59 results about "Body identification" patented technology

The invention provides an underground ore body positioning identification method and system based on a ground penetrating radar, and relates to the technical field of ore body detection, and the method comprises the steps: inputting a corrected multi-dimensional point cloud feature set into a pre-trained closed-loop convolutional neural network, optimizing the network parameters through transfer learning, and obtaining a multi-dimensional point cloud feature set; outputting the category confidence and the spatial position probability distribution point cloud of the ore body target; on the basis of the spatial position probability distribution point cloud, generating ore body depth and horizontal positioning point cloud through a reverse projection algorithm in combination with an electromagnetic wave two-way travel time calculation rule and a Kanay resistivity measurement result; and performing multi-scale two-dimensional empirical mode decomposition on the reverse projection positioning point cloud, and filtering noise components to extract a high-confidence target point cloud. According to the invention, the accuracy and stability of ore body identification are improved.

The invention discloses a device and a method for accurately acquiring working face abnormal body information by a multi-frequency ground penetrating radar. The device comprises a multi-frequency data acquisition module, a data alignment module, a data fusion module, a data management module, a model training optimization module and an abnormal body information identification module. The method comprises the following steps: acquiring ground penetrating radar data of different center frequencies of a target by using a multi-frequency ground penetrating radar; preprocessing the original multi-frequency ground penetrating radar data; performing spatial alignment on the preprocessed ground penetrating radar data; fusing the ground penetrating radar data through improved non-subsampled contourlet transform (NSCT) to obtain a multi-frequency ground penetrating radar fusion profile; fused images are collected to make a ground penetrating radar image data set; and importing an improved YOLOv11 model to perform underground anomalous body identification and marking on the ground penetrating radar image data set. Through multi-frequency data complementation and deep learning optimization, anomalous body detection precision and efficiency are significantly improved, and the method is especially suitable for geological exploration of complex underground environments such as a coal mine.

The invention provides a landslide mass recognition method and device, equipment, a storage medium and a program product, and can be applied to the field of landslide mass recognition. The method comprises the following steps: based on a landslide mass identification model, respectively carrying out feature extraction on images from a plurality of modalities to obtain extraction features corresponding to the images; converting the extracted features into corresponding frequency domains and carrying out frequency domain compression to obtain frequency domain features corresponding to the image; converting the frequency domain features into weight factors, and obtaining weighted features corresponding to the images according to the weight factors and extraction features corresponding to the same image; splicing the weighted features of the images to obtain fusion features; processing the fused features through a multilayer dense network, and carrying out multiple interpolations on the processed features by using a bilinear interpolation method; and obtaining the category of each pixel in the features after multiple interpolations by using a maximum parameter value decision method, and determining the landslide mass.

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 provides an intelligent inspection method for harbor container body damage. The method comprises the steps of S1, judging whether a container transport vehicle exists in a designated position of the view of each camera or not, and if yes, turning to the step S2; if not, returning to the step S1; s2, controlling each camera to zoom the focal length to shoot the container transport vehicle in different directions so as to obtain container body images of different surfaces of the container body; s3, identifying each box body image to obtain box number information, and transmitting each box body image to an identification server for box body damage identification to obtain a corresponding damage identification result; and S4, the container number information and the damage identification result are linked with a tallying platform, and a worker at the gate is prompted to carry out manual intervention or release. The beneficial effects of the invention are that the system can achieve the automatic and intelligent container defect detection, and improves the defect detection efficiency and accuracy.

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 discloses a convection monomer identification method, device and equipment and a storage medium, and the identification method comprises the steps: extracting a maximum value from radar three-dimensional networking data along a height axis, and obtaining a two-dimensional combined echo; performing multi-stage progressive threshold segmentation on the two-dimensional combined echo to obtain each stage of echo region; performing combination optimization on each level of echo region; calculating the mass center of a convection single body according to each level of echo region after combination and optimization; and based on the mass center of the convective single body, extracting the contour of the convective single body from the first-stage echo region to realize convective single body identification. According to the method, the over-segmentation problem caused by noise points or isolated areas is effectively avoided, so that the convection monomer identification result is closer to an actual structure, the capability of distinguishing the multi-monomer storm is improved, and the identification accuracy is remarkably 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 present invention comprises: three-dimensional markers 1 disposed in a real space; a plurality of sensor devices 2 disposed in the real space; and a server device 3 connected to the sensor devices 2 in a communicable state. The three-dimensional markers 1 are labels each having a three-dimensional shape and individually disposed on the surface side of respective moving bodies M that move in the real space. The sensor devices 2 acquire image sensor data comprising a point group in the real space including the three-dimensional markers 1, and transmit the image sensor data to the server device 3. After aggregating the image sensor data comprising the point group, in the real space, transmitted from each of the sensor devices 2, the server device 3 detects the three-dimensional markers 1 in the real space and identifies the moving bodies M on which the three-dimensional markers 1 are respectively disposed.

