330 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.

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 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 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 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 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 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 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 crustal stress prediction method and device, electronic equipment and a storage medium, and relates to the technical field of seismic survey. The method comprises the following steps: obtaining observation seismic data of seismic wavelets in a strong VTI medium, and constructing a to-be-inverted parameter matrix based on a PP wave reflection coefficient corresponding to the strong VTI medium; constructing a posterior probability function obeyed by an inversion parameter matrix corresponding to the to-be-inverted parameter matrix based on a Bayesian inversion theory and observation seismic data, and determining a target functional based on a prior probability function and a likelihood function corresponding to the posterior probability function; determining medium density and each stiffness matrix coefficient based on an inversion parameter matrix solving result of the target functional, and determining a flexibility matrix of the strong VTI medium based on each stiffness matrix coefficient; and predicting the ground stress distribution of the target profile in the strong VTI medium based on the medium density and the positive strain matrix and the flexibility matrix corresponding to the strong VTI. Therefore, the crustal stress prediction accuracy under the strong VTI medium is improved.

The invention discloses a bare concrete appearance quality evaluation method, which belongs to the field of concrete quality evaluation, and adopts the technical scheme that process data are synchronously acquired in a bare concrete pouring process; obtaining concrete surface appearance index data after demolding; performing time synchronization and spatial inverse registration on the acquired data, and performing gridding association on the appearance indexes and the process data in the corresponding time windows; for each grid unit, extracting vibration effective energy, pumping pressure fluctuation characteristics, template deformation characteristics and environment correction factors from the process data in the corresponding time window and space range, and combining the vibration effective energy, the pumping pressure fluctuation characteristics, the template deformation characteristics and the environment correction factors into a joint characteristic vector; constructing a structural causal model, calculating the cause posterior probability of the appearance defect for each grid, and outputting cause probability distribution of under-vibration, over-vibration, unstable pumping, insufficient template rigidity and environmental sensitization; and generating an appearance quality score according to the cause probability distribution. The method has the beneficial effect that the method for evaluating the appearance quality of the fair-faced concrete is provided.

The invention relates to the technical field of highway bridge safety monitoring, in particular to a bridge dynamic weighing algorithm based on Bayesian maximum posterior probability. Obtaining influence lines through a bridge calibration test, and calculating an influence line matrix, a mean value and a covariance; using a Moses algorithm to obtain the vehicle axle load as the axle load of the initial main cycle i = 0; initial axle load distribution is set, the covariance of load response is obtained through the influence line covariance, the measurement noise and the axle load of the main cycle, and an axle load mean vector and a stable value of the covariance are iterated together with the influence line matrix, the initial axle load distribution and the load response to serve as the axle load distribution of the main cycle; according to the axle load distribution, the influence line matrix, the load response and the covariance thereof, the posterior probability of the axle load is obtained, and the axle load when the probability is the maximum serves as the axle load corresponding to the (i + 1) th main cycle; and repeating until the axle load update difference value is smaller than the preset value, and outputting the axle load result, thereby solving the problem of low axle load identification precision of the existing bridge dynamic weighing system.

The invention relates to the technical field of motor control and cloud computing, in particular to an asynchronous motor inertia response detection method and system based on cloud computing, and the method comprises the steps: initializing prior probability distribution according to preset parameters of a motor; generating an optimal excitation signal parameter; synthesizing an excitation signal according to the received optimal excitation signal parameter, injecting the excitation signal into a motor, synchronously collecting original dynamic response data, and generating a feature data set; uploading to a cloud inertia inference center; calculating posterior probability distribution; comparing the variance of the posterior probability distribution with a preset convergence precision threshold value; if the variance is smaller than a convergence precision threshold value, controller parameters of an edge side control execution unit are updated based on a final inertia identification result; and if the variance is not less than the convergence precision threshold, taking the currently calculated posterior probability distribution as the prior probability distribution of the next iteration, and returning to execute the step of generating the optimal excitation signal parameter. According to the invention, high-precision parameter identification under extremely low disturbance is realized.

