69 results about "Learning Recognition" patented technology

The embodiment of the invention provides a road disease inspection method and device based on AI recognition, equipment and a storage medium, and is used for road maintenance. The method comprises the steps of performing multi-source data synchronous acquisition on a road surface to obtain an original image-positioning data set, performing dynamic interference suppression and image enhancement on the original image-positioning data set to obtain a stable enhanced image sequence, and performing disease target detection and classification in combination with a deep learning recognition model to obtain a disease target set, and performing multi-target space-time correlation and satellite positioning data fusion on the disease target set to obtain a stable target trajectory set, performing geometric coordinate conversion on the stable target trajectory set to obtain a road disease data set, and performing clustering analysis through a spatial clustering analysis algorithm to generate a road disease maintenance strategy report. Through the technical means of multi-source cooperation, deep learning, space-time fusion and the like, high-precision and intelligent detection of road diseases is realized, the road maintenance efficiency is improved, and the manpower and material resource cost is reduced.

The invention discloses a manipulator grabbing control method and equipment based on position and posture recognition. The manipulator grabbing control method comprises the following steps that S1, initial image information of a target object is acquired through image acquisition equipment; s2, performing preprocessing and feature extraction on the initial image information, and obtaining three-dimensional position coordinates and attitude parameters of the target object through a preset position and attitude recognition algorithm; and S3, according to the three-dimensional position coordinates and the posture parameters, a grabbing path of the manipulator is planned by combining a kinematic model of the manipulator. By introducing technical means such as a deep learning recognition algorithm, forward / inverse kinematics model collaborative planning, real-time posture dynamic adjustment and force sensing feedback control, the problems that a traditional mechanical arm is low in grabbing precision, poor in adaptability and insufficient in operation stability are solved, intelligent and high-precision grabbing control in a complex scene is achieved, and the grabbing precision of the mechanical arm is improved. And the requirements of the modern industry on high efficiency, reliability and flexibility of automatic equipment are met.

The invention discloses a road damage identification and positioning method fused with multi-source information, and relates to the technical field of road and bridge facility health monitoring. The method comprises the following steps: S1, collecting multi-source image data of a road bridge through an unmanned aerial vehicle, a wall-climbing robot and / or an industrial camera; and S2, preprocessing the multi-source image data, respectively inputting the multi-source image data into a pre-trained deep learning recognition model, and outputting the type and initial position information of a disease. According to the method, the global view angle of the unmanned aerial vehicle and the local high-definition data of the wall-climbing robot / industrial camera are deeply fused, and the deep learning-based semantic segmentation model is utilized to perform pixel-level recognition on tiny diseases such as cracks, so that the recognition accuracy and reliability are greatly improved, and meanwhile, the recognition efficiency is improved. Image coordinates are accurately mapped to the three-dimensional live-action model through a coordinate transformation formula, centimeter-level space positioning of the disease is achieved, and detection accuracy and operation safety are remarkably improved.

The invention relates to the technical field of building disease detection, in particular to an urban building disease detection method and device, electronic equipment and a storage medium. Multi-modal image data formed by original visible light and thermal infrared image data is obtained, and an original thermal infrared image is subjected to geometric correction; calculating a mapping relation with an original visible light image so as to complete pixel-level registration, obtaining target multi-modal image data, inputting the target multi-modal image data into a hierarchical deep learning recognition model, recognizing building disease information, then performing three-dimensional space mapping, generating a building three-dimensional mesh model containing disease three-dimensional space setting coordinates, and finally performing three-dimensional mesh modeling. And then calculating a relationship between a model surface grid vertex and a disease point cloud density, generating a disease distribution thermodynamic diagram, analyzing disease aggregation characteristics in multiple dimensions according to the thermodynamic diagram, and quantitatively analyzing spatial correlation between the disease and a building construction node in combination with building component information. According to the invention, the urban building disease detection efficiency and precision are improved.

