41results about How to "Good prediction accuracy" patented technology

The invention discloses a vehicle driving track prediction method based on a large language model, and belongs to the technical field of automatic driving. The invention aims to provide a vehicle driving track prediction method based on a large language model, which is used for inputting a vehicle track into the large language model in a text format and evaluating the capability of the large language model in understanding the time dynamic aspect of a traffic agent. According to the method, tracks of different vehicles in a traffic scene are encoded by using a track encoder, track encoding features are mapped into a text embedding space of a large language model by using a reprogramming adapter, and the encoding features are translated into lexical combination which can be understood by the large language model. According to the method, powerful support is provided for realizing reliable and safe driving decisions by the large language model.

The invention provides a new energy transmission project input-output data mapping relation determination method, and belongs to the technical field of new energy transmission projects, and the method comprises the steps: carrying out the standardization processing of historical input data (construction, equipment and operation and maintenance) and output data (output electric energy and electric energy quality); then constructing an equipment parameter matrix, and obtaining an equipment feature vector through fast Fourier transform; a principal component analysis method is adopted to extract main characteristic components, and core equipment characteristic vectors after dimension reduction are constructed. Establishing an interaction influence matrix, determining a weight coefficient of each equipment parameter, constructing an equipment parameter comprehensive evaluation index based on the weight coefficient, and establishing an integrated mapping relation with input-output data; and finally, constructing a multi-input multi-output deep neural network, taking the core equipment feature vector as input, taking input-output data as output, and performing network training based on the integrated mapping relation to obtain a final mapping relation capable of fully considering the mutual relation between different data.

The invention relates to a method for predicting physical and chemical properties of crude oil, and the method comprises the following steps: obtaining library sample spectrums of a plurality of crude oil samples, and obtaining respective standard data of a first physical and chemical property and a second physical and chemical property; taking the first n main factors of the first characteristic spectrum and the standard data of the first physicochemical property of the crude oil sample as a first variable, taking the standard data of the second physicochemical property of the crude oil sample as a second variable, and performing partial least square regression analysis to obtain a correction model; collecting a to-be-detected sample spectrum of a to-be-detected crude oil sample, and obtaining detection data of a first physicochemical property of the to-be-detected crude oil sample; substituting the first n main factors of the second characteristic spectrum and the detection data of the first physicochemical property of the crude oil sample to be detected into the correction model to obtain the prediction data of the second physicochemical property of the crude oil sample to be detected. The method disclosed by the invention has a relatively wide range of physicochemical property prediction capability and better prediction precision.

The invention relates to the technical field of data analysis, and discloses a slurry pump fault detection and service life prediction system and method based on big data. According to the method, firstly, a slurry pump vibration signal is obtained, an abnormal vibration signal is detected and deleted, and then noise reduction processing is conducted on the slurry pump vibration signal; secondly, after the denoised slurry pump vibration signal is subjected to segmented aggregation approximation and Grubby angle field processing, a slurry pump vibration signal image is generated, the characteristics of the slurry pump vibration signal image are extracted, a convolutional neural network is trained, convolutional neural network parameters are optimized based on an electric eel foraging optimization algorithm, a convolutional neural network recognition model is obtained, and a recognition result of the convolutional neural network is obtained; fault detection of the slurry pump is realized; and decomposing the IMF component of the slurry pump vibration signal, training the LSTM neural network to obtain an LSTM neural network prediction model, and outputting a slurry pump life prediction value. The purposes of fault detection and service life prediction are achieved by processing the vibration signal of the slurry pump, and the method is accurate and objective.

The invention discloses an automobile part intelligent production control and quality early warning system based on an AI model. Comprising a data acquisition module, a production process monitoring module, an AI analysis engine module, an adaptive control module, a quality early warning module, an equipment collaborative scheduling module, a data governance and model training module, a user permission and audit module and a remote operation and maintenance and knowledge precipitation module. According to the system and the method, an artificial intelligence algorithm and an industrial automation control technology are fused, and deep perception, dynamic regulation and control and defect pre-recognition of a production process are realized. The prediction precision of the method is superior to that of a traditional statistical process control method in a plurality of actual production scenes.

