39 results about "Paired samples" patented technology

The invention provides a field NL2SQL data automatic synthesis method and system based on a large language model, and the method comprises the steps: building an SQL template library through collecting a public data set and real business query data, carrying out the grading according to the SQL complexity, and forming an SQL template set covering different difficulties; specific SQL queries are generated based on an SQL template library and a large language model, verification is carried out through multiple mechanisms such as grammar check, execution verification and result dimension consistency check, and correctness and performability of the SQL queries are ensured. Generating a plurality of candidate natural language questions according to the verified SQL query and the large language model, calculating a semantic consistency score of each candidate question and the SQL query through a cross consistency evaluation mechanism, screening out the question with the most consistent semantics as a final question-answer pair sample, and meanwhile, eliminating low-quality samples by setting a consistency threshold value, so as to obtain a final question-answer pair sample; and the data accuracy and consistency are further improved.

Example solutions for fine-tuning a language model include: generating a dataset that includes a plurality of paired samples, each paired sample of the plurality of paired samples includes (i) a factual question and a true outcome for that factual question and (ii) a counterfactual question and a true outcome for that counterfactual question; submitting a factual query to an answer model, the factual query including the factual question and the true outcome of the factual question, the answer model generating a factual answer in response to the factual query; submitting a counterfactual query to the answer model, the counterfactual query including the counterfactual question and the true outcome of the counterfactual question, the answer model generating a counterfactual answer in response to the counterfactual query; and performing fine-tuning on a target model using at least the factual question paired with factual answer and the counterfactual question paired with counterfactual answer.

The application relates to a heterogeneous feature clustering method based on a privacy image, characterized in that a diffusion generation process is converted from a pixel domain to a feature domain through a featureized noise and matrix matching technology, a to-be-trained diffusion generator model with a conditional denoising UNet architecture as a core is constructed, and training is conducted in combination with a sample-level consistency constraint and a distribution-level alignment constraint, then a feature pair with mutually aligned semantics is constructed by using a pseudo image generated by the trained diffusion generator model; subsequently, a variational autoencoder model is trained, cross-domain feature mapping is realized through a double-layer alignment loss, and finally a unified domain feature dataset is formed and clustering is conducted; the method has the advantages that cross-domain feature unification is completed under the premise that no source domain data and no paired samples are available, original image leakage is effectively avoided, a high-quality unified feature set can be directly output, and a safe and efficient solution is provided for cross-domain visual analysis under a privacy limited scene.

The invention discloses an intelligent evaluation method for operation of an energy-saving facility based on artificial intelligence. The method comprises the following steps: step 1, collecting multi-source time sequence data of the energy-saving facility; 2, extracting a supervision label and binding the supervision label with the paired sample; step 3, inputting the paired samples into an improved TSMixer model, generating feature mixing weight parameters and bias parameters in a feature mixing layer through a HyperNetwork sub-module, and embedding an SE-block channel attention structure to obtain intermediate feature representation; 4, the step 3 is executed repeatedly; 5, constructing a dynamic feature fusion network; 6, inputting the comprehensive feature set into the LightGBM model, and outputting a result sequence; and 7, comparing the result sequence with the corresponding reference data in the reference library, and outputting an energy-saving facility operation intelligent evaluation result. The method has the advantages of comprehensive evaluation, efficient processing and intelligent result.

The invention relates to a heterogeneous feature clustering method based on a privacy image, which is characterized in that a diffusion generation process is converted from a pixel domain to a feature domain through a characteristic noise and moment matching technology, and a to-be-trained diffusion generator model taking a conditional denoising UNet architecture as a core is constructed; training by combining the sample-level consistency constraint and the distribution-level alignment constraint, and constructing feature pairs with mutually aligned semantics by using a pseudo image generated by the trained diffusion generator model; then training a variational auto-encoder model, realizing cross-domain feature mapping through double-level alignment loss, and finally forming a unified domain feature data set and performing clustering; the method has the advantages that cross-domain feature unification is completed on the premise of passive domain data and no paired samples, original image leakage is effectively avoided, a high-quality unified feature set can be directly output, and a safe and efficient solution is provided for cross-domain visual analysis in a privacy limited scene.

