58 results about "Paired samples" patented technology

The embodiment of the invention provides a government affair digital human dynamic interaction method and system based on a multi-mode large model. The method is applied to the technical field of government affair intelligent services, and comprises the following steps: acquiring a policy announcement text, and performing cleaning and structuring processing to obtain a structured policy data set; and extracting old and new policy data, performing difference comparison, marking key change fields, and generating policy change data. Abstracting and element extraction are carried out on the change data to form structured semantic fragments, and the structured semantic fragments are incrementally embedded into the policy knowledge graph. And generating a question and answer pair sample based on the updated knowledge graph, and carrying out self-supervised fine tuning training on the multi-modal large model. A user inputs multi-modal data, and the model generates a government affair response and feeds back the government affair response. According to the scheme, the multi-modal large model can continuously keep the latest policy knowledge; the model is enabled to generate accurate government affair response with consistent context while understanding multi-modal input such as text, voice and image, and timeliness, accuracy and interactive experience of policy interpretation are improved.

The invention discloses a steel structure quality defect tracing and analysis method based on deep learning, and the method comprises the following steps: collecting image data, sensor data and construction log information of a steel structure member, and generating a tracing identifier; performing alignment based on the traceability identifier to generate an alignment data sequence; based on the aligned data sequence, outputting a defect segmentation result by using an SE (3) isovariant graph neural network; performing continuous coherence topology analysis on a defect segmentation result, and outputting topologically continuous defect areas and severity scores; extracting process parameters of the defect area, calculating statistical dependency by utilizing an independence criterion, and screening out a paired sample set; based on the sample set, performing stability screening on the causal edges to form a causal graph for output; calculating a causal contribution score output by the causal graph, and outputting a liability sorting list; and filing the responsibility sorting list, and visually outputting a defect traceability analysis atlas and report at the same time. According to the invention, steel structure quality defect tracing and analysis are realized.

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.

The invention provides a visual-text cross-modal panda behavior recognition method based on an attention mechanism, and relates to the technical field of attention mechanisms, and the method comprises the steps: inputting a multi-modal data set into an initial model, extracting panda behavior features, introducing a customized cross-modal attention mechanism to achieve feature alignment, and enhancing the visual and text feature interaction depth; the bottleneck of insufficient semantic fusion is broken through; constructing video frame sequence-text description paired samples based on features, carrying out bidirectional matching learning in a unified embedding space through a cross-modal representation network, optimizing parameters through symmetric cross entropy loss, curing a model in combination with a verification and early stop mechanism, capturing video time sequence information, and solving the problem of incomplete behavior dynamic representation; and finally, the visual part of the target panda behavior data is optimized, a preprocessing strategy is adjusted according to quality parameters, an optimization result assists an attention mechanism to focus on key features, the current situation that preprocessing does not have a unified standard and cannot be fed back is improved, and finally the target behavior category is accurately recognized.

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 bridge damage detection method and system, and the method comprises the steps: collecting a bridge vibration signal through an acceleration sensor, carrying out the band-pass filtering preprocessing, converting a time domain signal into a frequency domain vibration signal through fast Fourier transform, and extracting a feature frequency through gradient change detection and threshold screening; a pairing difference value sequence of a monitoring characteristic frequency and a healthy baseline frequency is constructed, and a pairing sample T test is applied to perform statistical significance analysis on a difference value, so that the abnormal frequency change caused by structural rigidity degradation is effectively identified. According to the method, complex modal analysis is replaced by simplified frequency domain features, environmental interference influences are eliminated in combination with a pairing inspection mechanism, the micro-damage detection sensitivity and the false alarm resistance are remarkably improved, and the method is suitable for engineering scenes of long-term bridge monitoring and rapid damage screening.

