78results about How to "Efficient and accurate identification" patented technology

The invention relates to the technical field of industrial data acquisition and processing, and discloses a different industrial data optimal acquisition scheme analysis system and method based on an SCADA system, and the system comprises an industrial data acquisition information processing module, an industrial data acquisition scheme analysis module, and an industrial data acquisition optimal scheme generation module. According to client target industrial data acquisition scheme type information, in combination with an intelligent identification algorithm and industrial data acquisition scheme equipment composition information, industrial data acquisition scheme equipment composition information efficient retrieval is carried out, and equipment information used by different types of industrial data acquisition schemes is efficiently and scientifically analyzed; the industrial application applicability of the SCADA system is improved; the unit price information of the client target industrial data acquisition scheme composition equipment and the cost information of the client target industrial data acquisition scheme are scientifically and accurately counted, the cost information of different types of industrial data acquisition schemes is autonomously measured on line, and the scientificity of the industrial application of the SCADA system is improved.

The invention relates to the technical field of man-machine interaction, in particular to a gesture-based instrument operation method, which comprises the following steps of: capturing gesture information of an operator in real time through gesture acquisition equipment; the gesture recognition module receives gesture information captured by the gesture acquisition equipment and converts the gesture information into a preset instruction signal; the medical instrument control module receives the instruction signal and executes corresponding operation based on the instruction signal, the method is based on a medical instrument gesture model, the model is integrated to acquisition equipment to realize edge calculation, and the gesture of wearing gloves is acquired in real time; instruction conversion is carried out based on gesture acquisition equipment, and the gesture of wearing the glove is converted into an instrument instruction; the collection device is driven based on the instrument state, it is ensured that the instrument enters the working state and is driven, system resource waste is avoided, it is ensured that the instrument and the collection device are high in availability, the glove wearing gesture is efficiently and accurately recognized, traditional input equipment does not need to be used, and operation convenience and man-machine interaction efficiency are improved.

The invention discloses a circuit breaker contact wear degree intelligent evaluation system, and belongs to the technical field of circuit breaker contact wear evaluation, the circuit breaker contact wear degree intelligent evaluation system comprises a camera, a data collector, a server and a display, the server comprises a circuit breaker type analysis module, a signal processing module, a wear evaluation module, a wear prediction module and a health monitoring module, the circuit breaker type analysis module, the signal processing module, the wear evaluation module, the wear prediction module and the health monitoring module are in communication connection in sequence; the display comprises a data display module; according to the invention, the wear degree of the contact head of the circuit breaker can be intelligently evaluated, and meanwhile, the system also has the functions of wear degree prediction, residual use frequency calculation and wear frequency early warning, so that the maintenance and management efficiency of the circuit breaker can be greatly improved, and the equipment fault risk is reduced.

The invention discloses a remote sensing image geographic element automatic identification method and system based on large model analysis, and relates to the image processing related technical field, and the method comprises the steps: obtaining remote sensing image data according to a plurality of remote sensing data sources based on a remote sensing data processing rule; obtaining a geographic element identification instruction; performing multi-level image segmentation constraint registration, and establishing an image segmentation constraint tree; performing segmentation processing on the remote sensing image data according to the SAM image segmentation large model to obtain a multi-level remote sensing image segmentation result; performing segmentation loss optimization to obtain a multi-level remote sensing image segmentation optimization result; and carrying out vectorization element identification to obtain a multi-level geographic element identification result. The technical problems that in the prior art, remote sensing image multi-level geographic element recognition precision is insufficient, efficiency is not high, and image segmentation is not accurate are solved, efficient and accurate geographic element recognition is achieved, and the technical effect of improving geographic element recognition efficiency and accuracy is achieved.

The invention provides a target tree species remote sensing recognition method based on space-air-ground cooperation, which comprises the following steps: performing adjacent superpixel combination on segmented pattern spots after superpixel segmentation, and performing feature factor-based model optimization on a current recognition model of a target tree species based on combined pattern spot distribution and field distribution data, so as to obtain a target tree species remote sensing recognition model. Target tree species recognition is executed based on the updated new recognition model, and correct recognition pattern spots are obtained; executing edge matching detection on the correctly recognized pattern spots, adjusting the current merging scale based on an edge matching detection result to obtain an optimal recognition model, and further obtaining a recognition result of the target tree species based on the optimal recognition model; according to the method, the accuracy of the pattern spot boundary is effectively improved while the recognition precision of the target tree species is ensured, so that the matching degree of the boundary of the segmented pattern spot and the actual boundary of the target tree species is effectively improved.

