659 results about "Selection algorithm" patented technology

The invention discloses a construction progress detection system and method based on three-dimensional model and field data fusion, and relates to the technical field of power grid construction engineering. The system comprises a data acquisition module, a data preprocessing module, a data matching module, a data fusion module, a progress detection module and a feedback module, the data matching module matches point cloud data with a panoramic image, a clonal selection algorithm is applied to optimize matching parameters, and on-site three-dimensional basic data is constructed; the data fusion module performs deep fusion on the BIM model and on-site three-dimensional basic data, and constructs a three-dimensional fusion model for construction progress detection by using a GeoBIM technology; and the progress detection module compares the real-time acquired panoramic image of the construction site with the three-dimensional fusion model frame by frame, and automatically detects the construction progress, so that efficient acquisition, accurate processing and real-time monitoring of construction site data are realized, and the efficiency and decision ability of construction management are remarkably improved through automatic progress detection and real-time feedback.

The invention provides a software supply chain risk detection protection method and system, and the method comprises the steps: obtaining a third-party component from a software supply chain, and analyzing the multi-level dynamic behaviors of the third-party component; predicting a risk behavior pattern by using a Bayesian network algorithm, and constructing a behavior pattern library; monitoring a behavior of the third-party component during operation, and recording a behavior data stream; identifying an abnormal behavior deviating from a normal behavior mode by applying a time sequence anomaly detection algorithm of deep learning; evaluating risk levels of the abnormal behaviors by using a graph neural network, and generating an abnormal behavior list in combination with relevance and propagation paths among the behaviors; according to the list, a response strategy in the safety control measure library is dynamically adjusted through an adaptive strategy selection algorithm, and safety control measures are formulated for the third-party component. According to the invention, the identification and prediction capabilities of potential risk behaviors are improved.

The invention provides a semiconductor data analysis and visualization method and device based on a large model, and relates to the technical field of data processing. The method comprises the steps that demand information input by a user is analyzed through a large language model, and key information corresponding to the demand information is obtained; the key information comprises time information, industry information, index information and geographic information; determining a target data set most relevant to the key information from a preset database by utilizing a data set automatic selection algorithm; determining a visualization strategy of the demand information according to the demand information and the target data set; and according to the visualization strategy and the large language model, generating a visualization code corresponding to the visualization strategy, and executing the visualization code to automatically generate a visualization chart. According to the scheme, the problems of automatic analysis and visualization of complex data in the semiconductor feasibility research report are solved, dynamic data display is supported, and the data visualization effect is improved.

The invention discloses a numerical control lathe tool wear monitoring method and system. The method comprises the steps that numerical control lathe tool operation process data are monitored in real time; a corresponding preset operation parameter threshold value is matched according to the operation process data, and when it is monitored that the preset operation parameter threshold value is exceeded, the operation process data of the numerical control lathe tool is transmitted to an edge computing node; matching a corresponding preset operation parameter combination and finishing operation parameter screening according to the operation process data by utilizing the edge computing node; based on the screened operation parameters, key features are selected and extracted by using a feature selection algorithm, and an optimal feature set is acquired and uploaded to a cloud platform; and matching a corresponding tool wear prediction model according to the operation process data at the cloud platform, inputting the optimal feature set into the matched tool wear prediction model, obtaining a wear value, a wear degree and a life prediction result, and performing early warning decision and early warning prompt. According to the invention, efficient, accurate and real-time tool wear monitoring under complex working conditions is realized.

The invention discloses a task length prediction-based large language model reasoning calculation service energy consumption optimization scheduling method, which comprises the following steps of: firstly, reasoning by taking an Alpaca-52k instruction data set as an input source and a large language model (such as Llama3-8B), counting the number of output tokens of the large language model, and labeling each piece of input data; then, a Qwen2-1. 5B large language model is finely tuned by using an Alpaca-52k instruction data set and the response length of Llama3-8B reasoning, and computing resources of the prediction method are reduced on the premise that the prediction performance is guaranteed; then, the response length of the Llama3-8B reasoning task is predicted through the fine-tuned Qwen2-1. 5B model, task balanced sorting scheduling is carried out according to the response length so as to improve the large language model reasoning speed, and finally, a deep reinforcement learning power selection algorithm is used to reduce the calculation power as much as possible on the premise that the large language model reasoning task time delay is met so as to improve the large language model reasoning efficiency. Therefore, the energy consumption of large language model reasoning calculation is reduced.

