481 results about "Posteriori probability" patented technology

The invention discloses a turbofan engine operation monitoring method and system based on digital twinning, belongs to the technical field of turbofan engine monitoring, and aims to solve the problems that weak fault signals such as early cracks and abrasion are difficult to extract and the prediction precision of a multi-source fault propagation path is low under a strong noise background. An original operation signal is collected through a sensing array, and is processed by an adaptive resonance demodulation chain to generate a demodulation signal. The method comprises the following steps: carrying out time-frequency transformation on a demodulation signal, constructing an initial candidate feature set by combining feature frequency prior matching actual measurement and theoretical feature frequency, and generating an independent feature set by fusing multi-scale decoupling network separation features of digital twin constraints; for independent features, effective causal pairs are screened by adopting a physical coupling relationship combining Granger causal analysis and digital twinborn simulation, a dynamic Bayesian network is constructed to simulate fault propagation, a posterior probability is calculated through digital twinborn verification and Monte Carlo simulation, early warning is triggered, and a maintenance decision is generated. And weak signal extraction and accurate fault prediction under strong noise are realized.

The invention discloses a geothermal field parameter inversion calculation method, device and system, and a storage medium. The method comprises the following steps: establishing a geophysical stratified model; through Monte Carlo sampling of preset parameter spaces of the crustal heat generation rate and the heat conductivity, a temperature value is calculated in combination with a heat conduction equation, and a Gaussian likelihood function evaluation model is constructed to predict the matching degree of the temperature and the actually measured temperature; obtaining the optimal estimation of the thermal parameters, a confidence interval and a correlation matrix among the parameters based on the posterior probability distribution; and according to the correlation matrix, taking a high-confidence result generated by Monte Carlo inversion as priori knowledge of a physical guidance neural network PINNs, and finally outputting a thermal parameter spatial distribution prediction result of the target area through parameter initialization constraint, output layer range limitation and a physical regularization loss item. By adopting the technical scheme of the invention, the defects that the existing actually measured geothermal field parameters are rare and the regional characteristics cannot be described and the geophysical joint inversion of the geothermal field parameters cannot be realized in the prior art are overcome.

Even when there is little measurement data of abnormal signals, it is an object to manage the abnormality of the signals. 【Means for solving the problem】 The abnormality management device 1 uses, as teacher data, normal data indicating a normal frequency spectrum among the frequency spectra of a plurality of signals, for each frequency component intensity, and the parameter of a probability model that outputs the posterior probability that each frequency component intensity is normal is learned by maximum likelihood estimation. And a derivation unit 12 configured to derive a probability distribution of abnormal data indicating a frequency spectrum including a frequency component with an abnormal intensity, based on the posterior probability estimated by the learned probability model, the probability distribution of the normal data, and the prior probability of being normal.

The invention discloses a multi-source environment monitoring data fusion method based on adaptive Bayesian reasoning. The method comprises the following steps: S1, collecting an environment monitoring data set; s2, obtaining a preprocessed environment monitoring data set; s3, carrying out uncertainty evaluation on the preprocessed environment monitoring data set; s4, forming an initial prior probability; s5, calculating the posterior probability distribution of each environment monitoring data source through the Bayesian theorem, and dynamically updating the weight of each data source in the environment monitoring data fusion process according to the posterior probability distribution; s6, performing weighted fusion on each environment monitoring data source according to the dynamically updated weight to form fused environment monitoring data; and S7, carrying out post-processing on the fused environment monitoring data. According to the invention, the data fusion process can adapt to environmental changes in real time, so that the accuracy of data fusion is improved.