The invention relates to the technical field of image recognition, in particular to a cigarette carton two-dimensional code-based order accurate recognition and anomaly repair system and method. The system comprises an anomaly detection unit used for detecting whether a two-dimensional code is abnormal when the two-dimensional code is scanned, scanning the two-dimensional code to recognize an order if the two-dimensional code is not abnormal, generating an abnormal signal and transmitting the abnormal signal to an image processing unit if the two-dimensional code is not abnormal, and the image processing unit performs image extraction on the two-dimensional code and analyzes and repairs the extracted two-dimensional code image; and a mixed connected domain labeling algorithm is introduced in the analysis and restoration process of the extracted two-dimensional code image for optimization. According to the order accurate identification and abnormity restoration system and method based on the cigarette carton two-dimensional code, each constituent part in the two-dimensional code image can be efficiently and accurately identified, the restoration quality of the two-dimensional code image is improved, and it is ensured that the restored two-dimensional code has higher robustness and accuracy in the identification and decoding process.

The invention discloses a student classroom behavior intelligent analysis and judgment system and method, and relates to the technical field of classroom behavior recognition, and the system comprises a data collection and storage module, a behavior analysis module, a monitoring time control module and an after-class feedback module. Through a target recognition algorithm, a labeling technology and a face recognition technology, generating a bounding box of a student position in each second frame image, recognizing a student identity and recording a coordinate range and a name of the corresponding bounding box; a behavior recognition model is constructed through a labeling technology and a key point technology, and corresponding student behaviors are analyzed when different analysis signals are received; monitoring face features of teachers in a classroom through a face recognition technology to adjust a classroom behavior monitoring period; analyzing the number of times and the total number of abnormal behaviors of each student, and carrying out special marking on a specified student; according to the invention, intelligent behavior identification of different types of students and intelligent adjustment of the monitoring period under different conditions are realized.

The invention relates to the technical field of intelligent traffic, and provides a highway toll evasion vehicle identification method, device, system and equipment and a medium, and the method comprises the steps: collecting the first vehicle feature information of any passing vehicle entering a toll station exit ramp, an exit lane log and billing transaction information, obtaining second vehicle feature information associated with the charging transaction information; and based on the consistency of the first vehicle feature information and the second vehicle feature information, determining whether the passing vehicle is a potential vehicle having a fee evasion behavior, and if yes, performing abnormality analysis on a vehicle passing event of an exit lane log according to the vehicle information of the potential vehicle, and determining whether the potential vehicle has a fee evasion behavior. According to the technical scheme of the invention, the features of the passing vehicle and the exit lane log are collected at the exit ramp, so that the vehicle with the fee evasion behavior can be accurately and efficiently recognized when the vehicle passes through the exit ramp and the transaction process, the follow-up auditing and resuming of the fee evasion vehicle are facilitated, and the auditing efficiency and resuming strength of the fee evasion vehicle are improved.

The invention relates to the technical field of fire identification, and discloses a fire identification method, system, equipment and medium used in a charging pile scene, and the method comprises the steps: obtaining a time sequence image and a time sequence environment parameter of a to-be-identified charging pile region, the time sequence image comprising a bitmap image and an infrared image, and the time sequence environment parameter comprising time sequence smoke data; inputting the bitmap image into a target recognition model to obtain a target image, and selecting an infrared target image from the infrared images according to the target image; extracting temperature data from the infrared target image of each infrared image to obtain time sequence temperature data; and inputting a target image, the smoke data and the temperature data into a fire identification model to obtain a fire identification result of the charging pile area. According to the method, the flame and the charging pile can be accurately and efficiently recognized through the target recognition model, multiple features are fused through the fire recognition model, the model recognition accuracy is improved, and the safety of the charging environment is further ensured.