The invention provides a zero-set differential privacy self-adaptive federated learning system oriented to Non-IID data. The system comprises a central server and a plurality of clients, the central server maintains a global contribution value list in the federation training process and sends contribution values back to the corresponding clients; according to the contribution values, clients meeting conditions are screened out through a client filtering mechanism and are used for carrying out gradient weighting and generating global model updating; in local model training, each client uses a self-adaptive sliding window mechanism to divide a data set sampling and training range, selects an optimal learning rate through a self-adaptive learning rate selection algorithm, selects a gradient cutting threshold value of each round by using a self-adaptive gradient cutting threshold value mechanism, and performs local model training. And the privacy budget allocated to each round of gradient updating is adaptively adjusted through an adaptive differential privacy federated learning algorithm according to the received contribution value. According to the method, the problem of how to consider both data privacy protection and model precision optimization in a non-independent identically distributed (Non-IID) data environment is effectively solved.

The invention discloses a cross-chain fragmentation scheduling method based on a DAG, and belongs to the technical field of cross-chain fragmentation scheduling. Comprising the following steps: constructing a transaction dependency graph DAG model; a node selection algorithm based on out-degree is adopted, and nodes with high priorities are selected from nodes without preorder in the directed graph for scheduling; the FAST-MCTS algorithm is adopted to allocate transactions to the optimal fragment, and the number and delay of cross-fragment communication are minimized; and traversing the dynamic DAG by adopting an ODS-BFS scheduling algorithm, and preferentially processing nodes with relatively high degree. According to the method, cross-chain transactions are modeled through the dynamic DAG, the dependency relationship between different transactions is captured, it is ensured that the transactions are executed according to the dependency sequence, and the rollback probability occurring in the cross-chain transaction process is avoided; according to the method, the transactions with the dependency relationship are scheduled to the same fragment, so that the conflict of cross-fragment transactions is reduced, the method adapts to the number of fragments of different scales, and a lower cross-fragment proportion and higher throughput are kept.

The invention relates to the technical field of wind power equipment monitoring, and discloses a wind power gear box intelligent fault early warning method and system based on machine learning. The method comprises the steps that multi-source monitoring data such as vibration signals, temperature data and oil analysis data of the wind power gear box are acquired, and multi-scale operation characteristics are extracted through time-frequency conjoint analysis; key fault sensitive features are determined through an adaptive feature selection algorithm, and a dynamic fault feature weight matrix is constructed in combination with a historical fault case library; multi-modal data fusion is adopted to generate an enhanced fault feature set, and modal decomposition is carried out on the enhanced fault feature set to obtain a trend component and a fluctuation component; a fault evolution feature space is constructed by using a deep neural network based on two components, then a fault development mode is identified by using a time sequence mode matching algorithm, and finally a graded early warning signal is generated according to a matching degree with a preset mode, so that fault features can be comprehensively captured, and safe operation of a wind power gear box is ensured.

The invention provides a digital twin hydrological station system which is characterized in that the system comprises a layered architecture system and a multi-dimensional safety protection mechanism, the layered architecture system is composed of a sensing layer, a transmission layer, a data layer, a model layer, an application layer and a display layer from bottom to top, the sensing layer comprises a heterogeneous hydrological monitoring equipment cluster, and the transmission layer comprises a heterogeneous hydrological monitoring equipment cluster. The equipment cluster is provided with a water level sensor array, a Doppler flowmeter, a spectrum water quality analyzer and an environment monitoring terminal, and total element acquisition of hydrological element data, equipment operation state data and environment data is realized; the transmission layer adopts a hybrid communication protocol stack, integrates an ultra-short wave communication module, an NB-IoT narrowband communication module and an optical fiber communication module, and constructs a multi-path redundant transmission channel, and a key data transmission channel is configured with a dynamic link selection algorithm; the data layer comprises a distributed storage architecture, adopts a mixed storage mode of a relational database cluster and an unstructured database, and is internally provided with a data cleaning engine and a spatio-temporal data fusion module.