The invention relates to the technical field of geological informatization, in particular to an artificial intelligence modeling analysis method for a hydrate pilot production data set, which comprises the following steps of: acquiring logging data, lithology data, stratum physical property parameters and natural gas hydrate production dynamic data; screening, cleaning, complementing, de-noising and standardizing are carried out in sequence to obtain an artificial intelligence modeling data set; and establishing a stratum lithology machine learning recognition model, a stratum physical property machine learning recognition model and a natural gas hydrate artificial intelligence historical fitting model through a support vector machine SVM, a random forest RF and a neural network DNN. According to the method, a serial modeling architecture of lithology identification, physical property prediction and production history fitting is created, and the prediction output of the upstream model is used as the optimization input of the downstream model, so that the downstream production prediction model can learn physical property parameters which are recalculated based on machine learning and have higher precision; and the accuracy of final production prediction is improved from the data source.

The invention discloses a ship cable identification method based on an improved VGG16 network and bidirectional feature fusion. The method comprises an image acquisition module, a preprocessing module, a deep learning recognition module and an output module, and comprises the following steps: firstly, carrying out gray enhancement and anti-interference filtering on a construction drawing through the image preprocessing module, and constructing a ship cable marking database; then, an improved VGG16 network deep convolutional layer is adopted to extract cable multi-scale features, a full connection layer module of an original network is deleted, a lightweight channel self-attention module is added, and BiFPN is adopted to carry out bidirectional feature fusion on shallow texture features and deep semantic features of the improved VGG16 network, so that the features of the ship cable are captured more effectively; and finally, outputting structured data of cable numbers, specifications and path coordinates. The identification precision mAP, the processing speed and the generalization ability of the method are superior to those of a traditional method, the method can be effectively applied to the field of ship cable identification and the like, and the industrial detection efficiency and the automation level are greatly improved.

The invention discloses a myopia image deep learning recognition model training method, particularly relates to the technical field of medical image processing and deep learning, and is used for solving the problem that an existing deep learning model lacks anatomical structure priori knowledge guidance in myopia eye bottom image analysis. The method comprises the following steps: acquiring a myopia eye bottom image and anatomical structure priori knowledge data, extracting a multi-scale feature map by using a deep learning model, analyzing the geometric morphology of a key anatomical component based on standard spatial relationship information, and generating a spatial constraint loss item; according to the method, key anatomical path topology coherence is evaluated based on topology connection information, topology constraint loss items are generated, a loss item fusion strategy is dynamically adjusted according to a training stage, finally, a model is iteratively trained to convergence through a gradient back propagation algorithm, and organic combination of medical priori knowledge and a deep learning model is realized. And the clinical rationality and reliability of model output are improved.

The invention relates to the technical field of coal mine data processing, and discloses a coal mine multi-source data fusion visualization system based on deep learning. The system comprises seven modules including a multi-source data acquisition module, a preprocessing module, a three-dimensional geologic model construction module, a fusion strategy generation module, a deep learning recognition module and a data optimization and evaluation module. The multi-source data acquisition module acquires geological sonar signals, environment monitoring signals and equipment operation image data of a coal mine target area; the preprocessing module performs noise reduction on the data and extracts features; the three-dimensional geologic model building module builds a regional three-dimensional geologic model according to the preprocessed data; the fusion strategy generation module combines the model and the preprocessed data to generate a fusion strategy; the deep learning recognition module recognizes the equipment image according to a strategy to obtain equipment state data; the data optimization module optimizes the data; and the evaluation module evaluates and analyzes the optimized data and generates a result, thereby providing support for coal mine production management.

An area on video is monitored that overlaps a radio range of an access point on the data communication network. Failure to identify a person currently within the Wi-Fi area and shown on video is detected. Parallel Wi-Fi parameters for a device associated with the person in the video, including a group identification assigned to the person is received. The parallel Wi-Fi parameters are used to identify the person. A machine learning module, or other image recognition system, can be updated with images to build a machine learning recognition model from a history of images associating the identified person. Finally, a surveillance security policy of the identified group is associated with the identified person.

The invention relates to the technical field of fire detection and early warning, in particular to an intelligent ultraviolet flame detection and early warning system and method suitable for complex industrial scenes. Comprising distributed multispectral ultraviolet detection nodes and a central processing early warning unit. The nodes carry out signal acquisition and preliminary identification through a multispectral sensor, local processing and high-precision time synchronization; and the central unit fuses multi-node data, performs three-dimensional flame positioning by using a TDOA algorithm, performs machine learning recognition and false alarm elimination, and performs trajectory tracking by using a Kalman filter so as to realize graded early warning linkage. According to the intelligent ultraviolet flame detection and early warning system and method suitable for the complex industrial scene, the problems that in the prior art, the false alarm rate is high, flame positioning is not accurate, trajectory tracking is missing, cooperative monitoring is limited and the like are solved or at least relieved, false alarms are effectively reduced, and the positioning precision, the tracking capacity and the early warning reliability are improved.

