141 results about "Disease area" patented technology

The invention discloses a plant disease and insect pest early warning method and system based on plant monitoring, and belongs to the field of plant disease and insect pest early warning, and the method comprises the steps: extracting the contour of a lesion region through an edge detection algorithm, carrying out the smoothing of the contour through the combination of morphological operation, and obtaining a more precise lesion region boundary; according to an image segmentation result and a disease type identification result, a plant health condition evaluation model is established, and the plant damage degree is quantified; environmental data and image data are acquired from a plurality of sensor nodes distributed in a field, and the data are gathered to a regional gateway through wireless transmission; carrying out preprocessing and feature extraction on the converged heterogeneous data in a regional gateway, removing noise data and redundant information, and extracting key features; and carrying out pest detection and counting on the preprocessed image by using a deep learning model, carrying out modeling on a pest number change trend, predicting population density change in a period of time in the future in combination with environmental factors, and generating a detection result.

The invention discloses a grape disease recognition and early warning method based on the Internet of Things, and relates to the technical field of plant disease recognition, image acquisition equipment and environment sensing nodes are arranged in a vineyard, and leaf images and corresponding temperature and humidity, illumination and soil moisture parameters are obtained; inputting the image into a neural network fusing dilated convolution and a residual attention mechanism, realizing extraction of a disease spot region and a disease spot variation feature, and generating a preliminary recognition result; constructing a multi-factor evolution sample set in combination with the recognition result and the environment state of the time node; constructing a space-time correlation graph model based on a graph neural network, estimating a disease propagation risk path and a diffusion probability, and performing early warning judgment at a gateway end through a multi-factor gating discrimination algorithm; the method disclosed by the invention is high in recognition precision and strong in response timeliness, has adaptive prediction and targeted treatment capabilities, and remarkably improves the intelligence and precision level of grape disease management.

The invention discloses a tunnel lining disease automatic identification method and system based on multi-source data fusion, and relates to the technical field of facility detection, and the method comprises the steps: collecting multi-modal time sequence data, carrying out the time-space alignment, and obtaining a time sequence multi-source data set; reconstructing a tunnel center line based on a vehicle pose and constructing a lining structure consistency coordinate framework, and performing structured projection and distortion correction on alignment data to obtain a multi-modal fusion data set; dividing a two-dimensional structure grid under the coordinate framework, extracting and fusing geometric, texture, depth and energy features, calculating a structure consistency damage index, and extracting a suspected disease area; and calculating a disease credibility index and judging a disease type in combination with multi-modal physical evidence, mapping a suspected disease region back to a three-dimensional space, completing disease boundary extraction and geometric quantization, and outputting structured disease information. According to the method, the structure expression and the structured alignment of the cross-modal data under the unified geometric reference are realized by constructing the consistent coordinate framework of the lining structure.

The invention relates to the technical field of road pavement disease recognition, in particular to an intelligent road pavement disease recognition method and system based on a multispectral image, and the method comprises the steps: obtaining road pavement image data collected by a multispectral camera carried by an unmanned aerial vehicle; generating a registered multispectral fusion image; performing enhancement processing on the multispectral fusion image based on a dark channel algorithm to generate an enhanced image; performing shallow feature extraction and deep feature extraction on the enhanced image according to a convolutional neural network to generate an initial disease extraction image; performing segmentation processing on the initial disease extraction image through adaptive threshold segmentation to generate a binary image; performing morphological processing on the binary image to generate a candidate disease image; extracting edge information of a disease area from the candidate disease image based on an instance segmentation technology, and generating a segmented image; the segmented image is subjected to crack refining processing, a crack refined image is generated, and more comprehensive disease information support is provided for collection and fusion of multispectral data.

