45results about How to "Achieve high-precision recognition" patented technology

The invention relates to a seat cushion robot self-adaptive tightening system and an operation method. The system provided by the invention is composed of an integrated industrial personal computer, an AGV mobile platform, a collaborative robot, a tightening end effector, an industrial camera and accessories thereof. The collaborative robot is installed on the AGV mobile platform, the tightening end effector and the industrial camera are installed at the tail end of the AGV mobile platform, and the whole system is controlled by the integrated industrial personal computer. The accurate position of the center of the to-be-tightened bolt can be obtained through a visual identification method in combination with a designed multi-layer screening method; meanwhile, different feeding modes are set according to different pre-tightening conditions, automatic cap searching is achieved, and it is guaranteed that tightening is in place; in addition, the counter-acting force arm is added to counteract counter-acting force in the tightening process, and robot damage is avoided. The tightening system and the operation method provided by the invention not only have extremely high flexibility, but also can adapt to a complex tightening environment, so that the accuracy of tightening work is ensured, and the working efficiency is also improved.

The invention discloses a PET waste plastic intelligent sorting method and system based on multi-feature fusion, and the method comprises the following steps: collecting an RGB-HSI original data set of PET waste plastic, carrying out the dimension reduction processing of the HSI data set based on a genetic algorithm to generate MSI data, and forming a feature fusion data set with RGB data; constructing a feature fusion network based on a YOLACT model, and training by adopting a feature fusion data set; the feature fusion network comprises an RGB branch, an MSI branch and a fusion prediction module, RGB is used for extracting features of RGB data, the MSI branch is used for extracting features of MSI data, and the fusion prediction module is used for combining output of the RGB branch and output of the MSI branch to make comprehensive prediction; and installing the trained feature fusion network to an industrial personal computer to realize intelligent sorting of the PET waste plastics. Compared with a traditional recognition algorithm, the method can improve the recognition precision.

The invention relates to a lightweight electrical equipment multi-target detection method and system, and the method comprises the steps: collecting multi-angle images of different types of electrical equipment, obtaining an experiment data set, constructing a Ghostnet lightweight network structure, introducing a CBAM attention mechanism and an SPPFCSPC convolution structure to improve a YOLOv8 model, and obtaining a multi-target detection model based on the improved YOLOv8 model. And training, verifying and evaluating the multi-target detection model based on the improved YOLOv8 model to obtain a multi-target detection optimal model for electrical equipment multi-target detection. According to the method and the system, the model complexity and the calculation amount can be reduced while the detection precision is ensured, and an effective solution is provided for multi-target identification detection of lightweight electrical equipment.

The invention discloses a deep learning-based transmission and transformation project progress identification and analysis method and system, and the method comprises the steps: firstly collecting and preprocessing the multimedia data of a transmission and transformation site, constructing a project progress data set, training a deep learning model through a plane graph and the progress data set, inputting new progress data into the model, and identifying a current project state; comparing the plan progress, analyzing the deviation, and finally performing comprehensive trend prediction based on the model and the key influence factors. In the application of the design, key factors are obtained through a recursive transformer fusion network model fusing various modules and various differential data, and comprehensive trend analysis is realized in combination with differences of different voltages, so that deep-level features can be effectively extracted from various data sources, high-precision identification of the project progress is realized, and the accuracy of the project progress is improved. Compared with a traditional manual checking and recording mode, the method has the advantages that the recognition efficiency and accuracy are greatly improved, the interference of human factors is reduced, and the real-time performance and reliability of progress recognition are ensured.

The invention discloses an intelligent shopping cart self-service payment system based on multi-sensor fusion and an implementation method thereof, and the system comprises a self-service payment system which comprises a commodity recognition module, a data processing module, a settlement and payment module, a safety management module, a user interaction module, and a communication and network module. According to the invention, through integration of the RFID reader, the high-definition camera, the weight sensor and the ultrasonic sensor, high-precision identification of commodities in the shopping cart is realized by adopting a multi-modal data fusion algorithm, the system supports multiple payment modes, and the shopping cart is convenient to use. The block chain technology and the multi-layer encryption algorithm are utilized to guarantee the security of the payment process, the response speed and the data processing capability of the system are improved through the combination of edge computing and cloud computing, and the system improves the shopping experience, is suitable for various retail scenes, and has a wide application prospect.

