193 results about "Imagery analysis" patented technology

The invention belongs to the technical field of road deformation monitoring, and discloses a road slope deformation monitoring system and method, and the system comprises a fixed point monitoring mechanism which monitors the displacement, strain, underground water level and inclination parameters of a slope in real time at key points and key positions of the road slope; the route planning module plans the flight route of the unmanned aerial vehicle; the cruise monitoring mechanism is used for monitoring the side slope in all directions; the weather data acquisition module is used for acquiring local weather data; the three-dimensional laser scanner data analysis module is used for identifying abnormal conditions on the side slope; the image analysis module is used for identifying abnormal conditions on the slope; the comprehensive evaluation module is used for comprehensively evaluating the stability of the slope by utilizing a comprehensive evaluation model; and the alarm module comprises an alarm. According to the invention, the fixed-point monitoring mechanism and the cruise monitoring mechanism are combined to monitor the slope in all directions, the comprehensive evaluation module fuses data of various monitoring means, and the comprehensive evaluation model is utilized to comprehensively evaluate the slope stability.

The invention relates to the technical field of garden monitoring, in particular to a landscaping monitoring system and method, and the system comprises a collection module which is used for obtaining multi-angle images of a garden in stages, and selectively collecting the images according to a correlation judgment result; the analysis module is used for extracting vegetation indexes, disease targets, leaf health features and falling object state features; the identification selection module is used for generating a dynamic acquisition strategy according to the relevance abnormal index and triggering the acquisition module to directionally acquire the second-level image; the weighted fusion module is used for performing adaptive weighted fusion on the multi-source features and calculating a comprehensive wilt risk level; the early-warning module is used for generating early-warning signals and early-warning grades, the view limitation of traditional single-view-angle monitoring is broken through, large-range vegetation vitality abnormal areas can be rapidly screened, detail features of trunk diseases, low leaf withering and microscopic disease spots can be accurately captured, and the comprehensiveness and reliability of early withering recognition are remarkably improved.

The invention provides a crop yield prediction method and system based on image analysis, relates to the field of yield prediction, and obtains a farmland multi-view remote sensing image through an unmanned aerial vehicle multispectral sensor, and executes aerial triangulation and ortho-rectification to obtain accurate exterior orientation elements and ortho-images. Then, vegetation indexes are extracted and combined with field yield measurement data, a yield regression model is constructed, and rapid prediction and grading of the crop yield are achieved. In order to reduce multi-view brightness inconsistency and give consideration to crop growth characteristics, prior information such as canopy row direction, leaf angle distribution and sealing degree is introduced, iterative correction is performed on the image by adopting a BRDF model, and radiation difference is corrected while geometric accuracy is guaranteed. Particularly, through staged trend analysis and multiple rounds of detection, priori conflicts can be found in time, incremental images can be flexibly collected, and resolving robustness and yield prediction precision are improved. The method can be widely applied to unmanned aerial vehicle remote sensing agricultural monitoring scenes.

The invention provides a control system, method and equipment based on intelligent treatment of flotation gas. The system comprises a monitoring unit, an evaluation unit, an operation unit, an identification unit and a control unit, the evaluation unit is connected with the monitoring unit and the operation unit. The control unit is connected with the operation unit and the recognition unit. The monitoring unit is used for monitoring flotation gas information of the target area; the evaluation unit is used for evaluating the flotation gas information by adopting a preset artificial intelligence (AI) algorithm to obtain an evaluation result corresponding to the flotation gas information; the operation unit is used for determining operation information related to the exhaust fan in the target area based on the evaluation result; the recognition unit is used for collecting picture information corresponding to abnormal working conditions in the target area and determining a recognition result corresponding to the picture information by adopting a preset AI image analysis algorithm; and the control unit is used for controlling technological parameters corresponding to the flotation gas in the target area based on the operation information and the recognition result.

The invention relates to the technical field of bird recognition, in particular to an infrared target image multi-target recognition method based on image analysis. According to the flight vector of the bird area in each frame of the environment infrared image, carrying out optical flow tracking on pixel points of the bird area in each frame of the environment infrared image, and according to the pixel point position distribution of the bird area in each frame of the environment infrared image and the consistent condition of the bird area and the movement direction of the pixel points, adjusting the movement condition of the pixel points in the bird area. And acquiring the flight distance of the bird area in each frame of the environment infrared image, and determining the risk degree of the bird area in each frame of the environment infrared image by combining the distance between the bird area and the facility area in the direction of the flight vector. According to the invention, the optical flow change caused by the wing movement is accurately adjusted in different flight states, so that the overall flight analysis of the bird is more accurate, and the accuracy of the judgment of the danger degree of the bird is improved.