The invention relates to a ConvNeXt-based multi-stage adaptive class balance operation stage identification method, which comprises the following steps of: firstly, preparing a data set, and pre-training the weight of a model of ConvNeXt on an Imagenet-22k large data set; then carrying out data preprocessing; training an image feature extraction module by using the data to obtain a BLConvNeXt model weight; training a time sequence feature module by using data to obtain a multilevel adaptive time convolution module weight; and finally, loading a model training weight, and completely operating the operation stage identification model to obtain a final prediction result. According to the method, the recognition capability of the model on few sample categories can be enhanced through mutual learning of high-dimensional image features among the same batch of images; the overall recognition result accuracy in the few-sample stage and the recognition result accuracy in the difficult-to-classify image stage are improved; the whole method is good in generalization, and the stage of the current operation picture can be efficiently and accurately recognized in different types of operations; the recognition effect of the model on the stage with the small sample size is improved, and therefore the recognition result of the whole operation stage is optimized.

The invention discloses an intelligent edge calculation method for fire target detection, and belongs to the technical field of fire target detection, and the method comprises the following steps: S1, making a fire target detection data set; s2, an improved YOLO-AFPN-LADH-WIOU model is formed, and an improved YOLO-AFPN-LADH-WIOU S3, carrying out evaluation and transverse comparison on the improved YOLO-AFPN-LADH-WIOU model, and carrying out visual analysis on a result; s4, based on the step S3, realizing remote deployment of the basic model on the development board; s5, establishing a connection between the edge detection system and the client monitoring end by using a TCP transmission control protocol so as to perform interaction; and S6, realizing image acquisition and user GUI display. According to the intelligent edge calculation method for fire target detection, the precision, the speed and the breadth of detection are improved, and meanwhile, the real-time performance and the accuracy of fire monitoring and protection in practice are ensured.

The invention discloses a coal-bearing target stratum lithology and macroscopic coal rock type identification method based on element logging, and relates to the technical field of unconventional natural gas exploration and development, and the method comprises the following steps: s1, constructing a coal rock boundary, and dividing a coal rock and a non-coal rock; s2, calculating the content of each component in the coal rock and the non-coal rock; and s3, constructing a non-coal rock lithology identification method and a macroscopic coal rock type identification method in the coal rock. According to the method, division of the coal rock and the non-coal rock can be realized only based on element logging data and a small amount of experimental data, calculation of the content of each component in the rock of the coal-containing target layer is realized, and efficient and accurate identification of the lithology of the non-coal rock and the macroscopic coal rock type can be completed by further innovatively combining with the sulfur element; important technical reference can be provided for geological evaluation and target layer selection of a coal-bearing target layer.

The invention discloses a remote operation and maintenance and fault diagnosis method and system for an energy storage system, and relates to the field of fault diagnosis of energy storage equipment, a factory three-dimensional model is established, the binding relationship between a battery and the model is determined, data is collected from a battery management system, and after the steps of battery internal resistance estimation, data sequence labeling, abnormal value processing and Z-score standardization preprocessing, the fault diagnosis of the energy storage system is completed. And key characteristics of charge-discharge multiplying power and internal resistance change rate are extracted. And a multi-layer perceptron is selected to establish an artificial neural network model to realize a fault diagnosis prediction target. Once an abnormal battery is identified, the position of the abnormal battery in the plant global coordinate system is determined by means of coordinate information stored in the hash table. The fault prediction accuracy is improved by utilizing comprehensive data modeling, model parameters can be adaptively optimized, an abnormal battery is efficiently and accurately identified, and stable operation of an energy storage system is guaranteed.

The invention discloses a power grid dispatching voice information checking method and system, the method is suitable for the power grid dispatching voice information checking system, and the system comprises a dispatching voice recognition module and an RAG checking module. According to the method, a Mel filter bank, a Conformer encoder, a CTC decoder and a Bi Transformer recognizer in a scheduling voice recognition module are used for recognizing and converting scheduling voice into a scheduling text, and a retrieval unit, a scheduling information knowledge base and a checking unit in an RAG checking module are used for performing knowledge retrieval and checking on the scheduling text; the scheduling voice is accurately and efficiently recognized and checked to generate the scheduling instruction text, the scheduling voice of a dispatcher is recognized and checked under the actual condition, and the problem that the scheduling instruction of the power system cannot be accurately and efficiently checked due to the fact that the scheduling voice instruction cannot be accurately and efficiently recognized in the prior art is solved. And safe and stable operation of the power grid cannot be guaranteed.

