895 results about "Spacetime" patented technology

The invention provides a combined wind power prediction method suitable for a distributed wind power plant, and the method comprises the steps: collecting the real-time meteorological data and historical power data of a wind power plant cluster, carrying out the cross-wind-plant data collaborative cleaning, and generating a time-space aligned standardized data set. Constructing an adaptive spatio-temporal feature extractor, outputting a spatio-temporal feature matrix, and inputting the spatio-temporal feature matrix into the spatio-temporal adaptive neural network, the graph attention prediction model and the physical constraint decision tree model to generate three prediction sequences. And the sequences are fused through a space-time collaborative attention mechanism to generate a dynamic weighted combination prediction result. And performing physical constraint correction on the result by using a space-time residual error correction network to generate a final prediction sequence. And updating the neural network topological structure based on the prediction error distribution, and outputting a prediction result with uncertainty evaluation to a power grid dispatching system. According to the method, the precision and reliability of wind power prediction of the distributed wind power plant can be improved, and the stability and economy of power grid dispatching are improved.

The invention discloses an earth and rockfill dam seepage-deformation early warning method and system based on time-space combined anomaly, and belongs to the field of dam body safety data research. The method comprises the following steps: constructing a spatio-temporal topological graph based on an engineering coordinate system, integrating multi-dimensional data by nodes, and constructing a dynamic adjacency matrix according to spatial distance and seepage relevance; extracting features by using a space-time diagram convolutional network, a self-loop mechanism and cross-layer attention; and executing dual-drive early warning through standard threshold preliminary screening, multi-scale LSTM prediction and a time decay evidence theory. The system comprises a sensor network and an intelligent computing module, and the intelligent computing module has adaptive modeling and visualization functions. According to the scheme, seepage-deformation space-time correlation quantitative analysis is achieved, the hysteresis effect is captured, the threshold value is dynamically corrected, multi-source evidences are fused, the early warning timeliness and accuracy are improved, and the risk of false alarm and missing alarm is reduced.

The invention discloses an urban water supply management data trend analysis method based on space-time analysis, and relates to the field of data processing, and the method comprises the steps: collecting data in real time through an urban water supply pipe network sensor network, building a space-time unified coordinate system, building a space-time Kriging interpolation model based on pipe network topology, and achieving the space-time alignment of multi-source data; dividing an adaptive space-time grid by using a Voronoi diagram and a sliding window mechanism, and calculating multi-dimensional features; constructing a dynamic space-time diagram by taking a grid as a node, performing multi-step prediction in combination with a space-time diagram convolution circulation network, fusing a Kriging interpolation result, and evaluating an abnormal probability and a confidence interval through a Bayesian neural network; a monitoring layer, a prediction layer and a risk layer are overlaid in a three-dimensional GIS, a dynamic thermodynamic diagram is generated, an early warning path is optimized based on a Dijkstra algorithm, and a minimum risk topology path is output. The method has the advantages that through space-time analysis and accurate prediction, the intelligence, stability and emergency response efficiency of urban water supply management are remarkably improved, and powerful support is provided for smart city construction.

The invention relates to the technical field of electrical cabinet monitoring, in particular to an electrical cabinet active anti-condensation method and system based on a condensation mechanism mathematical model. According to the invention, a mathematical model based on a condensation formation mechanism is constructed, and environmental meteorological parameters, station room environmental parameters, electrical cabinet internal microenvironment parameters and cable trench state parameters are monitored through a four-stage monitoring system; and fusing the multi-source heterogeneous data, inputting a condensation mechanism mathematical model to calculate a condensation risk index, and adopting a hierarchical response mechanism to cooperatively control an execution mechanism according to the obtained condensation risk index value to realize active defense of condensation. According to the method, the condensation phenomenon with the space-time dynamic characteristic is quantitatively described through a mass transfer and heat transfer coupling equation, a grading prevention and control strategy and a cooperative control algorithm are developed by calculating the condensation risk index, setting the threshold condition and judging the condensation formation risk level, the prevention and control intensity is dynamically adjusted according to the risk level, and the control accuracy is improved. And the risk response speed and the resource utilization efficiency under the complex working condition are obviously improved.