The invention discloses a time-frequency domain evidence fusion harmonic variable working condition detection method and device suitable for non-stationary harmonic data, electronic equipment and a storage medium, and belongs to the technical field of electric digital data processing. Performing fast Fourier transform on the original current waveform time domain data; dividing original current waveform time domain data into subsequences, and calculating a nonlinear weighted standardized Euclidean distance; calculating the posterior probability distribution of the time interval of the point change moment, and recording the number of times that each moment is deduced as a point change; carrying out normalization processing on the contour of the nonlinear weighting matrix and the number of times that each moment is deduced as a change point, respectively obtaining a sequence arranged according to the moment, intercepting a sub-sequence, calculating shape similarity, and obtaining an interval in which the change point exists; selecting candidate moments exceeding a threshold value as a mutually exclusive recognition framework, and constructing and synthesizing a basic probability distribution function; and calculating a trust function and a likelihood function, and obtaining a final change point through a discriminant formula. According to the method, the change point can be accurately and effectively found.

The invention discloses an evaluation method for predicting crack propagation and residual service life of a natural gas hydrogen-doped pipeline, which comprises the following steps of: firstly, performing numerical simulation on crack depth detection data by using a numerical simulation method, and simulating a crack depth increasing process to obtain crack depth detection data; then, according to crack depth detection data information, crack propagation parameter prior distribution obtained through statistics of a historical database is combined, crack propagation parameters are calculated through a Bayesian updating method, a likelihood function of Bayesian updating is constructed through a Forman equation, and posterior distribution of the crack propagation parameters is calculated; substituting the posterior probability distribution obtained in the current loop as prior into the next loop for iterative updating; finally, the critical crack depth of the pipeline is estimated according to the failure criterion, the number of remaining use cycles of the pipeline and the remaining service life of the pipeline are obtained, the more accurate parameter posterior probability can be obtained, and the influence of uncertain information in the operation environment is relieved.

The invention relates to the technical field of industrial process prediction, in particular to a tundish erosion prediction method based on a hierarchical hybrid expert framework. The method comprises the following steps: collecting time sequence physical field data of the tundish, and screening features to construct a unified feature space; shunting the feature space to obtain a time sequence feature and a statistical aggregation feature; a statistical aggregation feature training classifier is utilized to generate a calibration posterior probability, and a gating network is constructed; generating an initial mode subset based on a posterior probability and training a corresponding expert model; the confidence of data to be measured is obtained by the gating network, and a single expert model is selected for prediction or multiple expert models are fused through a self-adaptive strategy for weighted prediction according to whether the confidence exceeds a threshold value or not; and finally, reconstructing the predicted value into an erosion thickness absolute value through inverse transformation. According to the method, the accuracy and adaptability of tundish erosion prediction are effectively improved.

The invention discloses a crane fault diagnosis method and system based on data driving, and the method comprises the steps: collecting the data of a PLC and a multi-source sensor of a crane, carrying out the preprocessing, and inputting a prediction model with the fusion of multi-scale causal convolution and an attention mechanism, so as to obtain a feature value prediction sequence; then calculating a residual error between a prediction sequence and an actual measurement sequence, modeling by using a first-class support vector machine, and triggering third-class early warning; based on the constructed Bayesian network, inputting the early warning evidence and updating the posterior probability, and outputting a Top-N fault reason; and finally, a risk score is calculated by integrating the posterior probability, the residual amplitude and the abnormal frequency, grading is carried out, and a diagnosis result and a disposal suggestion are pushed to a user terminal. According to the scheme, accurate diagnosis of complex coupling faults can be realized, 'beforehand 'early warning is realized, and unplanned shutdown and even safety accidents caused by fault expansion are effectively avoided.