The invention relates to the field of image cultural relic disease recognition and evaluation, in particular to a cultural relic disease recognition and evaluation system and method based on image analysis, and the system comprises a multispectral image collection module, an image processing module, a disease recognition module, a repair evaluation module, a decision optimization module and a disease database. The multispectral image acquisition module optimizes a light source through a reflective area detection algorithm to obtain a multispectral image; the image processing module uses a multi-band collaborative filtering algorithm to eliminate noise and extract fusion features, and divides disease areas; the disease identification module determines disease positions through a connected region marking algorithm, and performs disease classification by means of a neural symbol model; the repair evaluation module outputs a quantitative score; the decision optimization module weighs the repair effect, the cost and the time by introducing a multi-objective optimization model of a genetic algorithm, and outputs an optimal scheme; according to the invention, the disease identification precision and the automation and scientificity of repair evaluation are improved.

The invention relates to the technical field of tunnel crack detection and artificial intelligence edge calculation, and provides a tunnel apparent disease detection method based on deep learning and knowledge distillation, which comprises the following steps: step 1, introducing spectral domain information enhancement to an original tunnel image, the edge texture features of the disease area in the image are enhanced through methods such as multi-scale wavelet transform and small-scale enhancement. Step 2, constructing a high-performance teacher model, introducing a flexible up-sampling structure to adapt to feature recovery requirements of different levels of semantic information, introducing an efficient visual coding module to enhance feature fusion capability of different scale channels, and designing a scale adaptive weighted loss function at the same time; by introducing a frequency spectrum enhancement mechanism, structural features of disease areas with low contrast, fuzzy edges and the like are remarkably enhanced in an image preprocessing stage, clearer information input is provided for a model, and the stable recognition capability of a system in environments of uneven illumination, complex background and the like is enhanced.

The invention discloses a pavement disease size accurate quantification method based on binocular vision, and the method specifically comprises the steps: collecting pavement image data through a vehicle-mounted binocular camera, achieving the real-time detection of a disease target through an improved RT-DETR model, and outputting the disease type and detection frame information; after a pavement disease target is detected, pixel-level segmentation is carried out on a disease area in the detection frame based on a semantic segmentation model, and a disease contour mask is extracted; an improved IGEV-Stereo stereo matching algorithm is used for calculating a disparity map, depth information is output based on camera calibration parameters and a disparity estimation result, and conversion from pixel coordinates to three-dimensional coordinates is achieved; and the binocular depth information and a disease detection segmentation result are combined to realize accurate size quantification of typical road surface diseases with different characters. Through the binocular stereoscopic vision technology, automatic detection and precise quantification of pavement diseases can be realized, the cost can be effectively reduced while the disease quantitative evaluation precision is improved, and decision support is provided for road management and maintenance.

The invention discloses a bridge support disease detection system and method based on a rail-mounted inspection robot, and belongs to the technical field of bridge detection. The system comprises a robot walking guide mechanism, the rail-mounted inspection robot and a self-adaptive following system; the rail type inspection robot comprises a chassis, and a mechanical arm is arranged on the chassis; the self-adaptive following system comprises a robot control system, an image processing system and a network communication system; the robot control system comprises a main control unit which is mainly used for controlling movement of a chassis and self-adaptive adjustment of a mechanical arm; the image processing system comprises an image acquisition device, an image processing and storage device and an edge computing device; and the image acquisition equipment is arranged on the mechanical arm and is mainly used for acquiring image data. According to the invention, intelligent identification and adaptive following can be carried out on the bridge support disease image, so that the image acquisition equipment can be ensured to lock and track the disease area from the front side, and high-quality and omission-free photographing detection can be realized.

The invention relates to the technical field of space-time prediction, in particular to a brucellosis space-time prediction method and system based on artificial intelligence, and the method comprises the following steps: obtaining case information, calculating an incidence relation, extracting a path sequence, screening a trend direction, and matching a new case to generate prediction data. According to the method, continuous identification of a propagation path is realized by constructing a propagation incidence relation between cases and introducing a spatial directivity index, a non-trend propagation process is screened out through an angle average value and a variance, spatial consistency of path screening is enhanced, and through joint matching of spatio-temporal characteristics of newly-added cases and an existing propagation trend, the propagation path screening efficiency is improved. The sensitivity of prediction to the trend attribution of a new case is improved, the spatial directivity of a potential disease area is enhanced through reverse projection of a trend path and area positioning drop point frequency analysis, and through linkage processing of multi-level path extraction, trend judgment and area coding, the probability of occurrence of the new case is lowered. And the capturing capability of the prediction data on the propagation and evolution characteristics of the brucellosis is improved.