The application discloses a CSI-based position-independent human activity continuous learning recognition method, which comprises the following steps: 1, collecting CSI action sample data; 2, pre-processing the CSI action sample data; 3, constructing positive samples by randomly scaling the pre-processed samples in the time dimension; 4, constructing a multivariate time graph neural network and extracting CSI action sample features; 5, calculating the similarity between the sample feature values and the positive samples and the feature values of the remaining samples, obtaining a comparison loss, and optimizing the feature extraction network; 6, freezing the feature extraction network, sending the features obtained from the input samples into a classifier for training to obtain a classification model. When the application continuously learns new action categories, the user does not need to retrain the feature extraction network, and the new and old action recognition in any position in the room can be realized by providing limited position new category samples to train the classifier, and the practicability is relatively high.

The invention relates to the technical field of immunohistochemical staining, and discloses an automatic scoring method and system for immunohistochemical staining results. The method comprises the following steps: performing spectral signal intelligent deconvolution processing on a multi-immunohistochemical staining image to obtain target image data; performing multi-scale context modeling on the target image data to obtain a sub-region segmentation mask; performing depth map feature extraction on the target image data based on the subregion segmentation mask to obtain a multi-dimensional image feature vector; inputting the multi-dimensional image feature vector into a mixed deep learning recognition model to carry out marker intelligent recognition to obtain a multi-marker expression state recognition result; collaborative scoring is carried out based on the multiple marker expression state recognition result, a personalized unified scoring value is obtained, cell heterogeneity analysis is carried out on the personalized unified scoring value, and an intelligent scoring report is generated. According to the invention, the problem of spectrum crosstalk in multiple staining is effectively solved, and high-precision intelligent identification of multiple immunohistochemical markers is realized.

The invention discloses a power grid fault data processing method and device and a medium, and the method comprises the steps: carrying out the multi-dimensional detection data collection of a target power grid, obtaining a power grid image and sensing parameters, and collecting the environment parameters in a target power grid environment; acquiring historical detection data of a target power grid, extracting a fault sample proportion coefficient, performing combination division on the historical detection data, and performing training of an integrated image fault identification branch and an integrated sensing fault identification branch; performing fault rate influence analysis and detection data influence analysis according to the environmental parameters to obtain a fault rate influence coefficient, an image influence coefficient and a sensing influence coefficient; according to the fault rate, the image and the sensing influence coefficient, the number of image recognition branches and the number of sensing recognition branches are calculated and obtained, fault recognition branch calling and fault recognition are carried out, a power grid fault recognition result is obtained, and the technical problem that the machine learning recognition fault perception rate and accuracy are low due to the fact that power grid line inspection fault data samples are few is solved.

The invention relates to the technical field of fusion gene detection, and discloses a high-throughput detection platform of a thyroid cancer specific fusion gene CCDC6. According to the detection platform disclosed by the invention, high-throughput and high-sensitivity detection on the thyroid cancer CCDC6 fusion gene is realized through a technical scheme of combining sample enrichment and deep learning recognition. Through combination of the sample enrichment module and the deep learning recognition module, the sample enrichment module promotes high-sensitivity recognition of the deep learning recognition module, an end-to-end integrated solution is provided, a large number of samples can be processed in one-time operation, the result can be automatically interpreted, and the detection efficiency and accuracy of the CCDC6 fusion gene are remarkably improved.

The invention is suitable for the technical field of computers, and provides a large language model training data generation method based on flow playback and implicit feedback, and the method comprises the steps: obtaining interaction session data, including user questions, model answers and user subsequent behaviors, of a user and a large language model in online service; based on the interactive session data, identifying a user preference signal through a hierarchical implicit feedback judgment algorithm; the hierarchical implicit feedback judgment algorithm preferentially processes high-confidence implicit signals, including collaborative learning recognition based on user editing behaviors, recognition based on user query reconstruction and automatic judgment based on confidence scoring; a structured preference training data set is generated according to the user preference signal and comprises a plurality of preference pairs, and each preference pair comprises user questions, correct answers and wrong answers; the authenticity and quality of the data are effectively improved, the problems of reward model cracking and model catering are relieved, and continuous optimization and rapid iteration of model performance are achieved.