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.

First and second component housing bodies, which accommodate a plurality of components, are attached to a component supply unit. A plurality of the component supply units are mounted on a unit base. A memory stores component housing body information in which mounting position information corresponding to a mounting position of the component supply units, first identification information assigned to the first component housing body, and second identification information assigned to the second component housing body are associated with each other. An information management unit identifies, based on the unit address information, a mounting position of the component supply unit corresponding to the first read identification information or the second read identification information.An information updating unit writes the second identification information as current package body information corresponding to the mounting position information of the component supply unit from which the first component package body is removed, and writes the package body identification information as next package body information into the unit address information after the first component package body is removed from the component supply unit.

A loading assistance method is executed by a loading assistance server (10) in order to assist a loading operator when loading a product onto a moving body that transports a product to which product identification information has been previously assigned, the loading assistance method being characterized in that: the loading assistance server acquires moving body identification information that has been previously assigned to the moving body; the mobile body identification information and the product identification information are compared with a pre-acquired order, and when the mobile body identification information and the product identification information are consistent with the content of the order, the loading worker is notified of shipping-available information.

The invention provides an intelligent agent body identification method and device, electronic equipment and a storage medium, the method is applied to an intelligent agent, and the method comprises the steps: randomly calling a control signal based on the intelligent agent in a preset scene, and monitoring the state change of a plurality of intelligent agent bodies in the preset scene after the control signal is sent out; according to the state change of each agent body, reasoning in the plurality of agent bodies to obtain a target agent body controlled by the control signal; and taking the target intelligent agent body as an intelligent agent body matched with the intelligent agent in the preset scene. According to the method, the intelligent body matched with the intelligent body can be effectively identified by the intelligent body in different scenes, the identification generalization ability is improved, and powerful guidance is provided for future intelligent body research and development.

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.

To provide a mobile body identification system that can accurately identify mobile bodies moving in a real space.SOLUTION: A mobile body identification system comprises: three-dimensional markers 1 disposed in a real space; a plurality of sensor devices 2 disposed in the real space; and a server device 3 connected to the sensor devices 2 in a communicable state. The three-dimensional markers 1 are labels each having a three-dimensional shape and individually disposed on the surface side of respective moving bodies M that move in the real space. The sensor devices 2 acquire image sensor data comprising a point group in the real space including the three-dimensional markers 1, and transmit the image sensor data to the server device 3. After aggregating the image sensor data comprising the point group, in the real space, transmitted from each of the sensor devices 2, the server device 3 detects the three-dimensional markers 1 in the real space and identifies the moving bodies M on which the three-dimensional markers 1 are respectively disposed.SELECTED DRAWING: Figure 1

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 PCCP production full-process tracing system and method, and an identification module of the system comprises RFID chips implanted in cement interlayers at two ends of each PCCP body, and the identification module transmits PCCP body identification information to a central processing server; the positioning module transmits the space coordinate information of the gantry crane to the central processing server; the data acquisition module comprises a sensor deployed in a set production process, a data interface and equipment for spraying codes on a bell and spigot and a steel cylinder, and the data acquisition module transmits production parameters and spraying code information to the central processing server; and the central processing server establishes a production environment space model according to data from the identification module, the positioning module and the data acquisition module so as to realize binding, processing, analysis and storage of the data and the PCCP body identity. The problems that in traditional PCCP production management, inventory management efficiency is low, quality tracing is difficult, the production process is not transparent, and intelligentization and full-process tracing are difficult to achieve are solved.

The invention relates to a product detection method and device based on artificial intelligence, equipment and a storage medium. The method comprises the steps that identification codes on a bottle body and a packaging box of a to-be-detected product are obtained through a camera; inputting the identification code into a deep learning network model, and if it is determined that the bottle body identification code and the packaging box identification code of the to-be-detected product meet preset requirements through the deep learning network model, determining that the to-be-detected product is a first product; if it is determined that the to-be-detected product is the first product, preset associated information is obtained, and the associated information represents the association relationship between the bottle body identification code and the packaging box identification code; and if the bottle body identification code and the packaging box identification code of the to-be-detected product accord with the association relationship, determining that the to-be-detected product is a qualified product. The detection efficiency and accuracy can be improved.

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.

A terminal is provided with: a reception unit that receives a second terminal body identification number from another terminal; and a transmission unit that transmits the first terminal device identification number and the second terminal device identification number to the network node device during the registration process.

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.