603 results about "Data expansion" patented technology

The invention relates to the field of medical care informatics, in particular to an ophthalmic disease prediction model training method based on artificial intelligence. Comprising the following steps which are executed in sequence: S1, acquiring a data set of ophthalmic diseases, and performing disease labeling on each image in the data set; s2, performing data expansion on the labeled data set to obtain a training data set of ophthalmic diseases; s3, performing feature extraction on the training data set of the ophthalmic disease by using a neural network algorithm based on dynamic weight redistribution as a feature extraction model to obtain extraction features of the ophthalmic disease; s4, using a self-encoding neural network based on feature refinement as a feature dimensionality reduction model to perform dimensionality reduction on the extracted features, and obtaining initial low-dimensional features of the ophthalmic disease after dimensionality reduction; s5, inputting the initial low-dimensional features of the ophthalmic diseases after dimension reduction into a classifier model for training, and obtaining a classification result; s6, classifying the data set by using the trained model; features of ophthalmic disease data are effectively captured.

The invention relates to the technical field of road maintenance intelligence, and discloses a road intelligent maintenance decision-making system based on multi-dimensional evaluation and deep reinforcement learning, and the system comprises a geographic space data preprocessing module, a fuzzy TOPSI S evaluation module, a context awareness DQN strategy module, a credibility driving recommendation module, and a security strategy library module. The method comprises the following steps: generating a road health degree and a clustering label through multi-source data geographical weighted preprocessing and fuzzy TOPSI S dynamic weight evaluation; a reinforcement learning reward function is configured based on label differentiation, and a strategy space is explored in combination with noise; the confidence is verified through multiple models, a historical security policy is matched, and model optimization is driven through priority sampling injection samples. According to the method, the evaluation precision is improved through entropy weight-clustering dynamic weight, and flexible decision is realized in combination with reinforcement learning; and three-level security verification and closed-loop optimization are constructed to ensure that the risk is controllable, historical experience migration and multi-source data expansion are supported, and the cross-scene adaptive capacity is enhanced.

The invention discloses a text travel sentiment analysis response method and system based on multi-modal fusion and incremental learning, and the method comprises the steps: obtaining a text, an image, an audio or a video input by a user, carrying out the query in a text and multi-modal knowledge base through employing a multi-modal retrieval technology, and optimizing a retrieval result through combining sentiment analysis, thereby achieving the purpose of improving the user experience. And finally generating a personalized tourism information response. The system integrates a large language model, text and multi-modal knowledge base construction, and a data vectorization processing and sentiment analysis technology, supports multi-modal input and output, can dynamically adjust retrieval and answer contents, and realizes personalized recommendation according to user feedback. The system also has the capabilities of multi-modal data expansion, dynamic knowledge base updating and voice interaction, can significantly improve the response speed and accuracy of tourism information service, is widely applicable to intelligent and personalized tourism information service scenes, and has relatively high innovativeness and practical value.

The invention relates to the technical field of new energy grid connection, in particular to a new energy grid connection flexibility resource prediction model training method based on artificial intelligence, and aims at solving the problems that a traditional prediction method is insufficient in precision and poor in adaptability when coping with new energy grid connection flexibility resources. By collecting and processing multi-dimensional historical data, adopting machine learning and deep learning algorithms and combining artificial intelligence, effective features can be extracted from the data, and new energy grid-connected flexibility resources are accurately predicted. Through data expansion and optimization, the problems of data scarcity and uneven distribution are effectively solved, the generalization ability and prediction precision of the model are improved, more scientific and accurate decision support is provided for power system scheduling, and stable operation and flexible scheduling of a new energy grid-connected system are promoted.

The invention discloses a satellite remote sensing target data identification method based on artificial intelligence, which belongs to the technical field of data processing, and realizes distributed model training and data mining of a multi-node joint model by performing satellite remote sensing target identification on an acquired spectral vector and adopting a model training architecture of distributed federated learning; carrying out manual annotation on the acquired spectral vector data; a generative adversarial network based on multi-scale spectral decomposition is adopted for sample generation, and then data expansion is achieved; and performing data identification by adopting a satellite remote sensing target data identification model. According to the method, the generative adversarial network based on multi-scale spectral decomposition is adopted for sample generation, and the data set is expanded. A self-encoder algorithm is used for carrying out feature dimension reduction, feature independence is enhanced through a nonlinear conversion layer, and redundant information between features is reduced. A kernel extreme learning machine algorithm based on conjugate gradient compensation is adopted, a kernel function is mapped to a high-dimensional space, and the classification accuracy and efficiency are improved.

