281 results about "A posteriori probability" patented technology

The embodiment of the invention provides a dynamic risk assessment method and system based on perception and causal reasoning, and belongs to the technical field of automatic monitoring. The method comprises the following steps: acquiring a video data stream in real time and analyzing the video data stream into a structured dynamic visual scene representation; through a neural network model used for executing cross-modal semantic alignment, mapping the dynamic visual scene representation into activation evidence of nodes in a pre-constructed causal knowledge graph in a multi-modal embedding space, the nodes of the graph being risk events or risk factors; and performing probabilistic reasoning on the map based on the activation evidence to calculate the posterior probability of the node corresponding to the to-be-predicted risk event. According to the method, the problems that visual perception and causal reasoning cannot be effectively integrated and the evaluation result lacks interpretability in the prior art are solved, obstacles between visual information and abstract causal concepts are overcome by constructing a perception-mapping-reasoning technology closed loop, and the accuracy, transparency and credibility of risk evaluation are remarkably improved.

The purpose is to easily manage abnormal program operation. [Solution] The abnormality management device 1 includes a first learning unit 11 that uses normal data indicating processing resource usage data in which the processing resource usage corresponding to each process ID is normal as training data from among a plurality of processing resource usage data each including the usage of processing resources used in executing a process corresponding to each process ID, and learns parameters of a probability model that outputs a posterior probability that the processing resource usage corresponding to each process ID is normal by maximum likelihood estimation, and a derivation unit 12 that derives a probability distribution of abnormal data indicating processing resource usage data including abnormal processing resource usage based on the posterior probability estimated by the learned probability model, the probability distribution of the normal data, and the prior probability of normality.

The invention discloses a communication fault diagnosis method and device based on a power system, equipment and a medium. The method comprises the following steps: S1, collecting and outputting multi-source data; s2, preprocessing the multi-source data; s3, performing topological constraint attention mechanism weighting processing on the data matrix; s4, performing dynamic threshold calculation on the fusion feature matrix; s5, positioning the abnormal node list; s6, calculating to obtain a posterior probability; s7, matching the rule base to obtain a standardized disposal instruction; according to the method, the communication traffic is collected through the compressed sensing collection algorithm, the line impedance parameters are integrated into the observation matrix in combination with the topological parameters, key node data are collected preferentially, and the effective data acquisition rate is increased; the accuracy of switch false alarm identification is improved by outputting a time-space unified fusion feature matrix; the computing resources are focused to the abnormal nodes and neighborhoods thereof through the abnormal node mask matrix, so that the positioning precision of the aged equipment is improved, and the judgment omission rate caused by switch aging is prevented from being improved.

The invention belongs to the technical field of communication, and particularly relates to a multiplication active code decoding method based on an SO-ORBGRAND decoder. The SO-ORBGRAND decoder outputs soft information by calculating a correct posterior probability of a conjecture code word sequence, and high-performance decoding is realized through soft information exchange and optimization processing between the row decoder and the column decoder. In the iteration process, a method for judging the validity of the code word through the check matrix is designed by utilizing the polarization characteristic of the multiplication active code, so that the iteration is supported to jump out in advance to reduce the complexity. The invention provides a multiplication active code decoding algorithm based on an SO-ORBGRAND decoder, and compared with various non-system multiplication active code decoding algorithms and the performance of traditional long-code polarization codes, the method has remarkable advantages in error correction performance; a method for judging the validity of the code word through the check matrix by utilizing the polarization characteristic design of the multiplication positive code is provided, the complexity is reduced while the judgment success rate is improved, and the implementation overhead is reduced.

The invention relates to the technical field of intelligent substations, and discloses a safety measure checking method and system for an intelligent substation. The method comprises the steps of collecting multi-source real-time monitoring data of a target intelligent substation, and performing edge federation aggregation on the multi-source real-time monitoring data to obtain collaborative feature data; constructing a dynamic topological graph of the target intelligent substation based on the collaborative feature data, and performing short-term fault prediction on the target intelligent substation to obtain a short-term fault prediction result; performing probabilistic reasoning on each short-term fault event according to the fault probability propagation model to obtain a cascading fault posterior probability of the corresponding short-term fault event; performing multi-objective optimization sorting on each safety measure strategy to obtain a safety measure priority sequence; and performing simulation verification on the safety measure priority sequence according to the digital twin to obtain a check report. According to the method, a complete closed loop from risk analysis, rapid verification to strategy optimization is formed, and efficient and intelligent technical guarantee is provided for safe and stable operation of the intelligent substation.