The invention discloses an underground safety level intelligent evaluation response method and system fusing fuzzy credibility weighting and Bayesian reasoning, and relates to the technical field of underground safety monitoring. According to the method, a multi-source heterogeneous sensor is deployed to collect data, a fuzzy membership matrix is obtained through fuzzy membership function normalization, a credibility factor matrix is constructed by combining time sequence stability, spatial neighborhood consistency and node long-term health weight, and the two are subjected to weighted fusion to obtain a fusion index vector. And inputting the Bayesian network fused with the credible nodes, reasoning to obtain security level posterior probability distribution, and triggering a linkage response. The system correspondingly comprises a plurality of modules for implementing the steps. According to the method, the dynamic evaluation and credibility quantification of the data quality of the underground multi-source sensor are realized, and an intelligent evaluation mechanism for deeply integrating the credibility of the data source into a reasoning structure is constructed, so that the risk evaluation robustness and accuracy are improved, the closed-loop management from evaluation to response is realized, and the underground safety is guaranteed.

The invention discloses a converter endpoint carbon temperature dynamic adaptive prediction method based on meta-learning SAE, and the method comprises the steps: firstly carrying out the clustering of historical steelmaking data through employing a Wasserstein distance weighted Dirichlet process Gaussian mixture model (WDPGMM), and automatically dividing a plurality of working condition modes; then, constructing a MetaSAE model for the data of each mode, and training the MetaSAE model to improve the generalization ability of the model to different working conditions; and finally, according to the posterior probability of a new sample of a to-be-detected heat, judging the working condition mode to which the new sample belongs, selecting the most similar sample subset from the historical data of the corresponding mode by using the mutual information weighted JS divergence, and performing real-time fine adjustment on the MetaSAE model, thereby realizing the dynamic prediction of the molten steel end point carbon content and temperature of the new sample. The method can adapt to data distribution changes caused by multiple working conditions and sensor drifting in the steelmaking process, and continuous real-time accurate prediction of the carbon content and temperature of the molten steel is achieved.

The invention discloses a personalized teaching course recommendation method and system based on artificial intelligence, and relates to the technical field of artificial intelligence and education recommendation, and the method comprises the steps: collecting text data for preprocessing, and extracting a standard target set, a teaching target candidate set and a knowledge point candidate set; calculating cosine similarity and weight based on the standard target set and the teaching target candidate set, calculating knowledge point mastery degree and weight based on the knowledge point candidate set, and splicing the knowledge point mastery degree and weight to generate a learning feature vector; based on the learning feature vector, clustering is carried out by using a k-means + + algorithm, a clustering label and a clustering center set are output, after the clustering center is updated in combination with a Thompson Sampling algorithm and Monte Carlo, the posterior probability is recalculated, and a final recommendation result is output; the robustness and recommendation accuracy of personalized teaching course recommendation are effectively improved.

The invention belongs to the field of seat control, and relates to a dynamic optimization method for seat adjustment parameters of a commercial vehicle. The method comprises the following steps: collecting and processing a motor signal to obtain a current vector sequence in an angle domain; extracting a composite feature matrix capable of reflecting the current state of the seat system through time-frequency analysis; taking the composite feature matrix sequence as an observation value of a factorial hidden Markov model, and diagnosing and distinguishing posterior probabilities of parallel hidden state chains corresponding to passenger loads and mechanical resistance; when the deviation between the diagnosed system state and the ideal state is too large, a multi-objective optimization program is started, and a plurality of performance indexes such as the adjustment time, the driving torque fluctuation rate and the predicted temperature rise are subjected to collaborative optimization through a non-dominated sorting genetic algorithm; and the optimal motor control parameters of the next period, which can balance the adjustment time, the riding comfort and the energy consumption, can be efficiently solved. The comprehensive requirements of a modern commercial vehicle for efficient seat adjustment, comfortable experience and reliable operation can be met.

