764 results about "Data Annotation" patented technology

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

The invention discloses a data annotation method and system of a collaborative computing architecture based on quantum computing, and belongs to the field of data annotation. The method comprises the steps that S1, multi-modal data are input and preprocessed; s2, extracting features of each mode after preprocessing; s3, coding the features of each mode into a quantum state, and carrying out mode fusion; s4, performing label reasoning on the quantum state after modal fusion, and performing label constraint optimization by using a quantum approximate optimization algorithm; s5, based on a quantum Bayesian network or an approximate causal graph generation method, generating explanation according to a modal contribution causal path, and deducing marginal contribution of each modal to final label prediction by using a joint probability measurement result; and S6, outputting a labeling result. According to the method, a quantum-classical cooperative computing architecture is designed, the efficiency and accuracy of multi-modal data labeling are remarkably improved, the interpretability, the distributed processing capacity and the high-dimensional feature modeling capacity of the system are enhanced, and a brand new solution thought is provided for development of the multi-modal labeling technology.

The invention discloses a science and technology public text intelligent classification and service method and device based on deep learning. The method comprises the steps that multi-source science and technology public text data are acquired and preprocessed; extracting keywords by adopting a keyword extraction algorithm, and splicing the keywords with the public text title to form enhanced text features; constructing a multi-dimensional public text classification system and performing data annotation; performing feature extraction and fine adjustment by adopting a BERT pre-training model to obtain a classification model; automatically classifying the newly-added public texts and visually presenting the newly-added public texts; and generating a personalized recommendation result based on the user portrait and the public text feature index. The invention further relates to a technical scheme of multi-objective quality diversity optimization, heterogeneous resource allocation and fusion of the LPLC2 neural network and the BERT. The technical problems that a traditional method is limited in complex semantic understanding ability, single in classification dimension and lack of an integrated solution are solved, and the accuracy of science and technology public text classification and the intelligent level of service are improved.

The invention provides an intelligent data labeling method and system based on multi-modal fusion and large model verification, belongs to the field of artificial intelligence and data processing, and innovatively fuses multi-modal information such as an OCR recognition result, a layout structure, original image visual features and deep semantic analysis of a large language model (LLM). And a precise automatic labeling result credibility evaluation mechanism is constructed. According to the method, various errors in automatic labeling can be accurately recognized and adaptively corrected, and the errors comprise conventional error correction based on hard coding rules and complex semantic error correction driven by LLM. Meanwhile, the system can continuously optimize the data labeling capability of the system through an efficient man-machine cooperation and closed-loop feedback learning mechanism, and automatically precipitate domain knowledge assets. The invention aims to solve the problems of recognition accuracy bottleneck, heavy manual proofreading burden, lack of intelligent judgment and error correction, knowledge accumulation lag and the like in traditional document data labeling, so that the efficiency, accuracy and automation level of document data labeling are remarkably improved.

The invention discloses an uncertainty and representativeness combined active learning method and system for medical image data annotation, and relates to the technical field of medical image data. The method comprises the following steps of: training a variational auto-encoder infoVAE on an image pool; m samples are randomly extracted from the image pool and labeled, an initial labeled image set is constructed, and a segmentation model is trained on the labeled image set; t rounds of active learning circulation are carried out, and in each round of t active learning circulation, the following steps are specifically executed: screening candidate samples based on a representative method; screening a final sample based on an uncertainty method; updating the labeled data set and the unlabeled data set; retraining the segmentation model on the updated labeled image set, and optimizing model parameters; and obtaining final model parameters. According to the method, by designing a sample selection strategy integrating uncertainty and representativeness, the global performance of the model and the difficult sample segmentation capability are improved.

The invention discloses an image data annotation method and system, and relates to the technical field of computer vision, and the method comprises the steps: collecting image and text data, carrying out the updating through kernel density estimation and a kinetic equation, carrying out the optimization through employing Euler discretization in combination with CLP propagation and EA, carrying out the conversion through amplitude coding, and carrying out the construction through a quantum state circuit. The method comprises the following steps: carrying out calculation by using partial traces, generating topological weighted entanglement entropy through weighted fusion, carrying out updating based on an attention mechanism, generating potential conflict pairs, calculating priorities, generating final conflict pairs through a CNN model, outputting region features through a NeRF model, carrying out extraction by using a CLIP model, and carrying out updating through a loss function. According to the method, VAE is combined with GLP propagation and EA for optimization, the precision and consistency of labeling are improved, and through joint verification of quantum entanglement entropy calculation and the NeBF model, the efficiency and accuracy of labeling are improved.