The invention discloses a methane column concentration inversion method and system based on a physically-driven variational auto-encoder, relates to the technical field of greenhouse gas monitoring, and is used for solving the technical problem of low inversion reliability of an existing data-driven methane column concentration inversion mode. The methane column concentration inversion method based on the physical driving variational auto-encoder comprises the following steps: pre-training a decoder module through a loss function containing Jacobian matrix consistency constraint, and freezing network weights of all layers of the decoder module; taking the observation spectrum as the input of the encoder module, outputting posterior probability distribution by the encoder module, sampling any concentration value, and inputting the concentration value into the decoder module for forward simulation to generate a simulation spectrum corresponding to the concentration value; when the difference between the simulated spectrum and the observed spectrum is minimum, determining the optimal methane column concentration as an inversion encoder, and inputting the observed spectrum into an encoder module; and outputting the mean value and the standard deviation of the posterior probability distribution.

The application provides a polar code soft input soft output decoding method, a decoding device and a communication device; the decoding method comprises the following steps: (S1) performing fast serial cancellation list decoding under dynamic frozen bit constraint for all special nodes constituting a polar code; the types of the special nodes comprise rate zero, rate one, repetition and single parity check; (S2) calculating the approximation of the sum of the posterior probabilities of the legal code words not accessed under each special node after decoding; (S3) using the approximation of the code book probability obtained in the step (S2) to calculate the bit level soft information of the output polar code. The low-latency polar code soft input soft output decoding method provided by the application improves the parallelism by realizing fast soft output decoding based on the Rate0, Rate1, REP and SPC nodes, and significantly reduces the decoding latency without losing the soft output precision compared with the SO-SCL decoding algorithm.

The invention discloses a finite geometric code decoding method and system based on a generalized check matrix, and relates to the communication technology, and the method comprises the following steps: constructing a corresponding generalized check matrix according to an algebraic structure of a multi-step large number logic decodable finite geometric code; sending a finite geometric code sequence, transmitting the sequence after coding and BPSK modulation, and receiving a posterior probability log-likelihood ratio sequence by a receiving end; and carrying out iterative decoding on the finite geometric code received by the receiving end based on the constructed generalized check matrix. According to the invention, a new generalized check matrix is constructed according to the algebraic structure of the finite geometric code, and multi-step large-number logic iterative decoding is carried out on the finite geometric code based on the matrix, so that the iterative decoding performance of the multi-step large-number logic decodable finite geometric code is improved.

This invention discloses a low-complexity decoding method for LDPC-Hadamard codes based on a prototype diagram, belonging to the field of digital signal processing. The implementation method is as follows: information bits are LDPC encoded, and then the constraint relationship of the check nodes of the LDPC code is encoded into Hadamard code to obtain the prototype diagram of the LDPC-Hadamard code; the encoded codewords are transmitted and received through a channel, and the PVN and HCN nodes are initialized with the received information; Hadamard decoding is performed using a symbol-by-symbol maximum a posteriori probability decoding algorithm, and DFHT approximation is used to reduce decoding complexity; the decoded information is transmitted to the PVN nodes; the updated HCN node information is summed with the information from the previous iteration, and the summation result is used to determine the decoding; a decoding decision is made on the PVN information, and decoding is performed. This invention combines LDPC encoding, Hadamard encoding, and Max-Log approximation calculation methods to reduce decoding complexity and enhance the stability and reliability of the communication system.

The invention discloses a bearing fault diagnosis method based on time-frequency double-flow complementation and adaptive gating fusion, and the method comprises the steps: collecting an original vibration signal of a bearing, segmenting an original vibration signal sequence into a plurality of sample segments with fixed lengths, and forming an original data set; dividing the original data set into a training set and a test set by adopting a physical segmentation strategy based on a time axis; constructing a time-frequency double-flow feature extraction module, and extracting a time-domain feature vector and a frequency-domain feature vector; constructing an adaptive gating fusion module to dynamically generate a gating weight, and performing weighted complementary fusion on the time domain feature vector and the frequency domain feature vector to obtain a fused fault feature vector; constructing a classification decision network to map the fusion fault feature vector to a decision space, and optimizing model parameters by using a label smooth regularization loss function and a cosine annealing strategy; and inputting the test set into the trained fault diagnosis model, calculating posterior probability distribution of a sample belonging to each fault category, and outputting a bearing fault diagnosis result.