The application discloses a method for detecting glioma chromosome abnormalities based on targeted sequencing, and belongs to the technical field of biological medicine. The method first acquires the allele frequency of a to-be-detected sample at preset SNP sites (covering 1p, 1q, 19p, 19q, chromosome 7 and chromosome 10), and then calculates and determines whether specific chromosome arms or chromosomes have loss of heterozygosity. Meanwhile, the copy number of the region where each SNP site is located is calculated based on the sequencing depth, and the total copy number of the above-mentioned chromosomes is obtained by integration. Finally, the loss of heterozygosity determination result and the chromosome copy number information are comprehensively combined, so that the simultaneous identification of 1p / 19q co-deletion, gain of chromosome 7 (+7) and deletion of chromosome 10 (-10) is realized. The method does not require paired samples, can accurately quantify the copy number, avoid false positives, and only needs to detect part of the SNP sites, that is, can be combined with hot spot mutation detection, thereby saving cost and improving detection efficiency.

The invention provides a program error positioning and explanation generation method and system based on reinforcement learning, and belongs to the field of AI auxiliary programming education and natural language processing, and the method comprises the steps: S1, based on student submission records of a real teaching scene, screening out error codes and correct code pairing samples through an editing distance, and constructing a training set; s2, sampling and filtering the training set by using a pre-training language model, and constructing a sampling rejection data set to supervise and finely adjust the pre-training language model to obtain a base model; s3, constructing a multi-signal fusion reinforcement learning reward function for performing reinforcement learning training on the base model to obtain a trained error positioning and explanation generation model; and S4, inputting the question description and the error code into the trained error positioning and interpretation generation model, and generating structured feedback. According to the method, the error positioning accuracy is remarkably improved, the false drop rate is effectively reduced, and a feasible technical path is provided for intelligent programming education.

The invention discloses a shallow lake turbidity remote sensing inversion method and system based on wind drive physical constraint, and the method comprises the steps: constructing a time sequence pairing sample through combining a remote sensing image and actually measured data, and extracting wind drive characteristics such as wind speed, wind stress and strong wind duration; establishing direction consistency priori by using monotone calibration to generate an expected direction signal, and constructing a wind-driven amplitude response surface interpolation to obtain an expected change amplitude; in model training, a double-moment regression network sharing weights is adopted, and an actual measurement supervision error and wind-driven physical constraints are jointly optimized; and finally, carrying out pixel-level reasoning and verification on the multi-temporal remote sensing image to realize continuous estimation of spatial distribution and dynamic change of turbidity. According to the method, a double-moment input and shared weight network structure is introduced, the physical mechanism and deep learning advantages are fused, the time sequence continuity, the physical interpretability and the cross-space-time generalization ability are remarkably improved while high inversion precision is kept, and an efficient and reliable technical approach is provided for water environment remote sensing monitoring.

Example solutions for fine-tuning a language model include: generating a dataset that includes a plurality of paired samples, each paired sample of the plurality of paired samples includes (i) a factual question and a true outcome for that factual question and (ii) a counterfactual question and a true outcome for that counterfactual question; submitting a factual query to an answer model, the factual query including the factual question and the true outcome of the factual question, the answer model generating a factual answer in response to the factual query; submitting a counterfactual query to the answer model, the counterfactual query including the counterfactual question and the true outcome of the counterfactual question, the answer model generating a counterfactual answer in response to the counterfactual query; and performing fine-tuning on a target model using at least the factual question paired with factual answer and the counterfactual question paired with counterfactual answer.

The invention discloses a noise correlation cross-modal matching loss adjustment method and device based on meta-learning. The method comprises the following steps: constructing a pairing sample set containing a multi-modal data pair; quantifying semantic correlation of each multi-modal data pair in the pairing sample set by using a cross-modal model, and selecting an initial loss function; constructing a loss adjustment network, and balancing the noise sensitive loss function and the noise insensitive loss function to obtain an adaptive loss function; optimizing the loss adjustment network by using a meta-learning algorithm; updating model parameters of the cross-modal model on the target noise data set by using the optimized loss adjustment network; and inputting the query target and each object in the matching database into the updated cross-modal model to obtain the similarity of the query object relative to each object in the matching database. The problem that the robustness and the matching accuracy of the cross-modal model are difficult to consider at the same time in the prior art is solved, and powerful support is provided for landing of the cross-modal model in an actual scene.