The embodiment of the invention provides an automatic parking method, device and equipment and a vehicle. The method comprises the steps that parking environment information and configuration information of a to-be-parked vehicle are acquired, and the parking environment information comprises the distance between an obstacle and the to-be-parked vehicle and azimuth information of the obstacle; the configuration information of the to-be-parked vehicle comprises the vehicle head terrain clearance, the vehicle tail terrain clearance and the vehicle size. Based on the deep learning recognition model, target parameters of the target obstacle are determined according to the parking environment information, and the target parameters of the target obstacle comprise the height of the target obstacle. And determining a parking mode of the to-be-parked vehicle based on the target parameter of the target obstacle and the configuration information of the to-be-parked vehicle. And based on the parking mode of the to-be-parked vehicle, the parking environment information and the to-be-parked vehicle configuration information, a parking path is determined to realize parking. The invention aims to improve the safety and success rate of automatic parking in a complex scene.

The invention relates to a panda behavior recognition method based on deep learning and time sequence prediction, and the method comprises the steps: S1, constructing a panda behavior deep learning recognition model which comprises a region division module, a multi-view motion analysis module, a multi-scale posture estimation module and a posture guide fusion module; s2, carrying out region division and part coding on the input video, and generating a part coding video comprising three regions, namely a panda head region, a panda whole region and a background region; s3, performing multi-view motion analysis on the part coding video, and extracting multi-view motion features of the panda; s4, extracting multi-scale features of the joint attitude information of the panda in the part coding video, and performing feature fusion to generate an attitude thermodynamic diagram; and S5, taking the posture features in the posture thermodynamic diagram as guide signals to be fused with the multi-view action features, and outputting the giant panda behavior category. According to the method, the accuracy and robustness of giant panda behavior monitoring can be improved.

The invention discloses an insulator degradation analysis method, system and device and a medium, and the method comprises the steps: inputting the real-time inspection data of an insulator into a multi-task deep learning identification model which is obtained through introducing an insulator feature extraction branch and a cross-modal feature attention fusion branch into a backbone network of a YOLOv5 model, and carrying out the degradation identification; obtaining degradation state information, and importing the degradation state information into a power grid geographic information comprehensive management system; according to historical inspection comprehensive data and real-time climate data of the insulator, an inspection polymorphic layer of the insulator is constructed through the system, a multi-dimensional algorithm is adopted to analyze the degradation rule of the insulator, and a degradation risk analysis result is obtained; on the basis of a power grid geographic information integrated management system, insulator degradation identification, map positioning and multi-dimensional algorithm fault law analysis are combined, so that the utilization rate of insulator degradation state information is improved, and solid data support and scientific basis are provided for formulating a power grid operation and maintenance strategy.

A method and an electronic device for detecting a danger to a vehicle are provided. The method for detecting a danger to a vehicle includes an operation of acquiring an original image captured using a camera mounted on the exterior of the vehicle, an operation of generating a first processed image for a deep learning image recognition method and a second processed image for a computer vision image recognition method based on the original image, an operation of generating deep learning recognition data for the first processed image using a deep learning image recognition model, an operation of generating computer vision recognition data for the second processed image using the computer vision image recognition model, and an operation of detecting a danger to the vehicle based on the deep learning recognition data and the computer vision recognition data.

This invention discloses a method, device, and medium for intelligent identification of wire harness defects based on deep learning, relating to the field of industrial defect identification technology. The method includes: using a deep learning recognition network to perform joint deep learning representation and defect candidate identification on topology-aligned image sequences, outputting an initial list of defect candidates; verifying the structural relationships of the initial list of defect candidates based on the wire harness topology prior graph, and incorporating the consistency verification conclusion into the defect candidates as a judgment criterion, while correcting the target region identifier, generating a structural constraint defect judgment list; and performing online inference calibration on the structural constraint defect judgment list and re-judging boundary samples to generate a wire harness defect judgment conclusion. This invention uses a deep learning recognition network to perform joint deep learning representation and defect candidate identification on topology-aligned image sequences, enabling defect candidate identification to simultaneously consider local texture anomalies and global morphological relationship information, and improving the distinguishability of defects in complex critical connection areas.