The invention discloses an intelligent precise farmland irrigation direction control system based on the Internet of Things, and relates to the technical field related to intelligent irrigation. Comprising a data acquisition module, a data processing and analyzing module, a data labeling and classifying unit, a three-dimensional coordinate model building module, a nozzle angle adjusting device, a nozzle water quantity adjusting unit and a control instruction execution unit. According to the invention, the distance between the nearest nozzle and the water-requiring position is calculated, so that irrigation water can reach the water-requiring area more efficiently, the distance is accurately calculated to ensure that water can be quickly and accurately conveyed to the required area, water loss and energy waste caused by long-distance water conveying are avoided, the water spraying direction is adjusted in combination with the wind direction and the wind speed, and the water spraying efficiency is improved. The device can effectively cope with the influence of natural wind on irrigation, and by monitoring wind direction and wind speed information in real time and combining the wind direction and wind speed information with water spraying parameters of the spray head, the system accurately calculates horizontal and vertical angle adjustment values of the spray head, so that sprayed water flow can accurately reach a water needing position.

The invention relates to the technical field of computer vision, and particularly discloses a weed root recognition method and device based on attitude estimation, and the method comprises the steps: obtaining field image data containing a weed plant, detecting a leaf key point through a pre-trained leaf attitude estimation model, fitting a leaf vein extension line, calculating an intersection point cluster, and carrying out the recognition of a weed root. And screening a high-density candidate point set through density clustering analysis, calculating a weighted center coordinate as a weed root positioning result according to density weight, and transmitting the weighted center coordinate to laser weeding equipment to execute precise weeding operation. According to the method, accurate weed root identification is realized, the positioning accuracy is higher, the robustness to a complex field environment is higher, the real-time performance can be improved by utilizing model lightweight, efficient deployment is realized, and the data labeling cost is reduced.

The invention discloses a melanoma lesion area segmentation method based on a CLIP multi-modal fusion network. The method comprises the following steps: 1, constructing an MA-CLIP model; 2, a BLIP language model is finely adjusted through a manually-labeled text-image pair, a large-scale multi-modal data set is constructed, a training set, a test set and a verification set are divided, and preprocessing is carried out; 3, training the MA-CLIP model; 4, evaluating the performance of the MA-CLIP model and optimizing parameters; and 5, inputting a to-be-segmented melanoma clinical image into the trained MA-CLIP model, and outputting a segmentation result. According to the method, the problem of insufficient traditional medical data annotation can be solved, accurate guidance of clinical semantics on image segmentation is realized, and the recognition precision and boundary segmentation capability of a focus in a complex form are improved.

The invention belongs to the technical field of new-generation information, and particularly relates to a power plant metal supervision entity relationship extraction method based on dual coding, which comprises the following steps: multi-modal data preprocessing: collecting and cleaning data, and carrying out data labeling; constructing a dual coding joint learning model: designing a network layer architecture, and training the dual coding joint learning model; constructing and querying a dynamic knowledge graph: extracting a model to generate a triple, and performing time sequence evolution analysis, causal reasoning interface and dynamic updating; and incremental knowledge updating and dynamic model optimization: an incremental learning and feedback module forms a bidirectional closed loop between the knowledge graph and the relationship extraction model, and continuous evolution of the system is ensured through dynamic knowledge updating and adaptive model optimization. According to the method, through bidirectional feature modeling of dual paths, collaborative representation optimization between entities and relationships is realized while context information is captured, so that the requirements of complicated data types and diversified semantic associations in an engineering scene are met.

