97 results about "Belief propagation" patented technology

The invention relates to a tunnel lining leakage infrared-millimeter wave fusion detection method and system, and belongs to the technical field of civil engineering detection, and the method comprises the steps: obtaining high-density millimeter wave radar data and thermal infrared imager data; performing data preprocessing to realize space alignment and time synchronization; topological features are extracted, manifold learning mapping is carried out on the preprocessed millimeter wave radar data, and a topological signature matrix is constructed; multi-phase spectrum analysis is carried out, nonlinear phase space reconstruction and multi-scale entropy feature analysis are carried out on the preprocessed infrared thermal imager data, and an entropy feature map is obtained; probability fusion and progressive reasoning are executed, and a leakage area probability graph is generated through a conditional random field model and a progressive belief propagation algorithm; the method overcomes the limitation of a single sensor detection method, achieves the high-precision detection and quantitative evaluation of the leakage of the tunnel lining, and has the technical effects of high detection accuracy, high environmental adaptability, high automation degree and the like.

The invention discloses a power distribution network topology state estimation method, electronic equipment, a medium and a product. The method comprises the steps of obtaining a topological structure and measurement data of a power system; constructing variable nodes and factor nodes according to the topological structure and the measurement data, and constructing a state-topological joint factor graph model according to the variable nodes, the factor nodes and the topological structure; based on the state-topology joint factor graph model, performing state estimation through a belief propagation algorithm to obtain an estimated value of state variable correction; if the estimated value of the state variable correction meets the convergence condition, correcting the topological state of the switch branch according to the active power and reactive power of the head end of the switch branch in the estimated value of the state variable correction; and outputting an estimation result of the topological state of the switch branch until the on-off state of the switch branch obtained according to the state variable is consistent with the original topological state of the switch branch. According to the method, asynchronous real-time updating of the topological state of the power system can be realized.

The invention discloses an SVG valve hall cooling efficiency evaluation method and system based on a probabilistic graph model, and the method comprises the steps: obtaining original time sequence data which is obtained through the collection of a cooling system multi-parameter monitoring sensor group disposed in an SVG valve hall in continuous T sampling periods; preprocessing the original time series data to obtain a credible time series data set; constructing a Bayesian network topological structure comprising three-level nodes of an environment layer, a component layer and an efficiency layer and causal dependence edges, and optimizing parameters of the Bayesian network topological structure by adopting a maximum likelihood estimation method to form a dynamic Bayesian network model after parameter calibration; and the credible time sequence data set is used as an evidence variable to be input into the Bayesian network model after parameter calibration, calculation is carried out through a belief propagation reasoning algorithm, a final control instruction set is generated through probability weighted scoring processing, the final control instruction set is fed back to a valve group monitoring system, and early warning and automatic load reduction are achieved. The problems of large evaluation deviation and early warning lag in the prior art are solved.

The invention discloses a long video content information acquisition method based on an OCR and voice recognition technology. The method comprises the following steps: S1, carrying out preprocessing n on input long video data to extract an image frame sequence and an audio stream; s2, inputting the image frame sequence into an OCR recognition module, inputting the audio stream into an ASR recognition module, and obtaining a preliminary recognition result; s3, constructing a multi-target fitness function, and optimizing an OCR and ASR parameter combination by using a Kanglizard optimization algorithm; s4, respectively applying the optimal parameter group to an OCR identification module and an ASR identification module to obtain an optimized identification result; s5, constructing a fusion factor graph, executing edge message passing by adopting a belief propagation algorithm, and generating a multi-modal semantic block set; and S6, processing the multi-modal semantic block set to generate a unified multi-modal content information set. According to the invention, through fusion of the horny lizard optimization algorithm and the belief propagation mechanism, high-precision recognition and multi-modal semantic consistency extraction of the image text and the voice information in the long video are realized.

