36 results about "Maximum a posteriori estimator" patented technology

The invention provides a multi-source rainfall prediction method and device fusing GNSS and communication satellites, and the method comprises the steps: building a plurality of satellite-ground microwave links which are used for transmitting GNSS data and communication satellite data; gNSS data and communication satellite data on the multiple satellite-ground microwave links are acquired; performing atmospheric rainfall inversion on the plurality of satellite-ground microwave links based on GNSS data, and constructing a two-dimensional water vapor field according to an inversion result; performing rainfall intensity inversion on the plurality of satellite-ground microwave links based on communication satellite data, and constructing a two-dimensional rainfall field according to an inversion result; and fusing the two-dimensional water vapor field and the two-dimensional rainfall field by adopting Bayesian-maximum posteriori estimation to obtain a fusion field, and converting the fusion field to obtain a rainfall prediction field. According to the method, the rainfall field and the water vapor field are fused for rainfall prediction, so that the whole rainfall process can be accurately predicted.

The invention relates to the technical field of vehicle intelligent management, and discloses a vehicle state monitoring method and system based on deep learning, and the method comprises the steps: analyzing the physical dependence relation among vibration abnormality detection, loosening positioning and fatigue evaluation monitoring tasks based on structural mechanics constraints, and carrying out the monitoring of the vibration abnormality detection, the loosening positioning and the fatigue evaluation; generating a task dependence directed graph; inputting the multi-modal monitoring data matrix into a shared encoder of a multi-task learning network to generate a unified representation vector; respectively inputting the unified representation vector into a vibration anomaly detection decoder, a loose positioning decoder and a fatigue evaluation decoder, and outputting an initial prediction result of each task; and establishing a conditional probability model between task outputs based on the constraint relationship defined by the task dependent directed graph, and generating a consistency diagnosis result meeting physical constraints through maximum posteriori estimation. The technical problems of one-sided diagnosis results, mutual contradiction and low calculation efficiency are solved.

The invention discloses an underground coal mine goaf gap space sensing method based on a multiple detection technology, and the method comprises the steps: obtaining a three-dimensional dielectric constant body through a ground penetrating radar, and determining the space position of a goaf gap; resistivity tomography is carried out through the optimally arranged electrode network, and a three-dimensional resistivity body is inverted; constructing a three-dimensional wave velocity body by adopting sound wave detection, and identifying a velocity low-value region; a dielectric constant body, a resistivity body and a wave velocity body obtained by the three technologies are uniformly registered to a cubic grid with a preset resolution and normalized, a caving zone void probability body is constructed by adopting adaptive weighted fusion based on information entropy and a Bayesian maximum posteriori estimation framework, and finally a three-dimensional void model is generated and the total volume is calculated. Through multi-source data fusion of ground penetrating radar, resistivity tomography and sound wave detection, the limitation of a single technology in goaf space detection is solved, the precision of gap detection is improved, and the method is suitable for goaf space perception and treatment.

The invention discloses an ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning, and belongs to the field of ultrasonic cross-metal communication channel estimation. Constructing a pilot signal relationship between the receiving end and the transmitting end on the two sides of the metal wall; dividing a to-be-estimated metal channel impulse response into zero-value blocks or non-zero blocks of block sparse Bayesian according to whether the to-be-estimated metal channel impulse response is zero or not; the method comprises the following steps: iteratively updating hyper-parameters a, b, Bi and lambda of a non-zero block by adopting an expectation maximization algorithm, realizing structured sparse learning, calculating a maximum posteriori estimation result by utilizing a final hyper-parameter of the non-zero block, and taking the maximum posteriori estimation result as an estimation value of ultrasonic cross-metal channel pulse response. According to the method, a block sparse Bayesian learning algorithm is improved by utilizing the characteristics of metal channel pulse response, and compared with an original algorithm, the accuracy of channel estimation in a low signal-to-noise ratio environment is improved.