The invention relates to the technical field of intelligent driving, and discloses a user service recommendation method for a vehicle-mounted scene, and the method comprises the steps: collecting the vehicle-using behavior data of a user, carrying out the correlation marking of the intention of the user through a multi-dimensional marking system, carrying out the vectorization, and storing the vectorized intention into a vector database; capturing real-time behavior data of a user to generate a real-time behavior vector; based on the vector database, generating an intention context corresponding to a real-time behavior vector through a hybrid retrieval strategy; and generating a prompt project based on the intention context, inputting a large model output service recommendation result, and pushing the result to the user. By integrating mechanisms such as data annotation, vector retrieval and large model reasoning, the problem of long cold start period depending on long-term buried point data learning in the prior art is solved, potential intentions are quickly matched, the system effective time is shortened, and the service recommendation accuracy based on the user intentions is effectively improved. The invention further discloses a user service recommendation device for the vehicle-mounted scene, the vehicle and a computer readable storage medium.

The invention discloses a water ecological environment decision response intelligent interaction system based on AI natural language processing, and the system carries out the data collection and preprocessing, and forms a training set. In combination with the training set, data annotation and feature engineering are carried out, and AI model training including data segmentation, a training method and transfer learning is further carried out; on the basis, continuously collecting monitoring data, and inputting the monitoring data into the trained AI model for real-time prediction; data analysis and decision support are carried out; and once an abnormal trend is detected, an early warning mechanism is triggered. Through an AI large model technology in the vertical field, big data and an artificial intelligence algorithm are utilized to simulate and improve intelligence, convenience and precision of water ecological environment supervision, through combination of an AI large model and an intelligent voice interaction technology, based on analysis and processing capability of the AI large model, deep analysis is performed on water ecological environment data, and a water ecological environment monitoring result is obtained. And managers are helped to quickly make scientific decisions.

The invention is suitable for the technical field of machine learning, and provides a feature selection method and system based on domain adaptation and domain adversarial training, and the method comprises the following steps: obtaining source domain data and target domain data, constructing a source domain feature selection target function, and generating a binary feature mask vector; constructing a deep transfer learning framework based on a domain adversarial neural network, training the domain adaptive neural network by using the source domain tagged data and the target domain untagged data in a domain adversarial form, and constructing a cross-domain shared feature representation space of the source domain and the target domain; and feature selection knowledge migration from the source domain to the target domain is realized through decoding conversion. According to the method, the feature distribution difference between the source domain and the target domain is effectively eliminated through the adversarial training strategy driven by the gradient inversion layer, the method has remarkable advantages in a target domain data scarcity scene, the data annotation cost can be reduced, cross-domain potential association can be captured, and redundant features and over-fitting risks are reduced.

The invention relates to the technical field of natural language processing, in particular to an archive structured information extraction method and system based on a multi-modal large model and a medium, and the method comprises the following steps: S1, establishing a mapping table of fields to be extracted; s2, data annotation; s3, constructing a layout analysis model, an archive structured extraction model and an archive structured integration model; s4, screening key information pages based on the layout analysis model; s5, extracting single-page structured information based on an archive structured extraction model; and S6, based on the archive structured integration model, integrating single-page structured information results. Through application of the multi-modal large model, accurate layout analysis, strong structured extraction capability and efficient information integration are realized, automatic extraction and integration from archive image data to structured information are realized, manual intervention is reduced, and processing efficiency is improved.

According to the small sample target detection method based on prototype network feature enhancement, firstly, a foreground feature aggregation module is adopted, redundant background information is removed in the category prototype construction process, and purer category features are extracted; and a condition information coupling module is utilized, and the category prototype is dynamically adjusted in combination with the characteristics of the query image, so that the adaptability is better. And then, dynamically supplementing the most similar support sample through a support sample expansion module in the training process, improving the expression ability of the category prototype, and enhancing the generalization performance of the model. Finally, an optimization strategy based on meta learning is adopted, and a joint optimization mode of classification loss, regression loss and meta loss is combined, so that the matching precision of the category prototypes is improved. The method has the advantages that the detection precision and generalization ability of small sample target detection are improved under the condition that extra data labeling cost is not increased, and the method is suitable for various application scenes.