The invention relates to the field of automobile electronic testing technology and equipment, and discloses a vehicle-mounted camera imaging quality testing method and device suitable for a rain and fog scene, and the method comprises the steps: installing a to-be-tested camera module in a rain and fog environment cabin, setting the spectrum and illumination of a top natural illumination simulation system, and simulating the illumination at different time periods; selecting a test target plate in the darkroom; starting an automatic centering module to align the optical axis with the standard plate reference; rain and fog parameters are set through the environment control host, the rain and fog supply system is started to simulate different levels of rain and fog environments, and the raindrop particle size, concentration, dripping speed, fog visibility and fogdrop particle size are dynamically regulated and controlled; shooting a target plate graph card, extracting characteristic parameters by an image analysis module, performing quantitative analysis, and generating an evaluation report, so as to solve the technical problem of singleness of rain and fog scene environment simulation in the prior art.

The invention belongs to the technical field of soil detection, and discloses a garden soil quality detection system and method based on environmental monitoring, and the method comprises the steps: a fixed-point data collection module carries out fixed-point monitoring; the route planning module plans an optimal flight route and a stop point of the unmanned aerial vehicle; the inspection module is used for performing all-around soil quality inspection on the garden; the image analysis module, the spectral data analysis module and the radar data analysis module identify conditions and abnormal conditions which need to be subjected to soil sampling analysis; the comprehensive evaluation module comprehensively evaluates the soil; the risk prediction module fuses multi-source data and predicts the potential risk of the garden in combination with historical data. According to the invention, through a mode of combining fixed-point detection and routing inspection detection, all-around soil quality detection is carried out on the garden; the sampling flexibility and reliability are guaranteed through the design of the sampling assembly, and the comprehensive evaluation module fuses multi-source data and comprehensively and objectively evaluates the soil quality; the risk prediction module predicts potential risks in combination with historical data.

The invention discloses an unmanned aerial vehicle forest inspection method and system based on Beidou positioning, and relates to the field of forest inspection, and the system comprises an area limiting module which is used for uploading position coordinates and limiting an inspection area based on the uploaded position coordinates; the control module is used for controlling the unmanned aerial vehicle to fly in the inspection area and collecting forest images; the analysis module is used for receiving the forest image collected in the flight process of the unmanned aerial vehicle, storing the forest image and analyzing the forest health situation tendency based on the stored forest image; according to the invention, by defining the inspection area and designing and deploying the analysis logic of the unmanned aerial vehicle, the unmanned aerial vehicle can autonomously and intelligently complete forest image acquisition, and in the forest image acquisition process, the forest image acquisition path is autonomously and randomly generated, so that the diversity of forest image acquisition is ensured; and the result of the system for subsequently evaluating whether the forest ecological environment is healthy is more reliable and effective.

The invention discloses an urban garbage removal and output prediction method and system based on deep learning, and the method comprises the following steps: S1, obtaining a road video image, analyzing the garbage distribution and density, predicting the garbage distribution evolution, and estimating the garbage amount of a cleaning region; s2, according to the estimated garbage amount and sweeping area distribution, a sanitation vehicle is dispatched for sweeping, a video image in sweeping is collected, and the rotating speed and the vehicle speed of a sweeping disc of the vehicle are adjusted; and S3, the difference between the actual garbage amount and the estimated garbage amount is analyzed and used for correcting the estimated garbage amount. Intelligent management of environmental sanitation work is achieved, garbage distribution and cleaning amount can be efficiently estimated, a scheduling scheme and cleaning parameters can be dynamically adjusted according to actual conditions, the cleaning efficiency and quality are greatly improved, meanwhile, through data analysis and model optimization, the accuracy and reliability of the system are enhanced, and the cleaning efficiency and quality are improved. The improvement of the urban environmental sanitation level and the reasonable utilization of resources are effectively promoted.

The invention relates to the technical field of image analysis and processing, in particular to a typhoon positioning and intensity determining method and device based on remote sensing data, and the method comprises the following steps: carrying out the radial gradient enhancement processing of a microwave brightness temperature remote sensing image, and extracting a gradient extreme point distribution diagram of an eye wall region; performing morphological segmentation on the infrared cloud top temperature remote sensing image to generate a binary contour feature map representing an eye region structure; and inputting the gradient extreme point distribution map and the binary contour feature map into an improved convolutional fusion network, and outputting typhoon center coordinates and strength grade parameters. According to the method, the continuous boundary is reconstructed under the complex condition that a double-eye wall or a broken eye wall exists, the adaptive capacity of a positioning algorithm to an asymmetric and fragmented structure is improved, the calculation range is limited through an eye region constraint mechanism, the interference data participation degree is reduced, and higher energy efficiency ratio and engineering deployability are achieved.