The invention relates to the technical field of mechanical fault diagnosis, and discloses a gearbox fault diagnosis method and system based on lightweight variational Bayesian learning, and the method comprises the steps: collecting a to-be-diagnosed vibration signal of a gearbox, carrying out the preprocessing of the to-be-diagnosed vibration signal, and obtaining an original vibration signal and the fault feature frequency of the original vibration signal; determining amplitude modulation-frequency modulation sparse combination representation of the original vibration signal according to the fault characteristic frequency of the original vibration signal, and constructing a joint probability model according to classification distribution containing sparse vectors and the amplitude modulation-frequency modulation sparse combination representation of the original vibration signal; performing variational Bayesian inference solution on the joint probability model by using the non-overlapping sub-sequence of the original vibration signal and natural gradient optimization to obtain posterior probability estimation of a sparse coefficient; and determining an activation component according to the posterior probability estimation of the sparse coefficient and the sparse precision parameter, and matching the activation component with a pre-established multi-scale amplitude modulation-frequency modulation sparse dictionary to obtain a fault type and a confidence coefficient thereof.

The invention discloses a distillation SAE and dynamic integration converter steelmaking carbon temperature soft measurement method, which comprises the following steps of: training a teacher model on a large-scale data set of a conventional production working condition, and compressing the model by adopting a knowledge distillation technology; then, a small amount of data from unconventional production working conditions is used for fine tuning to obtain a plurality of expert-type student stacking auto-encoder models SAE; in the prediction stage, a to-be-tested sample is mapped to respective feature space through a plurality of SAEs, and the posterior probability of the sample is calculated in each feature space based on a Gaussian distribution model; and finally, respectively inputting a to-be-measured sample into a plurality of corresponding regression devices to obtain a predicted value, and carrying out weighted fusion on the output of each regression device according to a posterior probability to realize dynamic integration selection of the soft measurement model. The method can effectively adapt to complex data distribution of multi-working-condition changes in converter steelmaking, the carbon temperature prediction precision under the unconventional working condition is improved, and the method has high robustness and engineering practicability.

The invention discloses a fault early warning method and system for a battery pack based on data analysis, and the method comprises the steps: obtaining asynchronous sampling data of voltage, current and temperature parameters from a plurality of data sources, mapping the asynchronous sampling data to a preset time grid through employing a linear interpolation algorithm, and generating time series data; inputting the time sequence data into a long short-term memory network, and calculating through forward propagation to obtain a hidden state sequence containing time dependence characteristics; calculating a feature weight of the hidden state sequence by adopting an attention mechanism, and if the feature weight is greater than a preset threshold value, reserving corresponding features to form a simplified feature set; calculating posterior probability distribution by adopting a Bayesian reasoning algorithm based on the simplified feature set, and determining a fault occurrence probability; and according to the fault occurrence probability, a dynamic threshold adjustment algorithm is adopted to update an early warning threshold, and if the fault probability exceeds the current early warning threshold, a graded early warning signal is triggered. According to the invention, a powerful guarantee is provided for safe and stable operation of power equipment.

The invention belongs to the technical field of robots, and discloses a feature fusion and refining embedding sparse Bayesian learning method and system for robot curved surface milling contour error monitoring, and the method comprises the steps: carrying out the structural fusion of feature vectors composed of technological parameters, robot rigidity, cutting force and tracking errors into a dictionary matrix; and a sparse Bayesian learning method is combined to realize robot processing error monitoring. During model training, the posterior distribution of the weight variables can be obtained, and the expectation and variance of the posterior distribution of the weight coefficients are estimated by maximizing the posterior probability. Wherein the variance and the hyper-parameter influencing the weight sparsity are estimated by maximizing an edge likelihood function, that is, optimization is carried out by the expectation of a joint likelihood function under maximized weight posterior distribution, and the minimum value solution can be solved for the converted loss function to optimize the hyper-parameter. The posterior expectation and variance of the model weight and the hyper-parameter are iteratively updated in the training process, and the iteration process is terminated according to a set change threshold value so as to realize the sparsification of the model weight vector and the construction of the sparse model. According to the method, robot milling error monitoring of curved surface parts with different features under the variable poses is achieved, and the part measurement cost is effectively reduced.

