984 results about "Manual annotation" patented technology

A multi-modal model generation method, a multi-modal processing method, and a device. The multi-modal model generation method comprises: acquiring modal information; determining a description text corresponding to the modal information, and by means of a pre-trained basic model, generating input information and output information on the basis of the description text to obtain an autonomously-generated training sample; constructing a multi-modal model on the basis of the basic model, and finely tuning the multi-modal model on the basis of the autonomously-generated training sample; and by means of the finely-tuned multi-modal model, generating input information and output information on the basis of the modal information, and continuing to finely tune the multi-modal model. In this way, the model can have the ability to process multi-modal information, thereby reducing the time spent for manual annotation, and improving the overall training efficiency, and rich detail features in the modal information can be mined, thereby improving the accuracy of the model.

The invention provides a large language model-driven code review and optimization method, which belongs to the field of computer science, and comprises the following steps of: firstly, performing intensive training on a model through rich code samples and accurate manual annotation to ensure that the model has accurate code understanding and analysis capability; and then, performing deep analysis on an input source code by utilizing the trained large language model, and constructing an abstract syntax tree (AST) to capture deep semantic information of the code. And finally, the model automatically analyzes the codes, identifies potential defects, non-standard coding practices and optimization possibilities, and generates specific optimization suggestions, including code repair schemes and guidance following optimal practices, according to identification results, so that efficient support is provided for developers, and the quality of software products is ensured.

The invention provides a composition scoring cue word optimization method based on a large language model. The method comprises the following steps: obtaining composition topic information and a labeled composition sample set; constructing an initial scoring cue word according to the composition topic information, and evaluating the performance of the scoring cue word; using the current scoring cue word to guide the large language model to score each composition in the composition sample set; calculating a difference value between the large language model score and the manual annotation score; guiding the large language model to analyze the composition with the maximum difference value and obtain a score difference reason, so as to guide the large language model to modify the score cue words and generate candidate score cue words; evaluating the performance of the candidate scoring cue word, and comparing the performance of the candidate scoring cue word with the performance of the current scoring cue word to judge whether the current scoring cue word is updated; and carrying out loop iteration optimization on the scoring cue word step until an iteration termination condition is reached, and outputting a final scoring cue word. According to the method, the scoring cue word is optimized, so that the composition scoring accuracy and consistency of the large language model are improved.

The invention discloses a chip defect weak supervision semantic segmentation method based on YOLO and a diffusion model, and the method comprises the steps: collecting a chip defect weak supervision data set, carrying out the weak supervision manual marking, and constructing a chip defect image weak supervision data set; constructing and training a target detection model based on the chip defect image weak supervision data set; obtaining a to-be-detected chip defect image, inputting the to-be-detected chip defect image to the trained target detection model for defect position prediction, and outputting a chip defect area positioning image; inputting the chip defect area positioning image into a diffusion model for image reconstruction, and outputting a chip reconstruction image; and performing difference value comparison and thermodynamic diagram analysis on the to-be-detected chip defect image and the chip reconstruction image, and outputting a pixel-level precision chip defect semantic segmentation result. According to the method, the model is trained by using the low-precision weak supervision annotation data, so that high-precision semantic segmentation is realized, the training cost is reduced, and the practicability of the model is improved.

The invention discloses a semi-supervised identification method and system for a small target of a container, and belongs to the technical field of artificial intelligence and computer vision, and the method comprises the steps: carrying out the target detection of a container image through a pre-trained target detection model, intercepting a sub-image, and inputting the sub-image into an initial classification model, and obtaining a classification confidence coefficient; the uncertainty of the model on a sample classification result is quantified through a Monte Carlo Dropout method; a feature space distance filtering and dynamic threshold adjusting mechanism is combined, and samples with high confidence, low uncertainty and consistent feature space are screened out to serve as pseudo label data; pseudo label data and initial synthesis data are mixed, and the generalization ability of the model is gradually improved through semi-supervised iterative training. According to the method, the dependence on manual annotation can be remarkably reduced, meanwhile, the distribution difference between synthetic data and real scene data is gradually reduced, and finally, high-precision recognition and strong generalization ability of a classification model in a real scene are achieved.