The system comprises a base, a power distribution box and a three-dimensional holder are arranged on the base, an infrared thermal imaging module and a camera are installed at the mobile end of the three-dimensional holder, and the power distribution box is provided with a Bluetooth module, a controller and a driver. The infrared thermal imaging module and the camera are respectively connected with the input end of the controller through network cables, the output end of the controller is connected and communicated with the computer through a network cable, the controller is connected with the three-dimensional holder through the driver, and the controller is connected with the IMU sensor mounted on the hoist through the Bluetooth module; the infrared thermal imaging module and the camera are used for synchronously collecting infrared thermal imaging data and visible light image data of a hydraulic hoist area. The system is used for solving the problems that the omission ratio of manual inspection is high, single infrared detection is easily interfered by the environment, fixed monitoring has a blind area, and static image analysis is poor in vibration resistance in oil leakage detection.

The invention relates to the technical field of traffic management, in particular to a road target detection system, method and device, and the method comprises the steps: collecting a vehicle image of a roadside parking region; analyzing the vehicle image data to detect vehicle information in the parking space; based on the vehicle information, blind area vehicles generated due to shielding of front and rear vehicles are recognized and marked; recording parking information of a vehicle in a parking space in a blind area, wherein the parking information comprises mark information, a position and parking time; after the vehicle in the blind area is driven out of the parking space, whether the marked vehicle is still in a shielding state or not is checked; if the marked vehicle is still in the shielding state, continuously acquiring feature information of the marked vehicle; searching and matching corresponding vehicles in other cameras in a preset range based on the feature information to obtain a target vehicle; and obtaining license plate information based on the target vehicle through a license plate recognition technology, and replacing the mark information in the parking information with the license plate information.

The invention discloses an intelligent barley pest breeding system, and relates to the technical field of barley pest breeding, and the system comprises an environment regulation and control module, an automatic feeding module, a growth monitoring module, an intelligent processing module and an early warning prompt module. A full-life-cycle data acquisition system covering an egg stage, a larva stage, a pupal stage and an adult stage is constructed, AI image analysis of a visual monitoring unit can accurately identify insect body length and body surface abnormity, a physiological monitoring unit tracks body weight change and body temperature fluctuation in real time, and a growth prediction model is combined to predict the growth of insects. According to the method, the incubation time and key growth nodes of an ecdysis window are accurately pre-judged, the strategy optimization unit dynamically adjusts environment parameters based on the growth stage, cross-dimension correlation analysis of breeding data is achieved, data support is provided for formulating a standardized breeding process, and the industry is promoted to be changed into intelligent management of data driving from experience driving.

The invention discloses a complete polarization synthetic aperture radar and visible light image text description method. The method comprises the following steps: acquiring a complete polarization synthetic aperture radar image and a visible light image; carrying out Lee filtering denoising processing on the image, fusing the features of a plurality of polarization channels, inputting the fused features into a visual converter encoder for feature extraction, and carrying out multi-layer converter feature extraction on the visible light image; and performing weighted fusion on the multilayer features of the visible light image and the synthetic aperture radar image, and inputting the fused multi-modal features into a text to a text migration converter model text encoder to generate natural language description. According to the method, the complementary advantages of the fully polarized synthetic aperture radar and the visible light image are fully exerted through the deep learning technology, dynamic fusion and semantic interaction of multi-modal features are achieved, the accuracy and robustness of automatic text description of the remote sensing image are remarkably improved, and the method is suitable for multi-modal remote sensing image analysis and understanding in a complex environment.

The invention provides a medical image quality control and organ phenotype extraction system, relates to the technical field of image analysis, and constructs a topological relation tree of organ phenotypes according to relevance of anatomical structures among organ phenotype features in a medical image. Determining a structure association graph of the organ phenotype in the medical image based on the topological relation tree and the distribution information of the organ contour in the organ phenotype feature; determining a semantic structure chart of the medical entities in the text report through a context relationship of clinical description statements in the text report and a logic association relationship between the medical entities; performing node-level alignment fusion on the structure association graph of the organ phenotype and the semantic structure graph of the medical entity to obtain an organ-entity feature association graph; and performing quality control judgment on the medical image data based on the feature correlation map, and if the medical image data is judged to have imaging quality abnormality, outputting a quality control warning. Based on the scheme, the multi-modal correlation quality control of the medical image data can be realized, so that the reliability of the data is improved.