The invention discloses an inland river water transportation facility monitoring data secure transmission method based on a block chain technology, and the method comprises the following steps: selecting an optimal sensor for data collection according to the geographic position and operation environment of a facility through a dynamic sensor switching mechanism, and eliminating errors caused by equipment faults and environment interference, after the collected data is subjected to time-space aggregation processing and classified according to geographic areas and time windows, based on a block chain node selection algorithm, an optimal node is selected for data transmission, the data is encrypted by adopting a hierarchical encryption strategy, the encrypted data is stored in a block chain network, and the data is transmitted to the block chain network through a threshold-triggered smart contract. And monitoring the stored data in real time, and automatically adjusting a monitoring scheme or starting an emergency mechanism when the stored data exceed a preset threshold value. The invention aims to improve the security and integrity of monitoring data transmission.

The invention relates to a priority scheduling generation method and system for Beidou satellite short message communication, and the method comprises the steps: collecting multi-dimensional features, such as message service types, user identities, spatio-temporal information, channel states and the like, dynamically calculating the priority score of each message through a configurable weighting model, and then, carrying out the dynamic calculation of the priority score of each message; the method comprises the following steps of: abstracting a message into a conflict graph vertex, establishing an edge according to a receiving conflict relationship, distributing candidate message sets which are not conflicted with each other for each scheduling time slot by adopting an independent set selection algorithm, and performing joint optimization on a message sending sequence and power distribution by using a genetic algorithm in each time slot by taking a maximized comprehensive utility value as a target, and generating a final scheduling instruction. Through dynamic evaluation, conflict avoidance and resource collaboration, intelligence and self-adaption of a scheduling strategy are realized, and the guarantee capability of a system for key messages and the overall resource utilization efficiency are remarkably improved.

The invention relates to the technical field of power transmission line monitoring, and discloses a power transmission line monitoring data transmission method based on Lora and 5G hybrid networking, and the method comprises the steps: determining a network access mode through a hybrid networking selection algorithm, and generating a communication link through dual-module networking configuration; original monitoring data are processed by using a data framing compression algorithm, and a path is selected and verified through dynamic routing optimization; the modulation format is adjusted through the space-time alignment data of the multi-modal data fusion model and the adaptive modulation technology; optimizing transmission by using an abnormal data filtering algorithm and a dynamic priority scheduling program; and feedback learning mechanism evaluation performance and updating algorithm parameters. According to the invention, network adaptability, data transmission efficiency and reliability are improved, data fusion processing is optimized, adaptive optimization of transmission performance is realized, operation and maintenance cost is reduced, and safe and stable operation of a power system is guaranteed.

The invention relates to the technical field of unmanned aerial vehicle assisted edge computing, and discloses a deep neural network task-oriented collaborative reasoning and unmanned aerial vehicle trajectory optimization algorithm, which comprises the following steps of S1, establishing a multi-unmanned aerial vehicle assisted mobile edge computing system model; s2, providing an optimal segmentation point selection algorithm, selecting an optimal segmentation point for each user task, and determining a task unloading proportion; s3, providing an unmanned aerial vehicle and ground user matching algorithm, and allocating computing resources of the unmanned aerial vehicle; and S4, modeling the unmanned aerial vehicle trajectory optimization and ground user transmitting power selection problem as a Markov decision process, providing a dynamic DNN task unloading and trajectory optimization algorithm based on deep reinforcement learning, and obtaining the optimal trajectory of the unmanned aerial vehicle and the transmitting power of the ground user in each time slot by training an intelligent agent. According to the algorithm, rewards can be continuously improved, and delay and energy consumption of DNN tasks in the MEC system are effectively reduced.

The invention provides a cue word optimization method and system based on an approximate sub-module function and continuous learning, and relates to the technical field of artificial intelligence multi-mode perception.The method comprises the steps that a candidate cue word set is constructed, a combined objective function based on the property of the approximate sub-module function is designed, and a candidate cue word set is constructed; solving the combined objective function by adopting a greedy selection algorithm combining random disturbance, multi-round iteration and a task self-adaptive mechanism, realizing optimal selection of a candidate cue word set, gradually selecting a cue word with the maximum gain from the candidate cue word set, adding the cue word into an optimal subset, and when a new cue word is selected, selecting the cue word with the maximum gain into the optimal subset. And if the target cue words are selected, carrying out local optimization once, after all the target cue words are selected, carrying out joint optimization on all the cue words in a continuous space by adopting an alternate optimization strategy, and carrying out continuous iteration until the optimized cue words are obtained. According to the method and the device, efficient self-adaptive updating of the cue words of the language model is realized, so that the generalization performance and robustness of the model in zero-sample, few-sample and concept drift scenes are improved.