The invention provides an optimization method and device for a retrieval enhancement generation system, equipment and a medium. Comprising the following steps: constructing model training data according to a document extracted from a knowledge base and text fragments obtained by segmenting the document; performing model parameter optimization on the vectorization model based on the model training data; processing the user question and the text fragment based on a vectorization model to obtain a retrieval fragment; according to the retrieval fragment, the user question and the session context, configuring a cue word; calling a generation model to process the user question and the cue word, and generating a question answer text; a supervised fine tuning and reward feedback training mode is adopted, and a generation model is optimized according to a question and answer data set formed by a standard answer text, a user question and a text retrieved according to the user question; and determining a target evaluation index according to the question answer text, the manual annotation data of the user question and the question and answer evaluation index set, so as to evaluate the performance of the retrieval enhancement generation system according to the target evaluation index.

The invention provides an intelligent detection system for forging defects of a forge piece product, and relates to the technical field of industrial intelligent detection.The intelligent detection system comprises the steps that multi-angle images of a to-be-detected workpiece are collected and then spliced, and a complete surface expansion view of the product is generated; obtaining defect types of the defect candidate regions through region coordinates and region sizes of the defect candidates; obtaining a confidence value of a corresponding defect type judgment result; a lightweight deep network recognition model is called for secondary judgment, new sample data are generated in a manner of supporting manual annotation, and an equipment end is connected with a programmable controller for intelligent defect detection of the workpiece to be detected. According to the invention, the problems of incomplete defect coverage, failure to realize high-precision automatic identification of multiple types of defects and influence on the detection accuracy and the production efficiency caused by diversified and complex forging surface defects and limited image acquisition angles in the prior art can be solved, comprehensive acquisition and splicing of multi-angle images are realized, and the detection accuracy and the production efficiency are improved. The technical effect of improving the defect detection accuracy of the forge piece product is achieved.

The invention relates to the technical field of artificial intelligence and data processing, in particular to a roadbed settlement data identification method based on artificial intelligence, and the method specifically comprises the following steps: collecting time sequence monitoring data of a plurality of roadbed settlement sensors, and carrying out the manual marking; carrying out adaptive time domain segmentation normalization on the collected time sequence monitoring data, designing a conditional diffusion process, and generating enhanced data conforming to a soil mass mechanics constraint in a submerged space; constructing a roadbed settlement data classification model based on a one-dimensional convolutional neural network, and inputting the enhanced data into the model for training to obtain a trained roadbed settlement data classification model; and preprocessing new time sequence monitoring data collected by the roadbed settlement sensor, and inputting the preprocessed data into the roadbed settlement data classification model to obtain a settlement category classification result. According to the invention, the roadbed settlement data classification model based on the one-dimensional convolutional neural network is constructed and trained, so that the discrimination capability of the model can be enhanced, and the accuracy and reliability of model identification can be improved.

The invention provides a multi-mode intelligent robot control method and device, and the method comprises the steps: obtaining a current mode instruction which at least comprises one of a video instruction, a text instruction, a picture instruction and an audio instruction; on the basis of a pre-trained multi-modal large model, according to the current modal instruction, obtaining post-collapse characterization; on the basis of a pre-trained robot imitation learning network, according to the representation after collapse and current environment observation, predicting an output target action; the multi-mode intelligent robot with the body is controlled to operate according to the target action; wherein the robot imitation learning network is obtained by performing training optimization according to a training sample set formed by single-modal data samples, corresponding environment observation data and real actions of the robot. According to the method, the post-collapse representation is obtained by pre-training the multi-modal large model, and the post-collapse representation is utilized to predict the target action, so that the requirement on multi-modal manual annotation data when a training robot simulates a learning network is avoided in the process, the effect of multi-modal reasoning can be achieved only according to single-modal data training, and the efficiency of multi-modal reasoning is improved. Not only is data collection and labeling cost reduced, but also the reasoning ability of the model is improved.

The invention discloses a multi-granularity visual reasoning model construction method and device based on reinforcement learning. The method comprises the following steps: constructing an'image-reasoning query-bounding box 'triple as a training data set; designing a composite reward function including positioning precision, target counting precision and format reward; training the multi-modal large language model by adopting a GRPO algorithm; the trained model can output a region-level bounding box, and a pixel-level mask is generated and a contour-level result is extracted in combination with the segmentation model. According to the method, the problems that in the prior art, a reasoning path depends on manual annotation, multi-granularity tasks cannot be expanded, and generalization is insufficient are solved, and the autonomous decision-making ability, task expansibility and generalization in a distribution offset scene of the model are improved.

