50 results about "Recursive computation" patented technology

The invention provides an unmanned aerial vehicle dual-polarized antenna multipath interference suppression and polarization mismatch joint optimization algorithm, and relates to the technical field of low-altitude communication, and the algorithm carries out the coupling modeling of a multipath interference problem and a polarization mismatch problem through building a unified dual-polarized channel model which considers multipath propagation and time-varying polarization characteristics; an adaptive polarization state estimation method based on received signal covariance matrix feature analysis is provided; a combined space-time-polarization domain adaptive filter structure is designed; a joint optimization objective function containing a minimum mean square error and a maximum polarization matching degree (MPM) is constructed; a low-complexity adaptive weight updating rule based on a recursive least square (RLS) thought and integrated with polarization constraints is deduced. According to the algorithm, interference suppression and polarization matching are collaboratively optimized through a unified framework, the complexity is reduced by adopting recursive calculation, rapid convergence and strong environmental adaptability are realized, and the low-altitude communication performance of the unmanned aerial vehicle is remarkably improved.

The invention discloses a cache optimization method for a cell-free multiple-input-output environment based on a VPPO algorithm, and relates to the technical field of edge cache optimization. Multiple independent environment instances are generated and run in parallel; training the strategy network based on the plurality of independent environment instances and the value network to obtain a trained strategy network; and inputting the cache proportion of the current edge server into the trained strategy network to obtain the adjustment of the cache proportion of the edge server. A complex dynamic scene is simulated through multi-environment parallel interaction, multi-scene data are collected in parallel, and the adaptability and decision-making ability of the model to dynamic changes are improved. According to the method, a non-Markov environment is adapted by using Retract advantage estimation, and the problem of deviation of traditional advantage estimation in such scenes is solved by truncation weight and recursive calculation and processing of the condition that state transition depends on history, so that the advantage function calculation is more accurate, a foundation is laid for strategy optimization, and the performance of a model in a complex dependency relationship scene is improved.

The invention discloses a geotechnical engineering investigation safety monitoring system and method based on artificial intelligence, and relates to the technical field of rock-soil body deformation safety monitoring, and the method specifically comprises the following steps: obtaining multi-source data including deformation, stress and environmental data of a rock-soil body, and building a model input data source; performing data fusion processing; a data fusion processing result is used as input, and a rock-soil body stress and deformation incidence relation is obtained through data simulation; the method comprises the following steps: respectively constructing a short-term recursive calculation model and a long-term trend analysis model through a time sequence prediction method based on deep learning by taking a rock-soil body stress and deformation incidence relation as a substrate; and obtaining a real-time multi-source data value, obtaining a displacement prediction vector of the rock-soil body according to the short-term recursive calculation model, comparing the displacement prediction vector with a preset threshold value, performing real-time early warning when the displacement prediction vector exceeds the preset threshold value, and visualizing a long-term trend analysis result. According to the invention, the engineering safety is enhanced, and the long-term stability and reliability of infrastructures are improved.

This invention discloses a two-end reliability assessment method and system for SDN based on OBDD. Leveraging the centralized control and easy programming characteristics of SDN, this method establishes an SDN two-end reliability assessment model. By designing the topology management module and path calculation module within the network model, a two-end minimum path set that meets bandwidth and latency constraints is obtained. A minimum path set-breadth-first search algorithm is used to determine the variable ordering of the BDD. The corresponding OBDD is constructed using the Shannon formula in the OBDD construction module. Finally, the reliability assessment module recursively calculates network reliability from the bottom up, completing the SDN two-end reliability assessment. This method combines methods such as the constrained adjacency endpoint matrix algorithm, the minimum path set-breadth-first search algorithm, and the ordered binary decision diagram (OBDD) to assess connectivity reliability in SDN, demonstrating good scalability.