The application discloses a retrieval enhancement generation method and system based on knowledge boundary perception, an electronic device and a storage medium, and belongs to the technical field of natural language processing. The method comprises the following steps: generating a high-quality supervised track by using a teacher model, and learning the ability of gap planning and answers by instruction fine-tuning of a weak model; paired samples reflecting overconfidence and over-conservatism are constructed, and a DPO algorithm is used for confidence calibration; gap planning is generated by a student model during actual prediction, and it is accurately determined whether each knowledge point needs retrieval according to cognitive information labels, and accurate retrieval is triggered only for the knowledge points with knowledge gaps. The application can be widely applied to open domain question answering, dialogue systems and knowledge-intensive tasks by explicitly identifying knowledge boundaries, dynamically adjusting thresholds and fine-grained on-demand retrieval, while ensuring the accuracy of answers, significantly reducing the consumption of computing resources and response delay.

The invention discloses a cross-instrument Raman spectrum data alignment method based on a Cycle-GAN network, and the method comprises the following steps: S1, collecting sample data through different Raman spectrometers, and obtaining a first data set and a second data set which have systematic differences and have no paired samples; s2, carrying out denoising, baseline removal and normalization preprocessing on the data set; s3, constructing a Cycle-GAN network model containing structure-symmetric double generators and double discriminators, the generators being used for data set bidirectional mapping, and the discriminators discriminating the authenticity of spectral data; s4, using the data set to train a network model in a self-supervision form; and S5, inputting the to-be-aligned spectral data into the trained corresponding generator to obtain alignment data consistent with the spectral characteristics of the other data set. According to the method, a self-supervised bidirectional Cycle-GAN network architecture is adopted, the method has an automatic optimizing capability, does not depend on a standard spectrum, is compatible with non-paired training data, can also retain differentiated spectrum characteristics of tumor heterogeneity, and can realize accurate alignment of cross-instrument Raman spectra.

The application discloses a kind of celery root system to water growth new induction mode determination method, belong to plant to drought stress adaptation mechanism field of inquiry.It is set to bottle celery of certain drought stress, and it is placed on one side independent open water body, cultivate after a period of time, using ImageJ image analysis software, respectively, the difference of the total length of each celery near, far water side visible root section is measured and compared, and using paired sample t-test is carried out difference significance analysis.According to the difference result, it is clear whether celery can perceive the existence of one side independent open water body through aboveground part and induce its root system to grow in the direction of the water body, so as to further determine whether there is a new induction mode for the water growth of celery root system.The application is simple and easy to operate, accurate data, reliable conclusion, not only is the new exploration and new expansion of the concept of plant water research, but also has guiding significance for seedling cultivation and root directional regulation in horticultural production.

The application discloses a CT image domain adaptive lung nodule classification method based on generative same-different learning and prototype guidance, a medium and equipment, and belongs to the technical field of image processing. Paired samples of a source domain are acquired, and a lung nodule classification model is supervisedly trained. Then, samples of a target domain are acquired, and the lung nodule classification model is alternately subjected to generative same-different learning training and prototype label guided classification training until a feature extractor and a classification head converge, so that an optimal lung nodule classification model is obtained, thereby realizing effective target domain classification capability learning. Finally, a CT image to be processed is input into the optimal lung nodule classification model for lung nodule classification. The application generates enhanced views with semantic invariance by interpolation in the feature space of a generative model, and enables the classification model to learn the ability to distinguish between samples with the same semantics and samples with different semantics in a self-supervised manner, so that the feature representation capability that can be generalized to different domains is more effectively obtained.

The application belongs to the technical field of fault detection, and discloses a bearing unknown fault detection method based on steady-state and non-steady-state feature similarity mining, which comprises phase one, a pre-training phase, which is used for training similar labels or dissimilar labels of paired sample data on a labeled set, and completing training of a similarity prediction network; phase two, a new class discovery phase, which is used for identifying and discovering new classes; the application proposes a similarity feature extraction framework based on differential diagnosis, provides a fault diagnosis method based on similarity measurement and differentiation, and adds a time-frequency attention mechanism to integrate information in two dimensions of time and frequency, improve the accuracy of feature representation, and weaken the influence of noise; a salient feature deep fusion module is used to maximize the use of information, deeply mine information correlation between features, and improve the accuracy of detection results.

The application relates to the technical field of data prediction, and discloses a marine foundation bearing capacity prediction method and system based on scour pit fractal reconstruction and deep learning, a terminal and a storage medium.The method comprises the following steps: acquiring measured three-dimensional point cloud data, performing interpolation and reconstruction, and generating a three-dimensional scour pit digital geometric model; based on the three-dimensional scour pit digital geometric model, the foundation limit bearing capacity under different scour pit morphologies is calculated, and a scour pit morphology quantitative descriptor is extracted to form a data pair sample library; the deep learning model is trained by taking the data pair sample library as a training set, and a trained deep learning prediction model is obtained; the newly detected scour pit morphology data is input into the deep learning prediction model after being reconstructed and quantitatively described, and the foundation prediction bearing capacity under the current morphology is obtained. The application realizes end-to-end rapid prediction from detection data to bearing capacity evaluation, and provides an efficient and accurate decision support tool for the safe operation and maintenance of marine structures.