According to the OpenCSG large model intelligent data annotation system provided by the invention, by virtue of an automatic annotation technology driven by a large model, the requirement of manual annotation is greatly reduced, and the data annotation efficiency is remarkably improved. The system can generate a high-precision labeling result by using the intelligent labeling module and the automatic quality control module, and ensures the consistency and accuracy of the labeling result through an automatic auditing process. No matter sentiment analysis, entity recognition or complex text classification, the system can efficiently adapt to various types of labeling tasks, and different data processing requirements are met. Through modular design, the system supports flexible expansion, and can meet various data labeling requirements from small-scale test projects to large-scale commercial projects. According to the overall architecture, all the function modules are closely coordinated in the system, so that efficient operation and high-quality output are always kept in a large-scale data annotation project.

The invention discloses a special disease queue data capturing method and system based on an intelligent medical knowledge graph, and relates to the technical field of medical information, and the method comprises the following steps: S1, constructing a special disease intelligent medical knowledge graph which comprises a bidirectional mapping relation between standard terms of a single disease category and clinical actual corpora, clinical text data is accumulated in a mode of combining manual annotation and machine learning, and a domain exclusive knowledge base containing symptoms, diagnosis and examination indexes is formed. According to the special disease queue data capturing method and system provided by the invention, by constructing the special disease intelligent medical knowledge graph, bidirectional mapping of single disease specification terms and clinical actual corpora is realized, and the problem of insufficient semantic understanding when non-standardized clinical corpora are processed by a traditional method is effectively solved; the entity information in the unstructured medical data can be accurately extracted by utilizing a natural language processing model and an inference engine.

The invention discloses a three-dimensional medical image visual language model pre-training method for reconstructing a hybrid strategy, which belongs to the technical field of medical image calculation and comprises the steps of constructing a medical image text pair data set, a language text mask reconstruction strategy, a visual image mask reconstruction strategy, a semantic perception fusion strategy and multi-task joint learning. According to the method, the large language model is finely adjusted, the diagnosis and attribute information in the medical report is extracted by using the finely-adjusted large language model, the efficient prompt is generated, and the large language model has relatively high generalization ability, so that the cost of manual annotation is greatly saved; according to the semantic perception fusion strategy, the text feature obtained by the text encoder and the image feature obtained by the image encoder are combined to obtain the new text feature, so that the text perceives the diagnosis and attribute information of the image in advance, the alignment of the image and the text in the embedding space is further optimized, and the pre-training efficiency is improved.

The invention belongs to the technical field of automatic classification of pathological images, and particularly discloses an intelligent analysis method and system for automatic classification of pathological images, and the method comprises the following steps: S1, carrying out the preprocessing of a hyperspectral image and a pathological section image; s2, performing deep analysis on the preprocessed hyperspectral image and pathological section image; s3, performing global feature fusion in a channel dimension through a three-stage fusion strategy; s4, generating a pseudo label and optimizing a loss function; s5, dynamic optimization and model construction; and S6, performing multi-level interpretive analysis on the hyperspectral data by using an attention mechanism. According to the intelligent analysis method and system for automatic classification of the pathological images, through cooperation of multiple modules, the accuracy of pathological image classification is effectively improved, meanwhile, the generalization ability and adaptability of the model to complex pathological data are remarkably enhanced, dependence of manual annotation is reduced, and the accuracy of pathological image classification is improved. And the automation degree and the diagnosis efficiency of the system are improved.

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 discloses an AI model service platform data set labeling method and system, and relates to the technical field of data set labeling, a to-be-labeled data set is split according to a data type, and whether an existing labeling model capable of respectively processing the currently split to-be-labeled data set exists in an AI model service platform is identified; evaluating the annotation performance of the annotation model based on the evaluation index of the annotation model, and then updating the annotation model based on data enhancement; labeling the split to-be-labeled data set by using the updated labeling model to generate a first labeling result; performing secondary annotation on the first annotation result based on multi-model joint annotation to generate a second annotation result; and screening and identifying an unqualified annotation data set in the second annotation result through the confidence interval so as to trigger manual annotation. An efficient, low-cost, high-quality, flexible and extensible data set labeling process is realized, so that the labeling capability and the service quality of an AI model service platform are remarkably improved.

