37 results about "Body identification" patented technology

The invention discloses a first-level road non-ETC vehicle toll collection method and system based on multi-source data fusion, and the method comprises the steps: scanning a vehicle in a radar sensing recognition region in real time through a radar, so as to obtain radar scanning information; generating a vehicle tracking record based on the radar scanning information, wherein the vehicle tracking record comprises the target vehicle and first vehicle information corresponding to the target vehicle; in response to the target vehicle entering the visual identification area, an image acquisition instruction is sent to image identification equipment by using a radar, and the image identification equipment comprises license plate identification equipment and vehicle body identification equipment; acquiring second vehicle information acquired by the image recognition equipment; fusing the first vehicle information and the second vehicle information to obtain vehicle fusion information; and determining identification information of the target vehicle based on the vehicle fusion information, and determining a charging amount of the target vehicle according to the identification information. According to the invention, through fusion processing of the multi-source data, the accuracy of vehicle identification is effectively improved, and the conditions of misidentification and missed identification are reduced.

The invention relates to the technical field of mine water disaster prevention and control, in particular to a mine water disaster treatment slurry stone body recognition system and method based on an infrared spectrum. According to the technical scheme, the mine water disaster treatment slurry stone body recognition system and method based on the infrared spectrum comprises a rock debris preprocessing and conveying control module, a spectral data acquisition and control module and an intelligent recognition and output control module; by arranging the rock debris conveying and preprocessing module, continuous receiving and rapid preparation of rock debris are achieved, meanwhile, the near infrared spectrum detection module conducts spectrum scanning on flowing rock debris above a conveying belt and captures molecular fingerprint information, and generated spectrum data are transmitted to the data processing and intelligent recognition module; the recognition result is synchronously fused with parameters such as drilling depth and pumping pressure, a dynamic slurry diffusion map is generated, the recognition process and the drilling process are kept synchronous, the distribution condition of slurry stone bodies at different depths can be visually seen, and therefore the real-time performance of slurry stone body recognition is effectively improved.

The invention provides a three-dimensional multi-parameter collaborative strong convective echo recognition method and computer equipment, and belongs to the technical field of radars. The method comprises the following steps: step 1, inputting and preprocessing radar data; 2, identifying a single-elevation-angle severe convection pixel; step 3, marking a single elevation angle pixel; 4, performing multi-elevation three-dimensional consistency statistics and comprehensive judgment; and step 5, outputting and storing the strong convection echo body. The method aims at solving the technical problems that an existing strong convection echo body recognition method is complex in calculation process, the number of iteration segmentation times is large, and the real-time processing efficiency is insufficient.

The invention provides a wall recognition method and a storage medium, and the method comprises the steps: dividing an indoor building region and a peripheral region, and processing the proportional scale information and wall feature information, so as to achieve the precise separation of the indoor building region and a peripheral irrelevant region, and prevent the interference information of the peripheral region from affecting the wall recognition process; automatic and accurate acquisition of the scale is realized by using natural reference scale features generally contained in a peripheral area, and extra manual labeling or intervention is not needed; vectorization data and thickness parameters of the wall are extracted in a targeted mode, and the integrity and accuracy of wall feature information are ensured; according to the method, the identification result of the wall is jointly determined based on the proportional scale information, the vectorized data and the thickness parameters, so that fusion application of multi-dimensional key information is realized, dimensions, forms and physical attributes required by wall identification are comprehensively covered, and the identification reliability in a complex environment is improved.

The application provides a ground penetrating radar-based underground ore body positioning and identification method and system, and relates to the technical field of ore body detection.The method comprises the following steps: inputting a corrected multi-dimensional point cloud feature set into a pre-trained closed-loop convolutional neural network, optimizing network parameters through transfer learning, and outputting a category confidence of an ore body target and a spatial position probability distribution point cloud; based on the spatial position probability distribution point cloud, combining electromagnetic wave double-path travel time calculation rules and Cagniard resistivity measurement results, and generating an ore body depth and horizontal positioning point cloud through a reverse projection algorithm; and performing multi-scale two-dimensional empirical mode decomposition on the reverse projection positioning point cloud, filtering noise components to extract a high-confidence target point cloud.The application improves the accuracy and stability of ore body identification.