The invention discloses a wafer classification method and device for a wafer rapid heating process, and the method comprises the steps: obtaining a plurality of types of wafers, carrying out the heating for a period of time, obtaining temperature change data, building a data set, enabling the temperature change data to comprise radiance, a temperature value after heating, and a temperature difference value with resolution in a heating process, training a support vector machine based on the data set; and heating the wafers needing to be classified for the same time, inputting the obtained temperature change data into the trained support vector machine to obtain the wafer category, and implementing a rapid heating process according to the obtained wafer category. According to the method, the classification accuracy and the production efficiency in the rapid wafer heating process are remarkably improved, the production cost is reduced, the consistency and the quality of products are improved, and the method has wide application prospects in the field of semiconductor manufacturing.

The invention provides an electric power insulator defect identification method and device based on depth decision fusion, and the method comprises the steps: carrying out the feature extraction of an electric power inspection image, obtaining k depth feature maps of different hierarchies, and carrying out the fusion of the k depth feature maps of different hierarchies, and obtaining a fused depth feature map F; based on the fused depth feature map F, obtaining a covariance descriptor feature matrix # imgabs0 #, a principal component analysis feature matrix # imgabs1 # and a maximum noise fraction method feature matrix # imgabs2 #; respectively inputting the # imgabs3 #, the # imgabs4 # and the # imgabs5 # into the corresponding support vector machine models, and outputting initial recognition results (# imgabs6 #, # imgabs7 # and # imgabs8 #); and analyzing the initial identification results (# imgabs9 #, # imgabs10 # and # imgabs11 #) by adopting a decision fusion model of a voting mechanism, and outputting a final insulator defect identification result. The method can achieve the high-precision recognition of the defects of the power insulator under the condition of a small number of training samples.

The application provides a radar working mode recognition method, device, equipment, medium and program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a pulse description word (PDW) sequence of a radar emitter; determining an intra-pulse feature of the radar emitter and an inter-pulse feature of the radar emitter based on the PDW sequence; inputting the intra-pulse feature and the inter-pulse feature into a working mode feature extraction model to obtain an intra-pulse feature vector and an inter-pulse feature vector output by the working mode feature extraction model; and inputting a feature fusion vector of the intra-pulse feature vector and the inter-pulse feature vector into a radar working mode recognition model to obtain a radar working mode recognition result output by the radar working mode recognition model. The intra-pulse feature and the inter-pulse feature are obtained first, and then the intra-pulse feature and the inter-pulse feature are subjected to feature extraction to obtain the intra-pulse feature vector and the inter-pulse feature vector with context information, so that high-precision recognition of the radar working mode can be realized for each scene.

The application discloses a kind of power forest fire intelligent monitoring method, device and equipment based on through remote fusion, and storage medium, it is related to forest fire intelligent monitoring technical field, comprising: obtaining the position information of first monitoring node, and obtaining the temperature and humidity data and smoke concentration data corresponding to position information as environmental data;Position information and environmental data are input into least square support vector machine model for processing, and fire risk probability is obtained;When fire risk probability is greater than preset risk threshold, position information, environmental data and fire risk probability are combined to obtain fire preliminary screening information, and fire preliminary screening information is uploaded to cloud server, so that cloud server fuses satellite remote sensing data to complete fire confirmation, realize the non-blind area monitoring coverage of power forest fire along transmission line, realize the preferential transmission of key fire data, reduce communication resource waste, improve system response speed and transmission reliability of key data.

The invention discloses a navigation mark visual identification and collision early warning method and system based on deep learning, and relates to the technical field of intelligent shipping, and the method comprises the steps: collecting a navigation channel monitoring video stream in real time, and carrying out the preprocessing of a key image frame; constructing a navigation mark detection model based on an improved Officient Det network, and outputting the position and category of the navigation mark in the key image frame; the method comprises the following steps: constructing a ship track prediction model through ship historical track data, predicting a short-time track of a ship, calculating the relative position, speed and course angle of the ship and a navigation mark, and constructing a dynamic collision risk field; and fusing the visual detection result, the radar ranging data and the AIS information to obtain a ship collision risk, and triggering graded early warning based on a preset risk threshold. According to the method, the improved deep learning model and the real-time calculation framework are combined, the robustness of navigation mark identification is improved, an efficient collision early warning mechanism is established, and technical support is provided for intelligent shipping.