The invention relates to a risk analysis method, and particularly provides a dynamic risk analysis method based on a Bayesian network. Converting the risk influence factors into a first group of network nodes based on a historical risk knowledge model, and constructing an initial topological structure according to a knowledge model logic relationship; secondly, acquiring multi-source real-time monitoring data, learning correlation among variables through a data mining algorithm, and generating a second group of nodes and a data-driven topological structure; then, connecting the historical information Bayesian network with the data-driven Bayesian network through a shared risk node, and constructing a comprehensive Bayesian network; meanwhile, a dynamic probability updating mechanism is established, and comprehensive network probability parameters are dynamically adjusted by adopting a weighted fusion algorithm in combination with a historical prior probability and a real-time posterior probability; and finally, performing risk reasoning based on the dynamically updated integrated network, and outputting risk quantitative indexes.

The invention relates to the technical field of generator monitoring, in particular to an online state monitoring method and system for a water-turbine generator set, and the method comprises the steps: collecting data, and carrying out the preprocessing of the data through a lightweight TinyML reasoning model; dimension reduction processing is carried out on the preprocessed data through PCA, and dynamic normalization is carried out on the data after dimension reduction; performing convolution feature extraction on the normalized data through a feature extraction module, and extracting attention enhancement features from the convolution features through a sparse attention mechanism; calculating a posterior probability based on Bayesian network topology through a Bayesian network feature fusion module; a fault probability vector and an integrated feature vector are obtained through combination of a multi-modal fusion model and a posterior probability; and through the fault probability vector, predicting residual life and current working condition characteristics, and outputting an early warning level, a fault type and predicted fault time. According to the scheme, the diagnosis efficiency and reliability are improved through multi-modal fusion and Bayesian reasoning.

The invention discloses a PC bridge service state and vulnerability prediction method based on the Bayesian theory, and relates to the technical field of bridge service state and vulnerability evaluation, and the method comprises the steps: building a three-dimensional finite element model of a bridge; key parameters influencing the service state of the bridge are determined, the three-dimensional finite element model is operated, the mid-span deflection value and the average prestress loss value of each bridge sample at different moments are obtained, and a database is formed; a GRU proxy model for prediction is obtained through database training; aiming at a to-be-studied bridge, obtaining an actually-measured midspan deflection measured value of the bridge; using the key parameters of the bridge and the GRU agent model to obtain a mid-span deflection prediction value of the bridge; performing Bayesian parameter estimation according to the measured value and the predicted value of the mid-span deflection of the bridge, and estimating bridge parameters of the bridge, which conform to posterior probability distribution; and substituting the estimated bridge parameters conforming to the posterior probability distribution into the GRU agent model to obtain a time-varying curve of the mid-span deflection value and the prestress average loss value of the bridge, and a time-varying vulnerability curve of the bridge. According to the method, the relatively accurate bridge key parameters are calculated through Bayesian parameter estimation, and more accurate prediction can be achieved.

The invention discloses an ice lake feedback monitoring and early warning method based on artificial intelligence and multi-source data fusion, and belongs to the technical field of natural disaster monitoring, and the method specifically comprises the steps: extracting ice lake state feature vectors of multiple spatio-temporal data through cross-modal self-supervised learning, and constructing a three-dimensional virtual ice lake model; based on the feature vectors, constructing a glacier three-dimensional stress field model by using a finite element method, constructing a seepage channel graph model by using a graph neural network, and fusing the features of the glacier three-dimensional stress field model and the seepage channel graph model to form mechanical-seepage coupling state vectors; constructing a pulse neural network to simulate glacier fracture extension and seepage mutation pulse events; constructing a heterogeneous graph federated architecture and outputting a federated weight matrix in combination with a dynamic weighted aggregation strategy; the federal learning weight and the three-dimensional model output result are fused, the outburst posterior probability is calculated through a Bayesian neural network, and a fourth-level early warning signal is generated; according to the invention, the multi-dimensional characterization and coupling process simulation of the state of the ice lake is realized, and the early warning accuracy and timeliness are improved.