The invention discloses a typical bridge disease characterization method, and relates to the technical field of bridge detection, and the method comprises the steps: firstly constructing a degraded disease sample library, and building a surrounding environment model and a bridge BIM model; secondly, constructing a virtual bridge scene by using a UE5 engine, and obtaining a virtual disease sample; performing data enhancement by adopting an improved double-branch generative adversarial network in combination with real and virtual samples, generating a similar real degradation sample, and expanding real sample data; carrying out disease area identification and extraction on the detection image by using an improved Unet network trained by using real sample data; and finally, mapping an identification result to a BIM model to realize three-dimensional visual representation. According to the method, the problem of insufficient disease samples is solved through the generative adversarial network, the fine-grained disease recognition precision is improved by utilizing the improved Unet, full-process digital representation of bridge diseases from data acquisition, intelligent recognition to three-dimensional dynamic display is realized, and the accuracy and visualization effect of disease detection are remarkably improved.

The invention discloses a leaf vegetable disease detection method based on few sample learning and prototype attention. The method comprises the following steps: S1, preprocessing and enhancing data; s2, performing multi-scale feature extraction on the image; s3, global feature modeling is carried out; s4, target detection and semantic segmentation; and S5, carrying out few-sample optimization and fine tuning. According to the leaf vegetable disease detection method based on few-sample learning and prototype attention, a few-sample learning network architecture is introduced for solving the problem of agricultural disease data sample scarcity, rapid and accurate disease area identification and segmentation can be realized under a limited data condition, target detection and semantic segmentation tasks are combined, and the detection efficiency is improved. Disease positioning and fine-grained segmentation are carried out, the detection precision and segmentation performance are improved, prototype extraction and an attention mechanism are fused, the learning ability of the model under the low-sample condition is effectively improved, the method can be applied to disease detection and control of the vegetable planting area in real time, and efficient disease management of farmers is supported.

The invention provides an interactive pavement disease segmentation method based on a large model, and the method comprises the steps: constructing pavement disease segmentation models which comprise a disease detection network model, a disease initial segmentation network model and a disease segmentation large model, carrying out the offline training of the disease detection network model and the disease initial segmentation network model, and obtaining a disease segmentation model; performing fine adjustment on the disease segmentation large model; inputting the disease image into a pavement disease segmentation model, and detecting a disease area by using a disease detection network model based on a deep learning algorithm; based on a deep learning algorithm, performing disease initial segmentation on the disease areas by using the disease initial segmentation network model to obtain a disease initial segmentation result corresponding to each disease area, and automatically obtaining a disease positive prompt point and a disease negative prompt point; performing interactive correction on the automatically obtained disease positive prompt point and negative prompt point, and updating the positive prompt point, the negative prompt point and the frame; and based on the updated positive and negative prompt points and the frame, automatically segmenting the disease by using a disease segmentation large model to obtain a final segmentation mask.

The embodiment of the invention provides a tunnel disease detection method and system based on an unmanned aerial vehicle, and belongs to the technical field of defect optical detection. The method comprises the steps that an unmanned aerial vehicle is controlled to fly along a tunnel to collect multichannel image data of the surface of a structure, and the flight attitude is adjusted based on environment illumination information to execute image illumination compensation; performing image alignment of each target anchor point based on a structure anchor point atlas constructed based on historical acquisition images in combination with the flight pose information of the unmanned aerial vehicle and the multi-channel image data after illumination compensation; identifying a disease area in the aligned image, extracting disease features of each target anchor point at this time, and updating the disease of each target anchor point at this time into a time sequence feature data sequence corresponding to each target anchor point; and based on the updated time sequence characteristic data sequence of each target anchor point, Bayesian point change detection is adopted to analyze the disease evolution trend of the tunnel. According to the scheme, the alignment precision, the time sequence comparability and the risk judgment capability of tunnel disease detection are integrally improved.

