35 results about "Natural field" patented technology

The invention provides an active and passive source metal mine seismic exploration method, which comprises the following steps of: acquiring geological exploration data of a target area, and analyzing and judging to obtain a metallogenic favorable part of the target area; obtaining seismic geologic features of the target area and distribution features of natural field sources; according to the acquired geological exploration data, the passive source seismometers are arranged in a grid mode in a target area to form a passive source detection grid; the active source seismographs are arranged in a cross shape at the favorable metallogenic part to form two active source detection lines; artificial source seismic waves are excited, active source seismic data of the active source seismograph and passive source seismic data of the passive source seismograph are collected, and after the artificial source seismic waves are excited, the passive source seismic data of the passive source seismograph are continuously collected. The problem of active and passive source seismic data fusion acquisition is solved, the exploration cost is reduced, and the working efficiency and the detection precision are improved.

The invention belongs to the technical field of remote sensing image intelligent processing and agricultural information extraction, and particularly relates to a remote sensing large model pre-training and multi-granularity boundary supervision natural field type extraction method, which comprises the following steps: firstly, pre-training a model on a large-scale space-time spectrum remote sensing data set, and combining anchor point sensing mask and geographic information coding; secondly, inputting the multi-scale features into a multi-branch structure sensing network, and outputting a semantic segmentation prediction map through high and low resolution double input and dynamic attention fusion; generating a multi-scale field boundary label through morphological operation, and outputting a boundary prediction map through multi-task supervision after domain enhancement of features by a frequency space double-domain enhancement module; and finally, aligning the two images and performing pixel-level operation to obtain a high-precision extraction result. Through the method, the global classification error during cross-region migration is greatly reduced, the method is adaptive to a low-pixel wide ridge, the boundary detection value and the closure rate of a small-scale field are improved, and the extraction precision of a large field and a small field is considered.

The invention provides a natural scene ship target detection method and system, and the method comprises the steps: firstly obtaining a ship detection data set, a COCO data set and a plurality of to-be-detected natural scene pictures, and inputting the plurality of to-be-detected natural scene pictures into a YOLOv5x network model trained based on the ship detection data set, inputting the plurality of natural scene pictures containing the ships into a DINO target detection model trained based on a COCO data set to obtain a first target detection result of all the ships in each natural scene picture, and cutting out a cutting area containing the ships according to the first target detection result of each ship; calculating an included angle between a connecting line of the two contour points and the horizontal direction by adopting a contour searching function, a rotation matrix function and an affine transformation function, and rotating according to the calculated included angle to obtain a cutting area after ship direction correction; and finally, inputting the cutting area after ship direction correction into the trained YOLOv5nano network model for verification so as to complete ship target detection of a natural scene.

The invention discloses a strawberry identification and positioning method in a natural picking scene, and relates to the technical field of image identification, and the method comprises the steps: obtaining an environment parameter range in a natural scene; constructing a cross-scene feature set; training an initial neural network model based on the cross-scene feature set, and generating a universal strawberry recognition model; for a target natural scene, obtaining real-time environment parameters of the target natural scene; carrying out transfer learning based on the universal strawberry recognition model, and generating a special recognition model matched with the target natural scene; under a plurality of natural scenes, applying the special identification models matched with the natural scenes, and collecting model execution results of the scenes; and optimizing the universal strawberry recognition model through incremental learning, and improving the generalization ability of the strawberry recognition model to a new scene. The strawberry recognition method has the advantages that the environmental adaptability and robustness of strawberry recognition in a natural scene are remarkably enhanced by constructing a cross-scene feature set and a hierarchical transfer learning mechanism.

The invention relates to the technical field of geophysical exploration, and provides a three-dimensional electromagnetic method fracturing monitoring method which comprises the following steps: collecting parameter data of a target well; establishing a target well work area geoelectric model, and performing forward modeling through the target well work area geoelectric model to select reasonable acquisition parameters; arranging a three-dimensional measuring net according to a fracturing construction scheme; collecting data of a natural field source and an artificial field source in the fracturing monitoring process; processing to obtain natural potential data before and after fracturing, induced polarization method data before and after fracturing, time-frequency electromagnetic method data before and after fracturing, and controllable source magnetotelluric method apparent resistivity data and controllable source magnetotelluric method phase data before and after fracturing; differential abnormal data are obtained through calculation, all the differential abnormal data are comprehensively analyzed, and fracturing fluid and fracture distribution form data in the complete fracturing process are obtained. According to the invention, multiplicity of solutions is reduced, precision and monitoring accuracy are improved, and construction cost is reduced.