The invention discloses a pathogenic microorganism infection immune feature recognition system and method, belongs to the technical field of immune feature recognition, and aims to solve the problems of incomplete feature extraction, weak model generalization ability, poor adaptability to novel pathogenic microorganisms and the like due to the fact that a traditional immune feature recognition technology mostly adopts single-dimensional features or a traditional machine learning model. In order to solve the problems of low recognition accuracy, high false positive rate and difficulty in meeting actual requirements of clinical diagnosis and epidemic situation monitoring in the prior art, the pathogenic microorganism infection immune feature recognition system comprises a data acquisition module, a data preprocessing module, a multi-dimensional immune feature extraction module, a fusion deep learning recognition model module and a result output and verification module, according to the method, through a deep learning architecture fused by Transform, CNN and LSTM, the long-distance dependency relationship, local features and time sequence features among the features are captured at the same time, optimization strategies such as transfer learning and regularization are combined, and the recognition accuracy and generalization ability of the model can be significantly improved.

This invention relates to the field of foreign object disturbance identification technology, and discloses a method, system, device, and medium for online foreign object disturbance identification in a weighing rain and snow gauge based on analog data-driven simulation. The method includes: outputting a formula for calculating the weight increase based on the rain-collecting area of ​​the weighing rain and snow gauge; generating a basic precipitation signal with noise and instantaneous disturbances based on the weight increase calculation formula and disturbance rules; generating a disturbance label array based on the basic precipitation signal; training a learner using window data filtered from the disturbance label array; using the trained classifier as a machine learning recognition model for online disturbance identification; deploying the machine learning recognition model into the main controller of the weighing rain and snow gauge; and using a two-stage pipeline architecture to achieve online identification of foreign object disturbances in the weighing rain and snow gauge. This invention employs a two-stage pipeline architecture for anomaly-triggered classification, ensuring high-precision real-time identification while maintaining low power consumption.

The application discloses a Parkinson disease severity recognition method based on tensor ring decomposition and region segmentation, relates to the technical field of machine learning recognition, and comprises the following steps: collecting VGRF signals, accurately dividing the VGRF signals according to target personnel indexes, gait window indexes, foot indexes, set region indexes and sampling point time indexes, constructing a five-order time domain tensor, and improving the learning ability for large-dimension tensors; converting the five-order time domain tensor into a frequency domain five-order tensor, extracting low-rank structures in the frequency domain five-order tensor as core tensors, reducing the data dimension of calculation, and improving the learning power of a classification model; extracting statistical features of patients in gait, feet and each set region in the core tensors, further maintaining the correlation features between each dimension of data on the basis of reducing the data dimension of calculation, and then accurately distinguishing the severity of Parkinson disease of the patients and improving the accuracy of the classification model in recognizing the disease severity of the patients.

This invention discloses a photovoltaic junction box defect detection method based on region-constrained two-stage visual recognition. The method includes: inputting the original image into a one-stage target detection model to obtain candidate junction box targets; dividing the image into left, center, and right regions according to the width of the original image; cropping the selected junction box targets to obtain multiple local images; inputting each local image into a two-stage instance segmentation model to obtain segmentation results for lead wires, soldering, and residual adhesive, respectively; and finally, performing an inverse mapping fusion algorithm according to the local cropping offset, superimposing the recognition results and quantification indicators onto the original image for output. This scheme combines deep learning recognition with spatial prior constraints, candidate scoring algorithms, mask deduplication algorithms, workstation assignment algorithms, parameterized defect rules, and result inverse mapping fusion, which can improve the stability, interpretability, and engineering applicability of photovoltaic junction box detection.

This invention discloses a method, system, device, and storage medium for hail cloud feature analysis and identification based on multi-source data. The method includes: obtaining satellite feature products, derived feature products, and radar feature products strongly correlated with the hail cloud formation and dissipation process; performing spatial registration processing on the satellite feature products, derived feature products, and radar feature products to obtain spatiotemporally aligned satellite feature sets and radar feature sets; constructing a dataset based on the spatiotemporally aligned satellite feature sets and radar feature sets; constructing a GSCF deep learning recognition model; and training and testing the model. This application effectively solves the problem of heterogeneous fusion of multi-source data, fully leverages the advantages of macroscopic meteorological information from satellite data and fine structural information from radar data, improves the quality and representational ability of fused features, provides reliable data support for accurate hail cloud identification, and is of great significance for improving disaster early warning efficiency.