The invention discloses a bridge disease detection method based on a diffusion model and a bitter fish optimization algorithm, and relates to the technical field of bridge detection. The method comprises the following steps: fixing a visual angle and a distance at an easy-to-peel or crack position of a bridge, and collecting and aligning visible light and near-infrared images; carrying out multi-scale downsampling on the image, and carrying out wavelet denoising, brightness correction and texture smoothing; inputting the preprocessing result into an improved diffusion model, weighting the edge during forward diffusion, and reversely generating and applying texture and contour smoothing; the multi-source feature channel and the reconstructed image are combined and input into a deep segmentation network, shadow and stain are eliminated by using a local difference function, and global search is performed on a segmentation threshold, a noise coefficient and the like based on a disease detection rate, a false detection rate and the like by using a bitter fish algorithm; training and correcting the high-noise area again according to the optimal parameters; and uniformly marking disease areas. According to the method, the recognition recall rate of tiny spalling and irregular cracks in an extreme environment can be greatly improved, and the intelligent level and the practical effect of bridge disease detection are improved.

The invention relates to a potato leaf disease area positioning method, system and device.The method is applied to the potato leaf disease area positioning device.The method comprises the steps that a target infrared image of a potato leaf is segmented into a normal area and an abnormal area according to the environment temperature through a disease segmentation model obtained in advance; determining the water content of the target infrared image according to the leaf spectrum information through a pre-obtained water content prediction model; the leaf emissivity characteristic is calculated according to the water content through a pre-obtained temperature correction model, the temperature of the abnormal area is corrected based on the emissivity characteristic and the water content characteristic, and the temperature correction model is obtained based on potato leaf characteristic training; and through a pre-trained disease identification model, identifying a disease area from the abnormal area after temperature correction.

The invention relates to the technical field of intelligent maintenance, in particular to an intelligent pavement disease sorting platform based on AI recognition. The platform takes a multispectral acquisition module and an edge calculation module as front acquisition and identification modules, and further comprises a disease sorting decision module and a disease interactive display module, and can acquire a pavement disease image frame in real time to perform frame extraction preprocessing and single-frame delay control disease area identification so as to obtain a disease position area in a pavement image single frame; performing crack disease 12-dimensional feature analysis and maintenance emergency evaluation on the disease position area in the single frame of the pavement image to obtain a maintenance emergency degree index corresponding to each single frame of the pavement image; and carrying out maintenance emergency sorting planning based on the maintenance emergency degree index corresponding to each pavement image single frame, and issuing a maintenance disposal instruction on the man-machine interaction terminal to execute corresponding pavement disease maintenance motorcade scheduling planning work. According to the method, the detection efficiency and the sorting accuracy can be improved, resources are optimized, and the investment of unnecessary maintenance is reduced.

The invention relates to the technical field of road detection, in particular to a multi-dimensional road disease detection method and system based on a ground penetrating radar. The method comprises the following steps: acquiring original ground penetrating radar data, performing DC component removal, gain adjustment and background denoising on the original ground penetrating radar data, positioning a suspected disease area and defining the suspected disease area as a disease entity; extracting a time domain feature, a frequency domain feature and a spatial context feature of each disease entity in parallel to form a multi-dimensional feature vector; constructing a road disease knowledge graph according to the multi-dimensional feature vectors, and distributing an initial weight for each graph relation; and performing node matching operation according to the multi-dimensional feature vector and a knowledge graph, performing logical reasoning according to a graph relation path, fusing similarity and reasoning confidence, and outputting a diagnosis tag signal and a confidence signal. According to the method, the positioning accuracy of the suspected disease area is improved, comprehensive utilization of multi-dimensional information is realized, and a visual causal link is provided for diagnosis.