The invention relates to a safety production supervision system and method based on artificial intelligence and big data analysis, and the system comprises a production equipment management system, a data and business overall planning server, a video analysis server and monitoring equipment. The video analysis server is used for forwarding and coordinating the service functions of the service terminals, docking with a fire fighting system, analyzing a security image corresponding to a field scene, and carrying out alarm reporting on an appearing scene picture conforming to security alarm; various monitoring problems in civil explosive production process application scenes are sensed, monitored, alarmed, evaluated and handled by using an artificial intelligence technology, and 24-hour uninterrupted unattended safety management is realized.

The invention relates to the technical field of image analysis and mode recognition, in particular to a state monitoring and fault diagnosis method and system based on artificial intelligence. The invention discloses a state monitoring and fault diagnosis method based on artificial intelligence. The method comprises the following steps: S1, acquiring oil sample type data, performing background purification processing, and then executing a high-precision panoramic imaging process; the X-direction shooting number and the Y-direction shooting number are obtained based on the two-axis motion platform; s2, generating a grid coordinate sequence covering the deposition area according to the X-direction shooting number and the Y-direction shooting number, and forming a line-by-line scanning path; and synthesizing a full-focal-plane image based on the scanning path, and outputting a fused panoramic depth panorama according to the synthesized full-focal-plane image. According to the invention, through background purification, panoramic imaging, improved Mask-R-CNN mode recognition model segmentation and multivariate index fusion, precise recognition of wear particles and intelligent early warning and grading of wear states are realized.

The invention relates to the technical field of image analysis, in particular to a needle point position identification method for thyroid fine needle puncture ultrasound images, which comprises the following steps: acquiring each frame of target ultrasound image in the current puncture process, and determining a local difference feature value corresponding to each pixel point; determining a characteristic state change index corresponding to each pixel point; screening out a target change area from each frame of target ultrasonic image, and carrying out edge detection on the target change area; screening out a target fine needle area from each frame of target ultrasonic image; determining a fine needle motion state corresponding to each frame of target ultrasonic image according to a motion change condition between the target fine needle areas in the adjacent frames of target ultrasonic images; and based on the fine needle motion state corresponding to each frame of target ultrasonic image, identifying the needle point position in each frame of target ultrasonic image. According to the method, the target ultrasonic image is analyzed, so that the needle point position is recognized, and the accuracy of needle point position recognition is improved.

The invention discloses a fault analysis method and system for an environment-friendly marine high-expansion foam fire extinguishing system, and relates to the field of foam fire extinguishing system.The method comprises the steps that a foam fire extinguishing agent concentrated solution is prepared, the foam fluidity of the foam fire extinguishing agent concentrated solution is analyzed, and the method is combined with a measuring device for use process simulation; meanwhile, collecting a process image generated in the simulation process; analyzing a precipitation process and a coarsening process of the foam extinguishing agent concentrated solution according to the process image, and judging the foam stability of the foam extinguishing agent concentrated solution by combining a performance testing technology; on the basis of foam fluidity and foam stability, the reliability of the foam extinguishing agent concentrated solution under the preparation proportion condition is predicted, and the fault generation probability of the high-expansion foam extinguishing system is analyzed according to the reliability. According to reliability prediction and fault probability analysis, the use state of the foam extinguishing agent concentrated solution can be further analyzed, possible fire extinguishing system faults are warned in advance, and accidents are avoided.

The invention belongs to the technical field of building monitoring, and discloses a civil engineering building monitoring system and method, and the system comprises a data collection module which collects the data, geological data and maintenance data of a civil engineering building; the environment data acquisition module acquires surrounding environment data of a building in real time; the underground water level monitoring module acquires underground water level data in real time; collecting meteorological data of the building area in real time; the building structure monitoring module collects building structure data in real time; the inspection module is used for monitoring the building in all directions; the image analysis module identifies abnormal conditions; the infrared data analysis module analyzes the infrared data; the comprehensive evaluation module comprehensively evaluates the abnormal condition; and the risk prediction module fuses the multi-source data and predicts potential problems and risks. According to the invention, abnormal conditions of the building are comprehensively evaluated; and the risk prediction module performs multi-source data fusion, comprehensively analyzes a large amount of different types of monitoring data, and predicts potential problems and risks. Preventive measures are taken in advance.