The invention provides a coastline reference plane determination method based on tide correction, and belongs to the technical field of coastline detection.The method comprises the steps that a high-resolution satellite image and airborne laser radar data are collected and input into a sea-land boundary recognition model constructed by a neural architecture growth algorithm based on attention guidance, and a refined instantaneous coastline is extracted; a coastal tide numerical model containing a shallow moisture tide term and a composite partial tide term is established, a coastal refined tide forecast tide level sequence is obtained through a variational data assimilation optimal control algorithm, and a refined instantaneous coastline is associated with a corresponding forecast tide level to establish a spatial distribution function; the coastline position corresponding to the lowest tide level of the multi-temporal remote sensing data inversion theory is integrated through the Bayesian maximum posteriori estimation method to serve as the coastline reference plane, and the technical problem that the determination precision of the coastline reference plane is affected by dynamic changes of tides and water density differences, and consequently spatial positioning is inaccurate is solved.

The application discloses a MRI paravertebral muscle segmentation method based on Bayesian fusion and probability shape modeling, and comprises the following steps: a) a Fourier-Gaussian process probability shape model is established to parameterize the muscle shape, the Fourier-Gaussian process probability shape model comprises radial modeling based on a Fourier basis function to represent a muscle cross-section profile, and axial modeling based on a Gaussian process to capture the axial variation law of the muscle shape; b) a muscle edge feature model based on a convolutional neural network is constructed to perform multi-scale feature extraction on the muscle edge in the MRI image; and c) a Bayesian segmentation framework is designed, and based on a maximum posterior estimation principle, online segmentation of the paravertebral muscle in the MRI image is realized. The application is customized according to the characteristics of the paravertebral muscle segmentation task through the idea of fusing high-quality artificial labeling and a powerful learning model, so that the application can achieve high segmentation and reconstruction quality under a small amount of interaction, and has strong shape change description capability.

The invention discloses a method for quickly generating high-resolution carbon emission raster data, which comprises the following steps of: fusing national carbon emission panel data and night light remote sensing images, establishing a provincial-scale logarithmic regression model, extracting parameters of the logarithmic regression model and constructing a parameter characteristic matrix; a space-time constraint K-Means algorithm is adopted to perform clustering analysis on provincial administrative units throughout the country and divide the provincial administrative units into K carbon emission type areas, for each carbon emission area, an area regression model is established by using logarithmic carbon emission and night light data, and a clustering center parameter is used as Gaussian prior to obtain a clustering result; and performing parameter updating through Bayesian maximum posteriori estimation, estimating the carbon emission by using the model, performing correction on a pixel scale by adopting a zero error method, and finally generating a national carbon emission grid data set. The space-time constraint clustering and Bayesian maximum posteriori estimation method is introduced on the basis of carbon emission modeling, and balance of few models and high precision is achieved.

The invention belongs to the related technical field of computer digital image processing, and particularly relates to an element geometric measurement-oriented edge data adjustment method, which comprises the following steps of: carrying out overlapped division on element edges in an image to obtain an edge fragment set; based on a finite Gaussian mixture model and by fusing wavelets and total variation, establishing an adjustment edge maximum posteriori estimation function corresponding to all the edge segments, minimizing the function, performing independent adjustment on the edge segments, and aggregating the adjusted edge segments to obtain a pre-adjustment edge; carrying out overlapping division on the pre-adjustment edge to obtain a pre-adjustment edge fragment set; and the obtained finite Gaussian mixture model is fused with second-order total variation regularization, an ideal edge maximum posteriori estimation function of each pre-adjustment edge fragment is established, the function is minimized, independent smooth correction is performed on the pre-adjustment edge fragments, all the edge fragments after smooth correction are aggregated, and a final adjustment edge is obtained. According to the method, the geometric element measurement precision is improved on the algorithm level.

