52 results about "Belief propagation" patented technology

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 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 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.

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 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 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 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.

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.

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.

The embodiment of the invention provides a communication method and device, a storage medium and a program product. In the method, a decoding device decodes a low density parity check (LDPC) code based on a belief propagation (BP) algorithm to obtain an initial bit sequence which does not meet a check equation set. Further, the coding device obtains a plurality of candidate bit sequences based on performing a plurality of bit flipping operations on the initial bit sequence. In one bit flipping operation, at least one bit of the initial bit sequence is flipped. In addition, the decoding device determines a target bit sequence satisfying a predetermined condition from the plurality of candidate bit sequences as a decoding result of the LDPC code. The belief propagation algorithm can comprise BP, Min-Sum and other algorithms. The initial bit sequence can be a bit sequence obtained after the value of the variable node obtained by BP decoding is subjected to hard decision. Therefore, certain randomness is introduced in a bit flipping mode, so that the influence of noise or interference is resisted, and the decoding performance is improved.

The invention provides a camouflage target segmentation method combining edge guidance and progressive local optimization, and relates to the technical field of target segmentation, and the method comprises the steps: extracting multi-level features through a mixed backbone module, and building a pairing relation based on a semantic structure and a local texture; an endogenous enhancement module is utilized to generate an enhanced sample according to boundary coherence and neighborhood self-consistency so as to improve feature quality; the fusion module realizes bidirectional adaptive fusion of coarse and fine granularity features under the condition of no explicit attention; the positioning module forms an amplification mask according to belief propagation; and the edge refining module realizes iterative optimization in the tentative zone according to neighborhood co-occurrence and scale backtracking, and forms a closed-loop system through an iterative feedback path to obtain a convergent camouflage target segmentation result.

The embodiment of the invention provides a communication method and device, a storage medium and a program product. In the method, a decoding device decodes a low density parity check (LDPC) code based on a belief propagation (BP) algorithm to obtain an initial bit sequence which does not meet a check equation set. In addition, the decoding device performs a plurality of 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 a predetermined bit range is flipped. In addition, the decoding device performs LDPC encoding on the plurality of flipped bit sequences to acquire a plurality of candidate bit sequences. In addition, the decoding device determines a target bit sequence satisfying a predetermined condition from the plurality of candidate bit sequences as a decoding result of the LDPC code. Therefore, certain randomness is introduced in a mode of performing bit flipping in a preset bit range, so that the influence of noise or interference is resisted, the decoding performance is improved, and the complexity is reduced.

The invention discloses a logistics scheduling method and device based on deep learning and adaptive belief propagation, and is used for solving the technical problem that the quality of a finally output logistics scheduling scheme is poor due to the fact that an existing logistics scheduling method is liable to fall into a local optimal solution. The method comprises the steps of obtaining a logistics scheduling instance to be solved and converting the logistics scheduling instance into a logistics scheduling factor graph; based on a self-adaptive message passing mechanism, a graph attention network is adopted to generate dynamic parameters according to the factor graph, and target messages between variable nodes and neighbor factor nodes are output; weighting and aggregating the target message to obtain a belief value of each variable node, wherein each variable node contains a plurality of logistics scheduling execution actions and each action corresponds to the belief value; and selecting an action corresponding to the minimum belief value of each variable node to form a target logistics scheduling scheme.

The invention relates to the technical field of surrounding rock label automatic optimization, and discloses a surrounding rock label automatic optimization method based on pseudo label weighting and belief propagation. The objective of the invention is to solve the problem that spatial correlation and feature coupling among data are not fully utilized in the prior art; a differential processing mechanism for samples with different confidence degrees is lacked; an iterative optimization closed-loop system is not formed; and fuzziness between adjacent levels cannot be effectively processed. The method comprises the following steps: S1, data preparation and preprocessing; s2, initial model training; s3, confidence evaluation and sample classification; s4, adjusting labels and weights; s5, iterative optimization is carried out; and S6, outputting a final model. Through multi-round iterative optimization, the label quality is gradually improved, and finally the accuracy and reliability of the surrounding rock stability identification model are improved.

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 meet a check equation set. In addition, the decoding apparatus obtains a plurality of candidate bit sequences on the basis of performing a plurality of bit flipping operations on the initial bit sequence. In one bit flipping operation, at least one bit of the initial bit sequence is flipped. Furthermore, the decoding apparatus determines, from among the plurality of candidate bit sequences, a target bit sequence, which meets a predetermined condition, to serve as a decoding result of the LDPC code. The BP algorithm may include algorithms such as BP and Min-Sum. The initial bit sequence may be a bit sequence obtained after hard decision is performed on the value of a variable node obtained by means of BP decoding. In this way, certain randomness is introduced by means of bit flipping, thereby counteracting the influence of noise or interference, and improving the decoding performance.