The invention discloses a micro-service deployment and task unloading optimization method for tasks with complex dependency relationships. The method comprises the following steps: a system architecture covers a plurality of base stations, edge servers and user equipment; an application program generated by the user equipment comprises a plurality of micro-service tasks with a complex dependency relationship, and the tasks are unloaded to an edge server to be processed; in a task unloading process, optimizing a task unloading decision in each time slot, and managing a micro-service layer cache by adopting a long-time scale updating strategy; through joint optimization of a micro-service layer caching strategy and a task unloading decision, a system performance problem is constructed into an optimization model; firstly, an unloading path of a task is planned by applying a belief propagation algorithm, and then a caching strategy is trained by means of a deep reinforcement learning model; the problem of complicated task unloading and cache updating in different time scale scenes is solved by utilizing an alternative optimization algorithm, and finally, a trained model is deployed in an edge server, so that the long-term average time delay of the system is minimized, and meanwhile, the load balancing of the server is realized. According to the method, the utilization efficiency of system resources can be remarkably improved, the time delay is reduced, and various application requirements sensitive to the delay are fully met.

The invention relates to the technical field of enterprise service digitalization and intelligent management, in particular to an enterprise management method and management platform based on dynamic portraits. The method comprises the following steps: S1, accessing and standardizing multi-source heterogeneous data, calculating a source-level quality score and generating an evidence chain; s2, constructing a portrait map and a time axis taking an enterprise as a main node; s3, the rule engine and the LLM-RAG output numbers in parallel; s4, consistent arbitration and belief propagation; s5, event-driven dependency subgraph increment recalculation is carried out; s6, carrying out portrait-strategy matching and task linkage; s7, carrying out closed-loop write-back and auditable playback; and S8, multi-tenant fine-grained permission. According to the scheme, the problems of data fragmentation and insufficient timeliness and traceability are solved, and the linkage of portrait credibility, interpretability and real-time decision is realized.

The invention discloses a defect repeated alarm screening method and system based on a twin network and topology analysis, and relates to the technical field of intelligent substations. The method comprises the steps of extracting image pair features through a twin network and generating matching potential energy; constructing an anti-environmental interference dynamic reference curved surface by using historical data; constructing a weighted coupling graph based on the potential energy residual error and the spatio-temporal context, and screening potential repeated alarm clusters through belief propagation; performing time sequence alignment and multi-dimensional consistency evaluation on intra-cluster alarms, and eliminating low-contribution noise nodes in combination with Shapley value game analysis; and finally, identifying a stable defect chain through topology persistent coherence analysis, selecting main alarms and screening out repeated alarms. According to the method, the problem of misjudgment caused by illumination change, equipment aging and complex interference is effectively solved, the recognition accuracy and the automation level are greatly improved, and the operation and maintenance rechecking workload is remarkably reduced.

The invention provides an equipment exception work order intelligent management method and device, equipment and a storage medium, and the method comprises the steps: carrying out the multi-source fusion collection and semantic standardization processing of equipment exception data, and obtaining the standardized exception work order data; performing space-time correlation analysis on the abnormal work order data, and obtaining an equipment abnormal correlation network and a propagation path by calculating space-time correlation intensity and applying a causal inference algorithm; performing state prediction on the equipment anomaly association network and the propagation path by applying a graph neural network model, and obtaining work order state probability distribution and a change trend by adopting a graph convolutional network, an attention mechanism and a belief propagation algorithm; and executing dynamic priority ranking and resource allocation on the work order state probability distribution and the change trend, and obtaining an optimal work order processing sequence and a resource allocation scheme according to the network influence index and the resource matching degree. According to the invention, through abnormal correlation analysis and state probability prediction, intelligent management of the abnormal work order is realized.

The invention discloses a typhoid theory teaching method based on a knowledge graph, and belongs to the technical field of typhoid theory teaching. The method comprises the steps of multi-version data acquisition and standardization, ancient Chinese context semantic unit construction, term semantic disambiguation based on comparative learning, semantic conflict detection based on consistency constraint, multi-source information fusion and map optimization and teaching path generation. Through ancient Chinese context semantic modeling and comparative learning, precise disambiguation of traditional Chinese medicine ancient book terms is realized, and knowledge errors caused by polysemy of one word are reduced; through an automatic detection mechanism of logic consistency constraint, the quality and reliability of the knowledge graph are improved; a version traceability and belief propagation optimization strategy is adopted, so that the finally constructed knowledge graph not only can integrate multi-version essence, but also can clearly trace knowledge sources and evaluate knowledge credibility; the teaching path chain generated based on the atlas can dynamically simulate the whole process of traditional Chinese medicine syndrome differentiation treatment, and the clinical thinking training effect is remarkably improved.