The invention relates to the technical field of meteorological monitoring and climate prediction, in particular to a Qinghai-Tibet Plateau composite extreme climate event attribution evaluation method. The method comprises the steps that ground observation, remote sensing and reanalysis data are integrated through a multi-source data dynamic space-time weight fusion technology, and abnormal value correction and non-uniform interpolation are achieved; identifying a composite event by adopting a multivariable combined extreme index and a space-time coupling graph model and generating a structured label, wherein the structured label comprises strength, range, duration and evolution path; constructing a multi-scale causal network to analyze the contribution of the driving factor, and implementing physical constraint disturbance based on causal weight; recovering high-resolution response by using a Bayesian agent model and combining topographic constraint random downsampling, and deducing spatio-temporal evolution through an event propagation network; and a kernel polynomial hybrid uncertainty propagation framework is adopted to generate a probabilistic scene set, and multi-level risk early warning and dynamic knowledge base optimization are realized. According to the invention, the attribution precision and early warning efficiency of plateau composite extreme events are comprehensively improved.

The invention provides a multivariate time sequence anomaly detection method based on an adaptive causal diagram and spatio-temporal evolution, and belongs to the technical field of time sequence anomaly detection. According to the technical scheme, firstly, unification, missing value filling and Min-Max normalization processing are carried out on multivariate time series data, on this basis, a graph attention network is utilized to construct an adaptive correlation graph, a causal relationship between variables is quantized through Granger causal test, then the correlation graph and a causal graph are fused to generate a causal correlation mixed graph, and then, the causal correlation mixed graph is subjected to data processing. And inputting the mixed graph into a space-time converter to carry out future numerical value and structure prediction, finally calculating a prediction residual error and generating a comprehensive anomaly score, and further judging an abnormal node. According to the method, dynamic detection and interpretable analysis of abnormal events can be realized, and the problems that in the prior art, static state, causality and correlation of a graph structure are not fused, structural evolution modeling is lacked, and the judgment dimension is single are solved. According to the method, the anomaly detection coverage and sensitivity are remarkably improved.

The invention discloses a tunnel portal construction risk assessment method and system based on multi-source monitoring data fusion. The method comprises the following steps: performing layered acquisition on monitoring objects in a tunnel portal construction area, obtaining multi-source monitoring data of an environment layer, a geological layer and a construction layer, and adding element attributes such as time, space coordinates and equipment health status; performing credibility correction and time sequence reconstruction based on the monitoring data to obtain a time sequence alignment data set with credibility weighting; performing dynamic weight fusion on the data set, and generating a fusion risk feature vector in real time; inputting the feature vectors into a spatio-temporal evolution model, and outputting collapse, water inrush and settlement risk probabilities in a plurality of time windows in the future in combination with coupling calculation of a spatial sub-model and a time sub-model; and forming a multi-dimensional risk portrait according to a prediction result, performing adaptive correction based on construction site feedback, and updating a monitoring index weight and a model parameter so as to realize dynamic optimization of a subsequent prediction period.

The invention discloses a visual prediction method for space-time manifold and implicit state deduction of a robot, and relates to the technical field of robot space visual perception. The motion state and the visual image of the robot are collected; calculating carrier pose offset and generating a reverse compensation matrix; modeling an image target into a visual cone probability manifold, and extracting an explicit manifold observation vector; monitoring the confidence coefficient in real time through an observation quality evaluation network, and activating an implicit deduction mode during observation degradation; reading historical time sequence characteristics by using an improved Transform model, and performing prediction and deduction on a future nonlinear motion state of the target; performing coordinate correction and probability field decoding on a prediction result in combination with the reverse compensation matrix, and outputting a three-dimensional prediction trajectory and a spatial covariance ellipsoid; according to the method, the problem that traditional visual tracking fails under the conditions of violent shaking of a carrier and target shielding is solved, and continuous and foresight physical scale positioning and safety decision making of a robot on a dynamic target are achieved.