Systems and methods are provided for detecting and tracking objects. A first set of observations for a plurality of locations includes a probability that a value associated with the location is consistent with local background. A second set of observations includes a probability that the value associated with the location is not consistent with local background. A first probability tensor representing probabilities for each of a plurality of states for each of the plurality of locations is generated from the first and second sets of observations. The first probability tensor is updated according to a second probability tensor representing the probabilities for each of the plurality of states in a previous time step. It is determined that a target is present in the region of interest when a posterior probability meets a threshold value.

The invention discloses a household energy centralized management method and device, electronic equipment and a storage medium. The method comprises the following steps: collecting real-time observation data of a family environment to construct a multi-dimensional context feature vector; generating a dynamic state transition probability matrix and emission parameters of the hidden Markov model through a dynamic parameter mapping mechanism, and calculating initial posterior probability distribution of a family activity state by using a forward algorithm; when the information entropy of the probability distribution exceeds a threshold value, executing an active state exploration strategy, issuing an optimal perturbation instruction and correcting a posterior probability based on an active response observation evidence so as to determine a final family activity state; and based on the state loading control constraint set, solving a real-time optimization objective function containing energy efficiency and comfort penalty, and generating an equipment control instruction. According to the method, the uncertainty of state judgment is reduced by combining a dynamic model and an active exploration mechanism, and household energy cost optimization is realized on the premise of ensuring comfort.

Disclosed are a transceiver decoding method and system based on protograph differential chaos shift keying (DCSK), the method including acquiring an information bit sequence, performing a coded modulation on the information bit sequence via a protograph DCSK transmitter, and outputting a plurality of modulated symbols; inputting each modulated symbol into a wireless channel for channel interference, and outputting a received symbol corresponding to each modulated symbol; using a target extrinsic information-aided network to determine a target a-posteriori probability vector corresponding to each received symbol; inputting each target a-posteriori probability vector into an a-priori calculation decoder for decoding, and outputting an initial decoded bit sequence; and determining a target decoded bit sequence according to the initial decoded bit sequence and a preset check matrix. The technical problem of lower decoding accuracy caused by the existing transceiver decoding methods designed for a DCSK system is solved.

The invention discloses a partial discharge detection method and device and electronic equipment, and relates to the field of power failure detection. The method comprises the following steps: acquiring M groups of partial discharge data of power equipment through M acquisition strategies; performing screening and dimension reduction on the features of each group of partial discharge data through a target verification strategy to obtain M groups of feature sets; determining a partial discharge detection result according to each group of feature sets to obtain M partial discharge detection results; converting each partial discharge detection result into a piece of posterior probability distribution data, obtaining M pieces of posterior probability distribution data in one-to-one correspondence with the M partial discharge detection results, and fusing the M pieces of posterior probability distribution data to obtain target probability distribution data; and determining a target partial discharge type and target discharge intensity of the power equipment according to the target probability distribution data. According to the invention, the problem of insensitive detection or wrong diagnosis of various defects in partial discharge detection by using a single detection means in the prior art is solved.

The embodiment of the invention discloses a non-contact multi-parameter elbow joint fault diagnosis method and device. Collecting multi-parameter original signals of the area where the elbow-shaped joint is located in the same time reference and the same physical microenvironment; performing denoising, zero offset correction and filtering on the multi-parameter original signal to obtain a pre-processed multi-parameter signal; performing compensation processing on the preprocessed multi-parameter signal by applying a space-time coupling compensation matrix; performing feature extraction on the compensated multi-parameter signals to obtain feature vectors, and calculating response time lag between channels of the parameter signals; calculating a weighted time distance between the typical mode of the current observation stage and each fault stage, and mapping the weighted time distance to a preset fault evolution chain, so as to calculate a posterior probability of the typical mode of each fault stage through the fault evolution chain; and according to a comparison result of the posterior probability and a preset fault threshold value, outputting a fault diagnosis result of the area where the elbow-shaped joint is located and a confidence coefficient corresponding to the fault diagnosis result.

The invention provides a speed limit information fusion method and device, and the method comprises the steps: obtaining map speed limit information and map speed limit confidence based on a map navigation system; acquiring image data in front of a road, and recognizing speed limit information in the image data through a speed limit recognition model to obtain camera speed limit information and camera speed limit confidence; taking the map speed limit confidence coefficient and the camera speed limit confidence coefficient as prior probabilities, and obtaining a posterior probability through a Bayesian algorithm based on the prior probabilities and the conditional probabilities; and the final speed limit information is determined based on the posterior probability, the map speed limit information and the camera speed limit information, so that the accuracy and reliability of the speed limit information are improved.

