119results about How to "Comprehensive description" patented technology

The invention relates to the technical field of behavior recognition, in particular to a machine learning-based surgical personnel video behavior recognition method and system. The method comprises the following steps: acquiring a video frame sequence and a point cloud frame sequence from a hand washing area of an operator through an RGB camera and laser ranging equipment to obtain the video frame sequence and the point cloud frame sequence; performing hand point cloud segmentation on the point cloud frame sequence to obtain hand point cloud data; performing action original data generation according to the video frame sequence and the hand point cloud data to obtain action original data; and extracting position and attitude information of hand key points from the original motion data to obtain video key point feature data. Visual information is captured through the RGB camera, three-dimensional depth information is obtained through the laser ranging device, fine analysis and evaluation are carried out on actions in combination with the deep learning network, and finally higher recognition precision, robustness and automation level are achieved.

The invention discloses a lower limb movement gait recognition method and system based on feature fusion, and belongs to the technical field of gait recognition. Comprising the following steps: acquiring a surface electromyogram signal and a motion inertia signal of a to-be-predicted object; carrying out feature extraction on the surface electromyogram signal and the motion inertia signal by utilizing wavelet transform, obtaining gait features and screening an optimal feature combination; and processing the optimal feature combination through the trained gait recognition model to obtain a gait recognition result. Complex multi-modal data can be effectively processed and analyzed, so that high-precision gait recognition and evaluation are realized; the problem that the accuracy of gait evaluation needs to be improved due to the fact that single signal processing is difficult to comprehensively reflect a complex gait dynamic process in the prior art is solved.

The invention relates to the technical field of railway power supply and distribution, particularly provides an operation and maintenance management method and system for railway power supply and distribution, and is used for solving the problem of low operation and maintenance management efficiency caused by poor accuracy of fault identification and positioning. The method comprises the following steps: receiving equipment operation data of a railway power supply and distribution system collected by a sensor network through the Internet of Things; inputting the data into a pre-trained graph neural network model, and identifying a fault equipment identifier and a fault mode; determining a target fault propagation path according to the fault equipment identifier, the fault mode and the multi-level power supply and distribution knowledge graph; wherein the multi-level power supply and distribution mapping knowledge domain comprises an equipment level mapping knowledge domain, a region level mapping knowledge domain and a system level mapping knowledge domain which are associated with one another; according to the target fault propagation path, a fault severity evaluation model is adopted to predict the fault severity, a corresponding operation and maintenance management report is determined, and corresponding processing is carried out according to the operation and maintenance management report. According to the technical scheme provided by the invention, the operation and maintenance management efficiency is improved.

The invention relates to the technical field of defect detection imaging, in particular to a method for multi-angle imaging and image fusion of an area-array camera, which can acquire a large amount of source image data in a short time by using the combination of the area-array camera and a light source, and improves the quality of a source image by preprocessing the source image data. The method comprises the following steps: firstly, creating a frame image, creating an image according to the frame image and a specific formula, extracting data of a specified row from each frame image, fusing the row data extracted from all frame images until the motion is finished, generating a preliminary fused image, and integrating information at different time points, so that features and changes in the motion process are more comprehensively described, and finally, the motion speed is improved. According to the method, the preliminary fusion image is decomposed through wavelet transform, the enhancement coefficient of the preliminary fusion image is obtained through convolution operation, the preliminary fusion image is enhanced through the enhancement coefficient, the fusion image is obtained, detail information in the image is highlighted, and the image is clearer and sharper.

The invention discloses an underground diaphragm wall steel bar lap joint bending resistance calculation method based on big data, and belongs to the technical field of underground engineering design. In order to solve the problems that the condition change of a construction site is difficult to respond in real time, the construction parameters are difficult to dynamically adjust and the complex construction environment is difficult to adapt, the data set quality and the bending resistance prediction precision are effectively improved through multi-source data acquisition and key feature screening, and a physical prediction model of a steel bar lap joint is established. According to the method, factors such as stress transmission and binding power are comprehensively considered, so that the model has good adaptability under different working conditions, model parameters are dynamically adjusted, the model responds to changes of environments and construction errors in real time, the weight is automatically adjusted when monitoring data fluctuates, accurate prediction of the bending resistance is achieved, potential failure risks are effectively prevented, and the construction quality is improved. And a failure mode pre-judgment and multi-level risk management mechanism helps a construction team to quickly identify a high-risk area and flexibly adjust a construction scheme.