The invention discloses a vehicle re-identification method based on multidirectional feature and position perception fusion, relates to the technical field of image processing, and realizes multidimensional feature interaction optimization through position coding module structured modeling (horizontal-vertical direction tensor initialization); a DOConv module performs global-local collaboration (capture of fine-grained textures by deep convolution + integration of a global structure by traditional convolution) and deep separable convolution multidirectional feature capture (a square kernel focuses on local details, horizontal bar-shaped kernel extraction depends on a horizontal long distance, and vertical bar-shaped kernel analyzes vertical spatial distribution); the three collaboratively guide a network to accurately focus a vehicle body identification area, suppress background noise interference and enhance robustness to illumination, shielding, extreme viewing angles and similar vehicle types, so that richer feature information is integrated, the problems caused by intra-class similarity and intra-class difference can be further relieved, the robustness of the model is enhanced, and in the aspect of quantitative indexes, the accuracy of the model is improved. And the current advanced comparison method is also exceeded.

The invention discloses an anomalous body recognition and roadway roof supporting method based on a phased array geological radar, belongs to the technical field of roadway supporting, and aims to solve the problem of how to improve the detection efficiency and recognition precision of anomalous body recognition. A directional electromagnetic beam is formed by controlling the phase of the array element to scan and detect the front rock mass and receive a reflection signal; carrying out preprocessing and high-resolution inversion imaging on the reflected signal to generate an image reflecting the internal structure of the rock mass; inputting the inversion image into a pre-trained convolutional neural network model, and identifying a geological anomalous body and characteristic parameters thereof; dynamically determining a roadway roof support scheme according to an identification result, adopting reinforced support in a geological abnormal area, and adopting conventional support in a stable area; according to the method, detection-identification-decision-making integration is realized, the detection efficiency and the identification precision are improved, the support design can be dynamically optimized, and the method has both safety and economy.

The invention discloses a building disaster-bearing body remote sensing recognition method based on angular point features, and relates to the technical field of building disaster-bearing body recognition, and the method comprises the following steps: S1, collecting high-resolution optical remote sensing images in a concentrated manner, and carrying out the preprocessing of the high-resolution optical remote sensing images; s2, extracting image angular points of the preprocessed high-resolution optical remote sensing image set; s3, determining newly added angular points according to the image angular points; and S4, generating an outer boundary according to the newly added angular points, and completing remote sensing identification of the building disaster-bearing body. According to the method, a ground object sample does not need to be manufactured to carry out model pre-training, the building disaster-bearing body can be recognized only by depending on changes of image corner point features, the omission phenomenon in manual visual interpretation is avoided, and the method can be suitable for two scenes of recognition of a newly-added building disaster-bearing body before a disaster and recognition of a damaged building disaster-bearing body after the disaster.

The invention discloses a deep ground ore body identification method based on deep learning and phased array radar wave velocity inversion, and relates to the technical field of deep ground ore body identification, and the method comprises the steps: S1, carrying out the two-dimensional or three-dimensional scanning of an underground space through a phased array deep ground detection radar system with a preset frequency, and carrying out the data collection; s2, preprocessing the collected data, and extracting the wave velocity and multi-dimensional signal characteristics of electromagnetic waves; s3, inverting the wave velocity to obtain the three-dimensional distribution of the dielectric constant, and inverting the three-dimensional distribution of the equivalent conductivity at the same time; s4, inputting the features into a pre-trained deep neural network model, and outputting an ore body type label and a prediction confidence coefficient; and S5, fusing the spatial position, depth and ore body type identification results, generating a three-dimensional ore body distribution model, and outputting a comprehensive exploration report. According to the invention, rapid, lossless and accurate identification and evaluation of a plurality of industrial ore bodies are realized, and the industrial problems of shallow detection depth, single identification ore type and dependence on artificial experience of a traditional method are solved.

