28 results about "Time correlation" patented technology

The invention provides a time-space evolution data processing method and system applied to tunnel deformation analysis, and the method comprises the steps: obtaining a time sequence monitoring sequence of different monitoring dimensions in a tunnel construction process, constructing a time-space evolution correlation model of tunnel deformation based on the time sequence monitoring sequence, and analyzing a dynamic coupling relation between structure response data and geological environment action data, generating a space-time correlation feature matrix, generating a feature evolution sequence of tunnel deformation based on the space-time correlation feature matrix, and performing nonlinear mutation feature extraction on the feature evolution sequence to obtain a mutation feature index set; according to the sudden change characteristic index set and a preset accumulated deformation rate threshold value, performing dynamic division of tunnel deformation safety levels, generating a safety level division result, and based on the safety level division result and the spatial distribution data of the tunnel construction section, executing spatial positioning and boundary contour extraction of the potential risk damage area. And obtaining a risk damage area distribution map. According to the invention, the tunnel construction safety risk can be accurately evaluated and effectively controlled.

The invention belongs to the technical field of wind power climbing event prediction, and particularly relates to a wind power climbing event prediction method considering extreme weather and time-space correlation information. The method comprises the following steps: acquiring historical actually measured meteorological data of each wind power station of a cluster; carrying out cold-wave weather event identification on historical actually measured meteorological data, generating an antagonistic network based on a time sequence, and carrying out cold-wave event sample expansion; an extreme learning machine is constructed, and cold-wave weather prediction is carried out; performing historical climbing event detection on historical power output results of each station of the cluster; dividing the climbing events into various climbing conditions with different severity degrees by using a K-Means clustering algorithm; and carrying out climbing event prediction. According to the method, sample support is provided for training of the climbing prediction model, the climbing events are clustered and divided by fusing the fan operation state and the climbing characteristics, and the harm degrees of different climbing events, especially the climbing events in extreme weather, are finely measured.

The invention discloses a marine ecological early warning management system and method based on multi-source data, and relates to the technical field of marine data monitoring. The method comprises the following steps: presetting sampling time and synchronously acquiring water body parameters and environmental parameters of each sampling layer at a sampling point; calculating a standard vertical gradient, and constructing an incidence matrix; calculating association strength to form an interlayer association vector; extracting a trend slope and a time difference degree of the inter-layer association vector, and splicing to obtain a space-time association vector; a sliding window method is adopted to calculate covariance between the space-time correlation vector and each environment parameter time sequence, a dynamic coupling coefficient is obtained, and a coupling coefficient matrix is constructed; and comparing the coupling coefficient matrix with the reference matrix element by element, and realizing accurate early warning of the red tide risk through a preset threshold value. According to the method, the time-space correlation characteristics of the multi-source data are fused, so that the early-stage and accurate early warning of the red tide is realized.

The invention provides a spatial propagation source identification method and system based on meteorological extreme events, and the method comprises the steps: carrying out the time sequence reconstruction of historical meteorological observation data of a research region, and generating a meteorological data sequence set containing multi-element meteorological indexes; performing event extraction processing on the meteorological data sequence set based on a preset extreme event judgment strategy to obtain a meteorological extreme event sequence set; performing space-time correlation intensity calculation on the spatial position events, generating a space-time correlation intensity matrix describing correlation degrees among the different spatial position events, performing significance test on the space-time correlation intensity matrix, screening correlation intensity elements, and constructing a significant correlation set; and carrying out propagation path reverse tracking analysis based on the spatial propagation source identification result, determining a propagation source position of the meteorological extreme event in the spatial dimension and a corresponding propagation influence parameter, and generating a spatial propagation source identification result. According to the method, the propagation source identification efficiency and the positioning accuracy can be improved, and excessive dependence of a traditional model driving method on initial conditions is avoided.