The invention relates to a distributed photovoltaic ultra-short-term prediction method and system based on satellite cloud picture information, and belongs to the technical field of photovoltaic power prediction. Firstly, the correlation degree of meteorological variables and distributed photovoltaic power is measured through a Pearson correlation coefficient, and cloud layer distribution and movement characteristics thereof are analyzed and found to be key factors influencing photovoltaic output precision, so that a cloud cluster movement prediction framework under multiple time scales is constructed: a CNN-LSTM hybrid model is adopted to capture a nonlinear evolution rule of a cloud cluster; secondly, a feature cloud region dynamic selection algorithm is innovatively designed by means of a satellite cloud picture data source, an ultra-short-term distributed photovoltaic power prediction model based on the convolutional neural network is constructed by accurately quantifying a cloud layer shielding effect and combining the convolutional neural network, and the prediction precision is remarkably improved by effectively fusing cloud motion features.

The invention provides a 5G message transmission method and system based on privacy protection, and relates to the technical field of communication. The method comprises the following steps: encrypting the content of a to-be-sent 5G message to generate an encrypted message; dividing the encrypted message into a plurality of encrypted message fragments, generating corresponding confusion information for each encrypted message fragment, and encrypting the sensitive metadata to form a first data packet; generating a second data packet containing anonymous verification information based on the first data packet; determining a plurality of transmission paths by using a multi-path selection algorithm in combination with a routing selection strategy, and performing task allocation for each transmission path by using a distributed hash table to obtain an allocation result; and performing scattered transmission on all the second data packets according to the distribution result, so that the message receiving equipment performs recombination and decryption processing on all the second data packets to recover the 5G message content. According to the technical scheme provided by the invention, privacy protection is enhanced, and the security and reliability of 5G message transmission are ensured.

The invention discloses a peasant household credit assessment method and system based on multi-modal data federated learning, and relates to the technical field of computer intelligence, and the method comprises the steps: constructing a dual-channel representation mode of land assets based on GIS space coding and property and age limit information, and generating multi-dimensional feature input of peasant household production capacity and asset conditions in combination with multi-source data; a three-level federated learning structure is constructed, data is safely exchanged through TLS communication encryption and equipment identity authentication, and differential privacy noise is injected; a Top-K gradient selection algorithm is adopted to carry out parameter sparse transmission, a sparse rate is dynamically adjusted in combination with a round increasing strategy, a grouping sparse convolution and parameter freezing mechanism is introduced, and a federal deep learning model is compressed and an adaptive edge is deployed through 8-bit distillation quantification; according to the method, multi-modal feature construction, layered federated training and a privacy enhancement mechanism are fused, and fine modeling, lightweight deployment and data security collaboration of peasant household credit assessment are realized.

A smart shopping cart identifies items using cameras and sensors. The cart captures images of items within its storage area and applies machine-learning models, such as a barcode detection model, an OCR model, and an image embedding model, to generate identifier predictions. These predictions are processed using an efficient selection algorithm, which may involve majority voting, weighted voting, or linear regression, to select the most accurate identifier. The cart updates its display and user interface with the identified item. The process may be performed primarily by the CPU to enhance computational efficiency, avoiding the latency associated with GPU data transfer. Additional techniques, such as circular buffers and frame skipping, are employed to further optimize resource usage.

The invention relates to a fault prediction method for a mining high-voltage power distribution device. Temperature, current, voltage and gas leakage sensors are installed in the mining high-voltage power distribution device; integrating the multi-dimensional data by using a multi-sensor data fusion algorithm to form a real-time operation portrait of the power distribution device; extracting time sequence characteristics of temperature and voltage key variables by combining an operation rule of the high-voltage power distribution device; capturing abnormal fluctuation of the key characteristic parameters by using a sequence analysis model, and identifying a specific fault type in the high-voltage environment; constructing a feature mode database based on a time sequence, and matching new data with a historical fault mode; applying a feature selection algorithm to extract representative fault prediction features from the sensor data; establishing a fault prediction risk level model according to the hazard degrees of different fault modes; based on the fault risk level predicted in real time, an optimized self-adaptive maintenance scheme is generated, and the maintenance period and strategy are dynamically adjusted in combination with the actual operation state of the equipment.

