38results about How to "Real-time identification" patented technology

The invention belongs to but not limited to the technical field of emotion recognition, and discloses a multi-modal classroom emotion recognition method and system based on modal adaptive learning, and the method comprises the steps: building a multi-modal data collection system, collecting the multi-modal data, such as texts, audios and videos, in a classroom teaching process in real time, and carrying out the real-time collection of the multi-modal data; and preprocessing and feature extraction are carried out on data of different modes. For emotion expression feature differences of different classroom scenes, a multi-modal data fusion method based on adaptive modal scoring (AMS) is provided, and a time sequence modeling and emotion classification model is constructed. The multi-modal fusion and time sequence modeling mechanism simulates the multi-dimensional perception of the teacher on the emotional states of the students in the class, and enhances the interpretability of the emotion recognition system. According to the system, weighted fusion is carried out on data features from texts, audios and videos through fusion of multi-modal data, and complete emotion feature representation is formed.

The invention discloses a high-frequency partial discharge type identification method and system, and the method employs a multi-channel collection and fine signal processing mode, can comprehensively capture partial discharge signals, reduces the signal missing, and improves the detection accuracy. A strategy of dynamically adjusting a threshold value is adopted, so that the system can adapt to the change of the environment, and the phenomena of false alarm and missing alarm caused by the change of the environment are effectively reduced; the adaptive capacity and robustness of the system under various working conditions are improved by comprehensively considering spectrum characteristics and dynamically adjusting a threshold value; when the system operates, abnormal signals can be recognized in real time, alarm can be automatically triggered, and maintenance personnel can be notified in time, so that the equipment fault risk is reduced, and safe operation of the power system is guaranteed.

The invention relates to the technical field of element security risk identification in an informatization system development process, in particular to a code and component security risk identification method and device and a medium, and the method comprises the following steps: collecting version history of codes and components, auditing security risk characteristics of different programming languages, and evaluating a dependency relationship between the codes and the components; and analyzing static security characteristics of the code and the component, and generating a development security risk model. According to the method, the identification capability of potential development security risks is enhanced through security risk auditing of codes and binary components, and the development security risks are revealed in advance and the accuracy of development security risk assessment is optimized through continuous left shift of the security identification stage of an informatization system; therefore, security vulnerability identification of codes and components does not depend on detection of past data or known vulnerabilities any more, security threat modes of codes and components which emerge newly or have complex logic structures can be identified in real time, and the application security level in a software application development period is remarkably improved.

The invention provides a fish school disease degree identification method and device, and is applied to the technical field of aquaculture. The method comprises the steps of collecting fish school image data, performing data enhancement processing on the fish school image data, and constructing a fish disease recognition data set based on the processed fish school image data; training a fish disease recognition model through the fish disease recognition data set, wherein the fish disease recognition model is used for recognizing the disease degree grade of a fish school; wherein the fish disease identification model comprises a depth separable convolution module, a compression and excitation SE attention mechanism module and a cavity convolution DCLS module with learnable spacing, the depth separable convolution module is used for capturing fish body lesion area features, and the SE attention mechanism module is used for enhancing the fish body lesion area features. The DCLS module is used for dynamically adjusting a sampling interval to extract multi-scale features.

The invention is suitable for the technical field of disaster monitoring, and provides a natural disaster monitoring method and device, a storage medium and electronic equipment, and the natural disaster monitoring method provided by the invention comprises the steps: responding to a natural disaster event flow acquired by a dynamic visual sensor, and carrying out the reconstruction based on the natural disaster event flow to obtain a natural disaster image; recognizing the natural disaster image through an edge detection algorithm and an optical flow estimation algorithm, and obtaining the occurrence position and spreading characteristics of the natural disaster; recognizing the natural disaster image through a spatial wavelet transform algorithm and a spatial neighborhood filtering algorithm to obtain the disaster level of the natural disaster; and under the condition that the disaster grade meets the early warning condition, generating early warning prompt information based on the occurrence position, the spreading characteristic, the disaster type and the disaster grade, and outputting the early warning prompt information.