The invention relates to the technical field of artificial intelligence, and discloses a geological mineral resource prediction model training method based on artificial intelligence, and the method comprises the steps: carrying out the expansion of geological mineral resource data through a data expansion model, and obtaining a training data set; building a prediction model through an extreme learning machine based on conjugate gradient, and inputting the geological mineral resource data in the training data set into the prediction model for training; randomly initializing hidden layer parameters of the extreme learning machine, and calculating a hidden layer output matrix; calculating an output layer weight of the extreme learning machine through a conjugate gradient method; and updating the hidden layer output matrix. According to the method, a belief propagation mechanism is adopted, the internal relevance of geological mineral resource data can be simulated, and samples are adjusted and generated through a covariance matrix, so that the method is more in line with the distribution characteristics of actual geological mineral resources; a dynamic constraint game strategy is adopted, and strategies of a generator and a discriminator are dynamically adjusted, so that adaptability to complex data is enhanced.

The invention relates to a spacecraft thermal control film coating defect identification method based on a multi-dimensional attention mechanism. According to the method, a correlation mapping model based on monitoring data-detection image sensitive features-coating defects is constructed, and accurate feature information extraction of the multi-type defects of the coating on the surface of the spacecraft by a convolution kernel is realized by constructing a channel-space attention mechanism convolution neural network; through modes of random rotation, mirroring, symmetry, blurring, brightness change, contrast adjustment, saturation change, Gaussian noise addition and the like, data expansion is performed on variety defect categories with relatively small sample sizes, a data set is enriched, and the problem that the sample sizes of multiple types of defects are unbalanced is solved; a lightweight module (LM-Block) and a feature enhancement module (FEM-block) based on an attention mechanism are designed on the basis of a GhostConv self-adaptive ghost convolution module, and a coating defect detection model architecture based on efficient self-adaptive convolution and channel-spatial pyramid pooling is proposed on the basis of the modules to realize accurate recognition of defects.

The invention relates to the field of electric digital data processing, in particular to a building digital twin data processing method based on artificial intelligence, which comprises the following steps of: acquiring real building digital twin data, training a data expansion model by adopting a generative adversarial network algorithm based on topology dual optimization to obtain training data, and calculating the real building digital twin data according to the training data. Obtaining a central building energy consumption evaluation model and a local building energy consumption evaluation model by adopting a distributed federated learning method, training the local building energy consumption evaluation model of each building by adopting a self-adaptive constraint-based extreme learning machine algorithm, continuously iterating the local building energy consumption evaluation model, and evaluating the energy consumption of each building. And evaluating new real building digital twin data by using the trained local building energy consumption evaluation model. An existing building digital twin data processing method has the problem that energy consumption grade evaluation is not accurate, and the method is accurate in energy consumption grade evaluation.

The invention provides a tea tree fresh leaf identification method and system based on YOLOv8 in the technical field of tea leaf detection, and the method comprises the following steps: S1, obtaining a large number of historical images of tea tree fresh leaves, carrying out the preprocessing of data annotation and data expansion of each historical image, and constructing a data set; s2, creating a tea tree fresh leaf recognition model based on a YOLOv8 network, and setting a loss function of the tea tree fresh leaf recognition model as an I nner-S I oU function; s3, dividing the data set into a training set, a verification set and a test set based on a preset proportion, and training, verifying and testing the tea tree fresh leaf recognition model through the training set, the verification set and the test set; and S4, deploying the tea tree fresh leaf recognition model passing the test, and performing tea tree fresh leaf recognition by using the deployed tea tree fresh leaf recognition model. The method has the advantage that the identification accuracy of the fresh tea leaves is greatly improved.

The invention belongs to the technical field of high-performance composite materials, and discloses a carbon fiber reinforced thermoplastic composite material performance database, a construction method and application thereof, and the method comprises the following steps: S1, database structure construction; s2, experimental sample collection and data standardization; s3, feature engineering and variable reduction; s4, training a machine learning model; s5, constructing and verifying an adaptive model; and S6, data expansion and feedback optimization. According to the method, material performance prediction and formula parameter reverse design under target performance are realized through systematic acquisition and normalization processing of three types of data of material components, preparation process and performance characterization and building of a nonlinear mapping model among a material structure, a process and performance through a machine learning method. The database can be used for intelligently recommending a high-performance composite material combination scheme, is suitable for rapid screening and customized development of various thermoplastic composite materials, effectively reduces the research and development cost and development cycle, and improves the material design efficiency.