The invention relates to the technical field of geophysics, in particular to a deep reflection seismic data random noise denoising method based on multi-scale feature fusion, and the method is executed by a processor, and comprises the steps: generating a training set based on a deep reflection random medium forward modeling model and seismic random noise of different frequency bands; building a training network, and performing training on the training set to obtain trained model parameters; and carrying out transfer learning on the trained model parameters, and carrying out actual deep reflection seismic data random noise reduction. According to the method and the device, the problems of low denoising quality of random noise of deep reflection seismic data, too complex implementation process and low efficiency in the prior art are solved.

The invention provides a vocal cord lesion detection method based on multi-modal information fusion, and the method comprises the following steps: S1, collecting vocal cord image data and a sound signal, and carrying out the preprocessing of the vocal cord image data and the sound signal; s2, constructing two same improved Resnet models, namely an improved Resnet model I and an improved Resnet model II; s3, determining sound features according to the preprocessed sound signals, and inputting the preprocessed vocal cord image data into the trained improved Resnet model I to obtain vocal cord image features; s4, fusing the vocal cord image features and the sound features to obtain multi-modal fusion features; and S5, inputting the multi-modal fusion features into the trained improved Resnet model II to obtain predicted vocal cord lesion information. Through the method, the diagnosis time can be shortened, and the diagnosis accuracy can be improved.

The invention provides an urban flood disaster risk assessment method based on crowd sensing, a medium and equipment, and the method comprises the steps: collecting flood disaster text data on a social platform, carrying out the preprocessing, and constructing a flood disaster text data set; extracting flood disaster common words from the flood disaster text data set, classifying the flood disaster common words according to subject terms, and constructing a disaster descriptive lexicon based on a co-occurrence network; designing a crowdsourcing questionnaire based on the disaster descriptive word bank, issuing the crowdsourcing questionnaire to a target user group, and collecting flood disaster feedback data and geographic information fed back by the target user group; performing spatial distribution on the collected flood disaster feedback data according to geographic information; and carrying out risk assessment by combining two types of indexes which respectively represent the flood disaster situation and the flood disaster influence area. According to the invention, real-time monitoring and accurate assessment of urban flood disasters are realized, and the accuracy and real-time performance of flood disaster risk assessment are improved.

The invention discloses an unmanned aerial vehicle image transmission signal modulation mode identification system and method based on multi-modal fusion. The method comprises the following steps: (S100) collecting and preprocessing unmanned aerial vehicle communication signal data; (S200) converting the one-dimensional time-domain signal into a two-dimensional Gramer angle field graph and estimating a two-dimensional time-frequency graph of the image transmission signal; (S300) carrying out feature merging; (S400) generating a training set and a test set; (S500) constructing a VGG-ResNet hybrid architecture deep learning network and carrying out training; and (S600) identifying the image transmission signal modulation mode of the unmanned aerial vehicle to be identified. The method disclosed by the invention is based on a multi-modal fusion technology and shows remarkable advantages, and data features of different modals can be comprehensively utilized by fusing the one-dimensional time domain signal and the two-dimensional time frequency graph information to realize more comprehensive description of data.

The invention discloses a construction method and system of a power transformer equipment defect knowledge graph and a storage medium. The method comprises the following steps: acquiring structured data and unstructured data of transformer equipment operation defects; constructing an ontology layer of the transformer equipment defects, and carrying out structured expression on the relationship among all ontologies in the ontology layer; taking the constructed ontology layer as a reference, extracting the structured data to obtain a first target triple, and extracting the unstructured data to obtain a second target triple; and performing knowledge fusion on the first target triad and the second target triad to obtain transformer equipment defect map data. According to the method, the accuracy of entity recognition and relation recognition can be improved, fusion of multi-dimensional data such as equipment basic information, operation state information and defect information is better realized, so that operation and maintenance personnel are more effectively helped to quickly retrieve, position and process transformer defects, and the workload of the operation and maintenance personnel is reduced.