The invention discloses an electric power system voice instruction recognition method based on machine learning, and belongs to the field of electric power operation control. Comprising the following steps: identifying a voice instruction of an electric power system operation order to assist an operator to operate a switching operation order operation instruction; reading of a switching operation order operation instruction and first-time voice retelling are carried out on the mobile terminal, and the system carries out first-time voice recognition; carrying out second voice retelling on the mobile terminal, and carrying out second voice recognition; and only if the repeating of the secondary voice instruction is successful, the matching of the single switching operation ticket is completed. Checking operation is carried out on the voice retelling of the first person and the voice retelling of the second person, and checking of retelling instruction identification information is completed; after the operation auditing is completed, the information of the operation ticket operation is synchronously filled into the operation ticket document, so that the step of manually filling the operation information of the operation ticket is reduced, the correctness of the switching operation is ensured, and the flow control of the switching operation ticket operation is realized in a wireless network environment.

The invention discloses an electric system operation instruction ticket identification method based on secondary voice verification, and belongs to the field of electric power system operation. Comprising the steps that the mobile terminal determines whether step verification is consistent with an original verification sequence; the user reads the first item of the power transformation switching operation ticket operation item; judging whether the characters read by the user are consistent with the characters corresponding to the power transformation switching operation ticket; if yes, reading the same content once by the auditor, and judging again; if not, immediately prompting the user that the reading content is not matched with the operation item; after matching of one operation item is completed, traversing the whole operation power transformation switching operation ticket item, and performing reading identification and matching by the user in sequence until reading of the whole power transformation switching operation ticket is completed, thereby completing the operation; through a secondary voice verification mode, verification of retelling instruction identification information is completed; and after the operation auditing is completed, the information of the operation ticket operation is synchronously filled into the operation ticket document, so that the correctness of the switching operation is ensured, and the flow control of the switching operation ticket operation is realized in a wireless network environment.

The invention discloses an intelligent robot identification and positioning system applied to metal forging and pressing equipment, which is characterized by comprising an image identification module based on deep learning and used for acquiring image data of a working area of the metal forging and pressing equipment in real time and identifying a metal workpiece; the three-dimensional positioning module based on laser scanning is used for carrying out three-dimensional space positioning on the identified metal workpiece; the data processing unit is used for processing data of the image recognition module and the three-dimensional positioning module and generating accurate position information of the workpiece; the control unit is used for controlling the movement of the robot according to the position information so as to accurately grab and place the metal workpiece; the feedback system is used for monitoring and adjusting actions of the robot in real time so as to ensure the accuracy of operation; and the user interface is used for displaying system states and operation instructions and allowing an operator to carry out intervention and adjustment.

The invention discloses a camera automatic identification tracking method and system based on machine vision, and relates to the technical field of camera identification tracking, and the method comprises the following steps: configuring and calibrating a high-definition camera, obtaining the image and video data of a target environment through the high-definition camera, and storing the image and video data in a database; the method comprises the following steps of: preprocessing image and video data acquired by a high-definition camera, storing the preprocessed image and video data according to time, place and category, deleting fuzzy image and video data, identifying a target object in the image and video data by using a target detection algorithm, and identifying and tracking the target object; through the target detection algorithm and the target tracking model, the target object of the image and video data can be accurately identified and tracked in real time, the monitoring efficiency is greatly improved, misjudgment and missed judgment caused by human factors are reduced, meanwhile, the labor cost is reduced, the monitoring work is more efficient and reliable, and the user experience is improved. And a powerful guarantee is provided for public safety.