This application belongs to the fields of software security, program analysis, and big data technology. Specifically, it discloses a method, apparatus, and device for constructing a multi-granularity vulnerability assessment dataset. The method includes: for multiple data sources, crawling raw data and performing data cleaning, deduplication, and cross-data source entity linking to obtain a unified vulnerability entity; based on the unified vulnerability entity, using abstract syntax tree analysis and in-process data dependency and control dependency analysis to obtain code slices and patch pair samples of different granularities; performing multi-dimensional annotation of metadata information to obtain annotated samples with vulnerability information, context integrity level, and compilation information; based on the annotated samples, obtaining a positive sample set and a negative sample set through positive sample filtering and negative sample construction; and performing data fusion, dynamic filtering, and structured encapsulation to construct a target vulnerability assessment dataset. This application enables the effective construction of vulnerability assessment datasets.

The invention relates to the technical field of e-commerce marketing, in particular to a method and device applied to e-commerce marketing activity intelligent effect evaluation, and the method comprises the following steps: collecting a full-amount behavior log of an e-commerce platform user, and dividing the full-amount behavior log into an intervention group sample set and a blank group sample set according to whether the e-commerce platform user participates in a target marketing activity; in the method, the average processing effect of the business core indexes is calculated on the basis of generating the homogenized paired sample set, so that the real business increment brought by the marketing activity is attributed and stripped under the condition of eliminating external interference and sample deviation, and the evaluation result is ensured to reflect the causal effect of the activity instead of the statistical correlation of the natural behavior of the user; and a decision basis with statistical significance is provided for e-commerce marketing strategy adjustment and resource optimization configuration.

The invention relates to the technical field of mechanical system fault diagnosis, and discloses an unbalanced Transform-based mechanical system fault zero sample positioning method, which comprises the following steps of: determining the type and the position of a shock absorber to be subjected to fault monitoring; vibration signals are collected and de-noised to divide samples of known and unknown fault position categories, and meanwhile, the samples of the known fault position categories are fitted to construct a training set and a test set; constructing a semantic description matrix containing a plurality of attributes; for each attribute, constructing a Transform model respectively, and performing training in combination with a training set input model to generate a mapping function set from the data to the attributes; constructing a paired sample, and inputting the paired sample into a KNN classifier for training; inputting each test sample into the mapping function set to obtain an attribute vector set of each test sample, inputting the attribute vector set into the trained KNN model for reasoning, and generating a fault positioning result of each test sample; according to the invention, accurate fault position identification under the condition that no fault position category sample participates in training is realized.

The application provides a semi-supervised deep learning image restoration enhancement method based on double-network cooperation. The method comprises the following steps: constructing a double-task branch image restoration network, training the double-task branch image restoration network by adopting a teacher-student semi-supervised training mode, performing feature extraction and restoration enhancement processing on an image to be restored by a restoration branch in the trained double-task branch image restoration network, outputting the restored image, low-order semantic features and high-order semantic features, fusing the low-order semantic features and the high-order semantic features by an evaluation branch, obtaining a structural similarity index SSIM score matrix by using a self-attention mechanism, and determining the confidence of pseudo-label screening in the teacher-student semi-supervised training mode training process of the double-task branch image restoration network according to the SSIM score matrix. The application fully excavates the value of a large number of low-quality samples and a small number of high-low quality paired samples, generates high-quality pseudo labels, and realizes accurate restoration and enhancement of trackside images.

The invention provides a sample expansion method and device for a geographic regression model, equipment and a storage medium, and the method comprises the steps: carrying out the pairing of samples in an initial sample set of the geographic regression model, and obtaining an expansion sample set containing a plurality of paired samples; constructing a plurality of panel data entries based on the plurality of pairing samples; for any panel data entry, performing interpolation simulation on the independent variable variable quantity of any panel data entry, and generating a plurality of pieces of simulation input data in combination with the independent variable state quantity of any panel data entry; inputting the plurality of pieces of analog input data into a trained machine learning regression model to obtain a plurality of pieces of analog output data; and adding the plurality of pieces of analog input data to the independent variable state quantity of any panel data entry, and adding the plurality of pieces of analog output data to the dependent variable state quantity of any panel data entry to obtain a plurality of analog geographic samples. Therefore, the training sample scale can be effectively expanded, and the training effect of the geographic regression model is improved.