The invention discloses a new unit equipment risk management and control and spare part demand optimization method and system, and relates to the technical field of intelligent operation and maintenance, and the method comprises the steps: collecting operation condition signals in real time, constructing a digital twin model of each part of unit equipment, and forming a unified discrete state vector; performing feature extraction on the operation condition signal according to a fixed window, and constructing a Bayesian network of a hierarchical causal structure in combination with process attributes; performing posterior reasoning on the Bayesian network through belief propagation to obtain a fault posterior probability of a causal node, and calculating a risk score according to a weight and a consequence cost; and life parameter estimation is carried out, Monte Carlo simulation is used to predict the demand quantity, and the optimal spare part order quantity is calculated in combination with inventory constraints. According to the method, potential risks can be found in time, non-planned shutdown is reduced, inventory redundancy and capital occupation are reduced, and the safety, reliability and economical efficiency of operation of a new unit are improved.

The invention provides a hydraulic floating bridge ontology-free data reliability analysis method based on a time sequence decoupling network and a Bayesian network, and relates to the technical field of hydraulic floating bridges. Comprising the steps of determining source domain equipment according to a bill of material and a structure diagram of a target hydraulic floating bridge; determining a standardized time sequence monitoring data set of the source domain equipment; training a time sequence decoupling network by using the standardized time sequence monitoring data set to obtain a health probability data set of each key component of the target hydraulic floating bridge; inputting the health probability data set into a four-layer Bayesian network, and recursively calculating the posterior failure probability of each layer from bottom to top by adopting a belief propagation algorithm to obtain failure probability data and posterior probability distribution data; and performing risk assessment according to the failure probability data and the posterior probability distribution data to obtain a risk assessment result. The technical problems that in the prior art, data scarcity and model island exist in operation and maintenance of a hydraulic floating bridge and similar equipment, and reliability evaluation cannot be carried out are solved.

A method of processing wireless communication signals for joint communication and environmental awareness in a region of interest (ROI) is presented. The method alternately uses a first iterative process and a second iterative process for determining an initial representation of a voxelized environment of the ROI and for determining the transmitted signal. The first iterative process and the second iterative process may implement a Gaussian approximation belief propagation process.

The invention discloses a situational medical care emergency training method based on an intelligent decision tree, and the method comprises the following steps: S1, collecting and preprocessing medical care emergency historical case data, and extracting an expert path; s2, constructing a medical emergency situation virtual environment based on the structured data and initializing an intelligent decision tree; s3, performing evolution operation on the plurality of decision paths by adopting a genetic algorithm to generate a decision path set; s4, fusing the decision path and the expert path by adopting a belief propagation algorithm to generate an emergency decision path; s5, emergency training is carried out in the virtual environment, recording and evaluation are carried out, and an adjusted path is formed; and S6, redeploying the adjusted path to the decision tree, and carrying out targeted drilling. According to the method, the intelligent decision tree integrating genetic optimization and a belief propagation mechanism is constructed, so that accurate and efficient training and path optimization of medical staff in a situational emergency environment are realized.

This invention discloses a code-assisted baud rate sampling clock recovery method and system, applicable to the field of optical fiber communication technology. The method includes the following steps: performing matched filtering on the received signal; using the M&M algorithm to complete primary clock recovery on data from an oscilloscope to obtain baud rate sampling data; performing equalization processing on the baud rate sampling data; using a belief propagation algorithm to decode the equalized baud rate sampling data and output reliability estimation information; modulating the reliability estimation information of each baud rate sampling data into a PAM4 auxiliary symbol according to the PAM4 mapping rule; using the PAM4 auxiliary symbol to replace the decision symbol in the M&M algorithm to re-estimate the clock error and perform high-precision clock recovery. This invention, by combining primary and auxiliary clock recovery stages, significantly reduces the bit error rate and provides more stable clock synchronization in high data rate scenarios.