This invention belongs to the field of text printing technology and discloses a text printing method and system based on speech recognition. The method includes the following steps: constructing a text printing template database; constructing a mixed-language speech recognition model; acquiring speech audio data in real time and performing speech recognition; matching several corresponding text printing templates; selecting a text printing template; fusing the real-time speech text data with the selected text printing template; and printing the real-time text printing data. The system includes a database construction unit, a model construction unit, a storage unit, a speech audio acquisition unit, a speech recognition application unit, a template matching unit, a template selection unit, a data fusion unit, and a printer. This invention solves the problems of low intelligence, poor speech recognition effect, low recognition efficiency, and lack of organic integration in existing technologies.

The invention discloses a park weed accurate identification method and system based on an improved YOLOv11 deep convolutional network, and the method comprises the steps: 1, constructing a multi-scene park weed image data set, 2, carrying out the data enhancement and preprocessing, 3, constructing an improved YOLOv11 lightweight network model, 4, designing a self-adaptive loss function, and 5, carrying out the recognition of a multi-scene park weed image data set. Multi-stage model training and optimization are executed; and step 6, weed real-time identification and result output are realized. According to the method, a lightweight attention mechanism and an improved multi-scale feature fusion structure are introduced, the extraction and distinguishing capability of the network on small-scale weed features under a complex background is remarkably enhanced, the false detection and omission ratio is effectively reduced, and the dynamic label distribution strategy and a loss function fusing global context information are adopted, so that the robustness of the network is improved. And the detection robustness and the positioning precision of the model in dense, shielded and form-variable weed scenes are improved.

The application discloses a recognition method, device, equipment and medium, the method comprises: obtaining an image to be recognized containing at least a target object; target detection is performed on the image to be recognized to obtain a first image; wherein the first image comprises an image containing the target object segmented from the image to be recognized; direction detection is performed on the first image to obtain direction information of the first image; the first image is corrected based on the direction information to obtain a second image; the second image is recognized to obtain a recognition result of the target object.

The present invention is applicable to the field of augmented reality technology, and provides a bird observation method, system, device and storage medium based on augmented reality. The method includes: obtaining video information of birds; processing the video information according to a computer vision method to obtain identification information of the birds; superimposing the identification information and the video information to obtain a virtual image of the bird; determining the position of the virtual image in the real world according to the user's head movement information, and providing real-time bird identification and rich virtual information superposition through augmented reality-based glasses, so that bird watchers can easily identify birds and understand their characteristics and ecological information, while promoting social interaction and data sharing among bird watchers, and improving the overall bird watching experience.

The present invention relates to the technical field of vehicle control systems. The present invention provides a vehicle posture control optimization system based on an electro-hydraulic brake valve, including units such as data acquisition, driving mode recognition, electro-hydraulic brake valve adjustment simulation, vehicle driving simulation, data comparison and control optimization. The system recognizes the driving mode in real time through a deep learning model, simulates the electro-hydraulic brake valve adjustment strategy according to the driving mode and vehicle driving state data, and verifies the adjustment effect using a vehicle driving simulation model. If the simulation result is inconsistent with the real-time data, the model parameters are optimized through a genetic algorithm. The system can adapt to different driving modes and road conditions, dynamically adjust the control strategy, improve the electro-hydraulic brake valve adjustment smoothness and fuel efficiency, and provide a stable power drive for the vehicle.

The invention discloses an aperture opening ratio calculation method and device based on a VAD algorithm. The method comprises the following steps: acquiring a sound recording file of an online classroom; based on a VAD algorithm, voice activity information of the teacher recording file and voice activity information of the student recording file are detected according to a preset time interval, and a first voice character string of the teacher recording file and a second voice character string of the student recording file are obtained; carrying out statistics on voice activity information in the first voice character string and the second voice character string, and according to a statistical result, carrying out calculation to obtain a student aperture opening ratio of the online classroom; the student aperture ratio is determined according to the ratio of the voice activity information in the second voice character string to the voice activity information in the first voice character string. The voice activities of teachers and students in the online class can be accurately and quickly identified in real time by using the VAD algorithm, so that the student aperture opening ratio in the online class can be accurately calculated, and the learning state of the students can be effectively monitored.