The application discloses a curtain wall system connecting node stress state recognition method based on deep learning, and the method comprises the following steps: collecting original stress time series data and synchronous environment temperature data of a curtain wall connecting node to form a sample set; based on dynamic time clustering, the original stress time series data of each sample is segmented and divided, and an adaptive feature mapping function combining segmented information and a wavelet base function is used for feature mapping, and then dimension reduction is performed through principal component analysis to obtain a dimension reduction feature vector; a deep learning recognition network is constructed, and a double supervision loss function containing a weighted time series focal loss and an attention consistency regularization term is used to train the deep learning recognition network; and the obtained enhanced feature sequence and external physical features are input into the trained deep learning recognition network to output a stress state category of a node to be recognized. The application realizes automatic and engineering deployable transformation from original multi-source monitoring data to a clear state grade.

The invention relates to an intelligent garbage classification system based on multi-mode perception. The intelligent garbage classification system is suitable for families, public places and intelligent city management systems. According to the system, the image and weight information of garbage are collected at the same time through the multi-mode sensing module, and precise garbage classification and recognition are achieved in combination with environment sensing data. And the deep learning recognition module adopts a CNN and LSTM mixed architecture, so that the classification accuracy and the real-time performance are remarkably improved. And the dynamic classification throwing module dynamically adjusts the garbage throwing path according to the recognition result, and it is ensured that the garbage is precisely thrown into the corresponding classification area. The system is equipped with an energy management system, energy self-sufficiency is realized through a solar panel and an energy recovery unit, and the use cost and the maintenance difficulty are reduced. The intelligent interaction module supports user interface display, voice prompt and remote monitoring, improves user experience and supports intelligent city management. The garbage classification process is optimized, the equipment cost is reduced, the classification efficiency and accuracy are improved, and remarkable energy conservation and emission reduction effects and social benefits are achieved.

The invention relates to the field of concrete detection, and discloses a concrete performance identification system based on a deep learning image processing technology, which is applied to detection of concrete of a building structure beam column and comprises an image processing module, a deep learning identification module and a performance prediction module. The image preprocessed by the image processing module has stronger edge information and clearer texture features, which is helpful for the deep learning model to more efficiently extract discrimination features of key defects such as cracks, holes and the like; after details such as crack edges and hole boundaries are accurately segmented through a deep learning recognition module and image segmentation is completed, intelligent prediction of concrete compressive strength and compactness grade is finally realized by combining a regression model.

The invention discloses a bone nail recognition system and method based on machine recognition and artificial intelligence, and relates to the technical field of bone nail intelligent recognition. Bone nail tray partition images are collected through an image collection module, and the image quality of a bone nail tray is optimized by automatically adjusting shooting parameters; a camera of the image acquisition module moves in a partition above the tray through a three-axis movement mechanism, and then an image splicing and fusion unit of the image processing and recognition module splices and fuses partition images of the bone nail tray to obtain a complete image of the bone nail tray; and the deep learning recognition unit inputs the complete image of the bone nail tray into a pre-trained deep learning model, and outputs to obtain a bone nail information recognition result, so that the accuracy and efficiency of bone nail recognition can be improved.

The application discloses an incremental machine learning recognition system for radar pulse description word sequences, comprising a preprocessing module, a radar signal pre-sorting module, a post-processing module and an incremental radar signal classification module; the incremental machine learning recognition system for radar pulse description word sequences provided by the application collects radar pulse description word sequences of multiple target aliasing through the preprocessing module, and performs data screening on the collected radar pulse description word sequences of multiple target aliasing, so that the parameter information contained in the radar pulse description word sequences which are modulated or have a sudden change can be better recognized; meanwhile, the incremental radar signal classification module utilizes an incremental radar signal recognition network to perform supervised training and unsupervised inference, so that not only radar pulse description words of known categories can be recognized, but also radar pulse description words of unknown categories can be rejected and the radar pulse description words of unknown categories which have been seen before can be recognized incrementally.