The invention discloses a PINN-based high-precision hydrodynamic numerical simulation method and system, and the method comprises the steps: firstly building a computational domain, setting reasonable geometric parameters and boundary conditions, and constructing a dimensionless Navier-Stokes control equation set; then designing a deep neural network architecture with space-time coordinate input and flow field variable output, and adopting a loss function combining physical constraint and data driving; the core innovation lies in providing a timing sequence sensing RAR-D adaptive sampling strategy, dividing a time domain into a plurality of time frames, performing residual error evaluation in each frame, constructing a probability density function related to residual errors, and balancing priority sampling and overall coverage of a high residual error region; adam and L-BFGS optimizers are adopted to carry out network training, the weight of a loss function is dynamically adjusted, and a sampling point set is periodically updated; and finally, the solution precision is verified through multi-dimensional flow field visualization analysis. Therefore, the prediction precision of the complex flow field is effectively improved, and the calculation efficiency is remarkably improved.

The invention relates to the technical field of remote sensing, discloses a geological environment monitoring method and system based on multi-source remote sensing, and aims to solve the monitoring problems of heterogeneous multi-source remote sensing data, difficulty in dynamic capture, disjunction of geological mechanism and interpretation and the like. According to the method and the system, multi-source remote sensing data is acquired and standardized, multi-modal geologic features are extracted, through cross-modal deep fusion and space-time modeling, abnormity is analyzed and identified, risks are evaluated, and visualization and decision support are realized. According to the technical scheme, comprehensive, accurate and high-timeliness monitoring of the geological environment can be realized, the premonition of the geological disaster can be effectively identified, the trend can be predicted, and support is provided for prevention and control of the geological disaster.

The invention provides an urban space intelligent processing method based on multi-modal fusion, and the method comprises the steps: taking multi-modal data as input, and constructing a unified data stream processing and feature alignment mechanism; a physical space is used as a core framework, and the multi-modal data is converted into a space behavior graph with space-time position semantics; constructing an entity attribute-relation type-influence weight ternary interaction model on the basis of an interaction layer of the spatial behavior map, and analyzing a human, object and environment ternary interaction relation in the city and the park based on the ternary interaction model; designing a space intelligent engine with time sequence modeling and dynamic prediction capabilities; and constructing a task processing system. According to the invention, through deep combination of multi-modal fusion and a space intelligent technology, full-link upgrading of urban space from data perception to intelligent decision is realized, and powerful technical support is provided for fine management, efficient operation and safety guarantee of complex space scenes.

The invention relates to a cross-attention mechanism optimization method and a cross-attention mechanism optimization system for multi-modal feature fine-grained alignment. The method comprises the following steps: constructing a space-time topological graph of a dynamic scene, and representing a space-time relationship among intelligent agents through nodes and various connecting edges; coding the heterogeneous spatio-temporal information by using a plurality of groups of parallel recurrent neural networks, and converting the heterogeneous spatio-temporal information into uniform dimension feature representation; establishing a multi-modal cross attention alignment component, and quantifying feature quality from multiple dimensions through a multi-criterion evaluation unit; an adaptive weight fusion system is adopted to dynamically integrate evaluation results, and a unified quality score is generated; constructing a progressive optimization architecture based on the quality score, and performing multi-task cooperative training on a double-layer attention mechanism by combining real-time sampling and a directional optimization strategy; and finally generating a movement decision. According to the method, fine-grained alignment and optimization of multi-modal features are realized, and the accuracy and adaptability of intelligent agent navigation and trajectory prediction in a complex dynamic environment are remarkably improved.

The invention discloses a traffic network toughness diagnosis method under flood disasters based on a space-time diagram neural network, and relates to the crossing field of traffic engineering and artificial intelligence. The method comprises the following steps: collecting traffic topology, flood monitoring and traffic flow data, and carrying out space-time alignment; a flood coupling dynamic space-time diagram is constructed, a water depth-traffic capacity response mechanism is introduced, and real-time mapping from a disaster physical state to a network topology is realized by utilizing an attenuation function meeting physical monotonicity constraint and dynamically updating an edge weight of a diagram structure according to real-time water depth; inputting the dynamic graph into a pre-trained space-time graph neural network model, extracting space-time evolution characteristics and outputting a toughness diagnosis result; model training adopts a toughness label generated based on an anti-fact baseline to carry out supervised learning, and introduces a physical constraint loss function. According to the method, the problems of decoupling of disaster features and graph structures and unavailability of toughness labels in the prior art are solved, and the physical consistency and accuracy of diagnosis are improved.