The invention relates to the technical field of animal behavioral experiments, and discloses an animal behavioral experiment multi-scene simulation system. According to the invention, through cooperative control of multiple environment modules and seamless switching of a dynamic scene, continuous switching of natural scenes such as illumination, sound, smell, terrain and the like is accurately simulated; on the basis of real-time biological signal detection and intelligent feedback, environment adjustment or scene switching is automatically triggered according to animal behaviors / physiological states, and closed-loop self-adaptive adjustment is formed; in combination with an intelligent decision and an AI scene generation engine, self-definition and iterative optimization of a complex script are supported; the parameter coupling controller simulates the interaction effect between environmental factors, the fault-tolerant mechanism and the effect evaluator guarantee the data reliability, the closed-loop verification loop efficiently establishes a causal model of environmental parameters and behavior response, and a high-authenticity, self-adaptive and reproducible experimental platform is provided for animal behavior research.

Micro-bioenergy system for generating electrical energy from formic acid, comprising at least one capillary tube with a semipermeable membrane for drawing in formic acid from an ant nest, a micropump for transferring the acid into a reaction cell, a reaction cell with magnesium and copper electrodes for electrochemical conversion, an energy storage device for intermediate storage of the generated energy, and a housing for integrating and protecting the components.

The invention discloses a cotton disease detection method and device used in a natural scene, and the method comprises the steps: collecting and marking cotton disease detection data in the natural scene, constructing a self-made data set based on the detection data, and evaluating the self-made data set; an information sharing header, a self-adaptive down-sampling module, a feature extraction network and a YOLO11 algorithm are combined, a feature dynamic redistribution network is established, and model evaluation is carried out on a self-made data set and a public data set; and deploying the feature dynamic redistribution network on edge equipment to realize a disease detection task. The device comprises a processor and a memory. According to the method, the average detection precision of cotton diseases is improved, the size of the model is reduced, and the method is suitable for on-site deployment of cotton disease detection edge equipment in intelligent agriculture.

The invention relates to the technical field of positioning, in particular to an auxiliary repositioning method and device for automatically extracting a natural scene landmark, in a mapping stage, an object in a mapping scene is automatically extracted through a zero-shot method to serve as the natural scene landmark, and multi-modal features and feature points are extracted from a color picture and an object area to serve as scene landmark features; in the auxiliary relocation stage, a zero-shot method is used for extracting natural scene landmarks and scene landmark features, the natural scene landmarks and the scene landmark features are compared with landmark features in the mapping stage, and a result with the similarity and feature points meeting matching conditions is obtained and serves as a matching result; and a transformation matrix is obtained through feature point matching between landmarks, so that a current auxiliary relocation pose result is obtained. Compared with the prior art, the method has the advantages that the natural scene landmarks can be accurately recognized without prior knowledge, generalization and adaptability of the method are remarkably improved, and the method can effectively work in various and complex natural environments.

The invention discloses a safety key scene generalization generation method and system oriented to a natural data set, and relates to the technical field of automatic driving, and the method comprises the steps: collecting scene data of a natural driving scene; labeling all the scene data; inputting the map information and the historical state sequence into a scene encoder for encoding; inputting the future state sequence with noise, the coding condition and the noise level into a diffusion decoder for iterative decoding to obtain a generated future state sequence; iteratively updating the basic scene diffusion model; calculating a safety key score of each scene in the natural scene data set by adopting a layering scheme; comparing the safety key score with a preset threshold value, and screening out a safety key scene; and utilizing the trained scene diffusion model to generate a future state sequence of the safety key scene. According to the method, the safety key scores of the scenes are calculated, and the sufficient number of safety key scenes are extracted and generalized, so that the time cost and the financial cost for generating the safety key scenes are effectively reduced.