The invention provides a rice disease identification method and system, and the method comprises the following steps: S100, carrying out the labeling of a disease region of a rice leaf image, and obtaining a segmented label image; s101, performing feature extraction on the rice leaf image; s102, enhancing partial feature boundary information to obtain a first enhanced feature; s103, searching partial feature targets to obtain second enhanced features; s104, receiving a part of the second enhancement feature and the first enhancement feature, and outputting a decoding feature; s105, receiving a part of decoding features, and outputting a semantic prediction result; s106, extracting the boundary of the segmented label image to obtain a boundary label image; s107, receiving the first enhanced feature and a part of the second enhanced feature, and constructing boundary supervision loss; s108, receiving a boundary prediction result and a semantic prediction result, and constructing regular term loss; and S109, training the constructed loss by using a rice disease identification model, realizing boundary information of a concerned disease area, obtaining an accurate boundary segmentation effect, and improving the rice disease detection precision.

The invention relates to the technical field of material analysis, in particular to an intelligent inspection device and method for bridge structure diseases, and the method comprises the steps: determining a plurality of disease suspected regions through obtaining a plurality of frames of unmanned aerial vehicle images of a bridge structure, carrying out the feature analysis of each disease suspected region, and determining the feature value of each disease suspected region; according to different candidate time interval lengths and characteristic values, determining interval change fluctuation degrees representing the possibility of rain and sewage flow marks corresponding to different candidate time intervals in each disease suspected area, and carrying out feature distinguishing on the rain and sewage flow marks and crack diseases; according to the interval change fluctuation degree, determining interval division fitness, comprehensive identification capability and timeliness of different candidate time intervals; according to the interval division fitness, the target interval change fluctuation degree corresponding to each disease suspected area is determined, so that the crack disease area is determined for intelligent inspection, the recognition accuracy is improved, and the intelligent inspection effect is ensured.

The invention relates to the technical field of tea production, in particular to a method for distinguishing tea leaves with diseases and insect pests based on an imaging hyperspectral technology, which comprises the following steps of: obtaining disease sensitive wavebands according to reflectivity distribution conditions of different spectral wavebands in a leaf spectral image; calculating a disease spectrum ratio index of the leaf spectrum image based on the disease sensitive wave band, obtaining a disease spectrum ratio index image, further obtaining a disease area mask, finally separating a disease area based on the disease area mask, and obtaining a classification result through a preset disease and pest classification model. According to the method, efficient and accurate distinguishing of tea leaves with diseases and insect pests is achieved through the imaging hyperspectral technology, the visual expression of early weak lesions is remarkably enhanced based on disease sensitive band extraction and spectral ratio index calculation, the problem that a traditional method is insufficient in early disease recognition sensitivity is solved, and the method is suitable for large-scale popularization and application. A multi-category disease diagnosis result can be quickly output in combination with the preset disease and pest classification model, and the problem of low tea disease detection efficiency in the prior art is solved.

The invention discloses a tunnel disease detection method based on illumination compensation and dynamic feature fusion. The tunnel disease detection method is specifically implemented according to the following steps: step 1, data preprocessing; step 2, constructing an illumination adaptive compensation module; step 3, constructing a hierarchical attention fusion module; and step 4, constructing an adaptive spatial pyramid rapid pooling module. According to the method, the problem of feature aliasing caused by uneven illumination, background interference and scale mismatching in tunnel disease detection can be effectively relieved, accurate recognition and positioning of multi-type and multi-size disease areas are achieved, and the robustness and universality of a detection system in an actual engineering scene are improved.