The invention discloses an ocean three-dimensional temperature field reconstruction method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source heterogeneous ocean observation data and a numerical model background field, and generating an input feature group; performing feature extraction on the input feature group by using a double-branch encoder; the extracted features are input to a multi-head space-time channel attention fusion module for dynamic calibration and fusion, and deep fusion hidden variables are obtained; jointly inputting the deeply fused hidden variables and the numerical model background field into a decoder based on a conditional variation auto-encoder, generating high-resolution three-dimensional temperature field grid data, and synchronously outputting a three-dimensional uncertainty field; according to the method, the continuous, complete and high-precision ocean three-dimensional temperature field in the whole research area is reconstructed through a mathematical method and a physical method by utilizing limited, sparse, multi-source and heterogeneous ocean observation data, and the problem that the ocean three-dimensional temperature field generated in the prior art is not accurate enough is solved.

The invention belongs to the technical field of computer vision, deep learning and time-space prediction, and discloses an end-to-end time-space prediction method based on improved three-dimensional rotation position coding, which is suitable for various time-space sequence prediction scenes such as weather, traffic flow and the like. According to the invention, through four key improvements, a position coding mechanism is optimized; three-dimensional coding proportions of time, height and width are dynamically adjusted so as to adapt to different scenes; fusing the absolute time and the relative space position, and strengthening local space-time correlation modeling; the position information directly guides attention calculation, and the fusion with an Attention module is deepened; and a rotation matrix cache mechanism is introduced to reduce redundant calculation. Meanwhile, the model is matched with a Patch embedding layer, an adaptive Transform encoder and an MLP de-wharf, a complete link of'feature embedding-position encoding-space-time fusion-prediction output 'is constructed, and the precision, generalization and reasoning efficiency of space-time prediction are effectively improved.

The invention discloses an electricity consumption anomaly detection method, system and device based on an environmental perception graph convolutional network and a medium, and belongs to the technical field of smart power grids. The method comprises the steps that historical electricity consumption of a power grid area unit and external environmental factors are acquired to construct a multi-dimensional space-time association graph; a periodic trend component and environment-related residual fluctuation are separated through the multi-dimensional space-time correlation diagram, and a two-channel diagram convolution feature is obtained; in combination with the convolution features of the two-channel graph, feature space decoupling of a periodic channel and a residual channel is realized, and decoupling features are obtained; and extracting a historical residual fluctuation sequence based on the decoupling features, and constructing an environmental state sensitive probability density function. According to the method, the historical electricity consumption and the external environment factors such as temperature, humidity or weather events are closely fused by constructing the multi-dimensional space-time association diagram, so that the space-time dependence of the electricity consumption behavior is effectively captured, and misjudgment caused by ignoring environment dynamics in a traditional method is avoided.

The invention provides a geological disaster intelligent early warning method based on fusion of a physical information neural network and space-air-ground monitoring, and the method comprises the following steps: S10, obtaining geological structure features of different depths under the ground of a city, and recognizing the spatial distribution and thickness of an underground abnormal body; acquiring a surface deformation time sequence, crack and landform information, a three-dimensional point cloud and a continuous vibration signal of a surface-shallow stratum; s20, constructing a three-dimensional twin substrate, performing space-time alignment and resampling on multi-source heterogeneous data, mapping the data to a three-dimensional grid of a unified coordinate system, and extracting and fusing geological disaster precursor features; s30, taking the generated fusion feature field as an input training neural network model, carrying out geological disaster forward prediction and parameter inversion, and outputting future stability probability distribution, a potential slip plane and key parameter evolution; and S40, adaptively adjusting a risk threshold and an early warning rule, constructing an incremental data set, and carrying out periodic incremental training and parameter optimization on the physical information neural network in the step S30.