The invention discloses a quantum image sensing method and device based on exposure modulation. The sensing end performs frame-by-frame exposure modulation on the same scene in continuous K frames, and regulates and controls the expected number of incident photons of each frame; the pixels output a 1-bit binary image in each frame and obey Poisson distribution. And the collection end takes K frames as a group to carry out maximum posteriori estimation to obtain reference intensity estimation of each pixel. In order to improve robustness and speed, an RED-PRO iterative algorithm based on FISTA acceleration is adopted in the reconstruction process, after gradient descent of a data consistency item, the data is projected to a pre-training de-noising device manifold, and a pixel adaptive step length is given based on Fisher information. According to the method, the signal-to-noise ratio is remarkably improved and artifacts are inhibited in a scene with low illumination, saturation and limited dynamic range, the 1-bit sensing circuit architecture does not need to be changed, and the existing QIS / SPAD sensor and the lens module can be compatible only through exposure modulation and software reconstruction.

The application discloses a kind of structural damage identification bayesian model fast solution method based on zero sample transfer learning, belong to structural damage identification and machine learning technical field.To solve the problem of generalization performance decline of structural damage identification bayesian model.The application includes constructing the bayesian model of structural damage identification;Combined with sparse auto-encoder and adversarial learning, construct and train the adversarial sparse auto-encoder, the adversarial sparse auto-encoder includes source domain sparse auto-encoder module, target domain sparse auto-encoder module and domain discriminator module;PhyCNN proxy model is constructed;Source domain label data set is generated, and source domain PhyCNN proxy model is constructed;Design parameter freezing transfer strategy, construct target domain PhyCNN proxy model;Based on target domain PhyCNN proxy model, sample posterior sample, update structural damage identification bayesian model using posterior sample, calculate the maximum posterior estimation value of structural damage parameter, realize the positioning and quantization of structural damage.

The invention belongs to the technical field of pseudo satellite positioning, discloses an indoor pseudo satellite signal carrier-to-noise ratio positioning method based on a commercial smart phone, and solves the core problem through innovative design. The invention provides a positioning technology based on Bayesian reasoning alternating optimization dynamic estimation. According to an attenuation relationship between a carrier-to-noise ratio observation value and a distance base station, maximum posteriori estimation (MAP) is calculated through Bayesian reasoning, and an alternating estimation strategy is adopted to iteratively optimize an attenuation coefficient and a position until the attenuation coefficient and the position converge, so that the change of the attenuation coefficient is effectively dealt with, and the performance difference between smart phone devices is relieved; compared with a traditional weighted centroid method, the average positioning precision is improved by 30%. The technology provided by the invention is not only suitable for smart phones with different performances, but also can be used. The technology provides a universal solution for high-reliability positioning of the smart phone under the same-frequency interference condition.

The present invention discloses a SLAM method for underwater motion drift and noise estimation based on maximum a posteriori estimation, comprising: 1) estimating the motion drift of an underwater robot based on EKF-SLAM; 1.1) initializing the robot SLAM system state; 1.2) constructing sampling points based on EKF-SLAM state estimation; 1.3) updating the covariance matrix based on the state estimation error; 2) updating the motion drift of the underwater robot based on maximum a posteriori probability; 3) estimating underwater noise parameters based on EKF-SLAM; 3.1) initializing the underwater noise parameters; 3.2) estimating the underwater noise parameter covariance matrix; 3.3) updating the covariance matrix based on the state estimation; and 4) updating the underwater noise parameters based on maximum a posteriori probability. This method improves the state prediction portion of the existing EKF-SLAM method, combines estimation of motion noise and observation noise in the system model, adaptively filters the Gaussian distribution noise variance of the system model, and then performs SLAM estimation, thereby improving underwater self-positioning accuracy by approximately 20%.