The invention belongs to the technical field of bridge engineering, and particularly discloses a bridge safety early warning method and system based on fusion of a data model and a physical model. Comprising the following steps: in a first stage, on the basis of a neural network monitoring site excitation-response mapping relation model established by an initial state full-bridge finite element model, calculating response information corresponding to field excitation, and comparing response monitoring signals to determine whether a structure is damaged from a full-bridge angle; in the second stage, through data expansion from a monitoring point position to a non-monitoring point position, a set threshold value is compared to determine a damage position and a damage grade; and in the third stage, adjusting parameters of the model are calculated according to excitation-response signals of a monitoring point position sensor, the finite element structure model is updated, the residual bearing capacity of the structure is calculated by combining state information borne by the structure model, and the service life of the component is determined. The invention provides a safety assessment method fusing monitoring data and a physical model for structural health monitoring, and the method is clear in physical significance, high in real-time performance and reliable in result.

The invention discloses a colorectal cancer metastasis risk prediction method and system fusing radiomics and clinical characteristics, and the method comprises the steps: S1, collecting image data and clinical risk factor data of a patient, carrying out the preprocessing, and carrying out the feature extraction; s2, performing data expansion, and fusing expanded features and original features; s3, constructing and training a multi-modal large model, and outputting a first prediction result of the colorectal cancer metastasis risk of the patient; s4, layering the patients according to the first prediction result, and performing dynamic prediction; s5, performing interpretable analysis on a dynamic prediction result and generating a report; and S6, verifying the multi-modal large model. According to the method, the aspects of data fusion, prediction performance, dynamic assessment, clinical interpretation and the like are remarkably improved, key problems in the prior art are solved, an efficient, accurate and easy-to-use transfer risk assessment tool is provided for clinicians, and the method has important academic value and wide clinical application prospects.

The invention relates to the technical field of image processing, in particular to a tobacco leaf disease spot segmentation method and system based on multi-scale residual cavity convolution, and the method comprises the steps: collecting a plurality of tobacco leaf disease images as original images, and carrying out the data expansion of each collected original image through an image enhancement method, establishing a training set, a verification set and a test set based on the original image and the extended data; a semantic segmentation model is constructed, the semantic segmentation model is formed by stacking two sub-networks, and the two sub-networks are connected through an ROIE + module; in combination with the training set, training the semantic segmentation model by using the constructed loss function; verifying the trained semantic segmentation model by adopting a verification set, and selecting an optimal semantic segmentation model; the test set is adopted to test the optimal semantic segmentation model, and the performance of the optimal semantic segmentation model is evaluated; and inputting a to-be-segmented tobacco leaf disease image into the trained semantic segmentation model to obtain a tobacco leaf disease spot segmentation map. According to the method, the tobacco leaf scab area can be accurately and efficiently segmented.

The invention provides a glutenite logging curve identification method and device based on deep learning, and the method comprises the steps: collecting logging curve data, marking the types, and obtaining an original training data set; performing sample generation based on a quantum state mixed generative adversarial network algorithm to realize data expansion; inputting the expanded data into a feature extraction model, and training the feature extraction model; inputting the data after feature extraction into a feature dimension reduction model to train the feature dimension reduction model; inputting the data subjected to dimension reduction into a classifier model to train a classifier; and carrying out glutenite logging curve identification by utilizing the trained model. According to the method, the processing efficiency and the recognition precision of the logging curve data are effectively improved, the diversity and the quality of data generation are enhanced, the optimization problem in a traditional deep learning method is solved, and the generalization ability and the classification accuracy of the model for unknown data are further improved.

The invention discloses a pavement crack segmentation method based on adaptive feature extraction and attention fusion, and relates to the technical field of image recognition, and the method comprises the steps: S1, obtaining a pavement crack data set which comprises a pavement crack training set, a pavement crack verification set and a pavement crack test set; and S2, performing data expansion on the pavement crack training set in the S1, not performing data expansion on the pavement crack verification set and the pavement crack test set to obtain a pavement crack sample set, and selecting a training crack detection network by using the pavement crack verification set obtained in the S1. A content-guided attention mechanism (CGA) is improved by using a convolution and attention fusion module (CAFM), the CGA is added into a content-guided attention fusion module (CGAFUS), a convolution and attention fusion mechanism (CAFMFUSon module is proposed, the model can adaptively focus on a complex topological structure of the crack, the adaptive feature extraction capability of the model on the pavement crack is enhanced, and the pavement crack feature extraction efficiency is improved. Moreover, the features of different levels can be fused more effectively, and the precision and robustness of crack detection are improved.