The invention provides an expressway disease detection method capable of accurately positioning. The expressway disease detection method comprises the following steps: S1, image acquisition and data annotation; s2, constructing an instance segmentation model; s3, extracting a multi-scale feature map and fusing multi-scale features; s4, outputting a category label, a bounding box coordinate and a confidence coefficient score of the detection box; s5, performing multi-target trajectory tracking; s6, eliminating global motion influence; s7, establishing an association relationship between the prediction trajectory and the current detection frame; s8, dynamically maintaining a track list; and S9, outputting bounding box coordinates and ID tags in each target continuous frame, and storing the bounding box coordinates and the ID tags as structured data. According to the method, a high-performance target detection framework, an advanced instance segmentation tracking technology and a GPS positioning technology are combined, pixel-level identification and continuous tracking of diseases can be realized while high detection precision is ensured, and the positions of the diseases can be accurately positioned.

The invention discloses a text generation image diffusion model enhancement method based on multi-target preference optimization. The method comprises the following steps: firstly, determining a plurality of reward models, and constructing a sample pair training set comprising positive and negative samples; and then, generating a loss weight of each sample pair in the sample pair training set, performing fine tuning training on the text map diffusion model by using the sample pair training set, and in the fine tuning training process, calculating a loss function value of each sample pair in combination with the loss weight of each sample pair until the training is completed, thereby obtaining an aligned text map diffusion model. According to the method provided by the invention, manual data annotation is not needed, the problems of preference inconsistency and over-optimization in a multi-reward scene are effectively solved, and the image quality, the text alignment capability and the multi-target optimization performance of the text-to-image generation model are remarkably improved. The method is superior to an existing optimization method under single-reward and multi-reward setting, shows higher generation quality and robustness, and can be seamlessly applied to various picture generation models.

The invention relates to the technical field of medical image processing, in particular to a cross-modal image fusion detection system for endoscopic early cancer lesions, which comprises a data acquisition module for acquiring a white light endoscope and a narrow-band imaging image; the data annotation module carries out pixel-level annotation, and the data enhancement module amplifies a data set by using CycleGAN; the white light data module and the narrowband data module train feature extraction models respectively; the feature alignment and attention module solves the problem that feature scales and semantics between two modes are inconsistent, and efficient fusion is achieved. The edge enhancement module enhances feature extraction of small focuses through the generative adversarial network; the fusion model construction module fuses the features and the transition area data to generate a fusion model; and the output module processes the white light and the narrow-band imaging image and outputs a fused image. Through feature alignment, an attention mechanism and a CyclGAN data enhancement technology, the problem of insufficient medical image data is effectively solved, the model generalization ability is enhanced, and efficient and accurate early cancer focus detection is realized.

The invention discloses a self-adaptive sampling and hyper-space attention apricot tree disease detection model, and belongs to the technical field of crop disease and insect pest protection, and the model comprises the steps: S1, obtaining a large number of apricot tree disease images; s2, performing data annotation on the acquired apricot tree disease image; s3, preprocessing the marked data, and then processing the data by adopting four data enhancement methods of Cutout, Cutmix, Mosaic and copy enhancement; s4, performing feature extraction on the image through a multi-modal large model architecture, then performing processing by using a spatial state attention mechanism and a dynamic latent variable network model, and finally training the model by using a Focal loss function; s5, carrying out model lightweight design through network pruning and knowledge distillation technologies; according to the adaptive sampling and hyper-space attention apricot tree disease detection model provided by the invention, efficient and accurate disease detection is realized, and the model is of great significance to improvement of agricultural production efficiency and guarantee of food safety.

The invention discloses an automatic evaluation method and system for an RAG intelligent agent system, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving original document data uploaded by a user, carrying out semantic segmentation on an original document based on a natural language processing technology, generating a plurality of document blocks with complete semantics, and according to different conditions, combining the content of the document blocks, carrying out the automatic evaluation of the RAG intelligent agent system. Questions and corresponding answers in different scenes are automatically generated, wherein the questions comprise a question generated based on a single document block, a cross-block question generated by combining a plurality of document blocks and a hidden question generated based on hidden semantic reasoning; according to the automatic evaluation method and system for the RAG intelligent agent system, through an automatic evaluation process and a problem generation method, the dependence on manual intervention is remarkably reduced, the complexity of data annotation and result verification is reduced, and the evaluation time is greatly shortened, so that the requirement of rapid iterative optimization is met.