The invention provides a geological disaster prediction method and device fusing space-time sequence analysis and causal reasoning, and belongs to the technical field of geological disaster monitoring and early warning. Aiming at the problems of non-uniform data space-time reference, lack of causal logic, poor real-time performance and weak scene adaptability of a model in the prior art, the method comprises the following steps: performing standardization processing on acquired multi-source data, processing missing values by adopting an improved K nearest neighbor algorithm in combination with stratum characteristics, and processing abnormal values through a 3 sigma criterion and geological verification; based on an information theory and an improved SURD algorithm, three types of causal entropies among variables are calculated, a time attenuation coefficient is introduced, a core causal chain is constructed, and a dynamic causal graph is constructed; a core causal variable is used as input, a multi-feature attention-multi-relation space-time diagram recursive network model is constructed, a hour-level predicted value is output through space-time diagram convolution, residual training and a geological physical constraint layer, and'causal-space-time 'fusion is realized through a causal weight adjustment model; the method can be widely applied to early warning of geological disasters such as landslide and debris flow.

The invention provides a personnel trajectory tracking method and system, and belongs to the technical field of personnel trajectory tracking, and the method comprises the steps: obtaining first data and second data; processing the first data to obtain a personnel bounding box set in the first data; processing the second data to generate a second coordinate system; obtaining a personnel point cloud clustering set in the second coordinate system; based on the personnel bounding box set and the personnel point cloud clustering set, determining a target personnel set through joint confidence calculation; calculating the current three-dimensional position of each target person in the second coordinate system as a current observation node; constructing a space-time association graph based on the current observation node and the historical trajectory node of the target person; and tracking a continuous motion track of the target person based on the space-time association diagram. According to the invention, cross-modal personnel target efficient alignment and continuous trajectory tracking can be realized.

The invention belongs to the field of computer application technology and geological disaster prediction and early warning, and particularly discloses a landslide real-time prediction method and early warning method based on multi-modal information fusion, and the prediction method comprises the steps: constructing a landslide event text data set; a BERT-BiLSTM-CRF model is trained after time-space element labeling, and a structured historical landslide knowledge graph is constructed; using the landslide boundary training data set to train a U-Net + + image segmentation model to identify a landslide space boundary; taking time and space information as space-time anchor points, combining with a U-Net + + image segmentation model identification result, and identifying a landslide occurrence range and time on a remote sensing cloud platform; and extracting a multi-dimensional dynamic environment factor by combining a landslide occurrence range and time, constructing a time sequence feature sample set, and training a landslide susceptibility prediction model to realize dynamic prediction of landslide risks. According to the invention, timeliness and space precision are taken into consideration, and prediction accuracy and response capability of landslide disasters can be effectively improved.

The invention provides a power load prediction method based on feature decoupling and time-space diagram modeling, and particularly relates to the technical field of power load prediction, and the method comprises the steps: preprocessing the historical data of a power load, obtaining a normalized multivariable load sequence, and decomposing the normalized multivariable load sequence into a trend term and a season term through a learnable convolution kernel decomposition algorithm; a dominant period is extracted from seasonal term spectrum analysis, and multi-scale down-sampling is carried out according to the dominant period to generate a multi-scale sequence. Based on a multi-scale sequence, an adaptive mixed hop message aggregation mechanism is adopted, cross-scale time dependence and inter-variable high-order association are dynamically fused, and aggregation features are obtained. And performing combined prediction on the trend term and the aggregation feature, and outputting a load prediction result after inverse normalization. According to the method, the technical problem that an existing graph neural network model is difficult to accurately model a time-space dependency relationship of dynamic change under multiple scales in power load prediction is solved, so that the power load prediction precision and the model generalization ability are remarkably improved.