The present application relates to a plug-and-play image shadow edge removal method, apparatus, device, and medium. A brightness change coordinate system is constructed based on the brightness changes of pixels in a shadow image and a shadow-free image, and a Gaussian mixture model is used for modeling. The maximum a posteriori estimation problem for the position and belonging region of each point data in the brightness change coordinate system is used as a problem model. An iterative algorithm is used to solve the problem. During each iteration, the belonging region division is updated, the distribution parameters representing each updated belonging region are updated, and the position of each point data in the brightness change coordinate system is updated according to the updated distribution parameters to obtain a distribution-matched reference image. The edge region in the image is then optimized and updated to obtain the current iteration result, until a preset iteration termination condition is met to obtain a shadow-free image. This method can enhance the performance of shadow edge region recovery and obtain an image with better shadow removal effect.

The invention belongs to the technical field of spaceflight and space situation awareness, and particularly relates to a space debris short arc data association method and device based on a factor graph, and the method comprises the steps: obtaining optical angle measurement short arc data from an observation station, and carrying out the preprocessing; an angle observation initial orbit determination algorithm is adopted to obtain an orbit state initial value; constructing a cost matrix, performing preliminary screening on track state initial values corresponding to different short arc observation data, and generating a limited number of multi-correlation hypotheses through disturbance sampling expansion; constructing a nonlinear factor graph under each association hypothesis; solving the maximum posteriori estimation, and selecting an optimal association hypothesis according to a residual error and an information criterion; and outputting the associated cluster, the track state and the uncertainty thereof, and carrying out consistency check. According to the method, efficient and robust multi-arc-segment association and preliminary orbit estimation can be performed on optical angle measurement short arc data in foundation and space-based observation scenes, and scene self-adaption, real-time performance and high accuracy are realized.

This invention discloses an online flow field estimation path planning method based on Bayesian inference, aiming to solve the problem of efficient detection and estimation of unknown flow fields by mobile robots. The method first discretizes the two-dimensional flow field region into a grid and constructs a probabilistic roadmap. In each planning round, a weighted maximum a posteriori (MAP) estimation problem is constructed based on the residuals of velocity measurements and fluid equations to solve for the flow field state and calculate the posterior accuracy matrix. Candidate paths are generated through bundle search and then prospectively expanded using subsequent information gain. After mapping path nodes to a set of proposed measurements, a joint prediction covariance matrix is ​​calculated. Based on a task weight matrix dynamically weighted according to velocity magnitude, a task-weighted posterior uncertainty reduction score is calculated for the candidate paths. The path with the highest score is selected for execution, and new measurements are collected. This process is repeated until a preset termination condition is met. This method integrates physical constraints and prospective search, guiding priority exploration of high-velocity regions and significantly improving the efficiency of online robot perception.

The application provides a digital twin method and system for realizing bone growth, which is suitable for the technical field of digital twin of orthopedics. The method comprises the following steps: acquiring two-dimensional tomographic image data of a fracture site, establishing a three-dimensional surface geometric model of the fracture site, and forming a finite element model of the fracture site; based on the finite element model, assembling a solid mechanics, pore seepage and biological reaction coupled solver, and performing multi-physical field coupled solving; periodically acquiring follow-up image data of bone fracture healing, realizing online data assimilation of modulus, and outputting a rehabilitation load scheme; the application uses variational assimilation based on maximum a posteriori estimation to fuse the observation modulus field generated by the follow-up image and the simulation result, realizes continuous convergence of the model and the real state, and on this basis, real-time batch calculation of different load parameter combinations is realized, and the rehabilitation load scheme meeting the safety constraint and the optimal healing speed is output.