The invention belongs to the technical field of software development and testing, and discloses an artificial intelligence-based software development automatic test case generation system, which is characterized in that an immune heuristic case self-repairing module is adopted, defects are regarded as antigens, antibody cases capable of being self-updated are generated by using a clone selection algorithm, and a gene rearrangement mechanism is automatically triggered when an interface is changed, so that the test efficiency is improved. The details of the use case are adjusted while the core detection logic is reserved; compared with a traditional method, the mechanism can realize use case dynamic adaptation without manual intervention, the maintenance workload is remarkably reduced, and the method is particularly suitable for a complex software system with frequent iteration; the space-time coupling test scene generation engine fuses dynamic scenes such as interaction and state transition of a module and short-time operation after precise coverage login by using a space-time convolutional network; the cross-dimension holographic use case synthesis module integrates multi-source data such as codes, hardware and user behaviors through tensor decomposition to generate a composite use case; functions and performance of software in a complex scene can be comprehensively verified, and test blind areas are remarkably reduced.

The invention relates to the field of edge computing, in particular to a task migration method and device based on an edge computing system, equipment and a medium. By acquiring the current service resources of each service node of the edge computing system, available resources of the edge nodes and the cloud can be mastered in real time, resource finiteness consideration is met, and a complex and high-consumption algorithm is avoided. Task execution data of a target user terminal is accurately captured, and an initial task migration strategy and initial global time delay are comprehensively analyzed in combination with service resources, which means that a decision can be flexibly made according to the actual situation of a task and the current state of a node. When edge node performance fluctuates and a queuing task changes, a strategy is dynamically generated based on real-time data, a switching decision is enabled to accord with a network working condition, and the problems of lack of dynamic adaptability and mismatching of a node selection algorithm and edge equipment in the prior art are solved.

The invention belongs to the technical field of artificial intelligence and medical diagnosis, and particularly relates to an interpretable Transform medical diagnosis method based on prototype learning, and the method comprises the following steps: data preprocessing; extracting prototype features; constructing a model; optimizing an attention mechanism; key value pair storage: storing the key value pair as a parameterized prototype; designing a loss function; an Adam algorithm is selected to train the model, and the learning rate and batch size hyper-parameters are adjusted according to the actual situation; and evaluating and optimizing the model. According to the method, a prototype learning mechanism is introduced, the attention mechanism of the Transform model is optimized, and the innovative method overcomes the defect of insufficient model interpretation in the prior art. By combining the prototype features and the attention weight, the model can more accurately position key information, and the accuracy of medical diagnosis is improved.

The invention provides a single tree segmentation-biological parameter estimation method based on a vehicle-mounted LiDAR point cloud, and belongs to the technical field of urban landscaping intelligent monitoring. The point cloud individual tree segmentation algorithm based on tree geometric feature constraint is designed for solving the problem that individual tree segmentation is difficult due to crown overlapping in an urban scene, and the method takes a tree geometric structure as a constraint, combines point cloud reflection intensity information, a clustering algorithm, a main direction index and other means, and obtains the individual tree segmentation algorithm based on the tree geometric feature constraint. And accurate extraction of trunks and crowns in the scene point cloud is realized. Aiming at the problems of high feature redundancy, poor model interpretability and the like in an existing estimation method, a random forest model is taken as a basis, an adaptive feature selection algorithm is introduced to improve variable screening efficiency, hyper-parameters are dynamically adjusted by utilizing pigeon inspired optimization, and generalization and stability of the model are enhanced; an estimation model used for estimating biological parameters such as leaf area index, biomass and carbon reserve is designed.

The invention discloses an unmanned aerial vehicle non-cooperative environment autonomous landing method and system based on three-dimensional visual semantic information, and relates to the technical field of unmanned aerial vehicle landing recognized.The method comprises the steps that during emergency landing, a semantic segmentation model is used for generating a two-dimensional semantic segmentation map and obtaining semantic tags, and a landing area is automatically screened and tracked; fusing vision and inertia information through an airborne binocular camera and an IMU (Inertial Measurement Unit), outputting an accurate pose and depth map, and inputting a map construction algorithm to reconstruct a landform map of a landing area; projecting the semantic tag to a three-dimensional space and fusing the semantic tag with a terrain map to generate an evaluation map; and finally, an optimal landing point is determined by a landing point selection algorithm, and autonomous landing is completed through a flight control system. The method has the characteristics of high precision and fast reasoning, gets rid of dependence on external signals, significantly improves the landing capability under complex terrains, and guarantees the safety and reliability of autonomous landing.