The invention discloses a mine comprehensive management and control system based on spatio-temporal data, and the system comprises an acquisition module which is used for collecting and synchronizing observation data of a mining area distributed ground sensor and unmanned plane remote sensing equipment, and mapping the observation data to a three-dimensional grid unit; the digital twinning modeling module is used for constructing a fragmented gridding four-dimensional digital twinning model; the dynamic micro hypergraph modeling module dynamically constructs a node-hyperedge structure based on spatial proximity, time relevance and the like; the propagation module is used for realizing main variable weighted propagation and gating residual error updating; the anomaly detection module is used for automatically identifying and registering time-space anomaly events of the grid units; and the scheduling optimization and instruction module is used for outputting a scheduling priority and a control instruction based on the main variable, the abnormal distribution and the hypergraph link. According to the invention, intelligent integration, abnormal accurate identification and scheduling optimization control of mining area multi-source spatio-temporal data are realized, and the method has the advantages of high real-time performance, intelligent management and control and adaptability to complex scenes.

The invention relates to the technical field of weather forecast, and discloses a wind speed forecast correction method, device and equipment and a readable storage medium, and the wind speed forecast correction method comprises the steps: carrying out the space-time alignment processing of multi-source wind speed data, and obtaining the wind speed alignment data corresponding to an observation station; constructing a corresponding initial spatial-temporal feature based on the wind speed alignment data, and performing standardization processing on the initial spatial-temporal feature to obtain a standardized spatial-temporal feature; inputting the standardized spatial-temporal characteristics into a prediction model to obtain predicted wind speed information; and generating a wind speed forecast correction result according to a dynamic fusion strategy based on the predicted wind speed information and the multi-source wind speed data. The accuracy and the stability of wind speed forecasting are remarkably improved, and the problem that the correction capability is insufficient under complex terrains and extreme weather is effectively solved.

The invention relates to the technical field of big data analysis, and discloses a prospecting target area positioning method and system based on big data, and the method comprises the steps: collecting multi-source exploration data in real time through distributed nodes, completing coordinate normalization, semantic alignment and time synchronization through a spatial heterogeneous data flow engine, and generating a standardized incremental data block; performing local feature sensitivity analysis based on the historical model library, identifying a newly added feature dimension, and performing parameter increment updating by adopting a sliding window gradient descent method; inputting the updated model into a target evolution model driven by a Bayesian space-time probability field, and dynamically calculating the metallogenic probability of each space grid in combination with a stress field, an element migration path and historical verification data; and generating high, medium and low three-level target area maps according to probability sorting, and pushing the high, medium and low three-level target area maps to a three-dimensional visual decision terminal. According to the method, minute-level dynamic response of the target region under triggering of newly-added data is realized, computing resource consumption is reduced to be less than 5% of that of an original system, and prospecting efficiency and abnormal region identification timeliness are improved.

The invention discloses a karst stratum tunnel disaster sensing method and system based on multi-source information, and belongs to the technical field of tunnel engineering safety monitoring, and the method comprises the steps: building a monitoring index data set, converging the monitoring index data set to a cloud end, carrying out the time-space registration, and generating a multi-dimensional time sequence data field; calculating data uncertainty of each monitoring area by adopting an information entropy theory, calculating spatio-temporal evolution characteristics, and performing classifier identification by combining a deformation field space gradient to obtain a key monitoring area; adaptively adjusting the acquisition frequency of the sensor, and starting supplementary monitoring equipment for encrypted observation; performing space-time response calculation by adopting a machine learning algorithm to generate a tunnel disaster evolution prediction result; and carrying out grading threshold comparison and numerical simulation verification on the prediction result to realize effective identification and perception of the disaster evolution state. According to the method, the technical means of combining multi-source data fusion, the information entropy theory, machine learning and self-adaptive monitoring is adopted, and dynamic, accurate and predictive perception of the tunnel disaster evolution process can be achieved.

The invention belongs to the field of artificial intelligence and intelligent traffic systems, particularly relates to a path control system combining multi-modal perception and dynamic trajectory prediction, and aims to solve the problems of insufficient perception fusion, low trajectory prediction precision and control response lag in a complex dynamic environment. The system comprises a multi-modal perception fusion unit, a dynamic interaction modeling unit, a space-time coupling prediction unit, a risk field construction unit and an adaptive path generation unit. Multi-source sensor data are fused through confidence coefficient weighting, an interaction weight matrix under an attention mechanism is constructed, a future trajectory is predicted in combination with individual dynamics and a social force model, a four-dimensional space-time risk field is generated, and a minimum risk path is solved based on an improved fast marching algorithm. The system realizes sensing-prediction-control closed-loop cooperation, the end-to-end delay is less than 250 milliseconds, and the driving safety and comfort in a complex traffic scene are improved.