The invention provides an electromechanical prefabricated component automatic integration method and system. The method comprises the following steps: firstly, receiving integration demand input through a user terminal; secondly, acquiring an image of a to-be-integrated electromechanical component in real time through a CCD camera; then, splicing the preprocessed integration demand input data, the image data of the to-be-integrated electromechanical component and the image information data in the feature database to form an N-dimensional input vector; thirdly, the N-dimensional input vector is input into the trained convolutional neural network, and the trained convolutional neural network outputs the name and the position of a current component needing to be integrated; thirdly, the PLC control system controls the manipulator and the transmission device to automatically integrate the components according to the names and the positions of the current components needing to be integrated; and finally, returning to repeatedly execute the steps until the assembly is completed. Accurate selection of the components is realized by adopting the convolutional neural network algorithm, and the accuracy and efficiency of automatic integration of the electromechanical prefabricated components are greatly improved by utilizing the PLC control system.

The invention provides a password device discovery method and a computer device, and relates to the technical field of password application. The method comprises the following steps: responding to an access event triggered by a software application on a current password device, obtaining device feature information of the current password device in multiple dimensions, generating device fingerprint information of the current password device according to the device feature information in multiple dimensions, and sending an access request to a server of the software application according to the device fingerprint information, the access request is used for enabling the server to detect the equipment fingerprint information by adopting an equipment fingerprint detection algorithm according to a preset equipment fingerprint database to obtain a fingerprint detection result of the current password equipment, receiving a first access response sent by the server, and sending the first access response to the server; the first access response is an access response triggered when the server determines that the current password device is the registered password device. Complicated and unique equipment fingerprint information is generated through multi-dimensional equipment feature information, and omnibearing and high-precision identification of the identity of the password equipment is realized.

The invention discloses a power transmission bare conductor identification method, system and device, a medium and a program product, and the method comprises the steps: obtaining a to-be-identified power transmission bare conductor image, and carrying out the preprocessing; inputting the preprocessed to-be-identified power transmission bare conductor image into a pre-trained power transmission bare conductor type identification model to obtain a power transmission bare conductor type in the to-be-identified power transmission bare conductor image; wherein the power transmission bare conductor type identification model is obtained by training a lightweight convolutional neural network embedded with a plurality of dynamic feature refiners. According to the method, a lightweight feature extraction module is utilized, model parameters and model complexity are greatly reduced, fine-grained feature extraction is performed on the difficulties of high inter-class similarity and high intra-class discrimination of the transmission bare conductor in combination with a dynamic feature refinement device, and the recognition capability of an artificial intelligence algorithm in a transmission bare conductor scene is improved. The model identification of the bare conductor can be completed under the condition of uninterruptible power supply, and the identification efficiency and precision are improved.

The invention provides a construction method, system, equipment, medium and program product of a traditional Chinese medicine origin traceability model fusing element fingerprints and secondary metabolite outlines, and correspondingly provides a traditional Chinese medicine origin traceability method, equipment, medium and program product based on the model. Relates to the field of medicinal material identification. According to the method, element fingerprints and chemical component profiles are fused according to metallomics and metabonomics, and an element fingerprint substance and secondary metabolite profile substance combined recognition mode is innovatively put forward and is used for geographical source recognition of traditional Chinese medicine with radix peucedani as a mode. And an aggregation model is innovatively adopted, and respective optimal models of elements and chemical components are further integrated, so that the classification precision is improved, and the scientificity and accuracy of traditional Chinese medicine quality evaluation are remarkably improved.

The invention belongs to the field of motor fault recognition, and particularly discloses a permanent magnet synchronous motor winding short circuit recognition method, which comprises the following steps: S1, constructing a convolutional neural network model, and inputting three-phase current signals and vibration signals as input signals; s2, establishing at least two input channel groups, introducing a space attention module, performing feature extraction on the three-phase current signal and the vibration signal, and splicing the extracted features; and S3, training the convolutional neural network model by adopting the public data set. According to the invention, through a multi-input mechanism, the current and vibration signals of the PMSM are effectively separated and processed, so that the characteristics of the sample data are accurately extracted. On the basis of current and vibration signals, an attention mechanism is introduced into a convolutional neural network, and a multi-input strategy is adopted, so that the signal feature extraction and selection capability of the network is enhanced.