The present application aims at the problem that the Sentinel-1 backscattering data is significantly short-term time autocorrelated due to the influence of rainfall, soil moisture and surface water content change, and further leads to the high false alarm rate of traditional difference cumulative sum and deforestation detection method, discloses a kind of fast-growing forest deforestation detection method and device considering Sentinel-1 time correlation, and proposes an improved difference cumulative sum detection method by fusing sliding time window and random block resampling significance test, which calculates local difference cumulative sum curve using sliding time window, and extracts candidate change points based on maximum value;To overcome the statistical test bias caused by time correlation, a random block resampling strategy is innovatively introduced to retain the local dependence structure of time series, and an extreme difference null hypothesis distribution is constructed for significance test.The present application effectively improves the precision and robustness of short-period fast-growing forest deforestation event automatic identification in large-scale, long-time series monitoring scenarios.

The invention discloses an intelligent visibility prediction method and device, equipment, a medium and a product, and relates to the field of intelligent visibility prediction, and the method comprises the steps: obtaining meteorological data and real visibility of a current time period; performing smooth processing on the real visibility to obtain current weight visibility, and performing difference calculation to obtain a weight visibility difference sequence; inputting factor data in the current meteorological data into the corresponding trained single meteorological factor prediction model to obtain meteorological data in a prediction time period; and inputting the predicted meteorological data and the current weight visibility difference sequence into the trained weight-difference visibility prediction model to obtain the predicted weight visibility in the prediction time period. According to the method, the meteorological data is predicted, the weight visibility processing and the difference sequence are combined, the multi-factor association is fully considered through the weight-difference visibility prediction model, and the time correlation and the data stability are comprehensively considered, so that the sensitivity to the low-visibility process is improved, and the visibility is predicted more accurately.

The invention provides a sky-ground integrated cultural relic safety monitoring method and system, and relates to the technical field of cultural relic safety monitoring, and the method comprises the steps: obtaining radar and unmanned plane image data of a target ruin in different time periods and ground vibration data of a collection device in a region, and building ground surface reference parameters through combining geological survey and meteorological data; comparing radar images to identify earth surface change information, and generating an earth surface difference chart marked with the deviation value of each geographic position according to the deviation degree of the earth surface change information and the reference parameters; for key positions meeting a preset deviation value in the image, corresponding unmanned aerial vehicle image slices are extracted, visual feature vectors are obtained through multi-scale texture decomposition and color space conversion, a risk index is calculated by combining ground vibration data space-time correlation analysis, and an alarm is generated if illegal activities exist. Accurate monitoring and rapid warning of illegal activities of cultural relics and ruins can be realized.

This invention provides a method for diagnosing condensation degradation in vector thermal analysis. The method includes: performing a thermal analysis reaction on the sample to obtain thermogravimetric data and three-dimensional mass spectrometry data of the gaseous phase; calculating the total mass change of the sample; analyzing the three-dimensional mass spectrometry data using the equivalent characteristic spectrum method to obtain the total mass change of all gaseous components; and determining the classification of condensation degradation based on time correlation coefficients. The classification of condensation degradation includes: condensation tailing defects, condensation drift defects, memory effect defects, and secondary reaction defects. This invention utilizes mass spectrometry in vector thermal analysis to qualitatively and quantitatively analyze the escaping gases during the thermal analysis reaction process. By comparing the mass yield of all gaseous components with the weight loss rate of the solid and liquid phases in the thermal analysis, and using a time correlation and mass balance system, it provides real-time intelligent diagnosis of condensation degradation phenomena and possible types occurring in the escaping gases during detection.

The application discloses a concrete dam crack opening prediction method, system, equipment and medium, the concrete dam crack opening prediction method first obtains the time sequence of crack opening and the time sequence of each environmental variable; then the time sequence of crack opening is decomposed once through IEMD; then the intrinsic mode function with similarity is reconstructed into trend component, periodic component and random component based on the mode reconstruction method of SPMFE and t-SNE; the random component with the highest complexity is decomposed twice by using SVMD; finally, the components are predicted by using the space-time attention mechanism of Crossformer, and the final prediction result is obtained by superimposing the prediction results of the components. The application can effectively reduce the complexity of the dam crack opening time sequence, thereby being conducive to learning deeper space-time correlation features from simple components, and improving the accuracy and robustness of non-stationary sequence prediction.