The invention relates to the technical field of Chinese chestnut screening, in particular to a Chinese chestnut shell maturity classification method and system and a storage medium. The classification method comprises the steps that a Chinese chestnut image data set in a natural scene is collected, a chestnut shell position frame and maturity grades are marked, and the maturity grades are divided into an immature stage, a half-mature stage of transition from turquoise to yellowish-brown, a yellowish-brown mature stage and an over-mature stage of cracking and deterioration according to chestnut shell color change characteristics; and forcibly marking the target of which the shielding area exceeds 30% as an immature stage, and not marking the long-range target of which the maturity cannot be identified. According to the method, a deep coupling mechanism of the target characteristics and the model architecture is established, a noise transmission path is blocked from a data source, and the tolerance of the model to illumination variation and angle distortion is remarkably enhanced in cooperation with the combined effect of pre-enhancement and online random enhancement.

A geophysical surveying method and assembly applying transient pulses of electric current to an airborne time-domain electromagnetic transmitter to generate a primary controlled electromagnetic field; measuring, using an airborne receiver, a secondary controlled electromagnetic field to generate controlled field data; measuring, using the airborne receiver, a magnetic component of a natural electromagnetic field at an above-ground position to generate first natural field data; measuring, using a ground receiver at a ground station, telluric electrical currents induced by the natural electromagnetic field and / or a magnetic component of the natural electromagnetic field at a ground position to generate second natural field data; merging the first natural field data and the second natural field data into combined natural field data; extracting, from the combined natural field data, off-time natural field data recorded between the pulses; and generating geophysical survey data based on the controlled field data and the off-time natural field data.

The invention discloses a field weed growing point detection model and method based on key points. Backbone is used for receiving a field weed image to realize learning of multi-scale weed image features and outputting feature maps of different stages; the Neck uses multi-scale fusion to fuse different stages of feature maps output by the Backbone, and the performance ability of weed features is enhanced; and the Head converts the feature map output by the Neck into specific information required by weed detection, wherein the specific information comprises category, position and confidence information of weeds. The constructed detection model can maintain high detection precision in a natural field environment, the model is light, the real-time detection capability is improved, and the growth points of weeds can be accurately identified in real time in the field environment. By adopting the key point detection method, the specific position coordinates of the growth points of the weeds can be directly output, and the problem that the traditional weed detection method can only detect the whole or part of the weeds and is difficult to determine the growth points is solved.

The present application provides a method and system for natural scene positioning of an AGV based on a 3D lidar, which relates to the technical field of automatic guided vehicle control. The method includes: constructing matching submap information to provide a key reference for subsequent positioning; using a vision sensor to capture multiple image data, and at the same time, real-time collecting IMU data and odometer data of the current pose of the AGV, and accurately analyzing the motion state of the AGV based on this. The 3D lidar obtains scene point cloud data to construct a local map, registers it with the matching submap to obtain an initial pose estimate, and finally integrates the initial pose, motion state, and image data, and determines the final pose information of the AGV through complex calculations. Multiple sources of data work together to comprehensively improve the accuracy and reliability of the AGV's positioning in the natural scene.

The invention belongs to the technical field of pesticides, and particularly discloses application of leaf acetate in prevention and treatment of fusarium graminearum, leaf surface spraying of leaf acetate can significantly reduce the incidence rate of corn stalk rot caused by fusarium graminearum (reduced by 24%), significantly improve the plant height of corn and the fresh weight of the overground part and the underground part, and improve the yield of the corn. It is proved that the leaf acetate has an excellent prevention and control effect on the corn stem rot in the natural field environment, normal growth of corn can be guaranteed, and the leaf acetate has the practical agricultural production application potential.

