159results about How to "Reliable technical support" patented technology

The invention discloses a dynamic control method for a complex curved surface five-axis numerical control machining tool vector, which comprises the following steps of: scanning the surface of a target workpiece by using a high-precision three-dimensional scanner to obtain point cloud data; based on the point cloud data, establishing a mathematical model S (u, v) of the target complex curved surface; a mathematical model based on the target complex curved surface; establishing a comprehensive dynamic model of the five-axis numerical control system; optimizing the initial tool posture in combination with the kinetic model; collecting dynamic operation data of the cutter and the workpiece; on the basis of the dynamic operation data, a model predictive control (MPC) algorithm is used for adjusting the machining path and the cutter direction in real time; according to the method, a comprehensive dynamic model of a five-axis numerical control system is established, dynamic coupling effects of a machine tool kinematic chain, cutter flexibility, cutting force, a temperature field and a stress field are brought into a unified framework, and complex dynamic behaviors are accurately described and controlled through multi-body dynamic modeling and an optimization algorithm.

The invention discloses a carbon emission index evaluation method and system based on a full-life-cycle power transmission and transformation project, and the method comprises the steps: S1, collecting data in the full life cycle of the power transmission and transformation project, including material consumption, energy use and equipment operation data in the stages of design, construction, operation, maintenance and demolition; s2, decomposing and optimizing a carbon emission path of each stage by adopting a hierarchical constraint path optimization algorithm; s3, introducing a self-correction energy consumption curve algorithm, and dynamically correcting the carbon emission data according to historical and real-time energy consumption data; s4, monitoring the energy consumption fluctuation in the operation maintenance stage, and automatically correcting the carbon emission deviation caused by the external environment change; s5, integrating hierarchical constraint path optimization and energy consumption curve data, and generating carbon emission assessment; s6, generating a standardized carbon emission evaluation report containing optimization suggestions; and S7, outputting an evaluation report, and supporting low-carbon planning implementation. According to the invention, the carbon emission management and optimization efficiency of the whole life cycle of the power transmission and transformation project is effectively improved.

The invention discloses a seismic source inversion data driven rock burst prediction method and system, and the method comprises the steps: carrying out the quantitative inversion calculation of the core parameters of a coal rock fracture seismic source mechanism through original sound wave data collected on site; a rock burst seismic source data prediction model is constructed based on an Informer algorithm, and the occurrence time and position of rock burst are accurately predicted. A rock burst risk evaluation model is constructed by combining the predicted occurrence time and position of a future fracture source and seismic source mechanism core parameters, the magnitude and property of rock burst are predicted, and a dual-drive rock burst intelligent model for seismic source data prediction and rock burst risk evaluation is established; and multi-dimensional intelligent prediction of sequential response, spatial distribution and evolution attributes in the rock burst catastrophe process is realized. According to the method, seismic source data prediction and risk evaluation are combined, the rock burst catastrophe risk can be predicted, and the rock burst evolution process and the disaster-causing mechanism can be visually represented.

The invention belongs to the technical field of live-action three-dimensional model construction, and particularly relates to a live-action three-dimensional model construction method based on ground multi-source heterogeneous point cloud fusion. The method comprises the steps of unmanned aerial vehicle image acquisition and three-dimensional modeling, ground multi-source heterogeneous point cloud data acquisition, ground multi-source heterogeneous point cloud registration, ground fusion point cloud processing, and unmanned aerial vehicle image three-dimensional model and ground fusion point cloud space registration and fusion modeling. According to the method, the unmanned aerial vehicle image and the ground multi-source heterogeneous point cloud are fused, so that the problem of detail missing in large-range scene modeling is solved. The high precision of the ground point cloud and the wide coverage of the unmanned aerial vehicle image are combined, and the integrity and fineness of the model are significantly improved. In addition, the point cloud color endowing technology enhances the sense of reality of the model, and provides efficient and reliable technical support for the fields of smart cities, cultural heritage protection and the like.