The invention discloses a generated text detection method and system based on lightweight rewriting conversion and a medium, and the method comprises the steps: carrying out the AI removal conversion of a to-be-detected text through a forward rewriting model, fusing the to-be-detected text and a generated human-like equivalent text, inputting the fused text into a classifier, and outputting a classification result; the training process of the forward rewriting model comprises the following steps: constructing a pairing sample set by utilizing an artificially written text and a machine generated text, and finely tuning the forward rewriting model by minimizing cross entropy loss, the machine generated text is a text which is obtained by generating and filtering redundant samples through at least one large language model in a reverse manner through the large language model; according to the generated text detection method and system and the medium, access to any external, closed source or exclusive large language model API is not needed completely, the cost and delay are reduced, the system architecture and the deployment process are greatly simplified, and the problems that a watermark technology and a similarity regeneration method are limited by a closed source model and are difficult to actually deploy are solved.

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 present application provides a kind of thermal exposure risk time partition determination method, device, equipment and storage medium, comprising: obtaining the time series thermal exposure risk spatial distribution layer of target city in target period under predetermined time scale, hierarchical clustering analysis is carried out based on thermal exposure risk spatial pattern similarity, obtain the time partition under predetermined time scale;Based on the time partition under predetermined time scale, the average city thermal exposure risk spatial distribution layer of time partition under target time scale is obtained and paired sample difference test is carried out for the spatio-temporal partition statistics of thermal exposure risk, to determine the reliability of the time partition under predetermined time scale according to test result.The present application considers the spatial pattern of thermal exposure risk, thereby realizing the quantitative division of thermal exposure risk time partition under different predetermined time scales, and the obtained time partition is matched with the travel time of urban residents, which can effectively guide the partition control of urban thermal exposure risk.

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.

This invention discloses an automatic assessment method and apparatus for deformed central cusps based on convolutional neural networks. The method includes acquiring X-ray images and intraoral photographs of the target to be detected; determining N X-ray regions in the X-ray image and N intraoral photograph regions corresponding to the N X-ray regions in the intraoral photograph; acquiring a deformed central cusp assessment model, wherein the deformed central cusp assessment model is trained using pseudo-paired samples as training samples; inputting each paired region of the target to be detected into the deformed central cusp assessment model to determine whether a deformed central cusp exists in the paired regions of the target to be detected. This invention significantly improves the assessment accuracy of deformed central cusps.

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 invention belongs to the technical field related to computer vision, and discloses a self-adaption method and system during model testing based on malicious sample purification, and the method comprises the steps: dividing a current sample batch into benign samples and malicious samples; calculating a significance index of each sample; determining to-be-purified samples in the current sample batch, wherein the to-be-purified samples comprise malicious samples; for each to-be-purified sample, selecting a benign sample with the largest significance distance from the to-be-purified sample as a paired sample, fusing the to-be-purified sample and the paired sample by using an image fusion technology, and generating a purified sample and a pseudo label of a model prediction probability of the purified sample; and in combination with the current sample batch and the obtained purified sample, performing parameter optimization on the current to-be-optimized model by taking minimization of a total loss function as an optimization target. By means of the scheme, the utilization rate of the test data can be increased, and therefore a good model parameter optimization effect can be achieved by collecting less test data.

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 discloses a key performance indicator query method. The method comprises the following steps: acquiring a natural query statement of a user for a key performance indicator (KPI); the elements of the natural query statement comprise statistical indexes, statistical dimensions, a time range and a calculation mode; analyzing the natural query statement, and extracting an index entity, a dimension entity, a time entity and a calculation type based on elements in the natural query statement; determining a time value range according to the index additive classification corresponding to the index entity and the time entity; combining the index entity, the dimension entity, the time entity, the calculation type, the time value range and the index additive classification to construct a question and answer pair sample; and generating an SQL query statement corresponding to the natural query statement by using the question and answer pair sample. According to the method, the accuracy and reliability of the Text2SQL in a complex KPI scene are remarkably improved through the whole-process design of structured extraction, characteristic adaptation, logic integration and precise generation.

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.