This invention discloses a method for bridge expansion joint status identification and early warning based on Bayesian data fusion, comprising: S1, constructing a disaster-causing factor system and a failure cause database for expansion joints; S2, collecting multi-dimensional data of expansion joints; S3, preprocessing and extracting features from the collected multi-dimensional data; S4, constructing a Bayesian network model based on a fault tree; S5, fusing multi-dimensional data to determine the prior probability of the Bayesian network model; S6, constructing a conditional probability table for each node of the Bayesian network based on maximum a posteriori estimation and the analytic hierarchy process; S7, quantifying the failure probability of expansion joints through forward inference using the Bayesian network, classifying risk levels and risk warnings; and screening the main disaster-causing factors through backward inference using the Bayesian network. This invention integrates multi-dimensional data and multi-disciplinary theoretical methods, solving the problems of single data dimensions, low fusion efficiency, and insufficient early warning accuracy in existing technologies, and significantly improving the accuracy of expansion joint damage identification and the reliability of risk assessment.

The invention discloses a cross-modal sensing system and method for three-dimensional positioning and tracking of a high-speed dynamic object. The system comprises an event processor, a depth processor and a factor graph optimization module. The event processor analyzes the asynchronous event flow of the event camera in real time, and generates a target two-dimensional plane position through dynamic threshold filtering and grid clustering; a depth processor processes the data stream of the depth camera, generates an ROI through coordinate projection after receiving the two-dimensional position, and analyzes and extracts target depth information by using a depth histogram; the factor graph optimization module constructs a unified optimization framework containing an event projection residual factor, a depth measurement residual factor and a motion constraint factor, and the six-degree-of-freedom pose of the target is jointly optimized through maximum posteriori estimation. The event and depth processor adopts a parallel processing mechanism, realizes heterogeneous data tight coupling through a factor graph module, and can output a sub-millisecond-level low-delay three-dimensional positioning result in a high-speed dynamic scene.

This invention discloses a high-precision positioning and mapping method, system, and storage medium based on FMCW velocity, comprising: S2, acquiring point cloud data and inertial data; S3, estimating the FMCW velocity of the corresponding frame's moving platform; and S4, performing pre-integration fusion to obtain the first... k +1 frame relative to the first k FMCW-IMU pre-integration model for frame motion; S4, obtain the first frame. k Frame and the k +1 frames of residual term; S5, minimize the residual term between frames. k Frame and the k S6. Sum of the squares of each term in the residual term between +1 frames to obtain the maximum a posteriori estimate of the corresponding frame; k The maximum a posteriori estimate of the frame is obtained. k +1 frame of pose parameters to form the first k Frame to the k +1 frame of transformation matrix, and the newly acquired first frame k The point cloud data of frame +1 is transformed using a transformation matrix and then added to the map to complete the reconstruction. This method can solve the problem of accumulated errors caused by inertial navigation drift, and improve the positioning accuracy and robustness of the map.

The invention provides a wireless channel identification method based on a conditional diffusion model, which belongs to the field of wireless communication, and comprises the following steps of: S1, expressing an identification task involving a plurality of different channel scenes as a maximum posteriori estimation problem of a given estimated channel, and converting the maximum posteriori estimation problem into a maximum likelihood estimation problem through a Bayesian theorem; s2, a likelihood function in the maximum likelihood estimation problem is modeled as a reverse generation process of a conditional diffusion model, the reverse generation process is related to a noise prediction model, the noise prediction model is designed based on a transformer network, the input of the noise prediction model is a noise channel, and the noise channel is generated according to an estimation channel and a forward process of true value noise; and S3, training the noise prediction model, and performing wireless channel identification according to the actual channel received by the receiver and the trained noise prediction model. The method can effectively improve the recognition precision.

The present invention provides a rotational modulation inertial navigation system and method for unmanned boats adaptable to challenging environments. The system includes a micro-electromechanical inertial unit (MEMS-IMU), a servo motor, a servo drive, a single-axis rotary stage, an active code ring, a navigation and positioning solution board, an aviation plug, and a housing frame. The method includes: establishing an iterative cubature Kalman filter based on maximum a posteriori estimation; designing a filter to simplify the iterative update structure; and introducing a penalty weight function to rapidly reduce the weight of outlier measurements. The present invention improves positioning accuracy and navigation parameter compensation, enhances the adaptability of single-axis rotational MEMS strapdown inertial navigation systems in challenging GNSS / DVL environments, and further meets the high-precision, high-reliability positioning, attitude determination, and navigation requirements of unmanned boats operating in GNSS / DVL challenging environments.