The invention provides a pile foundation buried pipe design optimization method, medium and system, and belongs to the technical field of ground source heat pump pile foundation buried pipe, and the method comprises the following steps: building a geologic model and a vibration parameter matrix by obtaining actually measured geology and vibration data of a to-be-placed pile foundation area; and a feature matching technology is utilized to find optimal matching in a preset typical soil database, clustering analysis is carried out, and the soil deformation amount of the region is determined. And in combination with load and vibration information of the pile foundation and the building, soil deformation in the whole using process is predicted. On the basis, an underground heat transfer model of the pile foundation buried pipe is established, and the heat exchange capacity of the ground source heat pump is calculated; meanwhile, the influence of the buried pipe on soil deformation is analyzed. And finally, the heat exchange capacity and soil deformation change serve as optimization targets, a game model is adopted for solving, and an optimal pile foundation buried pipe design scheme is obtained. In the whole process, the mode of combining measured data and modeling analysis is fully utilized, and intelligent optimization of pile foundation design is achieved.

The invention discloses an industrial control system and a control device, and relates to the technical field of industrial control, a data acquisition module obtains spraying path precision data and spraying thickness consistency data, and a data processing module analyzes and extracts a spraying path precision deviation value and a thickness drift speed abnormal value; the anomaly detection module comprehensively calculates a health score by using a machine learning model, evaluates the health state of the spraying robot and predicts a potential fault; the early warning module performs real-time early warning when fault prediction exceeds a threshold value, and the control module dynamically adjusts control parameters of the spraying robot, corrects the path and thickness and triggers maintenance suggestions through health state change trend prediction, so that the self-diagnosis capability and the control precision of the industrial control system are remarkably improved, potential problems can be found in the early stage, and the safety of the industrial control system is improved. The shutdown maintenance risk and the product rejection rate are reduced, so that the production efficiency and the product quality are improved.

The invention relates to the technical field of rural image inspection and recognition, and discloses a multi-source image collaborative inspection and recognition analysis system and method for a digital rural area, and the method comprises the steps: collecting multi-source image data in real time; obtaining a plurality of characteristic parameters corresponding to each image data item in the image data set, and carrying out space-time registration and multi-scale fusion processing on the plurality of characteristic parameters of each image data item; performing target detection and identification analysis on the plurality of feature parameters in the fusion feature parameter set, and constructing an abnormal point identification model; setting a plurality of abnormal point change thresholds according to the inspection coordinate data set for classification processing to obtain a plurality of abnormal point categories; and setting a corresponding co-processing scheme according to the plurality of abnormal point categories, and setting early warning information corresponding to the change trends of the plurality of abnormal point categories based on the co-processing scheme. According to the invention, the intelligent degree and response efficiency of rural inspection are improved, and the safety and sustainable development of digital rural construction are effectively guaranteed.

The invention belongs to the technical field of network security, and discloses an Internet of Things attack detection method based on deep learning, which eliminates redundant features through feature selection, enhances inter-class difference and intra-class similarity of selected feature subsets, solves the problem of class imbalance in an attack detection data set by utilizing EQLv2, and improves the attack detection efficiency. And meanwhile, constructing a space-time model to extract space-time information of the sample. According to the method, multi-classification performance evaluation is carried out on an Internet of Things attack detection reference data set NSL-KDD and a CIC-IDS-2017, the advantages of the method are verified from five indexes including the accuracy rate, the precision rate, the recall rate, the F1 score and the false alarm rate, and the effectiveness and feasibility of the method are reflected when the method is compared with a plurality of methods.

The invention provides a grouting curtain parameter design method for structure protection under the blasting vibration effect, and relates to the technical field of underground engineering and blasting engineering, and the grouting curtain parameter design method comprises the following steps: S1, determining main factors influencing the vibration velocity of a protection structure, and constructing a vibration velocity prediction model containing an undetermined coefficient by adopting a dimension analysis white golden-han theorem; s2, establishing a finite element blasting numerical model, and obtaining structural vibration velocity data under different parameter combinations; s3, fitting the structural vibration velocity data by using the vibration velocity prediction model, and determining an undetermined coefficient in the vibration velocity prediction model; and S4, a relational expression between the structural vibration velocity and stress is established, and grouting curtain design parameters meeting structural safety requirements are determined in combination with engineering conditions. By means of the method, the problem that the grouting curtain damping effect and grouting curtain parameters are difficult to determine in actual engineering is solved, the blasting vibration peak value of the protection structure under the blasting vibration effect is effectively reduced, and a guarantee is provided for underground and ground structure protection and blasting engineering safety construction.