The invention discloses an intelligent asphalt pavement disease detection method based on a YOLOv5 algorithm, and relates to the technical field of intelligent expressway and road maintenance, and the method comprises the steps: obtaining video data through a vehicle-mounted or airborne pan-tilt camera, and constructing an asphalt pavement disease image data set through manual marking; a YOLOv5 target detection model is adopted, an efficient multi-scale attention mechanism is introduced, and preliminary training is carried out; detecting a real-time database on the basis of a pre-trained improved YOLOv5 target detection model, and manually checking a detection result so as to optimize the model; and performing redundant data de-duplication on continuous multi-frame image information by using a dynamic planning method, correcting the image by using a chessboard method to obtain the actual size of the target, and performing evaluation according to a disease standard. Therefore, by adopting the method, the labor cost and the calculation cost can be saved, the pressure of storage requirements can be reduced, the real-time detection effect can be realized, and the method can be widely applied to asphalt pavement disease detection.

The invention relates to the technical field of power grid intellectualization, in particular to a digital power grid asset classification and evolution monitoring method, system and device based on self-supervised comparative learning and a storage medium. The method comprises the following steps: acquiring network flow data in a digital power grid, extracting multi-dimensional features such as a time interval, a direction, a data packet length, a protocol type and an address port, and constructing an asset behavior feature matrix; then constructing a self-supervised contrast learning model, taking the communication behavior sequences of the same asset in different time periods as a positive sample pair, taking the communication behavior sequences of different assets as a negative sample pair, and training an asset embedding model through an InfoNCE contrast loss function; then, asset communication behaviors are mapped to a semantic embedding space, and asset classification and identification are carried out based on embedded vectors; and finally, carrying out evolution monitoring on the assets by adopting a sliding time window mechanism, generating an asset evolution trajectory sequence, and carrying out abnormity perception early warning. Autonomous learning without manual annotation, high-precision asset identification and real-time state evolution monitoring are realized.

The invention relates to a meteorological data set automatic construction method and system based on modal bridging, and aims to meet the multi-modal large model training requirement in the meteorological field, and the method and system realize automatic conversion from an original meteorological image to a structured expert reasoning text through deep fusion of image and text information. The method comprises five stages of data preprocessing, image-text semantic modeling, causal reasoning generation, consistency screening and parallel processing, key meteorological elements are extracted by using a multi-modal model, a chained thinking process is constructed through a language model with meteorological knowledge, chained reasoning annotation is realized, cross-modal semantic alignment and a multi-round reasoning mechanism are introduced, and a multi-modal semantic model is established. The method is advantaged in that high-quality samples are screened in combination with rules and models, automation, high consistency and good expansibility are realized, manual annotation cost is substantially reduced, and the method is suitable for large-scale meteorological reasoning multi-modal data set construction.

The invention discloses a grid coding-based remote sensing image general basic model construction and analysis method, which comprises the following steps of: pre-processing multi-modal data, eliminating cloud layer influence, aligning space coordinates and unifying data scales; generating a unified identification code with geographical meaning by using efficient space-time grid integrated coding; performing multi-scale feature extraction by means of a visual Transform and a convolutional neural network, and optimizing multi-source data fusion in combination with an anchoring feature extraction strategy; and finally, cross-task downstream application adaptation is realized on the basis of a multi-task Transform architecture. According to the method, the problems of high manual annotation cost, difficulty in multi-source data integration, low mass data processing efficiency and the like in traditional remote sensing image analysis are effectively solved. Experiments show that the multi-source data integration efficiency is improved, the model generalization ability is enhanced, the classification precision in high-resolution series data tests reaches 90% or above, the segmentation IoU exceeds 80%, and an efficient solution is provided for remote sensing image analysis.