The invention relates to the technical field of fault detection, and discloses a fault detection method and system for a charger, and the method comprises the steps: obtaining the charging state of a battery, and the charging state of the battery comprises the charging current, the charging power, the state of charge, and the temperature of the battery; performing feature extraction on the battery charging state, and constructing a charging feature matrix; inputting the charging characteristic matrix into a battery health prediction model to obtain a battery charging demand prediction value; performing state analysis and calculation according to the battery charging state to obtain a charger state; performing correlation analysis according to the charger state and the battery charging demand prediction value to obtain fault prediction information; and obtaining a charger fault detection result according to the fault prediction information. The method can improve the fault detection efficiency of the charger.

The invention discloses an improved automatic driving target detection method based on YOLOv8. Relates to the field of automatic driving, in particular to the technical field of automatic driving target detection based on YOLOv8 improvement. According to the method, multi-target detection is realized, the detection precision of the model on dense small targets under a complex background is improved, and the problems of false detection and missing detection are effectively reduced. The method comprises the following steps: S1, establishing and dividing a data set, and preprocessing the data set; s2, a basic YOLOv8s model is constructed; s3, the YOLOv8s model is optimized, specifically, S31, an SPPF module in the trunk part of the YOLOv8s model is replaced with an improved SPPF module; s32, adding a small target detection layer to the head part of the YOLOv8s model; s33, four improved ACCoM modules are added between the trunk part and the neck part of the YOLOv8s model; s4, performing iterative training on the optimized YOLOv8s model by adopting the preprocessed data set; and S5, adopting the trained YOLOv8s model to detect the target.

The invention provides an interactive prediction risk field construction method and system based on dynamic environment characteristics, and the method comprises the steps: constructing a barrier field according to the attributes and prediction behaviors of surrounding vehicles; constructing a road field according to road conditions; configuring driving factors according to characteristics of a driver; and fusing and interacting the obstacle field, the road field and the driving factors to construct an interactive prediction risk field. According to potential factors in a proposed traffic scene, corresponding risk field models are respectively constructed, and the risk field models comprise obstacle fields caused by traffic participants, road fields caused by lane lines and boundaries and driver factors determined by characteristics of drivers. Due to the fact that track prediction and relative motion calculation of surrounding vehicles are integrated, the model can perceive upcoming risks earlier, and the potential of the method applied to decision making and planning subsequently is shown.

The present invention relates to the technical field of sleep diagnosis, and in particular to a sleep staging method based on deep learning, the method comprising: collecting a number of target physiological signals, preprocessing each target physiological signal to obtain a number of standard physiological signals; synchronously aligning and integrating each standard physiological signal in turn to obtain a standard sleep data set; determining the actual correlation between each physiological signal to be integrated in the standard sleep data set, and determining an actual signal combination strategy according to the actual correlation; generating an actual sleep data set according to the actual signal combination strategy, and extracting features from the actual sleep data set to generate actual feature parameters; inputting the actual feature parameters into a deep learning model to determine the actual sleep state; analyzing the actual sleep state, and performing corresponding sleep intervention according to the analysis results. The present invention realizes low-cost non-intrusive sleep monitoring, thereby improving people's physical health.

The invention relates to the field of steel ladle automatic intelligent control, in particular to a steel ladle lining detection method and system. The steel ladle lining detection method comprises the following steps: acquiring point cloud image information of a steel ladle lining; discrete points in the point cloud image information are removed to obtain target point cloud data; performing edge detection on the target point cloud data to obtain a Canny edge image, and calculating a gray level co-occurrence matrix based on the Canny edge image to obtain texture features; extracting edge features through the Canny edge image, and fusing the edge features and the texture features to obtain a target feature vector; segmenting a damaged area at the target point cloud data by using a U-Net segmentation model, and outputting damage information through target feature vector classification; and performing three-dimensional reconstruction according to the target point cloud data and the damage information.