The invention discloses an eye fundus image recognition method, system and device based on deep learning, the efficiency and precision of eye fundus disease recognition are improved through a portable handheld terminal and a cloud database, the portable handheld terminal comprises an eye fundus image feature acquisition module, an eye fundus image recognition and judgment device and a man-machine interaction module, wherein the eye fundus image recognition and judgment device comprises a feature engineering module, an image recognition module and a visual auxiliary fault module, the feature engineering module is responsible for data expansion, enhancement and normalization, the image recognition module performs image classification prediction by constructing a deep learning model, and model construction, parameter initialization, model training and pruning optimization are included. The visual auxiliary diagnosis module assists doctors in diagnosis by generating a thermodynamic diagram. Remote storage and analysis of data can be realized, the diagnosis efficiency is improved, the visual loss risk is reduced, and the system is particularly suitable for resource-limited regions.

The invention relates to a two-stage battery residual life intelligent prediction method and system considering small samples, and belongs to the technical field of reliability engineering, and the method comprises the steps: finding a knee point based on the original data of battery capacity cycle aging, and dividing a degradation stage into two stages through the knee point; wherein the original data of the battery capacity cycle aging comprises the number of charge-discharge cycles and the corresponding actual capacity; respectively selecting independent normalized stream models as generators of the two stages, and generating training data samples; converting the training data sample which is non-normal distribution data into approximately normal distribution data to obtain a data set after data expansion; and training an RUL prediction model based on the data set after data expansion, and predicting the remaining life of the battery through the trained RUL prediction model. The method has practical application value in life analysis of battery production test, is beneficial to full life cycle test and health management of the battery, and has certain engineering application value.

The invention discloses a parameter prediction method, system and equipment applied to a graphite spheroidization production line and a medium, and relates to the field of intelligent manufacturing, and the method comprises the steps: obtaining the feature data of a target graphite raw material and the target data of a finished product; inputting the characteristic data of the target graphite raw material and the target data of the finished product into a parameter prediction model, and determining spheroidization production line parameters; the parameter prediction model establishment method comprises the steps of obtaining an input data set and an output data set; expanding the input data set by adopting a small sample data expansion and enhancement method to obtain an expanded data set; training an XGBoost model by adopting the expansion data set and the output data set, and determining the XGBoost model as a parameter prediction initial model; and optimizing and adjusting parameters of the parameter prediction initial model by adopting a grid search method and a beetle antennae search algorithm, and determining the optimized and adjusted parameter prediction initial model as a parameter prediction model. According to the invention, the cost of sample acquisition is reduced and the prediction precision of the model is improved.

The invention discloses a tunnel fire data generation method based on a generative adversarial network, and belongs to the crossing field of civil engineering disaster prevention and reduction, fire protection engineering and artificial intelligence. A generative adversarial network architecture is designed, a bidirectional LSTM network module and Wasserstein distance loss are introduced, and measuring point temperatures, flue gas concentrations and flow velocity data under different fire source positions, powers and longitudinal jet fan wind speeds in a tunnel are collected for network training. Wherein the bidirectional LSTM can help the network to better capture the time sequence information of the data, and the Wasserstein loss can enable the gradient change of the network to be smoother, so that the mode collapse phenomenon is reduced, and the network training stability is improved. Compared with a traditional data expansion means, the method can accurately and quickly generate tunnel fire data under different working conditions, avoids tedious and time-consuming numerical simulation work, and realizes efficient and high-precision expansion of the tunnel fire database.

The invention discloses an extremely-short-term ship motion attitude prediction method and system based on data enhancement, and relates to the field of ship motion attitude prediction. The method is used for solving the problem that data quality and model generalization performance are affected due to insufficient completeness of input data of a ship motion attitude extremely-short-term prediction model. The method comprises the following steps: performing data expansion on original observation ship attitude data by adopting a Slim-based generative adversarial filling network to obtain an expanded data set; performing data splicing and feature zooming on the original observation ship attitude data and the extended data set; performing time delay correlation analysis on the data after feature scaling to determine optimal sample data, then dividing the optimal sample data into a training set and a test set according to a proportion, and inputting the training set and the test set into a prediction model for training; optimizing parameters of the prediction model according to the prediction value of the training set; and outputting a predicted value by adopting the parameter-optimized prediction model, and carrying out reverse normalization processing to obtain a final prediction result. The method is suitable for extremely short-term ship motion attitude prediction.