The invention discloses a concentrator dynamic routing method and system. The method comprises the following steps: storing an equipment code of each collector to a remote monitoring terminal; the concentrator obtains the equipment coding information of all collectors in the jurisdiction of the concentrator from the remote monitoring terminal; constructing a hierarchical routing network according to the broadcast response condition between the concentrator and the collector; and dynamically selecting a relay node for a lower-layer collector according to the communication quality evaluation score. According to the invention, all collector nodes can be automatically identified and layered, and a flexible and adjustable network topology is constructed; dynamically selecting an optimal relay node by adopting a comprehensive calculation method of multi-dimensional communication quality parameters; when a communication environment or an equipment state changes, automatic reconstruction of a hierarchical routing network and dynamic optimization of a relay node are supported. The method can effectively improve the communication efficiency and network stability between the concentrator and the collector, and is widely applied to the fields of intelligent meter reading, industrial control, environment monitoring and the like.

The invention discloses a two-stage potential diffusion model underwater image enhancement method based on a frequency domain. The method comprises the following steps: inputting a degraded image and ambient light estimated according to the degraded image into a lightweight underwater image enhancement model to obtain a preliminary recovery image; inputting the preliminary recovery image into a high and low frequency feature pyramid encoder to obtain a potential spatial feature map set and an intermediate feature map set, and inputting the potential spatial feature map set and the intermediate feature map set into a high and low frequency prior extraction module to obtain high and low frequency images and high and low frequency image prior; inputting the low-frequency guidance priori and the potential spatial feature map set of the low-frequency image into a low-frequency guidance condition module to generate a priori guidance condition, and sending the priori guidance condition into a potential diffusion model to obtain a reconstructed potential spatial feature map set; and inputting the reconstructed potential spatial feature map set, the intermediate feature map set and the high and low frequency images into a high and low frequency feature pyramid decoder in prior to obtain an enhanced image. And improving the performance of the potential diffusion model and recovering a high-quality underwater image by using high and low frequency information priori knowledge of the underwater image.

The invention discloses a data acquisition system based on a multi-user multiplexing mechanism, and the system comprises the following modules: a 4G communication terminal module which is used for accessing a VPN network through a VPDN protocol; the industrial gateway module is used for connecting the 4G communication terminal and the middle station through a VPN network, supporting data acquisition and classified storage of southbound equipment, and cooperating with the middle station to realize multi-user data management; the middle station module is used for receiving, classifying and storing the data uploaded by the industrial gateway and providing decryption and data access services according to user permission; the user information management module is used for maintaining a user basic information table, a southbound equipment information table and a data point information table; the encryption and security module is used for performing classified storage and encryption processing on the user data and distributing an independent secret key; and the communication protocol module is used for realizing data encapsulation and transmission based on an MQTT protocol. By utilizing 4G communication, industrial gateway centralization and multi-user multiplexing technologies, efficient data acquisition and classified transmission are realized, and the system has the advantages of low cost, high data security and the like.

The invention relates to the technical field of pressure early warning, in particular to an intelligent early warning method and system for the pressure of a liquid storage tank, and the method comprises the steps: 1, carrying out the real-time collection of the early warning data of the liquid storage tank, and obtaining the monitoring parameters of the intelligent early warning of the pressure, 2, analyzing the monitoring parameters of the step 1, and obtaining the influence indexes of the pressure early warning of the liquid storage tank, 3, evaluating the liquid storage tank pressure fused with the influence index obtained through analysis in the step 2 to obtain a pressure fluctuation parameter, and 4, comparing the results obtained in the step 2 and the step 3 with a pressure threshold value and a pressure fluctuation threshold value respectively to obtain the liquid storage tank pressure fused with the influence index. And step 5, carrying out man-machine interaction on the intelligent early warning information of the pressure of the liquid storage tank obtained in the step 4. Through the big data technology and the sensor technology, real-time monitoring and early warning of the pressure of the liquid storage tank can be achieved, and the production safety is improved.

The invention discloses a vehicle high beam detection method and system, and relates to the technical field of computer vision, a YOLOv8 network is improved by designing a feature fusion global attention module (CGM), a convolution feature recombination module (CCM) and a small target detection module (CSTC), and a vehicle high beam data set is adopted to train an improved YOLOv8 target detection model, so that the detection accuracy of a vehicle high beam is improved. A model adaptive to an edge computing hardware platform is obtained through format conversion and quantization; the core number matched with the hardware resource specification is adopted in an edge calculation hardware platform, collaborative reasoning is carried out on the quantized model based on night road vehicle images obtained in real time, and a real-time vehicle high beam detection result is rapidly obtained. Through targeted optimization of the architecture and deployment mode of the vehicle high beam detection model, the speed and performance of the model deployed in edge computing equipment are effectively improved, and real-time robust vehicle high beam detection is realized based on edge computing.