The invention discloses a CBN grinding wheel non-contact type grinding high-temperature alloy taper hole abrasion prediction method, and relates to the technical field of state monitoring and intelligent prediction in the precise grinding machining process. The method mainly comprises the following steps: calibrating a dynamic contact arc acoustic propagation model by using a system identification method to obtain dynamic geometric parameters, and constructing acoustic-vibration geometric coupling characteristics in combination with multi-mode signals in a grinding process; correcting the acoustic-vibration geometric coupling features by using a maximum mean difference algorithm and an adaptive window algorithm, training a wear prediction model by using the corrected acoustic-vibration geometric coupling features, and performing regression analysis on the corrected acoustic-vibration geometric coupling features by using the trained wear prediction model to obtain a prediction result. According to the grinding wheel abrasion prediction method for high-temperature alloy taper hole grinding, the adaptability, robustness and sensitivity of abrasion state characterization under complex variable working conditions can be improved.

This invention relates to the field of medical data-driven dose prediction technology, and discloses a method, system, device, and storage medium for individualized dose prediction of hypothyroidism based on medical data. The method includes: constructing a standardized feature vector based on the child's weight, age in days, corrected age in months, current L-T4 dose, TSH value, FT4 value, previous TSH value, TSH rate of change, feeding method, month of consultation, etiology of hypothyroidism, comorbidity status, previous dose adjustment magnitude, and age at which TSH first reached target levels; extracting TSH dynamic trajectory features from the child's TSH time-series data from previous follow-ups; obtaining a basic recommended dose using a gradient boosting decision tree model constructed with counterfactual filtering training data; and correcting the basic recommended dose to obtain an individualized recommended dose. This method improves the prediction accuracy of the gradient boosting decision tree model and allows the individualized recommended dose to simultaneously take into account multiple clinical confounding factors.

An alloy performance prediction method fusing vision-language-process multi-modal data comprises the steps that a multi-modal data set of an alloy material is constructed, and the multi-modal data set comprises three kinds of modal input including process parameters such as temperature and time of solid solution and aging treatment, SEM image visual information and SEM image description text information and corresponding alloy mechanical property true values; training a visual encoder ResNet50 model through comparative learning and training a language encoder BERT model through mask language modeling to obtain an SEM image and vector codes of language description of the SEM image; splicing and fusing process parameters such as temperature and time of solid solution and aging treatment and the vector codes obtained in the second step, and training a random forest regression device according to corresponding alloy mechanical properties; and the random forest regression device obtained through training is used for alloy performance prediction. According to the method, the structured process data, the unstructured SEM image data and the derived text description data are subjected to collaborative fusion and joint modeling for the first time, complementarity among different modal data is fully utilized, and more comprehensive and more three-dimensional digital representation of the alloy state is constructed; the multi-modal fusion framework and the feature learning mechanism are suitable for wide material systems. Meanwhile, the adopted'depth representation + random forest 'hybrid model has the advantage of high training efficiency while ensuring high prediction precision.

The invention discloses a photovoltaic power prediction method and system of an improved RFNN-QLSTM based on VECM, and relates to the field of photovoltaic power prediction, and the method comprises the steps: carrying out the preprocessing of original photovoltaic data; constructing a vector error correction model, capturing an equilibrium regression relationship between the photovoltaic data and the meteorological factors and between the meteorological factors, introducing an error correction term on a vector autoregression model to identify a co-integration relationship and short-term deviation between variables in the photovoltaic data, and screening and constructing a new photovoltaic data set through the vector error correction model; and constructing a photovoltaic power short-term prediction model, training the photovoltaic power short-term prediction model by adopting the new photovoltaic data, and predicting and outputting a photovoltaic output prediction result. According to the method, the problem that the balance relation and the co-integration relation between the photovoltaic data and the meteorological factors are not considered is effectively solved, and a new research thought is provided for photovoltaic power prediction.

The application discloses a kind of associated multi-intersection traffic flow prediction methods.The steps of the present application are as follows:1, collect the traffic flow data of associated intersections, and divide the intersection traffic data into training set, validation set and test set after preprocessing;2, use CNN to extract spatial features from the input intersection traffic data;3, use Transformer to extract time features by inputting spatial features;4, after the Decoder layer is fully executed, the three time window data are finally input into three vectors, and the three vectors are stacked and input into the average pooling layer;5, set the model parameters;6, train the model until the maximum training period, and use the final model to predict the traffic flow of associated multi-intersections.The application uses CNN and Transformer to extract the spatial and temporal features of associated multi-intersections.Learning time encoding is used to embed the position encoding of Transformer, which injects position information and time information into the model together, helping the model to better learn the time features of traffic volume.