The invention provides a power device defect detection method and system, and relates to the technical field of semiconductors, and the method comprises the steps: obtaining a power device defect image set with defect tags; expanding a defect image set of the power device through geometric modeling in combination with material attributes of the power device; constructing a decoupling detection model used for identifying defect types and positioning defect positions; in combination with a bimodal positioning loss function, training a decoupling detection model by using the expanded power device defect image set; collecting a real-time image of the power device; and inputting the real-time image of the power device into the trained decoupling detection model, and outputting the defect category and the defect position of the real-time image of the power device. Through accurate model training and data expansion, the accuracy and robustness of detection are improved, the problem of missing detection can be effectively solved, and the production quality and efficiency of power devices are improved.

The invention specifically discloses a method for evaluating the health state of a cigarette comprehensive test board, and the method comprises the steps: obtaining the fault type of the cigarette comprehensive test board, and carrying out the analysis of the fault type, so as to obtain the self degradation of equipment and the influence factors of the fault; carrying out fault influence factor analysis on influence factors of equipment degradation and faults, and carrying out numeralization on the influence factors; carrying out fault data acquisition and processing on the cigarette comprehensive test bench, structuring data of equipment faults, and further carrying out fault data expansion through a generative model GAN network; and a DNN deep neural network is constructed, training is performed by using the expanded fault data, and then fault probability evaluation is performed on the cigarette comprehensive test bench through the DNN deep neural network. According to the invention, the convenience and accuracy of health assessment of the cigarette comprehensive test board can be improved.

The invention discloses a small sample learning data expansion method and system based on multi-modal information fusion, and relates to the technical field of machine learning, and the method comprises the steps: obtaining multi-modal data in a power operation environment, and constructing a multi-modal information data set, environment features and equipment state features are extracted from the multi-modal information data set to construct a multi-modal feature set; preprocessing the multi-modal feature set, and performing weighted fusion on the preprocessed multi-modal features to generate a multi-modal feature vector; constructing a multi-modal data expansion model, generating a virtual sample, verifying and fusing the virtual sample, and expanding a multi-modal information data set; and balancing the expanded data set by adopting a data balance strategy, acquiring power operation data in real time, and regularly updating the multi-modal data expansion model through an online learning mechanism. According to the method, various features can be comprehensively extracted by combining the sensor data and the image data of the electric power working environment, and the diversity of the data set is greatly enriched.

The invention belongs to the technical field of transformers, and provides a transformer partial discharge defect voiceprint feature extraction method and system, and the technical scheme comprises the steps: obtaining the original voiceprint data of a transformer; performing noise reduction on the original voiceprint data of the transformer based on an improved spectral subtraction method to obtain noise-reduced voiceprint data, and performing data expansion on the noise-reduced voiceprint data to obtain data-expanded voiceprint data; based on the voiceprint data after data expansion, in combination with the fundamental frequency and frequency distribution characteristics of the transformer, feature extraction is carried out to obtain the features of the partial discharge defect of the transformer, the voiceprint feature distribution characteristics and the deep relation with the transformer fault are fully considered, and the problems that the time domain, frequency domain and time-frequency domain features of the power transformer are multiple in variety, and the power transformer fault is poor are solved. The extraction of effective features is improved, the extraction of redundant and invalid features is avoided, and the prediction precision of the partial discharge defect of the transformer is improved.

The invention relates to the technical field of remote sensing image processing, in particular to an intelligent landslide identification method based on deep learning of a multi-source remote sensing image, which comprises the following steps of: firstly, acquiring high-resolution optical and radar data of the multi-source remote sensing image by adopting a spatio-temporal data expansion method; the problem that an existing landslide identification method is insufficient in data type and data quantity is effectively solved, more comprehensive and accurate landslide data are provided, the generalization ability of the model is enhanced, then a deep learning model with semi-supervised learning and adaptive convolution multi-scale feature fusion is adopted, the size of a convolution kernel can be dynamically adjusted, and the robustness of the landslide identification method is improved. Landslide features of different scales are accurately captured, understanding of landslide morphology and structure is enhanced, robustness of the model in a complex terrain is improved, finally, a landslide boundary and morphology are corrected through a positioning correction method, inaccurate or discontinuous boundaries caused by prediction errors are eliminated, and stability of the model in a complex environment is further improved.