This invention provides a method, device, electronic device, and storage medium for detecting illegal dog walking incidents. The method fully utilizes the characteristics of the leash, designs a leash detection model to detect inclined target leashes, and designs a human-dog-leash relationship judgment model to detect the interaction between the human and the leash and / or the interaction between the leash and the dog. Thus, the detection results of illegal dog walking incidents determined based on the interaction between the human and the leash and / or the interaction between the leash and the dog are more accurate. In addition, the entire process is completed automatically and has good intelligence.

The invention discloses a pedestrian re-identification method and device, equipment and a medium, and the method comprises the steps: obtaining a to-be-identified pedestrian image through an embedded equipment end, and carrying out the preprocessing of the to-be-identified pedestrian image, and obtaining a preprocessed image; detecting a region image conforming to at least one body region category from the pre-processed image; respectively extracting region feature information from each region image; and combining and comparing the feature information of each region with the historical feature information to generate a re-identification result. Therefore, resources consumed by feature extraction are reduced, and efficient and accurate pedestrian re-identification can still be realized under the condition that the resources are limited.

The invention provides a data processing method. The data processing method comprises the following steps: dynamically adapting a protocol of an external system; collecting source data from an external system; performing primary processing on the source data, removing redundant data, and retaining target data; providing an empty data table, performing secondary processing on the target data, writing the target data into the empty data table, and generating a real data table; processing the data in the real data table for three times to generate a declarated data table, and declaring the declarated data table to an external system; receiving an audit receipt from an external system; performing finance and taxation compliance auditing on the declarable data table according to a receipt result; and notifying an external system to complete subsequent operation. According to the data processing method provided by the invention, the protocol of the external system is dynamically adapted, the external system is flexibly connected, and the cooperation efficiency is improved; in addition, uneven resource allocation of the execution units is avoided through an intelligent scheduling algorithm, and efficiency and stability are improved under high concurrency. The invention further provides a data processing system used for executing the data processing method.

The application discloses a kind of intelligent basketball live broadcast systems based on man-machine cooperation, belong to automatic live broadcast technical field.It includes: mobile terminal device, two-dimensional holder and PC end management system;Mobile terminal device is used to real-time acquisition ball game picture, and obtains live broadcast focus coordinates by target detection to ball game picture;Two-dimensional holder is used to change control signal pulse width based on live broadcast focus coordinates, drive PWM steering gear to correct ball game picture collected by mobile terminal device by microcontroller;PC end management system is used to receive ball game picture collected by mobile terminal device, and upload to server end for user to pull stream and watch.The application can realize the intelligent live broadcast of basketball event, and the identification process is accurate and efficient, flexible to use and low in cost, with wide application prospect and commercial value.

The present invention discloses a fiber optic gyroscope fault diagnosis method based on MiniRocket network model, comprising the following steps: calibrating multiple fiber optic gyroscopes of the same model using a standard angular velocity source, obtaining the output signal of each fiber optic gyroscope and the corresponding temperature stress signal; marking the output signal of each fiber optic gyroscope, and performing noise reduction processing through the EEMD algorithm to construct a data set; training the MiniRocket network model, and tuning the model hyperparameters in combination with the sparrow optimization algorithm; and performing fiber optic gyroscope fault diagnosis of the same model based on the optimized MiniRocket network model. The method can effectively improve the accuracy and reliability of fiber optic gyroscope fault diagnosis through precise data acquisition, effective preprocessing means, efficient feature extraction and model optimization, and real-time fault diagnosis, especially being able to accurately distinguish intermittent faults from permanent faults, thereby achieving better maintenance of the inertial navigation system.