Embodiments of the present application disclose a target detection method, a method and device for establishing a time correlation perception model. The main technical solution includes: obtaining multiple frames of sensor data; performing target detection on each frame of sensor data in the multiple frames of sensor data respectively to obtain a first target detection result of each frame of sensor data; performing target tracking on the first target detection result of each frame of sensor data to correlate information of the same target to obtain a detection result sequence of each target; and performing second correction prediction on the first target detection result of each frame of sensor data based on the time sequence correlation of the information of the same target in the detection result sequence of each target to obtain a third target detection result of each frame of sensor data. The technical solution provided by the present application can improve the accuracy of target detection.

The invention relates to the technical field of mine early warning, and discloses a mine geological disaster early warning method based on artificial intelligence, and the method comprises the steps: carrying out the multi-physics field cooperation verification analysis of a ground surface displacement sequence, a deep rock mass stress sequence and a micro-seismic event sequence of a mine, and obtaining a multi-source fusion evidence body; performing cross-modal space-time correlation on the multi-source fusion evidence body to obtain high-dimensional disaster precursor features; based on the high-dimensional disaster precursor features, topological construction is carried out on the spatial structure data of the mine, and a disaster causal knowledge graph is obtained; performing perceptual reasoning on the disaster causal knowledge graph to obtain a real-time enhanced view; performing mode matching on the high-dimensional disaster precursor features and the real-time enhanced view to obtain a specific disaster stage of the mine, and evaluating the state stability of the specific disaster stage; determining a graded early warning signal and a decision support report of the mine according to the specific stage and the state stability of the disaster; according to the invention, the timeliness of mine geological disaster early warning can be improved.

The invention discloses a rock slope stability real-time monitoring method and system based on a three-dimensional laser scanner and a deep sensor, and relates to the technical field of geological disaster monitoring and early warning. The method comprises the steps that multi-temporal point cloud data and a deep monitoring sequence are acquired, point cloud frames are constructed to be bound with a deep sequence window, and a point cloud frame index set and a deep abnormal event index set are output; converting abnormal space coordinates according to the abnormal measuring point depth, the drilling hole coordinates and the drilling track parameters, and projecting the abnormal space coordinates to the slope model to form an abnormal mapping relation; performing aggregation and diffusion according to the slope unit number to generate a risk hot area, performing instability mode discrimination based on the mapping weight, the trigger characteristic quantity and the normal consistency parameter, and outputting a hot area mode result set; according to the method, unified space-time correlation of deep anomaly and slope deformation, risk hot area aggregation and instability mode discrimination are realized, and the consistency and traceability of slope stability monitoring results are improved.

The application provides a greenhouse environment space distribution prediction method and device, the method comprises the following steps: according to a plurality of groups of environment characteristics of a prediction point at a current time and previous time, a feature vector matrix comprising a plurality of groups of time sequence characteristics is determined, the environment characteristics comprise greenhouse internal environment characteristics and greenhouse external environment characteristics; the feature vector matrix is input to a convolution network of a trained prediction model for feature extraction, and a global feature vector after extraction is obtained; the global feature vector is input to a time sequence network of the prediction model, and a prediction result of temperature and humidity, light intensity and carbon dioxide content after a preset time length is output. The method can accurately mine the characteristics of the greenhouse microclimate environment data, such as large hysteresis, time sequence, nonlinearity and spatial distribution difference, better mine time correlation in combination with the convolution network and the time sequence network, so that the spatial distribution trend of the multi-point environment at a future time of the greenhouse can be more accurately predicted, and a decision basis can be provided for the overall environment regulation of the greenhouse.