The invention discloses a thin sand body identification method and device based on multi-wavelet decomposition, electronic equipment and a medium. The method comprises the following steps: preprocessing a prestack angle gather, and further performing multi-wavelet decomposition; through comparative analysis of well data and an actual decomposition gather, screening a frequency band range sensitive to a thin sand body reservoir; performing multi-wavelet reconstruction on the data subjected to multi-wavelet decomposition to obtain a seismic angle gather data body; screening dominant angles according to the seismic angle gather data body; partial superposition is carried out according to the dominant angle, and superposed seismic data are acquired; and based on the superposed seismic data, sand body sensitive attributes are extracted to carry out thin sand body identification. According to the method, the frequency component sensitive to the thin sand body is fully utilized, interference of the tuning effect on thin sand body recognition can be effectively avoided, and high-precision recognition of the thin sand body reservoir is achieved.

The application discloses a geological anomaly body rapid identification method and system based on seismic exploration, and relates to the field of geological anomaly body identification.The method comprises the following steps: fine imaging based on historical seismic data, generation of a high-precision three-dimensional geological model, and construction of a geological regional risk distribution map; emergency evaluation of anomaly body identification, configuration of a plurality of adaptive identification time limits; prediction of the predicted seismic source signal sequence and the predicted noise signal sequence of the second geological region based on the historical seismic source signal sequence and the historical noise signal sequence, analysis of the anomaly body identification complexity, and output of the second optimal identification time limit; optimization of the identification strategy of the geological anomaly body, execution of the geological anomaly body identification of the second geological region, iterative optimization of the identification scheme, and completion of the coal mining task.The geological anomaly body rapid identification method and system solve the problem that the geological anomaly body identification strategy cannot be dynamically adjusted, resulting in low safety warning responsiveness and insufficient accuracy.

The invention provides a screen body information automatic transmission method, system and device and a medium, and belongs to the field of display screen manufacturing and debugging. The method comprises the following steps: establishing an automatic mapping relationship between an identification code and a configuration file, collecting a screen body identification code by adopting identification equipment, packaging the screen body identification code and station information into a data request packet, automatically calling a corresponding binary configuration file after validity verification, injecting the configuration file into a data frame through a preset communication protocol, and transmitting the data frame to a screen body control card, and completing screen body initialization lightening. According to the invention, full-automatic accurate transmission of the screen body configuration information is realized, the problems of low efficiency, easy error and poor consistency in traditional manual configuration are effectively solved, and the production efficiency and the product quality are remarkably improved.

The embodiment of the application discloses a vehicle file inspection method and device, electronic equipment and medium, the vehicle file inspection method comprises the following steps: obtaining a vehicle file comparison data source of a to-be-detected vehicle; obtaining a to-be-detected file image of a to-be-detected vehicle file of the to-be-detected vehicle, and identifying the to-be-detected file image to obtain first to-be-detected vehicle file data; obtaining a to-be-detected vehicle body identification image of a to-be-detected vehicle body identification of the to-be-detected vehicle, and identifying the to-be-detected vehicle body identification image to obtain second to-be-detected vehicle file data; comparing the first to-be-detected vehicle file data with the vehicle file comparison data source, and comparing the second to-be-detected vehicle file data with the vehicle file comparison data source to obtain a vehicle file inspection result of the to-be-detected vehicle. The technical scheme of the embodiment of the application improves the efficiency, accuracy and traceability of vehicle file inspection.

The invention provides a river channel sand body recognition method and device and a storage medium. The method comprises the following steps that logging information of a target well area is collected, a GR2 data body is obtained through inversion on the basis of the logging information, and horizon tracking interpretation is conducted on the basis of the GR2 data body through seismic waveform feature driving; sand body fine prediction is carried out based on the GR2 data body; and representing sand body plane distribution according to a sand body fine prediction result. The computer equipment comprises a processor and a memory in which a computer program is stored, and when the computer program is executed by the processor, the river channel sand body identification method is realized. A computer program is stored in the storage medium, and when the computer program is executed by the processor, the river channel sand body recognition method is achieved. According to the river channel sand body identification method, the sand body thickness prediction coincidence rate is 90% or above, the precision is good, and the well position deployment requirement is met.

