediction model containing an energy characteristic mutual compensation coefficient, and generating a hierarchical regulation and control strategy with a space-time priority; and finally, executing a regulation and control strategy to complete parameter adjustment, continuously monitoring through closed-loop feedback, and triggering multi-energy collaborative complementary regulation when the single energy regulation capability is saturated, thereby realizing water-wind-light efficient collaborative control.

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 provides a structure health early warning method and system based on space-time correlation characteristics and digital twinning, and relates to the technical field of data processing. The method comprises the following steps: acquiring multi-source heterogeneous data of a target structure; performing dynamic sampling alignment and wavelet packet decomposition on the multi-source heterogeneous data to extract energy features to obtain synchronous data, and performing abnormal data filtering on the synchronous data to obtain cleaned fusion data; performing wavelet decomposition on a high-frequency vibration signal in the fused data to obtain a damage impact feature, performing time sequence processing on low-frequency temperature data in the fused data to obtain a temperature time feature, and performing dynamic graph convolutional network processing on strain data in the fused data to obtain a spatial correlation feature; constructing an input vector; calculating a damage degree index; and according to the damage degree indexes, early warning grades are divided, and corresponding control instructions are triggered for different early warning grades. By implementing the technical scheme provided by the invention, the accuracy of structural health early warning can be improved.

The invention relates to the technical field of water quality monitoring, in particular to an aquaculture water quality monitoring and self-adaptive regulation and control platform and method.The aquaculture water quality monitoring and self-adaptive regulation and control platform comprises a data processing module used for obtaining water quality parameter data of aquaculture at multiple monitoring points and multiple time points and conducting preprocessing and time sequence serialization fitting on the water quality parameter data to obtain a data processing module; obtaining a water quality parameter time sequence; the feature extraction module is used for carrying out space-time semantic recognition and feature extraction on the water quality parameter time sequence and constructing a feature sequence of each water quality parameter; and the risk early warning module is used for carrying out inter-parameter space-time correlation calculation and dynamic risk threshold value adaptation according to the feature sequence to obtain water quality risk early warning features. By combining the water quality parameters and the culture organism state data, the system can accurately evaluate the risk level of the water quality, timely warn potential risk factors, help a culturist to take effective precautionary measures, and avoid the death or health problem of the culture organism.

The invention relates to the technical field of unmanned aerial vehicle vision, and discloses a vision system of an unmanned aerial vehicle and the unmanned aerial vehicle. The system comprises a visual data acquisition module, a dynamic feature extraction module, an environment modeling module and a decision control module. The visual data acquisition module captures a synchronous frame sequence containing infrared wave bands, visible light wave bands and depth information in a target area through a multispectral sensor array; the dynamic feature extraction module performs cross-modal fusion processing on the original visual data stream to generate a space-time correlation feature tensor containing a target contour geometric invariance descriptor and a motion trail differential topological structure; the environment modeling module constructs a three-dimensional semantic grid map according to the feature tensor, wherein each voxel unit codes the material reflectivity, the dynamic obstacle occurrence frequency and the illumination attenuation coefficient; the decision control module generates a flight path control instruction containing a pitch angle adjustment amount, a yaw angle compensation value and a speed change gradient based on the map, and assists the unmanned aerial vehicle to better cope with a complex environment.

The invention provides an industrial anomaly detection and root localization method and system based on data driving, and the method comprises the steps: carrying out the cleaning, feature extraction and normalization processing of original data collected in an industrial production process, and constructing a feature space; based on a local anomaly factor LOF and a mahalanobis distance MD method, jointly detecting local anomaly and global anomaly, and identifying an abnormal working condition; extracting space and time correlation characteristics of the abnormal variables through Pearson correlation weighting and Granger causal test to form a space-time correlation matrix; constructing an abnormal causal network based on the matrix, and tracing an abnormal root and a propagation path through depth-first search and abnormal propagation intensity evaluation; and finally, dynamic optimization of the anomaly detection and diagnosis method is realized based on parameter self-adaption and model incremental learning. According to the method, the anomaly detection accuracy and the anomaly traceability interpretation capability can be effectively improved, and the intelligent level and the self-adaptive capability of data processing are enhanced.