The invention provides a multi-parameter evaluation method and system for a TBM cutter in a tunneling state, and relates to the technical field of TBM construction monitoring. According to the method, geological radar detection data and tunneling parameters and cutter state parameters after noise reduction are fused, and fusion feature data representing time-space correlation of geological conditions and cutter operation states are extracted; geological condition adjustment factors are determined based on geological radar data, a fuzzy comprehensive evaluation model with a self-adaptive membership function is constructed, and dynamic accurate evaluation of the cutter state is achieved; determining the dynamic weight of an optimization target according to the evaluated cutter state grade, and carrying out multi-target optimization calculation to obtain an optimization parameter combination adaptive to the current cutter state; and determining a preferential recommendation scheme, predicting the residual tunneling distance of the cutter, and outputting construction decision suggestions and early warning information. The construction safety and the tunneling efficiency of the TBM are effectively improved.

The application provides a strong mine pressure roof dominant layer position determination method, device and system, which comprises the following steps: determining the breaking start and end time of each roof rock layer from the coal mining face to the ground of the to-be-monitored mine surface based on first sensing data; determining the pressure start and end time of the coal mining face based on second sensing data; performing time correlation degree analysis on the breaking start and end time of the target roof rock layer and the pressure start and end time of the coal mining face, and determining a correlation degree coefficient; and in the case that the correlation degree coefficient is greater than or equal to a first preset threshold, determining that the target roof rock layer is the dominant layer position. The strong mine pressure roof dominant layer position determination method, device and system provided by the application realize the entry from the time correlation angle of the roof breaking and the working face pressure, accurately screen and determine the strong mine pressure dominant layer position, and further provide favorable technical support for the working face strong mine pressure disaster mechanism research and on-site engineering management.

The invention relates to the technical field of real-time monitoring, in particular to a bridge debris flow impact force real-time monitoring system which comprises a waveform screening module, an impact recognition module, an impact offset module, a fatigue evolution module and an instability early warning module. According to the method, the time interval and the amplitude rise ratio of the adjacent peak values in the stress response sequence are subjected to double-ratio judgment, so that interference of background disturbance on impact signal calibration is avoided, and the accuracy of identifying the number of chipping impact can be improved by means of cross confirmation of the impact distribution density and the vibration data; time correlation analysis is carried out in combination with a strain release curve slope change point, real-time quantitative analysis of a structure bearing capacity loss trend is realized, and spatial hot area early warning marking can be carried out on a pier area before the structure yields by tracking a loss rate slope change trend and identifying a critical growth interval; and the response processing precision before disaster caused by sudden debris impact is improved.

The invention discloses a concrete dam crack opening degree prediction method, system and equipment and a medium. The concrete dam crack opening degree prediction method comprises the following steps: firstly, obtaining a crack opening degree time sequence and a time sequence of each environment variable; carrying out primary decomposition on the crack opening time sequence through IEMD (International Empirical Mode Decomposition); then, on the basis of a modal reconstruction method of SPMFE and t-SNE, the intrinsic modal function with similarity is reconstructed into a trend component, a periodic component and a random component; carrying out secondary decomposition on the random component with the highest complexity by utilizing SVMD; and finally, predicting each component by adopting a Crossform space-time attention mechanism, and superposing prediction results of each component to obtain a final prediction result. According to the method, the complexity of the dam crack opening time sequence can be effectively reduced, so that deeper-level space-time correlation characteristics can be learned from simple components, and the accuracy and robustness of non-stationary sequence prediction are improved.

The application discloses an intelligent visibility prediction method and device, equipment, medium and product, and relates to the field of intelligent visibility prediction. The method comprises the following steps: acquiring meteorological data and real visibility of a current time period; performing smooth processing on the real visibility to obtain current weighted visibility, and then performing difference calculation to obtain a weighted visibility difference sequence; inputting each factor data in the current meteorological data into a corresponding trained single-meteorological-factor prediction model to obtain meteorological data of a prediction time period; and inputting the predicted meteorological data and the current weighted visibility difference sequence into a trained weighted-difference visibility prediction model to obtain predicted weighted visibility of the prediction time period. The application fully considers the correlation of multiple factors by predicting meteorological data, combining weighted visibility processing and a difference sequence, and using a weighted-difference visibility prediction model, and also comprehensively considers time correlation and data smoothness, thereby improving the sensitivity to a low-visibility process and more accurately predicting visibility.