The invention discloses a time sequence-based convection monomer identification method and device, and a storage medium, and relates to the technical field of weather identification. The method comprises the following steps: positioning a point with a maximum reflectivity factor in a current radar two-dimensional combined echo based on a monomer position indicated by a previous storm monomer identification result, and defining the point as an ideal mark point; taking the ideal mark point as a core, and performing a watershed algorithm in the current radar two-dimensional combined echo to obtain a current continuous monomer recognition result; removing an area identified as a continuous monomer from the current radar two-dimensional combined echo, and performing monomer identification again based on the residual radar two-dimensional combined echo to obtain a new monomer identification result; and combining the current continuous monomer identification result with the new monomer identification result, and outputting a current final storm monomer identification result. According to the method, the irregular jumping of the single contour and the switching of the identity label caused by radar observation noise or storm internal fluctuation are effectively inhibited.

A kind of man-machine interaction system of robot, the weight and / or volume change information of article is collected by counting unit, the unloading article quantity and / or type of current destination is identified, the still or dynamic image of taking article body is collected by taking article body identification system, when the unloading article quantity and / or type identified by robot is inconsistent with the unloading article quantity and / or type preset in system, the still or dynamic image of taking article body collected can be compared with the taking article body data stored in system, it is judged whether taking article body is the taking article body corresponding to current destination or the taking article body from remaining destination, and according to the judgment result, the voice prompt device is used to issue the voice prompt of needing to carry out man-machine interaction to taking article side, and finally the exact abnormal taking article information is recognized by the man-machine interaction intervention of operating party.The abnormal taking article information that is different from setting procedure is recognized by robot in process article conveying by real-time man-machine interaction.

The application discloses a reservoir dam identification method and device based on satellite interferometric height measurement and remote sensing images, equipment, medium and product, and relates to the field of image recognition. The method comprises the following steps: acquiring water surface elevation data; preprocessing the water surface elevation data to obtain longitude-latitude-height sequence, and determining dam potential point longitude-latitude coordinate information based on a preset elevation mutation threshold; performing vectorization processing on a CSV coordinate file, and performing image interception, buffer zone generation and band selection processing based on satellite remote sensing images to determine an image file; inputting the image file into a dam body identification model, and outputting a reservoir dam prediction point to realize the identification of the reservoir dam; the dam body identification model is determined by using a Bayesian optimization algorithm, iteratively optimizing the hyperparameters of an improved YOLO11 model according to the image file with labeled information. The application aims to efficiently and reliably realize automatic dam identification.

The invention discloses a disaster-bearing body object extraction method integrating online map data, and the method comprises the steps: obtaining POI data, hierarchical road network data and land coverage data of a target region through an online map API, and constructing point-shaped, linear and planar disaster-bearing body feature databases respectively; performing superposition analysis on the block units cut by the linear road network and the land coverage built-up area, and determining planar disaster-bearing body space units; calculating a POI function equivalent number, mapping the POI function equivalent number to a predefined disaster-bearing body classification system, and calculating a correction density index, an average nearest neighbor index and a category strength index; summarizing the third-level PCII to obtain a second-level proportion index, and distinguishing a single-function disaster-bearing body unit from a mixed-function disaster-bearing body unit by using a Grubbs criterion; according to the function attributes of different disaster types, a differentiation screening rule is established, and a core POI is extracted; according to the method, the accuracy and efficiency of disaster-bearing body identification are improved, and high-quality data support is provided for disaster risk assessment, emergency response and the like.

This vehicle control device comprises: a first light-emitting body identification unit that acquires a single frame image including a periodically changing light-emitting body imaged by an imaging device mounted on a vehicle, and identifies the type and state of the light-emitting body from the acquired single frame image; a time-series data acquisition unit that acquires and holds time-series data of the light-emitting body by using a first identification result obtained by the first light-emitting body identification unit; and a second light-emitting body identification unit that receives input of the held time-series data to identify the type and state of the light-emitting body from the input time-series data.

The invention discloses a plane wall body identification method, system and equipment of an architectural drawing. The plane wall body identification method comprises the following steps: reading primitive data in the given architectural drawing; identifying a filler wall and a non-filler wall based on the primitive data, and acquiring first image data corresponding to the filler wall and second image data corresponding to the non-filler wall; and combining the first image data and the second image data to obtain the plane wall in the architectural drawing, so as to improve the recognition accuracy of the plane wall.