The application provides a FY4B remote sensing single-channel super-resolution reconstruction method based on ESRGAN, which comprises the following steps: analyzing, uniformly cutting, scale constraining and quality detecting the resolution data of FY4B and FY3D to obtain pretreatment data; generating a maximum effective pixel intersection mask according to the pretreatment data; calculating the effective pixel ratio and performing patch screening according to the maximum effective pixel intersection mask to obtain training data; constructing an ESRGAN network structure and performing data reconstruction on the training data to obtain a predicted image. Through the establishment of paired samples of the same day, the uniform cutting alignment and scale constraint, the patch screening and mask loss calculation based on the maximum intersection of effective pixels, and the reconstruction strategy combining pixels, high frequencies and adversarial learning, the FY4B single-channel data is realized from low resolution to high resolution in detail enhancement and structure reconstruction.

This invention discloses a high-temperature and high-pressure micro-displacement method based on an isoporosity dual-pore microfluidic model. Addressing the issues of irreversible diagenesis and the difficulty in obtaining paired samples before and after evolution, a baseline model A and an experimental model B are constructed. Isoporosity constraints are used to eliminate interference from reservoir property differences, enabling a quantitative evaluation of the influence of dissolution pore structure on seepage patterns. The method includes: S1, extracting the primary intergranular pore skeleton and secondary dissolution pore characteristics from typical core thin sections of the same stratum; S2, constructing the baseline model A; S3, embedding dissolution pore units within its topological space and using an equivalent volume compensation algorithm to adaptively scale the intergranular channels to form model B; S4, verifying the porosity accuracy of the completed baseline model A and experimental model to ensure that the pore volume deviation between the two models does not exceed 5%; S5, conducting comparative displacement experiments under real high-temperature and high-pressure conditions.

The application provides a visual-text cross-modal panda behavior recognition method based on an attention mechanism, relates to the technical field of the attention mechanism, and first inputs a multi-modal data set into an initial model, extracts panda behavior features, introduces a customized cross-modal attention mechanism to realize feature alignment, strengthens the interaction depth of visual and text features, and breaks through the bottleneck of insufficient semantic fusion; then, based on the features, a video frame sequence-text description pair sample is constructed, bidirectional matching learning is carried out in a unified embedding space through a cross-modal representation network, parameters are optimized with a symmetric cross-entropy loss, the model is solidified in combination with a verification and early stopping mechanism, video timing information is captured, and the problem of incomplete dynamic representation of behavior is solved; finally, the visual part of target panda behavior data is optimized, the preprocessing strategy is adjusted according to the quality parameters, the optimization result is used to assist the attention mechanism to focus on key features, the current situation that preprocessing has no unified standard and cannot be fed back is improved, and finally, the target behavior category is accurately recognized.

The invention provides an information acquisition and control linkage building energy consumption real-time simulation method, which comprises the following steps of: synchronously acquiring building static attribute data, dynamic environment parameters and equipment operation logs, and combining a BIM model and an internet of things sensing technology to construct a standardized operation event and a state representation vector; generating a causal pairing sample by adopting semantic analysis, feature compression and a multi-modal data alignment method, and performing causal reasoning by utilizing a dynamic causal embedding model to form a sparse directed causal map; the state transition detection and causal link matching mechanism can output interpretable diagnosis statements of specific operation behaviors on energy consumption and environmental changes; the model has an online updating capability and dynamically maintains the causal relationship, and the causal identification accuracy and the intelligent decision support level of the building operation event are remarkably improved.

The invention discloses a cancer prognosis continuum recognition and analysis method based on a pathological basic model, and relates to the technical field of cancer prognosis analys.The cancer prognosis continuum recognition and analysis method comprises the steps that a digital pathological full-slice image of a patient and corresponding clinical outcome follow-up visit information are collected, and space molecular data of the patient are obtained; performing tissue region detection on the pathological full-slice image, segmenting the pathological full-slice image into a plurality of image blocks, and pairing the image blocks with the spatial molecular data to generate a paired sample set of the pathological full-slice image and the spatial molecular data; and based on the paired sample set, extracting low-dimensional embedded features by using the pathological basic model, constructing low-dimensional characterization of the patient, and performing clustering analysis on the low-dimensional characterization of the patient to obtain a pathological molecular state cluster. According to the method, the accuracy of prognosis prediction and the clinical decision support capability are improved by combining the digital pathological image, the clinical outcome information and the spatial molecular data and adopting the pathological basic model and the pseudo-time inference technology.