The application relates to a weak underwater target azimuth detection pre-tracking method based on belief propagation, which comprises the following steps: converting a weak target tracking problem into solving a joint posterior probability density function (pdf) based on a Bayesian rule; factorizing the joint posterior pdf, constructing a corresponding factor graph through a graph model; solving the transmitted information in the factor graph by using a belief propagation (BP) algorithm, and converting multiple integrals into ordinary integrals by using a Goodman variable substitution principle; constructing a likelihood function by using a complex Wishart distribution to describe the relationship between original sonar data and target states, and simplifying the remaining pdf by using a Gaussian distribution to obtain a confidence approximation of a target state edge posterior pdf; and calculating the number of weak targets and corresponding states by using a minimum mean square error (MMSE) estimator. The application can simultaneously track multiple weak targets without a complex clustering algorithm, has fewer preset parameters, and can accurately and efficiently track target state information including an azimuth, an azimuth angular velocity and target intensity.

A method of decoding information encoded in respective activation modes of an activated subset of antennas of a MIMO transmitter transmitting orthogonally modulated signal components, the method comprising: decoupling a real part and an imaginary part of a signal component of a received signal vector, respective channel matrices of real and virtual channels are received, determined, or estimated, and real and virtual signal components are represented as respective products of transmitted symbol components and activation unit vectors. The respective activation unit vectors of the decoupled real and imaginary parts are then represented with corresponding random variable vectors that are dependent on a probability quality function of the activation pattern available at the transmitter. Then, a Gaussian belief propagation-based messaging process is applied to the random variables to jointly estimate activation unit vectors of the respective real and imaginary parts of the received signal vector, and finally the activation unit vectors are decoded to obtain the information.

The present disclosure provides an iterative receiving method of a polar coded continuous phase modulation signal and a related device. The method comprises: receiving, by a demodulator, a continuous phase modulation signal from a channel, and determining an initial accuracy parameter, a forward metric and a backward metric of the continuous phase modulation signal; performing, by the demodulator, an operation on the initial accuracy parameter to obtain a target accuracy parameter; performing, by the demodulator, a recursive operation on the forward metric and the backward metric based on the target accuracy parameter to obtain a multi-ary code word probability, and obtaining an estimated information stream at a receiving end based on the multi-ary code word probability and converting the estimated information stream into a decoder prior information stream; performing, by a decoder, a decoding process on the decoder prior information stream based on a serial cancellation list decoding algorithm to obtain a candidate code word sequence, performing a decoding process on the candidate code word sequence based on a belief propagation decoding algorithm, and judging whether the decoding process meets a preset termination condition, and outputting a source information bit or a target information stream at the receiving end according to a result of the judgment.

The invention discloses a construction and evaluation method for an applicant drug clinical test quality management system, and aims to solve the problems that traceable evidence chains are lacked among applicant clinical test quality system elements, evidence materials and evaluation conclusions, evaluation is difficult to quantify, and closed-loop improvement based on evaluation results is difficult. According to the method, quality system element data, evidence material data and evidence source data are obtained and standardized, and a time heterogeneous evidence graph comprising element nodes, evidence nodes, version nodes, responsibility nodes and evidence source nodes is constructed; executing graph neural network representation learning on the evidence graph to generate a relationship likelihood parameter and an evidence credibility likelihood parameter; constructing a Bayesian factor graph containing conflict factors, and executing belief propagation to obtain element compliance posterior distribution and evidence credibility posterior distribution; and further calculating a compliance score, an uncertainty score and a coverage rate score to form a three-dimensional score result, and extracting an evidence chain to generate traceable output, thereby achieving the technical effects of automatic establishment of the evidence chain, compliance quantitative evaluation, conflict evidence suppression and evaluation result driven closed-loop improvement.

This disclosure provides a motion planning method, apparatus, computer device, and storage medium for robots, applied to each robot in a robot swarm; each robot maintains its own local factor graph; the local factor graphs maintained by the multiple robots constitute a global factor graph; the method includes: in response to a state update event corresponding to the current robot being triggered, determining a trigger domain, and determining a replanning subnet from the current global factor graph based on the trigger domain; performing message passing processing based on Gaussian belief propagation in the replanning subnet with the goal of reducing the energy change of the local factor graph corresponding to the current robot, to obtain an updated local factor graph corresponding to the current robot; determining the confidence distribution information of the variables corresponding to multiple variable nodes in the local factor graph of the current robot based on the updated local factor graph of the current robot; and determining the motion replanning result of the current robot based on the confidence distribution information.