The invention discloses a CNN-LSTM-based high-voltage user power consumption data prediction method, which takes high-voltage user power consumption data as a research object, cleans historical power consumption data of high-voltage users by using an LOF method, removes unreasonable outlier data, and reduces the influence of the outlier data on the performance of a prediction model. Through data classification and feature engineering, original data are converted into a more representative feature set, which is convenient for model learning. And the volatility of the data is reduced through data normalization, so that the stability and convergence speed of model training are improved. And then constructing a CNN-LSTM hybrid model, that is, adding a convolutional neural network CNN on the basis of a long short-term memory network LSTM. The hybrid model is composed of a CNN part and an LSTM part, the CNN part is used for extracting local features of data, the LSTM part is used for capturing a dependency relationship of a time sequence, and then more accurate power consumption prediction is carried out. After the construction and training of the model are completed, the prediction result of the model is effectively and intuitively evaluated through a mean square error and a graphical method.

The invention relates to the technical field of artificial intelligence, big data mining and complex time sequence analysis, in particular to an AI quantization time sequence feature adaptive extraction algorithm system, which comprises a space-time multi-dimensional adaptive purification module used for optimizing a data signal-to-noise ratio through a volatility ratio and a time period mask; the microstructure sensing feature construction module is used for constructing a relative feature space and extracting fractal and entropy features; the dynamic asymmetric volatility label generation module is used for generating a balanced sample label based on the average real wave amplitude; the double-path parallel integration representation learning module is used for performing prediction through Bagging and Boosting path fusion; and the closed-loop feedback optimization module is used for feeding back and optimizing system parameters according to the simulation objective function. The method comprises corresponding steps. According to the method, the problems that the features lack state perception, sample construction destroys physical significance and the like are solved, and high-robustness and self-adaptive evolution time sequence feature extraction and trend identification are realized.

The invention provides a cross-traffic-mode passenger flow joint prediction method based on a data interoperation mechanism. The method comprises the following steps: constructing single traffic sub-networks based on a hypergraph theory according to network topology structure data of a passenger flow prediction area, and obtaining historical passenger flow data of all nodes on each single traffic sub-network; constructing a cross-mode traffic sub-network by using a data interoperation mechanism based on the topological structure of each single traffic mode sub-network; and constructing a multi-mode space-time hypergraph convolutional network, inputting the historical passenger flow data of all nodes on each single traffic sub-network and the traffic network topology structure of the cross-mode traffic sub-network into the multi-mode space-time hypergraph convolutional network, and outputting a passenger flow prediction result of each single traffic sub-network by the multi-mode space-time hypergraph convolutional network. According to the method, passenger flow characteristics of stations and routes are captured by building a space-time hypergraph convolutional network; based on feature aggregation and distribution, accurate prediction of passenger flow of multiple traffic modes is realized.

The present application relates to a ship heave motion prediction method and system based on a lightweight context-aware network, belonging to the field of ship and ocean engineering motion control technology, including the following steps: signal acquisition and preprocessing, heave motion multi-step prediction based on LCGNet network, model training and optimization, quantization and compression of the trained LCGNet model, and deployment on a shipborne edge computing device; during system operation, historical heave data is collected in real time and input into the model, and step S2 is executed in a loop to realize continuous online prediction of future heave motion; through innovative lightweight network structure design, high-precision, multi-step heave motion real-time prediction is realized at extremely low parameter and calculation cost to meet the engineering deployment requirements of the shipborne active heave compensation system.

This invention proposes a method and system for fault early warning and prediction of rotating equipment in thermal power plants based on neural networks. The method includes: acquiring multi-source heterogeneous operating data of rotating equipment in thermal power plants, including vibration signals, temperature signals, rotational speed signals, oil quality signals, and current signals; performing denoising and standardization processing on the operating data to construct a multi-dimensional feature vector containing time-domain, frequency-domain, and time-frequency-domain features; based on the multi-dimensional feature vector, using a convolutional neural network to extract spatial correlation features, modeling long-term dependencies through a bidirectional long short-term memory network, and dynamically strengthening the weight allocation of fault-sensitive features using an attention mechanism to generate a fault identification model; and using the fault identification model to analyze the preprocessed feature data in real time, outputting fault type, remaining life prediction results, and risk level classification, wherein the risk level classification is generated based on a joint decision of fault severity and remaining life.