The invention relates to a monitoring data recursive calculation method and application thereof in a CCUS project. An estimation error used for describing the difference between an estimation value and a true value and a measurement error used for describing the difference between a measurement value and the true value are introduced; the comprehensive error is obtained by dividing the previous estimation error by the sum of the previous estimation error and the current measurement error; an initial estimation value and an initial estimation error are randomly given, then an initial comprehensive error is estimated, then a subsequent estimation value is calculated through a recursive algorithm, and the initial comprehensive error is updated to be used for calculation of the subsequent estimation value. After a plurality of rounds of measurement and calculation, an estimated value is closer to a real value, and all measurement data in the earlier stage does not need to be backtracked, so that the problems of a large amount of repeated redundancy and large error of monitoring data are effectively reduced; and the method provided by the invention is used for realizing carbon dioxide leakage monitoring in a CCUS project.

The invention provides a binary program vulnerability mining method, device and system based on symbolic execution and taint analysis and a medium, and the method comprises the steps: disassembling a target program, extracting a control flow graph (CFG) and a data flow graph (DFG), recording a node state through a hash table, and generating a feature representation through nonlinear transformation; performing simulation execution based on CFG, DFG and feature representation, recording variable symbol values to obtain path conditions, and recursively solving constraints to generate path mapping; marking input as taint data, recursively calculating a propagation path to generate a taint flow diagram, and determining a taint state after sensitive operation; checking whether the stains are subjected to sensitive operation or not, and if the influence of integral formula calculation exceeds a threshold value, judging that potential vulnerabilities generate a candidate set; and calculating grades through a risk assessment formula, and generating a report containing positions, types, grades and repair suggestions. The method combines symbolic execution and taint analysis, can comprehensively and accurately detect vulnerabilities, has remarkable precision and efficiency advantages, and is suitable for complex program security analysis.

The invention relates to a pseudorandom sequence generation method and device and a medium, and the method comprises the steps: calculating weight vectors of different powers through modular operation based on a primitive polynomial of a Galois field, and obtaining a bit weight value of each bit; splitting the offset value into a plurality of accumulations in the form of power of 2 by using a binary decomposition method, and obtaining an initial vector at the offset position by combining the bit weight value at each bit; according to the bit width generation requirement, the one-dimensional weight matrix is expanded into a two-dimensional generation matrix based on the principle of a digital root method; based on the two-dimensional generation matrix, recursively calculating matrixes under different bit weights by using matrix power operation, and generating weight matrixes under different processing bit widths; generating a pseudorandom sequence starting point according to a mode of multiplying the initial vector at the offset position by the weight matrixes under different processing bit widths; and generating the pseudorandom sequence from the starting point of the pseudorandom sequence by using a recursive formula. According to the invention, waste of storage resources and bandwidth resources can be avoided.

The application discloses a kind of based on artificial intelligence's geotechnical engineering survey safety monitoring system and method, it is related to rock-soil mass deformation safety monitoring technical field, specifically including the following steps: obtaining multi-source data, including the deformation of rock-soil mass, stress and environmental data, model input data source is established;Data fusion processing is carried out;With the result of data fusion processing as input, through data simulation, the stress and deformation correlation of rock-soil mass is obtained;Based on deep learning, with the stress and deformation correlation of rock-soil mass as basement, through the method of time series prediction, short-term recursive calculation model and long-term trend analysis model are respectively constructed;Real-time multi-source data value is obtained, the displacement prediction vector of rock-soil mass is obtained according to the short-term recursive calculation model, compared with the preset threshold value, when exceeding the threshold value, real-time early warning is carried out, and the long-term trend analysis result is visualized.The application enhances engineering safety, improves the long-term stability and reliability of infrastructure.