According to the electronic nose lightweight gas identification method provided by the invention, the accuracy of gas identification and the lightweight of the model are both considered, and the method can be deployed on an electronic nose microprocessor with limited resources. The method comprises the following steps of: (1) acquiring data by using an electronic nose, and preprocessing the data; (2) pre-training the model, which comprises the following steps: (1) processing the preprocessed data to generate a virtual sensor signal; (2) constructing a Markov chain by using the generated virtual sensor signal, and generating a Markov transfer field space-time pseudo image; (3) taking the space-time pseudo image as input, performing multi-round parallel training on the convolutional neural network model, and selecting a pre-trained final model; (3) deploying the model on a microprocessor of the electronic nose; when the electronic nose is used in an actual scene, the electronic nose collects gas signal data, the pre-training model in the microprocessor of the electronic nose conducts reasoning and recognition, and a final recognition result is obtained.

The invention relates to a method and a system for identifying Shigella and / or diarrheagenic Escherichia coli. The method comprises the following steps: collecting mass spectrum data of known strains through paper spray ionization; generating a training sample based on mass spectrum data of a known strain; constructing a strain prediction model, and training the strain prediction model by using the training sample; collecting mass spectrum data of the strain to be detected through paper spray ionization; and predicting a to-be-detected strain by using the trained strain prediction model based on the mass spectrum data of the to-be-detected strain, and determining the type of the to-be-detected strain. The method and the system can directly identify the strain sample, do not need sample pretreatment, are low in analysis cost and simple and convenient to operate, can identify the strain category in high throughput and real time, and are high in identification result accuracy, high in reliability and high in identification speed.

The invention discloses a tunnel surrounding rock breaking degree intelligent identification method, and relates to the technical field of geological engineering and image processing, and the method comprises the steps: image preprocessing, including noise removal and image enhancement; feature extraction, including edge detection; the image segmentation comprises threshold segmentation; feature analysis, including crack analysis, contrast analysis and homogeneity analysis; the method has the beneficial effects that the identification efficiency and accuracy can be effectively improved, and an important guarantee is provided for safe construction of tunnel engineering.

The application discloses a kind of power grid dispatching support system fault diagnosis method and device, method includes: obtaining the operation and maintenance index acquisition data of server, process;At preset fixed time, based on index time series prediction algorithm, the index future prediction data of preset time range is obtained;In combination with operation and maintenance index anomaly detection algorithm, identify system operation failure, generate operation failure information;All operation failure information is summarized, complete operation failure detection, and send operation failure alarm.The application realizes the operation and maintenance index time series prediction technology under the condition of mass model, establishes adaptive and self-learning model construction and optimization architecture, based on index prediction automatically sets dynamic threshold, through the multi-technology fusion analysis based on mass operation and maintenance data, real-time, fast, accurately identify system failure, realize experience type to intelligent analysis type management change, realize system failure accurate perception, guarantee control system safe, reliable and stable operation.

The invention relates to the technical field of robots, in particular to a psychological counseling robot with a multi-mode emotion recognition capability and a counseling method thereof. Which comprises a robot main body and is characterized in that a multi-mode sensing module is installed on the head of the robot main body, a data processing and analyzing module is installed in the robot main body, and a real-time feedback generating module is installed in the robot main body and located on one side in the robot main body. According to the psychological counseling robot with the multi-mode emotion recognition capability and the counseling method of the psychological counseling robot, through cooperative work of multiple modules, the intelligent psychological counseling robot can recognize user emotion in real time at multiple angles, personalized psychological counseling feedback is generated, and the interactive experience and the emotion adjustment effect of the user are improved; meanwhile, through reasonable position layout and electrical connection among the modules, the system is stable in operation and has strong expansibility, and more intelligent psychological health service can be realized.