The invention discloses a parameter generation type model optimization method based on a diffusion model, and the method comprises the steps: obtaining a plurality of polluted model parameters as source parameters and corresponding clean model parameters as target parameters, constructing a pollution-clean parameter pair sample data set, and dividing the pollution-clean parameter pair sample data set into a training set, a verification set and a test set; dividing source parameters and target parameters in the training set, the verification set and the test set according to parameter clusters, and aligning the dimensions of the parameter clusters; a model parameter optimization system is built, the model parameter optimization system comprises an implicit manifold auto-encoder and a conditional diffusion model, the implicit manifold auto-encoder is composed of an encoder and a decoder, and the encoder and the decoder are both of a multi-layer perceptron (MLP) architecture and are used for learning low-dimensional manifold representation of parameter clusters; performing denoising optimization on the low-dimensional manifold by the conditional diffusion model; the implicit manifold auto-encoder and a conditional diffusion model are trained through the training set, the implicit manifold auto-encoder is optimized through a parameter clustering difference loss function, and the conditional diffusion model is trained in a forward noise adding and reverse denoising learning mode; and performing testing and reasoning by using the test set to realize parameter purification.

The invention discloses an evaluation and enhancement method and device for the concept mutual exclusion recognition capability of LLMs, and belongs to the technical field of evaluation of large language models. The method comprises the following steps: acquiring original data from a specified knowledge source, and constructing a structured concept hierarchy diagram; based on the original data and the concept hierarchy diagram, constructing an evaluation data set containing a mutual exclusion concept pair positive example and a non-mutual exclusion concept pair negative example; designing a multi-level cue word system, wherein the multi-level cue word system comprises a direct expression type cue word, a field limiting type cue word and a structure guiding type cue word; and combining the concept pair samples in the evaluation data set with the cue words, inputting the combined concept pair samples and cue words into the LLMs to be evaluated, and evaluating a judgment result output by the LLMs and a reasoning basis from three dimensions of judgment accuracy, interpretation reliability and cue word robustness and enhancement effect. According to the method, benchmark construction, prompt enhancement and multi-dimensional verification are organically integrated in one framework, so that the assessment result can directly guide and verify a capability enhancement strategy.

The present application relates to the technical field of nondestructive testing of agricultural products, and particularly relates to a shelled walnut fat content detection model, a detection method, a system and equipment based on near-infrared spectroscopy.The present application establishes a paired sample spectrum-chemical value data matrix of the shelled and unshelled states of the same walnut, migrates and corrects the spectrum of the shelled walnut to eliminate the interference of the shell, and then on the basis of the optimized characteristic wavelength, constructs and utilizes a global optimization algorithm to automatically optimize the key network structure parameters of the prediction model to obtain the shelled walnut fat content detection model.In actual application, only the near-infrared spectrum data of the shelled walnut sample to be detected needs to be input into the detection model, and the nondestructive determination of the fat content can be quickly completed.The method does not damage the sample, does not consume chemical reagents, and has a low operation threshold, and provides an efficient and reliable technical means for the online quality grading, variety breeding and oil processing of walnuts.

The invention discloses a modeling method of a multi-satellite collaborative spectral radiation observation migration conversion model. The modeling method comprises the following steps: firstly, acquiring and configuring an input parameter set, and performing forward analog calculation by using an MODTRAN atmospheric radiation transfer model to obtain hyperspectral radiation brightness data; then, standard spectral response functions of different intergenerational multispectral sensors in the selected satellite are loaded into an MODTRAN atmospheric radiation transfer model, or post-processing convolution integration is carried out on hyperspectral radiance data, and simulated channel radiance values of all channels of the selected satellite in the same input observation scene are output; then performing pairing analysis to obtain a pairing sample set, and constructing a radiation migration relation model to perform training and precision verification; and finally, applying the radiation migration relation model to the remote sensing observation data of the selected satellite to realize the unification of the radiation reference of the observation data among the multi-spectral sensors among different generations of the selected satellite, and generating a long-time-sequence radiation data set with time sequence continuity and cross-platform / cross-generation consistency.