The invention relates to the technical field of test bed operation monitoring, in particular to a fuel pump test bed operation monitoring method and system. The method comprises the following steps: acquiring monitoring time sequence data of operation of a fuel pump test bed, performing feature extraction, constructing a coupling prediction model of a space-time diagram attention network-unscented Kalman filter, and predicting a process noise covariance matrix and a measurement noise covariance matrix according to the coupling prediction model, and performing state estimation by using an unscented Kalman filter to obtain vector posterior probability distribution, constructing a fault evolution trajectory manifold, calculating a mahalanobis distance between the vector posterior probability distribution and the fault evolution trajectory manifold, and taking the mahalanobis distance as a monitoring index. According to the scheme of the invention, the deep features which can better reflect the inherent nonlinear and complex dynamic characteristics of the system can be extracted from the multi-source data, the estimation accuracy of the filter on the potential health state of the system is improved, and the defects that model parameters are fixed and gradual change faults are difficult to capture in a traditional method are overcome.

The invention discloses a fiber cement board production line information big data system and method, and the method employs a data processing and analysis algorithm based on artificial intelligence and deep learning to carry out the time sequence feature analysis and fine-grained time sequence feature response correlation coding of real-time temperature, real-time humidity, real-time viscosity and real-time stirring speed. In this way, time sequence fine-grained response interaction characteristics between the temperature, humidity and viscosity and the stirring speed in the stirring process are learned and captured, and time sequence reasoning of stirring speed control is carried out through Bayesian posterior probability calculation among the time sequence fine-grained response interaction characteristics of the temperature, humidity and viscosity in the stirring process, so that the stirring speed value of the next time point is determined. And generating an adjusting instruction. In this way, intelligent control over the stirring process of fiber cement board production can be achieved, and the stability and uniformity of the stirring process can be guaranteed.

The invention relates to an index weight dynamic correction method and device for comprehensive evaluation and a medium. The method comprises the steps that all indexes needed by the comprehensive evaluation at the initial moment and index values and weight values of all the indexes are obtained; according to the change of the index required at the current moment relative to the index required at the previous moment, constructing a column connection table for representing the change degree of each index; calculating a posterior probability matrix representing the posterior probability of each index through the Bayesian probability based on the concatenation table; correcting the posterior probability matrix through a correction matrix obtained based on a Probit function to obtain a correction probability matrix; performing standard normal correction on the correction probability matrix; and obtaining a weight value of each index at the current moment based on a result after standard normal correction. According to the invention, dynamic correction of the index weight is realized.

The invention provides an intelligent power grid fault prediction and operation and maintenance method and system based on cloud computing and a medium. According to the system, intelligent sensors are deployed at key nodes of a power grid, operation data, including voltage, current, temperature and other parameters, of equipment are collected, and the data are transmitted to a cloud platform. And the cloud platform analyzes the real-time data by adopting a deep learning model and a Bayesian inference method so as to predict the potential fault of the equipment and send out an early warning signal in advance. In the data analysis process, a kernel function is introduced to perform feature extraction and smoothing processing on the data, so that the recognition capability of the model on a complex data mode is enhanced. Bayesian inference is used to calculate the posterior probability of the device state to provide more reliable uncertainty estimation, helping to improve the accuracy of fault prediction.

The invention provides an unmanned ship collision avoidance decision-making method based on navigation intention perception, and the method comprises the steps: collecting the motion state data of a ship and a target ship in real time through a multi-source sensor, and constructing a collision risk assessment model for screening a threat target; the posterior probability distribution of the navigation intention of the target ship is dynamically updated based on recursive Bayesian reasoning of the historical trajectory of the target ship, and the navigation intention is dispersed into a plurality of behavior modes; constructing the state of the ship, the motion characteristics of the target ship and intention distribution into a state vector, inputting the state vector into a strategy network based on reinforcement learning, and outputting a collision avoidance control action; policy network optimization is guided through a composite reward function, and the reward function fuses distance reward, rule penalty and collision risk penalty.