The invention provides a distributed optical fiber temperature sensing logging data blind denoising method and system based on physical self-supervised learning. The method comprises the following steps: acquiring original noisy distributed temperature sensing (DTS) logging data, and generating a self-supervised training sample through a space-time alternating downsampling strategy by using the space-time coherence of the original noisy distributed temperature sensing (DTS) logging data; constructing a physical self-supervised blind denoising network model by using a simplified structure, and inputting a self-supervised training sample for training; in the training process, a multi-physical constraint loss function optimization model is introduced until a loss function converges, and a denoising model is obtained; and inputting to-be-processed complete DTS logging data into the denoising model to obtain denoised DTS logging data. According to the method, manual labeling and large-scale labeling of the data set are not needed, the inherent physical characteristics and structural information of the DTS data can be fully utilized, efficient and accurate blind denoising of the DTS logging data is achieved, and the problem that an existing deep learning denoising method needs to depend on a large amount of labeled data is solved.

The invention discloses an SAR image and optical image deep fusion ground object classification method based on self-supervised learning, and aims to solve two challenges in remote sensing image classification: firstly, reducing dependence on a large amount of label data, and secondly, improving multi-modal data classification precision. The modal features of an SAR image and an optical image are extracted through independent network branches, deep feature extraction is performed in combination with a convolutional neural network and a double-branch structure of an attention mechanism, and unique advantages and complementary information of the two modals are fully utilized. By constructing a self-supervised learning loss function, the model is trained without label data; then, the trained feature encoder is migrated to a downstream ground feature classification task, and only a small number of marked samples are used for fine adjustment of the model; according to the method, complementary information among different modes is integrated, dependence on manual annotation of remote sensing images is reduced through a self-supervised learning method, and the precision and robustness of ground feature classification are improved.

The invention relates to a ship trajectory data mining and macro-micro fusion route prediction method and system, and the method comprises the steps: S1, extracting ship trajectory data near an inland waterway from the data of an automatic ship recognition system, and carrying out the cleaning, filtering, sorting and preliminary analysis; s2, performing clustering analysis by using the features of the ship trajectory data, preliminarily mining trajectory features in combination with manual annotation, and establishing a ship trajectory database; s3, on the basis of historical data, constructing a deep learning model aiming at the inland river driving key path nodes of the ship from a macroscopic level; s4, training a deep learning model combining a convolutional neural network and a long-short-term memory network based on historical data, and predicting a ship position track from a micro level; and S5, performing macro-micro fusion prediction on the new ship data to obtain a ship navigation path and trajectory prediction coupling result. According to the method, coupling of macro and micro prediction means is adopted, and a certain guarantee is provided for the precision of ship trajectory prediction.

The invention relates to a data annotation method and device, equipment and a readable storage medium. The data annotation method comprises the following steps: firstly, inputting to-be-annotated data and cue words into a first pre-trained large language model, generating a first prediction data annotation result, analyzing the confidence coefficient of the first prediction data annotation result, and generating a first data annotation result based on a result conforming to a first confidence coefficient threshold value; and inputting a result which does not accord with the first confidence threshold value into a second pre-trained large language model for re-labeling, analyzing the confidence, and generating a second data labeling result. And performing manual annotation on the result which does not accord with the second confidence threshold, and finally combining the first, second and manual annotation results to determine a final annotation result. According to the method, through automatic annotation and confidence analysis of a multi-round pre-trained large language model, and in combination with manual annotation, low-confidence tasks are processed, so that the annotation accuracy and efficiency are remarkably improved, and the dependence on manual intervention is reduced.

The invention discloses a multi-modal six-dimensional classroom effect evaluation and analysis method using AI, and the method comprises the steps: obtaining six-dimensional historical classroom data based on multi-modal historical classroom data; constructing an influence factor-weight mapping table; training a multi-modal fusion neural network model, performing feature fusion on the six-bit data, and constructing a classroom effect evaluation model; obtaining an influence factor value based on the real-time classroom time, and obtaining a weight distribution scheme with the highest matching degree with the current influence factor value; acquiring a classroom effect score, and performing targeted improvement based on the difference between the score and the optimal classroom effect score; and based on correction feedback data of students and teachers on the evaluation result, calculating the difference degree between model output and manual annotation through a contrast learning algorithm, and triggering model increment training. The method has the advantages that the weight is automatically adjusted by fusing multi-modal data and influence factors, the classroom effect is accurately evaluated, and personalized optimization suggestions are provided.