The invention discloses a single-phase earth fault line selection method based on double-layer parameter adaptive optimization, and the method comprises the following steps: collecting three-phase current corresponding to each feeder line of a target system after the single-phase earth fault of the target system occurs, and carrying out the calculation to obtain zero-sequence transient current; inputting the zero-sequence transient current into a self-adaptive optimization decomposition model for self-adaptive decomposition to obtain an intrinsic mode component of a corresponding feeder line; extracting transient characteristics based on the intrinsic mode component and constructing a transient characteristic matrix; and inputting the transient characteristic matrix into a pre-trained optimization mechanism fault line selection model, outputting a fault posterior probability corresponding to each feeder line, and determining a fault feeder line according to the fault probability. According to the invention, double-layer parameter adaptive optimization is used for carrying out joint optimization on key parameters of a decomposition model and key parameters of a line selection model, so that effective feature extraction and reliable discrimination can still be realized under the conditions of complex zero-sequence transient current high-frequency components and unclear features caused by the access of the distributed power supply; therefore, misjudgment and missed judgment are reduced, and the fault line selection accuracy and robustness are improved.

The invention discloses an uncertainty reasoning state scoring and early warning system for gear machining equipment, and relates to the technical field of gear machining monitoring. Data are collected in real time through an industrial Ethernet, time synchronization is realized based on an IEEE1588PTP protocol, and a data cleaning sub-module is designed to repair an abnormal value and keep a record; the method comprises the following steps of: extracting 128-dimensional potential features of a vibration signal by adopting a variational auto-encoder, fusing a temperature drift coefficient and a current harmonic component, quantifying feature uncertainty through Monte Carlo dropout, and marking low-reliability features; and constructing a Bayesian network containing 5 state nodes and 12 observation nodes, calculating a posterior probability through a Markov chain Monte Carlo algorithm, dynamically adjusting a conditional probability table, and fusing multi-feature scores. According to the method, the state evaluation accuracy and the early warning timeliness are improved, the resource consumption is optimized, full-link monitoring is realized, the fault handling efficiency is enhanced, and reliable support is provided for precision manufacturing.

The invention relates to the technical field of stereoscopic vision, and discloses a synchronous speed visual matching method and system, electronic equipment and a storage medium, and the method comprises the steps: synchronously collecting a stereoscopic video sequence with a predefined frame rate; executing multi-target hybrid tracking and motion induction detection, and outputting target motion information including position and velocity vectors; extracting hierarchical motion features of the target from continuous multiple frames of the stereoscopic video sequence, and performing unified space-time coding; under geometric constraints of stereoscopic vision, scale cosine similarity, direction similarity and trajectory consistency measurement are calculated and serve as observation evidences to be input into the probabilistic reasoning model for fusion, and a posterior probability representing matching reliability is output; the weight distribution of the speed similarity and the direction similarity is adjusted according to the motion characteristics of the targets in the scene, and the stable corresponding matching relation between the left view target and the right view target is established. According to the method, high-time-resolution information can be utilized, motion features and geometric constraints can be effectively fused, and the method has self-adaptive capacity.

The invention discloses a sensitivity-based network intrusion detection resampling method, system and device and a medium, and relates to the technical field of data detection, and the method comprises the steps: obtaining network data containing normal traffic and attack traffic; prior probabilities and class condition probabilities of the two classes of data are calculated respectively, then posterior probabilities are obtained, and the sensitivity weight of each sample is calculated; carrying out putting-back undersampling on a normal flow sample according to the sensitivity weight; carrying out replacement oversampling on the attack traffic sample according to the sensitivity weight; a sampling process is repeated to generate a plurality of balance training subsets, and a base classifier is trained based on each subset; and calculating voting weights based on the performance evaluation indexes of the base classifiers on the verification set, and obtaining weighted voting results based on the voting weights to classify new data. Random undersampling neglects the importance of boundary samples to decision boundary learning, and sensitivity weighted sampling can guide the model to sample the boundary samples, so that important information is prevented from being lost.