This invention discloses a method, system, equipment, and medium for calculating pressure loss in perfluorohexanone (PFH) pipeline networks, relating to the field of computer-aided design technology. The method traverses the component tree of the pipeline network building information model using a three-dimensional geometry engine to form a set of pipeline network features; constructs a directed graph topology representing the flow direction of the pipeline network, calculates potential energy loss, converts local resistance into equivalent length, and constructs a weighted directional topology weight matrix; obtains hydraulic balance under single-phase flow conditions through iterative calculation of node simultaneous equations, and generates the corresponding static pressure distribution results of the pipeline network; recursively calculates the dryness and density changes of the gas-liquid mixture using the equation of state, introduces a two-phase flow friction multiplication factor for nonlinear compensation, outputs a three-dimensional dynamic compensation pressure field model, and performs rendering and digital judgment; this solution solves the problem of low accuracy in pressure loss calculation caused by the inability of existing technologies to accurately simulate the flash evaporation effect of PPHH, thus improving the reliability of pipeline network design.

The invention discloses a storage pool creation method and device, equipment, a medium and a product, which are applied to the technical field of storage, and comprise the following steps: obtaining the number of fault domain units and the number of copies; when the number of the fault domain units is greater than the number of the copies, judging whether each fault domain unit meets a first preset capacity balance condition or not based on the minimum effective capacity and the expected effective capacity corresponding to each fault domain unit; if the first preset capacity balance condition is not met, grouping recursive calculation is carried out on each fault domain unit to obtain a target effective capacity, and whether a second preset capacity balance condition is met is judged based on the target effective capacity and the expected effective capacity; and under the condition that a second preset capacity balance condition is not met, creating a main pool based on the target effective capacity and creating an auxiliary pool based on the residual capacity. Therefore, the utilization rate of storage resources can be improved.

The invention provides a time sequence coupling constraint embedded power resource scheduling method and system and a storage medium, and relates to the technical field of energy management, and the method comprises the steps: obtaining a classical security constraint economic scheduling model; recursive calculation is carried out on the classic security constraint economic dispatching model; wherein in the forward propagation process of the recursive calculation process of the neural network model, parameters of the neural network at all time points are kept consistent, and meanwhile, the output of the calculation unit of the current time step serves as the input of the calculation unit corresponding to the next time step so as to connect all the calculation units; embedding the constraint condition into an output end of a set neural network model, and constructing a gradient return information highway; and training a set neural network model by using the power training data, and adjusting neural network parameters to obtain a power scheduling model so as to output a power resource scheduling scheme. Through the coupling calculation unit, the parameter sharing and the recursive structure, the calculation efficiency is remarkably improved, and the power resource scheduling scheme can be quickly generated.

The application discloses a kind of man-machine mapping and deft operation method and system based on virtual hand drive, form complete closed loop from hand perception to deft hand control.It includes: image is collected with monocular RGB camera, after normalization etc., 21 three-dimensional coordinates of joint of hand are predicted through multi-task deep neural network, then data is smoothed through dynamic filtering;Particle swarm optimization algorithm is used to fit the shape parameters of MANO hand model;Based on weighted singular value decomposition, the rotation matrix of root joint is calculated, and the global rotation matrix of non-fingertip joint is calculated recursively combined with hand topology structure;Local coordinate system of hand is established, and angle mapping function is designed to convert joint angle into deft hand control quantity;Through double buffering mechanism interpolation processing low frame rate perception result, hybrid control is realized by fusing current data.The scheme improves precision, robustness, adaptability and safety, and provides technical support for efficient and reliable human-computer interaction.

The invention belongs to the technical field of carbon management, and particularly relates to a product carbon footprint modeling method and system. According to the method, through the dynamic construction and optimization mechanism of the node example network, the modeling precision and calculation efficiency of carbon emission data in a complex production process are remarkably improved, and compared with a traditional data model based on a tree structure, the scheme not only solves the problems of unclear data hierarchy semantics, low storage efficiency, high recursive calculation complexity and the like, but also has the advantages of being high in practicability and the like. The dynamic adjustment capability of the carbon emission tracking model is enhanced, so that the model can better adapt to frequent change of the production process, verification of the emission proportion of main and by-products and integration of multi-dimensional carbon emission data are realized, reliable data support is provided for carbon management decision, and meanwhile, through a computing resource dynamic scheduling mechanism between heterogeneous subgraphs, the calculation efficiency is improved. The stability and the response speed in a large-scale carbon footprint calculation scene are effectively improved, and the period of carbon footprint evaluation is greatly shortened while the calculation precision is ensured.