The invention is suitable for the technical field of image annotation and intelligent transportation, and provides an intelligent image data annotation method and system based on multi-modal semantic fusion, and the method comprises the following steps: obtaining image data, point cloud data, voice data and environment information of the same time and space; performing semantic segmentation on the image data to obtain image segmentation information; performing alignment processing on the point cloud data and the image data to generate a 3D point cloud aligned with image pixels; according to the 3D point cloud, deducing the geometric contour of the shielded target to obtain point cloud geometric information; fusing the image segmentation information and the point cloud geometric information in combination with environment information to obtain image fusion information; and in combination with the voice data, marking visible targets and shielded targets in the image data with behavior semantics according to the image fusion information. According to the method, multi-modal data such as images, voices and point clouds are fused, so that semantic annotation can be accurately carried out on the occluded target in a complex scene.

The embodiment of the invention relates to the technical field of image processing, in particular to a garbage incinerator flame combustion situation recognition method based on unsupervised learning. According to the method, the problem of lack of labeling data in flame combustion situation recognition of the garbage incinerator is effectively solved through an unsupervised learning framework; a mutual information maximization objective function driving model is utilized to autonomously excavate essential feature association in a flame image, and a corresponding relation between a combustion state and a visual feature can be established without depending on manual labeling; and hierarchical description of the combustion state is realized through dual-label output of main clustering and super clustering, so that the macroscopic working condition classification capability is reserved, and the microscopic dynamic change characteristics are captured. Visibly, according to the embodiment of the invention, the adaptability of the model to a complex combustion scene is enhanced while the data annotation cost is reduced, and refined monitoring and recognition of the combustion state of the incinerator can be realized.

The invention discloses a rolling bearing cross-domain fault diagnosis method and device and a storage medium, and the method comprises the steps: collecting fault data of a rolling bearing, dividing the fault data into source domain data and target domain data, and determining a corresponding source domain data set and a target domain data set based on the source domain data and the target domain data; training the deep migration network model by using the source domain data set and the target domain data set; and performing fault diagnosis on the rolling bearing through the trained deep migration network model. The problems of data scarcity, domain difference and the like in fault diagnosis can be effectively solved, the features of the target domain data are extracted in a targeted manner by utilizing the feature representation of the source domain data and the model, so that the generalization ability and the diagnosis accuracy of the model are remarkably improved, and the fault diagnosis efficiency is improved. The trained deep migration network model is used for fault diagnosis of the rolling bearing, so that the data annotation cost can be reduced, the convergence process of the model is accelerated, and support is provided for accurate fault diagnosis.

The invention relates to a paper surface defect detection method and system based on multi-modal data fusion. The method mainly comprises the following steps: collecting data of to-be-detected paper at the same time by using a laser radar and an image acquisition device; preprocessing the to-be-detected paper data acquired by the laser radar, and performing multi-modal fusion on the preprocessed to-be-detected paper data and the to-be-detected paper data acquired by the image acquisition equipment; collecting paper surface defect data and data labels, and training the defect model by adopting an improved YOLOv7 algorithm in combination with a double-layer template algorithm to obtain a paper surface defect model; the target paper is detected and recognized, and defective paper is removed; and visual interface display: visually displaying the detection and identification result of the target paper. The image processing and deep learning technologies are introduced, the paper surface defect detection method and system are efficient and reliable, the limitation of a traditional manual detection method is overcome, and the quality and production efficiency of printed matter are improved.

The invention relates to the field of data annotation, and particularly discloses a data annotation method and system based on a large model, a terminal and a medium. Loading the fine-tuned domain model to carry out batch pre-labeling on the standardized data set to obtain a pre-labeling result of the standardized data set; calculating the prediction uncertainty of each sample in the standardized data set, and selecting a plurality of samples according to the prediction uncertainty to form a first to-be-audited data set; predicting the contribution degree of each sample in the standardized data set to the improvement of the domain model, and selecting a plurality of samples according to the improvement contribution degree to form a second to-be-audited data set; taking a union set of the first to-be-audited data set and the second to-be-audited data set to generate a to-be-audited target data set, and manually auditing the pre-labeling result of each sample in the to-be-audited target data set; and obtaining a labeling result of the standardized data set according to a manual auditing result. According to the invention, the data labeling efficiency and precision are improved.