The invention discloses an automobile part detection system and method based on image recognition. The system comprises an image acquisition assembly, a preprocessing assembly, a recognition assembly, a data analysis assembly and a control assembly, the image acquisition assembly is used for acquiring an image of a to-be-detected automobile part, and the image acquisition assembly communicates with the preprocessing assembly through a data channel; the pre-processing assembly is used for pre-processing the received image and then transmitting the image to the identification assembly; the recognition assembly processes the received image data based on a preset recognition model and generates recognition data of the automobile parts, the data analysis assembly is used for generating a quality evaluation report of the automobile parts based on the recognition data, and the control assembly is used for adjusting the working state of the production line according to the quality evaluation report. The whole structure is simple, through cooperation of the image acquisition assembly and the preprocessing assembly, the image of the to-be-detected automobile part can be rapidly acquired and processed, manual operation steps are reduced, and the whole efficiency of a production line is greatly improved.

The application discloses a vehicle cleanliness detection method, device and computer readable storage medium, the vehicle cleanliness detection method comprises: acquiring a to-be-detected vehicle image; segmenting the to-be-detected vehicle image to obtain a plurality of vehicle component images, the vehicle component image being an image in which a key component of a vehicle in the to-be-detected vehicle image is located; extracting features of the vehicle component images by using a feature extraction model in a cleanliness identification model to obtain global features and local features; fusing the global features and the local features by using a cleanliness prediction model in the cleanliness identification model to obtain fused features, and predicting the fused features to obtain a cleanliness score of each vehicle component image. In the foregoing manner, the application can improve the accuracy of vehicle cleanliness detection.

The present application relates to a kind of based on multi-modal fusion's weld magnetic detection system and method, system includes: multi-modal sensor component, for obtaining the two-dimensional image data and three-dimensional point cloud data of weld;Industrial control module, for according to two-dimensional image data and three-dimensional point cloud data, using instance segmentation model and three-dimensional point cloud feature extraction network to identify weld area, and extract weld three-dimensional structure information, according to weld three-dimensional structure information to generate detection path, based on detection path to carry out weld defect analysis and positioning;Mechanical arm actuating mechanism, for connecting detection probe, drive detection probe to move according to detection path, and detection probe real-time acquisition weld detection signal and transmission to industrial control module.The present application realizes the full process automation of weld identification, three-dimensional modeling, detection path generation and defect identification, compared with prior art has significant technical advantage and practical application value.

The application discloses a kind of agricultural laser weeding safety decision method and system, the method in which includes: obtaining the hyperspectral imaging data of weeding target area, ecological environment data and plant three-dimensional point cloud data;Based on hyperspectral imaging data, weed identification is carried out using pre-trained plant phenotype classifier;Based on hyperspectral imaging data, ecological environment data and plant three-dimensional point cloud data, the minimum lethal energy of laser for killing weeds is calculated using pre-trained energy optimization model, and laser emission parameter result is generated according to the minimum lethal energy of laser;Based on plant three-dimensional point cloud data, plant three-dimensional model is constructed, and weed positioning result and operation path planning result are generated;Operation equipment positions laser emission end to target weed according to positioning result and operation path planning result, and controls laser emission end to emit laser corresponding to laser emission parameter result.The method in the application realizes the synchronization of weeding efficiency, crop protection and ecological safety.

This application discloses a method, device, and storage medium for air gesture recognition, relating to the field of gesture recognition technology. The method includes: acquiring reflected signals after a hand gesture reflects a target acoustic signal through at least one microphone in a wearable device, wherein the target acoustic signal is constructed from a signal emitted by at least one speaker in the wearable device; performing frequency domain processing on each reflected signal to obtain target time-series spectrum information; performing differential processing on each target time-series spectrum information to obtain differential spectrum information; extracting potential gesture segments based on each differential spectrum information; inputting each potential gesture segment into a pre-constructed gesture recognition model, and outputting gesture recognition results. This application can improve the accuracy of gesture recognition during interaction.