This application relates to a nonlinear recursive calculation and monitoring method for relative blood volume. The method includes acquiring prior baseline parameters, discrete laboratory data sequences, and real-time physiological data streams; performing nonlinear dimensional compression mapping on continuously fluctuating parameters to generate dynamic mapping factors; calculating biochemical characteristics for discrete sampling parameters and triggering neutral degradation assignment to generate static modulation factors when anomalies occur; constructing a nonlinear decaying negative feedback term based on the previous blood volume state value and performing truncation processing to generate an adaptive saturation factor; fusing the above factors to update the unconstrained blood volume; and using numerical pinch intervals for physical boundary constraints to obtain the relative blood volume monitoring value. This application has the advantages of improving the system's anti-interference capability in high-frequency data sampling environments, blocking the propagation of missing data, and effectively suppressing system computational divergence and numerical oscillations.

The application discloses a large-scale data analysis method based on a DBSCAN algorithm, which comprises the following steps: step one, calculating an adjacency list through ring communication; step two, locally preliminarily determining a temporary cluster number of a core point; step three, alternately broadcasting a synchronous core point cluster number; and step four, updating a boundary point cluster number through reverse ring communication. The application can well solve the efficiency problem of the DBSCAN algorithm in large-scale data clustering analysis, ingeniously decomposes the calculation step dependence problem of the recursive calculation process of the DBSCAN algorithm, makes the algorithm applicable to a distributed execution model, makes the work load of different nodes in a cluster as balanced as possible, greatly reduces the communication amount in distributed calculation, and uses a non-blocking communication mode to make the calculation and communication overlap, reduces idle waiting time, fully utilizes the memory of a calculation node, and makes the massive data clustering analysis on the cluster become a reality.

The application discloses a user residence point identification method and device, electronic equipment and storage medium. Target signaling is added to each user in first user signaling data obtained, and second user signaling data is obtained. The target signaling is signaling with maximum time stamp for start time and end time and invalid values for longitude and latitude. Recursive calculation is performed on the second user signaling data through a recursive algorithm. A plurality of residence segments corresponding to each user are identified according to a distance threshold and a time threshold. The signaling length of each signaling in each residence segment is used to assign a corresponding weight to each signaling, and the offset calibration residence point of each residence segment is calculated. Finally, the offset calibration residence point of each residence segment and the second user signaling data are judged through the recursive algorithm, and the corresponding residence point of the plurality of residence segments of each user is output. Thus, the accuracy of identifying the residence state of the user can be improved.

The invention relates to the field of data analysis and intelligent decision, and particularly discloses a multi-level contribution deconstruction and aggregation method for an element system based on machine learning, which comprises the following steps: extracting element observation data, generating a standardized element feature vector, and forming a system feature matrix; training a relational machine learning model by using the system feature matrix and the overall efficiency data, and automatically generating a multi-level contribution deconstruction tree; selecting nodes in the contribution deconstruction tree for verification, and dynamically adjusting and optimizing the model and the tree structure according to the deviation between a predicted value and an actual value to obtain a final contribution deconstruction tree; based on the final contribution deconstruction tree, contribution value weighted aggregation is carried out from bottom to top, and the comprehensive contribution rate of each element is calculated through recursion. According to the method, the complex contribution relationship is automatically and objectively deconstructed through machine learning, the accuracy and adaptability of the model are ensured by means of verification of a closed loop, the quantitative contribution rate with a clear structure and high interpretability is finally output, and the reliability and decision support value of a calculation result are remarkably improved.