The invention relates to the technical field of remote sensing data processing and earth surface parameter inversion, and discloses an earth surface matrix parameter hyperspectral data inversion method and system based on machine learning. Comprising the following steps: constructing a multi-source heterogeneous hyperspectral data set; performing feature screening on the preprocessed hyperspectral data set based on an adaptive band selection algorithm, constructing a dynamic weight matrix by calculating mutual information entropy and inter-class distance measurement between spectral bands to realize intelligent screening of key feature bands, and combining spectral derivative conversion and spectral index calculation to generate an enhanced feature vector; and a multi-task transfer learning neural network model is constructed, and an output layer realizes multi-parameter collaborative inversion based on a multi-task learning architecture. And performing preprocessing and feature enhancement operation which is the same as that of the training data on the hyperspectral image data of the to-be-inverted region, inputting the trained neural network model, and outputting a surface matrix parameter inversion result.

The invention discloses a probe position selection method and system, and solves the problem in the prior art that the precision is low when signal spatial characteristics are reconstructed through a probe antenna. The probe position selection method comprises the following steps: determining a target channel model, and extracting target channel parameters; the target channel parameters comprise time, frequency and space parameters; determining a reconstruction criterion, and calculating a target reconstruction parameter according to the target channel parameter; determining at least one coordinate as a probe position; calculating the weight of the probe at each position according to the target reconstruction parameter, and determining a reconstruction error; the reconstruction error is a difference value between a target reconstruction parameter and a target channel parameter; and iterating the probe position, and determining the probe position corresponding to the lowest reconstruction error. According to the probe position selection algorithm provided by the invention, the required calculation amount can be reduced, and the spatial feature precision of the probe antenna reconstruction signal can be effectively improved.

A storage device may be coupled to a host device and the storage device may support a host performance booster (HPB) feature. A controller on the storage device may execute a background relocation operation. During the background relocation operation, the controller may identify a block to relocate data from. The controller may also determine that a relocation system threshold has not been reached. The controller may execute a source selection algorithm configured to minimize HPB entry inactivation resulting from data relocation. The source selection algorithm may include a criterion to enable the controller to select a source block for relocation.

The invention provides a distributed photovoltaic data acquisition method and device based on adaptive encryption communication and multi-link redundancy, and the method comprises the steps: collecting real-time power generation data, equipment state data and environment parameters of a photovoltaic system through a multi-source sensor, and forming original data; an MQTT over TLS adaptive encryption communication protocol is adopted to perform end-to-end encryption on original data, and dynamic key management is combined to ensure transmission security; based on a dynamic link selection algorithm, encrypted data is transmitted to a power master station through 4G and LoRaWAN double-link redundancy, so that the transmission reliability is improved; and the master station side decrypts the data and then integrates the data to a database to support power grid dispatching and predictive analysis. According to the invention, the problems of safety and reliability in distributed photovoltaic data acquisition are solved, and the method is suitable for a smart power grid monitoring scene with high reliability and high real-time performance requirements.

A system (100) for processing medical image data regarding a clinical question uses patient data (110), medical image data (120) and clinical guidelines (170). The system has a processor subsystem (160) to determine, based on user input (140), a use case regarding the clinical question for a patient. The processor retrieves the patient data and image data regarding the use case to determine an anatomical object. Then, the processor selects an algorithm from an algorithm repository (AM) based on the determined anatomical object and executes the algorithm on the image data to provide the output data for assessing the clinical question. The processor may also use the codified clinical guidelines regarding the use case to select said algorithm. Effectively the most appropriate algorithm is executed for the specific use case.

The invention discloses a reliability parameter assisted low-complexity polarization code rate matching method, and belongs to the technical field of wireless communication. Aiming at the problem that a proper punching site is difficult to select during rate matching due to a recursive structure of a polar code, the invention provides a polar code punching site selection algorithm based on a reliability parameter. An information bit reliability parameter is defined to calculate the reliability of candidate punching bits, and the optimal punching position is selected by traversing and solving the minimum value of the maximum reliability parameter. In order to solve the problem of high complexity of the existing polar code rate matching method, an interleaving algorithm and a pruning serial offset list decoding algorithm based on rate matching parameter mapping are provided, secondary sorting calculation of an interleaver is avoided, and a calculation unit updating strategy of a decoder is simplified. The polarization code rate matching method provided by the invention not only can improve the rate matching coding gain, but also can effectively reduce the calculation complexity.