The application provides a fruit and vegetable recognition and supply management method and system based on edge computing, and relates to the technical field of Internet of Things and smart home, and comprises the following steps: acquiring fruit and vegetable management multi-source data; pre-processing fruit and vegetable images to obtain to-be-recognized fruit and vegetable images, inputting the to-be-recognized fruit and vegetable images into a preset fruit and vegetable recognition model for recognition to obtain fruit and vegetable categories; updating inventory states according to the fruit and vegetable categories and corresponding weight data, and adjusting storage environment conditions based on the fruit and vegetable categories and a preset storage condition suggestion library; calculating historical consumption based on the weight data, and generating fruit and vegetable management decisions according to the inventory states, inventory time information and the historical consumption; and outputting the fruit and vegetable management decisions to terminal equipment. The application realizes high-precision recognition, dynamic inventory management and intelligent supply decisions of fruits and vegetables.

The invention discloses an ionized layer electron density profile abnormal fluctuation detection method, system and equipment. The method comprises the following steps: calculating a relative electron density change rate between adjacent height points of each effective electron density profile; the height axis is divided into a plurality of intervals, for each effective electron density profile, the robust percentile index of the relative electron density change rate is counted in each height interval, and a noise upper limit threshold value, a sawtooth amplitude threshold value and an extreme sudden change threshold value are generated; formulating a three-level progressive judgment process comprising a high noise background criterion, an extreme single-point jump criterion and an effective sawtooth structure criterion based on a noise upper limit threshold, an extreme sudden change threshold and a sawtooth amplitude threshold; and if any criterion is triggered, determining that the electron density profile is invalid. According to the method, non-physical sawtooth oscillation and extreme jump can be efficiently and accurately identified, and the quality control capability of data before ionosphere modeling is remarkably improved.

The embodiment of the invention provides a product disassembly and repair detection method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring an appearance quality inspection report and an X-ray image of an internal structure of a to-be-detected product; performing multi-modal anomaly detection processing on the appearance quality inspection report and the X-ray image to obtain a detection result of the to-be-detected product; wherein the detection result comprises the maintenance processing condition of the to-be-detected product; and if it is determined that the detection result represents that the to-be-detected product has been repaired, performing deep repair processing on the to-be-detected product. The method can improve the efficiency and accuracy of product maintenance detection.

The present application relates to a safety helmet wearing recognition detection method and system, an electronic device and a medium, and belongs to the technical field of oil and gas station recognition. The method comprises model establishment and recognition detection of on-site safety helmet wearing images. The model establishment comprises collecting on-site safety helmet wearing images, labeling and dividing the collected on-site safety helmet wearing images into a data set, establishing a safety helmet target recognition model based on the divided data set, and establishing a personnel posture estimation model based on the divided data set. The present application utilizes the automatic recognition function of the safety helmet target recognition model and the recognition function of the personnel posture estimation model on human body posture, realizes high-precision recognition of on-site safety helmet wearing, solves the problems of low monitoring efficiency and high labor cost of artificial management, solves the problem of low detection robustness of the existing safety helmet recognition model in the application scenario of oil and gas stations, and improves the accuracy of recognition.

The invention discloses a method and system for dynamic monitoring of target organisms in a marine ranch. The method comprises the following steps: receiving echo signals emitted by different sonar array elements in real time, performing distance Fourier transform and azimuth Fourier transform on each echo signal, and obtaining a frequency spectrum of each echo signal; performing phase shift operation and inverse Fourier transform on each frequency spectrum, and obtaining a plurality of transformed signals; performing phase estimation based on each transformed signal, and obtaining a phase error; obtaining a phase compensation function based on the phase error; compensating and coherently superimposing each echo signal according to the phase compensation function, and obtaining an image to be identified; inputting the image to be identified into a constructed target identification network for identification, and obtaining a target organism characteristic map, so that personnel can judge the category and state of the target organism according to the target organism characteristic map. The method realizes high-precision identification of target organisms in marine organisms, thereby improving the accuracy of monitoring.