The invention relates to the technical field of image processing, in particular to a pavement disease identification method and system based on multiple deep learning models. According to the pavement disease recognition method based on the multiple deep learning models, a unified disease recognition input standard is established through image normalization preprocessing; carrying out disease image classification by adopting an improved ResNet50 model; a trained first-stage target detection model is adopted to preliminarily mark a disease area in a road image, a second-stage target detection model is adopted, dense small frame marking training is combined with intersection operation, refined disease recognition is performed on a disease image, and the accuracy is improved. According to the pavement disease recognition method and system based on the multiple deep learning models, the robustness of the system to illumination, noise and complex disease forms is enhanced, the efficiency of the detection process is optimized, the accuracy and reliability of pavement disease detection are remarkably improved, and efficient and accurate technical support is provided for road maintenance and management.

The invention relates to a class-independent comparative learning and supervised tissue mapping method for plant disease classification, and belongs to the technical field of plant disease classification. The method comprises the following steps: firstly, pre-training a plant disease data set through class-independent comparative learning, and enhancing the positioning capability of a model in a complex background and a randomly distributed lesion area through a constructed supervised comparative learning framework; and then, standard classification training is carried out by using the positioned disease area, so that distinguishable features are automatically extracted, powerful initialization is provided, and the learning ability of the model to disease features is ensured. Finally, a novel supervision mapping model is provided, and the discrimination capability of the model is enhanced through an innovative node determination method and introduction of orthogonality sparse representation. According to the method, through joint optimization of feature fusion and back propagation, the separation degree of features between categories is further improved, and the robustness of disease classification is enhanced.

The invention discloses a crop disease intelligent identification method and system based on an unmanned aerial vehicle image, and belongs to the technical field of intelligent agriculture, and the method comprises the steps of data multi-dimensional collection, early disease identification, disease space-time deduction and crop management suggestion. According to the method, early disease recognition based on the spectrum sensitive vegetation index and the attention guidance double-branch network is adopted, the spectrum sensitive vegetation index capable of amplifying weak disease spot features is constructed, the weak disease features are highlighted through multispectral information enhancement and time sequence feature inhibition, meanwhile, the attention mechanism is utilized to focus on potential disease areas, and the disease recognition accuracy is improved. Therefore, the accuracy and stability of early disease recognition are remarkably improved. Disease space-time deduction combining propagation potential energy and a space-time diagram convolutional network is adopted, on the basis of considering factors such as environmental conditions, disease time sequence evolution and spatial neighborhood influence, a disease propagation path is dynamically simulated, future risks are quantified, and a visual risk prediction map and a propagation vector diagram are generated, so that agricultural management is assisted to be optimized.

The invention discloses a bridge intelligent inspection unmanned aerial vehicle integrated measurement and control system, and relates to the technical field of unmanned aerial vehicle measurement and control, and the system comprises a reference construction module, a pose acquisition module, a pose matching module, a coordinate generation module, and a disease locking module. According to the method, the reference three-dimensional model and the space reference control point are constructed, so that accurate registration of the real-time video stream and the historical model is realized; driving the unmanned aerial vehicle to actively adjust the pose and lock the disease area through visual servo control; the absolute three-dimensional space coordinates of the diseases are calculated through reverse ray tracing, integration of disease recognition, active observation and accurate positioning is achieved, the automation degree of bridge inspection is improved, and the problems that in the prior art, bridge disease recognition and space positioning links are disjointed, cross-cycle data cannot be aligned, and an active observation mechanism is lacked are solved.

The invention relates to the technical field of image analysis, in particular to a pinus massoniana disease model training method based on image analysis, and the method comprises the following steps: collecting a multi-time sequence image, constructing a boundary sample through gray scale and Sobel, obtaining a gradient deviation through CNN through outer normal gradient increment, and combining contour comparison, differential sequence and clustering recognition mutation points and marking to form a marking set; according to the marking and transition cluster, convolution kernel weight is adjusted to optimize boundary representation, the model is iteratively updated to improve the pinus massoniana disease identification precision, the time sequence stability is enhanced, the robustness in a complex scene is improved, the edge detail recovery capability is improved, and the detection consistency is enhanced. According to the method, contour gradient operation is carried out by extracting image gray and combining Sobel, subtle change is identified and time sequence information is fused to capture disease development, gradient response and error comparison are carried out to improve detection precision, mutation points are identified through differential clustering analysis and disease areas are marked, and the model is optimized to reduce boundary deviation.