The embodiment of the disclosure discloses a dual-nuclide PET reconstruction method and device based on a joint Poisson model, comprising: coincidence data acquisition and random triple coincidence estimation, retaining decay inner photon correlation through a consistent coincidence delay window, and estimating random triple coincidence by using decay inter photon irrelevance; a joint Poisson distribution model is established, non-pure positron nuclide, pure positron nuclide and random radioactive source distribution are simultaneously introduced into the distribution model, and a joint Poisson likelihood function is constructed; a prior function is constructed according to image characteristics, and a target function is composed of the joint Poisson likelihood function; the maximum a posteriori estimation of the target function is solved, so that the spatial distribution of the non-pure positron nuclide, the pure positron nuclide and the random radioactive source is obtained. The disclosure can be directly applied to the field of triple coincidence-based dual-nuclide PET imaging, and has the advantages of being capable of effectively suppressing random triple coincidence artifacts and the like.

This invention relates to the field of bridge digital twin technology, specifically to a dynamic update method for bridge digital twins based on multi-fidelity learning and sequential Bayesian inference. It constructs a parameterized finite element model and trains a multi-fidelity deep neural network surrogate model using high-fidelity and low-fidelity simulation data, replacing expensive simulations at extremely low cost. During online updates, modal features are extracted from monitoring data, and the posterior of the previous time step is used as the prior. The probability distribution of parameters is sequentially updated using Bayes' theorem, and finally, the digital twin is updated with the maximum a posteriori estimate. This invention overcomes computational bottlenecks through a multi-fidelity surrogate model, achieves probabilistic quantification and dynamic reduction of parameter uncertainty through sequential Bayesian inference, and verifies the physical consistency of the surrogate model and constructs an information-based prior through interpretable AI technology, thereby constructing an efficient, probabilistically robust, and physically reliable dynamically evolving digital twin.

The invention discloses a multi-agent maneuvering target tracking method, system and device based on a Gaussian process state space model. The method comprises the following steps: establishing an unknown target kinematics model and a sensor observation model by using a Gaussian process; carrying out local analysis on observation data collected by the intelligent agent based on a sliding window mechanism, and carrying out local estimation on the state of the target by the intelligent agent; approximation is carried out on an unknown target kinematics model and a sensor observation model based on a reduced-rank Gaussian process model; calculating the process likelihood and observation likelihood of the target state of the unknown target kinematics model and the sensor observation model after approximation, and deriving the posterior distribution of the target state; and maximizing the target posteriori distribution by using an alternating direction multiplier method so as to obtain the maximum posteriori estimation of the target state. According to the method, the advantages of a multi-agent system are effectively exerted, maneuvering target tracking under the condition that a model is unknown is achieved, and the method has good estimation precision and robustness.

The invention provides a multi-sensor fusion train positioning method and system based on factor graph optimization, and belongs to the technical field of train positioning. 16-dimensional state variables are constructed to serve as factor graph optimization input variables; constructing an INS pre-integral factor; when the attitude change of the train exceeds a set threshold value, selecting the laser radar frame at the moment as a key frame, discarding the other laser radar frames in the two key frames, and constructing a local map by adopting a sliding window; constructing a LiDAR odometer factor; constructing a TLC factor; and adding the INS pre-integration factor, the LiDAR odometer factor and the TLC factor into the factor graph, calling iSAM2 for optimization based on maximum posteriori estimation, and outputting a train pose estimation result under the ENU coordinate system. According to the method, the factor graph model is constructed, and the trackside features such as the transponder, the kilometer post and the hectometer post are utilized, so that high-precision positioning of the train in the satellite navigation signal limited and denial environment is realized.