The invention discloses a TBM dynamic attitude prediction method and system based on an ALA hybrid model, and relates to the technical field of TBM dynamic attitude prediction, and the method comprises the steps: collecting time sequence data, and carrying out the preprocessing; performing parameter estimation and optimization based on an ARIMA model; predicting results are fused and verified. The TBM dynamic attitude prediction method based on the ALA hybrid model provided by the invention has the advantages of high prediction result precision and high accuracy, realizes multi-attitude prediction of the TBM under front horizontal, front vertical, rear horizontal and rear vertical dynamic attitudes, provides richer information for efficient and safe tunneling of a shield tunneling machine, and improves the prediction efficiency. According to the method, better effects are achieved in the aspects of fusing various data characteristic processing capability, long-term prediction capability and model adaptability.

The invention discloses a GPU (Graphic Processing Unit) chip temperature sensing thread bundle management method, relates to the technical field of GPU chip application, and solves the problems of low efficiency of a thermal management strategy and the like caused by the fact that a thermal prediction method in the prior art cannot cover hot spots of a whole chip, is high in modeling complexity and poor in real-time performance. The thermal prediction model is used for predicting the hot spot temperature of the chip in the running process of the graphics processor; the TAWS scheduling module is constructed based on a two-stage Warp scheduling strategy and is used for comparing the hot spot temperature with a preset maximum temperature threshold value and selectively executing a normal scheduling mode or a temperature control scheduling mode; the temperature of the chip is regulated and controlled; and when the duration of the high-temperature state exceeds the preset maximum allowable duration, the TAWS scheduling module triggers a scheduling freezing mechanism to realize safety control of the chip temperature. According to the method, cross-architecture high-precision generalization, a low-overhead instruction-level thermal sensing scheduling mechanism, refined analysis of instruction-level thermal characteristics and the like are realized.

The invention discloses a spontaneous combustion temperature step prediction method and system and a medium, and the method comprises the steps: judging whether there is droplet evaporation in the environment where positive standard fuel is located: if there is no droplet evaporation, collecting the gas phase temperature, pressure, equivalence ratio and n-heptane concentration ratio data, and calculating the slope of the initial temperature based on the equivalence ratio and the n-heptane concentration ratio; calculating to obtain a cross temperature based on the pressure and the n-heptane concentration ratio, and calculating to obtain a first temperature step based on the gas phase temperature, the slope of the initial temperature and the cross temperature; if liquid drops are evaporated, collecting gas phase temperature, pressure, equivalence ratio, liquid drop diameter and liquid phase n-heptane concentration ratio data to calculate a first temperature step; the method can predict the first-stage temperature increase under the conditions of different temperatures, pressures, equivalence ratios, droplet diameters, n-heptane concentrations and the like, is good in prediction precision, is suitable for a pure gas phase condition and a droplet evaporation condition, and is simple to operate.

The invention discloses a lithium ion battery health state estimation method based on physical information Transform. The method comprises the following steps: acquiring voltage and current time sequence data of a lithium ion battery under a pulse charging condition; feature parameters are extracted from the time series data, and a training data set containing the feature parameters and corresponding health state labels is constructed; and constructing a physical information neural network model based on a Transform architecture, inputting the training data set into the physical information neural network model for training, processing pulse charging data of the lithium ion battery to be tested by using the trained physical information neural network model, and outputting a battery health state estimation result. According to the method, a framework which is high in principle and can be popularized is established, and an important contribution is made for constructing a battery health prediction model which is higher in interpretability, better in data efficiency and higher in physical credibility.

The invention relates to the technical field of material informatics, in particular to an aluminum alloy mechanical property prediction method based on deep learning, which comprises the following steps: collecting an original data set containing alloy components, process parameters and performance actual measurement data, and cleaning, preprocessing and dividing the original data set; constructing a fusion neural network prediction model, wherein the fusion neural network prediction model integrates a Mama module to extract process sequence features, a KAN network module to extract a component nonlinear relation and a Transform encoder module to perform global feature fusion; training the model by using the training set, and performing hyper-parameter tuning and monitoring by using the verification set; evaluating model precision and engineering applicability through a test set and an external experimental sample; and finally, inputting alloy components to be predicted and process parameters into the trained model, and synchronously outputting predicted values of the yield strength, the tensile strength and the ductility. The method provides an efficient and accurate prediction tool for aluminum alloy design and optimization.