The invention discloses a gravel aggregate mixing and stirring device. The gravel aggregate mixing and stirring device comprises a horizontal stirring barrel, a stirring regulation and control system, a feeding system and a quality detection and feedback system. Wherein a plurality of layers of stirring blade groups are arranged in the horizontal stirring barrel, a cooling spraying assembly is installed, the stirring regulation and control system controls the rotating speed of a driving motor and starting and stopping of the spraying assembly through a central controller in combination with data of a temperature sensor and a pressure sensor, and the feeding system controls feeding of each storage bin through an electric feeding valve according to a preset proportion. The quality detection and feedback system analyzes and judges the mixing uniformity degree through a discharge port image and feeds back the mixing uniformity degree to the central controller so as to adjust the opening degree of the feeding valve and the rotating speed of the motor, and closed-loop control is formed. The device is mainly used for efficiently mixing and stirring gravel aggregate in constructional engineering, and the aggregate mixing uniformity and the intelligent regulation and control level in the stirring process can be improved.

The invention discloses a small and micro water body quality monitoring method and system based on the Internet of Things, and relates to the field of water body quality monitoring, the system is composed of a plurality of functional modules, and the system comprises a data acquisition module which uses a laser radar and an RGB camera carried on an unmanned aerial vehicle to acquire a whole image of a water body, and uses Canny edge detection to extract a water body boundary; according to the boundary of the water body, the unmanned aerial vehicle flies in a gridding path, laser vertical irradiation is carried out on the water surface, and point cloud data are collected; during vertical irradiation, collecting a water surface image of a vertical area; the point cloud processing module is used for carrying out noise reduction processing on the point cloud data by utilizing statistical filtering; on the basis of IMU and real-time position data, auxiliary positioning is carried out, an NDT algorithm is adopted to carry out registration on the point cloud data, three-dimensional parameter calculation is carried out on the registered point cloud data, and vegetation points and non-vegetation points are distinguished through machine learning; and the image analysis module is used for carrying out RGB color space clustering on the water surface image.

The invention belongs to the technical field of surveying and collecting equipment, and discloses geographic information surveying and collecting equipment and method for urban and rural planning, and the method comprises the steps: a data collection module collects geological data, building data and vegetation data of an urban and rural planning region; the route planning module plans an optimal flight route of the unmanned aerial vehicle; the unmanned aerial vehicle carries a data acquisition module to carry out data survey and acquisition on urban and rural planning regions; the image analysis module analyzes the collected image; the thermal imaging analysis module analyzes the collected thermal images; the radar data analysis module performs preprocessing identification and modeling on the point cloud data acquired by the laser radar; and the model building module fuses the image data, the thermal imaging data and the radar data to build a two-dimensional or three-dimensional model of the urban and rural planning region. According to the invention, the working efficiency can be improved, data acquisition is carried out on urban and rural planning regions from different angles, image data, thermal imaging data and radar data are fused, and an accurate two-dimensional or three-dimensional model is constructed.

The invention belongs to the technical field of hydraulic engineering monitoring, and discloses a hydraulic engineering inspection system and method, and the method comprises the steps: a positioning monitoring deployment module reasonably distributes the number and positions of positioning monitoring assemblies; the route planning module plans an optimal unmanned aerial vehicle flight route; planning an advancing route, a water entry position and a water surface leaving position of the underwater robot; the cruise monitoring module carries out all-directional inspection on a water conservancy area; the image analysis module identifies abnormal conditions; the infrared data analysis module recognizes abnormal conditions in time; the radar data analysis module identifies abnormal conditions; the comprehensive analysis module comprehensively evaluates the water conservancy condition; the risk prediction module predicts potential problems and risks. According to the invention, the positioning monitoring assembly and the cruise monitoring module are combined to realize omnibearing monitoring of a water conservancy area; omnidirectional three-dimensional inspection is realized, multi-modal data collaborative analysis is realized, abnormal conditions are identified, potential problems and risks are predicted, and possible faults or disaster hidden dangers of water conservancy facilities can be found in advance.