The invention discloses a regional target and environment dynamic sensing method for construction site video monitoring, and the method comprises the steps: carrying out the initial deployment of distributed cameras for a construction site, and carrying out the deployment optimization; collecting video data based on Kafka Broker cluster multithreading, collecting environmental parameters, and performing fusion based on Mahalanobis distance to obtain fused environmental parameters; performing multi-target detection on the collected video data, and analyzing behavior characteristics of the collected video data to obtain video target data; environment parameter feature representation is extracted from the fused environment parameters, multi-modal dynamic fusion is carried out on the environment parameter feature representation and video target data based on learnable parameters, and a comprehensive situation is analyzed through a situation awareness model to obtain real-time comprehensive situation data; and performing real-time risk assessment on personnel behaviors and environmental conditions in the construction site by adopting Bayesian network analysis based on the real-time comprehensive situation data, identifying risk events, and automatically generating alarm information. According to the invention, the intelligent level of construction site video monitoring is obviously improved.

The invention discloses a standard section automatic identification method based on 3D ultrasonic data, and belongs to the technical field of medical imaging, and the method comprises the following steps: obtaining a 2D ultrasonic standard section image and 3D ultrasonic data; the obtained 2D standard section image is preprocessed, and corresponding parameters are set according to classification of different ultrasonic acquisition devices for batch automatic cutting; and meanwhile, automatic cutting is realized by adopting a maximum connected domain method, so that interference of a pure color background and an equipment interface is removed, and pure standard section data is obtained. According to the method, anatomical information is provided by utilizing 3D ultrasonic data, the neural network is guided to realize efficient standard section automatic identification, interference of a background and an equipment interface is removed by designing an automatic cutting method and a 2D data expansion method, a large number of 2D slice images are generated, and the richness of training data is increased. And a VGG16 and OC-CNN design network is combined, and the preprocessed and expanded 2D image is trained to obtain a model for automatically identifying the standard section.

The invention discloses an optimization method of a pea tissue culture regeneration system. The optimization method comprises the following steps: step 1, preparing pea aseptic seedlings; step 2, preparing an explant; step 3, inducing cluster buds; 4, carrying out bud elongation culture; step 5, rooting culture; and 6, domesticating and transplanting regenerated seedlings. By systematically optimizing the tissue culture conditions of the peas, an efficient regeneration system is established and is suitable for multiple pea varieties, and the problem of low regeneration efficiency in the prior art is solved. By means of the optimization method, researchers can obtain high-efficiency regenerated pea plants in a short time, the success rate and rooting efficiency of tissue culture regeneration are improved, meanwhile, the survival rate of regenerated seedlings is increased, and an important technical guarantee is provided for molecular breeding and gene modification of peas and other leguminous crops.

The invention relates to the technical field of house detection, in particular to an urban building house safety detection method and device, equipment and a storage medium. The method comprises the following steps: acquiring an image, temperature distribution data, humidity distribution data and structural deformation data of a to-be-detected urban building house; preprocessing the data, inputting a preset identification model to obtain a structural damage position of the urban building house, and determining a potential safety hazard area; generating a safety assessment report according to the structural damage position and the potential safety hazard area; according to the method, the safety evaluation report is returned to the detection equipment of the field staff, and the report content is displayed on the detection equipment, so that the real-time performance and accuracy of detection are improved, the detection operation efficiency of the field staff is improved by predicting potential safety hazards, and the problem of safety detection lag in the prior art is effectively solved.

The invention discloses a crop disease and pest identification method based on an agricultural production service platform. The method comprises the following steps: collecting crop data information; the crop data information comprises crop environment data, crop remote sensing image data and crop physical image data; performing feature extraction on the crop environment data, the crop remote sensing image data and the crop physical image data to obtain an environment feature vector, a remote sensing image feature vector and an image feature vector; performing feature splicing on the environment feature vector, the remote sensing image feature vector and the image feature vector to obtain a comprehensive feature vector; inputting the comprehensive feature vector into a disease and insect pest recognition model for model training to obtain a trained disease and insect pest recognition model; inputting to-be-detected crop data information into the trained pest and disease identification model, and outputting a pest and disease identification result of the to-be-detected crop. According to the invention, rapid and accurate identification and dynamic monitoring of plant diseases and insect pests can be realized.