The invention discloses a portable plant surface potential detection device and method, the device comprises a detection electrode assembly, a portable electrochemical workstation and a terminal computer, the detection electrode assembly is electrically connected with the electrochemical workstation through a shielding conductive wire, and the electrochemical workstation is in bidirectional communication connection with the terminal computer. Plant surface potential monitoring is carried out by creatively adopting an open circuit electrochemical method, traditional high-price patch clamp equipment is replaced with a low-cost portable electrochemical workstation, and all parts of the device are in portable modular design and can be quickly assembled and disassembled, so that the device is convenient to use and popularize. And the field real-time monitoring of the surface potential of plant stalks and leaves in a field natural growth state is realized. According to the device, under set detection and stimulation parameters, personal errors are effectively reduced, detection data are accurate, stability is high, potential response rules of plants under stimulation of fire, damage and the like can be captured, and the device has the advantages of being low in cost, portable, high in adaptability and wide in application prospect.

The present invention provides a method for seismic exploration of active and passive source metal mines, comprising the following steps: obtaining geological exploration data of a target area, analyzing and judging to obtain the favorable ore-forming parts of the target area; obtaining the seismic geological characteristics and the distribution characteristics of natural field sources of the target area; according to the obtained geological exploration data, arranging passive source seismographs in a grid manner in the target area to form a passive source detection grid; arranging active source seismographs in a cross shape in the favorable ore-forming parts to form two active source detection lines; exciting artificial source seismic waves and collecting the active source seismic data of the active source seismographs and the passive source seismic data of the passive source seismographs, and continuing to collect the passive source seismic data of the passive source seismographs after the excitation of the artificial source seismic waves is completed. This method combines the two data acquisition methods of active and passive sources, solves the problem of integrated acquisition of active and passive source seismic data, reduces exploration costs, and improves work efficiency and detection accuracy.

The embodiment of the invention provides a vehicle atmosphere control method and device, a vehicle and a storage medium. The method comprises the following steps: acquiring a target natural scene mode applied to a vehicle; target environment parameter information corresponding to the target natural scene mode is determined in a first mapping relation, and the first mapping relation records the corresponding relation between the multiple natural scene modes and the multiple pieces of environment parameter information; each piece of environmental parameter information is used for indicating execution parameters of a vehicle-mounted system associated with vision, smell, hearing, touch and climate in the vehicle in the corresponding natural scene mode; and controlling the vehicle to run according to the target environment parameter information. The method is used for providing dynamic changing natural environment simulation in a vehicle, so that a user obtains deep relaxation experience.

The invention belongs to the technical field of electromagnetic detection, and particularly relates to an aviation natural field source tipper detection data processing method and system, and the method comprises the steps: decomposing an original time domain signal into the superposition of a noise reduction target signal and noise, and constructing a data matrix after window segmentation; constructing sparse representation for the data matrix, separating a noise component from an effective signal, and removing the noise component to obtain a preliminary noise reduction signal; performing modal decomposition on the preliminary noise reduction signal, and extracting a modal component and a residual signal of the signal; and superposing all the modal components and the residual signal to obtain a final noise reduction signal. The problems of low detection precision and efficiency of the underground deep abnormal body structure are solved, and the processed signal has higher smoothness and consistency.

The invention relates to a method for identifying and positioning flower buds of fruit trees, which is used for segmenting the flower buds through a deep learning model in a complex natural scene and positioning the flower buds on branches through a geometric constraint method so as to accurately identify the flower buds in the complex scene and obtain the growth positions of the branches. The distance between the flower buds is measured through the coordinates of the position points where the flower buds grow, so that the dense flower buds are thinned through a distance method, and the purpose of further intelligent flower thinning is achieved. According to the method, multiple targets including flower buds, branches, leaves, flower branches and the like in the flower bud stage can be recognized and segmented through the optimized deep learning model, flower bud branch distribution and individual forms are analyzed on the basis of multi-target recognition and segmentation, and the method is suitable for scene configuration of an orchard planting mode, a fruit tree branch structure and the like in a natural scene. The influence of different data acquisition view field points on space positioning between flower buds and positioning of a flower bud thinning area is explored, and a basis is provided for automatically thinning dense flower buds.