The invention relates to an LDPC (Low Density Parity Check) decoding method and system based on a dynamic scale factor, belongs to the technical field of circuit design, and solves the problem of low decoding performance caused by adopting a fixed value to correct deviation in the prior art. Comprising the following steps: S1, receiving to-be-decoded information, initializing posterior probability information and check node information, and iteratively executing S2-S4 until a termination condition is met; s2, acquiring a decoding decision value according to the check matrix and the hard decision value of the posterior probability information, identifying whether a termination condition is met or not according to the decoding decision value and the number of iterations, if yes, outputting a decoding result, and otherwise, entering S3; s3, updating variable node information according to posterior probability information and check node information of the last iteration; calculating a dynamic scale factor, and updating check node information according to the dynamic scale factor and the updated variable node information; and S4, updating the posterior probability information according to the updated check node information to obtain the posterior probability information of the current iteration, and returning to S2. And the decoding performance is improved.

The invention discloses a wind turbine generator component life probability prediction method and system, and a wind power plant energy control method and system. The prediction method comprises the steps of constructing a state space model of a degradation process of a wind turbine generator component; acquiring real-time state information of the component; inputting the real-time state information into the state space model, evaluating the degradation state of the component by adopting a sequential data assimilation algorithm, and outputting posterior probability distribution of the degradation state; and based on the posterior probability distribution, forward simulating a future degradation path of the component by adopting Monte Carlo simulation until the future degradation path exceeds a preset failure threshold, and obtaining residual life probability distribution of the component. According to the method, the personalized residual life of each unit can be accurately evaluated, and an innovative wind power plant energy management control strategy is created on the basis, so that the absolute maximization of the full-life-cycle economic benefit of the wind power plant in a complex market environment is realized.

The invention relates to a structure vibration grading early warning method and device and a storage medium, and the method comprises the following steps: obtaining and preprocessing a structure vibration time sequence signal, firstly preprocessing the structure vibration time sequence signal, and dividing a data set; constructing a CNN-LSTM time sequence prediction model to carry out training optimization and short-term prediction; through historical data feature extraction and manifold dimensionality reduction, a vibration state mapping area is determined based on Gaussian mixture model clustering, and a posterior probability is calculated; a classification boundary is determined through maximum likelihood function iteration, and a multi-level early warning threshold value is established; and finally, three-level intelligent early warning of safety, warning and danger is realized by combining the dimension reduction point probability and the threshold value of the prediction data. Compared with the prior art, the intelligent multi-level early warning system is used for realizing high-dimensional time sequence prediction and dimension-reduction multi-level classification early warning in a high-efficiency and high-precision manner, and aims to establish the intelligent multi-level early warning system which can perform structure safety assessment more accurately and more timely.

The invention discloses a hybrid clustering optimization method and system for memristor array weight mapping, belongs to the technical field of storage and calculation integrated chips, and solves the problem that the reasoning precision is reduced due to the fact that edge weights are forcibly classified into unmatched clustering centers due to the fact that an existing clustering method is poor in adaptability to weight distribution characteristics. According to the method, the posterior probability is obtained based on the weight of the target neural network and the discrete level number of the conductance state of the initialized target memristor array; performing hierarchical clustering based on a result obtained in the step 1 to obtain a corresponding clustering center; performing resistance drift pre-compensation based on all the clustering centers to obtain a corrected clustering center; and carrying out weight-conductance mapping deployment based on the corrected clustering center. The memristor array weight mapping method is used for memristor array weight mapping.

The invention discloses an efficient identification method for connection rigidity of a steel pipe pole of a power transmission line, which relates to the field of structural health monitoring, and comprises the following steps: constructing a multi-scale finite element model of the steel pipe pole, acquiring displacement and frequency response of the structure to carry out parameter sensitivity analysis, and screening key correction parameters; constructing a key parameter-structure response sample data set, and training and optimizing an FEA-Net neural network model; and constructing a Bayesian correction framework, solving posterior probability distribution by adopting a TMCMC algorithm, and realizing the probabilistic identification of the connection stiffness and the systematic correction of the finite element model. By constructing a local refined contact model, a bolt pretightening force-connection rigidity corresponding relation is established, and an effective means is provided for verifying whether the rigidity of the corrected model is matched with the rigidity of an actual structure or not. The structure rigidity damage position can be accurately recognized, the rigidity degradation degree can be quantified, and a high-credibility model basis is provided for safety evaluation, digital twinborn construction and operation and maintenance decision making of engineering structures such as electric transmission line steel pipe poles and the like.