The invention discloses an operation method of a high-voltage transformer substation tubular bus drainage wire detaching and erecting robot, and relates to the technical field of power construction. Comprising the steps of obtaining parameter data outside a tubular busbar drainage wire; a positioning model is constructed, the positioning model is based on the parameter data, an operation plan of the disassembly and assembly robot is output, and the operation plan comprises an operation position, an operation path and operation time; constructing a detection model, and obtaining environment data influencing the operation of the dismounting and building robot outside the tubular busbar drainage wire based on the detection model and the parameter data; obtaining attribute data of the disassembly and assembly robot; and constructing an influence discrimination model. According to the method, the attribute data and the environment data are efficiently matched through the feature tree diagram to form an accurate mapping relation, so that it is ensured that the disassembly and assembly robot can accurately recognize and understand the working environment in the working process, the data processing efficiency is improved, and the adaptability of the robot to the working environment is further enhanced.

The application discloses a kind of multifunctional low-temperature degradation corn stalk composite microbial inoculant, preparation method and application, the composite microbial inoculant is compounded by multiple microorganisms with straw degradation, potassium solution and promoting function, comprising the following strains: bacillus subtilis K15, curvularia geniculata L11 and bacillus mycoides L31.The composite microbial inoculant constructed in the application realizes synergistic effect and functional complementation, through strain scientific compounding, straw decomposition rate is significantly improved, and decomposition period is obviously shortened;Enhance the adaptability and colonization competitiveness of microorganisms in winter low temperature and complex soil environment.The composite microbial inoculant of the application also has multiple functions such as straw degradation, potassium solution and IAA production, etc.At the same time of releasing nutrients by rapid decomposition of straw, it can convert difficult-to-use soil potassium into available potassium.

The invention belongs to the technical field of deep learning, and particularly relates to an ozone concentration prediction method based on a CNN-AM-BiLSTM residual network. Comprising the following steps: acquiring historical conventional pollutant and meteorological factor data of a monitoring station and carrying out data cleaning; carrying out feature extraction on the input multi-dimensional data by the convolutional neural network; a multi-head attention mechanism is constructed to help the model to better pay attention to important features, so that the feature representation capability is enhanced; the BiLSTM performs time modeling on the extracted features, so that the long-term dependency relationship in the time sequence data is better captured; a residual network is constructed to relieve the gradient disappearance problem, so that the model better learns nonlinear mapping of input data; the characteristics output by the BiLSTM are mapped to the target output space through the full connection layer, the invention provides a practical and effective method for ozone concentration prediction, the ozone concentration can be accurately predicted, and effective technical support is provided for atmospheric environment treatment.

The invention relates to the technical field of electric power Internet of Things, in particular to a time-space hierarchical data compression method and system for multi-source heterogeneous data on a power distribution network side, and the method comprises the following steps: S1, carrying out the classification and lightweight compression processing of original data, and outputting intermediate data with a priority identifier; s2, based on the intermediate data obtained in the step S1, a gateway layer carries out topological grouping according to the electrical distance, and then carries out time alignment and weighted fusion on the data in the group to generate a fusion data packet; and S3, based on the fusion data packet obtained in the step S2, the cloud platform performs panoramic insight and value evaluation, generates and dynamically issues an optimization strategy instruction, and the strategy instruction is dynamically issued back to the edge layer and the gateway layer through a control channel. According to the method, the real-time advantage of edge calculation and the big data processing capability of a cloud platform can be fully exerted, modules of all levels work cooperatively, a set of complete power distribution network multi-source heterogeneous data processing solution is formed, and the overall efficiency of the system can be effectively improved.

The invention discloses a power battery pack health state monitoring system, and relates to the field of battery pack health state monitoring, and the system comprises a vehicle information input module which is used for receiving vehicle unique identification information inputted by a user; the standard data acquisition module is used for acquiring historical operation and charging and discharging data corresponding to the unique identification information of the vehicle from a cloud data source; the data fusion and feature construction module is used for constructing a battery state evaluation feature set; the battery health assessment module is used for constructing a battery pack health state assessment model and outputting a battery health state assessment result; the safety risk grading module is used for carrying out grading evaluation on the potential safety risk of the vehicle battery; and the report generation and display module is used for generating a structured detection report. The method has the advantages that the full life cycle of the power battery pack from real-time health state accurate evaluation to future safety risk grading early warning is realized, and intelligent monitoring can be explained.