The invention discloses a multi-energy combined supply system collaborative scheduling optimization method based on reinforcement learning, and the method comprises the following steps: collecting and preprocessing real-time operation data and external environment data of a multi-energy combined supply system, and forming a standardized data set; establishing a mathematical model of the multi-energy combined supply system, and constructing a system dynamic behavior equation; generating a system dynamic data set; introducing a dynamic belief propagation mechanism, and outputting the confidence of a prediction result; forming a candidate scheduling action set; generating a multi-energy collaborative scheduling strategy by using a dynamic game and strategy updating mechanism; evaluating the long-term income of the multi-energy cooperative scheduling strategy to obtain an optimal scheduling strategy; outputting an optimal collaborative scheduling strategy; and performing joint iterative training to obtain an updated improved MBRL framework, and realizing coordinated and optimized operation of the multi-energy combined supply system, thereby reducing the operation cost of the system, improving the energy utilization efficiency and enhancing the operation safety and stability of the system.

The invention discloses a polarization code belief propagation decoding method for correcting insertion and deletion errors, and relates to the field of digital communication error control coding. The method comprises the following steps: mixing information bits with the length of 1 and all-zero frozen bits with the length of 1 according to position indexes of preset information bits and fixed bits to obtain a bit sequence; encoding the bit sequence to obtain a sending sequence with the length of 1; the sending sequence is transmitted through an IDS channel to obtain a receiving sequence with the length of the IDS channel; and carrying out insertion and deletion error correction processing on the receiving sequence through a BP decoder based on a weighted Lelwstein distance, and outputting an information sequence estimated value. According to the method, the drift distance is introduced, and the weighted Levinstein distance is adopted as probability measurement, so that the problem that insertion and deletion errors cannot be corrected by a traditional BP decoding algorithm is solved. The inherent problem of an SC framework is also avoided, and the error correction capability is improved; and through parallel operation, the decoding time delay is reduced, and the unit time processing capacity is improved.

The application provides a high-order modulation coding and decoding method for anti-nonlinear distortion of a power amplifier, comprising a transmitting end processing procedure, a transmission procedure and a receiving end processing procedure. The transmitting end processing procedure comprises: performing LDPC coding on an information bit stream to obtain a coded bit stream; grouping the coded bit stream, searching for a corresponding modulation constellation in set 64AANC modulation constellation data, obtaining a mapping symbol stream, and then performing power backoff to obtain a transmitting symbol stream; the transmission procedure comprises: adding a Gaussian white noise simulation signal to the transmitting symbol to enter an AWGN channel; and the receiving end processing procedure comprises: performing demapping by using set 64AANC demodulation constellation data, sequentially calculating 6-bit log-likelihood ratio values corresponding to each symbol and flattening the 6-bit log-likelihood ratio values, taking each n log-likelihood ratio values as a group, inputting the group into a belief propagation decoder for iterative decoding, and then flattening to obtain a receiving information bit stream. The application has the advantages that the power efficiency and bandwidth efficiency of an existing system can be improved.

The invention belongs to the technical field of data analysis, and particularly relates to a tracing and analyzing method and system for adhesive tape production process data, and the method comprises the following steps: S1, obtaining multidimensional process parameter data containing timestamps and multisource heterogeneous batch identification data in the adhesive tape production process; aiming at the multi-dimensional process parameter data and the batch identification data, respectively calculating a time sequence kernel matrix based on dynamic time bending and a classification data kernel matrix based on semantics, and carrying out multi-kernel fusion to obtain a uniform affinity matrix; and S2, taking each data point as a vertex, defining a vertex subset of which the affinity between any two vertexes in the affinity matrix is higher than a first preset threshold and the scale is N as a hyperedge, and obtaining a hypergraph representing the high-order relevance of the production process. The method has the beneficial effects that when a fault occurs, reverse reasoning is carried out on a state transition path by utilizing a belief propagation algorithm, and tracing from a fault phenomenon to key influence factors is realized.

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.

This invention provides a network threat assessment method, medium, and system based on dynamic Bayesian networks, belonging to the field of network security technology. The invention uses a parameter adaptive learning unit with a sliding time window mechanism and a Bayesian online change point detection algorithm to update the conditional probability table parameters. It inputs the topology and parameters into a threat temporal inference model that integrates a liquid neuron layer and a hypergraph matching layer, receives real-time security event evidence, and outputs the posterior probability distribution of nodes. Based on the posterior probability distribution, it calculates the neuron activation regulation function value and dynamically adjusts the liquid neuron parameters. When the posterior probability of a threat node exceeds a preset threat threshold, it executes an iterative belief propagation algorithm through a hierarchical distributed inference collaboration module to generate an attack path probability graph and output the network security assessment result. This solves the technical problem of not being able to simultaneously and accurately model the causal dependencies and high-order collaborative modes of network threat events.