Embodiments of the present application provide a method for communication, a device, a storage medium, and a program product. In the method, a decoding apparatus decodes a low density parity check (LDPC) code on the basis of a belief propagation (BP) algorithm to obtain an initial bit sequence that does not satisfy a parity-check equation set. In addition, the decoding apparatus performs multiple bit flipping operations on a predetermined bit range of the initial bit sequence to obtain a plurality of flipped bit sequences. In one bit flipping operation, at least one bit in the predetermined bit range is flipped. In addition, the decoding apparatus performs LDPC encoding on the plurality of flipped bit sequences to obtain a plurality of candidate bit sequences. Furthermore, the decoding apparatus determines, from among the plurality of candidate bit sequences, a target bit sequence meeting a predetermined condition as a decoding result of the LDPC code. In this way, a certain degree of randomness is introduced by performing bit flipping within the predetermined bit range, thereby resisting the influence of noise or interference, improving decoding performance, and reducing complexity.

The present invention discloses a fast sensor data fusion method based on sigma point belief propagation, comprising the following steps: step (1), predicting the state and covariance of target k at time t; step (2), performing sigma point sampling on the predicted state; step (3), transferring sigma points on the sampled samples in step (2); step (4), calculating iterative data association within all sensors s in parallel; step (5), performing confidence calculation after completing the iterative data association within the sensor; step (6), estimating the state and covariance of each target k at time t+1 based on the distribution of the state of target k at time t+1. The method solves the problems of high computational complexity and poor scalability of traditional multi-sensor information fusion algorithms, and reduces computational complexity based on the implementation of the sigma point belief propagation algorithm.

The invention discloses a polar code decoding method, polar code decoding equipment and a medium, which are used for adopting a low-difference random number sequence as a random number sequence in a random bit stream generator and improving the convergence of the random number sequence when the sequence is short, thereby improving the decoding performance when the random bit stream is short. The method comprises the following steps: acquiring information of N channels to be decoded; respectively extracting amplitude bit information and sign bit information from each path of channel information, and respectively converting the amplitude bit information corresponding to each path of channel information by using a pre-generated low-difference random number sequence to obtain random bit stream information corresponding to each path of channel information; and belief propagation BP decoding calculation is carried out based on the sign bit information and the random bit stream information corresponding to each channel information.

The application discloses a network propagation control method and device based on belief propagation, which comprises the following steps: step one: merging nodes with small influence in the network to simplify the network structure, and obtaining a coarsened network G c (V c ,E c ); step two: identifying key nodes from the coarsened network G c (V c ,E c ) by using an improved belief propagation algorithm (BPD-v), and outputting a propagation control node sequence S; and step three: locally optimizing part of the node sequence in a fine-tuning manner, further improving the effect of the control method, and then obtaining a better control sequence. By the method provided by the application, the resource consumption required for control propagation can be minimized under the premise of meeting the same propagation control effect, or the propagation control effect can be maximized under the premise of the same control resource consumption. In addition, the method has low time complexity and is suitable for super-large-scale network propagation control.

This invention provides a sparse Bayesian time-varying channel estimation method based on time correlation and confidence propagation. In a single-carrier phase-shift keying (PSK) modulation system, to reduce the impact of error propagation during block data processing in time-varying channel environments on the confidence propagation-based sparse Bayesian learning channel estimation algorithm, a time correlation-based channel estimation algorithm is proposed. This algorithm models channel estimation in block data processing as a hidden Markov model and utilizes a first-order autoregressive model to capture the time correlation of the channel between data blocks, thereby improving the algorithm's stability in the presence of error propagation. The proposed algorithm achieves almost the same performance as traditional methods with the same computational complexity; it reduces the impact of error propagation during block data processing in time-varying channel environments on the stability of the BP-SBL channel estimation algorithm; and the proposed TC-BP-SBL can be easily extended to other message passing systems.

This application provides a map generation method and system based on multi-source heterogeneous geographic information processing. It acquires three types of heterogeneous data: a geographic element attribute database, a geographic coordinate semantic database, and a geographic reference image database. Spatial spectrum reconstruction processing is then performed to generate a spatial spectrum cube. Belief propagation fusion processing is then used to construct a belief network from the spatial spectrum data. Combined with spatially constrained probability field optimization and D-S evidence synthesis, a geographic evidence chain is generated. Next, spatiotemporal field reconstruction processing is performed to separate the static and dynamic states of the geographic evidence chain and couple them into fields, constructing a hyperdimensional spatiotemporal map. Finally, holographic projection conversion processing is used to perform phase field compression and coherent diffraction reconstruction on the hyperdimensional spatiotemporal map, generating a holographic geographic projection map that supports 3D interaction. This method achieves the automatic generation of a structurally unified, highly reliable geographic map that supports dynamic behavioral reasoning and true 3D interaction from multi-source heterogeneous geographic data, significantly improving the decision-making value and cognitive efficiency of geographic information.