The invention discloses an on-orbit service oriented multi-branch spacecraft design evaluation system and method, and relates to a multi-branch spacecraft design evaluation system and method. The invention aims to solve the problems that the performance indexes such as single-arm and double-arm operability are all quantitative measurement for the performance of a certain single aspect, and the comprehensive consideration on the overall performance of the spacecraft is lacked when the design is carried out only by relying on the indexes. The method comprises the following steps: step 1, establishing a standard interface D-H parameter model of a multi-branch spacecraft; 2, building a performance evaluation system of the multi-branch spacecraft; and step 3, completing design and optimization of the multi-branch spacecraft configuration by taking the optimal performance in the evaluation system as a target through a multi-target optimization algorithm. The invention belongs to the technical field of aerospace.

The invention provides a precision shaft health state diagnosis method and system based on data twinning, and relates to the technical field of precision shaft health state monitoring, and the method comprises the steps: outputting a fusion health feature of a target precision shaft from a health state model of data twinning, and determining a linear offset during the linear change of the health state of the target precision shaft; determining a health response label of the target precision shaft in the operation state under the variable working condition according to the balance health index, and determining a cross degradation index of the health state of the target precision shaft according to the health response label; determining a health deviation point position of the target precision shaft at each moment during operation, and determining a time sequence health gradient of the full life cycle of the target precision shaft according to the health deviation point position and the linear offset; and performing fusion diagnosis on the health state of the precision shaft according to the cross degradation index and the time sequence health gradient. According to the method, the health state of the precision shaft can be accurately captured in variable working conditions and a complex degradation process, so that the sensitivity of a health diagnosis model to dynamic performance degradation of the precision shaft is improved.

The invention discloses a vehicle-mounted target tracker based on computer vision, and relates to the technical field of computer vision, and the vehicle-mounted target tracker comprises a sensor data collection module which is used for collecting real-time data from various sensors; the data fusion module is used for integrating the data from the plurality of sensors of the sensor data collection module and generating comprehensive information about the environment and the target; the data processing module is used for processing the complex data, extracting features and carrying out target identification and classification; according to the invention, by fusing the environment information and the target information, a more comprehensive and accurate context environment is provided for the system, the current scene can be understood more accurately, the authenticity of the target object can be judged more accurately, so that a more reasonable decision can be made, and the accuracy of the system is improved. And through combined use of an advanced target tracking algorithm, the precision, robustness and real-time performance of target tracking can be significantly improved.

The invention relates to the technical field of building construction supervision, in particular to a safety early warning method and system for a concrete construction process, and the method comprises the steps: firstly obtaining data of a monitoring point of a support frame and an adjacent monitoring point, and constructing a deformation feature vector of the support frame; the support frame deformation feature vector comprises a distance feature, an angle feature, a deformation feature and a vibration frequency feature; then determining the number of clustering clusters according to a BIM model of a building, carrying out clustering analysis on the deformation feature vector of the support frame, and judging whether an abnormal cluster exists or not; support frame monitoring points in the abnormal cluster are obtained, and an abnormal support frame monitoring point set is obtained; and obtaining the distance feature, the angle feature, the pouring feature component and the vibration feature component of the abnormal support frame monitoring point to construct an abnormal evaluation index, and if the abnormal evaluation index is greater than a preset abnormal threshold value, triggering safety early warning. By means of the method, real-time monitoring of the formwork supporting frame in the concrete construction process is achieved, and construction safety is improved.