The invention provides an air conditioning system fault diagnosis method and device based on a parameter correlation Bayesian network, and the method comprises the steps: rapidly determining the real-time numerical values of key performance parameters of parts through the correlation between the material parameters of the parts, comparing the calibration numerical values of the parts when the parts leave a factory, and determining the real-time fault rates of various faults of the parts; and in combination with a Bayesian network model of the air conditioning system, calculating the real-time fault rate and the real-time posterior probability of each stage of sub-node in sequence, and judging whether the sub-node is a fault node or not by comparing the difference between the statistical posterior probability and the real-time posterior probability. According to the method, in real-time data measurement and processing, measurement is convenient, and operability is high; according to the method for judging the faults through the Bayesian network and the posterior probability, the diagnosis process is more flexible, fault positioning is more accurate, the method can be suitable for fault diagnosis of various types of air conditioning systems through slight adjustment, and high practical generalization is achieved.

The invention discloses a shield risk tracing method based on a knowledge graph, and belongs to the technical field of shield risk analysis. Determining an entity set and a relationship set, and constructing a mode layer of the knowledge graph; performing data preprocessing on the document data to obtain a processed text, extracting a knowledge triple meeting requirements, and outputting a structured knowledge triple according to a predetermined format; performing entity alignment to obtain a normalized knowledge triad to construct a knowledge graph; calculating the membership degree of the node on the basis of the knowledge graph, and obtaining the posterior probability of the node in combination with the posterior probability of the node; and inferring a potential risk propagation path and relation strength based on the posterior probability of the node in the knowledge graph, and realizing dynamic analysis and prediction of the knowledge network. According to the method, structural association of risk factors is realized through the knowledge graph, and the uncertainty is quantified by combining fuzzy Bayesian reasoning, so that the problem that a complex causal relationship and information fuzziness are difficult to process by a traditional method is solved.

The invention discloses a VAGCN-based complex industrial process operation state evaluation method. The method comprises the following steps: collecting process data and carrying out standardization processing; performing data division on the data by using a sliding window to obtain a time sequence; the method comprises the following steps: converting time sequence data into cross-time-space variable interaction graph structure data through a variable interaction graph structure, and then capturing variable interaction characteristics and local space-time dependency in each time window by using a moving window; time pooling is used, advanced features of variable interaction are extracted and then input into the operation state evaluation model, posterior probabilities of the online data belonging to different operation state evaluation grades are obtained, and a final evaluation result is a state grade corresponding to the maximum posterior probability at the current moment; and performing similarity analysis on the variable interaction graph structure in the non-optimal state and the optimality graph structure in the optimal state so as to evaluate the variable dynamic offset degree in the non-optimal state. According to the method, the interaction among the variables at different time points can be captured, and more accurate process operation state evaluation can be realized.

The invention discloses a wind power plant fan fault prediction method based on a deep learning algorithm, and the method comprises the following steps: obtaining and inputting fan operation state data, and obtaining a matrix capable of representing the characteristics of the operation state data; reducing the data size scale of a high-frequency signal sequence generated by the fan sensor by using a box line downsampling method; aiming at the fault sequence data of the positive sample, combining prior knowledge and observation data by using a Bayesian multivariate autoregression model; for negative sample data, normal behavior sequence data is mapped to approximate posterior probability distribution of potential variables output by an encoder by using a variational auto-encoder model in combination with an attention mechanism, a decoder samples from the distribution through a re-parameterization technique, and reconstructed data similar to input data distribution is generated; a self-adaptive threshold adjustment strategy is used, and a classification decision can be dynamically optimized by automatically adjusting the threshold of positive and negative reconstruction errors. According to the method, the fan fault prediction precision can be effectively improved.