The invention discloses an old city boundary intelligent extraction and texture calculation system based on vector topology analysis and semantic segmentation. According to the method, the topological semantic association module provides a multi-dimensional association basis for boundary extraction by integrating spatial structure information of vector topology and functional attribute tags of semantic segmentation. In the coarse extraction stage, the objective space law of the topological features and the subjective function orientation of the semantic tags are mutually verified, so that the misjudgment of'similar forms but inconsistent functions' possibly caused by single dependence on the topological structure or the deviation of'tag coverage but space breakage 'possibly caused by only dependence on the semantic tags is avoided; in the fine correction stage, the module further dynamically calibrates the boundary range through historical-current situation space-time correlation and multi-source data conflict detection, so that the continuity of historical stable elements is reserved, the updating requirement of current situation semantic tags is included, the final boundary better fits the essential characteristics of'active inheritance 'of the old city, and the accuracy of the final boundary is improved. And human experience interference and errors are obviously reduced.

The invention relates to the technical field of geological engineering, and discloses a geological safety risk dynamic assessment method based on multi-source data fusion, which comprises the following specific steps: step 1, collecting and standardizing multi-source geological data; 2, performing semantic fusion and conflict resolution on the geologic features; 3, constructing a dynamic risk assessment model; 4, risk situation real-time updating and early warning are carried out; the satellite remote sensing system in the first step adopts the synthetic aperture radar interference measurement technology, the spatial resolution is better than 3 meters, the revisit period is shorter than 7 days, and the earth surface deformation monitoring precision reaches the millimeter level. Through standardized processing and semantic fusion of the multi-source geological data, the problem of heterogeneous data integration is effectively solved, the data basic quality of risk assessment is improved, a space-time coupling neural network model is adopted, nonlinear features and space-time correlation characteristics of geological risk evolution are accurately captured, and prediction precision and timeliness are improved.

The invention discloses a central control system of a digital multimedia exhibition hall, and relates to the technical field of central control systems, a time-space correlation analysis module constructs a fault propagation probability graph of exhibition hall equipment and audience behaviors, and a coupling degree analysis module verifies a cross-layer causal relationship by using a directional disturbance injection method based on the fault propagation probability graph; a coupling degree index is fitted by analyzing an incidence matrix of a resource scheduling operation and a fault propagation chain, a dynamic priority decision module fits a fault propagation cost gradient, the coupling degree index and the fault propagation cost gradient are input into a priority function, and a collaborative priority of a short-term suppression action and a long-term eradication action is calculated. And the strategy execution module calls the execution sequences of the two types of actions through the interface of the central control platform, and executes corresponding strategy actions. The central control system is not only high in dynamic adaptive capacity, but also capable of responding to audience demands and equipment state changes in real time, improving exhibition management efficiency and enhancing immersive experience of audiences.

The invention relates to the technical field of information security, in particular to a network security event association detection method based on big data analysis. Comprising the following steps: data acquisition; feature extraction and fusion; correlation detection is carried out, wherein an improved Apriori-Bayesian fusion algorithm is adopted, and discretization processing is carried out on the event feature vectors; mining a frequent item set by using an improved Apriori algorithm; and risk assessment and result output. According to the method, an improved Apriori-Bayesian fusion algorithm is adopted, discretization processing is carried out on event feature vectors according to types, meanwhile, a security event weight factor is introduced to calculate the item set weighted support degree, and a minimum support degree threshold value is dynamically adjusted to mine a frequent item set; the association confidence coefficient is calculated in combination with the Bayesian network, and the confidence coefficient is corrected through the space-time association coefficient, so that the association relationship between the network security events can be scientifically judged, the problems of limited association judgment accuracy and lack of quantitative correction in the traditional technology are solved, and the association false alarm and missing report probability is reduced.