The invention provides an intelligent detection and classification method and system for dendrobium officinale diseases and insect pests, and relates to the field of data processing, and the method comprises the steps: obtaining a plurality of pieces of sample data; based on the multiple sample data, establishing an optical flow calculation model, a feature extraction model and a pest and disease classification model; obtaining 3D point cloud data of the dendrobium officinale based on a three-dimensional reconstruction technology according to the three-dimensional data of the dendrobium officinale; through an optical flow calculation model, according to the two-dimensional image sequence of the dendrobium officinale, determining optical flow information of the dendrobium officinale; according to the 3D point cloud data and the optical flow information of the dendrobium officinale, whether an optical flow generation area exists in a leaf area of the dendrobium officinale is judged; if yes, extracting image features of the dendrobium officinale from the two-dimensional image sequence of the dendrobium officinale through a feature extraction model; the pest and disease damage type of the dendrobium officinale is determined through the pest and disease damage classification model according to the image features and the optical flow information of the dendrobium officinale, and the method has the advantage of improving the detection and classification efficiency and accuracy of the pest and disease damage of the dendrobium officinale.

The present invention relates to the field of pollution source monitoring technology, and in particular to a method and system for online monitoring of pollution sources. The method comprises the following steps: acquiring target pollution area data, and performing area cutting through simulation model restoration processing to obtain simulated cut pollution areas; then, using a uniform measurement point array to measure the oxygen flux of the cut area, and analyzing the flux fluctuation to generate a normal fluctuation range; based on this fluctuation range, reversely track the pollution source and locate the initial pollution source data. Subsequently, combining the oxygen flux data of each area, the initial pollution source is regionally calibrated, and the candidate direction of pollution diffusion is calculated; finally, according to the diffusion direction and the initial positioning data, the pollution area is dynamically monitored and adjusted to generate the final pollution source monitoring results. The present invention realizes more flexible and accurate online monitoring of pollution sources.

The present invention discloses a road construction site supervision information collection device and a collection method thereof, which relate to the technical field of road construction information collection, and include a high-strength lightweight board body, a high-efficiency battery shell is installed on the top of the high-strength lightweight board body, a concrete degree detection component is installed on the inner side of the high-efficiency battery shell, and a solar panel is installed on the surface of the high-strength lightweight board body. With the cooperation of the concrete degree detection component, the support adjustment component and the drone detection component, the overall device can effectively collect data during road construction operations, and help operators remotely identify potential problem areas, make timely adjustments to facilitate better and more accurate detection and collection, so that a large amount of data can be collected and processed in real time, and a comprehensive detection report can be provided to help operators quickly identify potential problems and make timely responses, reduce errors in manual detection operations, and improve overall work efficiency.

The present invention provides an information processing method, device, equipment and storage medium. The information processing method comprises: sending a first weighing control signal to a control unit of a weighing platform, the first weighing control signal enables the weighing object on the weighing platform to be supported only by the first batch of weight sensors among multiple sensors; calculating the first center of gravity component of the weighing object on the placement surface along the first direction according to the weight signals of the first batch of weight sensors; sending a second weighing control signal to the control unit of the weighing platform, the second weighing control signal enables the weighing object on the weighing platform to be supported only by the second batch of weight sensors among multiple sensors; calculating the second center of gravity component of the weighing object on the placement surface along the second direction according to the weight signals of the second batch of weight sensors; calculating the center of gravity position of the weighing object on the placement surface according to the first center of gravity component and the second center of gravity component. The present invention prevents goods from tipping over.

The invention provides an intelligent accompanying robot for intelligent medical care for the aged. The intelligent accompanying robot comprises an intelligent control middle table, a visual acquisition device, an acquisition unit, a reconstruction processing unit, an estimation processing unit, a track optimization processing unit and an intelligent monitoring accompanying unit. The intelligent control middle table controls the visual acquisition equipment to acquire video data of the to-be-accompanied object; the acquisition unit acquires a target point cloud of a to-be-accompanying object in the video data; the reconstruction processing unit performs reconstruction processing on the target point cloud to obtain a reconstructed point cloud model; the estimation processing unit performs target frame size estimation processing based on the point cloud model to obtain a target frame of a target size; a trajectory optimization processing unit performs trajectory optimization processing on the to-be-accompanying object based on the target frame of the target size to obtain an optimized target trajectory; and the intelligent monitoring accompanying unit performs accompanying tracking on the to-be-accompanying object based on the optimized target trajectory, and performs social interaction with the to-be-accompanying object according to the emotional state information of the to-be-accompanying object. Accurate and effective accompanying is achieved.