The application discloses a kind of power equipment defect identification and warning method and system based on deep learning.The method comprises: synchronously collecting and registering the visible light and infrared thermal imaging image on the surface of power equipment, constructs the instance segmentation network including light weight feature extraction network, multiscale feature fusion network and frequency domain mask prediction branch;Adopt the generative adversarial strategy to enhance the diversity of training sample;Based on graph neural network, analyze the association between defect and equipment topology, historical record, infer the cause-effect relationship of defect and risk level;Generate the interpretable warning information including heat map, natural language report and repair suggestion;Real-time detection and deep analysis are realized using end-edge-cloud collaborative architecture;Through closed-loop optimization mechanism, continuously improve system performance.The application realizes high-precision defect detection under multi-modal data fusion, has strong robustness and interpretability, significantly improves the intelligent level of power equipment operation and maintenance.

The invention provides a farmland surface disturbance and desertification monitoring method and system based on multi-modal remote sensing, and the method comprises the steps: obtaining a remote sensing image, carrying out the preprocessing of the remote sensing image, dividing the obtained multi-modal dual-temporal remote sensing image, constructing a Token sequence, inputting the Token sequence into a Transform backbone network, and carrying out the feature extraction, and obtaining an optical modal feature and an SAR modal feature; a low-rank adapter is introduced into an attention module of the network, gradient contribution is regulated and controlled based on modal deviation fractions, and cross-time-phase differential change features are constructed and fused to obtain fusion features; and generating a change probability graph, applying an optical and SAR prediction result consistency constraint, and outputting a final change detection result. According to the method, the fusion balance of optical and SAR features is remarkably improved through a modal depolarization LoRA fine tuning mechanism, so that weak modal information is fully expressed; and meanwhile, a cross-modal consistency constraint is introduced to ensure that a change detection result has consistency and credibility.

This invention discloses a pure vision-based 3D reconstruction and adaptive completion method in a collapse environment, belonging to the field of computer vision and emergency rescue 3D reconstruction technology. The method completes initial 3D reconstruction by acquiring data through binocular vision, constructs an anisotropic point cloud density distribution field to achieve accurate detection of low-density blind spots, establishes safe and reachable spatial constraints based on an implicit distance field, and optimizes the completion viewpoint by fusing an observability model. Map iteration is completed through local reconstruction and point cloud updates, and a closed-loop completion mechanism is formed using point cloud density as feedback until the reconstructed model meets the integrity requirements. This invention solves the problems of permanent voids in reconstruction caused by viewpoint blind spots, poor consistency between completion results and the real scene, and insufficient safety in the reconstruction process in existing technologies in collapse environments. It can achieve complete and high-precision autonomous reconstruction of 3D models of collapse disaster sites, providing reliable spatial data support for emergency rescue decision-making.

The application relates to a pantograph-catenary arc detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring current information in the process of train contact current collection with a pantograph-catenary through a current sensor arranged in a power supply circuit at the rear end of a pantograph insulator; acquiring arc radiation information in the process of train contact current collection with a pantograph-catenary through an antenna arranged at the top of a pantograph frame; decomposing the current information and the arc radiation information to obtain corresponding intrinsic mode function components and corresponding residual components; dividing each intrinsic mode function component into a plurality of time windows, and extracting time-frequency domain characteristic values of each time window; inputting the time-frequency domain characteristic values into a pre-established pantograph-catenary arc detection model, and outputting a detection result of whether an arc exists in the time window by using the pantograph-catenary arc detection model. The implementation of the application can improve the safety of train operation.

This invention belongs to the field of satellite remote sensing and red tide detection technology, and discloses an integrated method and system for generating and identifying broadband 3D samples of dominant red tide species. The method constructs an integrated model for generating broadband 3D red tide samples and identifying dominant species based on deep learning. It utilizes a red tide image generation module based on spectral feature transfer to obtain expanded samples of different dominant red tide species. A red tide dominant species identification module is constructed by integrating multi-scale feature extraction and cross-spatial attention mechanisms. The module is trained using a student-teacher semi-supervised learning model. Experimental results show that the model has good adaptability and can effectively identify three dominant red tide species: *Noctiluca scintillans*, *Noctiluca chloroticus*, and *Hacochloa erythrophora*, with an overall identification accuracy of 94.48%, which is 3.52%–8.66% higher than other comparative methods, providing a basis for red tide prevention and management.