This method comprises: comparing a calculated value of a jumping torque (Tq) obtained by substituting various parameter values of the product (ES) of a longitudinal modulus (E) and the cross-sectional area (S) of a toothed belt (1), a tooth height (h), a pulley pitch diameter (Rr), a pulley pitch diameter (Rn), an initial tension (T0), a tight-side span length (Lt), a slack-side span length (Ls), a wrap angle (θr), and a wrap angle (θn), into an expression (1)- an expression (3) and an actually measured value of the jumping torque (Tq) to each other; storing, in a storage unit (12), the value of an overall correction coefficient (K) and the value of a mesh correction coefficient (Kmn) of a driven pulley, each of which is determined in advance as a value that minimizes the average value of the difference between the compared values; and substituting the stored various parameter values, the value of the overall correction coefficient (K), and the value of the mesh correction coefficient (Kmn) into the expression (1)- the expression (3) to calculate the jumping torque (Tq) of the toothed belt (1) wound around a driving pulley (DR) and the driven pulley (DN).

The invention relates to the technical field of environmental health monitoring, and discloses a droplet distribution prediction method and system based on an adaptive polynomial random forest, and the method comprises the steps: firstly simulating the droplet distribution condition based on a set simulation condition, and constructing a droplet distribution prediction model according to a simulation result; the droplet distribution is predicted based on the constructed droplet distribution prediction model according to the real-time cough time, direction and size, a numerical calculation method is combined with an ML algorithm, and compared with a traditional numerical simulation method, the method can greatly save calculation time and cost and has excellent prediction precision.

The present invention discloses a method for predicting wind speed at coastal sites based on a Top-K spatiotemporal graph attention network, comprising: step 1: inputting wind speed and related auxiliary feature data collected at each site in the coastal area; step 2: constructing a wind speed prediction model; step 3: inputting the processed data in step 1, and training the prediction model constructed in step 2; step 4: calculating the accuracy of the model prediction, and if the accuracy exceeds a preset threshold, executing step 5, otherwise, returning to step 3; step 5: inputting the wind speed data and related auxiliary feature data of each site in the coastal area into the trained prediction model, and obtaining wind speed prediction values ​​of multiple sites in the coastal area. Compared with the prior art, the present invention has the advantages of good prediction effect, good practicality, etc.

The invention provides a traffic network flow prediction method and system based on a pre-training space-time diagram neural network model. The method comprises the following steps: in a pre-training stage, training an encoder and a decoder by using a long-term space-time sequence, and freezing parameters of the trained encoder; in a fine tuning stage, a long-term space-time sequence is coded based on a frozen encoder, features of a short-term space-time sequence are extracted by using a space-time diagram neural network STGNN, then long-term feature representation and short-term feature representation are fused by using a multi-layer perceptron, and after a fusion result passes through a linear layer, a future traffic flow sequence is obtained. According to the invention, accurate short-time traffic flow prediction can be realized.

This invention belongs to the field of thermal energy storage control technology, specifically involving a thermal energy storage control method and control system based on flue gas parameter fluctuations. First, flue gas parameters from the boiler tail end are collected and preprocessed. Then, the parameters are decomposed and fluctuation characteristics are extracted using the EEMD algorithm. An LSTM-BP fusion model is used to predict the fluctuation range and trend. Next, a multi-objective optimization function is constructed based on relevant parameters, and the dynamic operating condition baseline value is obtained by solving it. Then, based on a fuzzy adaptive PID algorithm, the thermal energy storage device is adjusted by combining the deviation and fluctuation frequency. Finally, the actual fluctuations are monitored, and the model and optimization function are corrected to form a closed-loop control. This invention can accurately capture the fluctuation characteristics of flue gas parameters, achieve accurate prediction of fluctuation trends and dynamic adaptation to the operating condition baseline value, improve the accuracy and stability of thermal energy storage control, solve the pain points of existing methods such as adjustment lag and insufficient accuracy, and improve the efficiency of flue gas waste heat recovery.