This invention discloses a method and system for rapid identification of risk areas on rock slopes, belonging to the field of rock slope engineering monitoring and geological disaster early warning technology. The method includes: a model construction stage, constructing a three-dimensional point cloud model of the original rock surface of a steep rock slope based on UAV oblique photography and three-dimensional laser scanning; a preliminary risk zone delineation stage, delineating preliminary risk zones on the original rock surface based on the three-dimensional point cloud model; a system deployment stage, deploying a slope microseismic monitoring system according to the preliminary risk zones before slope excavation; a risk level evaluation stage, using joint spacing and the moment magnitude of microseismic events as preliminary evaluation indicators to delineate slope excavation risk areas and evaluate risk levels; and a risk verification stage, issuing early warnings based on risk levels, verifying risk levels using in-depth evaluation indicators, and dynamically delineating the final risk area range through multi-source parameters. This invention can dynamically and rapidly identify risk areas on rock slopes in real time as excavation progresses.

The invention provides a ray tracing-based composite target scattering center modeling method and system. The method comprises the following steps of: constructing a nested geometric model of a medium-PEC composite target; the direction of the outer normal direction of the target surface is defined according to the number of layers of the closed surface of the nested geometric model, and a multi-region surface discrete grid model is obtained through surface discretization and component decomposition; acquiring a ray path set containing regional source information and scattering mechanism information, and classifying according to whether the two types of information are the same or not to obtain ray path subsets; screening and acquiring a ray path set of the main contribution area from the acquired ray path subset; according to the corresponding relation between the three-dimensional attribute scattering center model and the geometric structure, a plurality of model parameters are deduced in the forward direction, and a parameterized attribute scattering center model is obtained. The invention provides a dielectric material electromagnetic parameter definition method, and high-frequency electromagnetic modeling of a dielectric-PEC composite target with a higher order and a transmission complex scattering mechanism is realized.

This invention provides an intelligent in-machine inspection device and method. The device includes: a probe signal transmitter, mounted on the tool post of a CNC machine tool to move with each axis of the machine tool for in-machine inspection of the machined workpiece; a probe signal receiver, mounted on the CNC machine tool and wirelessly connected to the probe signal transmitter for receiving inspection data collected by the probe signal transmitter; a CNC machine tool control system, communicatively connected to the probe signal receiver for receiving inspection data fed back by the probe signal receiver and generating error detection results; and a host computer system, communicatively connected to the CNC machine tool for receiving error detection results transmitted by the CNC machine tool and generating an inspection report. This invention achieves automatic in-machine inspection, real-time error compensation, and automatic data transmission for wheel hub machining, solving the technical problem that existing inspection methods cannot meet the requirements of fully closed-loop intelligent in-machine inspection.

The invention discloses a lightweight garbage detection method and system based on an improved YOLOv11s model, and the method comprises the following steps: S1, only employing a monocular camera, and obtaining the RGB image data of a road through the monocular camera; s2, cutting the acquired image data, cutting off the upper half part of the road image, and keeping key information related to a ground area below the image; and S3, calling an improved YOLOv11s network model, inputting the preprocessed image into the network model, then starting a detection code to carry out image reasoning detection, and drawing a detection result on the image to obtain result image data containing junk bounding box coordinates, junk categories and detection confidence information. According to the method, garbage in complex environments such as urban roads, green belts and community scenes can be efficiently detected and recognized in real time at low cost, and particularly, the detection performance of small-size target garbage is excellent.