The invention discloses a rice leaf folder imago image detection and counting method and system for a complex environment, and belongs to the technical field of image processing and computer vision. Comprising the following steps: collecting rice leaf folder imago images in an actual rice field scene or an insect trap, constructing an image training set, and rejecting seriously fuzzy or seriously shielded samples through an image cleaning strategy to improve data quality and model effectiveness; an efficient attention backbone module and a cross-scale fusion network are introduced on the basis of a YOLOv8 model structure, and the detection performance of the model on small insect bodies and multi-scale targets is enhanced; a freezing-unfreezing two-stage fine tuning training method is adopted for a newly added image, and the stability and recognition robustness of the model in a complex environment are improved; experimental results show that the method has good detection precision, applicability and real-time performance in a natural field environment, and is suitable for an insect situation monitoring terminal, an agricultural image recognition platform and a pest and disease damage intelligent diagnosis scene.

The invention discloses a method for generating a graphic trademark in a natural scene, relates to the technical field of image generation, and can enable a stable diffusion model to learn spatial logic between the graphic trademark and a background object through a multi-strategy training mechanism of spatial semantic relationship modeling, trademark identity feature binding and trademark fusion processing fusion. Therefore, embedding generation of the trademark in various backgrounds is naturally completed, the authenticity and context consistency of image synthesis are remarkably improved, and the technical problems of insufficient fidelity and limited style control capability in a complex scene in the prior art are solved.

The invention provides an improved YOLOv7-based fruit tree fruit detection method in a natural scene, which belongs to the technical field of computer vision and intelligent agriculture crossing and comprises three stages of data acquisition and enhancement and model construction and training. In the data acquisition stage, a multi-modal data set containing RGB and multispectral images is constructed. In the data enhancement stage, data is expanded by adopting three-stage strategies of instance shearing and pasting, geometric enhancement and spectrum enhancement. In the model construction stage, a convolution block attention module is integrated into a YOLOv7 neck network to enhance the feature discrimination capability, and a loss function is replaced by SIoU loss to optimize bounding box regression. According to the method, fruit feature extraction is enhanced through a channel-space double-attention mechanism, the small target positioning precision is improved by using an angle perception loss function, and high-precision detection is realized in a complex natural environment.

This invention relates to the field of geophysical electromagnetic detection technology, specifically disclosing a method and system for noise suppression of electromagnetic detection data from UAV-based natural field sources. The method includes: dividing the original time-domain three-component magnetic field signal into time windows and constructing a data matrix; constructing an overcomplete dictionary matrix containing typical noise morphology characteristics of various UAV platforms; selecting a subset of data as a training set and globally optimizing key hyperparameters of the denoising process to obtain the optimal denoising parameter set; performing sparse entropy adaptive denoising processing on the data of all time windows based on the optimal denoising parameter set, and concatenating and reconstructing the denoising results of each window to obtain the final denoised three-component magnetic field data. By introducing a "sparse entropy adaptive" mechanism, it achieves precise removal of structured interference and adaptive preservation of effective signals; combined with the "gray wolf optimization" algorithm, it overcomes the difficulty of balancing the sparse threshold and iteration depth in traditional methods.

The present invention discloses an underwater electromagnetic measurement deployment test device and an electromagnetic testing method, which belong to the field of underwater geological exploration technology. The underwater electromagnetic measurement deployment test device mainly includes a field source body, an underwater electromagnetic instrument deployment module and a bearing head, which can simultaneously complete the underwater electromagnetic instrument deployment operation, artificial field source test operation and natural field source test operation, thereby realizing an integrated design of underwater electromagnetic measurement. When deploying the underwater electromagnetic instrument, the deployment position of each underwater electromagnetic instrument can be accurately controlled to form an underwater electromagnetic instrument node array. In terms of the electromagnetic testing method, the underwater electromagnetic instrument node array deployed by the embodiment of the present invention can efficiently obtain artificial and natural electromagnetic phase response data in the same area, thereby performing high-precision comparison in subsequent analysis. By collecting and analyzing the response data of different frequencies by the nodes, the resource distribution in the underwater stratum can be accurately located.