The invention relates to a medical image recognition method and device, computer equipment and a storage medium. The method comprises the following steps: performing principal component analysis on an initial connected domain of a tubular object in a region of interest of a medical image to obtain a principal component analysis result corresponding to the initial connected domain, and determining a target connected domain from the initial connected domain according to the principal component analysis result corresponding to the initial connected domain, and determining attribute information of the tubular object according to a principal component analysis result corresponding to the target connected domain. By adopting the method, the embedded tubular object can be identified from the medical image.

The present application relates to the field of data processing technology, and discloses a method for docking a device with an Internet of Things platform using a large language model. The method includes: collecting network traffic data of Internet of Things devices, extracting traffic fingerprints from the network traffic data, obtaining traffic fingerprint data, and constructing device description information based on the traffic fingerprint data; matching the traffic fingerprint data and device description information with the Internet of Things knowledge base to obtain target device parameters; defining device service attributes, and creating a physical model based on the device service attributes; matching the communication mode and device communication protocol to the physical model, inputting the communication mode and device communication protocol into the large language model to generate device communication plug-in code, and obtaining communication plug-in code; transmitting the communication plug-in code to the plug-in management unit to generate a device communication plug-in, and establishing the physical model and the device docking of the Internet of Things platform through the device communication plug-in. The present application improves the efficiency of docking between the device and the Internet of Things platform.

The invention discloses a method for filtering webpage access based on a kernel driver, which belongs to the technical field of internet communication and comprises the following steps: step 1, a user inputs an access website for a user browser to generate a DNS (Domain Name Server) query request message; 2, judging whether a DNS query request message exists in a local DNS cache or not, if yes, obtaining an IP address for access, and if not, executing a step 3; and step 3, sending a query request to a DNS server, constructing a complete DNS query request network packet according to a network protocol standard, capturing all DNS query request network packets sent to the 53 port by a kernel driving program, and then analyzing target domain names of the DNS query request network packets through a DNS protocol. Centralized and automatic management and control are achieved, the system is particularly suitable for scenes such as machine rooms and classrooms needing unified network behavior management, and the operation and maintenance cost is greatly reduced.

The application discloses a structural damage identification method based on a proxy model and an improved covariance matrix adaptive evolution strategy, and relates to the technical field of structural damage identification.The application comprises the following steps: obtaining a structure to be identified, constructing a finite element model of the structure to be identified, and respectively applying excitation to the structure to be identified and the finite element model to obtain a data set; a hybrid network model based on a convolutional neural network and a long short-term memory network is constructed, and the data set is used for training to obtain a proxy model; the proxy model is improved by using a covariance matrix adaptive evolution strategy based on a model gradient to obtain an optimal solution; a variational Bayesian model correction method is used to correct the optimal solution to obtain the most possible value of a structural damage geometric size parameter, so that damage geometric size and boundary information of the structure to be identified are determined.The application can efficiently and accurately identify structural damage geometric size information.

The invention provides a wind speed-power curve abnormal point identification method and device, and belongs to the technical field of wind power generation. The method comprises the following steps: acquiring a plurality of wind speed-power data points of a wind turbine generator in a preset time period; identifying the plurality of wind speed-power data points by using different curve abnormal value identification methods to obtain wind speed-power abnormal data points under the different curve abnormal value identification methods; and determining the same wind speed-power abnormal data points under different curve abnormal value identification methods as wind speed-power target abnormal data points. According to the method, the density clustering algorithm and the wind speed segmentation algorithm are fused, so that accurate identification of the abnormal points of the wind turbine generator is realized, the operation efficiency is improved, the maintenance cost is reduced, and safe and stable operation of the generator is ensured.

The embodiment of the invention provides a video encoding and decoding method and device, a computer readable medium and electronic equipment. The video decoding method comprises the following steps: determining a plurality of pixel regions in a current frame based on a preset pixel region size, wherein each pixel region comprises at least one pixel; determining a skipped area in the current frame according to whether each pixel area is processed in a skipped mode or not; and generating reconstruction data corresponding to the current block according to the position relationship between the current block to be decoded and the skipping region. According to the technical scheme of the embodiment of the invention, the coding and decoding efficiency can be improved, and the coding and decoding complexity can be reduced.