The invention discloses an Internet of Things intelligent risk processing method and related equipment thereof, belongs to the technical field of artificial intelligence, and is applied to intelligent risk control and transaction risk management of a financial system. According to the method, the device state, the time sequence data and the spatial topological relation are combined by obtaining the multi-dimensional parameters such as the device operation state, the network environment and the service scene and constructing the context feature vector. In a risk early warning analysis link, the layered intelligent agent accurately predicts local and global risks through a space-time association mechanism, and feeds back a prediction result to a state space to drive dynamic adjustment of control parameters. And in combination with the online optimization capability of the reinforcement learning algorithm, the control strategy can automatically adapt to the real-time change of the equipment state, and the collaborative effect of quick response of the equipment end, local optimization of the edge end and global regulation and control of the cloud end is realized. According to the invention, the timeliness and accuracy of risk early warning are improved, and the adaptive and intelligent management capability of the system in a complex network environment is enhanced.

The invention discloses a distributed wind power multi-level power intelligent prediction method, and belongs to the technical field of wind power prediction, and the method comprises the steps: building a physical model layer: based on wind power plant geographic information, fan parameters and historical meteorological data, building a physically-driven wind power conversion model, and generating an initial power prediction sequence; the characteristic decomposition layer is processed, wherein the initial power sequence is decomposed into a trend component, an oscillation component and a random component through variational mode decomposition; performing multi-level independent prediction; carrying out dynamic weight fusion: dynamically distributing the weights of trend, oscillation and random components through a gating attention mechanism, and generating a final power prediction value; performing error feedback correction: performing spatial-temporal correlation modeling on the prediction error based on a Seq2Seq-AM model, dynamically judging the correction opportunity in combination with an auto-encoder, and outputting corrected power. The initial prediction sequence is generated by constructing the physical model layer, the basic physical reasonability is guaranteed, and the problem that the distributed wind power prediction precision is insufficient is solved.

The invention discloses an unmanned aerial vehicle assisted artificial intelligence intelligent health care data collection and analysis system, relates to the technical field of data processing, and solves the problems that firstly, indoor and outdoor centimeter-level precision positioning and dynamic monitoring coverage are difficult to realize; secondly, multi-dimensional health data are difficult to fuse and calibrate in real time; thirdly, a dynamic health assessment and risk prediction system is difficult to construct, space-time correlation analysis on health states and environmental factors is lacked, a health baseline cannot be updated in real time, a health risk index and the probability of environmental induction risks cannot be accurately calculated, and effective prediction of future risks is also difficult to realize; and finally, a health data acquisition strategy is difficult to optimize. The multi-modal sensor array is carried by the unmanned aerial vehicle cluster, the centimeter-level precision positioning system is constructed by combining the UWB positioning technology, and the health data collection, processing, analysis and management modules are integrated, so that the whole-process intelligent processing of the health data is realized.

The invention belongs to the technical field of industrial robot control, and provides an error compensation method and system for an industrial robot, and the method comprises the steps: collecting a composite sensing signal and a pre-tightening force of the industrial robot, and constructing a contribution degree matrix; an error transfer link is extracted, a space-time correlation function is constructed based on the link, and a pretightening force attenuation dominant area is determined through a dynamic cloud atlas algorithm; gradient change features and attenuation trend coefficients of the attenuation dominant region are extracted, a to-be-compensated region is screened, and the saturation compensation risk of the execution mechanism is judged; and if the saturation risk exists, screening pretightening force sensitive joints to construct a compensation distribution model, outputting a segmented compensation demand quantity, and dynamically calibrating the segmented compensation quantity based on a real-time compensation result. The system comprises a contribution analysis module, an attenuation positioning module, a saturation analysis module and a compensation calibration module. The method is beneficial to positioning an error source dominated by pretightening force attenuation, and is beneficial to improving compensation precision and stability.