The invention discloses a method for simulating irradiation displacement damage and influence of an MOSFET (Metal-Oxide-Semiconductor Field Effect Transistor) gate oxide layer, which mainly solves the problems of incomplete description of irradiation defect evolution parameters and inaccurate simulation result in the prior art. Comprising the following steps: 1) obtaining the type and number of traps generated in a gate oxide layer after irradiation through molecular dynamics simulation; 2) utilizing first principle software to construct a gate oxide layer supercell and introducing a trap type; (3) acquiring the energy level position of a trap by applying a first principle to the supercell containing the defect, and calculating a capture cross section; 4) inputting the types, the number, the energy level positions and the capture cross sections of the traps into semiconductor device simulation software for performance simulation; and 5) obtaining the influence of trapped charges in the irradiated gate oxide layer on the electrical performance of the device. According to the method, the influence of irradiation displacement damage on the gate oxide layer can be comprehensively and accurately described, the influence of defects on the electrical performance of the device is determined, and a reliable basis is provided for further guiding and optimizing the performance of the semiconductor device.

The invention relates to an acoustic metamaterial general database construction method, device and equipment. The method comprises the steps that acoustic metamaterial models are generated in batches through modeling software; for each acoustic metamaterial model, internal entity object recognition and file rewriting are firstly carried out, and each entity object in the model is exported as an independent grid model file to be stored; discretization processing, voxel surface reconstruction and material physical parameter information addition are sequentially carried out on each grid model file, and a point cloud file of each acoustic metamaterial model is constructed; and finally, on the basis of the grid model file and the point cloud file corresponding to each acoustic metamaterial model and an acoustic absorption coefficient table of the acoustic metamaterial model calculated by simulation modeling software, constructing an acoustic metamaterial universal database. By adopting the method, the problem of cross-configuration data loss can be solved, and three-dimensional space feature aggregation and comprehensive characterization of the acoustic metamaterial model are realized.

The invention relates to a network security monitoring method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a first network entity relation graph and a second network entity relation graph according to multi-source log data of a to-be-monitored network environment; the first network entity relation graph is used for representing a direct interaction relation between network entities of different or the same entity type in the network environment to be monitored; the second network entity relation graph is used for representing a dependency interaction relation between network entities of the same or different entity types in the network environment to be monitored; fusing the first network entity relation graph and the second network entity relation graph to obtain a fused entity relation graph; and according to the graph node feature vector representation corresponding to the node in the fused entity relation graph, identifying an abnormal event type corresponding to the abnormal sub-graph in the fused entity relation graph. By adopting the method, the accuracy of network security monitoring can be improved.

The invention provides a value optimization method of a power grid architecture based on multi-dimensional technical upgrade of power grid online equipment, which belongs to the technical field of power grid architectures and comprises the following steps: firstly, establishing a power grid topological graph by collecting power grid data in a transformer area; then, calculating power flow among nodes in the power grid topological graph according to a preset time period, and dividing the power flow into two logic layers, namely low-power flow and high-power flow, according to a power threshold value; next, respectively calculating active power and reactive power of each node, and establishing a power balance constraint equation; based on the double-layer logic diagram, the method further calculates the stability index of each node, and normalizes the stability index to obtain a node stability coefficient. And according to the node stability coefficient, judging a power grid operation state and formulating a corresponding optimization strategy. And finally, constructing a power grid architecture optimization matrix as a value optimization target of the power grid architecture, thereby solving the technical problem that it is difficult to comprehensively and accurately evaluate the operation state of the power grid for optimization in the prior art.

The present invention proposes an online monitoring and diagnosis system for hydropower equipment, which relates to the field of monitoring and diagnosis of hydropower equipment, and includes: a data acquisition module for collecting real-time data; a data processing module for performing error detection using a verification method to obtain verified data; a data analysis module for performing clustering analysis using a clustering algorithm, marking normal data and abnormal data according to the clustering results, and obtaining clustered data; a model analysis module for performing feature extraction on the clustered data according to a pre-trained autoencoder, and performing secondary clustering on the clustered data according to the features extracted by the autoencoder to obtain the results of the secondary clustering; an abnormal diagnosis module for matching the results of the secondary clustering based on a pattern matching algorithm, determining the diagnosis type, and outputting a diagnosis report. The present invention can overcome the limitations of traditional methods, provide more accurate and timely fault identification, and can handle complex and new fault modes.