The invention provides a medical image data annotation generation method, system and device and a medium, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining a medical image, carrying out the standardization processing of the medical image, and obtaining a standardized medical image; performing target detection and image segmentation on the standardized medical image in sequence by using the trained medical image analysis model to obtain bounding box information and a segmentation mask; generating a binary mask of the standardized medical image according to the segmentation mask; and inputting the standardized medical image, the bounding box information and the binary mask into a trained data annotation generation model, and generating a medical image data annotation by the generation model. According to the method, the specific organ contour or the pathological region shape in the standardized medical image can be accurately marked, the influence of noise and a fuzzy region is effectively overcome, the marking precision is remarkably improved, more comprehensive medical image analysis information is provided, and the generalization ability is high.

The invention relates to the technical field of image generation, and discloses a country style and appearance design image generation method and system based on a knowledge graph and a large model, and the method comprises the steps: constructing a core element covering country style and appearance design and a knowledge graph; constructing a multi-level label system based on the knowledge graph; pre-processed rural style and appearance sample images are obtained, the rural style and appearance sample images are labeled based on a multi-level label system, and a special training set in the field of rural style and appearance design is obtained; constructing a basic diffusion large model architecture, and training the basic diffusion large model architecture by using the special training set for the country style and appearance design field to obtain a trained country style and appearance design image generation model; and generating a rural style and appearance design image by using the trained rural style and appearance design image generation model. According to the method, the problems that a general large model is insufficient in domain knowledge support and insufficient in data labeling specialty in rural style and appearance design image generation are solved, and the rural style and appearance design image generation efficiency and quality are improved.

The invention provides a gust front wind shear identification method based on artificial intelligence, and the method comprises the steps: carrying out the noise filtering, missing value supplementary measurement, data smoothing, wind shear value calculation and sample screening extraction of collected radial speed data, and obtaining a gust front wind shear sample; performing coordinate system conversion, sample set division and data annotation on the basis of gust and front wind shear samples to obtain an expanded data set; designing and training a Mask R-CNN model architecture to obtain a gust and front wind shear identification model; and inputting the expanded data set into the gust and front wind shear identification model to carry out gust and front wind shear detection. According to the method, dependence on reflectivity factor data can be reduced, an identification model is constructed based on gust front radial speed data, gust front wind shear can be accurately identified, pixel-level segmentation and positioning of a wind shear area can be realized, and identification efficiency is improved.

The invention relates to the technical field of medicine, and particularly discloses a semi-supervised medical image segmentation method based on an improved Transform, which designs a sphere embedded improved Transform module, constructs a boundary enhancement module based on morphological difference, and aims to improve the medical image segmentation performance. According to the method, a semi-supervised learning framework is adopted, and the dependence on a large-scale annotated data set is reduced by effectively utilizing limited annotated data and rich unannotated data, so that the accurate segmentation of the medical image is realized under the condition of limited resources. The method is expected to reduce the cost and time of data annotation while improving the segmentation precision, and brings a new breakthrough to the field of medical image segmentation.

The invention discloses a graph isomorphic network model-based power distribution network wiring mode identification method, which comprises the following steps of: according to a wiring mode analysis requirement, defining a sub-graph generation method, a rapid feature rule and a node mode category, and constructing and training a GIN model to realize analysis and identification of a power distribution network wiring mode. The specific process comprises the following steps: acquiring power grid data and simplifying subgraph extraction; performing preliminary classification analysis based on simple rules; marking equipment nodes according to the wiring mode, and generating a training data set; constructing a GIN model and carrying out model training; and according to the training model, applying all the simplified subgraph node classification data of the power distribution network to generate a wiring mode identification result. According to the method, a traditional graph search algorithm and a GIN graph neural network model are integrated, the defect that a traditional method depends on a manual rule arrangement process and a complex method for rule implementation is overcome, and a data annotation and model training method is adopted to achieve the recognition target of the wiring mode in the power distribution network.