The invention discloses an intelligent risk early warning method and system based on multi-source data fusion, and belongs to the technical field of construction risk assessment and early warning, and the method comprises the steps: obtaining multi-source information, and obtaining an early warning control quantity based on the multi-source information and a preset quantification processing mode; constructing an initial early warning cloud picture based on the early warning control quantity and a preset grading early warning standard; obtaining a three-dimensional early warning cloud picture based on the initial early warning cloud picture, the two-dimensional matrix distribution and a preset two-dimensional normal cloud model; optimizing and correcting the three-dimensional early warning cloud atlas by adopting a posterior probability support vector method to obtain an improved prediction model; and obtaining a risk early warning result based on the improved evidence fusion method and the improved prediction model. According to the intelligent risk early warning method and system based on multi-source data fusion provided by the invention, a corrected three-dimensional early warning model is constructed by combining historical data on the basis of multi-source data fusion and adopting a posterior probability support vector method and an improved evidence fusion method, and the accuracy, effectiveness and applicability of risk early warning are remarkably improved.

The invention belongs to the technical field of artificial intelligence analysis, and particularly relates to a military target behavior analysis agent test method and equipment. The method comprises the following steps: step 1, acquiring multi-source data of a military target, and performing data preprocessing on each category of data in the multi-source data; 2, extracting tensor features of the input tensor; defining a multi-layer intention hierarchical structure based on the tensor features; 3, defining a likelihood function of each intention layer; modeling a prior probability using a recurrent neural network; calculating a posterior probability by using a Bayesian rule; when the number of times of iterative execution reaches the set number of times, executing the step 4: performing intention reasoning by using approximate posterior distribution, and calculating the uncertainty of intention reasoning. According to the method, the precision, the real-time performance, the interpretability and the reliability of military target behavior analysis are remarkably improved.

The invention discloses an evidence optimization method and device based on a Bayesian model, computer equipment and a storage medium, and relates to the technical field of medical care informatics. The method comprises the following steps: acquiring a text file containing a variation classification result; calculating posterior probabilities corresponding to 17 evidence combinations given by the ACMG / AMP guide, and when selected evidence combination rules meet posterior probability classification, obtaining theoretical pathogenicity probabilities corresponding to the evidence intensity grades; finely classifying the clinical significance unknown variation VUS into six levels; determining thresholds corresponding to different evidence intensity grades by comparing the positive likelihood ratio or the local positive likelihood ratio with the theoretical pathogenicity probability corresponding to each evidence intensity grade; and the variation is marked with an ACMG / AMP evidence label again, and a pathogenicity classification result of the variation is given. According to the method, the evidence optimization difficulty can be reduced, and the accuracy and consistency of genetic variation interpretation in clinical gene detection can be improved.

The invention discloses an unmanned aerial vehicle-unmanned vehicle system collaborative target detection method and system and application thereof, and the method comprises the steps: integrating the high-altitude multi-modal data of an unmanned aerial vehicle and the ground three-dimensional point cloud / multi-view image of an unmanned vehicle through constructing a collaborative architecture of an unmanned aerial vehicle subsystem, an unmanned vehicle subsystem and a data fusion center; a deep learning algorithm is adopted to extract image features, Bayesian estimation is combined to fuse multi-source data, and posterior probability distribution of a target state is generated. And task allocation and dynamic path optimization are realized based on a path planning algorithm and a Hungary algorithm, and a detection strategy is adjusted through real-time communication. The system disclosed by the invention comprises a high-precision sensor, a communication module and a high-performance data processing center. According to the scheme, the target detection precision, the environmental adaptability and the efficiency in a complex scene are remarkably improved, and the method can be widely applied to urban security and protection, field exploration and logistics distribution.