The present application relates to the technical field of concrete dam structure safety monitoring, in particular to a concrete dam operation key position partition monitoring method, model and storage medium. The concrete dam operation key position partition monitoring method provided by the present application first divides the key positions of the concrete dam by using the extracted monitoring data time-frequency vector, and on this basis, obtains the time sequence measurement data of different types of monitoring instruments with high space-time correlation, thereby establishing a graph structure, capturing the time dimension and variable dimension dependency relationship of the multivariate time sequence data, providing a graph attention network to further learn and represent this relationship, obtaining the final feature representation of the time sequence measurement data, and finally calculating an anomaly score by using the final feature representation to detect anomalies; the complementary mutual verification of multiple types of monitoring instruments and multiple measuring points is realized, and the structural integrity and spatial distribution law of the concrete dam are fully reflected.

The application discloses a kind of abandoned stone coal mine ecological restoration effect evaluation method and system, method includes: the dataset of the multi-source ecology of repair area is established, and the multidimensional time series matrix is formed after data set is spatially registered, time marker and abnormality is eliminated;The time series matrix is analyzed in time sequence consistency, and the time sequence dependence between ecological factors is calculated using the time lag correction algorithm based on mutual information, and the coupling feature set is generated using variable step delay matching to correct time difference;The coupling feature set is input into the graph convolution network model, the node is ecological factor, and the edge weight is time correlation, and the associated features of space and time are extracted by graph convolution.The application realizes the comprehensive evaluation of precision, stability and interpretability of abandoned mine restoration effect by constructing multi-source ecological coupling model and dynamic intelligent analysis mechanism.

The application provides a kind of space-time evolution data processing method and system applied to tunnel deformation analysis, by obtaining the time series monitoring sequence of different monitoring dimensions in tunnel construction process, based on this, the space-time evolution correlation model of tunnel deformation is constructed, by analyzing the dynamic coupling relationship of structure response data and geological environment action data, generate a space-time correlation feature matrix, and based on this, generate the feature evolution sequence of tunnel deformation, nonlinear mutation characteristics extraction is carried out on the feature evolution sequence, and the mutation feature index set is obtained;According to the mutation feature index set and the preset cumulative deformation rate threshold, the dynamic division of tunnel deformation safety level is carried out, and the safety level division result is generated, based on the safety level division result and the spatial distribution data of tunnel construction section, the spatial positioning and boundary contour extraction of potential risk damage area are carried out, and the risk damage area distribution map is obtained.The application can accurately evaluate and effectively control the safety risk of tunnel construction.

The present application relates to the technical field of data processing, in particular to a kind of multi-source geological disaster information intelligent processing method and equipment, comprising the following steps: collecting displacement and acceleration monitoring information and unified access time alignment generates multi-source time series data, geological boundary is divided according to range analysis geological unit and is established mapping, monitoring point and remote sensing data attribution unit form index, depict time series variation reflects unit evolution characteristics, identify disaster state and output fusion result.In the present application, time correlation is carried out to multi-source monitoring information, and geological unit space constraint is introduced, so that the data of different sources form a stable corresponding relationship in a unified spatial framework.The monitoring information is no longer aligned with time and coordinates, but is directly associated with specific geological units.Combining the time series variation characteristics of multi-source information, the dynamic evolution within the geological unit is continuously depicted, and the support capability of monitoring information in disaster analysis and application output is enhanced.