The invention relates to the technical field of industrial vision and intelligent monitoring, and discloses a loom control system operation state monitoring and fault diagnosis method based on machine vision, which comprises the following steps: acquiring a video stream in a loom shed area and constructing a two-dimensional space-time slice tensor; performing global motion compensation processing on the space-time slice tensor by using a homography transformation matrix, mapping a compensated dynamic texture feature sequence to a three-dimensional phase space by using a time delay embedding algorithm, and reconstructing a closed phase space trajectory representing periodic operation logic of the loom; the discrete Frechet distance between the phase space trajectory of the current operation cycle and the preset reference trajectory is calculated, and a control instruction is generated. The health degree of the sequential logic of the system is directly quantified on the premise that specific components are not recognized by using the invariant characteristic of the phase space manifold topology; the technical problems that small phase lag is difficult to perceive and nonlinear faults cannot be early warned in a strong noise environment are solved.

The invention provides a mine slope risk assessment method and system based on multi-source data space-time fusion, and relates to the technical field of mine monitoring, and the method comprises the steps: obtaining geological and engineering data, and constructing a basic model representing a slope three-dimensional space structure; collecting multi-source heterogeneous monitoring data on a mine slope field, and aligning and fusing the multi-source heterogeneous monitoring data with the basic model in a space-time dimension to generate a continuous dynamic parameter field; and setting a data fusion model, and taking the basic model and the dynamic parameter field as input to obtain a four-dimensional space-time fusion risk field. According to the method, the basic model representing the three-dimensional space structure of the slope is constructed, and a unified space-time bearing base is provided for all heterogeneous monitoring data, so that originally isolated data such as GNSS displacement, deep displacement, blasting vibration and underground water level can be fused in a unified coordinate system; the problem of data splitting in the prior art is fundamentally solved, and integral and integrated expression of slope engineering and geological conditions is formed.

The invention discloses a quick early warning method for regional geological disasters, particularly relates to the field of early warning of geological disasters, and is used for solving the problems of early warning lag and inaccurate positioning caused by dependence on a single monitoring index in the prior art. According to the method, a multi-dimensional monitoring data stream with time-space continuity is constructed by acquiring a slope body deep inclination angle, earth sound event count and earth surface micro-displacement data of a geological early warning area; generating a regional deformation synchronization index based on phase-space reconstruction and correlation dimension analysis, and calculating a system entropy value through density clustering and displacement fluctuation analysis; performing real-time identification on the regional critical state by adopting a critical state judgment model of double-threshold grading; and finally, combining real-time rainfall intensity data to carry out spatial superposition to divide into multiple levels of warning subareas, and outputting corresponding early warning signals. According to the method, pre-disaster megasses can be captured from the aspect of internal structure instability, the accuracy and foresight of regional geological disaster pre-warning are improved, and regional public safety is guaranteed.

The invention relates to the technical field of unmanned aerial vehicle cooperative surveying and mapping, and discloses an unmanned aerial vehicle cluster cooperative space-space-ground integrated surveying and mapping system which comprises a space-time reference construction module, a multi-source data acquisition module, a distributed fusion optimization module, a system adaptive reconstruction module and a four-dimensional model generation module. An internal high-precision space-time reference independent of the GNSS is constructed; uniformly modeling the whole surveying and mapping task into a space-time factor graph through a distributed fusion optimization module, and solving the space-time factor graph; and closed-loop adaptive adjustment is performed on the geometric configuration of the beacon network and the measurement data quality through a system adaptive reconstruction module. The technical problem that a traditional surveying and mapping system is poor in precision and reliability in a GNSS disturbed environment is solved, the robustness, autonomy and environment adaptability of the system are remarkably improved by constructing a self-consistent internal reference and introducing intelligent feedback control, and a globally consistent high-precision four-dimensional space-time model can be efficiently generated.