The application discloses a high-performance low-complexity LDPC decoding method for satellite communication and belongs to the field of digital signal processing. Based on the low-density parity-check (LDPC) code in the 5G standard, the received channel information is iteratively decoded. The implementation method of the application is as follows: the amplitude of the current check node information is used to dynamically set the erasure range when updating the variable node; the amplitude of the check node information gradually increases with the increase of the iteration number, and the entire iteration process is divided into two parts, and different parameters are used to set the erasure range. When the decoding result is correct or the maximum iteration number is reached, the decoding result is output. The application increases the reliability of the variable node information, prevents reliable variable information from being erased and prevents the propagation of unreliable variable node information through the above two points, and has excellent decoding performance. There is no nonlinear operation in the log-likelihood ratio belief propagation algorithm (LLR-BPA) in the decoding algorithm of the application, and the decoding performance is high and the decoding complexity is low.

The invention discloses a message passing method and system for a multi-commodity flow problem in a communication network, a medium and equipment, and relates to the technical field of communication network routing. The method comprises the following steps: determining a multi-commodity minimum cost flow problem about a target communication network, and constructing a network diagram according to the multi-commodity minimum cost flow problem; constructing a plurality of calculation trees based on the network graph, and carrying out iterative optimization on commodity flow information among vertexes in the plurality of calculation trees by adopting a Min-sum belief propagation algorithm to obtain a flow distribution result among root vertexes of each calculation tree; and according to a flow distribution result between the root vertexes of each calculation tree, carrying out message transmission of multiple commodity flows in the target communication network. According to the scheme, the expandability of solving the multi-commodity minimum cost flow problem in the large-scale data network can be improved, so that the capability of solving the multi-commodity minimum cost flow problem in the large-scale data network is improved.

A method for training a layered belief propagation-based deep learning model for decoding a quantum error correction code, according to an embodiment of the present invention, comprises the steps of: initializing a weight assigned to the deep learning model including at least one neural belief propagation (NBP) model; and training the deep learning model so as to minimize a loss function determined in consideration of layered constraints and degeneracy, wherein the deep learning model includes at least one first deep learning model designed by concatenating the at least one NBP model by the number of first layers, and the at least one first deep learning model is configured by being concatenated by the number of second layers corresponding to a preset number of repetitions.

The invention relates to a wireless communication transmission and detection method based on a BP deep neural network. The method comprises the following steps: S1, expanding a belief propagation iterative algorithm factor graph and mapping the belief propagation iterative algorithm factor graph to a neural network structure to construct a deep neural network for large-scale MIMO system detection; s2, carrying out offline training on the constructed deep neural network; and S3, carrying out online detection by using the trained neural network. The number of hidden layers of the constructed deep neural network is small, so that the complexity of off-line training is reduced; the online calculation complexity of the constructed deep neural network large-scale MIMO system detection algorithm is the same as that of an original belief propagation algorithm, but a lower bit error rate can be achieved, and better robustness is achieved under various channel conditions. The method can be widely applied to the scenes of high-dimensional MIMO communication between a 5G base station and a terminal, large-scale access detection of Internet of Things nodes, high-speed signal decoding of a Wi-Fi / Wi Gi g network and the like.

A subspace-constrained unlicensed large-scale MIMO active user and channel joint estimation method is proposed. Under the GEC framework, the subspace strategy is integrated, and matrix inversion operations within the subspace are used to replace matrix inversion operations in the full-dimensional space. The low-precision quantized uplink received signal matrix received by the base station is iteratively processed to achieve joint channel estimation and user active state estimation, which greatly reduces the computational complexity of the algorithm. In addition, by modeling the hybrid channel as mutually independent conditional distributions, the channel column correlation is decoupled using cyclic belief propagation (LBP), thereby enabling parallel operation of hybrid channel posterior mean estimation, which greatly reduces the algorithm's running time.