The invention relates to the technical field of coal mine drilling geological exploration, in particular to a frequency-selecting water exploration method and device and electronic equipment, and the frequency-selecting water exploration method comprises the steps: obtaining three target detection frequencies corresponding to a self area, an upper area and a lower area of a horizontal drill hole respectively; a plurality of test points are preset in the extending direction of the horizontal drill hole, each test point is detected, and in the same detection period, target natural field induction electric field signals corresponding to the target detection frequencies are obtained; respectively obtaining a target apparent resistivity set corresponding to each target detection frequency based on each target detection frequency of the plurality of test points and the corresponding target natural field induction electric field signal; and based on the target apparent resistivity set corresponding to each target detection frequency, determining whether a water-containing low-resistance geologic body exists around the horizontal drill hole or not. The underground coal mine tunneling water inrush accident risk can be effectively reduced, and scientific and reliable technical support is provided for underground coal mine horizontal drilling advanced water exploration.

The present application belongs to the technical field of geographic information engineering, and discloses a meadow three-dimensional model boundary optimization method, system, terminal and storage medium, comprising: acquiring a meadow three-dimensional model to be subjected to boundary optimization in a natural scene three-dimensional model; using a distance field-based boundary fusion method to perform gradual blending of material properties of the meadow three-dimensional model and an adjacent area model; dividing a boundary domain and an internal domain of the meadow three-dimensional model, and generating model points corresponding to the boundary domain and the internal domain based on a region density control algorithm and a rejection sampling algorithm; based on the generated model points and the gradually blended material properties, optimizing the meadow three-dimensional model, and outputting an optimized meadow three-dimensional model; the present application enhances the visual consistency of the meadow three-dimensional model at the boundary, and improves the realism of the meadow three-dimensional model.

This application relates to the fields of computer graphics and artificial intelligence, and in particular to a single-image SVBRDF reconstruction method based on the fusion of lighting cues in natural scenes. The method includes: extracting ambient light distribution in a natural scene using a mirror sphere and performing geometric occlusion modeling; constructing planar light source cues and normal light source cues based on predicted normals; adaptively fusing material appearance features with bi-branch lighting cues using a multi-head cross-attention mechanism; and predicting diffuse albedo, normals, roughness, and specular reflection intensity maps using a parametric regression network. This application effectively decouples complex lighting and material properties, eliminates ambiguity in parameter estimation, and achieves high-precision, physically consistent material reconstruction without requiring a controlled darkroom environment, thus improving the robustness and practical value of single-image material digitization in natural scenes.

The invention belongs to the technical field of geographic information engineering, and discloses a grassland three-dimensional model boundary optimization method and system, a terminal and a storage medium, and the method comprises the steps: obtaining a grassland three-dimensional model to be subjected to boundary optimization in a natural scene three-dimensional model; using a boundary fusion method based on a distance field to carry out gradient mixing of material attributes on the grassland three-dimensional model and the adjacent region model; dividing a boundary domain and an internal domain of the grassland three-dimensional model, and generating model point locations corresponding to the boundary domain and the internal domain based on a region density control algorithm and a sampling rejection algorithm; optimizing the grassland three-dimensional model on the basis of the generated model point location and the material attribute after gradient mixing, and outputting the optimized grassland three-dimensional model; according to the method, the visual consistency of the grassland three-dimensional model at the boundary is enhanced, and the sense of reality of the grassland three-dimensional model is improved.

The invention discloses application of Exiguobacterium indicum and cyclo (leucine-proline) in plant tillering regulation, and belongs to the technical field of agricultural production. According to the present invention, the Exiguobacterium indicum R2567 (the registration number of the preservation center is GDMCC No.66084) is separated and identified from rice rhizosphere, and the strain can significantly inhibit plant tillering in a solar greenhouse and a natural field environment. The ring (leucine-proline) is extracted from exudates of Exiguobacterium indicum R2567, and experiments prove that plant tillering can be inhibited by adding the ring (leucine-proline) into a culture solution or planting soil. According to the present invention, the Exiguobacterium indicum R2567 and / or cyclo (leucine-proline) are / is used to adjust the tillering number of paddy rice and other crops, control and optimize the plant type, reduce the energy loss caused by the inefficient tillering, improve the quality and the yield of the crops, reduce the field operation, save the manpower, and improve the economic benefit.