The invention discloses a village-town crossing road maintenance construction safety risk dynamic evaluation method, and belongs to the field of artificial intelligence modeling and road engineering safety, and the method comprises the steps: building a risk index system comprising multi-level indexes; based on the risk indicator system, a dynamic Bayesian network model is constructed, and the model comprises a root node and a conditional dependency relationship between the nodes; determining the fuzzy probability of each root node in the dynamic Bayesian network model by adopting a fuzzy set theory in combination with an expert scoring method; the conditional probability of a non-root node in the dynamic Bayesian network model is corrected on the basis of a Leak Noise-or Gate expansion model; forward reasoning is carried out through a dynamic Bayesian network model, and a dynamic probability curve of the town-crossing highway maintenance construction safety risk is generated; backward reasoning is carried out through a dynamic Bayesian network model, and the posterior probability of the key risk factors is calculated. According to the method, the safety in the village-town-penetrating maintenance construction process can be guaranteed.

The invention discloses a shipping accident analysis method. The method comprises the following steps: constructing a shipping accident knowledge graph; inputting questions, retrieving the shipping accident knowledge graph by adopting a retrieval enhancement generation method, and generating answers; wherein the retrieval enhancement generation method is adopted to retrieve the shipping accident knowledge graph and generate the answer, and the method comprises the following steps of: analyzing a question by adopting a large language model to generate a query statement; retrieving in the shipping accident knowledge graph according to the query statement, outputting nodes and edges matched with the query statement from the shipping accident knowledge graph, and generating a Bayesian network according to the matched nodes and edges; generating a query result according to the conditional probability of a target node and a father node thereof and / or the posterior probability of a child node thereof in the Bayesian network, and inputting the query result into the large language model; and the large language model generates an answer according to the query result. The invention further discloses a shipping accident analysis device, computer equipment and a storage medium. The method comprises the following steps: extracting and storing shipping accident data by adopting a large language model in combination with a knowledge graph, and analyzing the shipping accident data in combination with a Bayesian network analysis model to obtain an occurrence probability of a complete causal chain from causes to accident results in an accident, so that objective and accurate accident causals are sorted out for complex shipping accidents, and the probability of occurrence of the complete causal chain is improved. And reliable conclusions and suggestions are provided for subsequent shipping businesses.

The invention provides a smart financial product recommendation method and device, equipment and a storage medium, and the method comprises the steps: firstly generating a key pair and a digital ID according to a user certificate, and constructing a safety identity identification system; and obtaining user financial data through the digital ID, obtaining a credit value by combining a credit calculation intelligent contract, and screening selectable financial products from a product library. And then the data is stored in a distributed manner according to business departments to form a multi-node computing architecture. And each node adopts a K-means + + algorithm to perform initial clustering and calculate model parameters, and optimal model parameters are determined through target function evaluation and neighbor node parameter migration. And calculating the posterior probability of the user data point based on the optimal parameter, determining the category of the user data point, and outputting a clustering result. And finally, generating a grouping label according to a clustering result, and screening and recommending financial products from the selectable products. According to the method, through multi-technology fusion, accurate grouping and recommendation are realized while the data security is guaranteed, and the recommendation efficiency and reliability are improved.

A decoding method includes: receiving and storing input data as a channel value in a channel value memory in a form of codeword; generating a posterior probability and a variable-to-check message according to a specific chunk of the channel value; converting the variable-to-check message from variable node domain into check node domain to generate a converted variable-to-check message; using a check node unit to generate a check-to-variable message according to the converted variable-to-check message; converting the check-to-variable message from check node domain into variable node domain to generate a converted check-to-variable message; determining whether to flip at least one bit of the specific chunk according to the posterior probability; and, determining whether to ignore and skip execution of decoding calculation for the specific chunk in a next iterative decoding operation according to whether a decoding calculation result in a current iterative decoding operation matches with a specific condition.

The invention discloses a data processing method, device and equipment for a QC-LDPC decoder. The method comprises the following steps: storing posterior probability information into a first memory; in the reading stage of the QC-LDPC decoder, soft information corresponding to a layer where a check matrix is located is read from a first memory or a second memory, a check node value corresponding to the layer where the check matrix is located is read from a third memory, and a variable node update value is stored in the second memory; and in a write-in stage of the QC-LDPC decoder, reading the variable node update value from the second memory, storing the check node update value to a third memory, storing the soft information update value to the first memory and the second memory, and storing the soft information update value to the second memory. And the decoding result is read from the second memory after iteration is completed. By optimizing caching and calling of information such as check nodes, variable nodes and soft information, use of memory resources is reduced.