The invention discloses an emotion recognition method based on a multi-modal fusion data enhancement framework, and the method comprises the following steps: S1, extracting multi-modal input sequences comprising n physiological signals, carrying out the modal fusion of the multi-modal input sequences through a CNXAF model, and obtaining multi-modal fusion data; s2, a conditional self-attention generative adversarial network C-SAGAN is used to perform data enhancement on the multi-modal fusion data to obtain an enhanced data set, and the C-SAGAN comprises two components, namely a generator and a discriminator; and S3, performing emotion recognition on the enhanced data set by using a pre-trained improved ConvNeXt-t model. The invention further discloses an emotion recognition system based on the multi-modal fusion data enhancement framework. According to the invention, data fusion and data expansion can be carried out on the multi-modal physiological signals, and high-precision multi-modal emotion recognition is realized end to end.

The invention discloses a titanium alloy microscopic structure prediction method based on deep learning. The method comprises the following steps: S1, constructing a microscopic structure sample data expansion module based on deep learning and a cross-working-condition microscopic structure prediction module based on transfer learning; s2, carrying out acquisition and data preprocessing on microscopic structure sample data; s3, in a microscopic structure sample data expansion module based on deep learning, constructing a data expansion model, and expanding the sample data under the new working condition according to the sample data under the existing working condition; s4, dividing the sample data under the existing working condition and the expanded sample data under the new working condition into a source domain data set and a target domain data set in a transfer learning-based cross-working-condition microstructure prediction module, pre-training the source domain data set, and then performing parameter fine tuning on the target domain data to obtain a transfer learning model; and S5, predicting the microscopic structure sample data under the new working condition by using the transfer learning model.

The invention discloses a teaching implementation method based on combination of motion perception and AI driving, and relates to the technical field of education and teaching. The implementation of the teaching method comprises the steps of multi-modal data expansion and acquisition, cross-dimensional dynamic evaluation and cognitive diagnosis, emotion adaptive feedback and intervention, cross-scene learning map construction and capability migration, and whole-process data closed loop and systematic evolution. And the multi-modal data expansion acquisition comprises the steps of dynamic data acquisition, physiological data acquisition, environmental behavior data acquisition and data preprocessing. The method has the advantages that a multi-modal data acquisition scheme of a common mobile phone camera and a built-in sensor is adopted; the system replaces the traditional dance teaching which depends on special three-dimensional modeling equipment and sensor gloves for boxing teaching, reduces the hardware cost, can realize full scene coverage of families, classrooms, outdoors and the like without professional site deployment, and solves the core problems of high hardware cost and limited scenes in the prior art.

The invention discloses a bridge cluster post-earthquake rapid evaluation method based on improved XGBoost. The method comprises the steps that firstly, a bridge cluster is classified, a bridge type is selected as a research object, modeling parameters are determined according to the selected bridge type, and parameter samples are expanded; then establishing a finite element model to extract output parameters related to bridge behaviors; according to the extracted output parameters related to the bridge behaviors, the bridges are divided into three classes to serve as bridge classification labels, and data expansion is carried out; performing classification model training on the obtained new training set by using an XGBoost model, and finally determining the XGBoost model; and using the determined XGBboost model, taking bridge parameters and seismic oscillation data as input, and outputting a classification label as a post-earthquake evaluation index / evaluation basis. According to the method, the efficiency and accuracy of bridge safety assessment after the earthquake are remarkably improved, and a rapid and reliable technical support is provided for emergency repair and recovery of the bridge after the disaster.

The invention relates to a multi-spectral wavefront aberration restoration method and system based on double roof prisms, and the method comprises the following steps: taking a sub-pupil image as the input of a classification model, obtaining classification feature data, and carrying out the data expansion of the classification feature data, the sub pupil image is obtained by inputting incident wavefront aberration into a simulation light path of the double-ridge prism wavefront sensor, the double-ridge prism wavefront sensor comprises two groups of double-ridge prisms, each group of double-ridge prisms comprises two ridge prisms which are vertically and oppositely arranged, the characteristics of the ridge prisms are equal, and the characteristics of the double-ridge prisms are equal. The two groups of double-roof prisms are made of a material combination for eliminating chromatic aberration; splicing the sub pupil images and the classification feature data after data expansion to serve as input of a regression model, and obtaining a Zernike coefficient matrix corresponding to incident wavefront aberration; and according to the Zernike coefficient matrix corresponding to the incident wavefront aberration and the Zernike polynomial, obtaining a recovered wavefront aberration. Compared with the prior art, the method has the advantage that the wavefront aberration recovery accuracy is improved.