The invention discloses a dry and hot valley area soil health assessment method and system, and relates to the technical field of soil environment monitoring, and the method comprises the steps: arranging a surface layer humidity sensor and a root layer humidity sensor in target area soil, continuously collecting humidity time sequences in a day and night period, performing time correlation analysis on the surface layer humidity sequence and the root layer humidity sequence in each day and night period, calculating correlation lag time to obtain a lag time sequence taking day as a unit, calculating an average value and a variance of the lag time sequence in a set time window, and performing linear regression analysis to obtain a change trend slope, when the average value is in a first preset health interval, the change trend slope is in a second preset health interval with the midpoint being zero, and the variance is lower than a stable threshold value, it is judged that the soil is in a healthy state. The method is especially suitable for soil health detection in a typical environment with obvious day and night humidity fluctuation in a dry and hot valley region, and has the advantage of large-scale detection.

The application relates to the technical field of gas concentration prediction, in particular to a gas concentration inspection prediction method based on space-time correlation information, which comprises the following steps: collecting methane concentration and wind speed data of each monitoring point; then determining a time sequence dimension emission confidence factor by comparing the time sequence dispersion of single-point data and the change of adjacent periods; meanwhile, calculating a space dimension anti-interference confidence factor by combining the wind speed fluctuation characteristics and the multi-point space distribution; then obtaining a gas emission confidence degree by comprehensively combining the two, and fusing the gas emission confidence degree with physical statistical characteristics of the methane concentration to construct a three-dimensional feature tensor and acquire a prediction model; the application effectively eliminates environmental interference through multi-dimensional confidence correction, and significantly improves the authenticity and accuracy of gas concentration prediction.

The application discloses a kind of dry-hot valley area soil health assessment method and system, it is related to soil environmental monitoring technical field, including in target area soil layout surface layer humidity sensor and root layer humidity sensor, continuous acquisition humidity time series in diurnal cycle, time correlation analysis is carried out to surface layer and root layer humidity sequence in each diurnal cycle, calculate relevant lag time, obtain lag time sequence with day as unit, calculate the mean and variance of lag time sequence in the set time window, and carry out linear regression analysis to obtain variation trend slope, when mean is in the first preset health interval, variation trend slope is in the second preset health interval with midpoint zero, and variance is lower than stable threshold value, determine that soil is in healthy state.The application is especially suitable for soil health detection in such typical environment with obvious diurnal humidity fluctuation in dry-hot valley area, and has the advantage of large-scale detection.

The application discloses a kind of quantitative relationship determination methods of supercritical CO2 flow and circulating pump power, belong to the technical field of medium deep geothermal energy development.The method includes the following steps: step one: build synchronous monitoring system, obtain original monitoring data, step two: collect data under all conditions, form basic data set, step three: preprocess basic data set, obtain effective data set, step four: calculate supercritical CO2 real-time physical property parameters, step five: calculate supercritical CO2 total resistance loss in pipe, step six: build initial quantitative relationship model, step seven: verify and iteratively adjust initial model, step eight: field correction model, obtain final quantitative relationship model, step nine: verify model and promote application;The application guarantees the time correlation of temperature, pressure, flow and power data by unified timestamp, effectively eliminates data anomalies, dimension differences and time lag problems by combining 3σ rule and z-score standardization preprocessing method, and guarantees the accuracy of basic data.

The application provides a tunnel surrounding rock dynamic grading and early warning method and related equipment, through integration of two types of core data sources of static basic data and dynamic monitoring data, overall coverage of geological information is realized; with the aid of the space-time correlation binding mechanism of mileage coordinates and time stamps, the information barrier between static data and dynamic data is broken, ensuring accurate correspondence of different dimension data in spatial position and time dimension, providing a unified data basis for subsequent grading calculation; based on the dynamic weight distribution mechanism of the construction stage, the grading calculation can adapt to the geological characteristics in different periods of tunnel construction, the dynamic update of the quality grade of each tunnel segment surrounding rock is realized through the preset grading standard algorithm, the technical difficulty that the traditional fixed weight grading method cannot adapt to the dynamic evolution of surrounding rock in the whole construction process is solved, and the timeliness and accuracy of surrounding rock grading are significantly improved.