The invention discloses an insurance fraud risk real-time evaluation method based on a dynamic Bayesian network. The method comprises the following steps: S1, collecting time sequence behavior data of an insurance user; s2, performing preprocessing to generate a structured observation sequence; s3, dividing into continuous time slices according to time, and constructing a time sequence sample; s4, constructing a dynamic Bayesian network; s5, calculating a posterior probability of a state variable in each time slice, and generating a sample weight; s6, executing an improved increment EM algorithm based on the sample weight, and optimizing dynamic Bayesian network parameters; s7, monitoring posterior probability entropy change of the state variable, and performing structure adjustment; and S8, the reasoning process is executed again, and a risk scoring report is generated. According to the method, the dynamic Bayesian network and the incremental EM algorithm are fused, the insurance fraud risk time sequence evaluation model is constructed, and the method has the advantages of being high in real-time performance, self-adaptive in structure, interpretable in result and the like.

The invention discloses a photovoltaic cleaning robot path planning method fusing string level power generation analysis, and belongs to the technical field of data processing, and the method specifically comprises the steps: dividing historical string power generation data according to the same interval to construct an entropy model, collecting the data in real time, calculating an interval entropy, comparing the interval entropy with the model, and recognizing a suspected low power generation interval; key cleaning group strings are screened in combination with spatial neighborhood information, and a posterior probability is calculated through a Bayesian probability model to determine a cleaning priority; power station layout constraints and robot motion characteristics are combined, and an optimal cleaning path is generated by adopting a path planning algorithm; through multi-dimensional data fusion and an intelligent algorithm, precise identification and path optimization of photovoltaic string cleaning requirements are realized, the cleaning efficiency is effectively improved, and the operation and maintenance cost is reduced.

The purpose is to appropriately manage abnormal communications with a simpler configuration. [Solution] The abnormality management device 1 includes a learning unit 12 that uses the characteristic directions of normal data as training data to learn, by maximum likelihood estimation, parameters of a probability model that outputs the posterior probability that the number of packets received in each time period corresponding to each of the characteristic directions of the normal data is normal; a derivation unit 13 that derives a probability distribution for the characteristic directions of abnormal data that indicates the number of abnormal packets received in each time period that deviates from the range of normal packet numbers, based on the posterior probability estimated by the probability model learned by the learning unit 12, the probability distribution for the characteristic directions of the normal data, and the prior probability of normality; and a calculation unit 14 that calculates a first index value that indicates the degree of spatial agreement formed by the probability distribution for the characteristic directions of the abnormal data derived by the derivation unit 13 and the probability distribution for the characteristic directions of the normal data.

The invention relates to the technical field of concrete detection, in particular to a concrete structure crack damage evaluation system based on acoustic emission sensing, which comprises a signal acquisition module, a characteristic discrete evolution module, a probability inference module, an energy gradient analysis module and a boundary defining module. According to the method, a multi-dimensional evolution set is constructed by extracting the amplitude and energy standard deviation of an acoustic emission signal in a continuous time window, the synchronous change trend of the multi-parameter standard deviation is analyzed by using a Bayesian probability model, and the non-uniform expansion posterior probability is calculated to lock a key signal set of dominant damage expansion. An energy fluctuation coefficient is calculated and a space sequence is generated by combining sensor space coordinates, and a fluctuation coefficient stable interval is identified according to a gradient attenuation rule of energy along with a distance, so that a physical boundary of a crack damage dynamic active region is quantitatively defined, and accurate evaluation of a non-uniform expansion state and an active range of a concrete crack is realized.

The object is to manage signal abnormalities even when there is little measurement data of the abnormal signal. [Solution] The abnormality management device 1 includes a learning unit 12 that uses the characteristic directions of normal data as training data to learn, by maximum likelihood estimation, parameters of a probability model that outputs the posterior probability that the intensity of each frequency component corresponding to each of the characteristic directions of the normal data is normal; a derivation unit 13 that derives a probability distribution for the characteristic directions of abnormal data that shows abnormal intensities of frequency components that deviate from a normal intensity range, based on the posterior probability estimated by the probability model learned by the learning unit 12, the probability distribution for the characteristic directions of the normal data, and the prior probability of normality; and a calculation unit 14 that calculates a first index value that indicates the degree of spatial agreement formed by the probability distribution for the characteristic directions of the abnormal data derived by the derivation unit 13 and the probability distribution for the characteristic directions of the normal data.