The application discloses a noise image smoothing denoising method and relates to the technical field of computer digital image processing. The method comprises the following steps: in a Bayesian probability framework, a Gaussian probability model is used to model noise, and a gradient l0 norm and an l1 norm are used to model a clear image; three regularization constraint coefficients are introduced, the three models in the step 1) are weighted and summed, and a noise image restoration problem model is constructed; a quadratic penalty function method is used for solving, two auxiliary variables corresponding to horizontal and vertical gradients of the clear image are introduced, a penalty coefficient is introduced, and the noise image restoration problem model is converted. In a Bayesian maximum posterior estimation framework, the clear image is modeled by introducing the l0 norm and the l1 norm of the image gradient, the regularization constraint condition is formed, and an optimal estimation problem is comprehensively constructed, and an iterative optimization algorithm is designed, so that the noise can be effectively suppressed, and the significant edges can be sharpened and enhanced, thereby generating a high-quality restored image.

The application discloses a kind of based on echo repair strategy's adaptive sparse foresight super-resolution imaging method, first, scanning radar super-resolution model is established, then based on regularization theory, use L1 norm as constraint term to improve the angular resolution of sparse target, then the signal and noise in echo are separated using the multiscale representation characteristics of wavelet variation, the problem that the performance of existing L1-IRN method is poor under low signal-to-noise ratio is solved while improving echo signal-to-noise ratio, and based on preprocessed echo, new optimization cost function is constructed, finally, based on bayesian theory, the sparse estimation problem is converted into maximum posterior estimation problem, adaptive iteration weight is obtained, and foresight super-resolution imaging is realized.The echo repair strategy of the method of the application can weaken the influence of noise on super-resolution imaging, realize adaptive sparse foresight super-resolution imaging under the condition of very low signal-to-noise ratio, obtain high-quality imaging results, improve the efficiency of foresight two-dimensional super-resolution imaging algorithm, and has strong robustness.

The invention relates to the field of intelligent operation and maintenance of a remote sensing data processing system, and discloses an intelligent abnormal root cause positioning method and system for system operation and maintenance, and the method comprises the steps: obtaining a system topology, determining a component dependency relationship, and building a dynamic fault tree model based on the system topology to describe a fault logic dependency relationship of a hardware layer, a network layer and an application layer; the fault tree is converted into a Bayesian network, node prior probability distribution is trained by using a historical fault library, and hardware indexes, network parameters and application logs collected in real time are fused to construct an evidence chain; and calculating a Bayesian posterior probability by adopting a maximum posterior estimation (MAP) algorithm, and dynamically outputting a root cause probability sequence. According to the method, the problem that the monitoring system is large in automatic alarm information amount and complex in system structure, so that abnormity checking is difficult is solved, the accuracy and timeliness of abnormal root cause positioning are improved, rapid recovery of system faults can be supported, and thus the system stability is improved.

The application discloses a quantum image sensing method and device based on exposure modulation. The sensing end performs frame exposure modulation on the same scene in K continuous frames, and controls the expected number of incident photons of each frame; the pixel outputs a 1-bit binary image in each frame and obeys a Poisson distribution. The acquisition end performs maximum a posteriori estimation on K frames as a group to obtain a reference intensity estimation of each pixel. In order to improve robustness and speed, the reconstruction process adopts a RED-PRO iterative algorithm based on FISTA acceleration: after the gradient descent of the data consistency term, the projection is performed to the pre-trained denoiser manifold, and the pixel adaptive step is given based on the Fisher information. The method significantly improves the signal-to-noise ratio and suppresses artifacts in low-light, saturation and dynamic range limited scenes, and does not need to change the 1-bit sensing circuit architecture, and only needs to be compatible with the existing QIS / SPAD sensor and lens module through exposure modulation and software reconstruction.