The invention provides a coal logistics park traffic flow statistical system and method based on YOLOv8 and OpenCV, and the method comprises the following steps: S101, deploying high-definition monitoring equipment at a key intersection in a coal logistics park, and configuring equipment parameters; s102, variables are initialized, a model is loaded, and a data loader is configured; s103, acquiring camera video data in real time through the rtsp video stream; s104, traversing the data frame, and carrying out object detection and tracking; s105, processing a detection result to realize vehicle counting; and S106, deploying the model to a management end to display a result. According to the method, targeted training is carried out on the YOLOv8 model for the special environment of the coal logistics park, the OpenCV and the vehicle tracking algorithm are combined, efficient and accurate recognition, tracking and statistics of the vehicles are achieved, the detection problem caused by shielding, light change and other factors is solved, and a basis is provided for park traffic management. In addition, the system is flexible and expandable, a user can configure the system according to needs, the system can be conveniently expanded to other logistics parks or traffic scenes, and large-range vehicle monitoring is achieved.

The invention discloses an action recognition method and device, equipment and a storage medium, and relates to the technical field of recognition, and the method comprises the steps: creating a starting action database and an ending action database, carrying out the frame-by-frame analysis of video data, obtaining the position information of sports equipment in each frame, and determining the longitudinal displacement change of the sports equipment; when the longitudinal displacement change is not equal to zero, generating first dimension data based on target joint point information of the user in the current frame, comparing the first dimension data with file data in a starting action database, and determining whether the current action is a motion starting action or not according to a first comparison result; when the longitudinal displacement change is equal to zero, generating second dimension data based on the target joint point information, comparing the second dimension data with file data in an ending action database, and determining whether the current action is a movement ending action or not according to a second comparison result; according to the first comparison result and the second comparison result, determining the number of times of effective free power movement of the user. Therefore, the number of times of free force movement is recognized.

This invention discloses a method, system, device, and medium for identifying virtual location vulnerabilities on iOS devices. The method includes the following steps: invoking an iOS application to perform a location request operation; in response to the location request operation, obtaining partial callback location data; generating a first vulnerability identification result based on the data parameters of the partial callback location data; and in response to the first vulnerability identification result being a non-virtual location result, generating a final vulnerability identification result based on the request parameters of the location request operation and the time parameters of the partial callback location data. This invention can more accurately identify the GPX virtual location method on Apple devices. Through dual identification logic, it can efficiently and accurately determine whether a user has used virtual location, effectively improving the authenticity of the location function and application security of Apple applications, enhancing user experience, and possessing high application value.

The present invention relates to the technical field of lightning ablation material detection, and in particular to a method for detecting lightning ablation of metal composite materials. The technical solution includes the following steps: obtaining a data set of lightning ablation images of metal composite materials to be detected, preprocessing the image data, presetting a bounding box, and learning a two-dimensional representation of the metal composite material ablation image data through a predefined convolution layer, and then stretching it into one-dimensional data. The Transformer-based machine vision detection method introduced in the present invention can automatically, efficiently and accurately identify ablation phenomena, effectively reduce the impact of noise, effectively and selectively capture global information, improve computational efficiency, strengthen the capture of global information while effectively reducing the amount of data calculation, and improve the accuracy of prediction and computational efficiency.

The invention discloses an SRv6 network dynamic path optimization method and system fused with fault prediction, and the method comprises the steps: obtaining the real-time performance data and topological structure information of an SRv6 network, and carrying out the abnormality recognition and positioning based on the real-time performance data; constructing a heterogeneous graph comprising a physical topology layer and an SRv6 logic layer; outputting a fault probability prediction value and a potential fault type of each link through a pre-trained link fault prediction model; defining a comprehensive optimization cost function, and calculating an optimal main path and an optimal backup path based on the comprehensive optimization cost function; obtaining a corresponding Segment list, and issuing the Segment list to a source node to realize switching of traffic to a new path; and evaluating the real-time performance of the new path, and feeding back evaluation result data to the link fault prediction model for iterative training of the link fault prediction model. According to the scheme, the link fault is predicted by sensing the network state and the pre-training model in real time, and conversion from passive response to active fault avoidance is achieved.