The application discloses a kind of identification method and system for ship abnormal behavior, identification method includes: address acquisition step: the URL video stream address of at least one video stream is obtained;Initial data acquisition step: video source data and video parameter information are obtained based on URL video stream address, video source data and the video parameter information are encapsulated to form initial data packet;Identification result acquisition step: based on URL video stream address and initial data packet, through the serial identification of pre-deployed ship body identification model, ship number identification model and abnormal behavior identification model, identification result is obtained.

The present application belongs to the technical field of helicobacter pylori identification, and discloses a helicobacter pylori intelligent identification method and system based on computer vision processing; including: collecting a gastric mucosa sample, obtaining an HP fluorescent staining image through staining treatment; performing split processing on the HP fluorescent staining image to obtain multiple sub-pictures, and taking the coordinate information of a predetermined orientation of each sub-picture as first coordinate information; performing multi-process operations on each sub-picture to generate a first sub-picture with a rectangular frame and a confidence level; splicing and combining the position coordinates of the rectangular frame in the first sub-picture with the first information coordinates on the corresponding sub-picture to generate a labeled image; comprehensively analyzing the labeled image and outputting a structured detection report. Through distance transformation and adhesion separation of the watershed, and consistent double-layer verification, the present application can effectively reduce the false detection rate of various artifacts such as bright spots, debris, bubbles and the like, thereby improving the accuracy and robustness of HP bacterial body identification.

The invention relates to the technical field of coal mine safety, and discloses a coal mine radar signal anomalous body identification method based on an improved generative adversarial network, which comprises the following steps: S1, acquiring radar signals covering the geology inside a mine in real time, and constructing a multi-band radar signal set; s2, carrying out the preprocessing of the multi-band radar signal set; s3, constructing a double-layer generative adversarial network model in which a structured feature extraction module and a double attention module are introduced, and generating abnormal body feature information data; step S4, generating exception report data based on the exception body feature information data, wherein the exception report data comprises data of the position, the type and the property of the exception body; according to the method, the double-layer generative adversarial network model is introduced, and the structured feature extraction module and the double attention module are combined, so that features of abnormal bodies can be clearer and separable through layered extraction and processing aiming at common structural anomalies and material anomalies in the coal mine environment, and the recognition accuracy and precision are improved.

The invention relates to an identification card generation method and device, computer equipment and a storage medium. The method comprises the steps that an original identification card template is obtained according to an identification card generation command, the original identification card template comprises a dynamic element, a style rule and a typesetting rule, the style rule comprises the position of the dynamic element and the size of the dynamic element, and the typesetting rule comprises a typesetting mode of the identification card on a page; according to the dynamic elements, multiple pieces of original data are obtained from a database, each piece of original data comprises multiple structured fields, and each structured field corresponds to one dynamic element; filling the structured fields into the dynamic elements according to the positions and the sizes of the dynamic elements to generate single identification plates; according to a typesetting rule, typesetting the plurality of single identification cards on the page to obtain a whole page of identification cards; and according to the whole-page identification plate, outputting an identification plate printing file. According to the invention, the generation efficiency of the identification plate can be improved.

The invention relates to the technical field of radio astronomical signal processing, and discloses a monopulse candidate body identification method based on multi-modal feature fusion, and the method comprises the steps: firstly carrying out the preprocessing of Fits format data observed by a FAST telescope, and generating two types of multi-modal input of a dispersion-time (DM-Time) graph and a frequency-time (Freq-Time) graph; then, constructing a double-branch feature extraction network, capturing global correlation features of the DM-Time graph through a ConvNeXt network embedded with a CBAM attention mechanism, and extracting local discrimination features of the Freq-Time graph through a ResNet18 network embedded with the CBAM attention mechanism; deep interactive fusion of bimodal features is realized by using a cross attention mechanism, and feature expression is optimized in combination with learnable weight and residual connection; and finally, binary classification is completed through a full connection layer, and a real monopulse candidate recognition result is output. Compared with an existing FETCH method, the method has the advantages that the false positive rate is reduced by 89.54% or above, the F1-score is improved by 14.79% or above, and large-scale searching of transient radio phenomena such as pulsar and rapid radio storm can be efficiently supported.