Circuits and methods for computing an order N polynomial include V decimation stages, each stage including respective multiply-and-accumulate circuitry. The multiply-and-accumulate circuitry in each stage k, in response to an input r-term and a plurality of input z-terms 0 through (Nk−1), generates output z-terms 0 through (Nk / 2−1) and an output r-term as a square of the input r-term. Each output z-term i is a sum of input z-term (2i+1) of the input z-terms and a product of input z-term 2i and the input r-term. The multiply-and-accumulate circuitry in decimation stages k for k≤(V−1) provides the output r-term and one or more output z-terms from decimation stage k as the input r-term and one or more input z-terms to the respective multiply-and-accumulate circuitry of decimation stage k+1. A recursive stage inputs from decimation stage V, the output r-term as a recursive r-term and the output z-terms as a-terms, and generates a polynomial output value z by a recursive evaluation of the recursive r-term, the a-terms, and a modulus, p.

The invention is notably directed to computer-implemented method of planning motion of a vehicle. The method comprises receiving (S20) real-time signals as to positions of N traffic participants. At each time step of multiple time steps, the method plans (S50) motion for the vehicle by computing (S30) states of each of the N traffic participants according to the signals received (S20). Said states include current states, which are estimated for each of the N traffic participants, as well as future states of each of the participants, wherein the future states are predicted over a prediction horizon T. This is achieved using an interaction-aware model of the N traffic participants. This model is designed to cause the method to recursively compute (S32-S38), at said each time step, the states of each of the N traffic participants according to a hierarchical list. The N traffic participants are ordered in the list from a most determinative one to a least determinative one of the N traffic participants. As a result, the states of the η-th participant in the list are computed based on the states of each of the η−1 participants in the list, ∀η∈[2, . . . , N]. This model is further designed to cause the method to update and sort (S40) the hierarchical list based on rules of the road, wherein the rules are evaluated based on the states of each of the N traffic participants as computed at said each time step. The invention is further directed to related computerized systems, vehicles, and computer program products.

The invention discloses an electric power system scheduling method and device based on multi-dimensional full-pure embedding approximate dynamic programming, terminal equipment and a storage medium, and belongs to the field of electric power systems.The method comprises the steps that optimization constraints are constructed, and an optimization model represented by a plurality of decoupled value functions is constructed; converting each decoupled value function into a corresponding Lagrange function, and then solving a gradient to obtain a plurality of KKT conditions; embedding preset complex parameters into each KKT condition to obtain a plurality of polynomial equation sets; calculating a zero-order coefficient of each polynomial equation set, and performing recursive calculation on a non-zero-order coefficient of each polynomial equation set; and obtaining an approximate expression of a value function corresponding to each polynomial equation set according to the obtained coefficient solution of each order, obtaining a solution of an optimization model according to the approximate expression, and scheduling the power system. By implementing the method and the device, the defects of low efficiency and poor effect in solving the scheduling problem of the power system in the prior art can be solved.

The invention discloses a system performance analysis and evaluation method based on a fuzzy function dependence network, and the method comprises the following steps: constructing a performance evaluation model based on the fuzzy function dependence network through the analysis of an equipment system, and the fuzzy dependency strength and the fuzzy dependency criticality of the dependency relationship of each pair of nodes and the autonomous efficiency value of each node are specified, and the output efficiency value of the nodes is calculated through hierarchical step-by-step recursion, so that the comprehensive evaluation of the behavior efficiency of the system is realized. According to the method, attribute parameters are processed through the triangular fuzzy number and the fuzzy logic, and the defect that values are too subjective and absolute when two parameters of dependency intensity and dependency criticality of the node dependency relationship are determined during system performance analysis and evaluation based on a traditional function dependency network is overcome. The fuzziness of manually set parameters is effectively reflected, the scientific reliability and accuracy of analysis are improved, and the system efficiency analysis and evaluation work can be scientifically, reasonably and comprehensively implemented.