The invention provides a multifunctional turbine system comprehensive experiment table and method, the multifunctional turbine system comprehensive experiment table comprises a steam generation system, an electric control system, a turbine structure, a dynamic balance rotor structure, a damping unit, a sensor and a data acquisition system, the steam generation system is used for providing high-temperature and high-pressure gas; the electric control system is used for providing power; the turbine structure is used for converting kinetic energy of high-temperature and high-pressure gas provided by the steam generation system into mechanical energy; the dynamic balance rotor structure is used for carrying out dynamic balance calibration; the damping unit is used for providing damping for the dynamic balance rotor structure; according to the invention, various complex working conditions can be simulated, and synchronous monitoring and comprehensive analysis of different parts of the turbine system are realized, so that reliable technical support is provided for industrial production and equipment maintenance.

The invention provides a three-dimensional fluorescence spectrum analysis method based on unsupervised contribution modeling. The method comprises the following steps: acquiring an original three-dimensional fluorescence spectrum of a sample; carrying out independent component analysis on the three-dimensional fluorescence spectrum, and reconstructing the spectrum by utilizing an analysis result; and carrying out modeling on the reconstructed spectrum by using a multivariate curve resolution-alternating least square algorithm to obtain an analysis report. According to the method, interference signals such as scattering in an original three-dimensional fluorescence spectrum are automatically removed through combination of two two-dimensional unsupervised modeling algorithms, then modeling is carried out on a reconstructed spectrum, dependence of three-dimensional fluorescence spectrum analysis on data, tools, platforms and personnel is reduced, good fault tolerance and accuracy are guaranteed while flexibility is improved, and the method is suitable for popularization and application. And reliable technical support is provided for water quality monitoring.

The invention discloses a multi-sensor fused all-weather operation target sensing and measuring system and method, and belongs to the field of intelligent sensing and measuring, the system comprises a data acquisition module, an all-weather image enhancement module, a three-dimensional modeling and semantic segmentation module and a motion detection module, and the method comprises the following steps: performing data acquisition on an operation target; performing all-weather image enhancement processing on the acquired image data; performing image semantic segmentation on the enhanced image; performing point cloud semantic modeling by using a result after image semantic segmentation in combination with laser point cloud and IMU (Inertial Measurement Unit) data; and using a result after image semantic segmentation to assist optical flow method calculation to realize motion detection. According to the invention, the limitation of a single sensor can be overcome, the sensing and measuring capabilities of the system in a complex environment are improved, and all-weather and all-time high-precision sensing and measuring tasks are realized. More reliable technical support is provided for application in the fields of automatic driving, unmanned aerial vehicles, intelligent robots and the like, and more intelligent sensing and measurement are achieved.

The invention discloses an MEMS vibration sensor resonant frequency self-checking method, system and device, and the method comprises the following steps: employing time domain continuous signal excitation as a response input signal, and collecting a dynamic response signal of an MEMS vibration sensor; establishing a dynamic response mathematical model of the MEMS vibration sensor; the collected dynamic response signals serve as identification data, and resonant frequency parameters in the dynamic response mathematical model are subjected to high-precision identification through a BP neural network optimized through an improved particle swarm optimization algorithm; and calculating the mechanical sensitivity of the MEMS vibration sensor based on the identification result of the resonant frequency parameter. The invention relates to the technical field of MEMS vibration sensor dynamic model parameter identification, and can effectively improve the identification precision of the resonant frequency of an MEMS vibration sensor.

The invention discloses a method and system for predicting the fatigue life of a rusted steel bar, and the method comprises the steps: considering the influence of a physical field factor on the crack propagation of the steel bar, and constructing a fatigue life prediction correction factor; performing nonlinear regression fitting on the fatigue life prediction correction factor to obtain an expression of the correction factor; setting the expression of the correction factor as a correction item to the Paris formula to form a corrected Paris formula; and performing integral processing on the corrected Paris formula to obtain a fatigue life prediction model. According to the method, the accuracy and applicability of steel bar corrosion fatigue life prediction can be remarkably improved.