The invention discloses a structure displacement visual identification method and system fused with a three-dimensional deformable model, and the method comprises the steps: collecting the vibration video data of an engineering structure through a consumer-level monocular camera, building a three-dimensional deformable grid model, and generating a pose rendering image and displacement value data; removing environmental noise interference; estimating dense optical flow data in the rendered image through an optical flow model, and constructing an optical flow feature data set containing pose information; inputting an integral structure image sequence and a local structure image sequence of the two groups of data sequences, and generating corresponding integral features and local features respectively after multi-layer convolution operation of a convolution layer; and generating a feature query vector, a key vector and a value vector through linear transformation, establishing a dynamic weight between two features according to the vectors, performing global average pooling after two fusion features are obtained, and outputting a pose estimation value through a multi-layer perceptron. The displacement of the three-dimensional structure can be accurately identified only through the monocular consumer camera.

The invention belongs to the field of model detection and symbol simulation, and relates to a symbol track value counting method and system based on a satisfiability model theory solver and limit model detection. The method comprises the following steps of: taking a hardware description language Chisel and a circuit specification described by extended track counting logic as input, reading the extended track counting logic through a preprocessing module, and performing logic synthesis and SMT (Surface Mount Technology) coding on a Chisel circuit; and the model detection rear-end module verifies the circuit specification based on SMT coding in combination with an STE algorithm and a BMC algorithm. According to the method, the respective advantages of the BMC and the STE are combined to enhance the solving capability of the symbol track value, the efficiency and the reliability of model detection are improved, meanwhile, the standard description capability of the STE is expanded on the Chisel level, and the verification process is greatly simplified.

The present application provides an asphalt pavement apparent disease evolution deduction method, system, device and medium, which belongs to the field of road engineering, transportation infrastructure operation and maintenance and intelligent detection technology, and comprises the following steps: constructing a unified space-time reference system, aligning and normalizing multi-source data in space-time; forming section-level disease quantification characteristics based on the recognition results of the inspection images and the structural indexes; event coding the maintenance measures of the road surface and pre-processing the section-level disease quantification characteristics corresponding to the time of the maintenance measures; constructing the correlation, time lag effect and spatial consistency between the disease types, disease characteristics and structural indexes to generate the collaborative evolution characteristics of the coupling relationship between diseases; constructing a deep time series prediction model to obtain the disease development trend at a specified time scale in the future, and forming the section-level disease evolution prediction result. The present application can depict the disease development law at different road sections and different time scales, and predict the future disease expansion.

The invention discloses a method for identifying multiple sensors at the tail end of a stacking robot and a mounting mechanism thereof, and relates to the field of robot sensors, the identification method comprises the following steps: identifying the surrounding environment, controlling the operation mode of the stacking robot according to the environment, and processing and identifying data of multiple sensors through a sensor chip module; the sensor chip module comprises a data acquisition module, a data processing module, an identification algorithm module, a data fusion module and a feedback and control module. According to the invention, data collected by a plurality of sensors are fused together through the data fusion module, by fusing data from different sensors, the fused data can help to optimize path planning, avoid obstacles and realize more accurate positioning, and meanwhile, the problem of time difference between operation steps and actual conditions is solved, so that the accuracy of path planning is improved. Therefore, normal and effective stacking operation cannot be carried out, and collision between the robot and an obstacle due to time difference is avoided.

The invention discloses an intelligent blank label image recognition and composite coding system, and relates to the technical field of image processing. The system comprises a label image feature processing part, an embedded code part and a composite code generation part, the label image feature processing part is used for obtaining multi-dimensional features of a target label image; curvature flow feature analysis is carried out, and local curvature distribution is calculated; calculating a geometric invariant moment of the target label image based on the local curvature distribution; the embedded coding part is used for extracting continuous homology-based topological characteristics of a target label image; according to the topological features, feature popular codes are constructed by using the Gaussian-Blog theorem; and the composite code generation part is used for carrying out unique coding based on differential topology by adopting a Hessian matrix according to a feature popular code and a fusion result. According to the method, the geometric and topological characteristics of the label image can be accurately identified, and a code with uniqueness and robustness is generated.