This invention belongs to the field of carbon management technology, specifically relating to a product carbon footprint modeling method and system. Through a dynamic construction and optimization mechanism of node example networks, this invention significantly improves the modeling accuracy and computational efficiency of carbon emission data in complex production processes. Compared to traditional tree-based data models, this solution not only solves problems such as unclear data hierarchy semantics, low storage efficiency, and high complexity of recursive calculations, but also enhances the dynamic adjustment capability of the carbon emission tracking model, enabling it to better adapt to frequent changes in production processes. It achieves the verification of emission ratios of main and by-products and the integration of multi-dimensional carbon emission data, providing reliable data support for carbon management decisions. Simultaneously, through a dynamic scheduling mechanism of computational resources among heterogeneous subgraphs, it effectively improves stability and response speed in large-scale carbon footprint calculation scenarios, significantly shortening the carbon footprint assessment cycle while ensuring computational accuracy.

The application discloses a storage pool creation method and device, equipment, medium and product, applied to the storage technical field, including: obtaining the number of fault domain units and the number of copies; when the number of fault domain units is greater than the number of copies, whether each fault domain unit meets the first preset capacity balance condition is judged based on the minimum effective capacity corresponding to each fault domain unit and the expected effective capacity; if the first preset capacity balance condition is not met, the target effective capacity is obtained by grouping and recursively calculating each fault domain unit, and whether the second preset capacity balance condition is met is judged based on the target effective capacity and the expected effective capacity; in the case of not meeting the second preset capacity balance condition, the main pool is created based on the target effective capacity, and the auxiliary pool is created based on the remaining capacity. In this way, the utilization rate of storage resources can be improved.

The application relates to the technical field of database operation and maintenance, in particular to an automatic operation and maintenance method for an audit system database, which comprises the following steps: periodically collecting the inventory of each level of an LSM-Tree and a threshold value, and calculating a level capacity occupation ratio; using an eBPF technology to count the background I / O time consumption and flow in a sliding window, and calculating the background disk writing unit time consumption; performing recursive calculation in the reverse order direction by using the level capacity occupation ratio, so as to obtain a full-link logical write multiple; analyzing the coupling relationship between the level capacity occupation ratios of adjacent levels, determining a multi-layer concurrent congestion coefficient, obtaining a unit data estimated occupation time length, reversely deducing a front-end allowed write rate, and combining a token bucket algorithm to smooth the flow limiting of the front-end audit write request. The application aims to realize the early perception of the internal backlog trend of the LSM-Tree, and automatically perform predictive flow control, so as to optimize the operation and maintenance management of the audit system database.

The application belongs to the technical field of power electronic real-time simulation, and aims at the problems of insufficient simulation accuracy of traditional pure CPU simulation, limited simulation scale of FPGA simulation, communication bottleneck and poor flexibility of "CPU+FPGA" hybrid simulation, and proposes a power electronic converter small step real-time simulation method and system. The core is to build a multi-port equivalent model based on the switching function method or the improved node method, and to equivalent the nonlinear part of the converter into a controlled source and fuse the linear passive network; adopt the "CPU unique calculation + FPGA special communication" architecture, FPGA nanosecond level acquisition PWM pulse sequence transmission to CPU, CPU through the main step nesting small step recursive calculation, in the microsecond level main step, reproduce the hundred nanosecond level electrical dynamic. The matching special system deeply optimizes the CPU multi-core parallel ability, the communication cost is low, realizes the accuracy, the efficiency and the scale unification, the cost is low, the flexibility is high, can support the large-scale high switching frequency system high precision simulation, is applicable to the new energy grid connection, direct current transmission and other scenes.