The invention discloses a blind area collision judgment method and system based on 360-degree camera image analysis, and relates to the technical field of blind area collision judgment, and the method comprises the following specific steps: collecting image data of a 360-degree camera, and carrying out the preprocessing of the image data; acquiring the current motion state of the vehicle; comprising motion direction, speed and steering wheel angle; dividing the vehicle into three motion states according to the current motion parameters; performing feature recognition on the image data by using different strategies according to different motion states, judging a vehicle collision risk level according to a recognition result, and performing early warning; according to the invention, the obstacle in the image is identified, and the predicted route image area is established in the visual area of the image, so that the calculated amount of the system is reduced; according to the method, the collision risk of the vehicle is judged according to the specific value of the area of the predicted route image region occupied by the obstacle, and the early warning information is output through the early warning system after accurate judgment is carried out according to different preset strategies, so that the safety of blind area collision during vehicle driving is greatly improved, and the collision risk is reduced.

The invention provides an underwater environment area dynamic search method, which comprises the following steps of: transmitting and receiving a sound wave signal by using a multi-beam sonar system, obtaining a high-resolution underwater environment image, and extracting particle size distribution data of submarine sediments through image analysis; establishing a mapping model by combining the sound wave frequency and the sediment acoustic characteristics to calculate a scattering intensity distribution map; adopting a pattern recognition algorithm to recognize the sediment particle size and the sound wave incident angle, and correcting inertial navigation data errors; a spatial filtering parameter optimization model is established based on sediment particle size distribution, filtering parameters are adjusted to remove noise, and scattering signal features are extracted; the configuration of the receiving array and the search area are dynamically adjusted according to the particle size change of the sediment, accurate and real-time analysis and environment adaptability search of the particle size distribution of the underwater sediment are realized, and the efficiency and accuracy of underwater detection are greatly improved.

Systems and methods for performing a medical imaging analysis task using a foundation model are provided. One or more 3D (three-dimensional) medical images each comprising a plurality of 2D (two-dimensional) slices are received. A first set of features is extracted from the plurality of 2D slices of the one or more 3D medical images using a machine learning based encoding network. Each respective feature of the first set of features is resampled based on a spatial location of one or more pixels of the 2D slices from which the respective feature was extracted. The resampled first set of features is encoded into a second set of features. A medical imaging analysis task is performed based on the second set of features. Results of the medical imaging analysis task are output.

The invention belongs to the technical field of artificial intelligence, and provides a closed space unmanned aerial vehicle autonomous obstacle avoidance inspection method and equipment based on AI image analysis, and the method comprises the steps: firstly obtaining fixed structure features and multi-source data of a closed space; then, according to the fixed structure features, the multi-source data and an improved visual SLAM algorithm, high-precision positioning and three-dimensional map construction are carried out in a GPS-free environment, and dynamic obstacles are recognized in the constructed three-dimensional map in real time; according to the IMU data, the real-time pose of the unmanned aerial vehicle is determined; and finally, according to the real-time pose, the three-dimensional map and the dynamic obstacle information, a deep reinforcement learning model is adopted to carry out path planning and obstacle avoidance decision making. According to the method, enhanced loopback detection is realized through multi-source data fusion and an improved visual SLAM algorithm in combination with fixed structure features of a closed space, positioning accumulative errors in a GPS-free environment are effectively eliminated, and global consistency and positioning precision of a three-dimensional map are remarkably improved.

The invention provides a breast cancer-oriented traditional Chinese medicine syndrome type intelligent identification method and system, a terminal and a medium, and relates to the technical field of artificial intelligence and traditional Chinese medicine tongue diagnosis image analysis. Inputting the tongue picture image into a pre-constructed tongue picture element classification model to obtain a tongue picture element classification result; acquiring pulse condition data and clinical symptom data of the breast cancer patient; and according to the tongue picture element classification result, the pulse condition data, the clinical symptom data and a plurality of preset candidate syndromes, utilizing a preset matching function to calculate a matching score corresponding to each preset candidate syndrome, and taking the preset candidate syndrome with the highest matching score as a final syndrome. According to the invention, the analytic performance of intelligent recognition of the breast cancer-oriented traditional Chinese medicine syndrome type can be improved. Wherein the tongue picture element classification model is improved on the basis of a YOLOv8n-cls network architecture, the tongue picture classification accuracy can be improved, and meanwhile, the model deployment cost is effectively reduced.

Systems and methods for performing a medical imaging analysis task conditioned on multi-domain medical images with missing modalities are provided. 1) one or more medical images each in a different domain and 2) a domain code defining a presence of the different domains in a set of predefined domains are received. One or more weights are determined based on the domain code. One or more parameters of a machine learning based encoder are updated based on the one or more weights. Features are extracted from the one or more medical images using the machine learning based encoder with the one or more updated parameters. A medical imaging analysis task is performed based on the extracted features. Results of the medical imaging analysis task are output.