The invention relates to the technical field of artificial intelligence, and provides a visual language feature fine alignment method for a medical multi-modal large model, and the method comprises the steps: carrying out the deep learning and semantic understanding of medical expert knowledge text features through an artificial intelligence large model, and generating a question and answer pair instruction; extracting medical expert knowledge visual features, and converting the features into question-answer pair instructions; training the medical multi-modal large model by using a question and answer pair instruction; using the artificial intelligence large model to generate a medical image-text question-answer pair instruction; reinforcement learning is introduced into the medical multi-modal large model, and fine alignment of visual language features is realized by using a medical image-text question and answer pair instruction. The invention provides a method for fully automatically generating high-quality medical multi-mode instruction data, so that the medical multi-mode large model provides accurate and reliable output content in various medical applications. The whole process of data generation does not need manual annotation, and manpower and time cost is saved. Meanwhile, model reasoning illusion is reduced, and model performance is improved.

The invention relates to the technical field of artificial intelligence, and particularly discloses a visual space description method, system and product based on depth information fusion, and the method comprises the steps: obtaining an image and a corresponding object text; performing vision-plane position feature extraction on the image to obtain a vision feature and a plane position feature, and performing depth feature extraction on the image to obtain a depth feature; performing text coding on the object text to obtain text features; performing feature fusion on the visual features, the plane position features, the depth features and the text features to obtain fusion features; performing spatial relationship classification on the fusion features to obtain a spatial relationship; and inputting the instruction language, the spatial relationship and the object text into a large language model to obtain a natural statement describing the spatial relationship of the object. According to the method, the spatial relationship between the objects in the image can be accurately described, the spatial perception capability of artificial intelligence is improved, and meanwhile, the increase of redundant manual annotation workload is avoided.

The invention relates to the technical field of image recognition, in particular to a remote sensing image segmentation method and system, a storage medium and equipment. Taking the preprocessed remote sensing image as a real sample, and obtaining a generated sample by using a conditional diffusion model; respectively converting the real sample and the generated sample into a set color space, calculating the color value of each channel pixel by pixel, determining the color distribution of the real sample, aligning the color distribution of the generated sample with the color distribution of the real sample through linear transformation, and obtaining the generated sample after color correction; and performing similarity screening on the generated samples after color correction, mixing the generated samples with real samples according to a set proportion, taking a formed mixed sample set as a training set of a semantic segmentation model, and performing training to realize remote sensing image segmentation. By combining the conditional diffusion model and color correction, the spectrum consistency of the generated samples is improved, the quality of the generated samples is optimized, and by means of mixed sample training, the requirement for manual annotation is reduced, and the precision of remote sensing image interpretation is improved.

The invention provides a model fine tuning method and system based on feedback and reinforcement, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining an output text generated by a language model, and carrying out the embedded coding; identifying a structural semantic unit in the text, and generating structural mark information; constructing a low-layer capsule set based on the structure marking information, and executing dynamic routing to generate a high-layer semantic capsule set; constructing a structure expression matrix according to the mapping relation between the high-level semantic capsule set and the structure mark; and inputting the matrix into a reward scoring model to generate a reinforcement learning return value, and updating language model parameters according to the reinforcement learning return value. According to the method, closed-loop linkage of the language model structure perception capability and the strategy optimization path is realized, and the text generation structure and semantic consistency can be improved under the condition that manual annotation is not needed.

The invention discloses a remote sensing identification method and system for an area suitable for layout of photovoltaic facilities in pond aquaculture, and the method comprises the following steps: obtaining an optical image and an SAR image of a pond aquaculture area, and carrying out the preprocessing of the optical image and the SAR image, and obtaining a processed optical image and a processed SAR image; performing manual annotation and data enhancement on the processed optical image, and performing data division on the enhanced optical image and the processed SAR image to obtain a training data set; constructing an initial recognition model by using a double-branch encoder, a cross-modal attention mechanism and a multi-task learning framework, and training and optimizing the initial recognition model by using the training data set to obtain a region recognition model; and identifying a suitable layout area in the pond culture area by using the area identification model. Technical support is provided for reasonable layout of photovoltaic facilities, scientific allocation of resources is ensured, the influence on the aquaculture ecological environment is avoided, and the space utilization rate of photovoltaic power generation is increased.