This vehicle mass estimation method includes: detecting an acceleration generated in a vehicle in the front-rear direction; setting, on the basis of the state of the vehicle, a value of a forgetting factor for past data to be forgotten; varying the value of the forgetting factor according to the elapse of time; and estimating a vehicle mass by recursive computation based on an recursive least square method using the forgetting factor on the basis of a vehicle model including the vehicle mass, the driving force of the vehicle, and a detected value of the acceleration.

The embodiments of the present application provide a detection method, apparatus, device, and medium for a multi-input multi-output system, and relate to the field of communication technology. The method includes: searching a code tree to obtain a surviving path, including: if the number of parent nodes is equal to the search breadth threshold, for each parent node, retaining a branch with a minimum distance increment value as a surviving path, and obtaining a search breadth threshold surviving path; recursively calculating the path metric value of the surviving path, using the number of surviving paths as the number of parent nodes of the next layer, until the bottom layer is searched; merging the symbol vector elements on the surviving path to obtain an estimated symbol vector, and demapping the estimated symbol vector to obtain a decision bit sequence; for each decision bit in the search breadth threshold decision bit sequence, calculating the log likelihood ratio according to the minimum path metric value corresponding to the decision bit, and obtaining soft information. The embodiments of the present application can reduce the detection complexity.

The invention relates to the technical field of image analysis, in particular to a facial occlusion image redrawing network method, which comprises the following steps: migrating the time sequence modeling advantage of RWKV to the field of image redrawing to realize semantic perception and identity consistency reconstruction of an occlusion area. Reconstructing a calculation normal form of a WKV attention mechanism and a token conversion layer, and decoupling recursive calculation based on time steps in RWKV into feature interaction of spatial dimensions, so that the feature interaction adapts to structural characteristics of a two-dimensional image; a window circulation WKV attention module is designed, multi-direction feature propagation is simulated through sub-window scanning and a bidirectional circulation mechanism, and local fine-grained texture and global structure constraint modeling are synchronously captured in a single-layer network; according to the method, a multi-scale token transfer layer is constructed, and the context dependency relationship of fine structures such as interbrow wrinkles and lip lines is re-calibrated and strengthened through cross-scale features, so that the problem of detail homogenization caused by occlusion is avoided, and the redrawing effect of a facial occlusion image is improved.

The present invention discloses a polarization code decoding method for correcting insertion and deletion errors, including: mixing information bits of length K and all-zero fixed bits of length N-K according to a preset position index of information bits and fixed bits to obtain a bit sequence; encoding the bit sequence to obtain a transmission sequence of length N; after passing through the IDS channel, the transmission sequence generates a received sequence of length N<supgt;*< / supgt>; a weighted edit distance-based SC decoder corrects insertion and deletion errors in the received sequence and outputs an estimate of the information sequence. The present invention introduces a drift amount into the recursive calculation of the channel transition probability for the insertion and deletion-replacement channel, improves the recursive structure of the traditional SC decoding algorithm, and uses the weighted edit distance to measure the channel transition probability, obtaining good error correction performance and improving the accuracy of information transmission.

The invention provides a forward measurement normalization method and system for CCSDS131.0-B-3 standard Turbo decoding, electronic equipment and a storage medium, and the method and system are used for carrying out the systematic simulation analysis of the value change behavior of forward measurement Alpha of each state in the decoding process of a CCSDS131.0-B-3 standard Turbo code frame. Obtaining statistical characteristics of each state which becomes a forward measurement Alpha maximum value state in a forward recursion process; the method comprises the following steps: based on statistical characteristics, screening out a fixed state from all states of a CCSDS131.0-B-3 standard Turbo code grid diagram as a reference state for forward measurement Alpha normalization in the whole decoding process; when recursive calculation of forward measurement Alpha is carried out by a Log-MAP algorithm, for each moment t of a decoding process, a measurement value of a reference state at the current moment t is subtracted from non-normalized forward measurement values of all states at the current moment, so that normalization operation of the forward measurement Alpha of all states at the moment is completed.