The invention provides a severe convection short-imminent forecasting method based on deep learning and a polarization quantity vertical profile, and the method comprises the steps: recognizing a convection cloud according to the equal-height surface polarization quantity at a preset time interval in a historical time period within a radar networking range and the three-dimensional grid point data of a wind field; tracking the convective cloud by using a CLTREC method, establishing a front-back matching relationship of the convective cloud, and determining a time sequence lattice point position of the convective cloud in the historical time period according to the front-back matching relationship; according to the time sequence grid point position of the convective cloud, sampling the polarization quantity vertical profile time sequence characteristics of the convective cloud grid points from the three-dimensional grid point data, and determining the real-time posterior probability of the polarization quantity vertical profile time sequence characteristics of the convective cloud grid points; and comparing the real-time posterior probability with a probability threshold value, and determining whether severe convection occurs in a future time period according to a comparison result, the probability threshold value being obtained by optimizing a deep learning model. According to the invention, the forecasting advance and the forecasting precision are improved.

The invention relates to the technical field of radio, and particularly discloses a high-precision electromagnetic spectrum map construction method and system for a topographic relief environment, and the method comprises the steps: obtaining spectrum data in a to-be-constructed region, the spectrum data comprising signal intensity and geographic position; combining terrain digital elevation and topographic relief elevation information in a to-be-constructed region with the signal intensity, and matching with an observation point according to resolution to obtain topographic relief elevation information of the observation point; establishing a model for the topographic relief elevation information of the observation point based on a Gaussian regression process, and performing parameter optimization on the model by using hyper-parameters of a kernel function; and according to a posterior probability estimation formula of the Gaussian regression process, constructing a spectrum map of the to-be-constructed region by using the monitoring value of the spectrum data in the to-be-constructed region as input.

The invention discloses a power transmission tower structure safety evaluation method based on computer vision, and relates to the field of power transmission tower structure safety evaluation, and the method comprises the following steps: obtaining a structure static curvature parameter; the dynamic curvature modal area difference square ratio of the rod piece structure is calculated; combining the structure static curvature parameter and the dynamic curvature modal area difference ratio corresponding to each basic evaluation unit into a dynamic and static fusion feature data set; calculating the structure damage posterior probability of each basic evaluation unit through Bayesian reasoning; and according to the structure damage posterior probability distribution results of all the basic evaluation units, carrying out comprehensive safety grade evaluation on the power transmission tower structure. The non-contact monitoring method based on computer vision is adopted, the deformation and displacement information of the nodes is obtained through the image processing technology, the equipment arrangement and maintenance work is greatly simplified, and the overall monitoring cost is reduced.

The invention discloses an oil pipeline monitoring method and system based on spatio-temporal modeling and multi-dimensional analysis, and belongs to the technical field of oil and gas pipeline safety monitoring, data processing and intelligent prediction.The method comprises the steps that multi-source spatio-temporal data is collected to construct a probability generation model; performing inversion on the model by using the observation value to reconstruct four-dimensional posterior probability distribution; performing causal inference on the posterior probability distribution to generate a dynamic causal information flow map; and analyzing topological evolution of the atlas to generate a stability monitoring report. According to the method, a technical path of combining probability modeling based on an information field theory and causal dynamics inversion is adopted, and accurate prediction of a systematic risk critical transition precursor can be realized by reconstructing a pipeline holographic state field and analyzing topological evolution of a causal network of the pipeline holographic state field; and the operation safety and the intelligent monitoring level of the long-distance oil pipeline are obviously improved.

The invention provides a coal mine earthquake prediction method and system based on Bayesian reasoning, and the method comprises the steps: obtaining observed micro-earthquake event data in a coal mine region; a microseism prediction model is constructed, an optimal solution of model parameters of the microseism prediction model is obtained based on the microseism event data in the historical time period, and the model parameters comprise event triggering parameters and event occurrence parameters; the optimal solution of the model parameters is used as prior information, and posteriori distribution is obtained based on Bayesian reasoning for observed micro-seismic event data; and extracting posterior probabilities in a set probability range from the posterior distribution, obtaining the maximum magnitude corresponding to the prediction time period based on the model parameters corresponding to each posterior probability, and calculating the occurrence probability of the maximum magnitude event based on the maximum magnitudes corresponding to all the posterior probabilities in the set probability range and a given magnitude threshold.