According to the wall thickness prediction method for the uranium hexafluoride container, waste of resources such as maintenance personnel and maintenance equipment of the uranium hexafluoride container is reduced, the blank of artificial intelligence prediction of the state of the uranium hexafluoride container is filled up, professional inspection suggestions and maintenance suggestions are given according to the prediction result of the prediction model, and the prediction efficiency is improved. A direct prediction model and an indirect prediction model are constructed according to the time-varying rule of the wall thickness of the uranium hexafluoride container and used for calculating the residual wall thickness of the uranium hexafluoride container, and after the prediction precision of the prediction models is verified, the use conditions of the two prediction models are formulated in combination with the actual situation. The direct prediction model is M = thetaS * M0 + betaG * log (age) + delta, thetaS and betaG are parameters of the direct prediction model, M is the residual minimum wall thickness, M0 is the initial wall thickness, log (age) is the natural logarithm of the age of the uranium hexafluoride container, and delta is the random error of the model. The indirect prediction model is M (t) = C0-D (t), C0 is the initial wall thickness, and D (t) is the maximum corrosion depth obtained through power law relation calculation.

The invention discloses a front fracturing well closing flowback numerical simulation method considering reservoir damage, and belongs to the technical field of petroleum development reservoir transformation. The method comprises the steps that initial parameters of a target oil reservoir are obtained, fracturing fracture propagation simulation is conducted, and an embedded discrete fracture grid is constructed based on the fracture form obtained through simulation; determining a function relationship between the reservoir damage coefficient and the water saturation according to an experimental result; establishing a reservoir parameter dynamic calculation model; constructing a mass conservation equation and carrying out differential discretization to obtain a numerical discretization solving model; and performing iterative solution on the model, dynamically updating reservoir physical property parameters at each time step, and finally obtaining a pressure field, a saturation field and crude oil yield prediction in the whole fracturing process. The problem that reservoir damage caused by fracturing fluid invasion cannot be dynamically reflected through traditional numerical simulation is solved, accurate simulation of the full-period process of preposed fracturing, well closing and flowback is achieved, and a reliable tool is provided for fracturing optimization and productivity prediction.

The present invention discloses an artificial intelligence-based carbon emissions prediction method, system, device and storage medium, which specifically relates to the field of carbon emissions; by establishing an interface with a trusted data source, the latest carbon emissions data is acquired and integrated in real time to ensure that the model is corrected and adjusted on the most timely basis, and at the same time, by monitoring the correction time and analyzing the data distribution, the real-time performance of the model and the degree of data anomaly are accurately evaluated, thereby improving the overall prediction accuracy of the model; finally, through comprehensive analysis and continuous monitoring, the accuracy of the model output can be predicted and warned to ensure the reliability of decision-making, which not only enhances the adaptability and stability of the model, but also effectively prevents the generation of feedback loops, so that the model can maintain efficient carbon emissions prediction capabilities in a dynamically changing environment, and provide solid data support for the company's emission reduction decisions.

This invention discloses a comprehensive energy load forecasting method based on a multi-scale graph conditional state-space model. The method includes constructing a comprehensive energy load forecasting dataset for a park, performing data preprocessing, and then using Pearson correlation analysis to select highly correlated features; dividing the dataset into training, validation, and test sets, and standardizing the data; constructing a joint prediction model based on dynamic graph learning, a multi-scale graph conditional state-space model, and a three-dimensional attention mechanism; training the joint prediction model using the training set; adjusting hyperparameters and selecting the optimal model using the validation set; inputting the test set into the trained model, and outputting the predicted electricity, cooling, and heating loads; restoring the actual predicted values ​​through inverse normalization; and evaluating the model performance using multiple indicators. This invention ensures the real-time requirement of the forecast and is suitable for online application scenarios in park energy dispatching.

The application discloses a kind of composite material mixed connection performance modularization quick prediction method, comprising the following steps: S1. the circuit model of composite material connecting structure is constructed, and the circuit model is established based on the mechanical equivalent principle of spring stiffness method;S2. the displacement response signal and load information of connecting structure under the action of load are acquired by simulating composite material connecting structure using the circuit model;S3. based on the displacement response signal, whether the structure is damaged and damage type is judged in combination with corresponding damage criterion;S4. the circuit model is adjusted according to the judgment result;S5. based on the circuit model after adjustment, the complete load-displacement curve of the composite material connecting structure is output in combination with the load information predicted.The application can efficiently predict the nonlinear mechanical response of bolt connection, glue joint connection and glue screw mixed connection structure before damage occurs, and accurately simulate the whole process behavior of stiffness degradation and load redistribution after damage occurs.