The invention discloses a tomato leaf disease detection method, which is based on an improved lightweight target detection model MWD-YOLO11n to improve the accuracy and efficiency of disease identification. According to the method, three key improvements are carried out on the basis of an original YOLO11 model: firstly, a multi-scale dynamic attention mechanism (MSDA) is introduced, and the perception ability of the model to different-scale disease features is enhanced; secondly, wavelet convolution (WTConv) is adopted to replace part of a traditional convolution structure, and the capturing capacity of the model for disease edges and texture details is effectively improved; and finally, integrating a dynamic up-sampling operator (DySample), optimizing the resolution of the feature map, and further enhancing the characterization capability of the disease area. The method has high precision and low calculation amount, is suitable for deployment of edge equipment, shows excellent performance in tomato leaf disease detection tasks, and has good application prospects and popularization value.

The invention discloses an outer wall disease detection and health assessment method based on a fusion-segmentation joint network, and the method comprises the steps: collecting an infrared thermal image and a visible light image of an outer wall disease, and constructing an infrared image fusion data set after preprocessing; constructing a fusion-detection joint network comprising an image fusion network module and a segmentation detection network module, and realizing collaborative optimization through a joint loss function; pre-training is carried out by using an MSRS data set so as to obtain the best parameters of the dynamic pixel fusion weight and the loss function weight item; freezing parameters of the image fusion network module, and optimizing the segmentation detection network module in combination with a self-established fusion data set; using the trained fusion-detection joint network to carry out disease segmentation detection on the building exterior wall image; counting the disease area and distribution condition according to the segmentation detection result, and comprehensively evaluating the health degree of the outer wall. According to the invention, automatic disease segmentation detection and external facade health assessment are realized, and the disease detection accuracy is improved.

The invention discloses a bridge coating intelligent maintenance method and system based on an unmanned aerial vehicle, and aims to improve the efficiency and precision of bridge coating disease detection and maintenance decision through the combination of an unmanned aerial vehicle technology and a large language model. The method comprises the following steps: automatically scanning the surface of a target bridge through an unmanned aerial vehicle carrying a high-resolution camera, and collecting high-definition image data of a bridge coating; processing the image data by using a deep learning model, and automatically identifying the disease type and the disease area of the bridge coating; and according to a disease identification result, obtaining related maintenance knowledge and meteorological data from a knowledge base through a coating maintenance large language model, and generating a personalized coating maintenance scheme in combination with reasoning analysis. According to the method, efficient and accurate detection of bridge coating diseases can be realized, a scientific and reasonable maintenance scheme is generated based on historical data and environmental factors, the bridge coating maintenance accuracy and maintenance efficiency can be effectively improved, the service life of a bridge is prolonged, and the method has a wide application prospect.

The invention discloses a steel box girder bottom damage identification and synchronous positioning method based on a distributed camera network. Comprising the following steps: S1, forming a series of imaging equipment units according to a spatially parallel structure, and connecting the imaging equipment units together through Bluetooth and a wireless area network so as to perform image data acquisition on any position of the bottom of a steel box girder; s2, transversely dividing the bottom area of the steel box girder according to the shooting coverage range of each imaging equipment unit; s3, performing two-stage synchronous identification and positioning based on deep learning driving, in the first stage, performing lightweight reconstruction on a panoramic image by taking MobilenetV4 as a key feature extraction tool, and performing disease area identification and positioning on a global level; and in the second stage, a YOLOv9 target detection framework is utilized to analyze a disease area, and disease information is provided in a local level. The design of the invention can be widely applied to inspection of the bottom of a long-span bridge and efficient analysis of diseases.