The method, apparatus, equipment, and product for multi-layer salt body identification based on well-seismic fusion provided in this application acquire seismic data and well logging data of the target area; perform well-seismic fusion analysis on the seismic data and well logging data to obtain initial labeling results of multi-layer salt bodies; calculate the weights of the initial labeling results based on a preset distance attenuation weight function to obtain distance attenuation weight data; construct an initial salt body identification model based on the seismic data; construct a loss function based on the distance attenuation weight data and the initial salt body identification model, and train the initial salt body identification model to obtain a target salt body identification model for multi-layer salt body identification processing, thereby improving the accuracy of multi-layer salt body identification.

To provide an AI system, method and program capable of acquiring an appropriate answer to a user's question from a generation information processor.SOLUTION: The prompt generation system includes a document information acquisition unit configured to acquire document data from a business entity device, a title specifying unit configured to specify a character string satisfying a predetermined title condition as a title of a sentence included in the document data, a body specifying unit configured to specify a body of the sentence based on the specified title, a storage unit configured to store the specified title and the body in association with each other as divided data in a document database, a question information acquisition unit configured to acquire question information indicating question content from a user, and a processor configured to execute a process including: And a transmission / reception part for transmitting the question information and the first divided information to the generation AI in association with each other so that answer information to the question information with reference to the first divided information can be acquired from the generation AI.SELECTED DRAWING: Figure 1

The invention discloses a tunnel geological anomalous body identification method, medium, equipment and product, and relates to the field of tunnel geological anomalous body identification, and the method comprises the steps: mapping multi-modal geological data to a unified vector space, extracting entities, relationships and entity attributes, and constructing a multi-modal knowledge graph; obtaining a current engineering geological background and detection data, and carrying out matching calculation on the current engineering geological background and the detection data and the multi-modal knowledge graph to obtain a correlation degree value of the current data and the multi-modal knowledge graph; n knowledge graph nodes with the highest correlation degree with the current data and the relation of the knowledge graph nodes are screened out, the knowledge graph nodes are converted into structured text description according to an entity-relation-answer format, the structured text description, the current detection data and a to-be-interpreted tunnel geological anomalous body identification problem jointly form a structured prompt of the query, the structured prompt is input into a large language model, and the query is completed. And obtaining an answer to the to-be-interpreted tunnel geological anomalous body identification question. The tunnel geological anomalous body identification method is high in adaptive optimization capability.

A powerlifting training method is disclosed. The method may include obtaining a powerlifting video associated with a user from a user device. The powerlifting video may include a plurality of frames. The method may further include obtaining a plurality of body identification data associated with each frame from a server. Further, the method may include generating a plurality of feature maps associated with the plurality of frames by using a first trained machine learning module. The method may additionally include merging the plurality of body identification data and the plurality of feature maps to create a merged dataset. The method may further include determining, via a second trained machine learning module, one or more recommendations based on the merged dataset. Furthermore, the method may include transmitting the recommendations to the user device.

The invention belongs to the technical field of while-drilling geological anomalous body identification, and discloses a while-drilling seismic-electromagnetic fusion geological anomalous body identification system and method, and the system comprises a ground excitation system which is configured to excite seismic waves and electromagnetic waves into a rock mass at the same time; the while-drilling receiving system is configured to receive seismic wave and electromagnetic wave signals from the geological anomalous body in the drilling and digging process, perform noise reduction preprocessing on the signals and compress the signals into data packets; the underground transmission system is configured to transmit the preprocessed data packet to an early warning center on the ground in real time; the early warning center is configured to extract data packet parameters, calculate the data packet parameters and generate comprehensive early warning information including risk levels, positions and scales according to calculation results; a calculation result is obtained through a GAFF (geologic anomaly fusion factor) formula. According to the method, geological forecast is converted into a continuous and adjoint type working process from a discontinuous and staged working process, and the overall construction efficiency of tunnel and well drilling construction is greatly improved.